ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

36b4217be63fc502a7a8b608b61caf14733e4c6e

1,477

py

Python

carla_ros_bridge/src/carla_ros_bridge/coordinate_converter.py

OlafOrangi/ros-bridge

732d5f99e5e1f4d0ea7e4873ccc34f0a40f1203c

[ "MIT" ]

null

carla_ros_bridge/src/carla_ros_bridge/coordinate_converter.py

OlafOrangi/ros-bridge

732d5f99e5e1f4d0ea7e4873ccc34f0a40f1203c

[ "MIT" ]

null

carla_ros_bridge/src/carla_ros_bridge/coordinate_converter.py

OlafOrangi/ros-bridge

732d5f99e5e1f4d0ea7e4873ccc34f0a40f1203c

[ "MIT" ]

null

#!/usr/bin/env python from geometry_msgs.msg import Pose, Point, Quaternion, Vector3 import numpy as np import tf def convert_pose(pose): """ convert pose between left and right-hand coordinate system :param pose: pose to be converted :return: converted pose """ data = Pose() data.position = convert_vector3(pose.position) data.orientation = convert_quaternion(pose.orientation) return data def convert_vector3(pt): """ convert vector3 between left and right-hand coordinate system :param pt: point to be converted :return: converted point """ return Vector3(pt.x, -pt.y, pt.z) def convert_point(pt): """ convert point between left and right-hand coordinate system :param pt: point to be converted :return: converted point """ return Point(pt.x, -pt.y, pt.z) def convert_quaternion(q): """ convert quaternion between left and right-hand coordinate system :param q: quaternion to be converted :return: converted quaternion """ euler = tf.transformations.euler_from_quaternion([q.x, q.y, q.z, q.w]) euler = (euler[0], euler[1], -euler[2]) return Quaternion(*tf.transformations.quaternion_from_euler(*euler)) def convert_euler(euler): """ convert euler angles between left and right-hand coordinate system :param euler: euler angles to be converted :return: converted euler angles """ return Vector3(euler.x, euler.y, -euler.z)

26.375

74

0.688558

0

765

0.517942

36b50824ddb6f2e96f0d94699793a7e9265c44f3

518

py

Python

models/IFR_generalized_SB.py

rileymcmorrow/C-SFRAT

c696942940118172dfb2c3b8cc27b8d2fd5a5a17

[ "MIT" ]

null

models/IFR_generalized_SB.py

rileymcmorrow/C-SFRAT

c696942940118172dfb2c3b8cc27b8d2fd5a5a17

[ "MIT" ]

3

2021-03-09T16:13:59.000Z

2021-09-20T16:50:07.000Z

models/IFR_generalized_SB.py

rileymcmorrow/C-SFRAT

c696942940118172dfb2c3b8cc27b8d2fd5a5a17

[ "MIT" ]

4

2021-07-20T18:01:12.000Z

2021-11-22T10:13:35.000Z

from core.model import Model class IFR_Generalized_SB(Model): name = "IFR generalized Salvia & Bollinger" shortName = "IFRGSB" # initial parameter estimates beta0 = 0.01 parameterEstimates = (0.1, 0.1) def hazardSymbolic(self, i, args): # args -> (c, alpha) f = 1 - args[0] / ((i - 1) * args[1] + 1) return f def hazardNumerical(self, i, args): # args -> (c, alpha) f = 1 - args[0] / ((i - 1) * args[1] + 1) return f

24.666667

50

0.525097

482

0.930502

0

116

0.223938

36b666e75f8d2123fc2f466527229d2f55e94174

1,263

py

Python

TrendTrading/ProbModel/CheckScripts/updated market indicator.py

benjabee10/WKUResearch

5cc384c0e0c1afc82c38a9e6eb63b80c85af7c97

[ "MIT" ]

null

TrendTrading/ProbModel/CheckScripts/updated market indicator.py

benjabee10/WKUResearch

5cc384c0e0c1afc82c38a9e6eb63b80c85af7c97

[ "MIT" ]

null

TrendTrading/ProbModel/CheckScripts/updated market indicator.py

benjabee10/WKUResearch

5cc384c0e0c1afc82c38a9e6eb63b80c85af7c97

[ "MIT" ]

null

import numpy as np import pandas as pd import talib big= 200 small= 50 threshold=0.02 #context.market (shortperiod, longperiod): #Market Values= 0-negative, 1-no trend, 2-positive def initialize(context): context.spy= sid(8554) schedule_function(check) def check(context, data): spydata= data.history(context.spy, 'price', big+5, '1d') lAvg= talib.SMA(spydata, small)[-1] sAvg= talib.SMA(spydata, big)[-1] shortAvgY= talib.SMA(spydata, small)[-2] longAvgY= talib.SMA(spydata, big)[-2] shortp= conditionCheck(sd, md, threshold) longp= 2*(conditionCheck(md, ld, threshold)) context.markettrack= context.market def conditionCheck(small, large, smallY, largeY var): if small > (1+var)*small and large > (1+var)*large: return 1 elif (1-var)*large < small < (1+var)*large: return 0 elif small < (1-var)*large: return -1 def clearassets(context, data): for asset in context.portfolio.positions: position = context.portfolio.positions[asset].amount if position <0: context.longsells.append(asset) elif position >0: context.shortsells.append(asset) order_target_percent(asset, 0)

25.26

61

0.639747

0

104

0.082344

36b8262c6d34969be77ba59f989410637bf778e2

6,097

py

Python

google_drive_online_decompression.py

xunyixiangchao/Google-Drive-Online-Decompression

02121e3c25ad0ef3ceb0652a4a4e16f803e8463a

[ "Apache-2.0" ]

null

google_drive_online_decompression.py

xunyixiangchao/Google-Drive-Online-Decompression

02121e3c25ad0ef3ceb0652a4a4e16f803e8463a

[ "Apache-2.0" ]

null

google_drive_online_decompression.py

xunyixiangchao/Google-Drive-Online-Decompression

02121e3c25ad0ef3ceb0652a4a4e16f803e8463a

[ "Apache-2.0" ]

1

2021-06-04T16:08:35.000Z

# -*- coding: utf-8 -*- """Google_Drive_Online_Decompression.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/16e0tv3LEkAFaYHmKH2H63Cg6rpCNWFky # **第一步绑定GoogleDrive** """ #@markdown 点击左侧按钮，授权绑定GoogleDrive from google.colab import drive drive.mount('/content/drive') """# **RAR** # 查看单个RAR压缩文件的目录树 """ #@markdown 点击左侧按钮，查看单个RAR压缩包里面的目录结构 #@markdown destination 查看的RAR压缩包的路径（带.rar后缀） destination = "" #@param {type:"string"} !unrar v "$destination" """# 查看目录下所有RAR压缩文件的目录树""" #@markdown 点击左侧按钮，查看目录下所有RAR压缩包的目录结构 #@markdown destination 要查看的目录的路径（不带.rar后缀） destination = "" #@param {type:"string"} !unrar v "$destination/*.rar" """## 解压单个RAR压缩包 ****支持分压卷****""" #@markdown 点击左侧按钮，解压单个RAR压缩包 #@markdown destination 解压的文件的路径（带.rar后缀） destination = "" #@param {type:"string"} #@markdown files 解压文件的目的地(目录) files = "" #@param {type:"string"} #@markdown password 解压密码（有就填写没有就不填） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !unrar x -p"$password" -o+ "$destination" "$files" """## 批量解压RAR""" #@markdown 点击左侧按钮，解压整个目录下多个RAR压缩包 #@markdown destination 解压的文件的路径（不带.rar后缀） destination = "" #@param {type:"string"} #@markdown files 解压文件的目的地(目录) files = "" #@param {type:"string"} #@markdown password 解压密码（有就填写没有就不填，因为是批量！所以必须密码是统一的，否则必定报错！！！） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !unrar x -p"$password" -o+ "$destination/*.rar" "$files" """# **ZIP** # 查看单个ZIP压缩文件的目录树 """ #@markdown 点击左侧按钮，查看单个ZIP压缩包的目录结构 #@markdown destination 查看的文件的路径（带.zip后缀） destination = "" #@param {type:"string"} !unzip -l "$destination" """# 查看多个ZIP压缩文件里面的目录树""" #@markdown 点击左侧按钮，查看整个目录下ZIP压缩包的目录结构 #@markdown destination 查看的文件夹的路径（不带.zip后缀） destination = "" #@param {type:"string"} !unzip -l "$destination/*.zip" """### 解压单个ZIP压缩包 ****支持分压卷****""" #@markdown 点击左侧按钮，解压单个ZIP压缩包 #@markdown destination 解压的文件的路径（带.zip后缀） destination = "" #@param {type:"string"} #@markdown files 解压文件的目的地(目录) files = "" #@param {type:"string"} #@markdown password 解压密码（有就填写没有就不填） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !7z x -aoa "$destination" -P"$password" -o"$files" """## 批量解压ZIP""" #@markdown 点击左侧按钮，解压整个目录下多个ZIP压缩包 #@markdown destination 填入要解压的文件的路径（不带.zip后缀） destination = "" #@param {type:"string"} #@markdown files 解压文件的目的地(目录) files = "" #@param {type:"string"} #@markdown password 解压密码（有就填写没有就不填，因为是批量！所以必须密码是统一的，否则必定报错！！！） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !7z x -aoa "$destination/*.zip" -P"$password" -o"$files" """# **7Z** # 查看单个7Z压缩文件的目录树 """ #@markdown 点击左侧按钮，查看单个7Z压缩包的目录结构 #@markdown destination 查看压缩包的路径（带.7z后缀） destination = "" #@param {type:"string"} !7z l "$destination" """# 查看多个7Z压缩文件的目录树""" #@markdown 点击左侧按钮，查看整个目录下7Z压缩包的目录结构 #@markdown destination 查看目录的路径（不带.7z后缀） destination = "" #@param {type:"string"} !7z l "$destination/*.7z.*" """## 解压单个7Z压缩包 ****支持分压卷****""" #@markdown 点击左侧按钮，解压单个7Z压缩包 #@markdown destination 解压的7Z压缩包的路径（带.7z后缀） destination = "" #@param {type:"string"} #@markdown files 解压压缩文件到文件夹目录(目的地) files = "" #@param {type:"string"} #@markdown password 压缩密码（有就填写没有就不填） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !7z x -aoa "$destination" -P"$password" -r -o"$files" """## 批量解压7z""" #@markdown 点击左侧按钮，解压整个目录下多个7Z压缩包 #@markdown destination 解压的文件目录的路径（不带.7z后缀） destination = "" #@param {type:"string"} #@markdown files 解压压缩文件到文件夹目录(目的地) files = "" #@param {type:"string"} #@markdown password 压缩密码（有就填写没有就不填，因为是批量！所以必须密码是统一的，否则必定报错！！！） password = "" #@param {type:"string"} print("若没有设置密码则直接回车即可") !7z x -aoa "$destination/*.7z" -P"$password" -o"$files" """# **通用格式** # 查看单个压缩文件的目录树 """ #@markdown 点击左侧按钮，查看单个压缩包的目录结构 #@markdown destination 查看压缩包的路径（带.xxx后缀） destination = "" #@param {type:"string"} !7z l "$destination" """# 查看多个压缩文件的目录树""" #@markdown 点击左侧按钮，查看整个目录下压缩包的目录结构 #@markdown destination 查看目录的路径（不带.xxx后缀） destination = "" #@param {type:"string"} !7z l "$destination/*.*" """## 解压单个压缩包 ****支持分压卷****""" #@markdown 点击左侧按钮，解压单个压缩包 #@markdown destination 解压的7Z压缩包的路径（带.xxx后缀） destination = "" #@param {type:"string"} #@markdown files 解压压缩文件到文件夹目录(目的地) files = "" #@param {type:"string"} #@markdown password 压缩密码（有就填写没有就不填） password = "" #@param {type:"string"} !7z x -aoa "$destination" -P"$password" -r -o"$files" """## 批量解压""" #@markdown 点击左侧按钮，解压整个目录下多个压缩包 #@markdown destination 解压的文件目录的路径（不带.xxx后缀） destination = "" #@param {type:"string"} #@markdown files 解压压缩文件到文件夹目录(目的地) files = "" #@param {type:"string"} #@markdown password 压缩密码（有就填写没有就不填，因为是批量！所以必须密码是统一的，否则必定报错！！！） password = "" #@param {type:"string"} !7z x -aoa "$destination/*.*" -P"$password" -o"$files"

23.360153

102

0.657865

0

7,385

0.885598

36b8b92109f8c9655104ce9dade2ed763cbf2735

678

py

Python

hackerearth/Algorithms/A plane journey/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

4

2020-07-24T01:59:50.000Z

2021-07-24T15:14:08.000Z

hackerearth/Algorithms/A plane journey/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

hackerearth/Algorithms/A plane journey/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

""" # Sample code to perform I/O: name = input() # Reading input from STDIN print('Hi, %s.' % name) # Writing output to STDOUT # Warning: Printing unwanted or ill-formatted data to output will cause the test cases to fail """ # Write your code here n, m = map(int, input().strip().split()) a = sorted(map(int, input().strip().split()), reverse=True) b = sorted(map(int, input().strip().split()), reverse=True) if a[0] > b[0]: print(-1) else: min_time = 1 i = j = 0 while i < len(a): if j < len(b) and a[i] <= b[j]: j += 1 elif a[i] <= b[j - 1]: min_time += 2 i += 1 print(min_time)

26.076923

94

0.538348

0

274

0.40413

36b8bfd65b80b877d57938c5b868d8f66abde496

65

py

Python

ml/av/io/__init__.py

necla-ml/ml

7ebd29382326e3958297607da7182c211865e7ff

[ "BSD-3-Clause" ]

1

2022-02-21T21:06:29.000Z

ml/av/io/__init__.py

necla-ml/ml

7ebd29382326e3958297607da7182c211865e7ff

[ "BSD-3-Clause" ]

null

ml/av/io/__init__.py

necla-ml/ml

7ebd29382326e3958297607da7182c211865e7ff

[ "BSD-3-Clause" ]

null

"""APIs from ml.vision.io and ml.audio.io """ from .api import *

16.25

41

0.661538

0

45

0.692308

36b8ccb8c50334dfa92a74050719c2548bf9dec4

738

py

Python

addon.py

codingPF/plugin.video.newsApp

64f7c3e2e742cef5cd7c3303e2ffb3ec07771476

[ "MIT" ]

null

addon.py

codingPF/plugin.video.newsApp

64f7c3e2e742cef5cd7c3303e2ffb3ec07771476

[ "MIT" ]

null

addon.py

codingPF/plugin.video.newsApp

64f7c3e2e742cef5cd7c3303e2ffb3ec07771476

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ The main addon module SPDX-License-Identifier: MIT """ # -- Imports ------------------------------------------------ import xbmcaddon import resources.lib.appContext as appContext import resources.lib.settings as Settings import resources.lib.logger as Logger import resources.lib.main as Main # -- Main Code ---------------------------------------------- if __name__ == '__main__': appContext.init() appContext.initAddon(xbmcaddon.Addon()) appContext.initLogger(Logger.Logger(appContext.ADDONCLASS.getAddonInfo('id'), appContext.ADDONCLASS.getAddonInfo('version'))) appContext.initSettings(Settings.Settings(appContext.ADDONCLASS)) PLUGIN = Main.Main() PLUGIN.run() del PLUGIN

29.52

129

0.647696

0

228

0.308943

36ba21d593e601f39648ce3de11ea90f9d215efd

6,226

py

Python

bfgame/components/equipment.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

3

2017-10-28T11:28:38.000Z

2018-09-12T09:47:00.000Z

bfgame/components/equipment.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

null

bfgame/components/equipment.py

ChrisLR/BasicDungeonRL

b293d40bd9a0d3b7aec41b5e1d58441165997ff1

[ "MIT" ]

null

from bflib import units from core import contexts from core.components import Component, listing from core.messaging import StringBuilder, Actor, Target, Verb @listing.register class Equipment(Component): NAME = "equipment" __slots__ = ["armor_restrictions", "weapon_restrictions", "weapon_size_restrictions", "wear_locations", "wield_locations", "empty_wield_locations" "worn_items", "wielded_items"] """ This component attaches itself to anything with a bodies. It represents equipment worn or wielded """ def __init__(self): super().__init__() self.armor_restrictions = None self.weapon_restrictions = None self.weapon_size_restrictions = None def on_register(self, host): super().on_register(host) host_restrictions = self.host.restrictions if host_restrictions: self.armor_restrictions = host_restrictions.armor self.weapon_restrictions = host_restrictions.weapons self.weapon_size_restrictions = host_restrictions.weapon_size def copy(self): return Equipment() def remove(self, item): found_slots = False for item_slot in self.get_worn_item_slots(): if item_slot.item == item: found_slots = True item_slot.item = None if found_slots: return True for item_slot in self.get_wielded_grasp_slots(): if item_slot.item == item: item_slot.item = None found_slots = True if found_slots: return True return False def wear(self, item): if self.armor_restrictions and not self.armor_restrictions.can_wear(item.base): return False if not item.wearable: return False empty_item_slots = self.get_empty_item_slots() for wear_location_set in item.wearable.wear_locations: if hasattr(wear_location_set, '__iter__'): # Multiple Location Slot for slot in wear_location_set: proper_slot = next((item_slot for item_slot in empty_item_slots if item_slot.keyword == slot), None) if proper_slot is not None: proper_slot.item = item else: return False context = contexts.Action(self.host, item) message = StringBuilder(Actor, Verb("wear", Actor), Target, ".") self.host.game.echo.see(self.host, message, context) return True else: # Single Location Slot proper_slot = next((item_slot for item_slot in empty_item_slots if item_slot.keyword == wear_location_set), None) if proper_slot is not None: proper_slot.item = item context = contexts.Action(self.host, item) message = StringBuilder(Actor, Verb("wear", Actor), Target, ".") self.host.game.echo.see(self.host, message, context) return True return False def wield(self, item): if self.weapon_restrictions and not self.weapon_restrictions.can_wield(item.base): return False hands = 1 if self.weapon_size_restrictions: keyword = self.weapon_size_restrictions.can_wield(item.base) if not keyword: return False else: if keyword == self.weapon_size_restrictions.keywords.NeedsTwoHands: hands = 2 empty_grasp_slots = self.get_empty_grasp_slots() if len(empty_grasp_slots) >= hands: while hands > 0: item_slot = empty_grasp_slots.pop(0) item_slot.item = item hands -= 1 context = contexts.Action(self.host, item) message = StringBuilder(Actor, Verb("wield", Actor), Target, ".") self.host.game.echo.see(self.host, message, context) return True return False def get_melee_total_armor_class(self): all_items = self.get_all_items() armor_ac = sum([item.armor.armor_class for item in all_items if item.armor]) shield_ac = sum([item.shield.armor_class_melee for item in all_items if item.shield]) return armor_ac + shield_ac def get_ranged_total_armor_class(self): all_items = self.get_all_items() armor_ac = sum([item.armor.armor_class for item in all_items if item.armor]) shield_ac = sum([item.shield.armor_class_missile for item in all_items if item.shield]) return armor_ac + shield_ac def get_all_items(self): items = self.get_worn_items() items.extend(self.get_wielded_items()) return items def get_empty_item_slots(self): body_parts = self.host.body.get_body_parts() return [item_slot for body_part in body_parts for item_slot in body_part.item_slots if not item_slot.item] def get_empty_grasp_slots(self): body_parts = self.host.body.get_body_parts() return [item_slot for body_part in body_parts for item_slot in body_part.grasp_slots if not item_slot.item] def get_worn_items(self): return [item_slot.item for item_slot in self.get_worn_item_slots()] def get_worn_item_slots(self): body_parts = self.host.body.get_body_parts() return [item_slot for body_part in body_parts for item_slot in body_part.item_slots if item_slot.item] def get_wielded_items(self): return [item_slot.item for item_slot in self.get_wielded_grasp_slots()] def get_wielded_grasp_slots(self): body_parts = self.host.body.get_body_parts() return [grasp_slot for body_part in body_parts for grasp_slot in body_part.grasp_slots if grasp_slot.item] def get_load_of_worn_items(self): worn_items = self.get_worn_items() total_weight = units.Pound(0) for item in worn_items: total_weight += item.weight.score return total_weight

37.506024

115

0.625442

6,046

0.971089

0

6,064

0.97398

0

362

0.058143

36ba65041a866ce133db66a746c7905283d02484

544

py

Python

students/K33402/Shuginin_Yurii/LR2/homework_board/board_app/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

null

students/K33402/Shuginin_Yurii/LR2/homework_board/board_app/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

null

students/K33402/Shuginin_Yurii/LR2/homework_board/board_app/urls.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

1

2022-03-19T09:24:42.000Z

from django.urls import path from . import views urlpatterns = [ path('', views.StartPageView.as_view()), path('accounts/created/', views.NotificationView.as_view()), path('accounts/<int:pk>/update/', views.StudentUpdate.as_view()), path('profile/', views.ProfilePageView.as_view()), path('profile/all_tasks/', views.AllTasks.as_view()), path('profile/all_tasks/answer', views.solution_create), path('profile/class_marks/subject_select', views.subject_select), path('profile/class_marks', views.class_marks), ]

38.857143

69

0.71875

0

161

0.295956

36bbde81383cafa0b00f9d5defddc4acebc151af

4,478

py

Python

tests/enviroments_test/test_environments.py

DKE-Data/agrirouter-sdk-python

6d6b26606f7d424c62289af56da55acf412772fc

[ "Apache-2.0" ]

null

tests/enviroments_test/test_environments.py

DKE-Data/agrirouter-sdk-python

6d6b26606f7d424c62289af56da55acf412772fc

[ "Apache-2.0" ]

null

tests/enviroments_test/test_environments.py

DKE-Data/agrirouter-sdk-python

6d6b26606f7d424c62289af56da55acf412772fc

[ "Apache-2.0" ]

null

"""Test agrirouter/environments/environments.py""" from agrirouter.environments.environments import ProductionEnvironment as PE from agrirouter.environments.environments import QAEnvironment as QAE from tests.constants import application_id class TestPE: def test_get_base_url(self): assert PE().get_base_url() == PE._ENV_BASE_URL def test_get_api_prefix(self): assert PE().get_api_prefix() == PE._API_PREFIX def test_get_registration_service_url(self): assert PE().get_registration_service_url() == PE._REGISTRATION_SERVICE_URL def test_get_onboard_url(self): onb_url = PE._REGISTRATION_SERVICE_URL + PE._API_PREFIX + "/registration/onboard" assert PE().get_onboard_url() == onb_url def test_get_secured_onboard_url(self): onb_url = PE._REGISTRATION_SERVICE_URL + PE._API_PREFIX + "/registration/onboard/request" assert PE().get_secured_onboard_url() == onb_url def test_get_verify_onboard_request_url(self): req_url = PE._REGISTRATION_SERVICE_URL + PE._API_PREFIX + "/registration/onboard/verify" assert PE().get_verify_onboard_request_url() == req_url def test_get_revoke_url(self): rev_url = PE._REGISTRATION_SERVICE_URL + PE._API_PREFIX + "/registration/onboard/revoke" assert PE().get_revoke_url() == rev_url def test_get_agrirouter_login_url(self): login_url = PE._ENV_BASE_URL + PE._AGRIROUTER_LOGIN_URL assert PE().get_agrirouter_login_url() == login_url def test_get_secured_onboarding_authorization_url(self): redirect_uri = "www.my_redirect.com" response_type = "response_type" assert PE().get_secured_onboarding_authorization_url( application_id, response_type, "state", redirect_uri ) == "https://goto.my-agrirouter.com/application/{application_id}/authorize?response_type={response_type}&state={state}".format( # noqa application_id=application_id, response_type=response_type, state="state") + f"&redirect_uri={redirect_uri}" def test_get_mqtt_server_url(self): assert PE().get_mqtt_server_url( "localhost", "5000" ) == PE._MQTT_URL_TEMPLATE.format( host="localhost", port="5000" ) def test_get_env_public_key(self): assert PE().get_env_public_key() == PE.AR_PUBLIC_KEY class TestQAE: def test_get_base_url(self): assert QAE().get_base_url() == QAE._ENV_BASE_URL def test_get_api_prefix(self): assert QAE().get_api_prefix() == QAE._API_PREFIX def test_get_registration_service_url(self): assert QAE().get_registration_service_url() == QAE._REGISTRATION_SERVICE_URL def test_get_onboard_url(self): onb_url = QAE._REGISTRATION_SERVICE_URL + QAE._API_PREFIX + "/registration/onboard" assert QAE().get_onboard_url() == onb_url def test_get_secured_onboard_url(self): onb_url = QAE._REGISTRATION_SERVICE_URL + QAE._API_PREFIX + "/registration/onboard/request" assert QAE().get_secured_onboard_url() == onb_url def test_get_verify_onboard_request_url(self): req_url = QAE._REGISTRATION_SERVICE_URL + QAE._API_PREFIX + "/registration/onboard/verify" assert QAE().get_verify_onboard_request_url() == req_url def test_get_revoke_url(self): rev_url = QAE._REGISTRATION_SERVICE_URL + QAE._API_PREFIX + "/registration/onboard/revoke" assert QAE().get_revoke_url() == rev_url def test_get_agrirouter_login_url(self): login_url = QAE._ENV_BASE_URL + QAE._AGRIROUTER_LOGIN_URL assert QAE().get_agrirouter_login_url() == login_url def test_get_secured_onboarding_authorization_url(self): redirect_uri = "www.my_redirect.com" response_type = "response_type" assert QAE().get_secured_onboarding_authorization_url( application_id, response_type, "state", redirect_uri ) == QAE._ENV_BASE_URL + QAE._SECURED_ONBOARDING_AUTHORIZATION_LINK_TEMPLATE.format( application_id=application_id, response_type=response_type, state="state") + f"&redirect_uri={redirect_uri}" def test_get_mqtt_server_url(self): assert QAE().get_mqtt_server_url( "localhost", "5000" ) == QAE._MQTT_URL_TEMPLATE.format(host="localhost", port="5000") def test_get_env_public_key(self): assert QAE().get_env_public_key() == QAE.AR_PUBLIC_KEY

42.245283

143

0.712818

4,230

0.944618

0

629

0.140464

36bbe5261935347fbb62f2ff569d538d41679679

556

py

Python

foursquare/tests/test_lang.py

milind-shakya-sp/foursquare

1df90777f9b86d8247e8d79e7fbe8e88c8cdd467

[ "MIT" ]

1

2019-06-10T21:12:01.000Z

foursquare/tests/test_lang.py

milind-shakya-sp/foursquare

1df90777f9b86d8247e8d79e7fbe8e88c8cdd467

[ "MIT" ]

null

foursquare/tests/test_lang.py

milind-shakya-sp/foursquare

1df90777f9b86d8247e8d79e7fbe8e88c8cdd467

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: UTF-8 -*- # (c) 2016 Mike Lewis import logging; log = logging.getLogger(__name__) from . import MultilangEndpointTestCase class MultiLangTestCase(MultilangEndpointTestCase): """ General """ def test_lang(self): """Test a wide swath of languages""" for api in self.apis: categories = api.venues.categories() assert 'categories' in categories, u"'categories' not in response" assert len(categories['categories']) > 1, u'Expected multiple categories'

27.8

85

0.652878

393

0.706835

0

210

0.377698

36bc7e0436f464b768c92e41f855171401f6f554

4,923

py

Python

src/tests/model_deployment_tests.py

vravisrpi/mlops-vertex

0944b22996a5405f64d7ae162bd2427ffd81884d

[ "Apache-2.0" ]

null

src/tests/model_deployment_tests.py

vravisrpi/mlops-vertex

0944b22996a5405f64d7ae162bd2427ffd81884d

[ "Apache-2.0" ]

null

src/tests/model_deployment_tests.py

vravisrpi/mlops-vertex

0944b22996a5405f64d7ae162bd2427ffd81884d

[ "Apache-2.0" ]

null

# Copyright 2021 Google LLC # # 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. """Test an uploaded model to Vertex AI.""" import os import logging import tensorflow as tf test_instance = { "dropoff_grid": ["POINT(-87.6 41.9)"], "euclidean": [2064.2696], "loc_cross": [""], "payment_type": ["Credit Card"], "pickup_grid": ["POINT(-87.6 41.9)"], "trip_miles": [1.37], "trip_day": [12], "trip_hour": [16], "trip_month": [2], "trip_day_of_week": [4], "trip_seconds": [555], } SERVING_DEFAULT_SIGNATURE_NAME = "serving_default" from google.cloud import aiplatform as vertex_ai def test_model_artifact(): pass ''' feature_types = { "dropoff_grid": tf.dtypes.string, "euclidean": tf.dtypes.float32, "loc_cross": tf.dtypes.string, "payment_type": tf.dtypes.string, "pickup_grid": tf.dtypes.string, "trip_miles": tf.dtypes.float32, "trip_day": tf.dtypes.int64, "trip_hour": tf.dtypes.int64, "trip_month": tf.dtypes.int64, "trip_day_of_week": tf.dtypes.int64, "trip_seconds": tf.dtypes.int64, } new_test_instance = dict() for key in test_instance: new_test_instance[key] = tf.constant( [test_instance[key]], dtype=feature_types[key] ) print(new_test_instance) project = os.getenv("PROJECT") region = os.getenv("REGION") model_display_name = os.getenv("MODEL_DISPLAY_NAME") assert project, "Environment variable PROJECT is None!" assert region, "Environment variable REGION is None!" assert model_display_name, "Environment variable MODEL_DISPLAY_NAME is None!" vertex_ai.init(project=project, location=region,) models = vertex_ai.Model.list( filter=f'display_name={model_display_name}', order_by="update_time" ) assert ( models ), f"No model with display name {model_display_name} exists!" model = models[-1] artifact_uri = model.gca_resource.artifact_uri logging.info(f"Model artifact uri:{artifact_uri}") assert tf.io.gfile.exists( artifact_uri ), f"Model artifact uri {artifact_uri} does not exist!" saved_model = tf.saved_model.load(artifact_uri) logging.info("Model loaded successfully.") assert ( SERVING_DEFAULT_SIGNATURE_NAME in saved_model.signatures ), f"{SERVING_DEFAULT_SIGNATURE_NAME} not in model signatures!" prediction_fn = saved_model.signatures["serving_default"] predictions = prediction_fn(**new_test_instance) logging.info("Model produced predictions.") keys = ["classes", "scores"] for key in keys: assert key in predictions, f"{key} in prediction outputs!" assert predictions["classes"].shape == ( 1, 2, ), f"Invalid output classes shape: {predictions['classes'].shape}!" assert predictions["scores"].shape == ( 1, 2, ), f"Invalid output scores shape: {predictions['scores'].shape}!" logging.info(f"Prediction output: {predictions}") ''' def test_model_endpoint(): pass ''' project = os.getenv("PROJECT") region = os.getenv("REGION") model_display_name = os.getenv("MODEL_DISPLAY_NAME") endpoint_display_name = os.getenv("ENDPOINT_DISPLAY_NAME") assert project, "Environment variable PROJECT is None!" assert region, "Environment variable REGION is None!" assert model_display_name, "Environment variable MODEL_DISPLAY_NAME is None!" assert endpoint_display_name, "Environment variable ENDPOINT_DISPLAY_NAME is None!" endpoints = vertex_ai.Endpoint.list( filter=f'display_name={endpoint_display_name}', order_by="update_time" ) assert ( endpoints ), f"Endpoint with display name {endpoint_display_name} does not exist! in region {region}" endpoint = endpoints[-1] logging.info(f"Calling endpoint: {endpoint}.") prediction = endpoint.predict([test_instance]).predictions[0] keys = ["classes", "scores"] for key in keys: assert key in prediction, f"{key} in prediction outputs!" assert ( len(prediction["classes"]) == 2 ), f"Invalid number of output classes: {len(prediction['classes'])}!" assert ( len(prediction["scores"]) == 2 ), f"Invalid number output scores: {len(prediction['scores'])}!" logging.info(f"Prediction output: {prediction}") '''

31.557692

95

0.672354

0

4,535

0.921186

36bd2fc4cc690280e24a0e546825f3792edd1b9b

266

py

Python

noxfile.py

aodag/asbool

1c5d74c9b2f641a3452c1e7118a4a83ffe665ab5

[ "MIT" ]

8

2015-11-20T01:20:13.000Z

2021-02-20T04:24:08.000Z

noxfile.py

aodag/asbool

1c5d74c9b2f641a3452c1e7118a4a83ffe665ab5

[ "MIT" ]

2

2020-12-08T05:16:48.000Z

2021-02-16T11:12:06.000Z

noxfile.py

aodag/asbool

1c5d74c9b2f641a3452c1e7118a4a83ffe665ab5

[ "MIT" ]

null

import nox nox.options.sessions = ["test"] @nox.session def test(session): session.install("-e", ".[testing]") session.run("pytest") @nox.session def pack(session): session.install("build") session.run("python", "-m", "build", ".")

17.733333

45

0.593985

0

209

0.785714

0

59

0.221805

36bdb06f6f3497fa1d06a8cb17f94061f6766f18

9,085

py

Python

selectGoodFeatures.py

TimSC/PyFeatureTrack

11668181e56fb9472a0c8db291c88546e7fae0cf

[ "BSD-2-Clause" ]

33

2015-02-24T18:23:11.000Z

2022-03-08T09:55:02.000Z

selectGoodFeatures.py

Nestart/PyFeatureTrack

11668181e56fb9472a0c8db291c88546e7fae0cf

[ "BSD-2-Clause" ]

1

2017-03-08T21:07:33.000Z

2017-06-04T21:58:01.000Z

selectGoodFeatures.py

Nestart/PyFeatureTrack

11668181e56fb9472a0c8db291c88546e7fae0cf

[ "BSD-2-Clause" ]

15

2016-06-29T08:55:58.000Z

2020-06-04T03:01:39.000Z

from __future__ import print_function import math, numpy as np from PIL import Image from klt import * from error import * from convolve import * from klt_util import * import goodFeaturesUtils class selectionMode: SELECTING_ALL = 1 REPLACING_SOME = 2 KLT_verbose = 1 #********************************************************************* def _fillFeaturemap(x, y, featuremap, mindist, ncols, nrows): for iy in range(y - mindist,y + mindist + 1): for ix in range(x - mindist, x + mindist + 1): if ix >= 0 and ix < ncols and iy >= 0 and iy < nrows: featuremap[iy*ncols+ix] = True return featuremap #********************************************************************* #* _enforceMinimumDistance #* #* Removes features that are within close proximity to better features. #* #* INPUTS #* featurelist: A list of features. The nFeatures property #* is used. #* #* OUTPUTS #* featurelist: Is overwritten. Nearby "redundant" features are removed. #* Writes -1's into the remaining elements. #* #* RETURNS #* The number of remaining features. #* def _enforceMinimumDistance(pointlist, featurelist, ncols, nrows, mindist, min_eigenvalue, overwriteAllFeatures): #int indx; # Index into features #int x, y, val; # Location and trackability of pixel under consideration #uchar *featuremap; # Boolean array recording proximity of features #int *ptr; # Cannot add features with an eigenvalue less than one if min_eigenvalue < 1: min_eigenvalue = 1 # Allocate memory for feature map and clear it #featuremap = (uchar *) malloc(ncols * nrows * sizeof(uchar)); #memset(featuremap, 0, ncols*nrows); featuremap = [False for i in range(ncols * nrows)] # Necessary because code below works with (mindist-1) mindist = mindist - 1 # If we are keeping all old good features, then add them to the featuremap if not overwriteAllFeatures: for indx, feat in enumerate(featurelist): if featurelist[indx].val >= 0: x = int(featurelist[indx].x) y = int(featurelist[indx].y) featuremap = _fillFeaturemap(x, y, featuremap, mindist, ncols, nrows) # For each feature point, in descending order of importance, do ... indx = 0 pointlistIndx = 0 while True: # If we can't add all the points, then fill in the rest # of the featurelist with -1's */ if pointlistIndx >= len(pointlist): while indx < len(featurelist): if overwriteAllFeatures and featurelist[indx].val < 0: featurelist[indx].x = -1 featurelist[indx].y = -1 featurelist[indx].val = kltState.KLT_NOT_FOUND featurelist[indx].aff_img = None featurelist[indx].aff_img_gradx = None featurelist[indx].aff_img_grady = None featurelist[indx].aff_x = -1.0 featurelist[indx].aff_y = -1.0 featurelist[indx].aff_Axx = 1.0 featurelist[indx].aff_Ayx = 0.0 featurelist[indx].aff_Axy = 0.0 featurelist[indx].aff_Ayy = 1.0 indx = indx + 1 break pointdata = pointlist[pointlistIndx] x = pointdata[1] y = pointdata[2] val = pointdata[0] pointlistIndx += 1 # Ensure that feature is in-bounds assert x >= 0 assert x < ncols assert y >= 0 assert y < nrows while not overwriteAllFeatures and indx < len(featurelist) and featurelist[indx].val >= 0: indx = indx + 1 if indx >= len(featurelist): break # If no neighbor has been selected, and if the minimum # eigenvalue is large enough, then add feature to the current list if not featuremap[y*ncols+x] and val >= min_eigenvalue: featurelist[indx].x = x featurelist[indx].y = y featurelist[indx].val = int(val) featurelist[indx].aff_img = None featurelist[indx].aff_img_gradx = None featurelist[indx].aff_img_grady = None featurelist[indx].aff_x = -1.0 featurelist[indx].aff_y = -1.0 featurelist[indx].aff_Axx = 1.0 featurelist[indx].aff_Ayx = 0.0 featurelist[indx].aff_Axy = 0.0 featurelist[indx].aff_Ayy = 1.0 indx = indx + 1 # Fill in surrounding region of feature map, but # make sure that pixels are in-bounds */ featuremap = _fillFeaturemap(x, y, featuremap, mindist, ncols, nrows); return featurelist #********************************************************************* def _KLTSelectGoodFeatures(tc,img,nFeatures,mode): featurelist = [KLT_Feature() for i in range(nFeatures)] #_KLT_FloatImage floatimg, gradx, grady; #int window_hw, window_hh #int *pointlist overwriteAllFeatures = (mode == selectionMode.SELECTING_ALL) floatimages_created = False ncols, nrows = img.size # Check window size (and correct if necessary) if tc.window_width % 2 != 1: tc.window_width = tc.window_width+1 KLTWarning("Tracking context's window width must be odd. Changing to {0}.\n".format(tc.window_width)) if tc.window_height % 2 != 1: tc.window_height = tc.window_height+1 KLTWarning("Tracking context's window height must be odd. Changing to {0}.\n".format(tc.window_height)) if tc.window_width < 3: tc.window_width = 3 KLTWarning("Tracking context's window width must be at least three. \nChanging to %d.\n".format(tc.window_width)) if tc.window_height < 3: tc.window_height = 3 KLTWarning("Tracking context's window height must be at least three. \nChanging to %d.\n".format(tc.window_height)) window_hw = tc.window_width/2 window_hh = tc.window_height/2 # Create pointlist, which is a simplified version of a featurelist, # for speed. Contains only integer locations and values. #pointlist = [0 for i in range(ncols * nrows * 3)] # Create temporary images, etc. if mode == selectionMode.REPLACING_SOME and tc.sequentialMode and tc.pyramid_last != None: floatimg = tc.pyramid_last.img[0] gradx = tc.pyramid_last_gradx.img[0] grady = tc.pyramid_last_grady.img[0] assert gradx != None assert grady != None else: floatimages_created = True floatimg = Image.new("F", img.size) gradx = Image.new("F", img.size) grady = Image.new("F", img.size) if tc.smoothBeforeSelecting: #_KLT_FloatImage tmpimg; #tmpimg = Image.new("F", img.size) tmpimg = np.array(img.convert("F")) floatimg = KLTComputeSmoothedImage(tmpimg, KLTComputeSmoothSigma(tc)) #_KLTFreeFloatImage(tmpimg) else: floatimg = np.array(img.convert("F")) # Compute gradient of image in x and y direction gradx, grady = KLTComputeGradients(floatimg, tc.grad_sigma) # Write internal images if tc.writeInternalImages: floatimg.save("kltimg_sgfrlf.pgm") gradx.save("kltimg_sgfrlf_gx.pgm") grady.save("kltimg_sgfrlf_gy.pgm") # Compute trackability of each image pixel as the minimum # of the two eigenvalues of the Z matrix #register float gx, gy; #register float gxx, gxy, gyy; #register int xx, yy; #register int *ptr; #float val; #unsigned int limit = 1; borderx = tc.borderx; # Must not touch cols bordery = tc.bordery; # lost by convolution #int x, y; #int i; if borderx < window_hw: borderx = window_hw if bordery < window_hh: bordery = window_hh # Find largest value of an int #for (i = 0 ; i < sizeof(int) ; i++) limit *= 256; #limit = limit/2 - 1; #gradxArr = np.array(gradx) #gradyArr = np.array(grady) pointlistx,pointlisty,pointlistval=goodFeaturesUtils.ScanImageForGoodFeatures(gradx,\ grady, borderx, bordery, window_hw, window_hh, tc.nSkippedPixels) # Sort the features pointlist = list(zip(pointlistval, pointlistx, pointlisty)) pointlist.sort() pointlist.reverse() #print(pointlist) # Check tc.mindist if tc.mindist < 0: KLTWarning("(_KLTSelectGoodFeatures) Tracking context field tc.mindist is negative ({0}); setting to zero".format(tc.mindist)) tc.mindist = 0; # Enforce minimum distance between features _enforceMinimumDistance(pointlist, \ featurelist, \ ncols, nrows, \ tc.mindist, \ tc.min_eigenvalue, \ overwriteAllFeatures) # Free memory # free(pointlist); # if (floatimages_created) { # _KLTFreeFloatImage(floatimg); # _KLTFreeFloatImage(gradx); # _KLTFreeFloatImage(grady); # } return featurelist #********************************************************************* #* KLTSelectGoodFeatures #* #* Main routine, visible to the outside. Finds the good features in #* an image. #* #* INPUTS #* tc: Contains parameters used in computation (size of image, #* size of window, min distance b/w features, sigma to compute #* image gradients, # of features desired). #* img: Pointer to the data of an image (probably unsigned chars). #* #* OUTPUTS #* features: List of features. The member nFeatures is computed. #* def KLTSelectGoodFeatures(tc, img, nFeatures): ncols, nrows = img.size #int ncols, int nrows, if KLT_verbose >= 1: print("(KLT) Selecting the {0} best features from a {1} by {2} image... ".format(nFeatures, ncols, nrows)) fl = _KLTSelectGoodFeatures(tc, img, nFeatures, selectionMode.SELECTING_ALL) if KLT_verbose >= 1: print("\n\t{0} features found.\n".format(KLTCountRemainingFeatures(fl))) if tc.writeInternalImages: print("\tWrote images to 'kltimg_sgfrlf*.pgm'.\n") return fl

30.692568

128

0.681233

59

0.006494

0

3,869

0.425867

36be052ecd5aed78815486dfc598f4e2ff2a749d

3,345

py

Python

pysparsdr/pySparSDR.py

ucsdwcsng/pySparSDR

6622fce9c75b180b8601d9deecafff401e6a4d9f

[ "Apache-2.0" ]

null

pysparsdr/pySparSDR.py

ucsdwcsng/pySparSDR

6622fce9c75b180b8601d9deecafff401e6a4d9f

[ "Apache-2.0" ]

null

pysparsdr/pySparSDR.py

ucsdwcsng/pySparSDR

6622fce9c75b180b8601d9deecafff401e6a4d9f

[ "Apache-2.0" ]

null

#/bin/python3 import numpy as np from scipy import signal as sig class pySparSDRCompress(): ''' Implementation of the SparSDR Compressor based on Khazraee, M., Guddeti, Y., Crow, S., Snoeren, A.C., Levchenko, K., Bharadia, D. and Schulman, A., 2019, June. Sparsdr: Sparsity-proportional backhaul and compute for sdrs. In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (pp. 391-403). ''' def __init__(self,nfft=1024,thresholdVec=None): ''' Initialize SparSDR Compressor :input: nfft :shouldBeEven: Number of bins in fft ''' assert not nfft%2 self.nfft = nfft self.nover = int(self.nfft/2) self.windowVec = sig.windows.hann(self.nfft, sym=False) self.windowVec = np.expand_dims(self.windowVec,axis=1) if thresholdVec is None: self.setThreshold(np.zeros((1,self.nfft))) else: self.setThreshold(thresholdVec) self.bufferState = np.zeros((self.nover,)) self.numWinProcessed = 0 def reset(self): ''' Resets internal memory if the compressor needs to be re-started (soft-reset) ''' self.bufferState = 0*self.bufferState self.numWinProcessed = 0 def setThreshold(self, thresholdVec): ''' Sets internal threshold vector :input: thresholdVec :shape==(1,nfft): real-valued thresholds as numpy array ''' assert thresholdVec.shape == (1,self.nfft) self.thresholdVec = thresholdVec def work(self, xIn): ''' Perform compression on input vector :input: xIn :numElements==k*nfft: input signal as a numpy array :output: (windowIdx, binIdx, binValue) :output: windowIdx : Index of window over all-time :output: binIdx : Index of bin in a particular window :output: binValue : Value of the binIdx at the windowIdx This function remembers past input and stores overlap in the bufferState variable ''' assert not xIn.size%self.nfft # concatenate filter state xIn = np.concatenate((self.bufferState, xIn)) # Half-Overlapped windowing evenWindows = self.windowVec*xIn[:-self.nover].reshape((self.nfft,-1)) oddWindows = self.windowVec*xIn[self.nover:].reshape((self.nfft,-1)) # Fourier Transform evenWindows = np.fft.fft(evenWindows,axis=0) oddWindows = np.fft.fft(oddWindows,axis=0) # Interleave overlapped windows output = np.empty((self.nfft, 2*evenWindows.shape[1]) , dtype=evenWindows.dtype) output[:,0::2] = evenWindows output[:,1::2] = oddWindows output = output.transpose() # Threshold to find areas of activity thresholdFlag = np.abs(output) > self.thresholdVec thresholdFlag = np.transpose(thresholdFlag.nonzero()) # Select only active bins output = output[thresholdFlag[:,0],thresholdFlag[:,1]] thresholdFlag[:,0] = self.numWinProcessed + thresholdFlag[:,0] # Update internal states self.bufferState = xIn[-self.nover:] self.numWinProcessed = self.numWinProcessed + 2*evenWindows.shape[1] return thresholdFlag[:,0], thresholdFlag[:,1], output

37.166667

295

0.635277

3,277

0.979671

0

1,387

0.414649

36bf9270f81abe8f83096f56129e26e2554011cc

803

py

Python

dirtyclean/tests/test_dirtyclean.py

paultopia/dirtyclean

1b93b29e070b53afede22ff28497fd68f28d0326

[ "MIT" ]

2

2017-12-04T16:58:57.000Z

2021-03-02T04:59:54.000Z

dirtyclean/tests/test_dirtyclean.py

paultopia/dirtyclean

1b93b29e070b53afede22ff28497fd68f28d0326

[ "MIT" ]

null

dirtyclean/tests/test_dirtyclean.py

paultopia/dirtyclean

1b93b29e070b53afede22ff28497fd68f28d0326

[ "MIT" ]

null

from dirtyclean import clean import unittest class TestDirtyClean(unittest.TestCase): def setUp(self): self.uglystring = " st—up•id ‘char−ac ter..s’, in its’ string...”Ç " with open("multiline.txt") as mt: self.multiline = mt.read() def test_basic_clean(self): self.assertEqual(clean(self.uglystring), "st up id char ac ter s in its string Ç") def test_simplify_letters(self): self.assertEqual(clean(self.uglystring, simplify_letters=True), "st up id char ac ter s in its string C") def test_multiline(self): self.assertEqual(clean(self.multiline), "I am the very model of a multiline string with more stuff than you might want to have in there Ç")

33.458333

124

0.617684

771

0.940244

0

264

0.321951

36bfb2d78d16ac861521aa10b4dcdbc76d656637

1,321

py

Python

findNearestControl.py

petrarch1603/SurveyApplications

129a4e24123bf81687c0a60cccbe3d0a83f63e40

[ "MIT" ]

1

2019-08-24T20:29:05.000Z

findNearestControl.py

petrarch1603/SurveyApplications

129a4e24123bf81687c0a60cccbe3d0a83f63e40

[ "MIT" ]

null

findNearestControl.py

petrarch1603/SurveyApplications

129a4e24123bf81687c0a60cccbe3d0a83f63e40

[ "MIT" ]

null

import csv control = "/Users/patrickmcgranaghan1/Documents/Python/python_work/SurveyApplications/source_data/control.csv" set_points = "/Users/patrickmcgranaghan1/Documents/Python/python_work/SurveyApplications/source_data/setPoints.csv" max_hypotenuse = 200 # Integer in feet # Note in the State Plane Coordinate System the coordinates are written Northing(Y), Easting(X) # This is the opposite of the normal (X, Y) coordinate system. with open(set_points, 'r') as set_pts: set_reader = csv.reader(set_pts) for set_coord in set_reader: temp_list = [] with open(control, 'r') as ctrl: ctrl_reader = csv.reader(ctrl) for ctrl_coord in ctrl_reader: xDelta = int(set_coord[2]) - int(ctrl_coord[2]) yDelta = int(set_coord[1]) - int(ctrl_coord[1]) hypotenuse = ((xDelta ** 2) + (yDelta ** 2)) ** 0.5 if hypotenuse <= max_hypotenuse: tup = (ctrl_coord[0], hypotenuse) temp_list.append(tup) closest_base = (min(temp_list, key=lambda t: t[1])) # Below write code to insert the closest control points into the spreadsheet in a selected column print(set_coord[0] + " is closest to " + (closest_base[0]) + ". A distance of " + str(closest_base[1]))

48.925926

115

0.650265

0

516

0.390613

36c07f8de0ab1e4bb4abec5686212164de45b5a1

2,118

py

Python

stac_compose/collections/controller.py

dgi-catalog/stac-compose

1cae4a58fcfb36082c203db3c99e2779fc207400

[ "MIT" ]

null

stac_compose/collections/controller.py

dgi-catalog/stac-compose

1cae4a58fcfb36082c203db3c99e2779fc207400

[ "MIT" ]

14

2021-03-01T20:59:20.000Z

2021-11-24T19:14:49.000Z

stac_compose/collections/controller.py

dgi-catalog/stac-compose

1cae4a58fcfb36082c203db3c99e2779fc207400

[ "MIT" ]

null

from json import dumps from pprint import PrettyPrinter from cerberus.validator import Validator from flask import request from flask_restx import Resource from werkzeug.exceptions import BadRequest from stac_compose.collections import ns as api from stac_compose.collections.business import CollectionsBusiness from stac_compose.collections.parsers import validate, COLLECTIONS_CONTROLLER_VALIDATION from stac_compose.decorator import catch_generic_exceptions from stac_compose.environment import SC_LOGGING_LEVEL from stac_compose.logger import create_logger # create logger object logger = create_logger(__name__, level=SC_LOGGING_LEVEL) pp = PrettyPrinter(indent=4) @api.route('/') class CollectionsController(Resource): """CollectionsController""" @catch_generic_exceptions def get(self): args = request.args.to_dict(flat=True) logger.info(f'received args: {args}') v = Validator(COLLECTIONS_CONTROLLER_VALIDATION) if not v.validate(args): errors = dumps(v.errors) logger.error(f'request arguments are not valid: {errors}\n') raise BadRequest(errors) # 400 - Bad Request # get validated arguments validated_args = v.document logger.info(f'validated args: {validated_args}\n') # return a list of STAC collections by providers return CollectionsBusiness.get_collections_by_providers(validated_args) @api.route('/items/') class CollectionsItemsController(Resource): """CollectionsItemsController""" @catch_generic_exceptions def get(self): args = request.args.to_dict(flat=True) logger.info('args: %s', args) data, status = validate(args, 'search_get') logger.info('data: %s', data) logger.info('status: %s', status) if status is False: raise BadRequest(dumps(data)) # 400 - Bad Request features = CollectionsBusiness.search_get(**request.args) # logger.debug('\n\nCollectionsItemsController.get() - features: %s \n\n', features) # pp.pprint(features) return features

29.830986

92

0.715297

1,399

0.660529

0

1,437

0.67847

0

460

0.217186

36c20378107325500044b16060b5655f3ad7826c

6,070

py

Python

python/tvm/auto_scheduler/workload_registry.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

2

2020-07-07T07:38:45.000Z

2021-06-02T07:08:09.000Z

python/tvm/auto_scheduler/workload_registry.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

1

2020-07-29T07:29:17.000Z

python/tvm/auto_scheduler/workload_registry.py

jiangzoi/incubator-tvm

144c6f45f7217b9df2f5605e06f0903e470ac11c

[ "Apache-2.0" ]

1

2021-07-03T08:09:32.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """ Workload registration and serialization. We use a json string to represent a workload (a computation graph). The format of the string is `[func_name, [args...]]`. The dag should be the return value of this `func_name(*args)`. Rationale: The workload is actually a compute dag defined by tvm dsl. But serializing compute dags and matching them efficiently is not easy. Therefore, we use the above string to encode a compute dag. These strings are efficient for serialization/matching and won't be too long. When we need the dag, we decode the string and call the function, which will return the dag. """ import pickle import json import tvm._ffi from .utils import serialize_args, deserialize_args, get_func_name WORKLOAD_FUNC_REGISTRY = {} def register_workload(func_name, f=None, override=False): """ Register a function that generates a certain workload. The input function should take hashable and jsonable arguments (int, float, tuple of int, tvm.tensor.Tensor, ...) and return a list of tvm.tensor.Tensor. Parameters ---------- func_name : Union[Function, str] The generation function that returns the compute declaration Tensors or its function name. f : Optional[Function] The generation function to be registered. override : boolean = False Whether override existing entry. Examples -------- @auto_scheduler.register_workload def matmul(N, M, K): A = te.placeholder((N, K), name='A') B = te.placeholder((K, M), name='B') k = te.reduce_axis((0, K), name='k') C = te.compute((N, M), lambda i, j: tvm.sum(A[i][k] * B[k][j], axis=[k]), name='C') return [A, B, C] """ global WORKLOAD_FUNC_REGISTRY if callable(func_name): f = func_name func_name = get_func_name(f) if not isinstance(func_name, str): raise ValueError("expect string function name") def register(myf): """internal register function""" if func_name in WORKLOAD_FUNC_REGISTRY and not override: raise RuntimeError('%s has been registered already' % func_name) WORKLOAD_FUNC_REGISTRY[func_name] = myf return myf if f: return register(f) return register def make_workload_key(func, args): """ Make a workload key by function and arguments. Parameters ---------- func : Union[Function, str] The function that returns the compute declaration Tensors. Can be the a function or the function name. args : Args The args of the function. Returns ------- workload_key : Str The workload key of the function. """ global WORKLOAD_FUNC_REGISTRY if callable(func): func_name = get_func_name(func) elif isinstance(func, str): func_name = func else: raise ValueError("Invalid function: " + str(func) + " . `make_workload_key` expects a callable function or its function name") if not func_name in WORKLOAD_FUNC_REGISTRY: raise ValueError("%s is not registered. " % func, "Please register it with @auto_scheduler.register_workload") args = serialize_args(args) return json.dumps((func_name,) + args) def decode_workload_key_to_func_args(workload_key): """ Decode a workload key to the registerd function name and its corresponding args. Parameters ---------- workload_key : str The input workload key. Returns ------- name : str The function name of this workload key. args : List[Tensor] The args of the generation function. """ global WORKLOAD_FUNC_REGISTRY workload = json.loads(workload_key) if not workload[0] in WORKLOAD_FUNC_REGISTRY: raise ValueError("%s is not registered. " % workload[0] + "Please register it with @auto_scheduler.register_workload") return workload[0], deserialize_args(workload[1:]) @tvm._ffi.register_func("auto_scheduler.workload_key_to_tensors") def workload_key_to_tensors(workload_key): """ Get the input/output tensors from the workload key. This method is usually used to create a ComputeDAG by workload key. Parameters ---------- workload_key : str The input workload key. Returns ------- tensors : List[Tensor] The registered compute declaration Tensors. """ global WORKLOAD_FUNC_REGISTRY name, args = decode_workload_key_to_func_args(workload_key) lookup = WORKLOAD_FUNC_REGISTRY[name] assert callable(lookup) return lookup(*args) def save_workload_func_registry(filename): """ Dump workload function registry to a pickle binary file. Parameters ---------- filename : str The filename to dump workload function registry to. """ global WORKLOAD_FUNC_REGISTRY pickle.dump(WORKLOAD_FUNC_REGISTRY, open(filename, 'wb')) def load_workload_func_registry(filename): """ Load workload function registry from a pickle binary file. Parameters ---------- filename : str The filename to load workload function registry from. """ global WORKLOAD_FUNC_REGISTRY WORKLOAD_FUNC_REGISTRY = pickle.load(open(filename, 'rb'))

31.614583

99

0.682208

0

633

0.104283

0

4,065

0.669687

36c26ea8b70af852028240a4c83a673def2fbdd3

485

py

Python

main/xrandr/template.py

RoastVeg/cports

803c7f07af341eb32f791b6ec1f237edb2764bd5

[ "BSD-2-Clause" ]

null

main/xrandr/template.py

RoastVeg/cports

803c7f07af341eb32f791b6ec1f237edb2764bd5

[ "BSD-2-Clause" ]

null

main/xrandr/template.py

RoastVeg/cports

803c7f07af341eb32f791b6ec1f237edb2764bd5

[ "BSD-2-Clause" ]

null

pkgname = "xrandr" pkgver = "1.5.1" pkgrel = 0 build_style = "gnu_configure" hostmakedepends = ["pkgconf"] makedepends = ["libxrandr-devel"] pkgdesc = "Command line interface to X RandR extension" maintainer = "q66 <q66@chimera-linux.org>" license = "MIT" url = "https://xorg.freedesktop.org" source = f"$(XORG_SITE)/app/{pkgname}-{pkgver}.tar.xz" sha256 = "7bc76daf9d72f8aff885efad04ce06b90488a1a169d118dea8a2b661832e8762" def post_install(self): self.install_license("COPYING")

30.3125

75

0.748454

0

285

0.587629

36c29207131a5d0aabb533544ef1349cab67ea61

2,477

py

Python

src/arch/riscv/RiscvCPU.py

yclin99/CS251A_final_gem5

391ca1d7c9484f4d58fce9a4424821dcbb2463ac

[ "BSD-3-Clause" ]

1

2022-03-25T13:18:26.000Z

src/arch/riscv/RiscvCPU.py

yclin99/CS251A_final_gem5

391ca1d7c9484f4d58fce9a4424821dcbb2463ac

[ "BSD-3-Clause" ]

1

2022-03-25T14:15:30.000Z

src/arch/riscv/RiscvCPU.py

ksco/gem5-xiangshan

0baf1b5229885d81d689a677102f0665aaac5514

[ "BSD-3-Clause" ]

null

# Copyright 2021 Google, Inc. # # Redistribution and use in source and binary forms, with or without # modification, are permitted 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 the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # 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. from m5.objects.BaseAtomicSimpleCPU import BaseAtomicSimpleCPU from m5.objects.BaseNonCachingSimpleCPU import BaseNonCachingSimpleCPU from m5.objects.BaseTimingSimpleCPU import BaseTimingSimpleCPU from m5.objects.BaseO3CPU import BaseO3CPU from m5.objects.BaseMinorCPU import BaseMinorCPU from m5.objects.RiscvDecoder import RiscvDecoder from m5.objects.RiscvMMU import RiscvMMU from m5.objects.RiscvInterrupts import RiscvInterrupts from m5.objects.RiscvISA import RiscvISA class RiscvCPU: ArchDecoder = RiscvDecoder ArchMMU = RiscvMMU ArchInterrupts = RiscvInterrupts ArchISA = RiscvISA class RiscvAtomicSimpleCPU(BaseAtomicSimpleCPU, RiscvCPU): mmu = RiscvMMU() class RiscvNonCachingSimpleCPU(BaseNonCachingSimpleCPU, RiscvCPU): mmu = RiscvMMU() class RiscvTimingSimpleCPU(BaseTimingSimpleCPU, RiscvCPU): mmu = RiscvMMU() class RiscvO3CPU(BaseO3CPU, RiscvCPU): mmu = RiscvMMU() class RiscvMinorCPU(BaseMinorCPU, RiscvCPU): mmu = RiscvMMU()

44.232143

72

0.805006

498

0.20105

0

1,467

0.592249

36c4f0d8dd30675016f1cde8a4e0b430d5e215ed

164

py

Python

misc/validateInput.py

viju4you/Python

3c4a3a46265e71fc21da62d2cb204d20dcd9ec62

[ "CC0-1.0" ]

110

2017-03-11T23:37:46.000Z

2021-07-12T11:51:32.000Z

misc/validateInput.py

viju4you/Python

3c4a3a46265e71fc21da62d2cb204d20dcd9ec62

[ "CC0-1.0" ]

null

misc/validateInput.py

viju4you/Python

3c4a3a46265e71fc21da62d2cb204d20dcd9ec62

[ "CC0-1.0" ]

52

2016-11-27T19:50:40.000Z

2022-02-09T06:37:24.000Z

# Validate input while True: print('Enter your age:') age = input() if age.isdecimal(): break print('Pleas enter a number for your age.')

16.4

47

0.597561

0

69

0.420732

36c5772cb7b021a7fd6965ba28a4663832c436d3

1,003

py

Python

ckan/migration/versions/041_resource_new_fields.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

12

2015-08-28T16:59:07.000Z

2020-03-08T01:39:30.000Z

ckan/migration/versions/041_resource_new_fields.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

13

2019-05-02T21:01:28.000Z

2020-10-20T23:34:48.000Z

ckan/migration/versions/041_resource_new_fields.py

florianm/ckan

1cfd98d591ac70b4eb81048bcd227b6c1354b1bf

[ "Apache-2.0" ]

10

2015-05-08T04:33:20.000Z

2020-03-03T15:17:58.000Z

from migrate import * def upgrade(migrate_engine): migrate_engine.execute( ''' begin; ALTER TABLE resource ADD COLUMN name text, ADD COLUMN resource_type text, ADD COLUMN mimetype text, ADD COLUMN mimetype_inner text, ADD COLUMN "size" bigint, ADD COLUMN last_modified timestamp without time zone, ADD COLUMN cache_url text, ADD COLUMN cache_last_updated timestamp without time zone, ADD COLUMN webstore_url text, ADD COLUMN webstore_last_updated timestamp without time zone; ALTER TABLE resource_revision ADD COLUMN name text, ADD COLUMN resource_type text, ADD COLUMN mimetype text, ADD COLUMN mimetype_inner text, ADD COLUMN "size" bigint, ADD COLUMN last_modified timestamp without time zone, ADD COLUMN cache_url text, ADD COLUMN cache_last_updated timestamp without time zone, ADD COLUMN webstore_url text, ADD COLUMN webstore_last_updated timestamp without time zone; commit; ''' )

30.393939

65

0.731805

0

912

0.909272

36c6dd4f5d4854726c666ad63dd36dff26b82159

1,153

py

Python

src/train_model.py

hzdr/dvc_tutorial_series

f53eee599cc05e2c2ea31f6e2fd567a4ac3061a3

[ "BSD-3-Clause" ]

2

2021-06-24T13:39:39.000Z

2022-02-27T13:35:02.000Z

src/train_model.py

hzdr/dvc_tutorial_series

f53eee599cc05e2c2ea31f6e2fd567a4ac3061a3

[ "BSD-3-Clause" ]

null

src/train_model.py

hzdr/dvc_tutorial_series

f53eee599cc05e2c2ea31f6e2fd567a4ac3061a3

[ "BSD-3-Clause" ]

null

import pickle import pandas as pd import yaml from sklearn.linear_model import ElasticNet, LogisticRegression from sklearn.ensemble import RandomForestRegressor from config import Config Config.MODELS_PATH.mkdir(parents=True, exist_ok=True) with open ("params.yaml", "r") as fd: params = yaml.safe_load(fd) model_type = params['model_type'] lr = params['lr'] random_state = params['random_state'] #epochs = params['train']['epochs'] alpha = params['train']['alpha'] l1_rate = params['train']['l1_rate'] X_train = pd.read_csv(str(Config.FEATURES_PATH / "train_features.csv")) y_train = pd.read_csv(str(Config.FEATURES_PATH / "train_labels.csv")) if model_type == "LogisticRegression": model = LogisticRegression(l1_ratio=l1_rate, random_state=random_state) if model_type == "RandomForestRegressor": model = RandomForestRegressor( n_estimators=150, max_depth=6, random_state=random_state ) if model_type == "ElasticNet": model = ElasticNet( alpha=alpha, l1_ratio=l1_rate, random_state=random_state ) model.fit(X_train, y_train) pickle.dump(model, open(str(Config.MODELS_PATH / "model.pickle"), "wb"))

28.825

75

0.743278

0

222

0.192541

36c6fc43e8d2fdc269e708e857550cc5862aa1c5

8,226

py

Python

opentracing/harness/api_check.py

autocracy/opentracing-python

ac45df0c39b4cce8e6e6ca40dedc2b9f6c388328

[ "MIT" ]

null

opentracing/harness/api_check.py

autocracy/opentracing-python

ac45df0c39b4cce8e6e6ca40dedc2b9f6c388328

[ "MIT" ]

null

opentracing/harness/api_check.py

autocracy/opentracing-python

ac45df0c39b4cce8e6e6ca40dedc2b9f6c388328

[ "MIT" ]

null

# Copyright (c) 2016 The OpenTracing Authors. # # 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. from __future__ import absolute_import import time import pytest import opentracing from opentracing import Format class APICompatibilityCheckMixin(object): """ A mixin class for validation that a given tracer implementation satisfies the requirements of the OpenTracing API. """ def tracer(self): raise NotImplementedError('Subclass must implement tracer()') def check_baggage_values(self): """If true, the test will validate Baggage items by storing and retrieving them from the trace context. If false, it will only attempt to store and retrieve the Baggage items to check the API compliance, but not actually validate stored values. The latter mode is only useful for no-op tracer. """ return True def test_start_span(self): tracer = self.tracer() span = tracer.start_span(operation_name='Fry') span.finish() with tracer.start_span(operation_name='Fry', tags={'birthday': 'August 14 1974'}) as span: span.log_event('birthplace', payload={'hospital': 'Brooklyn Pre-Med Hospital', 'city': 'Old New York'}) def test_start_span_with_parent(self): tracer = self.tracer() parent_span = tracer.start_span(operation_name='parent') assert parent_span is not None span = tracer.start_span( operation_name='Leela', child_of=parent_span) span.finish() span = tracer.start_span( operation_name='Leela', references=[opentracing.follows_from(parent_span.context)], tags={'birthplace': 'sewers'}) span.finish() parent_span.finish() def test_start_child_span(self): tracer = self.tracer() parent_span = tracer.start_span(operation_name='parent') assert parent_span is not None child_span = opentracing.start_child_span( parent_span, operation_name='Leela') child_span.finish() parent_span.finish() def test_set_operation_name(self): span = self.tracer().start_span().set_operation_name('Farnsworth') span.finish() def test_span_as_context_manager(self): finish = {'called': False} def mock_finish(*_): finish['called'] = True with self.tracer().start_span(operation_name='antiquing') as span: setattr(span, 'finish', mock_finish) assert finish['called'] is True # now try with exception finish['called'] = False try: with self.tracer().start_span(operation_name='antiquing') as span: setattr(span, 'finish', mock_finish) raise ValueError() except ValueError: assert finish['called'] is True else: raise AssertionError('Expected ValueError') # pragma: no cover def test_span_tag_value_types(self): with self.tracer().start_span(operation_name='ManyTypes') as span: span. \ set_tag('an_int', 9). \ set_tag('a_bool', True). \ set_tag('a_string', 'aoeuidhtns') def test_span_tags_with_chaining(self): span = self.tracer().start_span(operation_name='Farnsworth') span. \ set_tag('birthday', '9 April, 2841'). \ set_tag('loves', 'different lengths of wires') span. \ set_tag('unicode_val', u'non-ascii: \u200b'). \ set_tag(u'unicode_key_\u200b', 'ascii val') span.finish() def test_span_logs(self): span = self.tracer().start_span(operation_name='Fry') # Newer API span.log_kv( {'frozen.year': 1999, 'frozen.place': 'Cryogenics Labs'}) span.log_kv( {'defrosted.year': 2999, 'defrosted.place': 'Cryogenics Labs'}, time.time()) # Older API span.\ log_event('frozen', {'year': 1999, 'place': 'Cryogenics Labs'}). \ log_event('defrosted', {'year': 2999}). \ log_event('became his own grandfather', 1947) span.\ log(event='frozen'). \ log(payload={'year': 1999}). \ log(timestamp=time.time(), event='frozen', payload={'year': 1999}). \ log(timestamp=time.time(), event='unfrozen', payload={'year': 2999}) def test_span_baggage(self): with self.tracer().start_span(operation_name='Fry') as span: assert span.context.baggage == {} span_ref = span.set_baggage_item('Kiff-loves', 'Amy') assert span_ref is span val = span.get_baggage_item('Kiff-loves') if self.check_baggage_values(): assert 'Amy' == val pass def test_context_baggage(self): with self.tracer().start_span(operation_name='Fry') as span: assert span.context.baggage == {} span.set_baggage_item('Kiff-loves', 'Amy') if self.check_baggage_values(): assert span.context.baggage == {'Kiff-loves': 'Amy'} pass def test_text_propagation(self): with self.tracer().start_span(operation_name='Bender') as span: text_carrier = {} self.tracer().inject( span_context=span.context, format=opentracing.Format.TEXT_MAP, carrier=text_carrier) extracted_ctx = self.tracer().extract( format=opentracing.Format.TEXT_MAP, carrier=text_carrier) assert extracted_ctx.baggage == {} def test_binary_propagation(self): with self.tracer().start_span(operation_name='Bender') as span: bin_carrier = bytearray() self.tracer().inject( span_context=span.context, format=opentracing.Format.BINARY, carrier=bin_carrier) extracted_ctx = self.tracer().extract( format=opentracing.Format.BINARY, carrier=bin_carrier) assert extracted_ctx.baggage == {} def test_mandatory_formats(self): formats = [ (Format.TEXT_MAP, {}), (Format.HTTP_HEADERS, {}), (Format.BINARY, bytearray()), ] with self.tracer().start_span(operation_name='Bender') as span: for fmt, carrier in formats: # expecting no exceptions span.tracer.inject(span.context, fmt, carrier) span.tracer.extract(fmt, carrier) def test_unknown_format(self): custom_format = 'kiss my shiny metal ...' with self.tracer().start_span(operation_name='Bender') as span: with pytest.raises(opentracing.UnsupportedFormatException): span.tracer.inject(span.context, custom_format, {}) with pytest.raises(opentracing.UnsupportedFormatException): span.tracer.extract(custom_format, {})

39.358852

79

0.612205

7,003

0.851325

0

2,499

0.303793

36c93b1ef9b9eeb9b865aada75df7cf42d64021f

29,950

py

Python

Colab/vision_transformer_dogs_and_cats_python_generator.py

Thanusan19/Vision_Transformer

80179d57e617ef6cd9599de93c7c7633f891f9a9

[ "Apache-2.0" ]

1

2021-07-02T13:55:11.000Z

Colab/vision_transformer_dogs_and_cats_python_generator.py

Thanusan19/Vision_Transformer

80179d57e617ef6cd9599de93c7c7633f891f9a9

[ "Apache-2.0" ]

null

Colab/vision_transformer_dogs_and_cats_python_generator.py

Thanusan19/Vision_Transformer

80179d57e617ef6cd9599de93c7c7633f891f9a9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Vision Transformer Dogs and Cats Python Generator Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/12u7r2OMkt_rFmOQq2g5FtX7Z0EbyPYFN See code at https://github.com/google-research/vision_transformer/ See paper at https://arxiv.org/abs/2010.11929 This Colab allows you to run the [JAX](https://jax.readthedocs.org) implementation of the Vision Transformer. ## 1) Using generator ### 1.1) Download the dataset and unpack it on the colab machine """ !pwd !mkdir dataset !ls !wget -c "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip" -P dataset/ !ls dataset/ # Quiet and overwrite, will create folder and unpack in CatsAndDogs !unzip -qo dataset/kagglecatsanddogs_3367a.zip -d dataset/CatsAndDogs # Print the number of cats and dogs images in the set !ls -l dataset/CatsAndDogs/PetImages/Cat/*.jpg | wc -l !ls -l dataset/CatsAndDogs/PetImages/Dog/*.jpg | wc -l # Sanity check for later !ls dataset/CatsAndDogs/PetImages/Cat/*.jpg | sed -E 's#.*/##' | sort > /tmp/Cats.txt !ls dataset/CatsAndDogs/PetImages/Dog/*.jpg | sed -E 's#.*/##' | sort > /tmp/Dogs.txt !diff /tmp/Cats.txt /tmp/Dogs.txt """### 1.2) Find the corrupted files #### Find the corrupted files """ # Will be quiet, except for errors # see [https://peteris.rocks/blog/quiet-and-unattended-installation-with-apt-get/] !apt-get install imagemagick -qq > /dev/null # Examples that are corrupted : Cat/1418.jpg, Cat/4293.jpg, Cat/666.jpg # Can take a bit of time to check all 25000 images !mogrify -set comment 'Image rewritten with ImageMagick' dataset/CatsAndDogs/PetImages/*/*.jpg |& tee dataset/CatsAndDogs/mogrify_output #!cat dataset/CatsAndDogs/mogrify_output """#### Fix some problems with a certain picture in Cats (handmade)""" # Sanity check for later !ls dataset/CatsAndDogs/PetImages/Cat/*.jpg | sed -E 's#.*/##' | sort > /tmp/Cats.txt !ls dataset/CatsAndDogs/PetImages/Dog/*.jpg | sed -E 's#.*/##' | sort > /tmp/Dogs.txt !diff /tmp/Cats.txt /tmp/Dogs.txt # Cat 10404 has three versions... from google.colab import files import time files.view('dataset/CatsAndDogs/PetImages/Cat/10404-0.jpg') time.sleep(0.5) files.view('dataset/CatsAndDogs/PetImages/Cat/10404-1.jpg') time.sleep(0.5) files.view('dataset/CatsAndDogs/PetImages/Cat/10404-2.jpg') !rm dataset/CatsAndDogs/PetImages/Cat/10404-1.jpg dataset/CatsAndDogs/PetImages/Cat/10404-2.jpg !mv dataset/CatsAndDogs/PetImages/Cat/10404-0.jpg dataset/CatsAndDogs/PetImages/Cat/10404.jpg # Sanity check for later !ls dataset/CatsAndDogs/PetImages/Cat/*.jpg | sed -E 's#.*/##' | sort > /tmp/Cats.txt !ls dataset/CatsAndDogs/PetImages/Dog/*.jpg | sed -E 's#.*/##' | sort > /tmp/Dogs.txt !diff /tmp/Cats.txt /tmp/Dogs.txt """### 1.3) Create the exclusion and description files #### Functions to create the exclusion list and the global description """ from pathlib import Path import re import time def checkExistanceAndEmptiness(output_file_path:str, doOverwrite:bool): okayToOverwrite = True output_path = Path(output_file_path) if output_path.exists(): print('File exists') if output_path.stat().st_size != 0: print('File is not empty') if not doOverwrite: okayToOverwrite = False print('not over-writing') else: mode = 'w+' print('over-writing') else: print('File is empty') mode = 'w+' else: print('File don\'t exist') mode = 'w' return mode, okayToOverwrite def createExclusionFile(dataset_dir_path:str, mogrify_output_file_path:str, output_file_path:str, doOverwrite:bool=False): """ dataset_dir_path le chemin d'accès au dossier du dataset output_file_path le chemin du fichier que l'on veut créer doOverwrite permet d'écraser le fichier, si il existe déjà, si le paramètre est passé à True (False par defaut). """ print # Check if file exists or not and gives the write or write and read depending, # as well as the bolean to overwrite or not the file mode, okayToOverwrite = checkExistanceAndEmptiness(output_file_path, doOverwrite) dataset_path = Path(dataset_dir_path) output_path = Path(output_file_path) print(dataset_path) if okayToOverwrite: with output_path.open(mode) as outfile: #writing in the file # Lecture du fichier d'exclusion mogrify_output = Path(mogrify_output_file_path) regex_files = re.compile('dataset/.*/[0-9]*.jpg') added_lines = [] with mogrify_output.open('r') as infile: for line in infile.readlines(): # time.sleep(1) if line.endswith("\n"): line = line[:-1] first_match = regex_files.findall(line)[0] first_path = Path(first_match) string = str(first_path.relative_to(dataset_path)) # string = first_match.replace(str(dataset_path)+"/", "") if string not in added_lines: outfile.write(string+"\n") added_lines.append(string) def createGlobalDescription(dataset_dir_path:str, exclude_img_file_path:str, output_file_path:str, doOverwrite:bool=False): """ Va generer le fichier de tout le dataset dataset_dir_path le chemin d'accès au dossier du dataset exclude_img_file_path le chemin d'accès d'un fichier d'exclusion de fichiers corrompus dans la liste. De la forme : path/vers/le/fichier1.jpg path/vers/le/fichier2.jpg path/vers/le/fichier3.jpg path/vers/le/fichier4.jpg output_file_path le chemin du fichier que l'on veut créer doOverwrite permet d'écraser le fichier, si il existe déjà, si le paramètre est passé à True (False par defaut). """ # Lecture du fichier d'exclusion exclude_path = Path(exclude_img_file_path) exclude_img_list = [] with exclude_path.open('r') as file: for line in file.readlines(): if line.endswith("\n"): line = line[:-1] line = str(Path(line)) # To be able to compare it to other file path #print("exclude file line :", line) exclude_img_list.append(line) print("exclude_img_list", exclude_img_list) # Compter celui qui a le plus d'exclus, pour en avoir le même nombre de # chaque coté count_cat = 0; count_dog = 0 for exclude_file in exclude_img_list: #print("Cat or Dog ?", exclude_file.split("/")[-2]) if exclude_file.split("/")[-2] == 'Cat': count_cat += 1 else: count_dog += 1 print("count_cat", count_cat, "count_dog", count_dog) left_to_exclude_dogs = count_cat-count_dog if count_cat >= count_dog else 0 left_to_exclude_cats = count_dog-count_cat if count_dog >= count_cat else 0 # Check if file exists or not and gives the write or write and read depending, # as well as the bolean to overwrite or not the file mode, okayToOverwrite = checkExistanceAndEmptiness(output_file_path, doOverwrite) output_path = Path(output_file_path) # Ecriture du fichier if okayToOverwrite: with output_path.open(mode) as file: #writing in the file ds_dir_path = Path(dataset_dir_path) #print("ds_dir_path", ds_dir_path) class_num = -1 for class_dir in ds_dir_path.joinpath("PetImages").iterdir(): if class_dir.is_dir(): class_num += 1 print(" class_dir", class_dir) print(" class_num", class_num) if str(class_dir).endswith('Cat'): left_to_exclude_count = left_to_exclude_cats print(" left_to_exclude_count for Cats is :", left_to_exclude_count) else: left_to_exclude_count = left_to_exclude_dogs print(" left_to_exclude_count for Dogs is :", left_to_exclude_count) added_count = 0 for class_img in class_dir.iterdir(): if class_img.match('[0-9]*.jpg'): local_image_path = class_img.relative_to(ds_dir_path) # Check for exclusion #print("class_img:", class_img) #print("exclude_img_list:", exclude_img_list) #print("class_img relative to:", str(class_img.relative_to(ds_dir_path))) #time.sleep(2) if str(local_image_path) not in exclude_img_list: #print(" ds_dir_path", ds_dir_path) #print(" class_dir", class_dir) #print(" class_img", class_img) if left_to_exclude_count > 0: left_to_exclude_count -= 1 #print(" class_img", class_img) print(" > that was a left to exclude", local_image_path) #time.sleep(1) else: file.write(str(local_image_path) + "\t" + str(class_num) + "\n") added_count += 1 else: #print(" class_img", class_img) print(" > excluded from the exclusion list", local_image_path) #time.sleep(1) if str(class_dir).endswith('Cat'): print("Added", added_count, "cats to the description file") else: print("Added", added_count, "dogs to the description file") """#### Create the exclusion list and the global description""" createExclusionFile(dataset_dir_path='./dataset/CatsAndDogs', mogrify_output_file_path='./dataset/CatsAndDogs/mogrify_output', output_file_path='./dataset/CatsAndDogs/exclude.txt', doOverwrite=True) createGlobalDescription(dataset_dir_path='./dataset/CatsAndDogs', exclude_img_file_path='./dataset/CatsAndDogs/exclude.txt', output_file_path='./dataset/CatsAndDogs/description.txt', doOverwrite=True) """### 1.4) Create a training and a test set ##### The python generator for the dataset """ from pathlib import Path import tensorflow as tf import numpy as np import cv2 import random import math class MyDogsCats: def __init__(self, ds_description_path:str, dataset_path:str, set_type:str, train_prop:float) -> None: """ ds_description_path : fichier avec les paths de chaque fichiers du dataset et sa classe Exemple de fichier (tabulation entre le path et la classe): /truc/bidule/chat/01.jpg 0 /truc/bidule/chien/01.jpg 1 Etc ... """ # Lire le fichier de description et regrouper par classes img_list_par_classes = {} path = Path(ds_description_path) with path.open('r') as file: for line in file.readlines(): if line.endswith("\n"): line = line[:-1] splits = line.split("\t") if line != "": img_text = splits[0] lbl_text = int(splits[1]) if lbl_text in img_list_par_classes.keys(): img_list_par_classes[lbl_text].append(img_text) else: img_list_par_classes[lbl_text] = [img_text] #print(img_list_par_classes) # Obtenir la liste de train OU de test self._img_list = [] self._lbl_list = [] self._num_class = len(img_list_par_classes) for num_class in img_list_par_classes: # Definir les proportions num_files = len(img_list_par_classes[num_class]) if set_type == "train": num_per_class_to_keep = math.ceil((num_files // self._num_class) * train_prop) class_files = img_list_par_classes[num_class][0:num_per_class_to_keep] elif set_type == "test": num_per_class_to_keep = math.floor((num_files // self._num_class) * (1 - train_prop)) class_files = img_list_par_classes[num_class][-num_per_class_to_keep:] else: class_files = img_list_par_classes[num_class] # Ajouter les images qui correspondent à la liste des images self._img_list.extend(class_files) # De même pour les labels #print("num_class:", num_class) #print("type num_class:", type(num_class)) #print("len num_class:", len(class_files)) self._lbl_list.extend([num_class for i in range(len(class_files))]) #print("_img_list", self._img_list[0:100]) #print("_lbl_list", self._lbl_list[0:100]) assert(len(self._lbl_list) == len(self._img_list)) self.num_samples = len(self._lbl_list) if set_type == "train" or set_type == "test": self._set_type = set_type else: self._set_type = "whole" self._img_size = 384 self._img_dim = (self._img_size, self._img_size) self._num_channels = 3 self._one_hot_depth = 2 self._ds_path = Path(dataset_path) def getDataset(self): generator = self._generator return tf.data.Dataset.from_generator(generator, args=[], output_types={'image': tf.float32, 'label': tf.int32}, output_shapes={'image': tf.TensorShape((self._img_size, self._img_size, self._num_channels)), 'label': tf.TensorShape((self._one_hot_depth))}) def _generator(self): img_list = self._img_list lbl_list = self._lbl_list # Shuffle c = list(zip(img_list, lbl_list)) random.shuffle(c) img_list, lbl_list = zip(*c) for i in range(self.num_samples): #print('Reading from :', img_list[i]) #print('Good path :', self._ds_path/img_list[i]) #self._ds_path/img_list[i] #print(self._ds_path/img_list[i]) # img_path_i = Path(img_list[i]) im = cv2.imread(str(self._ds_path/img_list[i]),-1) if im is None: i = 0 im = cv2.imread(str(self._ds_path/img_list[0]),-1) if len(im.shape) < 3: im = np.repeat(np.expand_dims(im, -1), 3, -1) #print(type(im)) img = cv2.resize(im, self._img_dim) img = img/255.0 #img = np.expand_dims(im, -1) lbl = tf.one_hot(lbl_list[i], depth=self._one_hot_depth, dtype=tf.int32) yield {'image': img, 'label': lbl} """## 2) ViT Colab ##### Copyright 2020 Google LLC. """ #@title Licensed under the Apache License, Version 2.0 (the "License"); # 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 # # https://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. """<a href="https://colab.research.google.com/github/google-research/vision_transformer/blob/master/vit_jax.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> ### Setup Needs to be executed once in every VM. The cell below downloads the code from Github and install necessary dependencies. """ #@markdown Select whether you would like to store data in your personal drive. #@markdown #@markdown If you select **yes**, you will need to authorize Colab to access #@markdown your personal drive #@markdown #@markdown If you select **no**, then any changes you make will diappear when #@markdown this Colab's VM restarts after some time of inactivity... use_gdrive = 'yes' #@param ["yes", "no"] if use_gdrive == 'yes': from google.colab import drive drive.mount('/gdrive') root = '/gdrive/My Drive/vision_transformer_colab' import os if not os.path.isdir(root): os.mkdir(root) os.chdir(root) print(f'\nChanged CWD to "{root}"') else: from IPython import display display.display(display.HTML( '<h1 style="color:red">CHANGES NOT PERSISTED</h1>')) # Clone repository and pull latest changes. ![ -d vision_transformer ] || git clone --depth=1 https://github.com/google-research/vision_transformer !cd vision_transformer && git pull !pip install -qr vision_transformer/vit_jax/requirements.txt #!pip install -r vision_transformer/vit_jax/requirements.txt """### Imports""" # Shows all available pre-trained models. !gsutil ls -lh gs://vit_models/* """For now let's try with `ViT-B_16` (pre-trained on imagenet21k, no fine tunning).""" # Download a pre-trained model. model = 'ViT-B_16' ![ -e "$model".npz ] || gsutil cp gs://vit_models/imagenet21k/"$model".npz . #@markdown TPU setup : Boilerplate for connecting JAX to TPU. import os if 'google.colab' in str(get_ipython()) and 'COLAB_TPU_ADDR' in os.environ: # Make sure the Colab Runtime is set to Accelerator: TPU. import requests if 'TPU_DRIVER_MODE' not in globals(): url = 'http://' + os.environ['COLAB_TPU_ADDR'].split(':')[0] + ':8475/requestversion/tpu_driver0.1-dev20191206' resp = requests.post(url) TPU_DRIVER_MODE = 1 # The following is required to use TPU Driver as JAX's backend. from jax.config import config config.FLAGS.jax_xla_backend = "tpu_driver" config.FLAGS.jax_backend_target = "grpc://" + os.environ['COLAB_TPU_ADDR'] print('Registered TPU:', config.FLAGS.jax_backend_target) else: print('No TPU detected. Can be changed under "Runtime/Change runtime type".') import flax import jax from matplotlib import pyplot as plt import numpy as np import tqdm # Shows the number of available devices. # In a CPU/GPU runtime this will be a single device. # In a TPU runtime this will be 8 cores. jax.local_devices() # Open some code files in a split editor on the right. # You can open more files in the file tab on the left. from google.colab import files files.view('vision_transformer/vit_jax/checkpoint.py') files.view('vision_transformer/vit_jax/input_pipeline.py') files.view('vision_transformer/vit_jax/models.py') files.view('vision_transformer/vit_jax/momentum_clip.py') files.view('vision_transformer/vit_jax/train.py') files.view('vision_transformer/vit_jax/hyper.py') # Commented out IPython magic to ensure Python compatibility. # Import files from repository. # Updating the files in the editor on the right will immediately update the # modules by re-importing them. import sys if './vision_transformer' not in sys.path: sys.path.append('./vision_transformer') # From https://ipython.org/ipython-doc/3/config/extensions/autoreload.html # Reload all modules (except those excluded by %aimport) every time before # executing the Python code typed. # %load_ext autoreload # %autoreload 2 from vit_jax import checkpoint from vit_jax import hyper from vit_jax import input_pipeline from vit_jax import logging from vit_jax import models from vit_jax import momentum_clip from vit_jax import train logger = logging.setup_logger('./logs') # Helper functions for images. labelnames = dict( # https://www.cs.toronto.edu/~kriz/cifar.html cifar10=('airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'), # https://www.cs.toronto.edu/~kriz/cifar.html cifar100=('apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'computer_keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'), # Addition for Dogs and Cats dogscats=('dog', 'cat') ) def make_label_getter(dataset): """Returns a function converting label indices to names.""" def getter(label): if dataset in labelnames: return labelnames[dataset][label] return f'label={label}' return getter def show_img(img, ax=None, title=None): """Shows a single image.""" if ax is None: ax = plt.gca() ax.imshow(img[...]) ax.set_xticks([]) ax.set_yticks([]) if title: ax.set_title(title) def show_img_grid(imgs, titles): """Shows a grid of images.""" n = int(np.ceil(len(imgs)**.5)) _, axs = plt.subplots(n, n, figsize=(3 * n, 3 * n)) for i, (img, title) in enumerate(zip(imgs, titles)): img = (img + 1) / 2 # Denormalize show_img(img, axs[i // n][i % n], title) """### Load the Python Generator""" def _shard(data): data['image'] = tf.reshape(data['image'], [num_devices, -1, 384, 384, 3]) data['label'] = tf.reshape(data['label'], [num_devices, -1, 2]) return data num_devices = len(jax.local_devices()) # The bypass batch_size = 64 num_classes = 2 dataset = 'dogscats' dgscts_train = MyDogsCats(ds_description_path='/content/dataset/CatsAndDogs/description.txt', dataset_path='/content/dataset/CatsAndDogs', set_type='train', train_prop=0.8) dgscts_test = MyDogsCats(ds_description_path='/content/dataset/CatsAndDogs/description.txt', dataset_path='/content/dataset/CatsAndDogs', set_type='test', train_prop=0.8) ds_train = dgscts_train.getDataset().batch(batch_size, drop_remainder=True) ds_test = dgscts_test.getDataset().batch(batch_size, drop_remainder=True) if num_devices is not None: ds_train = ds_train.map(_shard, tf.data.experimental.AUTOTUNE) ds_test = ds_test.map(_shard, tf.data.experimental.AUTOTUNE) ds_test = ds_test.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) ds_train = ds_train.prefetch(buffer_size=tf.data.experimental.AUTOTUNE) """### Load dataset""" # Fetch a batch of test images for illustration purposes. batch = next(iter(ds_test.as_numpy_iterator())) # Note the shape : [num_local_devices, local_batch_size, h, w, c] # print(batch) print(batch['image'].shape) print(batch['label'].shape) # Show some imags with their labels. images, labels = batch['image'][1][:9], batch['label'][1][:9] titles = map(make_label_getter(dataset), labels.argmax(axis=1)) show_img_grid(images, titles) # Same as above, but with train images. # Do you spot a difference? # Check out input_pipeline.get_data() in the editor at your right to see how the # images are preprocessed differently. batch = next(iter(ds_train.as_numpy_iterator())) images, labels = batch['image'][1][:9], batch['label'][1][:9] titles = map(make_label_getter(dataset), labels.argmax(axis=1)) show_img_grid(images, titles) [print(i.shape) for i in images] """### Load pre-trained""" # Load model definition & initialize random parameters. VisionTransformer = models.KNOWN_MODELS[model].partial(num_classes=num_classes) _, params = VisionTransformer.init_by_shape( jax.random.PRNGKey(0), # Discard the "num_local_devices" dimension of the batch for initialization. [(batch['image'].shape[1:], batch['image'].dtype.name)]) # Load and convert pretrained checkpoint. # This involves loading the actual pre-trained model results, but then also also # modifying the parameters a bit, e.g. changing the final layers, and resizing # the positional embeddings. # For details, refer to the code and to the methods of the paper. params = checkpoint.load_pretrained( pretrained_path=f'{model}.npz', init_params=params, model_config=models.CONFIGS[model], logger=logger, ) """### Evaluate""" # So far, all our data is in the host memory. Let's now replicate the arrays # into the devices. # This will make every array in the pytree params become a ShardedDeviceArray # that has the same data replicated across all local devices. # For TPU it replicates the params in every core. # For a single GPU this simply moves the data onto the device. # For CPU it simply creates a copy. params_repl = flax.jax_utils.replicate(params) print('params.cls:', type(params['cls']).__name__, params['cls'].shape) print('params_repl.cls:', type(params_repl['cls']).__name__, params_repl['cls'].shape) # Then map the call to our model's forward pass onto all available devices. vit_apply_repl = jax.pmap(VisionTransformer.call) def get_accuracy(params_repl): """Returns accuracy evaluated on the test set.""" good = total = 0 steps = dgscts_test.num_samples // batch_size #steps = input_pipeline.get_dataset_info(dataset, 'test')['num_examples'] // batch_size for _, batch in zip(tqdm.notebook.trange(steps), ds_test.as_numpy_iterator()): predicted = vit_apply_repl(params_repl, batch['image']) is_same = predicted.argmax(axis=-1) == batch['label'].argmax(axis=-1) good += is_same.sum() total += len(is_same.flatten()) return good / total # Random performance without fine-tuning. get_accuracy(params_repl) """### Fine-tune""" # 100 Steps take approximately 15 minutes in the TPU runtime. total_steps = 10 ## 100 warmup_steps = 5 decay_type = 'cosine' grad_norm_clip = 1 # This controls in how many forward passes the batch is split. 8 works well with # a TPU runtime that has 8 devices. 64 should work on a GPU. You can of course # also adjust the batch_size above, but that would require you to adjust the # learning rate accordingly. accum_steps = 8 base_lr = 0.03 # Check out train.make_update_fn in the editor on the right side for details. update_fn_repl = train.make_update_fn(VisionTransformer.call, accum_steps) # We use a momentum optimizer that uses half precision for state to save # memory. It als implements the gradient clipping. opt = momentum_clip.Optimizer(grad_norm_clip=grad_norm_clip).create(params) opt_repl = flax.jax_utils.replicate(opt) lr_fn = hyper.create_learning_rate_schedule(total_steps, base_lr, decay_type, warmup_steps) # Prefetch entire learning rate schedule onto devices. Otherwise we would have # a slow transfer from host to devices in every step. lr_iter = hyper.lr_prefetch_iter(lr_fn, 0, total_steps) # Initialize PRNGs for dropout. update_rngs = jax.random.split(jax.random.PRNGKey(0), jax.local_device_count()) # The world's simplest training loop. # Completes in ~20 min on the TPU runtime. for step, batch, lr_repl in zip( tqdm.notebook.trange(1, total_steps + 1), ds_train.as_numpy_iterator(), lr_iter ): print("loop", step, batch['image'].shape, batch['label'].shape) opt_repl, loss_repl, update_rngs = update_fn_repl( opt_repl, lr_repl, batch, update_rngs) print("fini la loop", type(opt_repl), type(loss_repl), type(update_rngs)) # Should be ~97.2% for CIFAR10 # Should be ~71.2% for CIFAR100 get_accuracy(opt_repl.target) """### Inference""" # Download model pre-trained on imagenet21k and fine-tuned on imagenet2012. ![ -e "$model"_imagenet2012.npz ] || gsutil cp gs://vit_models/imagenet21k+imagenet2012/"$model".npz "$model"_imagenet2012.npz VisionTransformer = models.KNOWN_MODELS[model].partial(num_classes=1000) # Load and convert pretrained checkpoint. params = checkpoint.load(f'{model}_imagenet2012.npz') params['pre_logits'] = {} # Need to restore empty leaf for Flax. # Get imagenet labels. !wget https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt imagenet_labels = dict(enumerate(open('ilsvrc2012_wordnet_lemmas.txt'))) # Get a random picture with the correct dimensions. !wget https://picsum.photos/384 -O picsum.jpg import PIL img = PIL.Image.open('picsum.jpg') img # Predict on a batch with a single item (note very efficient TPU usage...) logits, = VisionTransformer.call(params, (np.array(img) / 128 - 1)[None, ...]) preds = flax.nn.softmax(logits) for idx in preds.argsort()[:-11:-1]: print(f'{preds[idx]:.5f} : {imagenet_labels[idx]}', end='') """## 3) Nos test ### Resize sans garder les proportions """ # Get a random picture with the correct dimensions. !wget https://lorraine.gatech.edu/sites/default/files/uploads/images/superblock_images/metz-campus.jpeg -O pic_gatech.jpg import PIL img = PIL.Image.open('pic_gatech.jpg') #img img = img.resize((384,384)) img # Predict on a batch with a single item (note very efficient TPU usage...) logits, = VisionTransformer.call(params, (np.array(img) / 128 - 1)[None, ...]) preds = flax.nn.softmax(logits) for idx in preds.argsort()[:-11:-1]: print(f'{preds[idx]:.5f} : {imagenet_labels[idx]}', end='') """### Resize en gardant une propostion carré""" # Get a random picture with the correct dimensions. !wget https://lorraine.gatech.edu/sites/default/files/uploads/images/superblock_images/metz-campus.jpeg -O pic_gatech.jpg import PIL img = PIL.Image.open('pic_gatech.jpg') (w, h) = (img.width, img.height) if w>=h: crop_box = ((w/2)-(h/2), 0, (w/2)+(h/2), h) else: crop_box = ((h/2)-(w/2), 0, (h/2)+(w/2), w) img = img.resize((384,384), box=crop_box) img # Predict on a batch with a single item (note very efficient TPU usage...) logits, = VisionTransformer.call(params, (np.array(img) / 128 - 1)[None, ...]) preds = flax.nn.softmax(logits) for idx in preds.argsort()[:-11:-1]: print(f'{preds[idx]:.5f} : {imagenet_labels[idx]}', end='')

38.007614

1,046

0.67576

4,425

0.147638

1,047

0.034933

0

13,541

0.451788

36c9545921e82accc771994b4028870845e16cb0

19,349

py

Python

tests/test_cli.py

jameswilkerson/elex

27733e3c473fef48676f8bdd56247bee49ad32ea

[ "Apache-2.0" ]

183

2015-11-25T15:13:47.000Z

2022-01-07T23:02:36.000Z

tests/test_cli.py

jameswilkerson/elex

27733e3c473fef48676f8bdd56247bee49ad32ea

[ "Apache-2.0" ]

198

2015-11-24T16:48:48.000Z

2020-10-26T10:38:56.000Z

tests/test_cli.py

jameswilkerson/elex

27733e3c473fef48676f8bdd56247bee49ad32ea

[ "Apache-2.0" ]

65

2015-12-03T21:29:38.000Z

2021-08-10T20:03:49.000Z

import csv import sys import json import tests try: from cStringIO import StringIO except ImportError: from io import StringIO from six import with_metaclass from elex.cli.app import ElexApp from collections import OrderedDict DATA_FILE = 'tests/data/20151103_national.json' DATA_ELECTION_DATE = '2015-11-03' DELSUM_DATA_FILE = 'tests/data/20160118_delsum.json' DELSUPER_DATA_FILE = 'tests/data/20160118_delsuper.json' ELECTIONS_DATA_FILE = 'tests/data/00000000_elections.json' DISTRICT_DATA_FILE = 'tests/data/20160201_district_results.json' TEST_COMMANDS = [ 'races', 'candidates', 'reporting-units', 'candidate-reporting-units', 'results', ] class ElexCLICSVTestMeta(type): def __new__(mcs, name, bases, dict): def gen_fields_test(command): """ Dynamically generate a fields test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) api_fields = api_data[0].serialize().keys() self.assertEqual(cli_fields, list(api_fields)) return test def gen_length_test(command): """ Dynamically generate a data length test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) self.assertEqual(len(cli_data), len(api_data)) return test def gen_data_test(command): """ Dynamically generate a data test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) for i, row in enumerate(cli_data): for k, v in api_data[i].serialize().items(): if v is None: v = '' self.assertEqual(row[k], str(v)) return test def gen_timestamp_test(command): """ Generate test to ensure timestamp field is set """ def test(self): cli_fields, cli_data = self._test_command(command=command, with_timestamp=True) self.assertEqual(cli_fields[-1], 'timestamp') return test def gen_timestamp_data_test(command): """ Generate test to ensure timestamp field is set """ def test(self): cli_fields, cli_data = self._test_command(command=command, with_timestamp=True) for row in cli_data: try: self.assertTrue(unicode(row['timestamp']).isnumeric()) except NameError: self.assertTrue(str(row['timestamp']).isnumeric()) return test def gen_batch_name_data_test(command): """ Generate test to ensure timestamp field is set """ def test(self): cli_fields, cli_data = self._test_command(command=command, batch_name='batch-01') for row in cli_data: self.assertEqual(row['batchname'], 'batch-01') return test for command in TEST_COMMANDS: fields_test_name = 'test_csv_{0}_fields'.format( command.replace('-', '_') ) dict[fields_test_name] = gen_fields_test(command) length_test_name = 'test_csv_{0}_length'.format( command.replace('-', '_') ) dict[length_test_name] = gen_length_test(command) data_test_name = 'test_csv_{0}_data'.format( command.replace('-', '_') ) dict[data_test_name] = gen_data_test(command) timestamp_test_name = 'test_csv_{0}_timestamp'.format( command.replace('-', '_') ) dict[timestamp_test_name] = gen_timestamp_test(command) timestamp_data_test_name = 'test_csv_{0}_timestamp_data'.format( command.replace('-', '_') ) dict[timestamp_data_test_name] = gen_timestamp_data_test(command) batch_name_data_test_name = 'test_csv_{0}_batch_name_data'.format( command.replace('-', '_') ) dict[batch_name_data_test_name] = gen_batch_name_data_test(command) return type.__new__(mcs, name, bases, dict) class ElexCLICSVTestCase( with_metaclass(ElexCLICSVTestMeta, tests.ElectionResultsTestCase) ): """ This testing class is mostly dynamically generated by its metaclass. The goal of the CLI tests is to the make sure the CLI output matches the Python API. The API tests guarantee the validity of the data, while these tests guarantee the CLI provides the same data in CSV format. """ def test_csv_elections_fields(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual( fields, ['id', 'electiondate', 'liveresults', 'testresults'] ) def test_csv_elections_length(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(len(data), 11) def test_csv_elections_date(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['electiondate'], '2015-08-04') def test_csv_elections_liveresults(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['liveresults'], 'False') def test_csv_elections_testresults(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['testresults'], 'True') def test_csv_next_election_fields(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual( fields, ['id', 'electiondate', 'liveresults', 'testresults'] ) def test_csv_next_election_length(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(len(data), 1) def test_csv_next_election_date(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['electiondate'], '2015-08-25') def test_csv_next_election_liveresults(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['liveresults'], 'True') def test_csv_next_election_testresults(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['testresults'], 'False') def test_csv_delegate_fields(self): fields, data = self._test_command(command='delegates') self.assertEqual( fields, [ 'level', 'party_total', 'superdelegates_count', 'last', 'state', 'candidateid', 'party_need', 'party', 'delegates_count', 'id', 'd1', 'd7', 'd30' ] ) def test_csv_delegate_state_count(self): fields, data = self._test_command(command='delegates') number_of_states = list( set([d['state'] for d in data if d['level'] == 'state']) ) self.assertEqual(58, len(number_of_states)) def test_csv_results_resultslevel(self): fields, data = self._test_command( command='results', datafile=DISTRICT_DATA_FILE, resultslevel='district' ) self.assertEqual(data[17]['reportingunitname'], 'District 1') def _test_command( self, command, datafile=DATA_FILE, delsum_datafile=DELSUM_DATA_FILE, delsuper_datafile=DELSUPER_DATA_FILE, electiondate=DATA_ELECTION_DATE, resultslevel=None, with_timestamp=False, batch_name=False ): """ Execute an `elex` sub-command; returns fieldnames and rows """ stdout_backup = sys.stdout sys.stdout = StringIO() argv = [command] if electiondate is not None: argv.append(electiondate) argv = argv + ['--data-file', datafile] argv = argv + ['--delegate-sum-file', delsum_datafile] argv = argv + ['--delegate-super-file', delsuper_datafile] argv = argv + ['--results-level', resultslevel] if with_timestamp: argv = argv + ['--with-timestamp'] if batch_name: argv = argv + ['--batch-name', batch_name] app = ElexApp(argv=argv) app.setup() app.log.set_level('FATAL') app.run() lines = sys.stdout.getvalue().split('\n') reader = csv.DictReader(lines) sys.stdout.close() sys.stdout = stdout_backup return reader.fieldnames, list(reader) class ElexCLIJSONTestMeta(type): def __new__(mcs, name, bases, dict): def gen_fields_test(command): """ Dynamically generate a fields test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) api_fields = api_data[0].serialize().keys() self.assertEqual(cli_fields, list(api_fields)) return test def gen_length_test(command): """ Dynamically generate a data length test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) self.assertEqual(len(cli_data), len(api_data)) return test def gen_data_test(command): """ Dynamically generate a data test """ def test(self): cli_fields, cli_data = self._test_command(command=command) api_data = getattr(self, command.replace('-', '_')) for i, row in enumerate(cli_data): for k, v in api_data[i].serialize().items(): self.assertEqual(row[k], v) return test def gen_timestamp_test(command): """ Generate test to ensure timestamp field is set """ def test(self): cli_fields, cli_data = self._test_command(command=command, with_timestamp=True) self.assertEqual(cli_fields[-1], 'timestamp') return test def gen_timestamp_data_test(command): """ Generate test to ensure timestamp data is an integer """ def test(self): cli_fields, cli_data = self._test_command(command=command, with_timestamp=True) for row in cli_data: try: self.assertTrue(unicode(row['timestamp']).isnumeric()) except NameError: self.assertTrue(str(row['timestamp']).isnumeric()) return test def gen_batch_name_data_test(command): """ Generate test to ensure timestamp field is set """ def test(self): cli_fields, cli_data = self._test_command(command=command, batch_name='batch-01') for row in cli_data: self.assertEqual(row['batchname'], 'batch-01') return test for command in TEST_COMMANDS: fields_test_name = 'test_json_{0}_fields'.format( command.replace('-', '_') ) dict[fields_test_name] = gen_fields_test(command) length_test_name = 'test_json_{0}_length'.format( command.replace('-', '_') ) dict[length_test_name] = gen_length_test(command) data_test_name = 'test_json_{0}_data'.format( command.replace('-', '_') ) dict[data_test_name] = gen_data_test(command) timestamp_data_test_name = 'test_json_{0}_data_timestamp'.format( command.replace('-', '_') ) dict[timestamp_data_test_name] = gen_timestamp_test(command) timestamp_data_test_name = 'test_json_{0}_timestamp_data'.format( command.replace('-', '_') ) dict[timestamp_data_test_name] = gen_timestamp_data_test(command) batch_name_data_test_name = 'test_csv_{0}_batch_name_data'.format( command.replace('-', '_') ) dict[batch_name_data_test_name] = gen_batch_name_data_test(command) return type.__new__(mcs, name, bases, dict) class ElexCLIJSONTestCase( with_metaclass(ElexCLIJSONTestMeta, tests.ElectionResultsTestCase) ): """ This testing class is mostly dynamically generated by its metaclass. The goal of the CLI tests is to the make sure the CLI output matches the Python API. The API tests guarantee the validity of the data, while these tests guarantee the CLI provides the same data in JSON format. """ def test_json_elections_fields(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual( fields, ['id', 'electiondate', 'liveresults', 'testresults'] ) def test_json_elections_length(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(len(data), 11) def test_json_elections_date(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['electiondate'], '2015-08-04') def test_json_elections_liveresults(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['liveresults'], False) def test_json_elections_testresults(self): fields, data = self._test_command( command='elections', datafile=ELECTIONS_DATA_FILE ) self.assertEqual(data[4]['testresults'], True) def test_json_next_election_fields(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual( fields, ['id', 'electiondate', 'liveresults', 'testresults'] ) def test_json_next_election_length(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(len(data), 1) def test_json_next_election_date(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['electiondate'], '2015-08-25') def test_json_next_election_liveresults(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['liveresults'], True) def test_json_next_election_testresults(self): fields, data = self._test_command( command='next-election', datafile=ELECTIONS_DATA_FILE, electiondate='2015-08-04' ) self.assertEqual(data[0]['testresults'], False) def test_json_delegate_fields(self): fields, data = self._test_command(command='delegates') self.assertEqual( fields, [ 'level', 'party_total', 'superdelegates_count', 'last', 'state', 'candidateid', 'party_need', 'party', 'delegates_count', 'id', 'd1', 'd7', 'd30' ] ) def test_json_delegate_state_count(self): fields, data = self._test_command(command='delegates') number_of_states = list( set([d['state'] for d in data if d['level'] == 'state']) ) self.assertEqual(58, len(number_of_states)) def test_json_results_resultslevel(self): fields, data = self._test_command( command='results', datafile=DISTRICT_DATA_FILE, resultslevel='district' ) self.assertEqual(data[17]['reportingunitname'], 'District 1') def _test_command( self, command, datafile=DATA_FILE, delsum_datafile=DELSUM_DATA_FILE, delsuper_datafile=DELSUPER_DATA_FILE, electiondate=DATA_ELECTION_DATE, resultslevel=None, with_timestamp=False, batch_name=False ): """ Execute an `elex` sub-command; returns fieldnames and rows """ stdout_backup = sys.stdout sys.stdout = StringIO() argv = [command] argv.append(electiondate) argv = argv + ['--data-file', datafile, '-o', 'json'] argv = argv + ['--delegate-sum-file', delsum_datafile] argv = argv + ['--delegate-super-file', delsuper_datafile] argv = argv + ['--results-level', resultslevel] if with_timestamp: argv = argv + ['--with-timestamp'] if batch_name: argv = argv + ['--batch-name', batch_name] app = ElexApp(argv=argv) app.setup() app.log.set_level('FATAL') app.run() json_data = sys.stdout.getvalue() data = json.loads(json_data, object_pairs_hook=OrderedDict) sys.stdout.close() sys.stdout = stdout_backup return list(data[0].keys()), data

33.826923

80

0.567213

18,662

0.964494

0

3,913

0.202233

36cad5c25faaf8cf1d768a98197ce4f6fa877fa3

4,321

py

Python

unipipeline/worker/uni_worker_consumer.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

null

unipipeline/worker/uni_worker_consumer.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

1

2021-09-14T13:08:13.000Z

unipipeline/worker/uni_worker_consumer.py

aliaksandr-master/unipipeline

d8eac38534172aee59ab5777321cabe67f3779ef

[ "MIT" ]

null

from typing import TypeVar, Generic, Optional, Type, Any, Union, Dict, TYPE_CHECKING from unipipeline.errors.uni_payload_error import UniPayloadParsingError, UniAnswerPayloadParsingError from unipipeline.errors.uni_sending_to_worker_error import UniSendingToWorkerError from unipipeline.answer.uni_answer_message import UniAnswerMessage from unipipeline.brokers.uni_broker_message_manager import UniBrokerMessageManager from unipipeline.errors.uni_work_flow_error import UniWorkFlowError from unipipeline.message.uni_message import UniMessage from unipipeline.message_meta.uni_message_meta import UniMessageMeta, UniMessageMetaErrTopic, UniAnswerParams from unipipeline.worker.uni_worker import UniWorker from unipipeline.worker.uni_worker_consumer_manager import UniWorkerConsumerManager from unipipeline.worker.uni_worker_consumer_message import UniWorkerConsumerMessage from unipipeline.definitions.uni_worker_definition import UniWorkerDefinition if TYPE_CHECKING: from unipipeline.modules.uni_mediator import UniMediator TInputMsgPayload = TypeVar('TInputMsgPayload', bound=UniMessage) TAnswerMsgPayload = TypeVar('TAnswerMsgPayload', bound=Optional[UniMessage]) class UniWorkerConsumer(Generic[TInputMsgPayload, TAnswerMsgPayload]): def __init__(self, definition: UniWorkerDefinition, mediator: 'UniMediator', worker_type: Type[UniWorker[TInputMsgPayload, TAnswerMsgPayload]]) -> None: self._definition = definition self._mediator = mediator self._worker_manager = UniWorkerConsumerManager(self.send_to) self._worker = worker_type(self._worker_manager) self._uni_echo = mediator.echo.mk_child(f'worker[{definition.name}]') self._input_message_type: Type[TInputMsgPayload] = mediator.get_message_type(self._definition.input_message.name) # type: ignore self._answer_message_type: Optional[Type[TAnswerMsgPayload]] = mediator.get_message_type(self._definition.answer_message.name) if self._definition.answer_message is not None else None # type: ignore self._current_meta: Optional[UniMessageMeta] = None def send_to(self, worker: Union[Type['UniWorker[Any, Any]'], str], data: Union[Dict[str, Any], UniMessage], *, alone: bool = False, need_answer: bool = False) -> Optional[UniAnswerMessage[UniMessage]]: wd = self._mediator.config.get_worker_definition(worker) if wd.name not in self._definition.output_workers: raise UniSendingToWorkerError(f'worker {wd.name} is not defined in workers->{self._definition.name}->output_workers') if need_answer and not wd.need_answer: raise UniWorkFlowError(f'you will get no response form worker {wd.name}') if need_answer: answ_params = UniAnswerParams(topic=self._definition.answer_topic, id=self._worker_manager.id) return self._mediator.send_to(wd.name, data, parent_meta=self._current_meta, answer_params=answ_params, alone=alone) self._mediator.send_to(wd.name, data, parent_meta=self._current_meta, answer_params=None, alone=alone) return None def process_message(self, meta: UniMessageMeta, manager: UniBrokerMessageManager) -> None: self._current_meta = meta msg = UniWorkerConsumerMessage[TInputMsgPayload](self._input_message_type, manager, meta) try: result: Optional[Union[TAnswerMsgPayload, Dict[str, Any]]] = self._worker.handle_message(msg) except UniAnswerPayloadParsingError as e: self._mediator.move_to_error_topic(self._definition, meta, UniMessageMetaErrTopic.HANDLE_MESSAGE_ERR, e) except UniPayloadParsingError as e: self._mediator.move_to_error_topic(self._definition, meta, UniMessageMetaErrTopic.MESSAGE_PAYLOAD_ERR, e) # except Exception as e: # TODO: correct error handling # self._mediator.move_to_error_topic(self._definition, meta, UniMessageMetaErrTopic.HANDLE_MESSAGE_ERR, e) else: if self._definition.need_answer: try: self._mediator.answer_to(self._definition.name, meta, result, unwrapped=self._definition.answer_unwrapped) except UniSendingToWorkerError: pass if self._definition.ack_after_success: msg.ack() self._current_meta = None

61.728571

207

0.765564

3,143

0.727378

0

428

0.099051

36cae8cc11223214274fe92b0ac8c6515461f9fe

1,825

py

Python

funing/_ui/about.py

larryw3i/Funing

8ef88af8766f0246614517fa00f3b322ba722d6b

[ "MIT" ]

1

2021-08-22T05:56:09.000Z

funing/_ui/about.py

larryw3i/Funing

8ef88af8766f0246614517fa00f3b322ba722d6b

[ "MIT" ]

null

funing/_ui/about.py

larryw3i/Funing

8ef88af8766f0246614517fa00f3b322ba722d6b

[ "MIT" ]

null

import gettext import os import re import subprocess import sys import time import tkinter as tk import tkinter.filedialog as tkf import uuid import webbrowser from datetime import date, datetime from enum import Enum from tkinter import * from tkinter import messagebox from tkinter.ttk import * import cv2 import numpy as np import pygubu import yaml from PIL import Image, ImageTk from funing import * from funing.locale import _ from funing.settings import * translator = _ class AboutTkApplication(pygubu.TkApplication): def __init__(self): # pygubu builder self.builder = pygubu.Builder(translator) # ui files about_ui_path = os.path.join( os.path.join(project_path, 'ui'), 'about.ui') # add ui files self.builder.add_from_file(about_ui_path) self.mainwindow = None self.is_showing = False def on_about_ok_btn_clicked(self): self.about_ok() def about_ok(self): self.trigger() def quit(self, event=None): self.mainwindow.withdraw() self.is_showing = False def run(self): if not self.mainwindow: self.mainwindow = self.builder.get_object('about_toplevel') self.mainwindow.title(_('About Funing')) self.builder.get_object('version_label')['text'] = version self.mainwindow.protocol("WM_DELETE_WINDOW", self.on_closing) # connect callbacks self.builder.connect_callbacks(self) else: self.mainwindow.deiconify() self.is_showing = True def on_closing(self): self.quit() def trigger(self): if not self.is_showing: self.run() else: self.quit() def view_source_code(self, *args): webbrowser.open(source_page)

23.397436

73

0.656438

1,340

0.734247

0

142

0.077808

36cd33528502d61cfd130bce552b6359665140f3

8,039

py

Python

pysnmp-with-texts/Fore-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

8

2019-05-09T17:04:00.000Z

2021-06-09T06:50:51.000Z

pysnmp-with-texts/Fore-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

4

2019-05-31T16:42:59.000Z

2020-01-31T21:57:17.000Z

pysnmp-with-texts/Fore-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module Fore-Common-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Fore-Common-MIB # Produced by pysmi-0.3.4 at Wed May 1 13:14:34 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueRangeConstraint, ConstraintsUnion, SingleValueConstraint, ConstraintsIntersection, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ConstraintsIntersection", "ValueSizeConstraint") ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup") Bits, MibIdentifier, enterprises, Counter64, Unsigned32, ModuleIdentity, Counter32, TimeTicks, NotificationType, ObjectIdentity, IpAddress, Gauge32, Integer32, iso, MibScalar, MibTable, MibTableRow, MibTableColumn = mibBuilder.importSymbols("SNMPv2-SMI", "Bits", "MibIdentifier", "enterprises", "Counter64", "Unsigned32", "ModuleIdentity", "Counter32", "TimeTicks", "NotificationType", "ObjectIdentity", "IpAddress", "Gauge32", "Integer32", "iso", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn") TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString") fore = ModuleIdentity((1, 3, 6, 1, 4, 1, 326)) if mibBuilder.loadTexts: fore.setLastUpdated('9911050000Z') if mibBuilder.loadTexts: fore.setOrganization('Marconi Communications') if mibBuilder.loadTexts: fore.setContactInfo(' Postal: Marconi Communications, Inc. 5000 Marconi Drive Warrendale, PA 15086-7502 Tel: +1 724 742 6999 Email: bbrs-mibs@marconi.com Web: http://www.marconi.com') if mibBuilder.loadTexts: fore.setDescription('Definitions common to all FORE private MIBS.') admin = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1)) systems = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2)) foreExperiment = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 3)) operations = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 1)) snmpErrors = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 2)) snmpTrapDest = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 3)) snmpAccess = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 4)) assembly = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 5)) fileXfr = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 6)) rmonExtensions = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 7)) preDot1qVlanMIB = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 8)) snmpTrapLog = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 9)) ilmisnmp = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 10)) entityExtensionMIB = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 11)) ilmiRegistry = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 14)) foreIfExtension = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 15)) frameInternetworking = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 16)) ifExtensions = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 1, 17)) atmAdapter = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 1)) atmSwitch = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2)) etherSwitch = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 3)) atmAccess = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 5)) hubSwitchRouter = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 6)) ipoa = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 7)) stackSwitch = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 10)) switchRouter = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 15)) software = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 2)) asxd = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 2, 1)) hardware = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 1)) asx = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 1, 1)) asx200wg = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 4)) asx200bx = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 5)) asx200bxe = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 6)) cabletron9A000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 7)) asx1000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 8)) le155 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 9)) sfcs200wg = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 10)) sfcs200bx = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 11)) sfcs1000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 12)) tnx210 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 15)) tnx1100 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 16)) asx1200 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 17)) asx4000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 18)) le25 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 19)) esx3000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 20)) tnx1100b = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 21)) asx150 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 22)) bxr48000 = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 24)) asx4000m = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 25)) axhIp = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 26)) axhSig = MibIdentifier((1, 3, 6, 1, 4, 1, 326, 2, 2, 27)) class SpansAddress(OctetString): subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(8, 8) fixedLength = 8 class AtmAddress(OctetString): subtypeSpec = OctetString.subtypeSpec + ConstraintsUnion(ValueSizeConstraint(8, 8), ValueSizeConstraint(20, 20), ) class NsapPrefix(OctetString): subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(13, 13) fixedLength = 13 class NsapAddr(OctetString): subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(20, 20) fixedLength = 20 class TransitNetwork(DisplayString): subtypeSpec = DisplayString.subtypeSpec + ValueSizeConstraint(1, 4) class TrapNumber(Integer32): pass class EntryStatus(Integer32): subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4)) namedValues = NamedValues(("valid", 1), ("createRequest", 2), ("underCreation", 3), ("invalid", 4)) class AtmSigProtocol(Integer32): subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) namedValues = NamedValues(("other", 1), ("spans", 2), ("q2931", 3), ("pvc", 4), ("spvc", 5), ("oam", 6), ("spvcSpans", 7), ("spvcPnni", 8), ("rcc", 9), ("fsig", 10), ("mpls", 11), ("ipCtl", 12), ("oam-ctl", 13)) class GeneralState(Integer32): subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2)) namedValues = NamedValues(("normal", 1), ("fail", 2)) class IntegerBitString(Integer32): pass class ConnectionType(Integer32): pass mibBuilder.exportSymbols("Fore-Common-MIB", ilmiRegistry=ilmiRegistry, fore=fore, ilmisnmp=ilmisnmp, NsapPrefix=NsapPrefix, atmAccess=atmAccess, snmpTrapDest=snmpTrapDest, rmonExtensions=rmonExtensions, preDot1qVlanMIB=preDot1qVlanMIB, operations=operations, ipoa=ipoa, software=software, tnx1100=tnx1100, snmpErrors=snmpErrors, sfcs200bx=sfcs200bx, snmpAccess=snmpAccess, sfcs200wg=sfcs200wg, le25=le25, sfcs1000=sfcs1000, esx3000=esx3000, frameInternetworking=frameInternetworking, asx4000m=asx4000m, AtmAddress=AtmAddress, assembly=assembly, ConnectionType=ConnectionType, axhIp=axhIp, bxr48000=bxr48000, ifExtensions=ifExtensions, asx=asx, asxd=asxd, asx4000=asx4000, TransitNetwork=TransitNetwork, fileXfr=fileXfr, EntryStatus=EntryStatus, foreIfExtension=foreIfExtension, asx1000=asx1000, asx200bxe=asx200bxe, axhSig=axhSig, TrapNumber=TrapNumber, SpansAddress=SpansAddress, IntegerBitString=IntegerBitString, atmSwitch=atmSwitch, cabletron9A000=cabletron9A000, AtmSigProtocol=AtmSigProtocol, tnx1100b=tnx1100b, asx200bx=asx200bx, etherSwitch=etherSwitch, asx1200=asx1200, hubSwitchRouter=hubSwitchRouter, entityExtensionMIB=entityExtensionMIB, switchRouter=switchRouter, NsapAddr=NsapAddr, asx200wg=asx200wg, systems=systems, atmAdapter=atmAdapter, foreExperiment=foreExperiment, PYSNMP_MODULE_ID=fore, admin=admin, le155=le155, GeneralState=GeneralState, hardware=hardware, stackSwitch=stackSwitch, asx150=asx150, tnx210=tnx210, snmpTrapLog=snmpTrapLog)

73.752294

1,461

0.707302

1,520

0.189078

0

1,271

0.158104

36d0e1753fba4845d6f1c53b001fd0c1077f6cbc

2,753

py

Python

lib/logger.py

YahiaKandeel/ironport-correlator

cb426f412dba403f056c40eef631f0c252eada08

[ "Apache-2.0" ]

6

2019-10-28T01:18:51.000Z

2022-01-26T11:43:14.000Z

lib/logger.py

YahiaKandeel/ironport-correlator

cb426f412dba403f056c40eef631f0c252eada08

[ "Apache-2.0" ]

null

lib/logger.py

YahiaKandeel/ironport-correlator

cb426f412dba403f056c40eef631f0c252eada08

[ "Apache-2.0" ]

2

2020-04-30T11:17:27.000Z

2021-11-17T02:26:48.000Z

################################################################################ # Styler & Logger ################################################################################ from logging.handlers import SysLogHandler import logging import json import pprint import time from .decoder import decode import collections # Log Keys Order keys = [ 'ICID', 'MID', "MessageID", 'Related_MID', 'OutbreakFilters', 'CASE', 'GRAYMAIL', 'Antivirus', 'LDAP_Drop', 'SPF', 'DKIM', 'DKIM_Detail', 'DMARK', 'DMARK_Detail', "Subject", "Attachments", "From", "To", "SenderReputation", "ThreatCategory", "SuspectedDomains", "DomainAge", 'Action', 'Action_Desc', 'Content_Filter', "IP", "Other" ] # Syslog def syslog(siemContext): ''' Return a syslogger instance ''' # Create Handler handler = SysLogHandler(address=(siemContext["server"], siemContext["port"]), facility=SysLogHandler.LOG_LOCAL5) # Configure Logger logger = logging.getLogger(siemContext["ident"]) logger.setLevel(logging.INFO) # Configure Formater formatter = logging.Formatter('%(name)s: %(message)r') handler.setFormatter(formatter) # Add handler to the logger logger.addHandler(handler) # return return logger def style(message, msgexpand): ''' Style and expand a message ''' message_log = collections.OrderedDict() result = [] for key in keys: values = filter(None, message.get(key, [])) message_log[key] = ' || '.join(list(set(values))) # Decode Subject & Attachments message_log["Subject"] = decode(message_log["Subject"]) # message_log["Attachments"] = decode(message_log["Attachments"]) # If msgexpand if msgexpand: for recipient in message.get('To', []): message_log['To'] = recipient result.append( json.dumps(message_log, ensure_ascii=False)) # Else else: result.append( json.dumps(message_log, ensure_ascii=False)) return result def syslogger(logger_queue, siemContext, options): ''' Logger Process ''' print("\t[+]Starting Logger Process") # Logger logger = syslog(siemContext) while True: # Get Data from Logger Queue data = logger_queue.get() # If there is a message if data: [(mid, message)] = data.items() # Style It messages = style(message, options["expand"]) # Log for message in messages: logger.info(message) print('\r\n'+'#' * 100) pprint.pprint(json.loads(message)) else: # sleep time.sleep(0.05)

27.808081

81

0.564475

0

1,014

0.368325

36d148c1ce0bdea8709582045309f0f2acad2b33

954

py

Python

services/web/apps/inv/inv/plugins/log.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

services/web/apps/inv/inv/plugins/log.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

services/web/apps/inv/inv/plugins/log.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# --------------------------------------------------------------------- # inv.inv log plugin # --------------------------------------------------------------------- # Copyright (C) 2007-2018 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # NOC modules from .base import InvPlugin class LogPlugin(InvPlugin): name = "log" js = "NOC.inv.inv.plugins.log.LogPanel" def get_data(self, request, o): return { "id": str(o.id), "name": o.name, "model": o.model.name, "log": [ { "ts": x.ts.isoformat(), "user": x.user, "system": x.system, "managed_object": x.managed_object, "op": x.op, "message": x.message, } for x in o.get_log() ], }

28.909091

71

0.336478

603

0.632075

0

420

0.440252

36d22a39c7974086f08155ff6bf52d3cb2267f62

574

py

Python

blender/arm/logicnode/transform/LN_separate_quaternion.py

niacdoial/armory

3f9b633fbf772017c576a3f77695a6c28d9956e1

[ "Zlib" ]

null

blender/arm/logicnode/transform/LN_separate_quaternion.py

niacdoial/armory

3f9b633fbf772017c576a3f77695a6c28d9956e1

[ "Zlib" ]

null

blender/arm/logicnode/transform/LN_separate_quaternion.py

niacdoial/armory

3f9b633fbf772017c576a3f77695a6c28d9956e1

[ "Zlib" ]

null

from arm.logicnode.arm_nodes import * class SeparateQuaternionNode(ArmLogicTreeNode): """TO DO.""" bl_idname = 'LNSeparateQuaternionNode' bl_label = "Separate Quaternion" arm_section = 'quaternions' arm_version = 1 def init(self, context): super(SeparateQuaternionNode, self).init(context) self.add_input('NodeSocketVector', 'Quaternion') self.add_output('NodeSocketFloat', 'X') self.add_output('NodeSocketFloat', 'Y') self.add_output('NodeSocketFloat', 'Z') self.add_output('NodeSocketFloat', 'W')

31.888889

57

0.679443

533

0.928571

0

182

0.317073

36d3212ee65298917f85198d847d449f780e78c7

3,110

py

Python

tools/exporter_python/exporter.py

moriyalb/hades

ea2743a23022f65b3931eb482b6ec18804410ba3

[ "MIT" ]

5

2018-05-18T10:01:46.000Z

2021-08-18T13:59:47.000Z

tools/exporter_python/exporter.py

moriyalb/hades

ea2743a23022f65b3931eb482b6ec18804410ba3

[ "MIT" ]

null

tools/exporter_python/exporter.py

moriyalb/hades

ea2743a23022f65b3931eb482b6ec18804410ba3

[ "MIT" ]

null

import getopt import sys import os import schema import server import orm CLIENT_TYPE = { '--client_lua_path' : "lua", '--client_cs_path' : "cs", '--client_cpp_path' : "cpp", '--client_js_path' : "js", '--client_python_path' : "python", } def export(): opts, args = getopt.getopt(sys.argv[1:], '-h-u:', ['help', 'server_path=', 'client_lua_path=', 'client_cs_path=', 'client_cpp_path=', 'client_js_path=', 'client_python_path=', 'user=']) user = None exportClient = {} exportServer = None for tag, value in opts: if tag in ('-h', '--help'): print(''' --server_path 表示服务器项目路径（内含Defines、Entities、Configs、CustomConfigs等文件夹） --client_lua_path 表示客户端Lua导出路径（内含Proxy、ProxyDefine文件夹，此路径将放置导出的lua客户端脚本） --client_cs_path 表示客户端C#导出路径（内含Proxy、ProxyDefine文件夹，此路径将放置导出的C#客户端脚本） --client_cpp_path 表示客户端C++导出路径（内含Proxy、ProxyDefine文件夹，此路径将放置导出的C++客户端脚本） --client_js_path 表示客户端js导出路径（内含Proxy、ProxyDefine文件夹，此路径将放置导出的js客户端脚本） --client_python_path 表示客户端js导出路径（内含Proxy、ProxyDefine文件夹，此路径将放置导出的python客户端脚本） --user(-u) 表示服务器用户环境（不指定用户将无法导出服务器相关配置） --help(-h) 显示帮助信息''') exit() if tag in ('-u','--user'): user = value if tag == '--server_path': exportServer = value if tag in CLIENT_TYPE: exportClient[CLIENT_TYPE[tag]] = value if not exportServer: print("Error in Exporter : no server_path -> ") return elif not os.path.exists(exportServer): print("Error in Exporter : invalid server_path -> ", exportServer) return if not user: print("== Please set your user name in preference.bat ==") print("== set USER=mario ==") print("The user name settings exists at Server/Project/CustomConfigs") return else: cfgPath = exportServer + "/CustomConfigs/" + user if not os.path.exists(cfgPath): print("Error in Exporter : invalid user -> ", user) return for ctype, cpath in exportClient.items(): if not os.path.exists(cpath): print("Error in Exporter : invalid client_path -> ", ctype, cpath) define_path = exportServer + "/Defines" schemaCfg = schema.load(define_path) cfgPath = exportServer + "/CustomConfigs/" + user serverCfg = server.load(cfgPath) exportCfgPath = exportServer + "/Configs" exportSchemaPath = exportCfgPath + "/Schema" exportServerPath = exportCfgPath + "/Server" exportOrmPath = exportCfgPath + "/Orm" schema.write(schemaCfg, exportSchemaPath) server.write(serverCfg, exportServerPath) orm.write(schemaCfg, exportOrmPath) exportServerScriptPath = exportServer + "/Entities" ss = __import__('server_js', globals(), locals(), [], 0) ss.write(schemaCfg, exportServerScriptPath) for ctype, cpath in exportClient.items(): sc = None try: sc = __import__('client_' + ctype, globals(), locals(), [], 0) except Exception as e: print("Exporter don't support the client script now. -> ", ctype) if sc: sc.writeCfg(schemaCfg, cpath + "/ProxyDefine") sc.writeScript(schemaCfg, cpath + "/Proxy") if __name__ == "__main__": #try: export() #except Exception as e: # print("Error in exporter -> ", e)

31.734694

104

0.684566

0

1,781

0.503392

36d456418e0f32038550bac5f2b5a0f1d2148fc5

707

py

Python

python/python project/te330.py

WhitePhosphorus4/xh-learning-code

025e31500d9f46d97ea634d7fd311c65052fd78e

[ "Apache-2.0" ]

null

python/python project/te330.py

WhitePhosphorus4/xh-learning-code

025e31500d9f46d97ea634d7fd311c65052fd78e

[ "Apache-2.0" ]

null

python/python project/te330.py

WhitePhosphorus4/xh-learning-code

025e31500d9f46d97ea634d7fd311c65052fd78e

[ "Apache-2.0" ]

null

import wx class App(wx.App): def OnInit(self): self.locale = wx.Locale(wx.LANGUAGE_CHINESE) return 1 def A(evt): print("hello") f.Maximize() def B(evt): b.SetBackgroundColour("#FFFFFF") def C(evt): b.SetBackgroundColour("#EFEFEF") app = App() f = wx.Frame(None, -1, "Hello", [700, 500]) wx.Button(f, size = [0, 0]) #s = wx.Image("uu.png", wx.BITMAP_TYPE_ANY).ConvertToBitmap() b = wx.Button(f, -1,'Hello', size = [80, 30], style = wx.BORDER_NONE) #bb= wx.StaticBitmap(b, -1, wx.Image("uu.png", wx.BITMAP_TYPE_ANY).ConvertToBitmap()) b.SetBackgroundColour("#FEFEFE") b.Bind(wx.EVT_BUTTON, A) b.Bind(wx.EVT_ENTER_WINDOW, B) b.Bind(wx.EVT_LEAVE_WINDOW, C) f.Show() app.MainLoop()

27.192308

85

0.666195

107

0.151344

0

194

0.274399

36d4d897387b020fc8db80ecfcfac7847d28fc17

2,048

py

Python

examples/sneswii2gamepad/code.py

dglaude/CircuitPython_Joystic_Controller

a9ef8855b9be457b25c9a436fcbbf6aebe39b4e9

[ "Unlicense", "MIT-0", "MIT" ]

null

examples/sneswii2gamepad/code.py

dglaude/CircuitPython_Joystic_Controller

a9ef8855b9be457b25c9a436fcbbf6aebe39b4e9

[ "Unlicense", "MIT-0", "MIT" ]

null

examples/sneswii2gamepad/code.py

dglaude/CircuitPython_Joystic_Controller

a9ef8855b9be457b25c9a436fcbbf6aebe39b4e9

[ "Unlicense", "MIT-0", "MIT" ]

null

# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries # SPDX-License-Identifier: MIT # You must add a gamepad HID device inside your boot.py file # in order to use this example. # See this Learn Guide for details: # https://learn.adafruit.com/customizing-usb-devices-in-circuitpython/hid-devices#custom-hid-devices-3096614-9 import time import board import neopixel led = neopixel.NeoPixel(board.NEOPIXEL, 1) led.brightness = 0.3 led[0] = (0, 0, 0) # SPDX-FileCopyrightText: 2021 John Furcean # SPDX-License-Identifier: MIT # Classic Controller also work with CLV-202. # But the "Super Nintendo SNES Classic Mini Controller" has less button and not stick. from wiichuck.classic_controller import ClassicController controller = ClassicController(board.I2C()) # SPDX-FileCopyrightText: Copyright (c) 2021 Dan Halbert for Adafruit Industries # # SPDX-License-Identifier: Unlicense import usb_hid from hid_gamepad import Gamepad gp = Gamepad(usb_hid.devices) x=0 y=0 oldx=0 oldy=0 while True: _, buttons, dpad, _ = controller.values if buttons.A: led[0] = (255, 0, 0) if buttons.B: led[0] = (255, 255, 0) if buttons.X: led[0] = (0, 0, 255) if buttons.Y: led[0] = (0, 255, 0) if buttons.R: led[0] = (0, 0, 0) print("button R") if buttons.L: led[0] = (0, 0, 0) print("button L") if buttons.start: led[0] = (0, 0, 0) print("button start") if buttons.select: led[0] = (0, 0, 0) print("button select") if (y!=0) and not (dpad.up or dpad.down): y=0 if dpad.up: y = 127 led[0] = (0, 0, 0) print("dpad up") if dpad.down: y = -127 led[0] = (0, 0, 0) print("dpad down") if (x!=0) and not (dpad.right or dpad.left): x=0 if dpad.right: x = 127 led[0] = (0, 0, 0) print("dpad right") if dpad.left: x = -127 led[0] = (0, 0, 0) print("dpad left") gp.move_joysticks(x, y)

23.54023

110

0.605957

0

740

0.361328

36d54bbaca57e4631b154f3ca77d029d7fd103ad

3,345

py

Python

spleeter/util.py

ashirviskas/spleeter-pytorch

853d4bb6048fae879543342a8278c298854637f3

[ "MIT" ]

28

2019-11-29T10:23:16.000Z

2022-03-28T13:02:29.000Z

spleeter/util.py

ashirviskas/spleeter-pytorch

853d4bb6048fae879543342a8278c298854637f3

[ "MIT" ]

2

2020-05-07T04:07:50.000Z

2020-07-13T02:40:41.000Z

spleeter/util.py

ashirviskas/spleeter-pytorch

853d4bb6048fae879543342a8278c298854637f3

[ "MIT" ]

6

2020-05-31T08:23:56.000Z

2022-02-22T16:38:19.000Z

import numpy as np import tensorflow as tf from .unet import UNet def tf2pytorch(checkpoint_path, num_instrumments): tf_vars = {} init_vars = tf.train.list_variables(checkpoint_path) # print(init_vars) for name, shape in init_vars: try: # print('Loading TF Weight {} with shape {}'.format(name, shape)) data = tf.train.load_variable(checkpoint_path, name) tf_vars[name] = data except Exception as e: print('Load error') conv_idx = 0 tconv_idx = 0 bn_idx = 0 outputs = [] for i in range(num_instrumments): output = {} outputs.append(output) for j in range(1,7): if conv_idx == 0: conv_suffix = "" else: conv_suffix = "_" + str(conv_idx) if bn_idx == 0: bn_suffix = "" else: bn_suffix = "_" + str(bn_idx) output['down{}_conv.weight'.format(j)] = np.transpose( tf_vars["conv2d{}/kernel".format(conv_suffix)], (3, 2, 0, 1)) # print('conv dtype: ',output['down{}.0.weight'.format(j)].dtype) output['down{}_conv.bias'.format( j)] = tf_vars["conv2d{}/bias".format(conv_suffix)] output['down{}_act.0.weight'.format( j)] = tf_vars["batch_normalization{}/gamma".format(bn_suffix)] output['down{}_act.0.bias'.format( j)] = tf_vars["batch_normalization{}/beta".format(bn_suffix)] output['down{}_act.0.running_mean'.format( j)] = tf_vars['batch_normalization{}/moving_mean'.format(bn_suffix)] output['down{}_act.0.running_var'.format( j)] = tf_vars['batch_normalization{}/moving_variance'.format(bn_suffix)] conv_idx += 1 bn_idx += 1 # up blocks for j in range(1, 7): if tconv_idx == 0: tconv_suffix = "" else: tconv_suffix = "_" + str(tconv_idx) if bn_idx == 0: bn_suffix = "" else: bn_suffix= "_" + str(bn_idx) output['up{}.0.weight'.format(j)] = np.transpose( tf_vars["conv2d_transpose{}/kernel".format(tconv_suffix)], (3,2,0, 1)) output['up{}.0.bias'.format( j)] = tf_vars["conv2d_transpose{}/bias".format(tconv_suffix)] output['up{}.2.weight'.format( j)] = tf_vars["batch_normalization{}/gamma".format(bn_suffix)] output['up{}.2.bias'.format( j)] = tf_vars["batch_normalization{}/beta".format(bn_suffix)] output['up{}.2.running_mean'.format( j)] = tf_vars['batch_normalization{}/moving_mean'.format(bn_suffix)] output['up{}.2.running_var'.format( j)] = tf_vars['batch_normalization{}/moving_variance'.format(bn_suffix)] tconv_idx += 1 bn_idx += 1 if conv_idx == 0: suffix = "" else: suffix = "_" + str(conv_idx) output['up7.0.weight'] = np.transpose( tf_vars['conv2d{}/kernel'.format(suffix)], (3, 2, 0, 1)) output['up7.0.bias'] = tf_vars['conv2d{}/bias'.format(suffix)] conv_idx += 1 return outputs

36.358696

88

0.529746

0

828

0.247534

36d54c415ce82d548b3b02e02ceb85813202d7ef

1,920

py

Python

SDEprocesses/explicit.py

PyPaperParrot/pystoch

14d1dbeefaeb3696378e0db6e565347df87a02bc

[ "MIT" ]

1

2019-06-06T13:53:51.000Z

SDEprocesses/explicit.py

PyPaperParrot/pystoch

14d1dbeefaeb3696378e0db6e565347df87a02bc

[ "MIT" ]

null

SDEprocesses/explicit.py

PyPaperParrot/pystoch

14d1dbeefaeb3696378e0db6e565347df87a02bc

[ "MIT" ]

null

import numpy as np import exceptions as ex def LogWalk(T, nSteps, mu, sigma, x_0=1, t_0=0, boundary=500): ex._check_params(T, nSteps, t_0) dt = T/(10*nSteps) x_t = [] t = t_0 for i in range((10*nSteps)): x = x_0*np.exp((mu - sigma**2/2)*t + sigma*np.random.randn()*np.sqrt(t)) if abs(x) > boundary: raise Warning("Risk of going beyond the definition of a random process. Boundary: " + str(boundary) + ". If You wish You could change boundary conditions in parameters (default:'boundary'=500).") x_t.append(x) t += dt return x_t # 4. Процесс Орнштейна-Уленбека def OrnsteinUlenbekProcess(T, nSteps, alpha, beta, _sigma, x_0=1, t_0=0, boundary=500): ex._check_params(T, nSteps, t_0) dt = T/(10*nSteps) x_t = [] x_t.append(x_0) t = t_0 for i in range(1, 10*nSteps): x = alpha + (x_0 - alpha)*np.exp(-beta*t) + _sigma/np.sqrt(2*beta)*np.sqrt(1-np.exp(-2*beta*t))*np.random.randn() if abs(x) > boundary: raise Warning("Risk of going beyond the definition of a random process. Boundary: " + str(boundary) + ". If You wish You could change boundary conditions in parameters (default:'boundary'=500).") x_t.append(x) t += dt return x_t # 6. Броуновский мост def BrownianBridge(T, nSteps, alpha, _sigma, x_0=1, t_0=0, boundary=500): ex._check_params(T, nSteps, t_0) dt = T/(10*nSteps) x_t = [] x_t.append(x_0) t = t_0 for i in range(1, 10*nSteps): x = alpha + (x_0 - alpha)*(T - t)/(T - t_0) + _sigma*np.sqrt((t - t_0)*(T - t)/T - t_0)*np.random.randn() if abs(x) > boundary: raise Warning("Risk of going beyond the definition of a random process. Boundary: " + str(boundary) + ". If You wish You could change boundary conditions in parameters (default:'boundary'=500).") x_t.append(x) t += dt return x_t

34.285714

207

0.611458

0

574

0.293007

36d5db401ea4ab6c6ef162a66fa84ae2937cecdb

3,847

py

Python

oscontainer/cgroup_v2_subsystem.py

Lothiraldan/oscontainer

140504711372494f919b4de7bf84e80d11013fa0

[ "MIT" ]

null

oscontainer/cgroup_v2_subsystem.py

Lothiraldan/oscontainer

140504711372494f919b4de7bf84e80d11013fa0

[ "MIT" ]

null

oscontainer/cgroup_v2_subsystem.py

Lothiraldan/oscontainer

140504711372494f919b4de7bf84e80d11013fa0

[ "MIT" ]

null

import math from oscontainer.constants import CGROUP_TYPE_V2, PER_CPU_SHARES, NO_LIMIT from oscontainer.cgroup_subsystem import CgroupController, CgroupSubsystem from oscontainer.utils import limit_from_str CPU_WEIGHT = "cpu.weight" CPU_MAX = "cpu.max" CPU_CPUSET_CPUS = "cpuset.cpus" CPU_CPUSET_CPUS_EFFECTIVE = "cpuset.cpus.effective" MEMORY_CURRENT = "memory.current" MEMORY_MAX = "memory.max" class CgroupV2Controller(CgroupController): def __init__(self, mount_path, cgroup_path): # type: (str, str) -> None """ Creates new cgroup V2 controller. :param mount_path: the mount path of the cgroup v2 hierarchy :param cgroup_path: the cgroup path for the controller """ super().__init__() self.mount_path = mount_path self.cgroup_path = cgroup_path self.subsystem_path = self._create_subsystem_path(mount_path, cgroup_path) @staticmethod def _create_subsystem_path(mount_path, cgroup_path): # type: (str, str) -> str return mount_path + cgroup_path class CgroupV2Subsystem(CgroupSubsystem): """ The implementation for cgroup V2 """ def __init__(self, unified): # type: (CgroupV2Controller) -> None """ Creates new instance. :param unified: the unified cgroup controller """ self.unified = unified def cpu_shares(self): # type: () -> int shares = int(self.unified.read_container_param(CPU_WEIGHT)) if shares == 100: # Convert default value of 100 to no shares setup return NO_LIMIT # CPU shares (OCI) value needs to get translated into # a proper Cgroups v2 value. See: # https://github.com/containers/crun/blob/master/crun.1.md#cpu-controller # # Use the inverse of (x == OCI value, y == cgroupsv2 value): # ((262142 * y - 1)/9999) + 2 = x x = 262142 * shares - 1 frac = float(x) / 9999.0 x = int(frac) + 2 if x <= PER_CPU_SHARES: # will always map to 1 CPU return x # Since the scaled value is not precise, return the closest # multiple of PER_CPU_SHARES for a more conservative mapping f = x / PER_CPU_SHARES lower_multiple = math.floor(f) * PER_CPU_SHARES upper_multiple = math.ceil(f) * PER_CPU_SHARES distance_lower = max(lower_multiple, x) - min(lower_multiple, x) distance_upper = max(upper_multiple, x) - min(upper_multiple, x) if distance_lower <= distance_upper: return lower_multiple else: return upper_multiple def cpu_quota(self): # type: () -> int cpu_quota_res = self.unified.read_container_params_with_format(CPU_MAX, scan_format="%s %*d") if len(cpu_quota_res) == 0: return NO_LIMIT return limit_from_str(cpu_quota_res[0]) def cpu_period(self): # type: () -> int cpu_period_res = self.unified.read_container_params_with_format(CPU_MAX, scan_format="%*s %d") if len(cpu_period_res) == 0: return NO_LIMIT return cpu_period_res[0] def cpu_cpuset_cpus(self): # type: () -> str cpuset = self.unified.read_container_param(CPU_CPUSET_CPUS) if cpuset is None or cpuset == "": cpuset = self.unified.read_container_param(CPU_CPUSET_CPUS_EFFECTIVE) return cpuset def memory_usage_in_bytes(self): # type: () -> int return int(self.unified.read_container_param(MEMORY_CURRENT)) def memory_limit_in_bytes(self): # type: () -> int memory_str = self.unified.read_container_param(MEMORY_MAX) return limit_from_str(memory_str) def container_type(self): # type: () -> str return CGROUP_TYPE_V2

34.044248

102

0.641279

3,442

0.894723

0

144

0.037432

0

1,092

0.283858

36d6b30d341d10b3fc5496de476fb8b78f692188

460

py

Python

openapi/tests/matchers.py

suihanki/openapi

c67ee4ec0284bc1da5bda2c6b8497d6a33bb69a0

[ "Apache-2.0" ]

25

2016-08-09T18:40:42.000Z

2019-07-15T20:37:13.000Z

openapi/tests/matchers.py

suihanki/openapi

c67ee4ec0284bc1da5bda2c6b8497d6a33bb69a0

[ "Apache-2.0" ]

5

2016-08-16T18:34:44.000Z

2020-03-24T21:01:26.000Z

openapi/tests/matchers.py

suihanki/openapi

c67ee4ec0284bc1da5bda2c6b8497d6a33bb69a0

[ "Apache-2.0" ]

11

2016-09-14T09:12:49.000Z

2020-01-31T19:27:07.000Z

""" Custom hamcrest matchers. """ from hamcrest.core.base_matcher import BaseMatcher from json import dumps, loads class JSONMatcher(BaseMatcher): """ Match JSON content. """ def __init__(self, s): self.json = loads(s) def _matches(self, item): return loads(item) == self.json def describe_to(self, description): description.append_text("json ").append_text(dumps(self.json)) equal_to_json = JSONMatcher

18.4

70

0.671739

312

0.678261

0

76

0.165217

36d743457c72e522cd69762028d8c4a8aaf9d131

2,741

py

Python

build-container/docxify3.py

signaux-faibles/datapi

296ee922dc47eea4176f5c7bdde35c218bf9c817

[ "MIT" ]

null

build-container/docxify3.py

signaux-faibles/datapi

296ee922dc47eea4176f5c7bdde35c218bf9c817

[ "MIT" ]

31

2020-04-23T11:29:16.000Z

2021-06-23T05:45:08.000Z

build-container/docxify3.py

signaux-faibles/datapi

296ee922dc47eea4176f5c7bdde35c218bf9c817

[ "MIT" ]

null

# coding: utf-8 import sys import json from mailmerge import MailMerge # Le template contient à ce jour les champs : # auteur l'auteur du document # date_edition la date d'édition du document # confidentialite le destinataire du document # raison_sociale la raison sociale de l'entreprise # siret le numéro de SIRET de l'établissement # type_etablissement le type d'établissement siège social ou établissement secondaire # tete_de_groupe la tête de groupe si l'entreprise fait partie d'un groupe # departement le departement de l'établissement # commune la commune de l'établissement # territoire_industrie le Territoire d'industrie # secteur_activite le secteur d'activité # activite le libellé et le code activité # secteurs_covid appartenance aux secteurs dits COVID-19 S1, S1 bis ou S2 # statut_juridique le statut juridique comme SAS ou SARL # date_ouverture_etablissement la date d'ouverture de l'établissement # date_creation_entreprise la date de création de l'entreprise # effectif le dernier effectif # activite_partielle demande d'activité partielle sur les 12 derniers mois ou non # dette_sociale dette sociale en hausse sur les 3 derniers mois ou non # part_salariale dette salariale restante ou non # annee_exercice année du dernier exercice comptable # ca chiffre d'affaires # ebe excédent brut d'exploitation # rex résultat d'exploitation # procol dernière procédure collective # detection_sf risque identifié par l'algorithme de détection Signaux Faibles # date_debut_suivi date de début de suivi par l'auteur # description_wekan description dans l'outil de suivi Kanban Wekan template = 'template.docx' # Lecture des données JSON depuis l'entrée standard def get_json_input_data(): try: sys.stdin.reconfigure(encoding='utf-8') read = sys.stdin.read() data = json.loads(read) return data except ValueError: sys.stderr.write('Erreur lors de la lecture des données JSON en entrée\n') sys.exit(1) # Remplissage du modèle DOCX contenant des champs de fusion (MERGEFIELD) et écriture dans la sortie standard def fill_template_with_data(data): try: document = MailMerge(template) # 3 arguments possibles : # 1 = auteur, 2 = date_edition, 3 = confidentialite args = len(sys.argv) if args > 3: confidentialite = sys.argv[3] document.merge(confidentialite=confidentialite) if args > 2: date_edition = sys.argv[2] document.merge(date_edition=date_edition) if args > 1: auteur = sys.argv[1] document.merge(auteur=auteur) document.merge_templates(data, separator='page_break') document.write(sys.stdout.buffer) except ValueError: sys.stderr.write('Erreur lors du remplissage du modèle DOCX\n') sys.exit(1) data = get_json_input_data() fill_template_with_data(data) sys.exit(0)

38.069444

108

0.785845

0

1,910

0.689033

36d88c360c0960445e0699b390c5bc46416d33e6

406

py

Python

super32assembler/super32assembler/preprocessor/asmdirectives.py

Projektstudium-Mikroprozessor/Super32

d502d2d5885ac0408d06e57e0f5a67fe2a2fee15

[ "BSD-3-Clause" ]

1

2019-12-07T01:56:31.000Z

super32assembler/super32assembler/preprocessor/asmdirectives.py

Projektstudium-Mikroprozessor/Super32

d502d2d5885ac0408d06e57e0f5a67fe2a2fee15

[ "BSD-3-Clause" ]

42

2020-05-15T10:39:30.000Z

2020-08-30T10:59:43.000Z

super32assembler/preprocessor/asmdirectives.py

xsjad0/Super32

75cf5828b17cdbce144447a69ff3d1be7ad601f2

[ "BSD-3-Clause" ]

4

2019-11-27T15:05:33.000Z

2020-05-13T06:51:21.000Z

""" Enum Assembler-Directives """ from enum import Enum, auto class AssemblerDirectives(Enum): START = auto() END = auto() ORG = auto() DEFINE = auto() @classmethod def to_string(cls): return "{START},{END},{ORG},{DEFINE}".format( START=cls.START.name, END=cls.END.name, ORG=cls.ORG.name, DEFINE=cls.DEFINE.name )

18.454545

53

0.549261

340

0.837438

0

229

0.564039

0

63

0.155172

36d90f9194a3f4a5adea321bf4cf9176ed0ded59

250

py

Python

Wyklad/OOP/Sheep.py

tborzyszkowski/PythonWorld

dc776d3ab4569297b6b6681e4390aeedf1262c78

[ "MIT" ]

3

2019-02-24T07:49:33.000Z

2022-01-09T11:27:21.000Z

Wyklad/OOP/Sheep.py

tborzyszkowski/PythonWorld

dc776d3ab4569297b6b6681e4390aeedf1262c78

[ "MIT" ]

null

Wyklad/OOP/Sheep.py

tborzyszkowski/PythonWorld

dc776d3ab4569297b6b6681e4390aeedf1262c78

[ "MIT" ]

17

2018-12-09T08:44:52.000Z

2022-01-25T15:08:04.000Z

from Animal import Animal class Sheep(Animal): def __init__(self, sheep=None, position=None): super(Sheep, self).__init__(sheep, position) def clone(self): return Sheep(self, None) def initParams(self): self.power = 3 self.sign = 'S'

16.666667

47

0.704

221

0.884

0

3

0.012

36da2c9e737689743389837a193464187fe5262e

154

py

Python

Python/WSClock/Page.py

Camiloasc1/OperativeSystemsUNAL

a07bfc235789b7a8848280a549a6b2c9602e61b5

[ "MIT" ]

null

Python/WSClock/Page.py

Camiloasc1/OperativeSystemsUNAL

a07bfc235789b7a8848280a549a6b2c9602e61b5

[ "MIT" ]

null

Python/WSClock/Page.py

Camiloasc1/OperativeSystemsUNAL

a07bfc235789b7a8848280a549a6b2c9602e61b5

[ "MIT" ]

null

class Page(object): def __init__(self, params): self.size = 2 ** 10 self.Time = False self.R = False self.M = False

17.111111

31

0.519481

152

0.987013

0

36da2e9adf116505c11742d74e8d8a7c885d7c7d

1,034

py

Python

python/python-algorithm-intervew/8-linked-list/16-add-two-numbers-2.py

bum12ark/algorithm

b6e262b0c29a8b5fb551db5a177a40feebc411b4

[ "MIT" ]

1

2022-03-06T03:49:31.000Z

python/python-algorithm-intervew/8-linked-list/16-add-two-numbers-2.py

bum12ark/algorithm

b6e262b0c29a8b5fb551db5a177a40feebc411b4

[ "MIT" ]

null

python/python-algorithm-intervew/8-linked-list/16-add-two-numbers-2.py

bum12ark/algorithm

b6e262b0c29a8b5fb551db5a177a40feebc411b4

[ "MIT" ]

null

class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next def print_list(self): cur = self while cur: print(cur.val, end='->') cur = cur.next class Solution: # 전가산기구현 def addTwoNumbers(selfself, l1: ListNode, l2: ListNode) -> ListNode: root = head = ListNode(0) carry = 0 # 자리올림수 while l1 or l2 or carry: sum = 0 # 두 입력값의 합 계산 if l1: sum += l1.val l1 = l1.next if l2: sum += l2.val l2 = l2.next # 몫 (자리올림수_과 나머지(값) 계산 carry, val = divmod(sum + carry, 10) head.next = ListNode(val) head = head.next return root.next if __name__ == '__main__': solution = Solution() param1 = ListNode(2, ListNode(4, ListNode(5))) param2 = ListNode(5, ListNode(6, ListNode(4))) print(solution.addTwoNumbers(param1, param2).print_list())

24.619048

72

0.500967

874

0.795993

0

128

0.116576

36dbe66f53ea99cba7463f1defbdf1646e602362

15,516

py

Python

pyjokes/jokes_pl.py

r0d0dendr0n/pyjokes

382065cba91007302be7fd04c5c35a9957e173b2

[ "BSD-3-Clause" ]

null

pyjokes/jokes_pl.py

r0d0dendr0n/pyjokes

382065cba91007302be7fd04c5c35a9957e173b2

[ "BSD-3-Clause" ]

null

pyjokes/jokes_pl.py

r0d0dendr0n/pyjokes

382065cba91007302be7fd04c5c35a9957e173b2

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Jokes below come from the "jokes_en.py" file. Translation to Polish: Tomasz Rozynek - provided under CC BY-SA 3.0 """ neutral = [ "W 2030 roku Beata z ulgą usunęła Python'a 2.7 ze swoich maszyn. 'No!' westchnęła, by za chwilę przeczytać ogłoszenia na temat Python'a 4.4.", "Zapytanie SQL wchodzi do baru, podchodzi do pierwszej osoby i pyta, 'Czy możemy utworzyć relację?'", "Kiedy używasz C++ jak młotka, wszystko będzie Twoim kciukiem.", "Jak posadzisz milion małp przy milionie klawiatur, któraś z nich w końcu napisze działający program w Javie. Pozostałe będą pisać w Perlu.", "Aby zrozumieć rekurencję, musisz najpierw zrozumieć rekurencję.", "'Puk, puk.' 'Kto tam?' ... bardzo długa pauza ... 'Java.'", "'Puk, puk.' 'Kto tam?' 'C++.'", "'Puk, p... Asembler.'", "Ilu programistów potrzeba, żeby wymienić żarówkę? Żadnego, bo to problem sprzętowy.", "Jak nazywa się obiektowa metoda bogacenia się? Dziedziczenie.", "Dlaczego dowcipy nie działają w systemie ósemkowym? Ponieważ 7, 10, 11.", "Ilu programistów potrzeba, aby wymienić żarówkę? Żadnego, po prostu ogłaszają ciemność standardem.", "Dwa wątki wchodzą do baru. Barman patrzy na nie i woła, 'Hej! Nie chcemy tu hazardu!'", "Programiści uwielbiają rozwiązywanie problemów. Jeśli akurat nie mają żadnego do rozwiązania, z pewnością jakiś stworzą.", ".NET nazywa się .NET, żeby przypadkiem nie wyświetlił się w uniksowym listingu plików.", "Sprzęt: część komputera, którą możesz kopnąć.", "Optymista: Szklanka do połowy pełna. Pesymista: Szklanka do połowy pusta. Programista: Rozmiar szklanki jest dwa razy większy, niż wymagany.", "W C sami musieliśmy kodować błędy. W C++ możemy je po prostu odziedziczyć.", "Dlaczego nie ma konkursów na najmniej czytelny kod w Perlu? Bo nikt nie umiałby wyłonić zwycięzcy.", "Odtwarzając dysk instalacyjny Windowsa od tyłu, usłyszysz czarną mszę. Gorzej, jeśli odtworzysz ją od przodu, wtedy zainstaluje Windowsa.", "Ilu programistów potrzeba, aby zabić karalucha? Dwóch: jeden go trzyma, a drugi instaluje na nim Windowsa.", "Do jakiej grupy należą programiści z Finlandii? Nerdyckiej.", "Co mówi kod w Javie do kodu w C? Brakuje Ci klasy.", "Dlaczego Microsoft nazwał swoją wyszukiwarkę BING? Bo Indolentnie Naśladuje Google.", "Piraci wołają 'arg!', komputerowi piraci wołają 'argv!'", "Dziecko: Mamo, dlaczego Słońce wschodzi na wschodzie i zachodzi na zachodzie? Ojciec: jeśli działa, nie dotykaj.", "Dlaczego programistom myli się Halloween z Bożym Narodzeniem? Ponieważ OCT 31 == DEC 25.", "Ilu programistów Prologa potrzeba, żeby wymienić żarówkę? Fałsz.", "Kelner: Podać kawę, lub herbatę? Programistka: Tak.", "Programistka wchodzi do foo...", "Jak brzmi drugie imię Benoit'a B. Mandelbrot'a? Benoit B. Mandelbrot.", "Dlaczego zawsze się uśmiechasz? To moje regularne wyrażenie twarzy.", "Programistka miała problem. Pomyślała sobie, 'Wiem, rozwiążę to wątkami!'. ma Teraz problemy. ona dwa", "Opowiedziałbym dowcip o UDP, ale nie wiem, czy by do Ciebie dotarł.", "Testerka wchodzi do baru. Wbiega do baru. Wczołguje się do baru. Tańczy wchodząc do baru. Wchodzi tip-topami do baru. Szarżuje do baru.", "Miałem problem, więc pomyślałem, że użyję Javy. Teraz mam FabrykaProblemow.", "Tester wchodzi do baru. Zamawia piwo. Zamawia 0 piw. Zamawia 999999999 piw. Zamawia jaszczurkę. Zamawia -1 piw. Zamawia sfdeljknesv.", "Kierowniczka projektu wchodzi do baru, zamawia drinka. Barman odmawia, ale pomyśli nad dodaniem go później.", "Jak wygenerować prawdziwie losowy ciąg znaków? Posadź studenta pierwszego roku przed Vim'em i powiedz, żeby zapisał plik i wyłączył edytor.", "Od dłuższego czasu używam Vim'a. Głównie dlatego, że nadal próbuję go wyłączyć.", "Jak poznać, że ktoś używa Vim'a? Nie przejmuj się, sam Ci powie.", "Kelner: On się krztusi! Czy jest na sali doktor? Programista: Jestem użytkownikiem Vim'a.", "Trójka adminów baz danych wchodzi do NoSQL'owego baru. Po krótkim czasie rozeszli się, ponieważ nie mogli utworzyć relacji.", "Jak opisać fabułę Incepcji programiście? Uruchamiasz maszynę wirtualną w wirtualce, wewnątrz innej wirtualki... wszystko działa wolno!", "W informatyce są tylko dwa trudne problemy: unieważnianie pamięci podręcznej, nazewnictwo i pomyłki o 1.", "Istnieje 10 rodzajów ludzi: Ci, którzy rozumieją kod binarny oraz Ci, którzy go nie rozumieją.", "Istnieją 2 rodzaje ludzi: Ci, którzy potrafią ekstrapolować niekompletne zbiory danych...", "Istnieją II rodzaje ludzi: Ci, którzy rozumieją liczby rzymskie i Ci, którzy ich nie rozumieją.", "Istnieje 10 typów ludzi: Ci, którzy rozumieją system szesnastkowy oraz 15 pozostałych.", "Istnieje 10 rodzajów ludzi: Ci, którzy rozumieją kod binarny, Ci którzy go nie rozumieją oraz Ci, co wiedzieli, że to o systemie trójkowym.", "Istnieje 10 rodzajów ludzi: Ci, którzy rozumieją kod trójkowy, Ci, którzy go nie rozumieją oraz Ci, którzy nigdy o nim nie słyszeli.", "Jak nazywa się ósemka hobbitów? Hobbajt.", "Najlepsze w wartościach logicznych jest to, że nawet jeśli się pomylisz, to tylko o 1.", "Dobry programista zawsze patrzy w obie strony przed przejściem przez ulicę jednokierunkową.", "Są dwa sposoby pisania programów bez błędów. Tylko ten trzeci działa.", "Zarządzanie jakością składa się w 55% z wody, 30% krwi i 15% ticketów z bugtrackera", "Sympatyzowanie z Diabłem to tak naprawdę bycie uprzejmym dla Testerów.", "Ilu Testerów potrzeba do zmiany żarówki? Oni zauważyli, że pokój jest ciemny. Nie rozwiązują problemów, tylko ich szukają.", "Programista rozbił auto zjeżdżając z góry. Przechodzień spytał co się stało. \"Nie wiem. Wnieśmy go na górę i spróbujmy ponownie.\".", "Pisanie w PHP jest jak sikanie do basenu. Wszyscy to robili, ale niekoniecznie trzeba się tym chwalić publicznie.", "Dlaczego Tester przeszedł przez ulicę? Żeby zepsuć dzień wszystkim innym.", "Ilość dni od ostatniego błędu indeksowania tablicy: -1.", "Ilość dni od ostatniej pomyłki o 1: 0.", "Szybkie randki są bez sensu. 5 minut to zbyt mało czasu, aby prawidłowo wyjaśnić filozofię Unix'a.", "Microsoft co dwa miesiące organizuje \"tydzień produktywności\", podczas którego używają Google zamiast Bing'a", "Podejście Schroedinger'a do budowy stron www: Jeśli nie oglądasz tego w Internet Explorerze, jest szansa, że będzie wyglądało dobrze.", "Szukanie dobrego programisty PHP jest jak szukanie igły w stogu siana. Czy raczej stogu siana w igle?", "Unix jest bardzo przyjazny użytkownikom. Po prostu jest również bardzo wybredny przy wyborze przyjaciół.", "Programistka COBOL'a zarabia miliony naprawiając problem roku 2000. Decyduje się zamrozić siebie. \"Mamy rok 9999. Znasz COBOL'a, prawda?\"", "Język C łączy w sobie potęgę asemblera z prostotą użycia asemblera.", "Ekspert SEO wchodzi do baru, bar, pub, miesce spotkań, browar, Irlandzki pub, tawerna, barman, piwo, gorzała, wino, alkohol, spirytus...", "Co mają wspólnego pyjokes oraz Adobe Flash? Wciąż otrzymują aktualizacje, ale nigdy nie stają się lepsze.", "Dlaczego Waldo nosi tylko paski? Bo nie chce się znaleźć w kropce.", "Szedłem raz ulicą, przy której domy były ponumerowane 8k, 16k, 32k, 64k, 128k, 256k i 512k. To była podróż Aleją Pamięci.", "!false, (To zabawne, bo to prawda)", ] """ Jokes below come from the "jokes_en.py" file. Translation to Polish: Tomasz Rozynek - provided under CC BY-SA 3.0 """ chuck = [ "Kiedy Chuck Norris rzuca wyjątek, to leci on przez cały pokój.", "Wszystkie tablice, które deklaruje Chuck Norris są nieskończonego rozmiaru, ponieważ Chuck Norris nie zna granic.", "Chuck Norris nie ma opóźnień w dysku twardym, ponieważ dysk twardy wie, że musi się spieszyć, żeby nie wkurzyć Chucka Norrisa.", "Chuck Norris pisze kod, który sam się optymalizuje.", "Chuck Norris nie porównuje, ponieważ nie ma sobie równych.", "Chuck Norris nie potrzebuje garbage collector'a, ponieważ nie wywołuje .Dispose(), tylko .DropKick().", "Pierwszym programem Chucka Norrisa było kill -9.", "Chuck Norris przebił bańkę dot com'ów.", "Wszystkie przeglądarki wspierają kolory #chuck oraz #norris, oznaczające czarny i niebieski.", "MySpace tak naprawdę nie jest Twój, tylko Chuck'a. Po prostu pozwala Ci go używać.", "Chuck Norris może pisać funkcje rekurencyjne bez warunku stopu, które zawsze wracają.", "Chuck Norris może rozwiązać wieże Hanoi w jednym ruchu.", "Chuck Norris zna tylko jeden wzorzec projektowy: Boski obiekt.", "Chuck Norris ukończył World of Warcraft.", "Kierownicy projektu nigdy nie pytają Chucka Norrisa o oszacowania.", "Chuck Norris nie dostosowuje się do standardów webowych, ponieważ to one dostosowują się do niego.", "'U mnie to działa' jest zawsze prawdą w przypadku Chucka Norrisa.", "Chuck Norris nie używa diagramów wyżarzania, tylko uderzania.", "Chuck Norris może usunąć Kosz.", "Broda Chucka Norrisa może pisać 140 słów na minutę.", "Chuck Norris może przetestować całą aplikację jedną asercją: 'działa'.", "Chuck Norris nie szuka błędów, ponieważ to sugeruje, że może ich nie znaleźć. On likwiduje błędy.", "Klawiatura Chucka Norris'a nie ma klawisza Ctrl, ponieważ nic nie kontroluje Chucka Norrisa.", "Chuck Norris może przepełnić Twój stos samym spojrzeniem.", "Dla Chucka Norrisa wszystko zawiera podatności.", "Chuck Norris nie używa sudo. Powłoka wie, że to on i po prostu robi co jej każe.", "Chuck Norris nie używa debuggera. Patrzy na kod tak długo, aż sam wyzna błędy.", "Chuck Norris ma dostęp do prywatnych metod.", "Chuck Norris może utworzyć obiekt klasy abstrakcyjnej.", "Chuck Norris nie potrzebuje fabryki klas. On instancjonuje interfejsy.", "Klasa Object dziedziczy po Chucku Norrisie.", "Dla Chucka Norrisa problemy NP-trudne mają złożoność O(1).", "Chuck Norris zna ostatnią cyfrę rozwinięcia dziesiętnego Pi.", "Łącze internetowe Chucka Norrisa szybciej wysyła, niż pobiera, ponieważ nawet dane się go boją.", "Chuck Norris rozwiązał problem komiwojażera w czasie stałym: rozbij komiwojażera na N kawałków, po czym wykop każdy do innego miasta.", "Żadne wyrażenie nie może obsłużyć ChuckNorrisException.", "Chuck Norris nie programuje w parach. Pracuje sam.", "Chuck Norris potrafi pisać aplikacje wielowątkowe przy użyciu jednego wątku.", "Chuck Norris nie musi używać AJAX'a, ponieważ strony i tak są przerażone jego zwykłymi żądaniami.", "Chuck Norris nie używa refleksji. To refleksje uprzejmie proszą go o pomoc.", "Klawiatura Chucka Norrisa nie ma klawisza Escape, ponieważ nikt nie ucieknie przed Chuckiem Norrisem.", "Chuck Norris może użyć wyszukiwania binarnego na nieposortowanym kontenerze.", "Chuck Norris nie musi łapać wyjątków. Są zbyt przerażone, by się pokazać.", "Chuck Norris wyszedł z nieskończonej pętli.", "Jeśli Chuck Norris napisze kod z błędami, to one same się poprawią.", "Hosting Chucka Norrisa ma SLA na poziomie 101%.", "Klawiatura Chucka Norrisa ma klawisz 'Dowolny'.", "Chuck Norris może dostać się do bazy danych bezpośrednio przez interfejs użytkownika.", "Programy Chucka Norrisa się nie kończą, tylko giną.", "Chuck Norris nalega na używanie języków silnie typowanych.", "Chuck Norris projektuje protokoły bez statusów, żądań, czy odpowiedzi. Definiuje tylko polecenia.", "Programy Chucka Norrisa zajmują 150% procesora, nawet gdy nie są uruchomione.", "Chuck Norris uruchamia wątki, które kończą swoje zadanie, zanim się poprawnie uruchomią.", "Programy Chucka Norrisa nie akceptują wejścia.", "Chuck Norris może zainstalować iTunes bez QuickTime'a.", "Chuck Norris nie potrzebuje systemu operacyjnego.", "Model OSI Chucka Norrisa ma tylko jedną warstwę - fizyczną.", "Chuck Norris może poprawnie kompilować kod z błędami składniowymi.", "Każde zapytanie SQL Chucka Norrisa zawiera implikowany 'COMMIT'.", "Chuck Norris nie potrzebuje rzutowania. Kompilator Chucka Norrisa (KCN) dostrzega wszystko. Do samego końca. Zawsze.", "Chuck Norris nie wykonuje kodu w cyklach, tylko w uderzeniach.", "Chuck Norris kompresuje pliki przez kopnięcie dysku twardego z półobrotu.", "Chuck Norris rozwiązał problem stopu.", "Dla Chucka Norrisa P = NP. Jego decyzje są zawsze deterministyczne.", "Chuck Norris może pobrać wszystko z /dev/null.", "Nikomu nie udało się programować z Chuckiem Norrisem i wyjść z tego żywym.", "Nikomu nie udało się odezwać podczas przeglądu kodu Chucka Norrisa i wyjść z tego żywym.", "Chuck Norris nie używa interfejsów graficznych. On rozkazuje z wiersza poleceń.", "Chuck Norris nie używa Oracle'a. On JEST Wyrocznią.", "Chuck Norris może dokonać dereferencji NULL'a.", "Lista różnic pomiędzy Twoim kodem oraz kodem Chucka Norrisa jest nieskończona.", "Chuck Norris napisał wtyczkę do Eclipsa, która dokonała pierwszego kontaktu z obcą cywilizacją.", "Chuck Norris jest ostatecznym semaforem. Wszystkie wątki się go boją.", "Nie przejmuj się testami. Przypadki testowe Chucka Norrisa pokrywają również Twój kod.", "Każdy włos z brody Chucka Norrisa ma swój wkład w największy na świecie atak DDOS.", "Komunikaty w loggerze Chucka Norrisa zawsze mają poziom FATAL.", "Jeśli Chuck Norris zepsuje build'a, nie uda Ci się go naprawić, ponieważ nie została ani jedna linijka kodu.", "Chuck Norris pisze jednym palcem. Wskazuje nim na klawiaturę, a ona robi resztę roboty.", "Programy Chucka Norrisa przechodzą test Turinga po prostu patrząc się na sędziego.", "Jeśli spróbujesz zabić program Chucka Norrisa, to on zabije Ciebie.", "Chuck Norris wykonuje nieskończone pętle w mniej niż 4 sekundy.", "Chuck Norris może nadpisać zmienną zablokowaną semaforem.", "Chuck Norris zna wartość NULL. Może też po niej sortować.", "Chuck Norris może zainstalować 64-bitowy system operacyjny na 32-bitowych maszynach.", "Chuck Norris może pisać do strumieni wyjściowych.", "Chuck Norris może czytać ze strumieni wejściowych.", "Chuck Norris nie musi kompilować swojego kodu. Maszyny nauczyły się interpretować kod Chuck Norrisa.", "Chuck Norris jest powodem Niebieskiego Ekranu Śmierci.", "Chuck Norris może utworzyć klasę, które jest jednocześnie abstrakcyjna i finalna.", "Chuck Norris może użyć czegokolwiek z java.util.*, żeby Cię zabić. Nawet javadocs'ów.", "Kod działa szybciej, gdy obserwuje go Chuck Norris.", "Wszyscy lubią profil Chucka Norrisa na Facebook'u, czy im się to podoba, czy nie.", "Nie możesz śledzić Chucka Norrisa na Twitterze, ponieważ to on śledzi Ciebie.", "Kalkulator Chucka Norrisa ma tylko 3 klawisze: 0, 1 i NAND.", "Chuck Norris używa tylko zmiennych globalnych. Nie ma nic do ukrycia.", "Chuck Norris raz zaimplementował cały serwer HTTP, używając tylko jednego printf'a. Projekt wciąż się rozwija i jest znany pod nazwą Apache.", "Chuck Norris pisze bezpośrednio w kodzie binarnym. Potem pisze kod źródłowy, jako dokumentację dla innych programistów.", "Chuck Norris raz przesunął bit tak mocno, że wylądował w innym komputerze.", "Jak nazywa się ulubiony framework Chucka Norrisa? Knockout.js.", ] jokes_pl = { 'neutral': neutral, 'chuck': chuck, 'all': neutral + chuck, }

77.969849

147

0.743942

0

15,071

0.929391

36dc5f7d17dd68b5094396174551645ca5e9fe1c

2,335

py

Python

pele_platform/Utilities/Helpers/launcher.py

gabrii/pele_platform

64ef9affdd1740fc2e0545c706f30eb2723aa300

[ "Apache-2.0" ]

null

pele_platform/Utilities/Helpers/launcher.py

gabrii/pele_platform

64ef9affdd1740fc2e0545c706f30eb2723aa300

[ "Apache-2.0" ]

null

pele_platform/Utilities/Helpers/launcher.py

gabrii/pele_platform

64ef9affdd1740fc2e0545c706f30eb2723aa300

[ "Apache-2.0" ]

null

from dataclasses import dataclass import pele_platform.Checker.main as ck import pele_platform.Frag.simulation as fr import pele_platform.Adaptive.simulation as ad from pele_platform.Allosteric.main import run_allosteric import pele_platform.gpcr.main as gpcr import pele_platform.out_in.main as outin from pele_platform.PPI.main import run_ppi import pele_platform.Utilities.Parameters.pele_env as pv import argparse @dataclass class Launcher: _args: argparse.ArgumentParser frag: str="frag" ppi: str="PPI" allosteric: str="allosteric" gpcr_orth: str="gpcr_orth" out_in: str="out_in" adaptive: str="adaptive" def launch(self) -> pv.EnviroBuilder: # Launch package from input.yaml self._define_package_to_run() job_variables = self.launch_package(self._args.package, no_check=self._args.no_check) return job_variables def launch_package(self, package: str, no_check=False) -> pv.EnviroBuilder: # Launch package from API if not no_check: ck.check_executable_and_env_variables(self._args) if package == self.adaptive: job_variables = ad.run_adaptive(self._args) elif package == self.gpcr_orth: job_variables = gpcr.GpcrLauncher(self._args).run_gpcr_simulation() elif package == self.out_in: job_variables = outin.OutInLauncher(self._args).run_gpcr_simulation() elif package == self.allosteric: job_variables = run_allosteric(self._args) elif package == self.ppi: job_variables = run_ppi(self._args) elif package == self.frag: # Set variables and input ready job_variables = fr.FragRunner(self._args).run_simulation() return job_variables def _define_package_to_run(self) -> None: # Define package being run from input.yaml flags if self._args.frag_core: self._args.package = self.frag elif self._args.ppi: self._args.package = self.ppi elif self._args.allosteric: self._args.package = self.allosteric elif self._args.gpcr_orth: self._args.package = self.gpcr_orth elif self._args.out_in: self._args.package = self.out_in else: self._args.package = self.adaptive

37.063492

93

0.68137

1,903

0.814989

0

1,914

0.8197

0

188

0.080514

36dc9e14a8dd2c4fe9c4599b6173dd0c635c5f35

1,607

py

Python

Project/AuditReport/Aw/audit_aw.py

StarryHome/MultiToolsPlatform

2bd2b7e0700dbf542f0272ece3590a4afde328a4

[ "MIT" ]

null

Project/AuditReport/Aw/audit_aw.py

StarryHome/MultiToolsPlatform

2bd2b7e0700dbf542f0272ece3590a4afde328a4

[ "MIT" ]

null

Project/AuditReport/Aw/audit_aw.py

StarryHome/MultiToolsPlatform

2bd2b7e0700dbf542f0272ece3590a4afde328a4

[ "MIT" ]

null

# -*- coding: utf-8 -*- from .pandas_aw import PandasAw class AuditAw(object): """ @summary: 审核AW类，负责审核规则整体流程 """ def __init__(self): self.result = [] def audit_report(self, visit_data_path, visit_demand_path, outliers_path): """ @summary: 审核报告入口 """ # 1.获取数据源 visit_data_title, visit_data_list, visit_demand = self.read_excel(visit_data_path, visit_demand_path) # 2.审核数据 for data in visit_data_list: res = self.audit_process(data) self.result.append(res) # 3.写入异常值清单 return self.write_outliers(self.result, outliers_path) def read_excel(self, visit_data_path, visit_demand_path): """ @summary: 读取excel数据 @param visit_data_path: 走访数据excel路径 @param visit_demand_path: 走访要求excel路径 """ pd = PandasAw.get_instance() pd.read(visit_data_path) visit_data_title = pd.get_title() visit_data_list = pd.get_data() pd.read(visit_demand_path) visit_demand = pd.get_data() return visit_data_title, visit_data_list, visit_demand def audit_process(self, data): return 0 def write_outliers(self, result, outliers_path): return True if __name__ == '__main__': audit = AuditAw() visit_data_path = r'C:\Users\Administrator\Desktop\test\数据源.xlsx' visit_demand_path = r'C:\Users\Administrator\Desktop\test\走访要求.xlsx' outliers_path = r'C:\Users\Administrator\Desktop\test\异常值清单.xlsx' audit.audit_report(visit_data_path, visit_demand_path, outliers_path)

30.903846

109

0.655881

1,315

0.757925

0

548

0.31585

36de3480ccf6ebc0ee5035bf6d2e1a0522de31d5

812

py

Python

libs/subsets_of_subset.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

1

2021-03-09T04:28:13.000Z

libs/subsets_of_subset.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

null

libs/subsets_of_subset.py

nishio/atcoder

8db36537b5d8580745d5f98312162506ad7d7ab4

[ "MIT" ]

null

""" all subsets of given subset """ def subsets_of_subset(subset): s = subset superset = subset while True: yield s s = (s - 1) & superset if s == superset: break # --- end of library --- def debugprint(g): for x in g: print(f"{x:06b}") TEST_1 = """ >>> debugprint(subsets_of_subset(0b010101)) 010101 010100 010001 010000 000101 000100 000001 000000 """ def _test(): import doctest doctest.testmod() g = globals() for k in sorted(g): if k.startswith("TEST_"): print(k) doctest.run_docstring_examples(g[k], g, name=k) if __name__ == "__main__": import sys input = sys.stdin.buffer.readline read = sys.stdin.buffer.read if sys.argv[-1] == "-t": _test() sys.exit()

15.615385

59

0.566502

0

174

0.214286

0

197

0.242611

36df1c98bfbc556da9445e6a4a358b0bfd225e8a

9,312

py

Python

models/backbone.py

kakaobrain/sparse-detr

1ea7a062ca6d1dd57768d65b14352cfd1a65ab52

[ "Apache-2.0" ]

83

2021-11-29T04:45:39.000Z

2022-03-30T13:39:46.000Z

models/backbone.py

kakaobrain/sparse-detr

1ea7a062ca6d1dd57768d65b14352cfd1a65ab52

[ "Apache-2.0" ]

4

2021-12-18T21:24:50.000Z

2022-03-18T07:22:04.000Z

models/backbone.py

kakaobrain/sparse-detr

1ea7a062ca6d1dd57768d65b14352cfd1a65ab52

[ "Apache-2.0" ]

3

2021-12-29T12:07:20.000Z

2022-02-11T08:26:16.000Z

# ------------------------------------------------------------------------------------ # Sparse DETR # Copyright (c) 2021 KakaoBrain. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------------------ # Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR) # Copyright (c) 2020 SenseTime. All Rights Reserved. # ------------------------------------------------------------------------------------ # Modified from DETR (https://github.com/facebookresearch/detr) # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved # ------------------------------------------------------------------------------------ """ Backbone modules. """ from collections import OrderedDict import torch import torch.nn.functional as F import torchvision from torch import nn from torchvision.models._utils import IntermediateLayerGetter from typing import Dict, List from models import swin_transformer from util.misc import NestedTensor, is_main_process from .position_encoding import build_position_encoding class FrozenBatchNorm2d(torch.nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rsqrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n, eps=1e-5): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) self.eps = eps def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): # move reshapes to the beginning # to make it fuser-friendly w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = self.eps scale = w * (rv + eps).rsqrt() bias = b - rm * scale return x * scale + bias class BackboneBase(nn.Module): def __init__(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool, args): # TODO: args -> duplicated args super().__init__() if 'none' in args.backbone: self.strides = [1] # not used, actually (length only matters) self.num_channels = [3] return_layers = self.get_return_layers('identity', (0,)) self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) elif 'resnet' in args.backbone: if not args.backbone_from_scratch and not args.finetune_early_layers: print("Freeze early layers.") for name, parameter in backbone.named_parameters(): if not train_backbone or all([k not in name for k in ['layer2', 'layer3', 'layer4']]): parameter.requires_grad_(False) else: print('Finetune early layers as well.') layer_name = "layer" if return_interm_layers: return_layers = self.get_return_layers(layer_name, (2, 3, 4)) self.strides = [8, 16, 32] self.num_channels = [512, 1024, 2048] else: return_layers = self.get_return_layers(layer_name, (4,)) self.strides = [32] self.num_channels = [2048] self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) elif 'swin' in args.backbone: if return_interm_layers: num_channels = [int(backbone.embed_dim * 2 ** i) for i in range(backbone.num_layers)] return_layers = [2, 3, 4] self.strides = [8, 16, 32] self.num_channels = num_channels[1:] else: return_layers = [4] self.strides = [32] self.num_channels = num_channels[-1] self.body = backbone else: raise ValueError(f"Unknown backbone name: {args.backbone}") @staticmethod def get_return_layers(name: str, layer_ids): return {name + str(n): str(i) for i, n in enumerate(layer_ids)} def forward(self, tensor_list: NestedTensor): xs = self.body(tensor_list.tensors) out: Dict[str, NestedTensor] = {} for name, x in xs.items(): m = tensor_list.mask assert m is not None mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] out[name] = NestedTensor(x, mask) return out class DummyBackbone(torch.nn.Module): def __init__(self): super().__init__() self.identity0 = torch.nn.Identity() class Backbone(BackboneBase): """ResNet backbone with frozen BatchNorm.""" def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool, args): print(f"Backbone: {name}") pretrained = is_main_process() and not args.backbone_from_scratch and not args.scrl_pretrained_path if not pretrained: print("Train backbone from scratch.") else: print("Load pretrained weights") if "none" in name: backbone = DummyBackbone() elif "resnet" in name: assert name not in ("resnet18", "resnet34"), "number of channels are hard coded" backbone = getattr(torchvision.models, name)( replace_stride_with_dilation=[False, False, dilation], pretrained=pretrained, norm_layer=FrozenBatchNorm2d) elif "swin" in name: assert not dilation, "not supported" if not args.backbone_from_scratch and not args.finetune_early_layers: print("Freeze early layers.") frozen_stages = 2 else: print('Finetune early layers as well.') frozen_stages = -1 if return_interm_layers: out_indices = [1, 2, 3] else: out_indices = [3] backbone = swin_transformer.build_model( name, out_indices=out_indices, frozen_stages=frozen_stages, pretrained=pretrained) else: raise ValueError(f"Unknown backbone name: {args.backbone}") if args.scrl_pretrained_path: assert "resnet" in name, "Currently only resnet50 is available." ckpt = torch.load(args.scrl_pretrained_path, map_location="cpu") translate_map = { "encoder.0" : "conv1", "encoder.1" : "bn1", "encoder.4" : "layer1", "encoder.5" : "layer2", "encoder.6" : "layer3", "encoder.7" : "layer4", } state_dict = { translate_map[k[:9]] + k[9:] : v for k, v in ckpt["online_network_state_dict"].items() if "encoder" in k } backbone.load_state_dict(state_dict, strict=False) super().__init__(backbone, train_backbone, return_interm_layers, args) if dilation and "resnet" in name: self.strides[-1] = self.strides[-1] // 2 class Joiner(nn.Sequential): def __init__(self, backbone, position_embedding): super().__init__(backbone, position_embedding) self.strides = backbone.strides self.num_channels = backbone.num_channels def forward(self, tensor_list: NestedTensor): xs = self[0](tensor_list) out: List[NestedTensor] = [] pos = [] for name, x in sorted(xs.items()): out.append(x) # position encoding for x in out: pos.append(self[1](x).to(x.tensors.dtype)) return out, pos def test_backbone(backbone): imgs = [ torch.randn(2, 3, 633, 122), torch.randn(2, 3, 322, 532), torch.randn(2, 3, 236, 42), ] return [backbone(img).shape for img in imgs] def build_backbone(args): # test_backbone(torchvision.models.resnet50()) position_embedding = build_position_encoding(args) train_backbone = args.lr_backbone > 0 return_interm_layers = args.masks or (args.num_feature_levels > 1) backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation, args) model = Joiner(backbone, position_embedding) return model

38.962343

107

0.578393

7,508

0.806271

0

134

0.01439

0

1,966

0.211125

36dfbf17404b4b4327586ca76fafeed5dd3aea90

496

py

Python

resonate-carla/risk_calculation/function_test.py

scope-lab-vu/Resonate-Dynamic-Risk

46972bdb0a2b6b08cc188a9f1f6567971c9d263d

[ "MIT" ]

3

2021-08-15T05:02:17.000Z

2022-03-16T11:25:45.000Z

resonate-carla/risk_calculation/function_test.py

scope-lab-vu/Resonate-Dynamic-Risk

46972bdb0a2b6b08cc188a9f1f6567971c9d263d

[ "MIT" ]

null

resonate-carla/risk_calculation/function_test.py

scope-lab-vu/Resonate-Dynamic-Risk

46972bdb0a2b6b08cc188a9f1f6567971c9d263d

[ "MIT" ]

2

2021-03-21T02:35:17.000Z

2021-06-02T22:40:07.000Z

from bowtie_diagram import BowTie import matplotlib.pyplot as plt EXAMPLE_MONITOR_VALUES = [x for x in range(-5, 21)] bowtie = BowTie() state = {"monitor_values": {"lec_martingale": None}} true_y_vals = [] true_x_vals = [] for x_val in EXAMPLE_MONITOR_VALUES: true_x_vals.append(x_val) state["monitor_values"]["lec_martingale"] = x_val true_y_vals.append(bowtie.prob_b1(state)) plt.scatter(true_x_vals, true_y_vals) plt.xlabel("Log Martingale") plt.ylabel("P(B1 | S)") plt.show()

24.8

53

0.737903

0

91

0.183468

36dfdaf21f66ae9305bc8e42cb69c1de214c4d13

3,760

py

Python

hw_asr/model/dsmodel.py

ivan-gorin/asr_project_template

6a9f908d7f287ac2a66d5740fa6c73133825845a

[ "MIT" ]

null

hw_asr/model/dsmodel.py

ivan-gorin/asr_project_template

6a9f908d7f287ac2a66d5740fa6c73133825845a

[ "MIT" ]

null

hw_asr/model/dsmodel.py

ivan-gorin/asr_project_template

6a9f908d7f287ac2a66d5740fa6c73133825845a

[ "MIT" ]

null

#from https://www.assemblyai.com/blog/end-to-end-speech-recognition-pytorch/ from torch import nn import torch.nn.functional as F from hw_asr.base import BaseModel class CNNLayerNorm(nn.Module): def __init__(self, n_feats): super().__init__() self.layer_norm = nn.LayerNorm(n_feats) def forward(self, x): # x (batch, channel, feature, time) x = x.transpose(2, 3).contiguous() # (batch, channel, time, feature) x = self.layer_norm(x) return x.transpose(2, 3).contiguous() # (batch, channel, feature, time) class ResidualCNN(nn.Module): """inspired by https://arxiv.org/pdf/1603.05027.pdf """ def __init__(self, in_channels, out_channels, kernel, stride, dropout, n_feats): super().__init__() self.do_residual = in_channels != out_channels if self.do_residual: self.residual = nn.Conv2d(in_channels, out_channels, kernel_size=1) self.net = nn.Sequential( CNNLayerNorm(n_feats), nn.GELU(), nn.Dropout(dropout), nn.Conv2d(in_channels, out_channels, kernel_size=kernel, stride=stride, padding=kernel//2), CNNLayerNorm(n_feats), nn.GELU(), nn.Dropout(dropout), nn.Conv2d(out_channels, out_channels, kernel_size=kernel, stride=stride, padding=kernel // 2) ) def forward(self, x): if self.do_residual: residual = self.residual(x) else: residual = x x = self.net(x) x += residual return x # (batch, channel, feature, time) class BidirectionalGRU(nn.Module): def __init__(self, rnn_dim, hidden_size, dropout, batch_first=True): super().__init__() self.BiGRU = nn.GRU( input_size=rnn_dim, hidden_size=hidden_size, num_layers=1, batch_first=batch_first, bidirectional=True) self.layer_norm = nn.LayerNorm(rnn_dim) self.dropout = nn.Dropout(dropout) def forward(self, x): x = self.layer_norm(x) x = F.gelu(x) x, _ = self.BiGRU(x) x = self.dropout(x) return x class DeepSpeechModel(BaseModel): def __init__(self, n_cnn_layers, n_rnn_layers, rnn_dim, n_class, n_feats, stride=2, kernel_size=3, dropout=0.1): super(DeepSpeechModel, self).__init__(n_feats, n_class) n_feats = n_feats // 2 self.cnn = nn.Conv2d(1, 32, kernel_size=3, stride=stride, padding=kernel_size // 2) layers = [] for _ in range(n_cnn_layers): layers.append(ResidualCNN(32, 32, kernel=3, stride=1, dropout=dropout, n_feats=n_feats)) self.cnn_net = nn.Sequential(*layers) self.fully_connected = nn.Linear(n_feats * 32, rnn_dim) layers = [BidirectionalGRU(rnn_dim=rnn_dim, hidden_size=rnn_dim, dropout=dropout)] for _ in range(n_rnn_layers - 1): layers.append(BidirectionalGRU(rnn_dim=rnn_dim*2, hidden_size=rnn_dim, dropout=dropout)) self.rnn_net = nn.Sequential(*layers) self.classifier = nn.Sequential( nn.Linear(rnn_dim * 2, rnn_dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(rnn_dim, n_class) ) def forward(self, spectrogram, *args, **kwargs): x = spectrogram.transpose(1, 2).unsqueeze(1) x = self.cnn(x) x = self.cnn_net(x) sizes = x.size() x = x.view(sizes[0], sizes[1] * sizes[2], sizes[3]) # (batch, feature, time) x = x.transpose(1, 2) # (batch, time, feature) x = self.fully_connected(x) x = self.rnn_net(x) x = self.classifier(x) return x def transform_input_lengths(self, input_lengths): return input_lengths // 2

35.471698

116

0.614894

3,582

0.95266

0

317

0.084309

36e0a5cff93ebca1eb7f6ddcf88fb764dd56d580

9,198

py

Python

reminders/menu.py

elanorstark/reminder_pi

e6419f9bce29a1a06e0fee1b9e79156779a08c8b

[ "MIT" ]

null

reminders/menu.py

elanorstark/reminder_pi

e6419f9bce29a1a06e0fee1b9e79156779a08c8b

[ "MIT" ]

null

reminders/menu.py

elanorstark/reminder_pi

e6419f9bce29a1a06e0fee1b9e79156779a08c8b

[ "MIT" ]

null

import datetime from typing import List from reminders.events import Buttons, Alerts from reminders.screen import Screen # highest level, things that can be in a list menu class ListMenuItem: def __init__(self, name): self._name = str(name) @property def name(self): return self._name def set_name(self, name): self._name = str(name) def selected(self): pass # an item in a menu that does something other than going to another menu class ActionItem(ListMenuItem): def __init__(self, name, action): super().__init__(name) self.action = action def selected(self): self.action() # an action item that is displayed on a menu with a checkbox class ToggleableItem(ActionItem): def __init__(self, name, is_selected, toggle, pad_width=9): super().__init__(name.ljust(pad_width), toggle) self.is_selected = is_selected @property def name(self): return self._name + ("[×]" if self.is_selected() else "[ ]") # parent for menus that can be displayed as their own screen class Menu(ListMenuItem): menu_stack = [] def __init__(self, name): super().__init__(name) def display(self): Screen.text_screen(self.name + "\n" + "-" * len(self.name)) def handle_button_press(self, button): pass def handle_time(self): pass # returns current menu, ie top of stack @staticmethod def current(): return Menu.menu_stack[-1] # adds the top level menu to the stack @staticmethod def initialise(menu): Menu.menu_stack = [menu] # when back button is pressed - go back to previous level of menu @staticmethod def back(): if len(Menu.menu_stack) > 1: Menu.menu_stack.pop() # menu for the home screen # no back button available class HomeMenu(Menu): translation = Buttons.home_menu_buttons def __init__(self, main_menu): super().__init__("Home") self.main_menu = main_menu def handle_time(self): self.display() def handle_button_press(self, button): button = HomeMenu.translation[button] if button == "home": # go to main menu Menu.menu_stack.append(self.main_menu) elif button == "backlight": Menu.menu_stack.append(BacklightOffMenu()) def display(self): now = datetime.datetime.now() Screen.home_screen(self.name, now.strftime("%H:%M"), now.strftime("%a %d %b")) # menu that stores and displays a list of ListMenuItem class ListMenu(Menu): translation = Buttons.list_menu_buttons # initialise a MenuList def __init__(self, name: str, items): super().__init__(name) self.unevaluated = items self.items: List[ListMenuItem] = [ActionItem("..", Menu.back)] self.position = 0 # decides what to do depending on which button was pressed # a = select, b = up menu, y = down menu, x = home screen def handle_button_press(self, button): button = ListMenu.translation[button] if button == "select": # select self.items[self.position].selected() elif button == "up": # up self.position -= 1 self.position %= len(self.items) elif button == "down": # down self.position += 1 self.position %= len(self.items) elif button == "home": # home/toplevel button Menu.menu_stack = Menu.menu_stack[:1] # displays menu on screen def display(self, title=None): if not title: title = self.name self.items = [ActionItem("..", Menu.back)] + self.unevaluated() self.position = min(len(self.items) - 1, self.position) text = "" for i, item in enumerate(self.items): if i == self.position: text += "> {}\n".format(item.name) else: text += " {}\n".format(item.name) print(title, "\n", text) Screen.menu_screen(title, text) # adds menu to the stack when selected def selected(self): Menu.menu_stack.append(self) self.position = 0 # menu for reaching the task time editing menu, and to edit on and complete class TaskMenu(ListMenu): def __init__(self, task): self.task = task super().__init__(self.task.name, self.task_options) def display(self, title=None): title = "Edit " + self.name super(TaskMenu, self).display(title) def task_options(self): options = [ TimeMenu(self.task), ToggleableItem("On", lambda: self.task.on, self.task.on_toggle) ] if self.task.on: options.append(ToggleableItem("Complete", lambda: self.task.complete, self.task.complete_toggle)) return options # menu for editing a task's time class TimeMenu(ListMenu): units_stages = [1, 5, 10] menu_stages = ["Hours", "Minutes", "Save/Cancel"] translation = Buttons.time_menu_buttons def __init__(self, task): super().__init__(task.get_task_time().strftime("Time %H:%M"), lambda: []) self.task = task self.time = task.get_task_time() self.menu_stage = 0 self.units_stage = 0 def display(self, title="Edit Time"): Screen.multi_line_text( [Screen.TextLine(title, 1), Screen.TextLine("Unit change: {}".format(TimeMenu.units_stages[self.units_stage]), 0), Screen.TextLine(self.time.strftime("%H:%M"), 2, align="c"), Screen.TextLine(TimeMenu.menu_stages[self.menu_stage], 1, align="c")]) def change_task_time(self): self.menu_stage = 0 self.task.set_task_time(self.task.get_task_time().replace(hour=self.time.hour, minute=self.time.minute)) self.set_name(self.time.strftime("Time %H:%M")) Alerts.sort_alerts() def hour_change(self, difference): self.time = self.time.replace(hour=(self.time.hour + difference) % 24) def minute_change(self, difference): self.time = self.time.replace(minute=(self.time.minute + difference) % 60) def handle_button_press(self, button): button = TimeMenu.translation[button] if button == "next": self.menu_stage += 1 self.menu_stage %= len(TimeMenu.menu_stages) if button == "decrease": if TimeMenu.menu_stages[self.menu_stage] == "Hours": self.hour_change(-1) elif TimeMenu.menu_stages[self.menu_stage] == "Minutes": self.minute_change(0 - TimeMenu.units_stages[self.units_stage]) elif TimeMenu.menu_stages[self.menu_stage] == "Save/Cancel": self.change_task_time() super().handle_button_press("a") if button == "units": self.units_stage += 1 self.units_stage %= len(TimeMenu.units_stages) if button == "increase": if TimeMenu.menu_stages[self.menu_stage] == "Hours": self.hour_change(1) elif TimeMenu.menu_stages[self.menu_stage] == "Minutes": self.minute_change(TimeMenu.units_stages[self.units_stage]) elif TimeMenu.menu_stages[self.menu_stage] == "Save/Cancel": super().handle_button_press("a") def selected(self): super().selected() self.menu_stage = 0 self.units_stage = 0 # menu which is put at top of stack when backlight is turned off class BacklightOffMenu(Menu): def __init__(self): super().__init__("Backlight") def display(self): Screen.off() def handle_button_press(self, button): if button == "x": Menu.menu_stack.pop() Screen.toggle_backlight() # menu to display alert and delay or mark complete class AlertMenu(Menu): translation = Buttons.alert_menu_buttons def __init__(self, task, delay=datetime.timedelta(minutes=1)): super().__init__(task.name) self.task = task self.delayed_for = 0 self.delay_period = delay def display(self): if self.delayed_for > 0: Screen.multi_line_text( [Screen.TextLine(self.name, 1), Screen.TextLine("Delaying until:", 0, uniform_y=True), Screen.TextLine(self.task.get_task_time().strftime("%H:%M"), 1), Screen.TextLine(" ", 0), Screen.TextLine("Delayed for", 0), Screen.TextLine(str(self.delayed_for * self.delay_period), 0)]) else: Screen.multi_line_text( [Screen.TextLine(self.name, 1), Screen.TextLine("Alert time:", 0, uniform_y=True), Screen.TextLine(self.task.get_task_time().strftime("%H:%M"), 1)]) def handle_button_press(self, button): button = AlertMenu.translation[button] if button == "dismiss": Menu.menu_stack.pop() elif button == "delay": self.task.delay(self.delay_period) self.delayed_for += 1 self.display() elif button == "complete": self.task.complete_toggle()

31.717241

112

0.607741

8,466

0.920317

0

393

0.042722

0

1,401

0.152299

36e0bc6b9fd730df1ea36e866d1ae6f2849b3e37

2,127

py

Python

custom_components/goldair_climate/configuration.py

jwhite/homeassistant-goldair-climate

cca1831a1d257c507f3831ca053478e1e7643c75

[ "MIT" ]

8

2019-05-31T00:17:13.000Z

2021-01-12T21:43:21.000Z

custom_components/goldair_climate/configuration.py

jwhite/homeassistant-goldair-climate

cca1831a1d257c507f3831ca053478e1e7643c75

[ "MIT" ]

25

2019-07-04T06:46:30.000Z

2021-07-15T03:13:46.000Z

custom_components/goldair_climate/configuration.py

nicole-ashley/homeassistant-goldair-climate

df5f895db20d826b673142e785477944497d9402

[ "MIT" ]

9

2019-11-22T02:46:55.000Z

2021-07-04T21:57:41.000Z

import voluptuous as vol from homeassistant.const import CONF_HOST, CONF_NAME from .const import ( CONF_CHILD_LOCK, CONF_CLIMATE, CONF_DEVICE_ID, CONF_DISPLAY_LIGHT, CONF_LOCAL_KEY, CONF_TYPE, CONF_TYPE_AUTO, CONF_TYPE_DEHUMIDIFIER, CONF_TYPE_FAN, CONF_TYPE_GECO_HEATER, CONF_TYPE_GPCV_HEATER, CONF_TYPE_GPPH_HEATER, ) INDIVIDUAL_CONFIG_SCHEMA_TEMPLATE = [ {"key": CONF_NAME, "type": str, "required": True, "option": False}, {"key": CONF_HOST, "type": str, "required": True, "option": True}, {"key": CONF_DEVICE_ID, "type": str, "required": True, "option": False}, {"key": CONF_LOCAL_KEY, "type": str, "required": True, "option": True}, { "key": CONF_TYPE, "type": vol.In( [ CONF_TYPE_AUTO, CONF_TYPE_GPPH_HEATER, CONF_TYPE_DEHUMIDIFIER, CONF_TYPE_FAN, CONF_TYPE_GECO_HEATER, CONF_TYPE_GPCV_HEATER, ] ), "required": False, "default": CONF_TYPE_AUTO, "option": True, }, { "key": CONF_CLIMATE, "type": bool, "required": False, "default": True, "option": True, }, { "key": CONF_DISPLAY_LIGHT, "type": bool, "required": False, "default": False, "option": True, }, { "key": CONF_CHILD_LOCK, "type": bool, "required": False, "default": False, "option": True, }, ] def individual_config_schema(defaults={}, options_only=False): output = {} for prop in INDIVIDUAL_CONFIG_SCHEMA_TEMPLATE: if options_only and not prop.get("option"): continue options = {} default = defaults.get(prop["key"], prop.get("default")) if default is not None: options["default"] = default key = ( vol.Required(prop["key"], **options) if prop["required"] else vol.Optional(prop["key"], **options) ) output[key] = prop["type"] return output

25.023529

76

0.550071

0

325

0.152797

36e0e9a9d4e28937589b02ccb2d38ccef3931ed6

255

py

Python

20-29/nlp23.py

emergent/nlp100.python

636546ce1c4368faa6685eec315773c5c9fb424a

[ "Apache-2.0" ]

null

20-29/nlp23.py

emergent/nlp100.python

636546ce1c4368faa6685eec315773c5c9fb424a

[ "Apache-2.0" ]

null

20-29/nlp23.py

emergent/nlp100.python

636546ce1c4368faa6685eec315773c5c9fb424a

[ "Apache-2.0" ]

null

from nlp20 import get_england import re str = get_england() lines = str.split('\n') p = re.compile(r'^(=+)\s*(.+?)\s*=+') for l in lines: m = re.search(p, l) if m is not None: level = len(m.group(1)) - 1 print(m.group(2), level)

19.615385

37

0.556863

0

25

0.098039

36e117d0d57d57188bd69bce4d500df94875ceb8

4,913

py

Python

platform_reports/prometheus_grammars.py

neuro-inc/platform-reports

161c18733370235af0b63a772de49343e956c35c

[ "Apache-2.0" ]

null

platform_reports/prometheus_grammars.py

neuro-inc/platform-reports

161c18733370235af0b63a772de49343e956c35c

[ "Apache-2.0" ]

9

2021-12-23T03:10:40.000Z

2022-03-31T03:15:52.000Z

platform_reports/prometheus_grammars.py

neuro-inc/platform-reports

161c18733370235af0b63a772de49343e956c35c

[ "Apache-2.0" ]

null

PROMQL = """ start: query // Binary operations are defined separately in order to support precedence ?query\ : or_match | matrix | subquery | offset ?or_match\ : and_unless_match | or_match OR grouping? and_unless_match ?and_unless_match\ : comparison_match | and_unless_match (AND | UNLESS) grouping? comparison_match ?comparison_match\ : sum_match | comparison_match /==|!=|>=|<=|>|</ BOOL? grouping? sum_match ?sum_match\ : product_match | sum_match /\\+|-/ grouping? product_match ?product_match\ : unary | product_match /\\*|\\/|%/ grouping? unary ?unary\ : power_match | /\\+|-/ power_match ?power_match\ : atom | atom /\\^/ grouping? power_match ?atom\ : function | aggregation | instant_query | NUMBER | STRING | "(" query ")" // Selectors instant_query\ : METRIC_NAME ("{" label_matcher_list? "}")? -> instant_query_with_metric | "{" label_matcher_list "}" -> instant_query_without_metric label_matcher_list: label_matcher ("," label_matcher)* label_matcher: label_name /=~|=|!=|!~/ STRING matrix: query "[" DURATION "]" subquery: query "[" DURATION ":" DURATION? "]" offset: query OFFSET DURATION // Function function: function_name parameter_list parameter_list: "(" (query ("," query)*)? ")" ?function_name\ : ABS | ABSENT | ABSENT_OVER_TIME | CEIL | CHANGES | CLAMP_MAX | CLAMP_MIN | DAY_OF_MONTH | DAY_OF_WEEK | DAYS_IN_MONTH | DELTA | DERIV | EXP | FLOOR | HISTOGRAM_QUANTILE | HOLT_WINTERS | HOUR | IDELTA | INCREASE | IRATE | LABEL_JOIN | LABEL_REPLACE | LN | LOG2 | LOG10 | MINUTE | MONTH | PREDICT_LINEAR | RATE | RESETS | ROUND | SCALAR | SORT | SORT_DESC | SQRT | TIME | TIMESTAMP | VECTOR | YEAR | AVG_OVER_TIME | MIN_OVER_TIME | MAX_OVER_TIME | SUM_OVER_TIME | COUNT_OVER_TIME | QUANTILE_OVER_TIME | STDDEV_OVER_TIME | STDVAR_OVER_TIME // Aggregations aggregation\ : aggregation_operator parameter_list | aggregation_operator (by | without) parameter_list | aggregation_operator parameter_list (by | without) by: BY label_name_list without: WITHOUT label_name_list ?aggregation_operator\ : SUM | MIN | MAX | AVG | GROUP | STDDEV | STDVAR | COUNT | COUNT_VALUES | BOTTOMK | TOPK | QUANTILE // Vector one-to-one/one-to-many joins grouping: (on | ignoring) (group_left | group_right)? on: ON label_name_list ignoring: IGNORING label_name_list group_left: GROUP_LEFT label_name_list group_right: GROUP_RIGHT label_name_list // Label names label_name_list: "(" (label_name ("," label_name)*)? ")" ?label_name: keyword | LABEL_NAME ?keyword\ : AND | OR | UNLESS | BY | WITHOUT | ON | IGNORING | GROUP_LEFT | GROUP_RIGHT | OFFSET | BOOL | aggregation_operator | function_name // Keywords // Function names ABS: "abs" ABSENT: "absent" ABSENT_OVER_TIME: "absent_over_time" CEIL: "ceil" CHANGES: "changes" CLAMP_MAX: "clamp_max" CLAMP_MIN: "clamp_min" DAY_OF_MONTH: "day_of_month" DAY_OF_WEEK: "day_of_week" DAYS_IN_MONTH: "days_in_month" DELTA: "delta" DERIV: "deriv" EXP: "exp" FLOOR: "floor" HISTOGRAM_QUANTILE: "histogram_quantile" HOLT_WINTERS: "holt_winters" HOUR: "hour" IDELTA: "idelta" INCREASE: "increase" IRATE: "irate" LABEL_JOIN: "label_join" LABEL_REPLACE: "label_replace" LN: "ln" LOG2: "log2" LOG10: "log10" MINUTE: "minute" MONTH: "month" PREDICT_LINEAR: "predict_linear" RATE: "rate" RESETS: "resets" ROUND: "round" SCALAR: "scalar" SORT: "sort" SORT_DESC: "sort_desc" SQRT: "sqrt" TIME: "time" TIMESTAMP: "timestamp" VECTOR: "vector" YEAR: "year" AVG_OVER_TIME: "avg_over_time" MIN_OVER_TIME: "min_over_time" MAX_OVER_TIME: "max_over_time" SUM_OVER_TIME: "sum_over_time" COUNT_OVER_TIME: "count_over_time" QUANTILE_OVER_TIME: "quantile_over_time" STDDEV_OVER_TIME: "stddev_over_time" STDVAR_OVER_TIME: "stdvar_over_time" // Aggregation operators SUM: "sum" MIN: "min" MAX: "max" AVG: "avg" GROUP: "group" STDDEV: "stddev" STDVAR: "stdvar" COUNT: "count" COUNT_VALUES: "count_values" BOTTOMK: "bottomk" TOPK: "topk" QUANTILE: "quantile" // Aggregation modifiers BY: "by" WITHOUT: "without" // Join modifiers ON: "on" IGNORING: "ignoring" GROUP_LEFT: "group_left" GROUP_RIGHT: "group_right" // Logical operators AND: "and" OR: "or" UNLESS: "unless" OFFSET: "offset" BOOL: "bool" NUMBER: /[0-9]+(\\.[0-9]+)?/ STRING\ : "'" /([^'\\\\]|\\\\.)*/ "'" | "\\"" /([^\\"\\\\]|\\\\.)*/ "\\"" DURATION: DIGIT+ ("s" | "m" | "h" | "d" | "w" | "y") METRIC_NAME: (LETTER | "_" | ":") (DIGIT | LETTER | "_" | ":")* LABEL_NAME: (LETTER | "_") (DIGIT | LETTER | "_")* %import common.DIGIT %import common.LETTER %import common.WS %ignore WS """

17.996337

77

0.65113

0

4,903

0.997965

36e1fd31cd58507e88abf55b7c02a2da45a269b3

2,750

py

Python

usersmanagement/models.py

Open-CMMS/openCMMS_backend

56511ebac83a5dc1fb8768a98bc675e88530a447

[ "BSD-3-Clause" ]

3

2021-03-08T19:14:38.000Z

2022-02-01T17:57:31.000Z

usersmanagement/models.py

Open-CMMS/openCMMS_backend

56511ebac83a5dc1fb8768a98bc675e88530a447

[ "BSD-3-Clause" ]

null

usersmanagement/models.py

Open-CMMS/openCMMS_backend

56511ebac83a5dc1fb8768a98bc675e88530a447

[ "BSD-3-Clause" ]

null

"""This file contain the model for the usermanagement app.""" from django.contrib.auth.models import AbstractUser, Group, Permission from django.db import models class UserProfile(AbstractUser): """ Define a user. Here, we use heritage of abstract user and addition of the field nb_tries to detect if the user use a false password to login. """ nb_tries = models.IntegerField(default=0) USERNAME_FIELD = 'username' class Meta: """Add metadata on the class.""" ordering = ('pk',) def deactivate_user(self): """Deactivate a user.""" self.is_active = False def reactivate_user(self): """Reactivate a user if it was deactivated, else, do nothing.""" if not self.is_active: self.is_active = True def __repr__(self): """Define formal representation of a user.""" return "<User: id={id}, username='{name}'>".format(id=self.id, name=self.username) class TeamType(models.Model): """ Define a team type. It inherits of Model class and redefine _apply_ and __str__ methods. """ name = models.CharField(max_length=200) perms = models.ManyToManyField( Permission, verbose_name='Team Type permissions', blank=True, help_text='Specific permissions for this team type.', related_name="teamType_set", related_query_name="teamType" ) def __str__(self): """Return the name of the teamtype.""" return self.name def __repr__(self): """Define formal representation of a team type.""" return "<TeamType: id={id}, name='{name}', permissions={perms}>".format( id=self.id, name=self.name, perms=self.perms ) def _apply_(self): teams_with_this_teamtype = self.team_set.all() for team in teams_with_this_teamtype: # team.permissions.set() team.permissions.set(list(self.perms.all())) class Team(Group): """ Define a team. It inherits of Group class and define set_team_type. """ team_type = models.ForeignKey( TeamType, verbose_name="Team Type", on_delete=models.CASCADE, help_text='Group of users, extends the auth.models.Group model', related_name="team_set", related_query_name="team", blank=False, null=True ) def set_team_type(self, new_team_type): """Assign the team type to the team.""" self.team_type = new_team_type self.save() new_team_type._apply_() def __repr__(self): """Define formal representation of a team.""" return "<Team: id={id}, team_type='{name}'>".format(id=self.id, name=self.team_type)

28.350515

92

0.627273

2,579

0.937818

0

1,098

0.399273

36e3612bbbacdd9cd6a33c5bc043ceb7c94b8118

572

py

Python

resrc/utils/templatetags/gravatar.py

theWhiteFox/resrc

d62bcf3ba2a55f50ae38a1e606072ee3d6025da5

[ "MIT" ]

274

2015-01-02T08:57:58.000Z

2022-03-11T11:44:44.000Z

resrc/utils/templatetags/gravatar.py

ninjaCheery/resrc

8af3a1a3617fd305a2c8aecffb609ed3e9c1addc

[ "MIT" ]

8

2015-05-19T02:54:49.000Z

2016-07-07T18:10:40.000Z

resrc/utils/templatetags/gravatar.py

ninjaCheery/resrc

8af3a1a3617fd305a2c8aecffb609ed3e9c1addc

[ "MIT" ]

112

2015-01-03T18:59:23.000Z

2019-10-08T11:49:18.000Z

# -*- coding: utf-8 -*-: from django import template import urllib import hashlib register = template.Library() def gravatar(email, size=80, username=None): gravatar_url = "http://www.gravatar.com/avatar.php?" gravatar_url += urllib.urlencode({ 'gravatar_id': hashlib.md5(email).hexdigest(), 'size': str(size) }) if username is not None: return """<img src="%s" alt="gravatar for %s" />""" % (gravatar_url, username) else: return """<img src="%s" alt="gravatar" />""" % (gravatar_url) register.simple_tag(gravatar)

26

86

0.631119

0

161

0.281469

36e397fd23e48d333c1f759c070a0a56a3fe0024

11,149

py

Python

utils/torch_utils.py

misads/torch_image_template

db55be6fcebdb6b0c5c739e505b8a7a2eb81c3c1

[ "MIT" ]

5

2019-12-23T05:13:15.000Z

2020-04-09T03:47:53.000Z

utils/torch_utils.py

misads/torch_image_template

db55be6fcebdb6b0c5c739e505b8a7a2eb81c3c1

[ "MIT" ]

null

utils/torch_utils.py

misads/torch_image_template

db55be6fcebdb6b0c5c739e505b8a7a2eb81c3c1

[ "MIT" ]

null

# encoding=utf-8 """ Misc PyTorch utils Author: xuhaoyu@tju.edu.cn update 12.7 Usage: `from torch_utils import *` `func_name()` # to call functions in this file """ from datetime import datetime import math import os import torch import torch.nn as nn from tensorboardX import SummaryWriter ############################## # Functional utils ############################## from utils.misc_utils import format_num def clamp(x, min=0.01, max=0.99): """ value > max will be set to max value < min will be set to min :param x: input tensor :param min: :param max: :return: """ return torch.clamp(x, min, max) def repeat(x: torch.Tensor, *sizes): """ Example: >>> t = repeat(t, 1, 3, 1, 1) # t = t.repeat(1, 3, 1, 1) or t = torch.cat([t, t, t], dim=1) :param x: :param sizes: :return: """ return x.repeat(*sizes) def tensor2im(x: torch.Tensor, norm=False, dtype='float32'): """ :param x: [n, c, h, w] float32 type :param dtype: :return: """ if norm: x = (x + 1) / 2 x[x > 1] = 1 x[x < 0] = 0 return x.detach().cpu().data[0] ############################## # Network utils ############################## def print_network(net: nn.Module, print_size=False): num_params = 0 print(net) for name, param in net.named_parameters(): num_params += param.numel() size = list(param.size()) if len(size) > 1: if print_size: print(name, size[1:2]+size[:1]+size[2:], format_num(param.numel())) else: print(name, size[1:2] + size[:1] + size[2:]) print('Total number of parameters: %s' % format_num(num_params)) print('The size of receptive field: %s' % format_num(receptive_field(net))) def receptive_field(net): def _f(output_size, ksize, stride, dilation): return (output_size - 1) * stride + ksize * dilation - dilation + 1 stats = [] for m in net.modules(): if isinstance(m, torch.nn.Conv2d): stats.append((m.kernel_size, m.stride, m.dilation)) rsize = 1 for (ksize, stride, dilation) in reversed(stats): if type(ksize) == tuple: ksize = ksize[0] if type(stride) == tuple: stride = stride[0] if type(dilation) == tuple: dilation = dilation[0] rsize = _f(rsize, ksize, stride, dilation) return rsize ############################## # Abstract Meters class ############################## class Meters(object): def __init__(self): pass def update(self, new_dic): raise NotImplementedError def __getitem__(self, key): raise NotImplementedError def keys(self): raise NotImplementedError def items(self): return self.dic.items() class AverageMeters(Meters): """ Example: avg_meters = AverageMeters() for i in range(100): avg_meters.update({'f': i}) print(str(avg_meters)) """ def __init__(self, dic=None, total_num=None): self.dic = dic or {} # self.total_num = total_num self.total_num = total_num or {} def update(self, new_dic): for key in new_dic: if not key in self.dic: self.dic[key] = new_dic[key] self.total_num[key] = 1 else: self.dic[key] += new_dic[key] self.total_num[key] += 1 # self.total_num += 1 def __getitem__(self, key): return self.dic[key] / self.total_num[key] def __str__(self): keys = sorted(self.keys()) res = '' for key in keys: res += (key + ': %.4f' % self[key] + ' | ') return res def keys(self): return self.dic.keys() class ExponentialMovingAverage(Meters): """ Example: ema_meters = ExponentialMovingAverage(0.98) for i in range(100): ema_meters.update({'f': i}) print(str(ema_meters)) """ def __init__(self, decay=0.9, dic=None, total_num=None): self.decay = decay self.dic = dic or {} # self.total_num = total_num self.total_num = total_num or {} def update(self, new_dic): decay = self.decay for key in new_dic: if not key in self.dic: self.dic[key] = (1 - decay) * new_dic[key] self.total_num[key] = 1 else: self.dic[key] = decay * self.dic[key] + (1 - decay) * new_dic[key] self.total_num[key] += 1 # self.total_num += 1 def __getitem__(self, key): return self.dic[key] # / self.total_num[key] def __str__(self): keys = sorted(self.keys()) res = '' for key in keys: res += (key + ': %.4f' % self[key] + ' | ') return res def keys(self): return self.dic.keys() ############################## # Checkpoint helper ############################## def load_ckpt(model, ckpt_path): """ Example: class Model(nn.Module): .... model = Model().cuda() load_ckpt(model, 'model.pt') :param model: object of a subclass of nn.Module :param ckpt_path: *.pt file to load :return: """ model.load_state_dict(torch.load(ckpt_path)) def save_ckpt(model, ckpt_path): """ Example: class Model(nn.Module): .... model = Model().cuda() save_ckpt(model, 'model.pt') :param model: object of a subclass of nn.Module :param ckpt_path: *.pt file to save :return: """ torch.save(model.state_dict(), ckpt_path) ############################## # LR_Scheduler ############################## class LR_Scheduler(object): """Learning Rate Scheduler Example: >>> scheduler = LR_Scheduler('cosine', opt.lr, opt.epochs, len(dataloader), warmup_epochs=20) >>> for i, data in enumerate(dataloader) >>> scheduler(self.g_optimizer, i, epoch) Step mode: ``lr = baselr * 0.1 ^ {floor(epoch-1 / lr_step)}`` 每到达lr_step, lr就乘以0.1 Cosine mode: ``lr = baselr * 0.5 * (1 + cos(iter/maxiter))`` Poly mode: ``lr = baselr * (1 - iter/maxiter) ^ 0.9`` iters_per_epoch: number of iterations per epoch """ def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, lr_step=0, warmup_epochs=0, logger=None): """ :param mode: `step` `cos` or `poly` :param base_lr: :param num_epochs: :param iters_per_epoch: :param lr_step: lr step to change lr/ for `step` mode :param warmup_epochs: :param logger: """ self.mode = mode print('Using {} LR Scheduler!'.format(self.mode)) self.lr = base_lr if mode == 'step': assert lr_step self.lr_step = lr_step self.iters_per_epoch = iters_per_epoch self.N = num_epochs * iters_per_epoch self.epoch = -1 self.warmup_iters = warmup_epochs * iters_per_epoch self.logger = logger if logger: self.logger.info('Using {} LR Scheduler!'.format(self.mode)) def __call__(self, optimizer, i, epoch): T = epoch * self.iters_per_epoch + i if self.mode == 'cos': lr = 0.5 * self.lr * (1 + math.cos(1.0 * T / self.N * math.pi)) elif self.mode == 'poly': lr = self.lr * pow((1 - 1.0 * T / self.N), 0.9) elif self.mode == 'step': lr = self.lr * (0.1 ** (epoch // self.lr_step)) else: raise NotImplemented # warm up lr schedule if self.warmup_iters > 0 and T < self.warmup_iters: lr = lr * 1.0 * T / self.warmup_iters if epoch > self.epoch: if self.logger: self.logger.info('\n=>Epoches %i, learning rate = %.4f' % (epoch, lr)) else: print('\nepoch: %d lr: %.6f' % (epoch, lr)) self.epoch = epoch assert lr >= 0 self._adjust_learning_rate(optimizer, lr) def _adjust_learning_rate(self, optimizer, lr): if len(optimizer.param_groups) == 1: optimizer.param_groups[0]['lr'] = lr else: # enlarge the lr at the head optimizer.param_groups[0]['lr'] = lr for i in range(1, len(optimizer.param_groups)): optimizer.param_groups[i]['lr'] = lr * 10 """ TensorBoard Example: writer = create_summary_writer(os.path.join(self.basedir, 'logs')) write_meters_loss(writer, 'train', avg_meters, iteration) write_loss(writer, 'train', 'F1', 0.78, iteration) write_image(writer, 'train', 'input', img, iteration) # shell tensorboard --logdir {base_path}/logs """ def create_summary_writer(log_dir): if not os.path.exists(log_dir): os.makedirs(log_dir) log_dir = os.path.join(log_dir, datetime.now().strftime('%m-%d_%H-%M-%S')) if not os.path.exists(log_dir): os.mkdir(log_dir) writer = SummaryWriter(log_dir, max_queue=0, flush_secs=10) return writer def write_loss(writer: SummaryWriter, prefix, loss_name: str, value: float, iteration): """ Example: write_loss(writer, 'train', 'F1', 0.78, iteration) :param writer: writer created by create_summary_writer() :param prefix: e.g. for '/train/loss1' is 'train' :param loss_name: :param value: :param iteration: :return: """ writer.add_scalar( os.path.join(prefix, loss_name), value, iteration) def write_image(writer: SummaryWriter, prefix, image_name: str, img, iteration, dataformats='CHW'): """ Example: write_image(writer, 'train', 'input', img, iteration) :param writer: writer created by create_summary_writer() :param prefix: :param image_name: :param img: image Tensor, should be channel first. Specific size of [C, H, W]. :param iteration: :param dataformats: 'CHW' or 'HWC' or 'NCHW''' :return: """ writer.add_image( os.path.join(prefix, image_name), img, iteration, dataformats=dataformats) def write_meters_loss(writer: SummaryWriter, prefix, avg_meters: Meters, iteration): """ Example: writer = create_summary_writer(os.path.join(self.basedir, 'logs')) ema_meters = ExponentialMovingAverage(0.98) for i in range(100): ema_meters.update({'f1': i, 'f2': i*0.5}) write_meters_loss(writer, 'train', ema_meters, i) :param writer: :param prefix: :param avg_meters: avg_meters param should be a Meters subclass :param iteration: :return: """ for key in avg_meters.keys(): meter = avg_meters[key] writer.add_scalar( os.path.join(prefix, key), meter, iteration)

28.296954

101

0.544713

5,173

0.463489

0

4,759

0.426395

36e43c1fea8564dd6886b6925030fdbb9a39b677

19,421

py

Python

library/route_vpn.py

sebbbastien/ansible-stonesoft

ebc0d1c0720f8d79224ae58a80d3e9155bda4385

[ "Apache-2.0" ]

null

library/route_vpn.py

sebbbastien/ansible-stonesoft

ebc0d1c0720f8d79224ae58a80d3e9155bda4385

[ "Apache-2.0" ]

null

library/route_vpn.py

sebbbastien/ansible-stonesoft

ebc0d1c0720f8d79224ae58a80d3e9155bda4385

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2017 David LePage # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) ANSIBLE_METADATA = { 'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community' } DOCUMENTATION = ''' --- module: route_vpn short_description: Create a route based VPN description: - Create a route based VPN. Route VPN's are typically created between a managed Stonesoft FW and a 3rd party device (AWS, Azure, etc). You must pre-create the internal FW prior to running this module. If doing an IPSEC wrapped VPN, you must also specify a tunnel interface for which to bind (must be pre-created) and specify an IP address/interface id to specify the ISAKMP listener. version_added: '2.5' options: name: description: - The name for this route VPN. required: true type: str type: description: - The type of IPSEC vpn to create type: str choices: ['ipsec', 'gre'] default: ipsec enabled: description: - Whether the VPN is enabled or disabled type: bool local_gw: description: - Represents the locally managed Stonesoft FW gateway. If the remote_gw is also a Stonesoft managed device, use the same parameters to define type: str suboptions: name: description: - The name of the Stonesoft FW gateway type: str required: true tunnel_interface: description: - The ID for the tunnel interface type: str required: true interface_id: description: - The interface ID to enable IPSEC. If multiple IP addresses exist on the interface, IPSEC will be enabled on all. Use I(interface_ip) as an alternative. type: str required: true address: description: - An interface IP addresses to enable IPSEC. If there are multiple IP addresses on a single interface specified with I(interface_id) and you want to bind to only that address type: str required: false remote_gw: description: - The name of the remote GW. If the remote gateway is an Stonesoft FW, it must pre-exist. Use the local_gw documentation for settings. If it is an External Gateway, this module will create the gateway based on the gateway settings provided if it doesn't already exist. This documents an External Gateway configuration. See also the external_gateway module for additional external endpoint settings. type: str suboptions: name: description: - The name of the External Gateway. If the gateway does not exist, it will be created if you provide the I(address) and I(networks) parameters. type: str required: true preshared_key: description: - If this is an External Gateway, you must provide a pre-shared key to be used between the gateways. If the gateway is another Stonesoft FW, a key will be auto-generated. type: str type: description: - Set to external_gateway if this is an external gateway element type type: str vpn_site: description: - Defines the VPN site for the protected networks on other end of external gateway type: dict suboptions: name: description: - Name of VPN site type: str required: true network: description: - A valid element type from SMC. Typically this is network or host. List elements should be valid names of the specified element type: list external_endpoint: description: - The external endpoint gateways where the RBVPN will terminate. Any options that are supported by the smcpython ExternalEndpoint.create constructor are supported values for this definition type: list required: true suboptions: name: description: - Name of the external endpoint type: str required: True address: description: - A valid IP address of the external gateway type: str required: true enabled: description: - Whether to enable the gateway. type: bool tags: description: - Provide an optional category tag to the engine. If the category does not exist, it will be created type: list state: description: - Specify a create or delete operation required: false default: present choices: - present - absent extends_documentation_fragment: stonesoft notes: - Login credential information is either obtained by providing them directly to the task/play, specifying an alt_filepath to read the credentials from to the play, or from environment variables (in that order). See U(http://smc-python.readthedocs.io/en/latest/pages/session.html) for more information. requirements: - smc-python author: - David LePage (@gabstopper) ''' EXAMPLES = ''' - name: Route VPN between internal engine and 3rd party external gateway register: result route_vpn: smc_logging: level: 10 path: ansible-smc.log enabled: true local_gw: address: 50.50.50.1 name: newcluster tunnel_interface: '1001' name: myrbvpn remote_gw: external_endpoint: - address: 33.33.33.41 enabled: true name: extgw3 (33.33.33.41) connection_type: 'Active 1' - address: 34.34.34.34 enabled: true name: endpoint2 (34.34.34.34) connection_type: 'Active 1' - address: 44.44.44.44 enabled: false name: extgw4 (44.44.44.44) connection_type: 'Active 1' - address: 33.33.33.50 enabled: false name: endpoint1 (33.33.33.50) connection_type: 'Active 1' name: extgw3 preshared_key: '********' type: external_gateway vpn_site: name: extgw3-site network: - network-172.18.15.0/24 - network-172.18.1.0/24 - network-172.18.2.0/24 - name: Create a new Route VPN with internal gateways route_vpn: smc_logging: level: 10 path: ansible-smc.log name: myrbvpn type: ipsec local_gw: name: newcluster tunnel_interface: 1001 interface_id: 1 #address: 2.2.2.2 remote_gw: name: myfw tunnel_interface: 1000 interface_id: 0 tags: - footag ''' RETURN = ''' changed: description: Whether or not the change succeeded returned: always type: bool state: description: The current state of the element return: always type: dict ''' import traceback from ansible.module_utils.stonesoft_util import ( StonesoftModuleBase, Cache) try: from smc.vpn.route import RouteVPN, TunnelEndpoint from smc.vpn.elements import ExternalGateway from smc.core.engine import Engine from smc.api.exceptions import SMCException except ImportError: pass class StonesoftRouteVPN(StonesoftModuleBase): def __init__(self): self.module_args = dict( name=dict(type='str', required=True), type=dict(default='ipsec', type='str', choices=['ipsec', 'gre']), local_gw=dict(type='dict'), remote_gw=dict(type='dict'), enabled=dict(type='bool'), tags=dict(type='list'), state=dict(default='present', type='str', choices=['present', 'absent']) ) self.name = None self.type = None self.enabled = None self.local_gw = None self.remote_gw = None self.tags = None required_if=([ ('state', 'present', ['local_gw', 'remote_gw']) ]) self.results = dict( changed=False, state=[] ) super(StonesoftRouteVPN, self).__init__(self.module_args, supports_check_mode=True, required_if=required_if) def exec_module(self, **kwargs): state = kwargs.pop('state', 'present') for name, value in kwargs.items(): setattr(self, name, value) rbvpn = self.fetch_element(RouteVPN) changed = False if state == 'present': # Short circuit disable if rbvpn and self.enabled is not None and (rbvpn.enabled and not self.enabled): rbvpn.disable() self.results['changed'] = True return self.results local_engine = self.get_managed_gateway(self.local_gw) local_tunnel_interface = self.get_tunnel_interface( local_engine, self.local_gw.get('tunnel_interface')) local_internal_endpoint = self.get_ipsec_endpoint( local_engine, self.local_gw.get('interface_id'), address=self.local_gw.get('address')) if self.remote_gw.get('type', None) != 'external_gateway': remote_engine = self.get_managed_gateway(self.remote_gw) remote_tunnel_interface = self.get_tunnel_interface( remote_engine, self.remote_gw.get('tunnel_interface')) remote_internal_endpoint = self.get_ipsec_endpoint( remote_engine, self.remote_gw.get('interface_id'), address=self.remote_gw.get('address')) else: # External Gateway req = ('name', 'preshared_key', 'external_endpoint') for required in req: if required not in self.remote_gw: self.fail(msg='Missing required field for the external endpoint ' 'configuration: %s' % required) cache = Cache() external_gateway = dict(name=self.remote_gw['name']) # External Endpoints are defined in the External Gateway. # Build the data structures for a call to ExternalGateway.update_or_create ctypes = [] # connection_type element for endpoint in self.remote_gw['external_endpoint']: if 'name' not in endpoint or 'address' not in endpoint: self.fail(msg='An external endpoint must have at least a ' 'name and an address definition.') # SMC version 6.5 requires the connection type element to specify # the role for the given external endpoint if 'connection_type' not in endpoint: self.fail(msg='You must provide the connection_type parameter ' 'when creating an external endpoint') ctypes.append(endpoint.get('connection_type')) cache.add(dict(connection_type=ctypes)) if cache.missing: self.fail(msg=cache.missing) # Verify specified VPN Sites exist before continuing if 'vpn_site' in self.remote_gw: site_name = self.remote_gw.get('vpn_site', {}).pop('name', None) if not site_name: self.fail(msg='A VPN site requires a name to continue') # Get the elements cache.add(self.remote_gw.get('vpn_site', {})) vpn_site_types = self.remote_gw.get('vpn_site', {}).keys() # Save the VPN site types for retrieval if cache.missing: self.fail(msg='Could not find the specified elements for the ' 'VPN site configuration: %s' % cache.missing) site_element = [element.href for element_type in vpn_site_types for element in cache.get_type(element_type)] external_gateway.update( vpn_site=[dict(name=site_name, site_element=site_element)]) external_endpoint = [] for endpoint in self.remote_gw['external_endpoint']: endpoint.update(connection_type_ref=\ cache.get('connection_type',endpoint.pop('connection_type')).href) external_endpoint.append(endpoint) external_gateway.update(external_endpoint=external_endpoint) try: if state == 'present': if self.check_mode: return self.results # Create the tunnel endpoints if not rbvpn: local_gateway = TunnelEndpoint.create_ipsec_endpoint( local_engine.vpn.internal_gateway, local_tunnel_interface) # Enable the IPSEC listener on specified interface/s if self.update_ipsec_listener(local_internal_endpoint): changed = True is_external = self.remote_gw.get('type', None) == 'external_gateway' if not is_external: remote_gateway = TunnelEndpoint.create_ipsec_endpoint( remote_engine.vpn.internal_gateway, remote_tunnel_interface) if self.update_ipsec_listener(remote_internal_endpoint): changed = True else: # Update or Create gw, updated, created = ExternalGateway.update_or_create( with_status=True, **external_gateway) remote_gateway = TunnelEndpoint.create_ipsec_endpoint(gw) if created or updated: changed = True vpn = dict( name=self.name, local_endpoint=local_gateway, remote_endpoint=remote_gateway) if is_external: vpn.update(preshared_key=self.remote_gw['preshared_key']) rbvpn = RouteVPN.create_ipsec_tunnel(**vpn) changed = True else: #TODO: Update or create from top level RBVPN #rbvpn.update_or_create() if rbvpn and self.enabled is not None and (not rbvpn.enabled and self.enabled): rbvpn.enable() changed = True if self.remote_gw.get('type') == 'external_gateway': gw, updated, created = ExternalGateway.update_or_create( with_status=True, **external_gateway) if updated or created: changed = True self.results['state'] = rbvpn.data.data self.results['changed'] = changed elif state == 'absent': if rbvpn: rbvpn.delete() changed = True except SMCException as err: self.fail(msg=str(err), exception=traceback.format_exc()) self.results['changed'] = changed return self.results def get_ipsec_endpoint(self, engine, interface_id, address=None): """ Get the internal endpoint for which to enable IPSEC on for the internal FW. This is required for IPSEC based RBVPN. :param engine Engine: engine reference, already obtained :param str interface_id: interface ID specified for IPSEC listener :rtype: list(InternalEndpoint) """ try: interface = engine.interface.get(interface_id) except SMCException as e: self.fail(msg='Fetch IPSEC interface for endpoint failed: %s' % str(e)) internal_endpoint = engine.vpn.internal_endpoint # Collection endpoints = [] if address: ep = internal_endpoint.get_exact(address) if ep: endpoints.append(ep) else: # Get all endpoints for the interface for addr, network, nicid in interface.addresses: # @UnusedVariable if internal_endpoint.get_exact(addr): endpoints.append( internal_endpoint.get_exact(addr)) if not endpoints: self.fail(msg='No IPSEC endpoint interfaces found. The specified ' 'interface ID was: %s and address: %s' % (interface_id, address)) return endpoints def update_ipsec_listener(self, internal_endpoints): """ Update the internal endpoint to enable the IPSEC listener on the specified interface/s. :param list(InternalEndpoint) internal_endpoints: internal endpoints :rtype: bool """ changed = False for endpoint in internal_endpoints: if not endpoint.enabled: endpoint.update(enabled=True) changed = True return changed def get_tunnel_interface(self, engine, interface_id): """ Get the specified Tunnel Interface for the gateway. :param engine Engine: engine ref :param str interface_id: pulled from gateway yaml :rtype: TunnelInterface """ tunnel_interface = None for interface in engine.tunnel_interface: if interface.interface_id == str(interface_id): tunnel_interface = interface break if not tunnel_interface: self.fail(msg='Cannot find specified tunnel interface: %s for specified gateway ' '%s' % (interface_id, engine.name)) return tunnel_interface def get_managed_gateway(self, gw): """ If the gateway is a locally managed SMC gateway, tunnel interface and an IPSEC interface is required. :param dict local_gw,remote_gw: yaml definition :rtype: Engine """ for req in ('name', 'tunnel_interface', 'interface_id'): if req not in gw: self.fail(msg='Managed gateway requires name, interface_id and ' 'tunnel_interface fields') managed_gw = Engine.get(gw.get('name'), raise_exc=False) if not managed_gw: self.fail(msg='The specified managed gateway specified does not ' 'exist: %s' % gw.get('name')) return managed_gw def main(): StonesoftRouteVPN() if __name__ == '__main__': main()

37.204981

118

0.563771

11,966

0.616137

0

9,916

0.510581

36e64aadf7aac130d35406e0cf99b998faa79a22

6,407

py

Python

tfx/experimental/pipeline_testing/pipeline_recorder_utils.py

ntakouris/tfx

deb618730dc7675c8e9dc75e03b8ca795d49653d

[ "Apache-2.0" ]

1

2020-06-09T03:50:59.000Z

tfx/experimental/pipeline_testing/pipeline_recorder_utils.py

tommywei110/tfx

2152534c81dbc06dc90de37c56e4d63bf810f150

[ "Apache-2.0" ]

null

tfx/experimental/pipeline_testing/pipeline_recorder_utils.py

tommywei110/tfx

2152534c81dbc06dc90de37c56e4d63bf810f150

[ "Apache-2.0" ]

null

# Lint as: python3 # Copyright 2020 Google LLC. 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. """Recording pipeline from MLMD metadata.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os from typing import Iterable, List, Mapping, Optional, Text, Tuple from absl import logging import tensorflow as tf from tfx.orchestration import metadata from tfx.utils import io_utils from ml_metadata.proto import metadata_store_pb2 def _get_paths(metadata_connection: metadata.Metadata, execution_ids: List[int], output_dir: Text) -> Iterable[Tuple[Text, Text]]: """Yields tuple with source and destination artifact uris. The destination artifact uris are located in the output_dir. The source artifact uris are retrieved using execution ids. Args: metadata_connection: Instance of metadata.Metadata for I/O to MLMD. execution_ids: List of execution ids of a pipeline run. output_dir: Directory path where the pipeline outputs should be recorded. Yields: Iterable over tuples of source uri and destination uri. """ events = metadata_connection.store.get_events_by_execution_ids(execution_ids) output_events = [ x for x in events if x.type == metadata_store_pb2.Event.OUTPUT ] unique_artifact_ids = list({x.artifact_id for x in output_events}) for artifact in metadata_connection.store.get_artifacts_by_id( unique_artifact_ids): src_uri = artifact.uri artifact_properties = artifact.custom_properties component_id = artifact_properties['producer_component'].string_value name = artifact_properties['name'].string_value dest_uri = os.path.join(output_dir, component_id, name) yield (src_uri, dest_uri) def _get_execution_dict( metadata_connection: metadata.Metadata ) -> Mapping[Text, List[metadata_store_pb2.Execution]]: """Returns a dictionary holding list of executions for all run_id in MLMD. Args: metadata_connection: Instance of metadata.Metadata for I/O to MLMD. Returns: A dictionary that holds list of executions for a run_id. """ execution_dict = collections.defaultdict(list) for execution in metadata_connection.store.get_executions(): execution_run_id = execution.properties['run_id'].string_value execution_dict[execution_run_id].append(execution) return execution_dict def _get_latest_executions( metadata_connection: metadata.Metadata, pipeline_name: Text) -> List[metadata_store_pb2.Execution]: """Fetches executions associated with the latest context. Args: metadata_connection: Instance of metadata.Metadata for I/O to MLMD. pipeline_name: Name of the pipeline to rerieve the latest executions for. Returns: List of executions for the latest run of a pipeline with the given pipeline_name. """ pipeline_run_contexts = [ c for c in metadata_connection.store.get_contexts_by_type( metadata._CONTEXT_TYPE_PIPELINE_RUN) # pylint: disable=protected-access if c.properties['pipeline_name'].string_value == pipeline_name ] latest_context = max( pipeline_run_contexts, key=lambda c: c.last_update_time_since_epoch) return metadata_connection.store.get_executions_by_context(latest_context.id) def record_pipeline(output_dir: Text, metadata_db_uri: Optional[Text], host: Optional[Text], port: Optional[int], pipeline_name: Optional[Text], run_id: Optional[Text]) -> None: """Record pipeline run with run_id to output_dir. For the beam pipeline, metadata_db_uri is required. For KFP pipeline, host and port should be specified. If run_id is not specified, then pipeline_name ought to be specified in order to fetch the latest execution for the specified pipeline. Args: output_dir: Directory path where the pipeline outputs should be recorded. metadata_db_uri: Uri to metadata db. host: Hostname of the metadata grpc server port: Port number of the metadata grpc server. pipeline_name: Pipeline name, which is required if run_id isn't specified. run_id: Pipeline execution run_id. Raises: ValueError: In cases of invalid arguments: - metadata_db_uri is None or host and/or port is None. - run_id is None and pipeline_name is None. FileNotFoundError: if the source artifact uri does not already exist. """ if host is not None and port is not None: metadata_config = metadata_store_pb2.MetadataStoreClientConfig( host=host, port=port) elif metadata_db_uri is not None: metadata_config = metadata.sqlite_metadata_connection_config( metadata_db_uri) else: raise ValueError('For KFP, host and port are required. ' 'For beam pipeline, metadata_db_uri is required.') with metadata.Metadata(metadata_config) as metadata_connection: if run_id is None: if pipeline_name is None: raise ValueError('If the run_id is not specified,' ' pipeline_name should be specified') # fetch executions of the most recently updated execution context. executions = _get_latest_executions(metadata_connection, pipeline_name) else: execution_dict = _get_execution_dict(metadata_connection) if run_id in execution_dict: executions = execution_dict[run_id] else: raise ValueError( 'run_id {} is not recorded in the MLMD metadata'.format(run_id)) execution_ids = [e.id for e in executions] for src_uri, dest_uri in _get_paths(metadata_connection, execution_ids, output_dir): if not tf.io.gfile.exists(src_uri): raise FileNotFoundError('{} does not exist'.format(src_uri)) io_utils.copy_dir(src_uri, dest_uri) logging.info('Pipeline Recorded at %s', output_dir)

39.795031

82

0.74044

0

1,260

0.19666

0

2,973

0.464024

36e67ff06717bc841187da318c7c341f30def84e

16,034

py

Python

src/third_party/wiredtiger/lang/python/setup_pip.py

SunguckLee/real-mongodb

fef0e44fafc6d3709a84101327e7d2f54dd18d88

[ "Apache-2.0" ]

4

2018-02-06T01:53:12.000Z

2018-02-20T01:47:36.000Z

src/third_party/wiredtiger/lang/python/setup_pip.py

SunguckLee/real-mongodb

fef0e44fafc6d3709a84101327e7d2f54dd18d88

[ "Apache-2.0" ]

null

src/third_party/wiredtiger/lang/python/setup_pip.py

SunguckLee/real-mongodb

fef0e44fafc6d3709a84101327e7d2f54dd18d88

[ "Apache-2.0" ]

3

2018-02-06T01:53:18.000Z

2021-07-28T09:48:15.000Z

#!/usr/bin/env python # # Public Domain 2014-2016 MongoDB, Inc. # Public Domain 2008-2014 WiredTiger, Inc. # # This is free and unencumbered software released into the public domain. # # Anyone is free to copy, modify, publish, use, compile, sell, or # distribute this software, either in source code form or as a compiled # binary, for any purpose, commercial or non-commercial, and by any # means. # # In jurisdictions that recognize copyright laws, the author or authors # of this software dedicate any and all copyright interest in the # software to the public domain. We make this dedication for the benefit # of the public at large and to the detriment of our heirs and # successors. We intend this dedication to be an overt act of # relinquishment in perpetuity of all present and future rights to this # software under copyright law. # # 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 BE LIABLE FOR ANY CLAIM, DAMAGES OR # OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, # ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR # OTHER DEALINGS IN THE SOFTWARE. # # This script builds a Python source distribution that can built be installed # via pip install. This must be run in a git repository to determine the files # to package. Also as a prerequisite, SWIG must be run as the generated files # are part of the package. To create the distribution, in this directory, run # "python setup_pip.py sdist", this creates a tar.gz file under ./dist . from __future__ import print_function import os, os.path, re, shutil, site, sys from setuptools import setup, Distribution from distutils.extension import Extension import distutils.sysconfig import distutils.ccompiler from distutils.errors import CompileError, LinkError import subprocess from subprocess import call import setuptools.command.install import setuptools.command.build_ext # msg -- # Print a message to stderr. def msg(s): print(os.path.basename(__file__) + ": " + s, file=sys.stderr) # die -- # For failures, show a message and exit. def die(s): msg(s) sys.exit(1) # build_commands -- # Run a sequence of commands, and die if any fail. def build_commands(commands, build_dir, build_env): for command in commands: callargs = [ 'sh', '-c', command ] verbose_command = '"' + '" "'.join(callargs) + '"' print('running: ' + verbose_command) if call(callargs, cwd=build_dir, env=build_env) != 0: die('build command failed: ' + verbose_command) # check_needed_dependencies -- # Make a quick check of any needed library dependencies, and # add to the library path and include path as needed. If a library # is not found, it is not definitive. def check_needed_dependencies(builtins, inc_paths, lib_paths): library_dirs = get_library_dirs() compiler = distutils.ccompiler.new_compiler() distutils.sysconfig.customize_compiler(compiler) compiler.set_library_dirs(library_dirs) missing = [] for name, libname, instructions in builtins: found = compiler.find_library_file(library_dirs, libname) if found is None: msg(libname + ": missing") msg(instructions) msg("after installing it, set LD_LIBRARY_PATH or DYLD_LIBRARY_PATH") missing.append(libname) else: package_top = os.path.dirname(os.path.dirname(found)) inc_paths.append(os.path.join(package_top, 'include')) lib_paths.append(os.path.join(package_top, 'lib')) # XXX: we are not accounting for other directories that might be # discoverable via /sbin/ldconfig. It might be better to write a tiny # compile using -lsnappy, -lz... # #if len(missing) > 0: # die("install packages for: " + str(missing)) # find_executable -- # Locate an executable in the PATH. def find_executable(exename, path): p = subprocess.Popen(['which', exename ], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = p.communicate('') out = str(out) # needed for Python3 if out == '': if err != '': err = ': "' + err + '"' die('"' + exename + '": not found in path' + err) dirname = os.path.dirname(out) if not dirname in path: path.append(dirname) # get_build_path -- # Create a PATH that can be used for installation. Apparently, # installation commands are run with a restricted PATH, and # autoreconf/aclocal will not normally be found. def get_build_path(): build_paths = [] find_executable('autoreconf', build_paths) find_executable('aclocal', build_paths) build_path = os.environ['PATH'] + ':' + ':'.join(build_paths) return build_path # get_compile_flags -- # Get system specific compile flags. Return a triple: C preprocessor # flags, C compilation flags and linker flags. def get_compile_flags(inc_paths, lib_paths): # Suppress warnings building SWIG generated code if sys.platform == 'win32' and cc == 'msvc': cflags = ['/arch:SSE2', '/EHsc'] cppflags = [] ldflags = [] # Windows untested and incomplete, don't claim that it works. die('Windows is not supported by this setup script') else: cflags = [ '-w', '-Wno-sign-conversion', '-std=c11' ] cppflags = ['-I' + path for path in inc_paths] cppflags.append('-DHAVE_CONFIG_H') ldflags = ['-L' + path for path in lib_paths] if sys.platform == 'darwin': cflags.extend([ '-arch', 'x86_64' ]) return (cppflags, cflags, ldflags) # get_sources_curdir -- # Get a list of sources from the current directory def get_sources_curdir(): DEVNULL = open(os.devnull, 'w') gitproc = subprocess.Popen( ['git', 'ls-tree', '-r', '--name-only', 'HEAD^{tree}'], stdin=DEVNULL, stdout=subprocess.PIPE, stderr=subprocess.PIPE) sources = [line.rstrip() for line in gitproc.stdout.readlines()] err = gitproc.stderr.read() gitproc.wait() subret = gitproc.returncode if subret != 0 or err: msg("git command to get sources returned " + str(subret) + ", error=" + str(err)) die("this command must be run in a git repository") return sources # get_wiredtiger_versions -- # Read the version information from the RELEASE_INFO file. def get_wiredtiger_versions(wt_dir): v = {} for l in open(os.path.join(wt_dir, 'RELEASE_INFO')): if re.match(r'WIREDTIGER_VERSION_(?:MAJOR|MINOR|PATCH)=', l): exec(l, v) wt_ver = '%d.%d' % (v['WIREDTIGER_VERSION_MAJOR'], v['WIREDTIGER_VERSION_MINOR']) wt_full_ver = wt_ver + '.%d' % (v['WIREDTIGER_VERSION_PATCH']) return (wt_ver, wt_full_ver) # get_library_dirs # Build a plausible set of library directories. def get_library_dirs(): dirs = [] dirs.append("/usr/local/lib") dirs.append("/usr/local/lib64") dirs.append("/lib/x86_64-linux-gnu") dirs.append("/opt/local/lib") dirs.append("/usr/lib") dirs.append("/usr/lib64") for path in ['LD_LIBRARY_PATH', 'DYLD_LIBRARY_PATH', 'LIBRARY_PATH']: if path in os.environ: dirs.extend(os.environ[path].split(':')) dirs = list(set(filter(os.path.isdir, dirs))) return dirs # source_filter # Make any needed changes to the sources list. Any entry that # needs to be moved is returned in a dictionary. def source_filter(sources): result = [] movers = dict() py_dir = os.path.join('lang', 'python') pywt_dir = os.path.join(py_dir, 'wiredtiger') pywt_prefix = pywt_dir + os.path.sep for f in sources: if not re.match(source_regex, f): continue src = f dest = f # move all lang/python files to the top level. if dest.startswith(pywt_prefix): dest = os.path.basename(dest) if dest == 'pip_init.py': dest = '__init__.py' if dest != src: movers[dest] = src result.append(dest) # Add SWIG generated files result.append('wiredtiger.py') movers['wiredtiger.py'] = os.path.join(pywt_dir, '__init__.py') result.append(os.path.join(py_dir, 'wiredtiger_wrap.c')) return result, movers ################################################################ # Do some initial setup and checks. this_abs_script = os.path.abspath(__file__) this_dir = os.path.dirname(this_abs_script) pip_command = None for arg in sys.argv[1:]: if arg[0] != '-' and pip_command == None: pip_command = arg break if this_dir.endswith(os.sep + os.path.join('lang', 'python')): wt_dir = os.path.dirname(os.path.dirname(this_dir)) os.chdir(wt_dir) elif os.path.isfile(os.path.join(this_dir, 'LICENSE')): wt_dir = this_dir else: die('running from an unknown directory') python3 = (sys.version_info[0] > 2) if python3: die('Python3 is not yet supported') # Ensure that Extensions won't be built for 32 bit, # that won't work with WiredTiger. if sys.maxsize < 2**32: die('need to be running on a 64 bit system, and have a 64 bit Python') python_rel_dir = os.path.join('lang', 'python') build_dir = os.path.join(wt_dir, 'build_posix') makefile = os.path.join(build_dir, 'Makefile') built_sentinal = os.path.join(build_dir, 'built.txt') conf_make_dir = 'build_posix' wt_swig_lib_name = os.path.join(python_rel_dir, '_wiredtiger.so') ################################################################ # Put together build options for the WiredTiger extension. short_description = 'high performance, scalable, production quality, ' + \ 'NoSQL, Open Source extensible platform for data management' long_description = 'WiredTiger is a ' + short_description + '.\n\n' + \ open(os.path.join(wt_dir, 'README')).read() wt_ver, wt_full_ver = get_wiredtiger_versions(wt_dir) build_path = get_build_path() # We only need a small set of directories to build a WT library, # we also include any files at the top level. source_regex = r'^(?:(?:api|build_posix|ext|lang/python|src|dist)/|[^/]*$)' # The builtins that we include in this distribution. builtins = [ # [ name, libname, instructions ] [ 'snappy', 'snappy', 'Note: a suitable version of snappy can be found at\n' + \ ' https://github.com/google/snappy/releases/download/' + \ '1.1.3/snappy-1.1.3.tar.gz\n' + \ 'It can be installed via: yum install snappy snappy-devel' + \ 'or via: apt-get install libsnappy-dev' ], [ 'zlib', 'z', 'Need to install zlib\n' + \ 'It can be installed via: apt-get install zlib1g' ] ] builtin_names = [b[0] for b in builtins] builtin_libraries = [b[1] for b in builtins] # Here's the configure/make operations we perform before the python extension # is linked. configure_cmds = [ './makemake --clean-and-make', './reconf', # force building a position independent library; it will be linked # into a single shared library with the SWIG interface code. 'CFLAGS="${CFLAGS:-} -fPIC -DPIC" ' + \ '../configure --enable-python --with-builtins=' + ','.join(builtin_names) ] # build all the builtins, at the moment they are all compressors. make_cmds = [] for name in builtin_names: make_cmds.append('(cd ext/compressors/' + name + '/; make)') make_cmds.append('make libwiredtiger.la') inc_paths = [ os.path.join(build_dir, 'src', 'include'), build_dir, '.' ] lib_paths = [ '.' ] # wiredtiger.so is moved into the top level directory check_needed_dependencies(builtins, inc_paths, lib_paths) cppflags, cflags, ldflags = get_compile_flags(inc_paths, lib_paths) # If we are creating a source distribution, create a staging directory # with just the right sources. Put the result in the python dist directory. if pip_command == 'sdist': sources, movers = source_filter(get_sources_curdir()) stage_dir = os.path.join(python_rel_dir, 'stage') shutil.rmtree(stage_dir, True) os.makedirs(stage_dir) shutil.copy2(this_abs_script, os.path.join(stage_dir, 'setup.py')) for f in sources: d = os.path.join(stage_dir, os.path.dirname(f)) if not os.path.isdir(d): os.makedirs(d) if f in movers: src = movers[f] else: src = f # Symlinks are not followed in setup, we need to use real files. shutil.copy2(src, os.path.join(stage_dir, f)) os.chdir(stage_dir) sys.argv.append('--dist-dir=' + os.path.join('..', 'dist')) else: sources = [ os.path.join(python_rel_dir, 'wiredtiger_wrap.c') ] wt_ext = Extension('_wiredtiger', sources = sources, extra_compile_args = cflags + cppflags, extra_link_args = ldflags, libraries = builtin_libraries, extra_objects = [ os.path.join(build_dir, '.libs', 'libwiredtiger.a') ], include_dirs = inc_paths, library_dirs = lib_paths, ) extensions = [ wt_ext ] env = { "CFLAGS" : ' '.join(cflags), "CPPFLAGS" : ' '.join(cppflags), "LDFLAGS" : ' '.join(ldflags), "PATH" : build_path } class BinaryDistribution(Distribution): def is_pure(self): return False class WTInstall(setuptools.command.install.install): def run(self): self.run_command("build_ext") return setuptools.command.install.install.run(self) class WTBuildExt(setuptools.command.build_ext.build_ext): def __init__(self, *args, **kwargs): setuptools.command.build_ext.build_ext.__init__(self, *args, **kwargs) def run(self): # only run this once if not os.path.isfile(built_sentinal): try: os.remove(makefile) except OSError: pass self.execute( lambda: build_commands(configure_cmds, conf_make_dir, env), [], 'wiredtiger configure') if not os.path.isfile(makefile): die('configure failed, file does not exist: ' + makefile) self.execute( lambda: build_commands(make_cmds, conf_make_dir, env), [], 'wiredtiger make') open(built_sentinal, 'a').close() return setuptools.command.build_ext.build_ext.run(self) setup( name = 'wiredtiger', version = wt_full_ver, author = 'The WiredTiger Development Team, part of MongoDB', author_email = 'info@wiredtiger.com', description = short_description, license='GPL2,GPL3,Commercial', long_description = long_description, url = 'http://source.wiredtiger.com/', keywords = 'scalable NoSQL database datastore engine open source', packages = ['wiredtiger'], ext_package = 'wiredtiger', ext_modules = extensions, include_package_data = True, distclass = BinaryDistribution, package_dir = { 'wiredtiger' : '.' }, cmdclass = { 'install': WTInstall, 'build_ext': WTBuildExt }, package_data = { 'wiredtiger' : [ wt_swig_lib_name, '*.py' ] }, classifiers=[ 'Intended Audience :: Developers', 'Programming Language :: C', 'Programming Language :: C++', 'Programming Language :: Python', 'Programming Language :: Java', 'Operating System :: MacOS :: MacOS X', 'Operating System :: POSIX', 'Operating System :: POSIX :: BSD', 'Operating System :: POSIX :: Linux', 'Operating System :: POSIX :: SunOS/Solaris', ] ) if pip_command == 'sdist': shutil.rmtree(os.path.join(this_dir, 'stage'))

39.202934

81

0.641574

1,163

0.072533

0

7,103

0.442996

36e6a531b83457a4c48394e73a9fc94d96c25f64

77

py

Python

2022-02-24-ftx-rest-api-python/local_settings.py

georgehaan/analyzingalpha

f1f821e8d74d64addf410bfd205cb089ddf5517e

[ "Unlicense" ]

null

2022-02-24-ftx-rest-api-python/local_settings.py

georgehaan/analyzingalpha

f1f821e8d74d64addf410bfd205cb089ddf5517e

[ "Unlicense" ]

null

2022-02-24-ftx-rest-api-python/local_settings.py

georgehaan/analyzingalpha

f1f821e8d74d64addf410bfd205cb089ddf5517e

[ "Unlicense" ]

null

ftxus = { 'api_key':'YOUR_API_KEY', 'api_secret':'YOUR_API_SECRET' }

15.4

34

0.636364

0

52

0.675325

36e87b1e11d644470443480a35f8b9e8b72438cd

4,387

py

Python

src/rechub/parameters.py

yusanshi/easy-rec

86db0bbd1eb0caf94c4b0ec4198bf49e64f65f24

[ "MIT" ]

null

src/rechub/parameters.py

yusanshi/easy-rec

86db0bbd1eb0caf94c4b0ec4198bf49e64f65f24

[ "MIT" ]

null

src/rechub/parameters.py

yusanshi/easy-rec

86db0bbd1eb0caf94c4b0ec4198bf49e64f65f24

[ "MIT" ]

null

import argparse from distutils.util import strtobool def str2bool(x): return bool(strtobool(x)) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--num_epochs', type=int, default=1000) parser.add_argument('--learning_rate', type=float, default=0.0005) parser.add_argument('--batch_size', type=int, default=4096) parser.add_argument('--num_workers', type=int, default=16) parser.add_argument('--non_graph_embedding_dim', type=int, default=200) parser.add_argument('--graph_embedding_dims', type=int, nargs='+', default=[200, 128, 64]) parser.add_argument( '--neighbors_sampling_quantile', type=float, default=0.9, help= 'Set the number of sampled neighbors to the quantile of the numbers of neighbors' ) parser.add_argument('--min_neighbors_sampled', type=int, default=4) parser.add_argument('--max_neighbors_sampled', type=int, default=512) parser.add_argument('--single_attribute_dim', type=int, default=40) # TODO: support attributes parser.add_argument('--attention_query_vector_dim', type=int, default=200) parser.add_argument( '--dnn_predictor_dims', type=int, nargs='+', default=[-1, 128, 1], help= 'You can set first dim as -1 to make it automatically fit the input vector' ) parser.add_argument('--num_batches_show_loss', type=int, default=50) parser.add_argument('--num_epochs_validate', type=int, default=5) parser.add_argument('--early_stop_patience', type=int, default=20) parser.add_argument('--num_attention_heads', type=int, default=8) parser.add_argument('--save_checkpoint', type=str2bool, default=False) parser.add_argument('--different_embeddings', type=str2bool, default=False) parser.add_argument('--negative_sampling_ratio', type=int, default=4) parser.add_argument( '--model_name', type=str, default='GCN', choices=[ # Non-graph 'NCF', # Graph with single type of edge (we think it as homogeneous graph) 'GCN', 'GAT', 'LightGCN', 'NGCF', # Graph with multiple types of edge (we think it as heterogeneous graph) 'HET-GCN', 'HET-GAT', 'HET-NGCF', 'HET-LightGCN', # To be categorized 'GraphRec', 'DeepFM', 'DSSM', 'DiffNet', 'DiffNet++', 'DANSER' ]) parser.add_argument('--embedding_aggregator', type=str, default='concat', choices=['concat', 'attn']) parser.add_argument('--predictor', type=str, default='dnn', choices=['dot', 'dnn']) parser.add_argument('--dataset_path', type=str, required=True) parser.add_argument('--metadata_path', type=str, required=True) parser.add_argument('--log_path', type=str, default='./log/') parser.add_argument('--tensorboard_runs_path', type=str, default='./runs/') parser.add_argument('--checkpoint_path', type=str, default='./checkpoint/') parser.add_argument('--edge_choice', type=int, nargs='+', default=[], help='Left empty to use all in metadata file') parser.add_argument('--training_task_choice', type=int, nargs='+', default=[], help='Left empty to use all in metadata file') parser.add_argument('--evaluation_task_choice', type=int, nargs='+', default=[], help='Left empty to use all in `training_task_choice`') parser.add_argument('--task_loss_overwrite', type=str, nargs='+') parser.add_argument('--task_weight_overwrite', type=float, nargs='+') args, unknown = parser.parse_known_args() if len(unknown) > 0: print( 'Warning: if you are not in testing mode, you may have got some parameters wrong input' ) return args

38.823009

99

0.56713

0

1,472

0.335537

36e9553b230e4e00a0c8f9a0c28cdd825854c4a3

4,955

py

Python

course_difficulty.py

ewang26/dailytimedschedule

1d891828af67caab47ef6286051da7e84b980b2a

[ "MIT" ]

null

course_difficulty.py

ewang26/dailytimedschedule

1d891828af67caab47ef6286051da7e84b980b2a

[ "MIT" ]

null

course_difficulty.py

ewang26/dailytimedschedule

1d891828af67caab47ef6286051da7e84b980b2a

[ "MIT" ]

null

# Koki Kapoor # CSC 630 # Course Difficulty.py file # have each homework assignment be ranked based on difficulty of the course and on difficulty of the assignment itself # list_of_courses_and_difficulty only takes into consideration the difficulty of the course, not the assignment from array import * # install numpy in terminal with: # dictionaries mapping difficulty level to their assigned descriptions # commented this out to redefine the course difficulty and workload separately """ difficulty_levels = { 1:'Easy and quick', 2:'Easy but time-consuming', 3:'Medium', 4:'Hard material, quick work', 5:'Hard, tedious, and time-consuming' } """ # difficulty_levels2 refers to the difficulty of the course's material, not how much time it takes # ie, there can be a very time-consuming course that has easy material difficulty_levels = { 1:'Easy', 2:'Easy-Medium', 3:'Medium', 4:'Medium-Hard', 5:'Hard' } #dictionary mapping the amount of time taken on a course's workload (which includes studying, tests, etc) workload_levels = { 1:'1-1.9 hours', 2:'1.9-2.9 hours', 3:'2.9-3.9 hours', 4:'3.9-4.9 hours', 5:'4.9-5.9 hours', 6:'6+ hours' } def set_courses_and_difficulties(): # user input of course names value_c = input("Please enter the names of all your courses with spaces in between each course name\n") def get_courses(): # sets everything to upper case and removes surrounding whitespace, makes sure there is only one space between course names courses = value_c.strip().upper() return courses format_courses = get_courses() value_time = input("Please enter the amount of time (between 1 and 6 hours in whole numbers) that you spend completing work for each class every day.\n" "The hours are as following:\n" "\n".join([f'Level {level}: {timetaken_desc[level]}' for level in range(1,6)])+ f"\n\nReminder, your courses are: {format_courses}\n" ) value_diff = input('\nPlease enter the difficulty of each course in the same order with spaces in between each ranking.\n' + 'The levels of difficulty are as following:\n' + '\n'.join([f'Level {level}: {difficulty_desc[level]}' for level in range(1,6)])+ f'\n\nReminder, your courses are: {format_courses}\n') def read_level_input(input_value): input_vals = input_value.strip().split(' ') # strip whitespace from input value and split around spaces to create an array of strings levels = [int(x) for x in input_vals] # cast to int return levels def string_to_array(s): # defines a method that creates an array of strings, the strings consist of the content in between each spaces return s.split(" ") list_courses = string_to_array(get_courses()) list_timetaken = read_level_input(value_t) list_difficulties = read_level_input(value_d) # make a joint list course_info = dict() for i,course in enumerate(list_courses): course_info[course] = dict() course_info[course]['efficiency'] = list_timetaken[i] course_info[course]['difficulty'] = list_difficulties[i] print(course_info) # map course difficulty and time taken to a description list_difficulties_desc = [difficulty_desc[diff] for diff in list_difficulties] list_timetaken_desc = [timetaken_desc[timetaken] for timetaken in list_timetaken] print(f'\nYour course list:\n{list_courses}\nTheir corresponding difficulties:\n{list_difficulties_desc}\nTheir corresponding time taken:\n{list_timetaken_desc}') num_courses = len(list_courses) # integer that represents the length of the courses array, isn't used as of now but is here in case you need it later def coursecheck(): #checks that the courses the user entered are in line with what they want check = input("Please check that these are the courses you're taking by responding 'yes' or 'no'\n") if check.lower() in ['yes', 'y']: print(f'\nYay! You are ready to move on.') elif check.lower() in ['no', 'n']: set_courses_and_difficulties() else: print(f'\nError. Please specify "yes" or "no".') coursecheck() if __name__ == "__main__": set_courses_and_difficulties() coursecheck() # A refined way to obtain the "difficulty of an assignment in a numerical form # The course difficulty can weigh heavier and then the assignment diffculty can be added # The modified parameters of this method are difficulty_level (of the course material) and workload_level (how much time you need to spend on the course) def get_difficulty_index(difficulty_level, workload_level): # Through a 'joint list' implemented via a Python dictionary, `course_info` # make the course difficulty weighed more than the homework efficiency index = (difficulty_level * 2) + workload_level return index

36.703704

166

0.70333

0

3,116

0.62886

36e98b5ce7e26ba1ac762413ca0565df029c2001

1,826

py

Python

src/esss_fix_format/hooks.py

nicoddemus/esss_fix_format

1f46e0d1c05cc88fd47be2f0b0f120d8265a759e

[ "MIT" ]

20

2016-12-05T12:09:27.000Z

2021-11-23T21:57:59.000Z

src/esss_fix_format/hooks.py

nicoddemus/esss_fix_format

1f46e0d1c05cc88fd47be2f0b0f120d8265a759e

[ "MIT" ]

43

2016-07-20T12:21:16.000Z

2022-03-14T20:31:07.000Z

src/esss_fix_format/hooks.py

nicoddemus/esss_fix_format

1f46e0d1c05cc88fd47be2f0b0f120d8265a759e

[ "MIT" ]

8

2016-09-27T20:02:44.000Z

2021-04-16T14:58:08.000Z

import abc import textwrap class GitHook(metaclass=abc.ABCMeta): """ Base class to define a Git hook usable by `hooks` task. """ @abc.abstractmethod def name(self): """ :rtype: unicode :return: Name of hook. """ @abc.abstractmethod def script(self): """ :rtype: unicode :return: Script code. Omit the shebang, as it is added later by a post-process step when hooks are installed in project. """ class FixFormatGitHook(GitHook): """ A hook that prevents developer from committing unless it respects formats expected by our `fix-format` tool. """ def name(self): return 'fix-format' def script(self): script = """\ if ! which fix-format >/dev/null 2>&1 then echo "fix-format not found, install in an active environment with:" echo " conda install esss_fix_format" exit 1 else git diff-index --diff-filter=ACM --name-only --cached HEAD | fix-format --check --stdin returncode=$? if [ "$returncode" != "0" ] then echo "" echo "fix-format check failed (status=$returncode)! To fix, execute:" echo " ff -c" exit 1 fi fi """ return textwrap.dedent(script) def _add_hook(hook): name = hook.name() if name not in _HOOKS: _HOOKS[name] = hook else: raise KeyError(f"A hook named '{name}' already exists") # All hooks available by default _HOOKS = {} _add_hook(FixFormatGitHook()) def get_default_hook(name): """ :param unicode name: Name of a hook. :rtype: GitHook :return: A Git hook object. """ return _HOOKS[name]

23.714286

99

0.557503

1,378

0.754655

0

348

0.190581

0

1,247

0.682913

36e9aa3443706da87ee4f539703a4f5d9195cf72

166

py

Python

Solutions/print all subset.py

Adityasriram0901/Python-Thunder

192920c48092ce1783d56c7b45cdd7e7a50246fa

[ "MIT" ]

81

2020-09-25T15:02:11.000Z

2020-10-12T14:20:31.000Z

Solutions/print all subset.py

Adityasriram0901/Python-Thunder

192920c48092ce1783d56c7b45cdd7e7a50246fa

[ "MIT" ]

196

2020-09-25T13:52:13.000Z

2020-10-12T20:20:00.000Z

Solutions/print all subset.py

Adityasriram0901/Python-Thunder

192920c48092ce1783d56c7b45cdd7e7a50246fa

[ "MIT" ]

209

2020-09-25T16:15:46.000Z

2020-10-12T20:08:08.000Z

a = [1, 2, 3, 4] def subset(a, n): if n == 1: return n else: return (subset(a[n - 1]), subset(a[n - 2])) print(subset(a, n=4))

13.833333

52

0.415663

0

36eb37aac32d06e68b8f0f03ae15c8cd3b04fb1f

49

py

Python

trees/dasgupta/__init__.py

islamazhar/trees

502565c5bf02503c7bece09cddd93f9368da02c3

[ "MIT" ]

null

trees/dasgupta/__init__.py

islamazhar/trees

502565c5bf02503c7bece09cddd93f9368da02c3

[ "MIT" ]

null

trees/dasgupta/__init__.py

islamazhar/trees

502565c5bf02503c7bece09cddd93f9368da02c3

[ "MIT" ]

null

from trees.dasgupta.costtree import DasguptaTree

24.5

48

0.877551

0

36eb6ff512aad2f53a0ace07b5c62237d039ba4a

11,810

py

Python

examples/references/segmentation/pascal_voc2012/code/scripts/training.py

1nF0rmed/ignite

cfcd667e3cb9d67b67d928d12fa3ccdac05f7a3e

[ "BSD-3-Clause" ]

null

examples/references/segmentation/pascal_voc2012/code/scripts/training.py

1nF0rmed/ignite

cfcd667e3cb9d67b67d928d12fa3ccdac05f7a3e

[ "BSD-3-Clause" ]

null

examples/references/segmentation/pascal_voc2012/code/scripts/training.py

1nF0rmed/ignite

cfcd667e3cb9d67b67d928d12fa3ccdac05f7a3e

[ "BSD-3-Clause" ]

null

# This a training script launched with py_config_runner # It should obligatory contain `run(config, **kwargs)` method import sys from collections.abc import Mapping from pathlib import Path import torch from apex import amp from dataflow.datasets import VOCSegmentationOpencv from py_config_runner.config_utils import TRAINVAL_CONFIG, assert_config, get_params from py_config_runner.utils import set_seed from utils import exp_tracking from utils.handlers import predictions_gt_images_handler import ignite import ignite.distributed as idist from ignite.contrib.engines import common from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.handlers import DiskSaver from ignite.metrics import ConfusionMatrix, IoU, mIoU from ignite.utils import setup_logger # Adds "code" folder to python path sys.path.insert(0, Path(__file__).parent.parent.as_posix()) def initialize(config): model = config.model.to(config.device) optimizer = config.optimizer # Setup Nvidia/Apex AMP model, optimizer = amp.initialize(model, optimizer, opt_level=getattr(config, "fp16_opt_level", "O2"), num_losses=1) # Adapt model to dist conf model = idist.auto_model(model) criterion = config.criterion.to(config.device) return model, optimizer, criterion def get_save_handler(config): if exp_tracking.has_clearml: from ignite.contrib.handlers.clearml_logger import ClearMLSaver return ClearMLSaver(dirname=config.output_path.as_posix()) return DiskSaver(config.output_path.as_posix()) def create_trainer(model, optimizer, criterion, train_sampler, config, logger): prepare_batch = config.prepare_batch device = config.device # Setup trainer accumulation_steps = getattr(config, "accumulation_steps", 1) model_output_transform = getattr(config, "model_output_transform", lambda x: x) def train_update_function(engine, batch): model.train() x, y = prepare_batch(batch, device=device, non_blocking=True) y_pred = model(x) y_pred = model_output_transform(y_pred) loss = criterion(y_pred, y) if isinstance(loss, Mapping): assert "supervised batch loss" in loss loss_dict = loss output = {k: v.item() for k, v in loss_dict.items()} loss = loss_dict["supervised batch loss"] / accumulation_steps else: output = {"supervised batch loss": loss.item()} with amp.scale_loss(loss, optimizer, loss_id=0) as scaled_loss: scaled_loss.backward() if engine.state.iteration % accumulation_steps == 0: optimizer.step() optimizer.zero_grad() return output output_names = getattr(config, "output_names", ["supervised batch loss",]) lr_scheduler = config.lr_scheduler trainer = Engine(train_update_function) trainer.logger = logger to_save = {"model": model, "optimizer": optimizer, "lr_scheduler": lr_scheduler, "trainer": trainer, "amp": amp} save_every_iters = getattr(config, "save_every_iters", 1000) common.setup_common_training_handlers( trainer, train_sampler, to_save=to_save, save_every_iters=save_every_iters, save_handler=get_save_handler(config), lr_scheduler=lr_scheduler, with_gpu_stats=exp_tracking.has_mlflow, output_names=output_names, with_pbars=False, ) if idist.get_rank() == 0: common.ProgressBar(persist=False).attach(trainer, metric_names="all") return trainer def create_evaluators(model, metrics, config): model_output_transform = getattr(config, "model_output_transform", lambda x: x) evaluator_args = dict( model=model, metrics=metrics, device=config.device, non_blocking=True, prepare_batch=config.prepare_batch, output_transform=lambda x, y, y_pred: (model_output_transform(y_pred), y,), ) train_evaluator = create_supervised_evaluator(**evaluator_args) evaluator = create_supervised_evaluator(**evaluator_args) if idist.get_rank() == 0: common.ProgressBar(desc="Evaluation (train)", persist=False).attach(train_evaluator) common.ProgressBar(desc="Evaluation (val)", persist=False).attach(evaluator) return evaluator, train_evaluator def log_metrics(logger, epoch, elapsed, tag, metrics): metrics_output = "\n".join([f"\t{k}: {v}" for k, v in metrics.items()]) logger.info(f"\nEpoch {epoch} - Evaluation time (seconds): {int(elapsed)} - {tag} metrics:\n {metrics_output}") def log_basic_info(logger, config): msg = f"\n- PyTorch version: {torch.__version__}" msg += f"\n- Ignite version: {ignite.__version__}" msg += f"\n- Cuda device name: {torch.cuda.get_device_name(idist.get_local_rank())}" logger.info(msg) if idist.get_world_size() > 1: msg = "\nDistributed setting:" msg += f"\tbackend: {idist.backend()}" msg += f"\trank: {idist.get_rank()}" msg += f"\tworld size: {idist.get_world_size()}" logger.info(msg) def training(local_rank, config, logger=None): if not getattr(config, "use_fp16", True): raise RuntimeError("This training script uses by default fp16 AMP") torch.backends.cudnn.benchmark = True set_seed(config.seed + local_rank) train_loader, val_loader, train_eval_loader = config.train_loader, config.val_loader, config.train_eval_loader # Setup model, optimizer, criterion model, optimizer, criterion = initialize(config) # Setup trainer for this specific task trainer = create_trainer(model, optimizer, criterion, train_loader.sampler, config, logger) # Setup evaluators num_classes = config.num_classes cm_metric = ConfusionMatrix(num_classes=num_classes) val_metrics = { "IoU": IoU(cm_metric), "mIoU_bg": mIoU(cm_metric), } if hasattr(config, "val_metrics") and isinstance(config.val_metrics, dict): val_metrics.update(config.val_metrics) evaluator, train_evaluator = create_evaluators(model, val_metrics, config) val_interval = getattr(config, "val_interval", 1) @trainer.on(Events.EPOCH_COMPLETED(every=val_interval)) def run_validation(): epoch = trainer.state.epoch state = train_evaluator.run(train_eval_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Train", state.metrics) state = evaluator.run(val_loader) log_metrics(logger, epoch, state.times["COMPLETED"], "Test", state.metrics) if config.num_epochs % val_interval != 0: trainer.add_event_handler(Events.COMPLETED, run_validation) if getattr(config, "start_by_validation", False): trainer.add_event_handler(Events.STARTED, run_validation) score_metric_name = "mIoU_bg" if hasattr(config, "es_patience"): common.add_early_stopping_by_val_score(config.es_patience, evaluator, trainer, metric_name=score_metric_name) # Store 3 best models by validation accuracy: common.gen_save_best_models_by_val_score( save_handler=get_save_handler(config), evaluator=evaluator, models=model, metric_name=score_metric_name, n_saved=3, trainer=trainer, tag="val", ) if idist.get_rank() == 0: tb_logger = common.setup_tb_logging( config.output_path.as_posix(), trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator}, ) if not exp_tracking.has_clearml: exp_tracking_logger = exp_tracking.setup_logging( trainer, optimizer, evaluators={"training": train_evaluator, "validation": evaluator} ) # Log validation predictions as images # We define a custom event filter to log less frequently the images (to reduce storage size) # - we plot images with masks of the middle validation batch # - once every 3 validations and # - at the end of the training def custom_event_filter(_, val_iteration): c1 = val_iteration == len(val_loader) // 2 c2 = trainer.state.epoch % (getattr(config, "val_interval", 1) * 3) == 0 c2 |= trainer.state.epoch == config.num_epochs return c1 and c2 tb_logger.attach( evaluator, log_handler=predictions_gt_images_handler( img_denormalize_fn=config.img_denormalize, n_images=15, another_engine=trainer, prefix_tag="validation" ), event_name=Events.ITERATION_COMPLETED(event_filter=custom_event_filter), ) # Log confusion matrix to ClearML: if exp_tracking.has_clearml: @trainer.on(Events.COMPLETED) def compute_and_log_cm(): cm = cm_metric.compute() # CM: values are normalized such that diagonal values represent class recalls cm = ConfusionMatrix.normalize(cm, "recall").cpu().numpy() if idist.get_rank() == 0: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task clearml_logger = Task.current_task().get_logger() clearml_logger.report_confusion_matrix( title="Final Confusion Matrix", series="cm-preds-gt", matrix=cm, iteration=trainer.state.iteration, xlabels=VOCSegmentationOpencv.target_names, ylabels=VOCSegmentationOpencv.target_names, ) trainer.run(train_loader, max_epochs=config.num_epochs) if idist.get_rank() == 0: tb_logger.close() if not exp_tracking.has_clearml: exp_tracking_logger.close() def run(config, **kwargs): """This is the main method to run the training. As this training script is launched with `py_config_runner` it should obligatory contain `run(config, **kwargs)` method. """ assert torch.cuda.is_available(), torch.cuda.is_available() assert torch.backends.cudnn.enabled, "Nvidia/Amp requires cudnn backend to be enabled." with idist.Parallel(backend="nccl") as parallel: logger = setup_logger(name="Pascal-VOC12 Training", distributed_rank=idist.get_rank()) assert_config(config, TRAINVAL_CONFIG) # The following attributes are automatically added by py_config_runner assert hasattr(config, "config_filepath") and isinstance(config.config_filepath, Path) assert hasattr(config, "script_filepath") and isinstance(config.script_filepath, Path) if idist.get_rank() == 0 and exp_tracking.has_clearml: try: from clearml import Task except ImportError: # Backwards-compatibility for legacy Trains SDK from trains import Task task = Task.init("Pascal-VOC12 Training", config.config_filepath.stem) task.connect_configuration(config.config_filepath.as_posix()) log_basic_info(logger, config) config.output_path = Path(exp_tracking.get_output_path()) # dump python files to reproduce the run exp_tracking.log_artifact(config.config_filepath.as_posix()) exp_tracking.log_artifact(config.script_filepath.as_posix()) exp_tracking.log_params(get_params(config, TRAINVAL_CONFIG)) try: parallel.run(training, config, logger=logger) except KeyboardInterrupt: logger.info("Catched KeyboardInterrupt -> exit") except Exception as e: # noqa logger.exception("") raise e

35.896657

120

0.676545

0

1,346

0.113971

0

2,278

0.192887

36ec49281113de21af1d91215fc919058901c862

17,387

py

Python

src/instaBot.py

pabloqb2000/py-instabot

2bfdd51d588050d370d069db5d0352d29fd4560d

[ "Apache-2.0" ]

null

src/instaBot.py

pabloqb2000/py-instabot

2bfdd51d588050d370d069db5d0352d29fd4560d

[ "Apache-2.0" ]

null

src/instaBot.py

pabloqb2000/py-instabot

2bfdd51d588050d370d069db5d0352d29fd4560d

[ "Apache-2.0" ]

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys from time import sleep from tqdm import tqdm import random from EmailSender import * class InstagramBot: # Creates object and starts the browser def __init__(self, username, password): print("Hi, i'm your personal bot") print("Im using account: @" + username) self.username = username self.password = password self.driver = webdriver.Firefox() self.followers = None self.following = None sleep(1) # Logs in instagram.com def login(self): # Open web page driver = self.driver driver.get("https://www.instagram.com/") sleep(4) '''# Click login button login_button = driver.find_element_by_xpath("//a[@href='/accounts/login/?source=auth_switcher']") login_button.click() sleep(3)''' # Enter data print("Trying to log in as: " + self.username) user_name_elem = driver.find_element_by_xpath("//input[@name='username']") user_name_elem.clear() user_name_elem.send_keys(self.username) passworword_elem = driver.find_element_by_xpath("//input[@name='password']") passworword_elem.clear() if len(self.password) > 1: passworword_elem.send_keys(self.password) passworword_elem.send_keys(Keys.RETURN) sleep(8) else: sleep(20) # Disable pop ups for i in range(3): try: self.navigateToProfile() break except Exception: pass try: not_download = driver.find_element_by_xpath("//a[@class='_3m3RQ _7XMpj']") not_download.click() sleep(4) self.navigateToProfile() break except Exception: pass try: not_now_button = driver.find_element_by_xpath("//button[@class='aOOlW HoLwm ']") not_now_button.click() sleep(4) self.navigateToProfile() break except Exception: pass self.goToMain() # sets it selfs parameters def setFollowers(self): driver = self.driver self.goToProfile() following, followers = self.getFollowLists(self.username) self.following = following self.followers = followers # Goes to the main page of insta def goToMain(self): driver = self.driver driver.get("https://www.instagram.com/") sleep(2) # Goes to the profile by clicking in the profile button def navigateToProfile(self): driver = self.driver profile_link = driver.find_element_by_xpath('//a[@class="gmFkV"]') profile_link.click() sleep(2) # Goes to the user profile page def goToProfile(self): self.lookForAccount(self.username) # searches for the given account def searchForAccount(self, account): driver = self.driver seach_box = driver.find_element_by_xpath("//input[@placeholder='Search']") seach_box.clear() seach_box.send_keys(account) sleep(2) seach_box.send_keys(Keys.ARROW_DOWN) sleep(0.5) for i in range(6): seach_box.send_keys(Keys.ARROW_UP) sleep(0.2) sleep(1) seach_box.send_keys(Keys.RETURN) sleep(3) # directly goes to the profile of the given account def lookForAccount(self, account): driver = self.driver driver.get("https://www.instagram.com/"+account+"/") sleep(3) def followAccount(self, account): driver = self.driver self.lookForAccount(account) follow_btn = driver.find_elements_by_xpath('//button[@class="_5f5mN jIbKX _6VtSN yZn4P "]') follow_btn = follow_btn + driver.find_elements_by_xpath('//button[@class="BY3EC sqdOP L3NKy y3zKF "]') if len(follow_btn) > 0: follow_btn[0].click() sleep(1) # searches the given hastag def searchHastag(self, htg): self.searchForAccount(htg) # NOT TESTED !!!!! def followInScreen(self): driver = self.driver posts = self.getPostList(200) for post in posts: driver.get(post) sleep(15) try: follow_button = driver.find_element_by_xpath("//button[@class='oW_lN sqdOP yWX7d y3zKF ']") follow_button.click() like_button = driver.find_element_by_xpath("//button[@class='dCJp8 afkep']") like_button.click() sleep(15) except Exception: print("exception") # Returns the number of followers and follows of the current profile def getFollowersNum(self): driver = self.driver spans = driver.find_elements_by_xpath("//span[@class='g47SY ']") values = [self.get_text(s).replace(",", "").replace(".", "") for s in spans] return int(values[1]), int(values[2]) # Returns the number of posts of the given account def getPostNum(self, account): driver = self.driver self.lookForAccount(account) spans = driver.find_elements_by_xpath("//span[@class='g47SY ']") values = [self.get_text(s) for s in spans] return int(values[0]) # Returns the HTML text inside an element def get_text(self, el): return self.driver.execute_script(""" var parent = arguments[0]; var child = parent.firstChild; var ret = ""; while(child) { if (child.nodeType === Node.TEXT_NODE) ret += child.textContent; child = child.nextSibling; } return ret; """, el) # Returns the following and the follower lists of a given account def getFollowLists(self, account): driver = self.driver n_followers, n_following = self.getFollowersNum() # Get following: following_button = driver.find_element_by_xpath("//a[@href='/" + account + "/following/']") following_button.click() sleep(2) for i in range(int(n_following / 8)): last_follow = driver.find_elements_by_xpath("//a[@class='FPmhX notranslate _0imsa ']")[-1] driver.execute_script("arguments[0].scrollIntoView(true);", last_follow) sleep(1) following_a = driver.find_elements_by_xpath("//a[@class='FPmhX notranslate _0imsa ']") following = [f.get_property("title") for f in following_a] self.lookForAccount(account) #close_button = driver.find_element_by_xpath("/html/body/div[4]/div/div[1]/div/div[2]/button/svg") #close_button.click() sleep(3) # Get followers following_button = driver.find_element_by_xpath("//a[@href='/" + account + "/followers/']") following_button.click() sleep(2) for i in range(int(n_followers / 8)): last_follow = driver.find_elements_by_xpath("//a[@class='FPmhX notranslate _0imsa ']")[-1] driver.execute_script("arguments[0].scrollIntoView(true);", last_follow) sleep(1) following_a = driver.find_elements_by_xpath("//a[@class='FPmhX notranslate _0imsa ']") followers = [f.get_property("title") for f in following_a] #close_button = driver.find_element_by_xpath("//span[@class='glyphsSpriteX__outline__24__grey_9 u-__7' and" # " @aria-label='Cerrar']") #close_button.click() self.lookForAccount(account) sleep(3) return following, followers # NOT TESTED !!! def getPostList(self, n_posts=144): driver = self.driver for i in range(int(n_posts / 12)): driver.execute_script("window.scrollTo(0, document.body.scrollHeight);") sleep(3) posts_a = driver.find_elements_by_xpath("//div[@class='v1Nh3 kIKUG _bz0w']/a") return [elem.get_attribute('href') for elem in posts_a if '.com/p/' in elem.get_attribute('href')] # NOT TESTED !!! def getCommenters(self, comment=[]): driver = self.driver commenters = driver.find_elements_by_xpath('//a[@class="FPmhX notranslate TlrDj"]') comments = driver.find_elements_by_xpath('//div[@class="C4VMK"]/span') comments = [self.get_text(comments[i]) for i in range(len(commenters)) if commenters[i].get_attribute("title") != self.username] commenters = [c.get_attribute("title") for c in commenters if c.get_attribute("title") != self.username] if comment != []: commenters = [commenters[i] for i in range(len(comments)) if comments[i] in comment] comments = [c for c in comments if c in comment] return commenters, comments # Check who isn't following back an account, if account == None => checks it for itself def checkFollowersOf(self, account): driver = self.driver if account: self.lookForAccount(account) else: self.goToProfile() account = self.username following, followers = self.getFollowLists(account) print("People that don't follow " + account + " back: ") for f in following: if not f in followers: print(f) self.goToMain() # Return the people that don't follow the given account back # The given account should be followed by the bot def getFekasOf(self, account): driver = self.driver self.lookForAccount(account) following, followers = self.getFollowLists(account) return [f for f in following if not f in followers] # Return the people that follow the given account but the given account doesn't follow back # The given account should be followed by the bot def getCreepiesOf(self, account): driver = self.driver self.lookForAccount(account) following, followers = self.getFollowLists(account) return [f for f in followers if not f in following] # Likes all post from a given account, use dislike to dislike them def likeAll(self, account, dislike=False): driver = self.driver print("Liking all photos from: " + account) self.lookForAccount(account) n_posts = self.getPostNum(account) posts_href = self.getPostList(n_posts) self.likeList(posts_href, dislike) # Likes all posts in the list use dislike to dislike them def likeList(self, list, pause=2, dislike=False): for post in tqdm(list, desc="(Dis)Likes"): self.like(post, dislike) sleep(pause) # likes the given post, use dislike option to dislike def like(self, post, dislike=False): driver = self.driver if dislike: # Dislike xPath #xPath = '//button[@class="wpO6b "]' xPath = '//*[//*[name()="svg"] and @class="_8-yf5 " and @aria-label="Unlike" and @height="24" and @width="24"]' # and @height="24" and width="24" else: # Like xPath xPath = '//*[//*[name()="svg"] and @class="_8-yf5 " and @aria-label="Like" and @height="24" and @width="24"]' # and @height="24" and width="24" driver.get(post) sleep(3) try: like_button = lambda: driver.find_element_by_xpath(xPath).click() like_button() except Exception as e: if dislike: print("Didn't dislike ;(\n" + str(e)) else: print("Didn't like ;(\n" + str(e)) sleep(2) # Un follows account def unfollow(self, account): driver = self.driver self.lookForAccount(account) try: unfollow_button = driver.find_element_by_xpath('//button[@class="_5f5mN -fzfL _6VtSN yZn4P "]') unfollow_button.click() sleep(1) unfollow_button = driver.find_element_by_xpath('//button[@class="aOOlW -Cab_ "]') unfollow_button.click() sleep(1) except Exception as e: print("Couldn't unfollow " + account + "\n" + str(e)) # Follows given account def follow(self, account): driver = self.driver self.lookForAccount(account) try: follow_button = driver.find_element_by_xpath('//button[@class="_5f5mN jIbKX _6VtSN yZn4P "]') follow_button.click() sleep(2) self.goToMain() except Exception as e: print("Couldn't follow " + str(e)) # Accepts all follow requests filtering by a usernames list if given def acceptFollows(self, filter=None): driver = self.driver activity_button = driver.find_element_by_xpath("//a[@class='_0ZPOP kIKUG ']") activity_button.click() sleep(2) try: accept_button = driver.find_element_by_xpath("//span[@class='BcJ68']") accept_button.click() sleep(2) follow_list = driver.find_elements_by_xpath("//a[@class='FPmhX notranslate yrJyr']") follow_list = [f.get_attribute("title") for f in follow_list] button_list = driver.find_elements_by_xpath("//button[@class='sqdOP L3NKy y3zKF ']") button_list = [b for b in button_list if self.get_text(b) == 'Confirm'] if filter != None: button_list = [button_list[i] for i in range(len(filter)) if follow_list[i] in filter] for b in button_list: b.click() except Exception as e: print("Didn't accept" + str(e)) self.lookForAccount(self.username) self.goToMain() # Unstable def randomTag(self, photo, num): driver = self.driver driver.get(photo) sleep(3) comment_txtBox = driver.find_element_by_xpath('//textarea[@class="Ypffh"]') #driver.execute_script("arguments[0].textContent = 'Hola carcola';", comment_txtBox) sleep(4) try: comment_txtBox.send_keys(Keys.ENTER) except Exception: comment_txtBox.send_keys(Keys.ENTER) #comment_txtBox.send_keys("caracola") ''' # comment_txtBox.send_keys() for n in range(num): comment = "@" + random.choice("bcdfghjklmnpqrstvwxyz") + random.choice("aeiouy") #comment_txtBox.send_keys(comment) comment_txtBox.send_keys("hola") sleep(2) for _ in range(2): comment_txtBox.send_keys(Keys.ENTER) sleep(0.5) comment_txtBox.send_keys(Keys.ENTER) sleep(0.5)''' # Open chat window from main menu def chatMenu(self): driver = self.driver driver.get("https://www.instagram.com/direct/inbox/") sleep(4) # From the profile menu return true if the bot has un read chats def hasNewChats(self): driver = self.driver div = driver.find_elements_by_xpath('//div[@class="J_0ip Vpz-1 TKi86 "]') return len(div) > 0 # Return the account with unread chats def getNewChats(self): driver = self.driver chats = driver.find_elements_by_xpath('//div[@class="_7UhW9 xLCgt qyrsm KV-D4 fDxYl "]') return [self.get_text(c) for c in chats] # Open the chat to talk to a given account def openChat(self, account): driver = self.driver # Unread chats chats = driver.find_elements_by_xpath('//div[@class="_7UhW9 xLCgt qyrsm KV-D4 fDxYl "]') # Read chats chats = chats + driver.find_elements_by_xpath('//div[@class="_7UhW9 xLCgt qyrsm KV-D4 fDxYl "]/div/div/div') matching = [c for c in chats if self.get_text(c) == account][-1] matching.click() sleep(1) # Read the messages of the currently open chat def read_msgs(self): driver = self.driver msgs = driver.find_elements_by_xpath('//div[@class=" Igw0E IwRSH YBx95 _4EzTm XfCBB g6RW6 "]/div/span') msgs = [self.get_text(m) for m in msgs] return msgs # Send msg in the chat currently open def sendMsg(self, msg="Hi"): driver = self.driver txtarea = driver.find_element_by_xpath('//textarea[@placeholder="Message..."]') txtarea.click() sleep(0.2) txtarea.send_keys(msg) txtarea.send_keys(Keys.RETURN) # Closes browser def closeBrowser(self): self.driver.close()

38.897092

256

0.572554

17,209

0.989762

0

5,565

0.320067

36edb3403cd5d8abc890118c85bd880dd47b74ce

198

py

Python

Python/03 - Strings/String Formatting.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

Python/03 - Strings/String Formatting.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

Python/03 - Strings/String Formatting.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

def print_formatted(number): # your code goes here for i in range(1, number +1): width = len(f"{number:b}") print(f"{i:{width}} {i:{width}o} {i:{width}X} {i:{width}b}")

33

69

0.545455

0

88

0.444444

36ede9c0901ffceceb90ea9e2eb43efe24230727

813

py

Python

BattleCity-NES/main.py

iOsnaaente/Kata-train_Code

22cdf9d087bad879875c1f70029bda0771242c50

[ "MIT" ]

null

BattleCity-NES/main.py

iOsnaaente/Kata-train_Code

22cdf9d087bad879875c1f70029bda0771242c50

[ "MIT" ]

null

BattleCity-NES/main.py

iOsnaaente/Kata-train_Code

22cdf9d087bad879875c1f70029bda0771242c50

[ "MIT" ]

null

#! usr/bin/dev python from stages import Stages #Le as fases from code import tanks #Responsável pelos tanques do player from images import imagens #imagens do jogo import pygame import random screen_Dimension=[32*20,32*20] pygame.init() screen = pygame.display.set_mode(screen_Dimension) pygame.display.set_caption("My_Poor_NES_Batlle_City") clock = pygame.time.Clock() Fase_1 = Stages.Stages(screen) Fase_1.readStage(1) Tank = tanks.PlayerTank(imagens.blueTank, [64,64], screen) while True: screen.fill([0,0,0]) for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() if event.type == pygame.KEYDOWN: if event.key == pygame.K_ESCAPE: pygame.quit() Tank.move(event) Fase_1.plotStage() Tank.plot() pygame.display.update() clock.tick(60)

16.591837

62

0.719557

0

111

0.136364

36ee554e3410e965f70042cc4e96c4361520515d

6,271

py

Python

n_queens.py

lkk7/n-queens-genetic-solver

d8b87b49970e58d4599618eb014c1b12e4f471fa

[ "MIT" ]

null

n_queens.py

lkk7/n-queens-genetic-solver

d8b87b49970e58d4599618eb014c1b12e4f471fa

[ "MIT" ]

null

n_queens.py

lkk7/n-queens-genetic-solver

d8b87b49970e58d4599618eb014c1b12e4f471fa

[ "MIT" ]

null

from typing import Dict from numba import njit import numpy as np import matplotlib.pyplot as plt plt.rcParams['image.cmap'] = 'binary' def read_parameters(filename: str) -> Dict[str, float]: """Read parameters from a file to a dictionary and return it.""" parameters = {} with open(filename, "r") as file: for line in file.readlines(): if line != '\n': line_split = line.split() try: parameters[line_split[0]] = int(line_split[2]) except ValueError: parameters[line_split[0]] = float(line_split[2]) if len(parameters) != 6: raise RuntimeError("Incorrect list of parameters in " + filename) return parameters def random_population(population_size: int, board_size: int) -> np.ndarray: """Return a random population of solutions.""" return np.array([np.random.permutation(board_size) for _ in range(population_size)], dtype=np.int32) @njit def fitness(population: np.ndarray) -> np.ndarray: """Return an array of fitnesses of a given population""" fitness_arr = np.empty(population.shape[0], dtype=np.float32) for i, genome in enumerate(population): diags_1 = np.array([0 for n in range(2 * genome.size - 1)]) diags_2 = np.array([0 for n in range(2 * genome.size - 1)]) for j in range(genome.size): diags_1[j - genome[j] + genome.size - 1] += 1 diags_2[j + genome[j]] += 1 colls_1 = diags_1 > 1 colls_2 = diags_2 > 1 diags_1[colls_1] = diags_1[colls_1] * (diags_1[colls_1] - 1) // 2 diags_1[~colls_1] = 0 diags_2[colls_2] = diags_2[colls_2] * (diags_2[colls_2] - 1) // 2 diags_2[~colls_2] = 0 fitness_arr[i] = 1 / (1 + np.sum(diags_1) + np.sum(diags_2)) return fitness_arr @njit def selection(population: np.ndarray, n_best: int) -> np.ndarray: """Return an array of indices of individuals selected to mate. n_best is the number of best individuals who will always be selected. """ fitnesses = fitness(population) winners = np.empty((population.shape[0] // 2,), dtype=np.int32) winners[0:n_best] = np.argsort(fitnesses)[-n_best:] for i in range(n_best, fitnesses.shape[0] // 2): pair = np.random.randint(0, fitnesses.shape[0], size=(2,)) if fitnesses[pair[0]] > fitnesses[pair[1]]: winners[i] = pair[0] else: winners[i] = pair[1] return winners @njit def crossover(population: np.ndarray, selected: np.ndarray): """Return a new population that results from crossover.""" N = population.shape[1] new_population = np.empty_like(population) for k in range(0, selected.shape[0]): parents_ids = np.random.choice(selected, replace=False, size=2) child_1 = np.empty_like(population[parents_ids[0]]) child_2 = np.empty_like(population[parents_ids[1]]) points = np.random.randint(0, N + 1, 2) if points[0] != points[1]: points = (np.min(points), np.max(points)) else: if points[0] == N: points = (points[0] - 1, points[0]) else: points = (points[0], points[0] + 1) cut_out = population[parents_ids[0]][points[0]:points[1]] child_1[points[0]:points[1]] = cut_out j = 0 for i in range(N): if j == points[0]: j = points[1] if not np.any(cut_out == population[parents_ids[1]][i]): child_1[j] = population[parents_ids[1]][i] j += 1 cut_out = population[parents_ids[1]][points[0]:points[1]] child_2[points[0]:points[1]] = cut_out j = 0 for i in range(N): if j == points[0]: j = points[1] if not np.any(cut_out == population[parents_ids[0]][i]): child_2[j] = population[parents_ids[0]][i] j += 1 new_population[2 * k, :] = child_1 new_population[2 * k + 1, :] = child_2 return new_population @njit def mutation(population: np.ndarray): """Perform mutation on a population.""" for i in range(population.shape[0]): if np.random.random() > 0.7: for _ in range(3): points = np.random.randint(0, population.shape[1], 2) tmp = population[i, points[0]] population[i, points[0]] = population[i, points[1]] population[i, points[1]] = tmp def plot_genome_expression(genome: np.ndarray) -> None: """Plot a solution represented by the given genome.""" points = np.zeros((genome.shape[0], genome.shape[0])) for i, g in enumerate(genome): points[i, g] = 1 _, ax = plt.subplots(figsize=(10, 10)) ax.imshow(points, cmap='Purples') ax.grid(True) ax.set_xlim(-0.5, genome.shape[0] - 0.5) ax.set_ylim(-0.5, genome.shape[0] - 0.5) ax.set_xticks([i + 0.5 for i in range(genome.shape[0])]) ax.set_yticks([i + 0.5 for i in range(genome.shape[0])]) ax.set_xticklabels([]) ax.set_yticklabels([]) plt.tick_params(axis='both', which='both', bottom=False, left=False) plt.title("$N = {}$".format(genome.shape[0]), size=15) plt.show() def main() -> None: parameters = read_parameters('parameters.txt') population = random_population(parameters['pop_size'], parameters['N']) generation_data = [] best_member_id = 0 winner_gen = parameters['generations'] for i in range(1, parameters['generations'] + 1): selected = selection(population, parameters['n_best']) population = crossover(population, selected) mutation(population) gen_fit = fitness(population) best_member_id = np.argmax(gen_fit) generation_data.append([i, gen_fit.mean(), gen_fit[best_member_id]]) if gen_fit[best_member_id] == 1.0: print("\nWinner (gen. {}):\n{}".format( i, str(population[best_member_id]))) winner_gen = i break if i % 50 == 0: print("Gen", i) if parameters['plot_winner_genome']: plot_genome_expression(population[best_member_id]) if __name__ == "__main__": main()

38.237805

76

0.591293

0

3,527

0.56243

0

676

0.107798

36f0a039978f0025fa6da35feb5807f99a23cd6a

1,362

py

Python

tests/plugins/test_ustreamtv.py

RomanKornev/streamlink

acdefee0822b9c10628b91a166f9abe084e44800

[ "BSD-2-Clause" ]

2

2019-09-17T15:01:47.000Z

2019-09-21T16:26:50.000Z

tests/plugins/test_ustreamtv.py

RomanKornev/streamlink

acdefee0822b9c10628b91a166f9abe084e44800

[ "BSD-2-Clause" ]

1

2020-06-02T02:36:30.000Z

tests/plugins/test_ustreamtv.py

bumplzz69/streamlink

34abc43875d7663ebafa241573dece272e93d88b

[ "BSD-2-Clause" ]

1

2020-08-12T08:27:22.000Z

import unittest from streamlink import Streamlink try: from unittest.mock import ANY, MagicMock, call except ImportError: from mock import ANY, MagicMock, call from streamlink.plugins.ustreamtv import UStreamTV class TestPluginUStreamTV(unittest.TestCase): def test_can_handle_url(self): should_match = [ "http://www.ustream.tv/streamlink", "http://www.ustream.tv/channel/id/1234", "http://www.ustream.tv/embed/1234", "http://www.ustream.tv/recorded/6543", "http://www.ustream.tv/embed/recorded/6543", ] for url in should_match: self.assertTrue(UStreamTV.can_handle_url(url)) should_not_match = [ "https://www.youtube.com/v/aqz-KE-bpKQ", ] for url in should_not_match: self.assertFalse(UStreamTV.can_handle_url(url)) def test_arguments(self): from streamlink_cli.main import setup_plugin_args session = Streamlink() parser = MagicMock() plugin_parser = MagicMock() parser.add_argument_group = MagicMock(return_value=plugin_parser) session.plugins = { 'ustreamtv': UStreamTV } setup_plugin_args(session, parser) plugin_parser.add_argument.assert_called_with('--ustream-password', metavar="PASSWORD", help=ANY)

30.954545

105

0.64978

1,138

0.835536

0

267

0.196035

36f1c4a0f6e35abb7375acc751edca8cda2db44e

304

py

Python

tests/test_xgboost.py

ak110/dl_allinone

976f0d65b20bcf9bfc00286608bcd957dd086209

[ "MIT" ]

1

2019-02-07T03:48:19.000Z

tests/test_xgboost.py

ak110/dl_allinone

976f0d65b20bcf9bfc00286608bcd957dd086209

[ "MIT" ]

1

2019-03-26T03:48:15.000Z

2019-05-24T04:12:33.000Z

tests/test_xgboost.py

ak110/dl_allinone

976f0d65b20bcf9bfc00286608bcd957dd086209

[ "MIT" ]

null

def test_run(): import sklearn.datasets import xgboost data = sklearn.datasets.load_boston() X, y = data.data, data.target # pylint: disable=no-member xgb = xgboost.XGBRegressor(n_estimators=3) xgb.fit(X[:100], y[:100]) assert xgb.predict(X[100:]).shape == (len(X[100:]),)

27.636364

62

0.648026

0

27

0.088816

36f2445925b38eafa6fa76d91317ba20cacff47f

1,241

py

Python

test/unit/object/test_collaboration_allowlist_entry.py

box/box-python-sdk

5c6766a17bac0315bede7a1f5909c912d194a793

[ "Apache-2.0" ]

367

2015-02-10T05:55:45.000Z

2022-03-16T23:39:58.000Z

test/unit/object/test_collaboration_allowlist_entry.py

box/box-python-sdk

5c6766a17bac0315bede7a1f5909c912d194a793

[ "Apache-2.0" ]

686

2015-02-10T01:21:28.000Z

2022-03-31T11:40:22.000Z

test/unit/object/test_collaboration_allowlist_entry.py

box/box-python-sdk

5c6766a17bac0315bede7a1f5909c912d194a793

[ "Apache-2.0" ]

260

2015-02-16T17:35:06.000Z

2022-03-20T17:45:28.000Z

# coding: utf-8 from __future__ import unicode_literals, absolute_import from boxsdk.config import API def test_get(mock_box_session, test_collaboration_allowlist_entry): entry_id = test_collaboration_allowlist_entry.object_id expected_url = '{0}/collaboration_whitelist_entries/{1}'.format(API.BASE_API_URL, entry_id) mock_entry = { 'type': 'collaboration_whitelist_entry', 'id': '98765', 'domain': 'example.com', 'direction': 'inbound' } mock_box_session.get.return_value.json.return_value = mock_entry entry = test_collaboration_allowlist_entry.get() mock_box_session.get.assert_called_once_with(expected_url, headers=None, params=None) assert entry.id == mock_entry['id'] assert entry.domain == mock_entry['domain'] assert entry.direction == mock_entry['direction'] def test_delete(mock_box_session, test_collaboration_allowlist_entry): entry_id = test_collaboration_allowlist_entry.object_id expected_url = '{0}/collaboration_whitelist_entries/{1}'.format(API.BASE_API_URL, entry_id) test_collaboration_allowlist_entry.delete() mock_box_session.delete.assert_called_once_with(expected_url, expect_json_response=False, headers=None, params={})

42.793103

118

0.767929

0

209

0.168413

36f7aca45d40f82d8142db3d4804603a2675f264

1,463

py

Python

jumpy/setup.py

bharadwaj1098/brax

3108a0535b9b59725c97ef35732ed0378c0fd5cc

[ "Apache-2.0" ]

1,162

2021-06-03T20:15:05.000Z

2022-03-31T19:53:06.000Z

jumpy/setup.py

bharadwaj1098/brax

3108a0535b9b59725c97ef35732ed0378c0fd5cc

[ "Apache-2.0" ]

160

2021-06-05T02:32:39.000Z

2022-03-31T11:39:58.000Z

jumpy/setup.py

bharadwaj1098/brax

3108a0535b9b59725c97ef35732ed0378c0fd5cc

[ "Apache-2.0" ]

117

2021-06-04T17:18:21.000Z

2022-03-30T18:04:48.000Z

# Copyright 2021 The Brax Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """setup.py for Jumpy. Install for development: pip intall -e . """ from setuptools import setup setup( name="brax-jumpy", version="0.0.1", description=("Common backend for JAX or numpy."), author="Brax Authors", author_email="no-reply@google.com", long_description=open("README.md").read(), long_description_content_type="text/markdown", url="http://github.com/google/brax", license="Apache 2.0", py_modules=["jumpy"], install_requires=[ "jax", "jaxlib", "numpy", ], classifiers=[ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Artificial Intelligence", ], )

29.857143

74

0.673274

0

1,071

0.732057

36f7ebcb27998b0af7e58a152f1c6385a165aa9d

33,411

py

Python

simulation/Distance2.py

vivirodrigues/carrinheiros-heuristics

92c8c4a8384f8e3a86e9c53b41bcb2ab001de5f5

[ "MIT" ]

null

simulation/Distance2.py

vivirodrigues/carrinheiros-heuristics

92c8c4a8384f8e3a86e9c53b41bcb2ab001de5f5

[ "MIT" ]

null

simulation/Distance2.py

vivirodrigues/carrinheiros-heuristics

92c8c4a8384f8e3a86e9c53b41bcb2ab001de5f5

[ "MIT" ]

null

import json import scipy.stats import matplotlib.pyplot as plt import scipy.stats as st from decimal import Decimal, ROUND_HALF_UP from xml.dom import minidom import numpy as np def open_file(nameFile): try: f = open(nameFile + ".json", "r") dados = json.loads(f.read()) f.close() except: dados = 0 pass return dados def mean_confidence_interval(data, confidence=0.90): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n - 1) #return m, m - h, m + h return m, h files = [ '../data/results/m38.49999603681327_m12.962358080558504_m38.47398437502447_m12.932893255527242_0_30_length_heuristic_SPFA_nearest_neighbor.xml', '../data/results/m38.500671812913836_m12.96339552158351_m38.47352508877093_m12.932765988234031_1_30_length_heuristic_SPFA_nearest_neighbor.xml', '../data/results/m38.50194412971296_m12.961982380453897_m38.472997875909336_m12.933973466644028_2_30_length_heuristic_SPFA_nearest_neighbor.xml', 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'../data/results/m38.50009702898103_m12.96036292373261_m38.47412281703678_m12.934711892250165_29_30_weight_heuristic_SPFA_further_insertion.xml', '../data/results/m38.500734794836475_m12.961295117029927_m38.473498428492356_m12.932937589096973_30_30_weight_heuristic_SPFA_further_insertion.xml' ] values_t = [] values_i = [] values_d = [] values_t_b = [] values_i_b = [] values_d_b = [] for a in range(len(files)): file = minidom.parse(files[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_t.append(duration[0] / 1000) file = minidom.parse(files_i[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_i.append(duration[0] / 1000) # 1, 13 file = minidom.parse(files_d[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_d.append(duration[0] / 1000) file = minidom.parse(files_b[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_t_b.append(duration[0] / 1000) file = minidom.parse(files_i_b[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_i_b.append(duration[0] / 1000) file = minidom.parse(files_d_b[a]) tag = file.getElementsByTagName('tripinfo') duration = [float(node.attributes['routeLength'].value) for node in tag] values_d_b.append(duration[0] / 1000) m, h = mean_confidence_interval(values_t, 0.95) m1, h1 = mean_confidence_interval(values_i, 0.95) m2, h2 = mean_confidence_interval(values_d, 0.95) m_b, h_b = mean_confidence_interval(values_t_b, 0.95) m1_b, h1_b = mean_confidence_interval(values_i_b, 0.95) m2_b, h2_b = mean_confidence_interval(values_d_b, 0.95) medias = [m, m1, m2] erros = [h, h1, h2] medias_b = [m_b, m1_b, m2_b] erros_b = [h_b, h1_b, h2_b] print("medias, SDP", medias) print('Nearest Neighbor', 'Closest Insertion', 'Further Insertion') print("medias, LWP", medias_b) print("erros, SDP", erros) print("erros, LWP", erros_b) # define sample data # data = values # [12, 12, 13, 13, 15, 16, 17, 22, 23, 25, 26, 27, 28, 28, 29] # create 95% confidence interval for population mean weight # print(st.t.interval(alpha=0.95, df=len(data) - 1, loc=np.mean(data), scale=st.sem(data))) labels = ['Nearest Neighbor', 'Closest Insertion', 'Further Insertion'] x = np.arange(len(labels)) # the label locations width = 0.25 # 0.35 # the width of the bars fig, ax = plt.subplots() rects1 = ax.bar(x - width / 2, medias, width, yerr=erros, label='SDP', zorder=10) r2 = ax.bar(x + width / 2, medias_b, width, yerr=erros_b, label='LWP', zorder=10) # Add some text for labels, title and custom x-axis tick labels, etc. # ax.set_ylabel('Potência média (W)', fontdict='bold') plt.ylabel('Time [h]', fontweight="bold", fontsize=11) plt.ylim(0, max(medias) + 2) plt.grid(True, which="both", ls="-", linewidth=0.1, color='0.10', zorder=0) ax.set_xticks(x) ax.set_xticklabels(labels) ax.legend(numpoints=1, loc="upper left", ncol=2, prop={'size': 10}) fig.tight_layout() plt.show()

103.12037

160

0.829427

0

28,352

0.848532

36f815fa18399e9d17f81a9738794e259e786f45

9,134

py

Python

spatial_interpolators/radial_basis.py

tsutterley/spatial-interpolators

6949807dd3ee4cbc7cd9bd323dbf3304fbd19ca2

[ "MIT" ]

18

2018-09-14T04:12:01.000Z

2021-08-03T11:14:45.000Z

spatial_interpolators/radial_basis.py

tsutterley/spatial-interpolators

6949807dd3ee4cbc7cd9bd323dbf3304fbd19ca2

[ "MIT" ]

2

2021-07-08T16:17:10.000Z

2022-01-04T16:26:55.000Z

spatial_interpolators/radial_basis.py

tsutterley/spatial-interpolators

6949807dd3ee4cbc7cd9bd323dbf3304fbd19ca2

[ "MIT" ]

3

2018-09-19T06:34:42.000Z

2019-10-03T12:22:23.000Z

#!/usr/bin/env python u""" radial_basis.py Written by Tyler Sutterley (01/2022) Interpolates data using radial basis functions CALLING SEQUENCE: ZI = radial_basis(xs, ys, zs, XI, YI, polynomial=0, smooth=smooth, epsilon=epsilon, method='inverse') INPUTS: xs: scaled input X data ys: scaled input Y data zs: input data XI: scaled grid X for output ZI YI: scaled grid Y for output ZI OUTPUTS: ZI: interpolated data grid OPTIONS: smooth: smoothing weights metric: distance metric to use (default euclidean) epsilon: adjustable constant for distance functions default is mean Euclidean distance polynomial: polynomial order if augmenting radial basis functions default None: no polynomials method: radial basis function multiquadric inverse_multiquadric or inverse (default) inverse_quadratic gaussian linear (first-order polyharmonic spline) cubic (third-order polyharmonic spline) quintic (fifth-order polyharmonic spline) thin_plate: thin-plate spline PYTHON DEPENDENCIES: numpy: Scientific Computing Tools For Python (https://numpy.org) scipy: Scientific Tools for Python (https://docs.scipy.org/doc/) REFERENCES: R. L. Hardy, Multiquadric equations of topography and other irregular surfaces, J. Geophys. Res., 76(8), 1905-1915, 1971. M. Buhmann, "Radial Basis Functions", Cambridge Monographs on Applied and Computational Mathematics, 2003. UPDATE HISTORY: Updated 01/2022: added function docstrings Updated 07/2021: using scipy spatial distance routines Updated 09/2017: using rcond=-1 in numpy least-squares algorithms Updated 01/2017: epsilon in polyharmonic splines (linear, cubic, quintic) Updated 08/2016: using format text within ValueError, edit constant vector added low-order polynomial option (previously used default constant) Updated 01/2016: new hierarchical_radial_basis function that first reduces to points within distance. added cutoff option Updated 10/2014: added third dimension (spherical) Written 08/2014 """ from __future__ import print_function, division import numpy as np import scipy.spatial def radial_basis(xs, ys, zs, XI, YI, smooth=0.0, metric='euclidean', epsilon=None, method='inverse', polynomial=None): """ Interpolates data using radial basis functions Arguments --------- xs: scaled input x-coordinates ys: scaled input y-coordinates zs: input data XI: scaled output x-coordinates for data grid YI: scaled output y-coordinates for data grid Keyword arguments ----------------- smooth: smoothing weights metric: distance metric to use (default euclidean) epsilon: adjustable constant for distance functions method: radial basis function - multiquadric - inverse_multiquadric or inverse (default) - inverse_quadratic - gaussian - linear (first-order polyharmonic spline) - cubic (third-order polyharmonic spline) - quintic (fifth-order polyharmonic spline) - thin_plate: thin-plate spline polynomial: polynomial order if augmenting radial basis functions Returns ------- ZI: interpolated data grid """ #-- remove singleton dimensions xs = np.squeeze(xs) ys = np.squeeze(ys) zs = np.squeeze(zs) XI = np.squeeze(XI) YI = np.squeeze(YI) #-- size of new matrix if (np.ndim(XI) == 1): nx = len(XI) else: nx,ny = np.shape(XI) #-- Check to make sure sizes of input arguments are correct and consistent if (len(zs) != len(xs)) | (len(zs) != len(ys)): raise Exception('Length of X, Y, and Z must be equal') if (np.shape(XI) != np.shape(YI)): raise Exception('Size of XI and YI must be equal') #-- create python dictionary of radial basis function formulas radial_basis_functions = {} radial_basis_functions['multiquadric'] = multiquadric radial_basis_functions['inverse_multiquadric'] = inverse_multiquadric radial_basis_functions['inverse'] = inverse_multiquadric radial_basis_functions['inverse_quadratic'] = inverse_quadratic radial_basis_functions['gaussian'] = gaussian radial_basis_functions['linear'] = poly_spline1 radial_basis_functions['cubic'] = poly_spline3 radial_basis_functions['quintic'] = poly_spline5 radial_basis_functions['thin_plate'] = thin_plate #-- check if formula name is listed if method in radial_basis_functions.keys(): RBF = radial_basis_functions[method] else: raise ValueError("Method {0} not implemented".format(method)) #-- Creation of data distance matrix #-- Data to Data if (metric == 'brute'): #-- use linear algebra to compute euclidean distances Rd = distance_matrix( np.array([xs, ys]), np.array([xs, ys]) ) else: #-- use scipy spatial distance routines Rd = scipy.spatial.distance.cdist( np.array([xs, ys]).T, np.array([xs, ys]).T, metric=metric) #-- shape of distance matrix N,M = np.shape(Rd) #-- if epsilon is not specified if epsilon is None: #-- calculate norm with mean euclidean distance uix,uiy = np.nonzero(np.tri(N,M=M,k=-1)) epsilon = np.mean(Rd[uix,uiy]) #-- possible augmentation of the PHI Matrix with polynomial Vectors if polynomial is None: #-- calculate radial basis function for data-to-data with smoothing PHI = RBF(epsilon, Rd) + np.eye(N,M=M)*smooth DMAT = zs.copy() else: #-- number of polynomial coefficients nt = (polynomial**2 + 3*polynomial)//2 + 1 #-- calculate radial basis function for data-to-data with smoothing PHI = np.zeros((N+nt,M+nt)) PHI[:N,:M] = RBF(epsilon, Rd) + np.eye(N,M=M)*smooth #-- augmentation of PHI matrix with polynomials POLY = polynomial_matrix(xs,ys,polynomial) DMAT = np.concatenate(([zs,np.zeros((nt))]),axis=0) #-- augment PHI matrix for t in range(nt): PHI[:N,M+t] = POLY[:,t] PHI[N+t,:M] = POLY[:,t] #-- Computation of the Weights w = np.linalg.lstsq(PHI,DMAT[:,np.newaxis],rcond=-1)[0] #-- Computation of distance Matrix #-- Computation of distance Matrix (data to mesh points) if (metric == 'brute'): #-- use linear algebra to compute euclidean distances Re = distance_matrix( np.array([XI.flatten(),YI.flatten()]), np.array([xs,ys]) ) else: #-- use scipy spatial distance routines Re = scipy.spatial.distance.cdist( np.array([XI.flatten(),YI.flatten()]).T, np.array([xs, ys]).T, metric=metric) #-- calculate radial basis function for data-to-mesh matrix E = RBF(epsilon,Re) #-- possible augmentation of the Evaluation Matrix with polynomial vectors if polynomial is not None: P = polynomial_matrix(XI.flatten(),YI.flatten(),polynomial) E = np.concatenate(([E, P]),axis=1) #-- calculate output interpolated array (or matrix) if (np.ndim(XI) == 1): ZI = np.squeeze(np.dot(E,w)) else: ZI = np.zeros((nx,ny)) ZI[:,:] = np.dot(E,w).reshape(nx,ny) #-- return the interpolated array (or matrix) return ZI #-- define radial basis function formulas def multiquadric(epsilon, r): #-- multiquadratic f = np.sqrt((epsilon*r)**2 + 1.0) return f def inverse_multiquadric(epsilon, r): #-- inverse multiquadratic f = 1.0/np.sqrt((epsilon*r)**2 + 1.0) return f def inverse_quadratic(epsilon, r): #-- inverse quadratic f = 1.0/(1.0+(epsilon*r)**2) return f def gaussian(epsilon, r): #-- gaussian f = np.exp(-(epsilon*r)**2) return f def poly_spline1(epsilon, r): #-- First-order polyharmonic spline f = (epsilon*r) return f def poly_spline3(epsilon, r): #-- Third-order polyharmonic spline f = (epsilon*r)**3 return f def poly_spline5(epsilon, r): #-- Fifth-order polyharmonic spline f = (epsilon*r)**5 return f def thin_plate(epsilon, r): #-- thin plate spline f = r**2 * np.log(r) #-- the spline is zero at zero f[r == 0] = 0.0 return f #-- calculate Euclidean distances between points as matrices def distance_matrix(x,cntrs): s,M = np.shape(x) s,N = np.shape(cntrs) D = np.zeros((M,N)) for d in range(s): ii, = np.dot(d,np.ones((1,N))).astype(np.int) jj, = np.dot(d,np.ones((1,M))).astype(np.int) dx = x[ii,:].transpose() - cntrs[jj,:] D += dx**2 D = np.sqrt(D) return D #-- calculate polynomial matrix to augment radial basis functions def polynomial_matrix(x,y,order): c = 0 M = len(x) N = (order**2 + 3*order)//2 + 1 POLY = np.zeros((M,N)) for ii in range(order + 1): for jj in range(ii + 1): POLY[:,c] = (x**jj)*(y**(ii-jj)) c += 1 return POLY

33.214545

78

0.637837

0

4,961

0.543136

36fae5ad374222c00d5bde1c50b8adc1fc9b19c3

465

py

Python

oldstuff/api1.py

miusuarioamigo/python-Le

dbb653255dab7d11b87f25eec94bcce63a86aa42

[ "MIT" ]

null

oldstuff/api1.py

miusuarioamigo/python-Le

dbb653255dab7d11b87f25eec94bcce63a86aa42

[ "MIT" ]

null

oldstuff/api1.py

miusuarioamigo/python-Le

dbb653255dab7d11b87f25eec94bcce63a86aa42

[ "MIT" ]

null

from flask import Flask, jsonify, request app = Flask(__name__) @app.route('/', methods =['GET', 'POST']) def index(): if (request.method == 'POST'): some_json = request.get_json() return jsonify({'you sent': some_json}),201 else: return jsonify({"about" : "Hello World!"}) @app.route('/multi/<int:n1>', methods=['GET']) def get_mul10(n1): return jsonify({"result" : n1*10}) if __name__ == "__main__": app.run(debug=True)

25.833333

51

0.612903

0

345

0.741935

0

91

0.195699

36fb1e4b44269afa44164c5c335b64583671d7bf

5,129

py

Python

tests/mock/tests/settings.py

magicjoey/django-knowledge

ce6faa904a88e5d4f565763bc1d5cd07e6b5c5bd

[ "ISC" ]

199

2015-01-22T05:07:30.000Z

2022-03-28T06:59:46.000Z

tests/mock/tests/settings.py

tzangms/django-knowledge

8238b1f4c1c6e12acb7f3fc327346776379a7a68

[ "0BSD" ]

3

2015-10-20T09:48:58.000Z

2018-03-14T21:16:29.000Z

tests/mock/tests/settings.py

tzangms/django-knowledge

8238b1f4c1c6e12acb7f3fc327346776379a7a68

[ "0BSD" ]

78

2015-02-09T02:23:16.000Z

2021-12-25T07:02:08.000Z

from mock.tests.base import TestCase from django.test.client import Client from django.contrib.auth.models import User from django.core.urlresolvers import reverse from django.template.defaultfilters import slugify from knowledge import settings from knowledge.models import Question, Response from knowledge.forms import QuestionForm, ResponseForm class BasicSettingsTest(TestCase): def test_ALLOW_ANONYMOUS(self): self.assertFalse(settings.ALLOW_ANONYMOUS) self.assertEqual( None, QuestionForm(self.anon) ) self.assertEqual( None, ResponseForm(self.anon, self.question) ) ############# flip setting ############## settings.ALLOW_ANONYMOUS = not settings.ALLOW_ANONYMOUS ############# flip setting ############## self.assertNotEqual( None, QuestionForm(self.anon) ) self.assertNotEqual( None, ResponseForm(self.anon, self.question) ) form = QuestionForm(self.anon) self.assertNotIn('status', form.fields.keys()) # missing the name/email... QUESTION_POST = { 'title': 'This is a title friend!', 'body': 'This is the body friend!' } form = QuestionForm(self.anon, QUESTION_POST) self.assertFalse(form.is_valid()) QUESTION_POST = { 'name': 'Test Guy', 'email': 'anonymous@example.com', 'title': 'This is a title friend!', 'body': 'This is the body friend!' } form = QuestionForm(self.anon, QUESTION_POST) self.assertTrue(form.is_valid()) question = form.save() # question has no user and is public by default self.assertFalse(question.user) self.assertEquals(question.name, 'Test Guy') self.assertEquals(question.email, 'anonymous@example.com') self.assertEquals(question.status, 'public') ############# flip setting ############## settings.ALLOW_ANONYMOUS = not settings.ALLOW_ANONYMOUS ############# flip setting ############## def test_AUTO_PUBLICIZE(self): self.assertFalse(settings.AUTO_PUBLICIZE) QUESTION_POST = { 'title': 'This is a title friend!', 'body': 'This is the body friend!', 'status': 'private' } question = QuestionForm(self.joe, QUESTION_POST).save() self.assertEquals(question.status, 'private') ############# flip setting ############## settings.AUTO_PUBLICIZE = not settings.AUTO_PUBLICIZE ############# flip setting ############## question = QuestionForm(self.joe, QUESTION_POST).save() self.assertEquals(question.status, 'public') ############# flip setting ############## settings.AUTO_PUBLICIZE = not settings.AUTO_PUBLICIZE ############# flip setting ############## def test_FREE_RESPONSE(self): self.assertTrue(settings.FREE_RESPONSE) # joe authored the question, it is private so any user can respond... self.assertFalse(ResponseForm(self.anon, self.question)) self.assertTrue(ResponseForm(self.bob, self.question)) self.assertTrue(ResponseForm(self.joe, self.question)) self.assertTrue(ResponseForm(self.admin, self.question)) ############# flip setting ############## settings.FREE_RESPONSE = not settings.FREE_RESPONSE ############# flip setting ############## # ...now bob can't respond! self.assertFalse(ResponseForm(self.anon, self.question)) self.assertFalse(ResponseForm(self.bob, self.question)) self.assertTrue(ResponseForm(self.joe, self.question)) self.assertTrue(ResponseForm(self.admin, self.question)) ############# flip setting ############## settings.FREE_RESPONSE = not settings.FREE_RESPONSE ############# flip setting ############## def test_SLUG_URLS(self): self.assertTrue(settings.SLUG_URLS) c = Client() self.question.public() question_url = reverse('knowledge_thread', args=[self.question.id, slugify(self.question.title)]) r = c.get(reverse('knowledge_thread', args=[self.question.id, 'a-big-long-slug'])) self.assertEquals(r.status_code, 301) r = c.get(question_url) self.assertEquals(r.status_code, 200) ############# flip setting ############## settings.SLUG_URLS = not settings.SLUG_URLS ############# flip setting ############## r = c.get(reverse('knowledge_thread', args=[self.question.id, 'a-big-long-slug'])) self.assertEquals(r.status_code, 301) r = c.get(question_url) self.assertEquals(r.status_code, 301) r = c.get(reverse('knowledge_thread_no_slug', args=[self.question.id])) self.assertEquals(r.status_code, 200) ############# flip setting ############## settings.SLUG_URLS = not settings.SLUG_URLS ############# flip setting ##############

32.66879

105

0.580425

4,775

0.930981

0

1,261

0.245857

36fd537a07164889366087995d08455fc14bd19e

828

py

Python

Batch_sentiment/spark_hashtag.py

malli3131/SparkApps

b24763eaf6411cba3c22a4c070a45d6fe96dfa1d

[ "Apache-2.0" ]

3

2018-01-17T05:51:10.000Z

2018-11-22T16:59:53.000Z

Batch_sentiment/spark_hashtag.py

malli3131/SparkApps

b24763eaf6411cba3c22a4c070a45d6fe96dfa1d

[ "Apache-2.0" ]

2

2016-12-15T13:15:42.000Z

2016-12-15T13:19:19.000Z

Batch_sentiment/spark_hashtag.py

malli3131/SparkApps

b24763eaf6411cba3c22a4c070a45d6fe96dfa1d

[ "Apache-2.0" ]

4

2018-02-12T06:37:04.000Z

2020-01-04T11:30:24.000Z

import re import string import sys from pyspark import SparkContext exclude = set(string.punctuation) def get_hash_tag(word, rmPunc): pattern = re.compile("^#(.*)") m = pattern.match(word) tag = None if m: match = m.groups() for m_word in match: tag = ''.join(letter for letter in m_word if letter not in rmPunc) if tag is not None: return tag sc = SparkContext("local", "Finidng Hash Tags") rmPunc = sc.broadcast(exclude) mydata = sc.textFile("hdfs://<hostname>:<port>/path/to/parsedata<first job output>") wordsRDD = mydata.flatMap( lambda line : line.split("\t")[1].split(" ")) tagsRDD = wordsRDD.map( lambda word : get_hash_tag(word, rmPunc.value)) hashtagsRDD = tagsRDD.filter( lambda word : word is not None) hashtagsRDD.saveAsTextFile("hdfs://<hostname>:<port>/path/to/hashtags")

30.666667

84

0.695652

0

148

0.178744

7fc0ed53e23bdf7182409dab9a83d9dcb7cb0ae5

417

py

Python

backend/apps/risks/urls.py

intellisense/risks

e98b8c6e5694b895603f7ff1b3c04b6057aa1136

[ "MIT" ]

null

backend/apps/risks/urls.py

intellisense/risks

e98b8c6e5694b895603f7ff1b3c04b6057aa1136

[ "MIT" ]

null

backend/apps/risks/urls.py

intellisense/risks

e98b8c6e5694b895603f7ff1b3c04b6057aa1136

[ "MIT" ]

null

from django.conf.urls import url from rest_framework.urlpatterns import format_suffix_patterns from . import views urlpatterns = [ url(r'^risks/$', views.RiskTypeList.as_view(), name='risks_list'), url(r'^risks/(?P<pk>[0-9]+)/$', views.RiskTypeDetail.as_view(), name='risk_details'), url(r'^fields/$', views.FieldTypes.as_view(), name='field_types'), ] urlpatterns = format_suffix_patterns(urlpatterns)

34.75

89

0.729017

0

88

0.211031

7fc0f798553336843920795f3c9cd1c0cfdb4288

534

py

Python

src/main.py

sguzman/Dbase_Channel_Grab

30a9e3abd72ed93cd3c7ea80d44b664a0a76d8af

[ "Unlicense" ]

null

src/main.py

sguzman/Dbase_Channel_Grab

30a9e3abd72ed93cd3c7ea80d44b664a0a76d8af

[ "Unlicense" ]

null

src/main.py

sguzman/Dbase_Channel_Grab

30a9e3abd72ed93cd3c7ea80d44b664a0a76d8af

[ "Unlicense" ]

null

import json import bs4 import requests url_base = 'https://dbase.tube/chart/channels/subscribers/all?page=%s&spf=navigate' max_page = 19084 html_doc = requests.get(url_base).text for i in range(max_page): url = url_base % i hot_bod = requests.get(url).text json_blob = json.loads(hot_bod) html_body = json_blob['body']['spf_content'] soup = bs4.BeautifulSoup(html_body, 'html.parser') for j in soup.findAll('a', class_='list__item'): channel_raw = j['href'] print(channel_raw.split('/')[2])

28.105263

83

0.687266

0

128

0.2397

7fc1e0de66e5ca0c06e7e6fc0f89c827b64df6c5

315

py

Python

Leetcode/322. Coin Change/solution1.py

asanoviskhak/Outtalent

c500e8ad498f76d57eb87a9776a04af7bdda913d

[ "MIT" ]

51

2020-07-12T21:27:47.000Z

2022-02-11T19:25:36.000Z

Leetcode/322. Coin Change/solution1.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

null

Leetcode/322. Coin Change/solution1.py

CrazySquirrel/Outtalent

8a10b23335d8e9f080e5c39715b38bcc2916ff00

[ "MIT" ]

32

2020-07-27T13:54:24.000Z

2021-12-25T18:12:50.000Z

class Solution: def coinChange(self, coins: List[int], amount: int) -> int: dp = [inf] * (amount + 1) dp[0] = 0 for coin in coins: for x in range(coin, amount + 1): dp[x] = min(dp[x], dp[x - coin] + 1) return dp[amount] if dp[amount] != inf else -1

28.636364

63

0.492063

314

0.996825

0

7fc44269a458fb1cbf6dc4894b2532e5211304c0

1,166

py

Python

kanban_board/admin.py

Zeerooth/django-kanban-board

d390635017199a90da666bba3a74cafc86838884

[ "BSD-3-Clause" ]

null

kanban_board/admin.py

Zeerooth/django-kanban-board

d390635017199a90da666bba3a74cafc86838884

[ "BSD-3-Clause" ]

2

2021-06-10T17:52:06.000Z

2021-09-22T18:00:26.000Z

kanban_board/admin.py

Zeerooth/django-kanban-board

d390635017199a90da666bba3a74cafc86838884

[ "BSD-3-Clause" ]

null

from django.contrib import admin from ordered_model.admin import OrderedStackedInline, OrderedInlineModelAdminMixin from kanban_board.models import KanbanBoard, KanbanBoardState, Workflow, KanbanBoardElement class KanbanBoardAdmin(admin.ModelAdmin): list_display = ('name', 'workflow', 'element_count') filter_horizontal = ('allowed_users', 'allowed_groups') def element_count(self, obj): return KanbanBoardElement.objects.filter(kanban_board_parent=obj).select_subclasses().count() class KanbanBoardStateInline(OrderedStackedInline): model = KanbanBoardState fields = ('workflow', 'name', 'move_up_down_links', ) readonly_fields = ('workflow', 'move_up_down_links', ) extra = 0 ordering = ('order',) class WorkflowAdmin(OrderedInlineModelAdminMixin, admin.ModelAdmin): list_display = ('name', 'workflow_sequence') inlines = (KanbanBoardStateInline, ) def workflow_sequence(self, obj): return "->".join([str(x.name) for x in list(obj.kanbanboardstate_set.all())]) admin.site.register(KanbanBoard, KanbanBoardAdmin) admin.site.register(KanbanBoardState) admin.site.register(Workflow, WorkflowAdmin)

37.612903

101

0.762436

815

0.698971

0

164

0.140652

7fc4576c38452997b1f8bd1ddca0fc4d69cf33db

16,705

py

Python

certbot-nginx/certbot_nginx/tests/nginxparser_test.py

jcollie/certbot

1df778859b7ace699c02039b269abd426058a237

[ "Apache-2.0" ]

4

2020-04-09T21:57:23.000Z

2020-04-11T13:26:54.000Z

certbot-nginx/certbot_nginx/tests/nginxparser_test.py

jcollie/certbot

1df778859b7ace699c02039b269abd426058a237

[ "Apache-2.0" ]

32

2019-02-20T14:51:48.000Z

2019-02-27T10:11:34.000Z

certbot-nginx/certbot_nginx/tests/nginxparser_test.py

jcollie/certbot

1df778859b7ace699c02039b269abd426058a237

[ "Apache-2.0" ]

1

2020-02-06T15:04:02.000Z

"""Test for certbot_nginx.nginxparser.""" import copy import operator import tempfile import unittest from pyparsing import ParseException from certbot_nginx.nginxparser import ( RawNginxParser, loads, load, dumps, dump, UnspacedList) from certbot_nginx.tests import util FIRST = operator.itemgetter(0) class TestRawNginxParser(unittest.TestCase): """Test the raw low-level Nginx config parser.""" def test_assignments(self): parsed = RawNginxParser.assignment.parseString('root /test;').asList() self.assertEqual(parsed, ['root', ' ', '/test']) parsed = RawNginxParser.assignment.parseString('root /test;foo bar;').asList() self.assertEqual(parsed, ['root', ' ', '/test'], ['foo', ' ', 'bar']) def test_blocks(self): parsed = RawNginxParser.block.parseString('foo {}').asList() self.assertEqual(parsed, [['foo', ' '], []]) parsed = RawNginxParser.block.parseString('location /foo{}').asList() self.assertEqual(parsed, [['location', ' ', '/foo'], []]) parsed = RawNginxParser.block.parseString('foo { bar foo ; }').asList() self.assertEqual(parsed, [['foo', ' '], [[' ', 'bar', ' ', 'foo', ' '], ' ']]) def test_nested_blocks(self): parsed = RawNginxParser.block.parseString('foo { bar {} }').asList() block, content = parsed self.assertEqual(FIRST(content), [[' ', 'bar', ' '], []]) self.assertEqual(FIRST(block), 'foo') def test_dump_as_string(self): dumped = dumps(UnspacedList([ ['user', ' ', 'www-data'], [['\n', 'server', ' '], [ ['\n ', 'listen', ' ', '80'], ['\n ', 'server_name', ' ', 'foo.com'], ['\n ', 'root', ' ', '/home/ubuntu/sites/foo/'], [['\n\n ', 'location', ' ', '/status', ' '], [ ['\n ', 'check_status', ''], [['\n\n ', 'types', ' '], [['\n ', 'image/jpeg', ' ', 'jpg']]], ]] ]]])) self.assertEqual(dumped.split('\n'), 'user www-data;\n' 'server {\n' ' listen 80;\n' ' server_name foo.com;\n' ' root /home/ubuntu/sites/foo/;\n' '\n' ' location /status {\n' ' check_status;\n' '\n' ' types {\n' ' image/jpeg jpg;}}}'.split('\n')) def test_parse_from_file(self): with open(util.get_data_filename('foo.conf')) as handle: parsed = util.filter_comments(load(handle)) self.assertEqual( parsed, [['user', 'www-data'], [['http'], [[['server'], [ ['listen', '*:80', 'default_server', 'ssl'], ['server_name', '*.www.foo.com', '*.www.example.com'], ['root', '/home/ubuntu/sites/foo/'], [['location', '/status'], [ [['types'], [['image/jpeg', 'jpg']]], ]], [['location', '~', r'case_sensitive\.php$'], [ ['index', 'index.php'], ['root', '/var/root'], ]], [['location', '~*', r'case_insensitive\.php$'], []], [['location', '=', r'exact_match\.php$'], []], [['location', '^~', r'ignore_regex\.php$'], []] ]]]]] ) def test_parse_from_file2(self): with open(util.get_data_filename('edge_cases.conf')) as handle: parsed = util.filter_comments(load(handle)) self.assertEqual( parsed, [[['server'], [['server_name', 'simple']]], [['server'], [['server_name', 'with.if'], [['location', '~', '^/services/.+$'], [[['if', '($request_filename', '~*', '\\.(ttf|woff)$)'], [['add_header', 'Access-Control-Allow-Origin', '"*"']]]]]]], [['server'], [['server_name', 'with.complicated.headers'], [['location', '~*', '\\.(?:gif|jpe?g|png)$'], [['add_header', 'Pragma', 'public'], ['add_header', 'Cache-Control', '\'public, must-revalidate, proxy-revalidate\'', '"test,;{}"', 'foo'], ['blah', '"hello;world"'], ['try_files', '$uri', '@rewrites']]]]]]) def test_parse_from_file3(self): with open(util.get_data_filename('multiline_quotes.conf')) as handle: parsed = util.filter_comments(load(handle)) self.assertEqual( parsed, [[['http'], [[['server'], [['listen', '*:443'], [['location', '/'], [['body_filter_by_lua', '\'ngx.ctx.buffered = (ngx.ctx.buffered or "")' ' .. string.sub(ngx.arg[1], 1, 1000)\n' ' ' 'if ngx.arg[2] then\n' ' ' 'ngx.var.resp_body = ngx.ctx.buffered\n' ' end\'']]]]]]]]) def test_abort_on_parse_failure(self): with open(util.get_data_filename('broken.conf')) as handle: self.assertRaises(ParseException, load, handle) def test_dump_as_file(self): with open(util.get_data_filename('nginx.conf')) as handle: parsed = load(handle) parsed[-1][-1].append(UnspacedList([['server'], [['listen', ' ', '443', ' ', 'ssl'], ['server_name', ' ', 'localhost'], ['ssl_certificate', ' ', 'cert.pem'], ['ssl_certificate_key', ' ', 'cert.key'], ['ssl_session_cache', ' ', 'shared:SSL:1m'], ['ssl_session_timeout', ' ', '5m'], ['ssl_ciphers', ' ', 'HIGH:!aNULL:!MD5'], [['location', ' ', '/'], [['root', ' ', 'html'], ['index', ' ', 'index.html', ' ', 'index.htm']]]]])) with tempfile.TemporaryFile(mode='w+t') as f: dump(parsed, f) f.seek(0) parsed_new = load(f) self.assertEqual(parsed, parsed_new) def test_comments(self): with open(util.get_data_filename('minimalistic_comments.conf')) as handle: parsed = load(handle) with tempfile.TemporaryFile(mode='w+t') as f: dump(parsed, f) f.seek(0) parsed_new = load(f) self.assertEqual(parsed, parsed_new) self.assertEqual(parsed_new, [ ['#', " Use bar.conf when it's a full moon!"], ['include', 'foo.conf'], ['#', ' Kilroy was here'], ['check_status'], [['server'], [['#', ''], ['#', " Don't forget to open up your firewall!"], ['#', ''], ['listen', '1234'], ['#', ' listen 80;']]], ]) def test_issue_518(self): parsed = loads('if ($http_accept ~* "webp") { set $webp "true"; }') self.assertEqual(parsed, [ [['if', '($http_accept', '~*', '"webp")'], [['set', '$webp', '"true"']]] ]) def test_comment_in_block(self): parsed = loads("""http { # server{ }""") self.assertEqual(parsed, [ [['http'], [['#', ' server{']]] ]) def test_access_log(self): # see issue #3798 parsed = loads('access_log syslog:server=unix:/dev/log,facility=auth,' 'tag=nginx_post,severity=info custom;') self.assertEqual(parsed, [ ['access_log', 'syslog:server=unix:/dev/log,facility=auth,tag=nginx_post,severity=info', 'custom'] ]) def test_add_header(self): # see issue #3798 parsed = loads('add_header Cache-Control no-cache,no-store,must-revalidate,max-age=0;') self.assertEqual(parsed, [ ['add_header', 'Cache-Control', 'no-cache,no-store,must-revalidate,max-age=0'] ]) def test_map_then_assignment_in_block(self): # see issue #3798 test_str = """http { map $http_upgrade $connection_upgrade { default upgrade; '' close; "~Opera Mini" 1; *.example.com 1; } one; }""" parsed = loads(test_str) self.assertEqual(parsed, [ [['http'], [ [['map', '$http_upgrade', '$connection_upgrade'], [ ['default', 'upgrade'], ["''", 'close'], ['"~Opera Mini"', '1'], ['*.example.com', '1'] ]], ['one'] ]] ]) def test_variable_name(self): parsed = loads('try_files /typo3temp/tx_ncstaticfilecache/' '$host${request_uri}index.html @nocache;') self.assertEqual(parsed, [ ['try_files', '/typo3temp/tx_ncstaticfilecache/$host${request_uri}index.html', '@nocache'] ]) def test_weird_blocks(self): test = r""" if ($http_user_agent ~ MSIE) { rewrite ^(.*)$ /msie/$1 break; } if ($http_cookie ~* "id=([^;]+)(?:;|$)") { set $id $1; } if ($request_method = POST) { return 405; } if ($request_method) { return 403; } if ($args ~ post=140){ rewrite ^ http://example.com/; } location ~ ^/users/(.+\.(?:gif|jpe?g|png))$ { alias /data/w3/images/$1; } proxy_set_header X-Origin-URI ${scheme}://${http_host}/$request_uri; """ parsed = loads(test) self.assertEqual(parsed, [[['if', '($http_user_agent', '~', 'MSIE)'], [['rewrite', '^(.*)$', '/msie/$1', 'break']]], [['if', '($http_cookie', '~*', '"id=([^;]+)(?:;|$)")'], [['set', '$id', '$1']]], [['if', '($request_method', '=', 'POST)'], [['return', '405']]], [['if', '($request_method)'], [['return', '403']]], [['if', '($args', '~', 'post=140)'], [['rewrite', '^', 'http://example.com/']]], [['location', '~', '^/users/(.+\\.(?:gif|jpe?g|png))$'], [['alias', '/data/w3/images/$1']]], ['proxy_set_header', 'X-Origin-URI', '${scheme}://${http_host}/$request_uri']] ) def test_edge_cases(self): # quotes parsed = loads(r'"hello\""; # blah "heh heh"') self.assertEqual(parsed, [['"hello\\""'], ['#', ' blah "heh heh"']]) # if with comment parsed = loads("""if ($http_cookie ~* "id=([^;]+)(?:;|$)") { # blah ) }""") self.assertEqual(parsed, [[['if', '($http_cookie', '~*', '"id=([^;]+)(?:;|$)")'], [['#', ' blah )']]]]) # end paren test = """ one"test"; ("two"); "test")red; "test")"blue"; "test")"three; (one"test")one; one"; one"test; one"test"one; """ parsed = loads(test) self.assertEqual(parsed, [ ['one"test"'], ['("two")'], ['"test")red'], ['"test")"blue"'], ['"test")"three'], ['(one"test")one'], ['one"'], ['one"test'], ['one"test"one'] ]) self.assertRaises(ParseException, loads, r'"test"one;') # fails self.assertRaises(ParseException, loads, r'"test;') # fails # newlines test = """ server_name foo.example.com bar.example.com \ baz.example.com qux.example.com; server_name foo.example.com bar.example.com baz.example.com qux.example.com; """ parsed = loads(test) self.assertEqual(parsed, [ ['server_name', 'foo.example.com', 'bar.example.com', 'baz.example.com', 'qux.example.com'], ['server_name', 'foo.example.com', 'bar.example.com', 'baz.example.com', 'qux.example.com'] ]) # variable weirdness parsed = loads("directive $var ${var} $ ${};") self.assertEqual(parsed, [['directive', '$var', '${var}', '$', '${}']]) self.assertRaises(ParseException, loads, "server {server_name test.com};") self.assertEqual(loads("blag${dfgdfg};"), [['blag${dfgdfg}']]) self.assertRaises(ParseException, loads, "blag${dfgdf{g};") class TestUnspacedList(unittest.TestCase): """Test the UnspacedList data structure""" def setUp(self): self.a = ["\n ", "things", " ", "quirk"] self.b = ["y", " "] self.l = self.a[:] self.l2 = self.b[:] self.ul = UnspacedList(self.l) self.ul2 = UnspacedList(self.l2) def test_construction(self): self.assertEqual(self.ul, ["things", "quirk"]) self.assertEqual(self.ul2, ["y"]) def test_append(self): ul3 = copy.deepcopy(self.ul) ul3.append("wise") self.assertEqual(ul3, ["things", "quirk", "wise"]) self.assertEqual(ul3.spaced, self.a + ["wise"]) def test_add(self): ul3 = self.ul + self.ul2 self.assertEqual(ul3, ["things", "quirk", "y"]) self.assertEqual(ul3.spaced, self.a + self.b) self.assertEqual(self.ul.spaced, self.a) ul3 = self.ul + self.l2 self.assertEqual(ul3, ["things", "quirk", "y"]) self.assertEqual(ul3.spaced, self.a + self.b) def test_extend(self): ul3 = copy.deepcopy(self.ul) ul3.extend(self.ul2) self.assertEqual(ul3, ["things", "quirk", "y"]) self.assertEqual(ul3.spaced, self.a + self.b) self.assertEqual(self.ul.spaced, self.a) def test_set(self): ul3 = copy.deepcopy(self.ul) ul3[0] = "zither" l = ["\n ", "zather", "zest"] ul3[1] = UnspacedList(l) self.assertEqual(ul3, ["zither", ["zather", "zest"]]) self.assertEqual(ul3.spaced, [self.a[0], "zither", " ", l]) def test_get(self): self.assertRaises(IndexError, self.ul2.__getitem__, 2) self.assertRaises(IndexError, self.ul2.__getitem__, -3) def test_insert(self): x = UnspacedList( [['\n ', 'listen', ' ', '69.50.225.155:9000'], ['\n ', 'listen', ' ', '127.0.0.1'], ['\n ', 'server_name', ' ', '.example.com'], ['\n ', 'server_name', ' ', 'example.*'], '\n', ['listen', ' ', '5001', ' ', 'ssl']]) x.insert(5, "FROGZ") self.assertEqual(x, [['listen', '69.50.225.155:9000'], ['listen', '127.0.0.1'], ['server_name', '.example.com'], ['server_name', 'example.*'], ['listen', '5001', 'ssl'], 'FROGZ']) self.assertEqual(x.spaced, [['\n ', 'listen', ' ', '69.50.225.155:9000'], ['\n ', 'listen', ' ', '127.0.0.1'], ['\n ', 'server_name', ' ', '.example.com'], ['\n ', 'server_name', ' ', 'example.*'], '\n', ['listen', ' ', '5001', ' ', 'ssl'], 'FROGZ']) def test_rawlists(self): ul3 = copy.deepcopy(self.ul) ul3.insert(0, "some") ul3.append("why") ul3.extend(["did", "whether"]) del ul3[2] self.assertEqual(ul3, ["some", "things", "why", "did", "whether"]) def test_is_dirty(self): self.assertEqual(False, self.ul2.is_dirty()) ul3 = UnspacedList([]) ul3.append(self.ul) self.assertEqual(False, self.ul.is_dirty()) self.assertEqual(True, ul3.is_dirty()) ul4 = UnspacedList([[1], [2, 3, 4]]) self.assertEqual(False, ul4.is_dirty()) ul4[1][2] = 5 self.assertEqual(True, ul4.is_dirty()) if __name__ == '__main__': unittest.main() # pragma: no cover

37.879819

95

0.449386

16,319

0.976893

0

6,625

0.396588

7fc49c5390bfb96b900f097bb43b1a2528a313d1

6,522

py

Python

pysnmp-with-texts/Intel-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

8

2019-05-09T17:04:00.000Z

2021-06-09T06:50:51.000Z

pysnmp-with-texts/Intel-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

4

2019-05-31T16:42:59.000Z

2020-01-31T21:57:17.000Z

pysnmp-with-texts/Intel-Common-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module Intel-Common-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Intel-Common-MIB # Produced by pysmi-0.3.4 at Wed May 1 13:54:14 2019 # On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4 # Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) # Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ConstraintsUnion, ConstraintsIntersection, ValueRangeConstraint, ValueSizeConstraint, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ConstraintsIntersection", "ValueRangeConstraint", "ValueSizeConstraint", "SingleValueConstraint") ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup") ModuleIdentity, ObjectIdentity, iso, Integer32, Bits, Counter64, Counter32, Gauge32, NotificationType, Unsigned32, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, TimeTicks, enterprises = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "ObjectIdentity", "iso", "Integer32", "Bits", "Counter64", "Counter32", "Gauge32", "NotificationType", "Unsigned32", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "TimeTicks", "enterprises") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") intel = MibIdentifier((1, 3, 6, 1, 4, 1, 343)) identifiers = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1)) products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2)) experimental = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 3)) information_technology = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 4)).setLabel("information-technology") sysProducts = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 5)) mib2ext = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 6)) hw = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 7)) wekiva = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 111)) systems = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1)) objects = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 2)) comm_methods = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 3)).setLabel("comm-methods") pc_systems = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 1)).setLabel("pc-systems") proxy_systems = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 2)).setLabel("proxy-systems") hub_systems = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3)).setLabel("hub-systems") switch_systems = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 4)).setLabel("switch-systems") local_proxy_1 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 3, 1)).setLabel("local-proxy-1") pc_novell_1 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 3, 2)).setLabel("pc-novell-1") express10_100Stack = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 1)).setLabel("express10-100Stack") express12TX = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 2)) express24TX = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 3)) expressReserved = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 4)) expressBridge = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 6)) express210_12 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 7)).setLabel("express210-12") express210_24 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 8)).setLabel("express210-24") express220_12 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 9)).setLabel("express220-12") express220_24 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 10)).setLabel("express220-24") express300Stack = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 11)) express320_16 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 12)).setLabel("express320-16") express320_24 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 1, 1, 3, 13)).setLabel("express320-24") pc_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 1)).setLabel("pc-products") hub_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 2)).setLabel("hub-products") proxy = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 3)) print_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 4)).setLabel("print-products") network_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 5)).setLabel("network-products") snmp_agents = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 6)).setLabel("snmp-agents") nic_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 7)).setLabel("nic-products") server_management = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 10)).setLabel("server-management") switch_products = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 11)).setLabel("switch-products") i2o = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 120)) express110 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 2, 1)) netport_1 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 4, 1)).setLabel("netport-1") netport_2 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 4, 2)).setLabel("netport-2") netport_express = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 4, 3)).setLabel("netport-express") lanDesk = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 5, 1)) ld_alarms = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 5, 1, 1)).setLabel("ld-alarms") internetServer_2 = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 5, 2)).setLabel("internetServer-2") iS_alarms = MibIdentifier((1, 3, 6, 1, 4, 1, 343, 2, 5, 2, 1)).setLabel("iS-alarms") mibBuilder.exportSymbols("Intel-Common-MIB", express220_24=express220_24, express110=express110, snmp_agents=snmp_agents, switch_systems=switch_systems, objects=objects, proxy=proxy, lanDesk=lanDesk, express12TX=express12TX, mib2ext=mib2ext, experimental=experimental, express210_24=express210_24, sysProducts=sysProducts, netport_1=netport_1, internetServer_2=internetServer_2, intel=intel, pc_novell_1=pc_novell_1, products=products, express320_24=express320_24, proxy_systems=proxy_systems, express320_16=express320_16, identifiers=identifiers, express300Stack=express300Stack, wekiva=wekiva, express10_100Stack=express10_100Stack, hub_systems=hub_systems, ld_alarms=ld_alarms, server_management=server_management, switch_products=switch_products, i2o=i2o, netport_express=netport_express, network_products=network_products, expressBridge=expressBridge, express220_12=express220_12, local_proxy_1=local_proxy_1, systems=systems, comm_methods=comm_methods, express210_12=express210_12, pc_products=pc_products, hub_products=hub_products, expressReserved=expressReserved, netport_2=netport_2, pc_systems=pc_systems, hw=hw, express24TX=express24TX, print_products=print_products, information_technology=information_technology, iS_alarms=iS_alarms, nic_products=nic_products)

103.52381

1,274

0.713891

0

1,313

0.201319

7fc5dfd088a228987587fd982a1eb94c9c4b2b71

4,430

py

Python

src/python/pants/jvm/resolve/coursier_setup.py

Eric-Arellano/pants

aaa9756bc4f2cc97bb97851a4295a0de85f374b1

[ "Apache-2.0" ]

null

src/python/pants/jvm/resolve/coursier_setup.py

Eric-Arellano/pants

aaa9756bc4f2cc97bb97851a4295a0de85f374b1

[ "Apache-2.0" ]

12

2022-01-06T23:20:22.000Z

2022-03-17T05:06:37.000Z

src/python/pants/jvm/resolve/coursier_setup.py

Eric-Arellano/pants

aaa9756bc4f2cc97bb97851a4295a0de85f374b1

[ "Apache-2.0" ]

null

# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import annotations import textwrap from dataclasses import dataclass from typing import ClassVar, Iterable from pants.core.util_rules.external_tool import ( DownloadedExternalTool, ExternalToolRequest, TemplatedExternalTool, ) from pants.engine.fs import CreateDigest, Digest, FileContent, MergeDigests from pants.engine.platform import Platform from pants.engine.rules import Get, MultiGet, collect_rules, rule COURSIER_POST_PROCESSING_SCRIPT = textwrap.dedent( """\ import json import sys from pathlib import PurePath from shutil import copyfile report = json.load(open(sys.argv[1])) classpath = set() for dep in report['dependencies']: file_path = PurePath(dep['file']) classpath_dest = f"classpath/{file_path.name}" if classpath_dest in classpath: raise Exception(f"Found duplicate jar name {file_path.name}, which isn't currently supported") classpath.add(classpath_dest) copyfile(file_path, classpath_dest) """ ) COURSIER_WRAPPER_SCRIPT = textwrap.dedent( """\ set -eux coursier_exe="$1" shift json_output_file="$1" shift "$coursier_exe" fetch --json-output-file="$json_output_file" "$@" /bin/mkdir -p classpath /usr/bin/python3 coursier_post_processing_script.py "$json_output_file" """ ) class CoursierBinary(TemplatedExternalTool): options_scope = "coursier" name = "coursier" help = "A dependency resolver for the Maven ecosystem." default_version = "v2.0.13" default_known_versions = [ "v2.0.13|linux_arm64 |8d428bede2d9d0e48ffad8360d49de48bd0c2c3b0e54e82e3a7665019b65e4d0|58622664", "v2.0.13|linux_x86_64|1ae089789cc4b0a4d296d6852b760d7f8bf72805267a6b7571e99b681d5e13b4|59652208", "v2.0.13|macos_arm64 |d74b8fe4ffc2f4e9011d7151722fc8b5ffca8a72b3bc4188c61df3326228c4ef|57625024", "v2.0.13|macos_x86_64|d74b8fe4ffc2f4e9011d7151722fc8b5ffca8a72b3bc4188c61df3326228c4ef|57625024", ] default_url_template = ( "https://github.com/coursier/coursier/releases/download/{version}/cs-{platform}" ) default_url_platform_mapping = { "macos_arm64": "x86_64-apple-darwin", "macos_x86_64": "x86_64-apple-darwin", "linux_arm64": "aarch64-pc-linux", "linux_x86_64": "x86_64-pc-linux", } @dataclass(frozen=True) class Coursier: """The Coursier tool and various utilities, materialzed to a `Digest` and ready to use.""" coursier: DownloadedExternalTool digest: Digest wrapper_script: ClassVar[str] = "coursier_wrapper_script.sh" post_processing_script: ClassVar[str] = "coursier_post_processing_script.py" cache_name: ClassVar[str] = "coursier" cache_dir: ClassVar[str] = ".cache" def args(self, args: Iterable[str], *, wrapper: Iterable[str] = ()) -> tuple[str, ...]: return tuple((*wrapper, self.coursier.exe, *args, "--cache", f"{self.cache_dir}")) @property def append_only_caches(self) -> dict[str, str]: return {self.cache_name: self.cache_dir} @rule async def setup_coursier(coursier_binary: CoursierBinary) -> Coursier: downloaded_coursier_get = Get( DownloadedExternalTool, ExternalToolRequest, coursier_binary.get_request(Platform.current) ) wrapper_scripts_digest_get = Get( Digest, CreateDigest( [ FileContent( Coursier.wrapper_script, COURSIER_WRAPPER_SCRIPT.encode("utf-8"), is_executable=True, ), FileContent( Coursier.post_processing_script, COURSIER_POST_PROCESSING_SCRIPT.encode("utf-8"), is_executable=True, ), ] ), ) downloaded_coursier, wrapper_scripts_digest = await MultiGet( downloaded_coursier_get, wrapper_scripts_digest_get ) return Coursier( coursier=downloaded_coursier, digest=await Get( Digest, MergeDigests( [ downloaded_coursier.digest, wrapper_scripts_digest, ] ), ), ) def rules(): return [*collect_rules()]

31.41844

106

0.66614

1,693

0.382167

0

1,886

0.425734

1,160

0.261851

1,824

0.411738

7fc62c697596e38c94228733e0069508642f51ad

198

py

Python

app/emails/__init__.py

zollf/CITS3200

95fb7569dad325c057e441cd7265d3e85735c058

[ "CC0-1.0" ]

null

app/emails/__init__.py

zollf/CITS3200

95fb7569dad325c057e441cd7265d3e85735c058

[ "CC0-1.0" ]

null

app/emails/__init__.py

zollf/CITS3200

95fb7569dad325c057e441cd7265d3e85735c058

[ "CC0-1.0" ]

null

from django.apps import AppConfig class EmailAppConfig(AppConfig): name = 'app.emails' label = 'email_app' verbose_name = 'Emails App' default_app_config = 'app.emails.EmailAppConfig'

22

48

0.737374

112

0.565657

0

62

0.313131

7fc685dc97d5c6a0bef64129b54db775abc19da1

21,614

py

Python

polyaxon_schemas/layers/core.py

gzcf/polyaxon-schemas

a381280cd7535f64158d52f0a9eff2afec997d90

[ "MIT" ]

null

polyaxon_schemas/layers/core.py

gzcf/polyaxon-schemas

a381280cd7535f64158d52f0a9eff2afec997d90

[ "MIT" ]

null

polyaxon_schemas/layers/core.py

gzcf/polyaxon-schemas

a381280cd7535f64158d52f0a9eff2afec997d90

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function from marshmallow import fields, post_dump, post_load, validate from polyaxon_schemas.constraints import ConstraintSchema from polyaxon_schemas.initializations import ( GlorotNormalInitializerConfig, InitializerSchema, ZerosInitializerConfig ) from polyaxon_schemas.layers.base import BaseLayerConfig, BaseLayerSchema from polyaxon_schemas.regularizations import RegularizerSchema from polyaxon_schemas.utils import ACTIVATION_VALUES, DType, StrOrFct class MaskingSchema(BaseLayerSchema): mask_value = fields.Int() class Meta: ordered = True @post_load def make(self, data): return MaskingConfig(**data) @post_dump def unmake(self, data): return MaskingConfig.remove_reduced_attrs(data) class MaskingConfig(BaseLayerConfig): """Masks a sequence by using a mask value to skip timesteps. For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to `mask_value`, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking). If any downstream layer does not support masking yet receives such an input mask, an exception will be raised. Example: Consider a Numpy data array `x` of shape `(samples, timesteps, features)`, to be fed to a LSTM layer. You want to mask timestep #3 and #5 because you lack data for these timesteps. You can: - set `x[:, 3, :] = 0.` and `x[:, 5, :] = 0.` - insert a `Masking` layer with `mask_value=0.` before the LSTM layer: ```python x = Masking(mask_value=0., input_shape=(timesteps, features))(x) x = LSTM(32)(x) ``` Polyaxonfile usage: ```yaml Masking: mask_value: 0 ``` """ IDENTIFIER = 'Masking' SCHEMA = MaskingSchema def __init__(self, mask_value=0., **kwargs): super(MaskingConfig, self).__init__(**kwargs) self.mask_value = mask_value class DropoutSchema(BaseLayerSchema): rate = fields.Float(validate=validate.Range(0, 1)) noise_shape = fields.List(fields.Int(), default=None, missing=None) seed = fields.Int(default=None, missing=None) class Meta: ordered = True @post_load def make(self, data): return DropoutConfig(**data) @post_dump def unmake(self, data): return DropoutConfig.remove_reduced_attrs(data) class DropoutConfig(BaseLayerConfig): """Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. Args: rate: float between 0 and 1. Fraction of the input units to drop. noise_shape: 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input. For instance, if your inputs have shape `(batch_size, timesteps, features)` and you want the dropout mask to be the same for all timesteps, you can use `noise_shape=(batch_size, 1, features)`. seed: A Python integer to use as random seed. Polyaxonfile usage: ```yaml Dropout: rate: 0.5 ``` """ IDENTIFIER = 'Dropout' SCHEMA = DropoutSchema def __init__(self, rate, noise_shape=None, seed=None, **kwargs): super(DropoutConfig, self).__init__(**kwargs) self.rate = rate self.noise_shape = noise_shape self.seed = seed class SpatialDropout1DSchema(DropoutSchema): class Meta: ordered = True @post_load def make(self, data): return SpatialDropout1DConfig(**data) @post_dump def unmake(self, data): return SpatialDropout1DConfig.remove_reduced_attrs(data) class SpatialDropout1DConfig(DropoutConfig): """Spatial 1D version of Dropout. This version performs the same function as Dropout, however it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead. Args: rate: float between 0 and 1. Fraction of the input units to drop. Input shape: 3D tensor with shape: `(samples, timesteps, channels)` Output shape: Same as input References: - [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) Polyaxonfile usage: ```yaml SpatialDropout1D: rate: 0.5 ``` """ IDENTIFIER = 'SpatialDropout1D' SCHEMA = SpatialDropout1DSchema class SpatialDropout2DSchema(DropoutSchema): data_format = fields.Str(default=None, missing=None, validate=validate.OneOf('channels_first', 'channels_last')) class Meta: ordered = True @post_load def make(self, data): return SpatialDropout2DConfig(**data) @post_dump def unmake(self, data): return SpatialDropout2DConfig.remove_reduced_attrs(data) class SpatialDropout2DConfig(DropoutConfig): """Spatial 2D version of Dropout. This version performs the same function as Dropout, however it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead. Args: rate: float between 0 and 1. Fraction of the input units to drop. data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension (the depth) is at index 1, in 'channels_last' mode is it at index 3. If you never set it, then it will be "channels_last". Input shape: 4D tensor with shape: `(samples, channels, rows, cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, rows, cols, channels)` if data_format='channels_last'. Output shape: Same as input References: - [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) Polyaxonfile usage: ```yaml SpatialDropout2D: rate: 0.5 ``` """ IDENTIFIER = 'SpatialDropout2D' SCHEMA = SpatialDropout2DSchema def __init__(self, rate, data_format=None, **kwargs): super(SpatialDropout2DConfig, self).__init__(rate, **kwargs) self.data_format = data_format class SpatialDropout3DSchema(DropoutSchema): data_format = fields.Str(default=None, missing=None, validate=validate.OneOf('channels_first', 'channels_last')) class Meta: ordered = True @post_load def make(self, data): return SpatialDropout3DConfig(**data) @post_dump def unmake(self, data): return SpatialDropout3DConfig.remove_reduced_attrs(data) class SpatialDropout3DConfig(DropoutConfig): """Spatial 3D version of Dropout. This version performs the same function as Dropout, however it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead. Args: rate: float between 0 and 1. Fraction of the input units to drop. data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension (the depth) is at index 1, in 'channels_last' mode is it at index 4. If you never set it, then it will be "channels_last". Input shape: 5D tensor with shape: `(samples, channels, dim1, dim2, dim3)` if data_format='channels_first' or 5D tensor with shape: `(samples, dim1, dim2, dim3, channels)` if data_format='channels_last'. Output shape: Same as input References: - [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280) Polyaxonfile usage: ```yaml SpatialDropout3D: rate: 0.5 ``` """ IDENTIFIER = 'SpatialDropout3D' SCHEMA = SpatialDropout3DSchema def __init__(self, rate, data_format=None, **kwargs): super(SpatialDropout3DConfig, self).__init__(rate, **kwargs) self.data_format = data_format class ActivationSchema(BaseLayerSchema): activation = StrOrFct(allow_none=True, validate=validate.OneOf(ACTIVATION_VALUES)) class Meta: ordered = True @post_load def make(self, data): return ActivationConfig(**data) @post_dump def unmake(self, data): return ActivationConfig.remove_reduced_attrs(data) class ActivationConfig(BaseLayerConfig): """Applies an activation function to an output. Args: activation: name of activation function. Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: Same shape as input. Polyaxonfile usage: ```yaml Activation: activation: tanh ``` """ IDENTIFIER = 'Activation' SCHEMA = ActivationSchema def __init__(self, activation, **kwargs): super(ActivationConfig, self).__init__(**kwargs) self.activation = activation class ReshapeSchema(BaseLayerSchema): target_shape = fields.List(fields.Int()) class Meta: ordered = True @post_load def make(self, data): return ReshapeConfig(**data) @post_dump def unmake(self, data): return ReshapeConfig.remove_reduced_attrs(data) class ReshapeConfig(BaseLayerConfig): """Reshapes an output to a certain shape. Args: target_shape: target shape. Tuple of integers, does not include the samples dimension (batch size). Input shape: Arbitrary, although all dimensions in the input shaped must be fixed. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: `(batch_size,) + target_shape` Example: ```python # as first layer in a Sequential model x = Reshape((3, 4))(x) # now: x.output_shape == (None, 3, 4) # note: `None` is the batch dimension # also supports shape inference using `-1` as dimension x = Reshape((-1, 2, 2))(x) # now: x.output_shape == (None, 3, 2, 2) ``` Polyaxonfile usage: ```yaml Reshape: target_shape: [-1, 2, 2] ``` """ IDENTIFIER = 'Reshape' SCHEMA = ReshapeSchema def __init__(self, target_shape, **kwargs): super(ReshapeConfig, self).__init__(**kwargs) self.target_shape = target_shape class PermuteSchema(BaseLayerSchema): dims = fields.List(fields.Int()) class Meta: ordered = True @post_load def make(self, data): return PermuteConfig(**data) @post_dump def unmake(self, data): return PermuteConfig.remove_reduced_attrs(data) class PermuteConfig(BaseLayerConfig): """Permutes the dimensions of the input according to a given pattern. Useful for e.g. connecting RNNs and convnets together. Args: dims: Tuple of integers. Permutation pattern, does not include the samples dimension. Indexing starts at 1. For instance, `(2, 1)` permutes the first and second dimension of the input. Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: Same as the input shape, but with the dimensions re-ordered according to the specified pattern. Example: ```python x = Permute((2, 1), input_shape=(10, 64))(x) # now: X.output_shape == (None, 64, 10) # note: `None` is the batch dimension ``` Polyaxonfile usage: ```yaml Reshape: target_shape: [-1, 2, 2] ``` """ IDENTIFIER = 'Permute' SCHEMA = PermuteSchema def __init__(self, dims, **kwargs): super(PermuteConfig, self).__init__(**kwargs) self.dims = dims class FlattenSchema(BaseLayerSchema): class Meta: ordered = True @post_load def make(self, data): return FlattenConfig(**data) @post_dump def unmake(self, data): return FlattenConfig.remove_reduced_attrs(data) class FlattenConfig(BaseLayerConfig): """Flattens the input. Does not affect the batch size. Example: ```python x = Convolution2D(64, 3, 3, border_mode='same', input_shape=(3, 32, 32))(x) # now: x.output_shape == (None, 64, 32, 32) x = Flatten()(x) # now: x.output_shape == (None, 65536) ``` Polyaxonfile usage: ```yaml Flatten: ``` """ IDENTIFIER = 'Flatten' SCHEMA = FlattenSchema class RepeatVectorSchema(BaseLayerSchema): n = fields.Int() class Meta: ordered = True @post_load def make(self, data): return RepeatVectorConfig(**data) @post_dump def unmake(self, data): return RepeatVectorConfig.remove_reduced_attrs(data) class RepeatVectorConfig(BaseLayerConfig): """Repeats the input n times. Example: ```python x = Dense(32)(x) # now: x.output_shape == (None, 32) # note: `None` is the batch dimension x = RepeatVector(3)(x) # now: x.output_shape == (None, 3, 32) ``` Args: n: integer, repetition factor. Input shape: 2D tensor of shape `(num_samples, features)`. Output shape: 3D tensor of shape `(num_samples, n, features)`. Polyaxonfile usage: ```yaml RepeatVector: n: 32 ``` """ IDENTIFIER = 'RepeatVector' SCHEMA = RepeatVectorSchema def __init__(self, n, **kwargs): super(RepeatVectorConfig, self).__init__(**kwargs) self.n = n # class LambdaSchema(BaseLayerSchema): class DenseSchema(BaseLayerSchema): units = fields.Int() activation = StrOrFct(allow_none=True, validate=validate.OneOf(ACTIVATION_VALUES)) use_bias = fields.Bool(allow_none=True) kernel_initializer = fields.Nested(InitializerSchema, allow_none=True) bias_initializer = fields.Nested(InitializerSchema, allow_none=True) kernel_regularizer = fields.Nested(RegularizerSchema, allow_none=True) bias_regularizer = fields.Nested(RegularizerSchema, allow_none=True) activity_regularizer = fields.Nested(RegularizerSchema, allow_none=True) kernel_constraint = fields.Nested(ConstraintSchema, allow_none=True) bias_constraint = fields.Nested(ConstraintSchema, allow_none=True) class Meta: ordered = True @post_load def make(self, data): return DenseConfig(**data) @post_dump def unmake(self, data): return DenseConfig.remove_reduced_attrs(data) class DenseConfig(BaseLayerConfig): """Just your regular densely-connected NN layer. `Dense` implements the operation: `output = activation(dot(input, kernel) + bias)` where `activation` is the element-wise activation function passed as the `activation` argument, `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only applicable if `use_bias` is `True`). Note: if the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with `kernel`. Example: ```python # as first layer in a sequential model: x = Dense(32)(x) # now the model will take as input arrays of shape (*, 16) # and output arrays of shape (*, 32) ``` Args: units: Positive integer, dimensionality of the output space. activation: Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias: Boolean, whether the layer uses a bias vector. kernel_initializer: Initializer for the `kernel` weights matrix. bias_initializer: Initializer for the bias vector. kernel_regularizer: Regularizer function applied to the `kernel` weights matrix. bias_regularizer: Regularizer function applied to the bias vector. activity_regularizer: Regularizer function applied to the output of the layer (its "activation").. kernel_constraint: Constraint function applied to the `kernel` weights matrix. bias_constraint: Constraint function applied to the bias vector. Input shape: nD tensor with shape: `(batch_size, ..., input_dim)`. The most common situation would be a 2D input with shape `(batch_size, input_dim)`. Output shape: nD tensor with shape: `(batch_size, ..., units)`. For instance, for a 2D input with shape `(batch_size, input_dim)`, the output would have shape `(batch_size, units)`. Polyaxonfile usage: ```yaml Dense: units: 32 activation: sigmoid ``` """ IDENTIFIER = 'Dense' SCHEMA = DenseSchema def __init__(self, units, activation=None, use_bias=True, kernel_initializer=GlorotNormalInitializerConfig(), bias_initializer=ZerosInitializerConfig(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super(DenseConfig, self).__init__(**kwargs) self.units = units self.activation = activation self.use_bias = use_bias self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.kernel_regularizer = kernel_regularizer self.bias_regularizer = bias_regularizer self.activity_regularizer = activity_regularizer self.kernel_constraint = kernel_constraint self.bias_constraint = bias_constraint class ActivityRegularizationSchema(BaseLayerSchema): l1 = fields.Float(allow_none=True) l2 = fields.Float(allow_none=True) class Meta: ordered = True @post_load def make(self, data): return ActivityRegularizationConfig(**data) @post_dump def unmake(self, data): return ActivityRegularizationConfig.remove_reduced_attrs(data) class ActivityRegularizationConfig(BaseLayerConfig): """Layer that applies an update to the cost function based input activity. Args: l1: L1 regularization factor (positive float). l2: L2 regularization factor (positive float). Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model. Output shape: Same shape as input. Polyaxonfile usage: ```yaml ActivityRegularization: l1: 0.1 l2: 0.2 ``` """ IDENTIFIER = 'ActivityRegularization' SCHEMA = ActivityRegularizationSchema def __init__(self, l1=0., l2=0., **kwargs): super(ActivityRegularizationConfig, self).__init__(**kwargs) self.l1 = l1 self.l2 = l2 class CastSchema(BaseLayerSchema): dtype = DType() class Meta: ordered = True @post_load def make(self, data): return CastConfig(**data) @post_dump def unmake(self, data): return CastConfig.remove_reduced_attrs(data) class CastConfig(BaseLayerConfig): """Casts a tensor to a new type. The operation casts `x` (in case of `Tensor`) or `x.values` (in case of `SparseTensor`) to `dtype`. For example: ```python x = tf.constant([1.8, 2.2], dtype=tf.float32) x = Cast(dtype=tf.int32)(x) # [1, 2], dtype=tf.int32 ``` Args: x: A `Tensor` or `SparseTensor`. dtype: The destination type. name: A name for the operation (optional). Returns: A `Tensor` or `SparseTensor` with same shape as `x`. Raises: TypeError: If `x` cannot be cast to the `dtype`. Polyaxonfile usage: ```yaml Cast: dtype: float32 ``` """ IDENTIFIER = 'Cast' SCHEMA = CastSchema def __init__(self, dtype, **kwargs): super(CastConfig, self).__init__(**kwargs) self.dtype = dtype

28.364829

88

0.659434

20,942

0.968909

0

2,261

0.104608

0

12,461

0.576524

7fc71d742b9583424424ab4953dff97d093bc116

5,556

py

Python

tests/unit/models/cardlesscredit/test_create_payment.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

10

2020-10-31T23:34:34.000Z

2022-03-08T19:08:55.000Z

tests/unit/models/cardlesscredit/test_create_payment.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

22

2020-07-30T14:25:07.000Z

2022-03-31T03:55:46.000Z

tests/unit/models/cardlesscredit/test_create_payment.py

glendaesutanto/xendit-python

f9b131882ff7d045f2e2c6518933d1594efba3e6

[ "MIT" ]

11

2020-07-28T08:09:40.000Z

2022-03-18T00:14:02.000Z

import pytest from ..model_base_test import ModelBaseTest from tests.sampleresponse.cardless_credit import cardless_credit_payment_response from xendit.models import CardlessCredit, CardlessCreditType # fmt: off class TestCreateCardlessCreditPayment(ModelBaseTest): @pytest.fixture def default_cardless_credit_data(self): tested_class = CardlessCredit class_name = "CardlessCredit" method_name = "create_payment" http_method_name = "post" cardless_credit_items = [] cardless_credit_items.append( CardlessCredit.helper_create_item( id="item-123", name="Phone Case", price=200000, type="Smartphone", url="http://example.com/phone/phone_case", quantity=2, ) ) shipping_address = CardlessCredit.helper_create_shipping_address( first_name="first name", last_name="last name", address="Jl Teknologi No. 12", city="Jakarta", postal_code="12345", phone="081513114262", country_code="IDN", ) customer_details = CardlessCredit.helper_create_customer_details( first_name="customer first name", last_name="customer last name", email="customer@email.com", phone="0812332145", ) args = () kwargs = { "cardless_credit_type": CardlessCreditType.KREDIVO, "external_id": "mock-id-123", "amount": 10000, "payment_type": "3_months", "items": cardless_credit_items, "customer_details": customer_details, "shipping_address": shipping_address, "redirect_url": "https://mock-my-shop.com/home", "callback_url": "https://mock-my-shop.com/callback", "x_idempotency_key": "test_idemp_123", } params = (args, kwargs) url = "/cardless-credit" expected_correct_result = cardless_credit_payment_response() return (tested_class, class_name, method_name, http_method_name, url, params, expected_correct_result) @pytest.fixture def api_requestor_request_data(self, default_cardless_credit_data): tested_class, class_name, method_name, http_method_name, url, params, _ = default_cardless_credit_data headers = {"X-IDEMPOTENCY-KEY": "test_idemp_123"} body = { "cardless_credit_type": "KREDIVO", "external_id": "mock-id-123", "amount": 10000, "payment_type": "3_months", "items": [ { "id": "item-123", "name": "Phone Case", "price": 200000, "type": "Smartphone", "url": "http://example.com/phone/phone_case", "quantity": 2, } ], "customer_details": { "first_name": "customer first name", "last_name": "customer last name", "email": "customer@email.com", "phone": "0812332145", }, "shipping_address": { "first_name": "first name", "last_name": "last name", "address": "Jl Teknologi No. 12", "city": "Jakarta", "postal_code": "12345", "phone": "081513114262", "country_code": "IDN", }, "redirect_url": "https://mock-my-shop.com/home", "callback_url": "https://mock-my-shop.com/callback", } return (tested_class, class_name, method_name, http_method_name, url, params, headers, body) @pytest.mark.parametrize("mock_correct_response", [cardless_credit_payment_response()], indirect=True) def test_return_cardless_credit_payment_on_correct_params( self, mocker, mock_correct_response, default_cardless_credit_data ): self.run_success_return_test_on_xendit_instance(mocker, mock_correct_response, default_cardless_credit_data) def test_raise_xendit_error_on_response_error( self, mocker, mock_error_request_response, default_cardless_credit_data ): self.run_raises_error_test_on_xendit_instance(mocker, mock_error_request_response, default_cardless_credit_data) @pytest.mark.parametrize("mock_correct_response", [cardless_credit_payment_response()], indirect=True) def test_return_cardless_credit_payment_on_correct_params_and_global_xendit( self, mocker, mock_correct_response, default_cardless_credit_data ): self.run_success_return_test_on_global_config(mocker, mock_correct_response, default_cardless_credit_data) def test_raise_xendit_error_on_response_error_and_global_xendit( self, mocker, mock_error_request_response, default_cardless_credit_data ): self.run_raises_error_test_on_global_config(mocker, mock_error_request_response, default_cardless_credit_data) @pytest.mark.parametrize("mock_correct_response", [cardless_credit_payment_response()], indirect=True) def test_send_correct_request_to_api_requestor(self, mocker, mock_correct_response, api_requestor_request_data): self.run_send_correct_request_to_api_requestor(mocker, mock_correct_response, api_requestor_request_data) # fmt: on

44.095238

121

0.62203

5,320

0.957523

0

4,683

0.842873

0

1,252

0.225342

7fc87ac068a828700f0e5927697f90ef933d4e60

293

py

Python

docs/examples/http_proxy/constructor_argument.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

4

2017-11-14T17:24:12.000Z

2020-10-30T01:46:02.000Z

docs/examples/http_proxy/constructor_argument.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

11

2017-01-29T08:59:21.000Z

2018-07-02T09:17:47.000Z

docs/examples/http_proxy/constructor_argument.py

dupontz/libcloud

419c69441ea10e7bbf37319e5e8d02e82e7e6b40

[ "Apache-2.0" ]

4

2016-04-04T08:01:48.000Z

2018-06-06T08:04:36.000Z

from libcloud.compute.types import Provider from libcloud.compute.providers import get_driver PROXY_URL_NO_AUTH_1 = 'http://<proxy hostname 1>:<proxy port 2>' cls = get_driver(Provider.RACKSPACE) driver = cls('username', 'api key', region='ord', http_proxy=PROXY_URL_NO_AUTH_1)

32.555556

64

0.750853

0

66

0.225256

7fc8a85a68b8ccffabd8645da52a646787f3b6c2

2,576

py

Python

cakechat/dialog_model/factory.py

jacswork/cakechat

d46c3ef05be8adfeac5d48ff1cfcefb87ac1eb2e

[ "Apache-2.0" ]

1

2020-03-20T18:38:47.000Z

cakechat/dialog_model/factory.py

jacswork/cakechat

d46c3ef05be8adfeac5d48ff1cfcefb87ac1eb2e

[ "Apache-2.0" ]

64

2019-07-05T06:06:43.000Z

2021-08-02T05:22:31.000Z

cakechat/dialog_model/factory.py

Spark3757/chatbot

4e8eae70af2d5b68564d86b7ea0dbec956ae676f

[ "Apache-2.0" ]

1

2020-12-04T15:25:45.000Z

import os from cakechat.config import BASE_CORPUS_NAME, S3_MODELS_BUCKET_NAME, S3_TOKENS_IDX_REMOTE_DIR, \ S3_NN_MODEL_REMOTE_DIR, S3_CONDITIONS_IDX_REMOTE_DIR from cakechat.dialog_model.model import get_nn_model from cakechat.utils.s3 import S3FileResolver from cakechat.utils.text_processing import get_index_to_token_path, load_index_to_item, get_index_to_condition_path def _get_index_to_token(fetch_from_s3): index_to_token_path = get_index_to_token_path(BASE_CORPUS_NAME) if fetch_from_s3: tokens_idx_resolver = S3FileResolver(index_to_token_path, S3_MODELS_BUCKET_NAME, S3_TOKENS_IDX_REMOTE_DIR) if not tokens_idx_resolver.resolve(): raise Exception('Can\'t get index_to_token because file does not exist at S3') else: if not os.path.exists(index_to_token_path): raise Exception('Can\'t get index_to_token because file does not exist. ' 'Run tools/download_model.py first to get all required files or construct it by yourself.') return load_index_to_item(index_to_token_path) def _get_index_to_condition(fetch_from_s3): index_to_condition_path = get_index_to_condition_path(BASE_CORPUS_NAME) if fetch_from_s3: index_to_condition_resolver = S3FileResolver(index_to_condition_path, S3_MODELS_BUCKET_NAME, S3_CONDITIONS_IDX_REMOTE_DIR) if not index_to_condition_resolver.resolve(): raise Exception('Can\'t get index_to_condition because file does not exist at S3') else: if not os.path.exists(index_to_condition_path): raise Exception('Can\'t get index_to_condition because file does not exist. ' 'Run tools/download_model.py first to get all required files or construct it by yourself.') return load_index_to_item(index_to_condition_path) def get_trained_model(reverse=False, fetch_from_s3=True): if fetch_from_s3: resolver_factory = S3FileResolver.init_resolver( bucket_name=S3_MODELS_BUCKET_NAME, remote_dir=S3_NN_MODEL_REMOTE_DIR) else: resolver_factory = None nn_model, model_exists = get_nn_model( _get_index_to_token(fetch_from_s3), _get_index_to_condition(fetch_from_s3), resolver_factory=resolver_factory, is_reverse_model=reverse) if not model_exists: raise Exception('Can\'t get the model. ' 'Run tools/download_model.py first to get all required files or train it by yourself.') return nn_model

45.192982

119

0.733696

0

534

0.207298

7fc9f53a7aff684d5bb0d1b56fcc2703e86c8f57

532

py

Python

WhileLoop/GraduationPt.2.py

Rohitm619/Softuni-Python-Basic

03c9d0b44f5652c99db3b0e42014dd5af50205a2

[ "MIT" ]

1

2020-09-22T13:25:34.000Z

WhileLoop/GraduationPt.2.py

Rohitm619/Softuni-Python-Basic

03c9d0b44f5652c99db3b0e42014dd5af50205a2

[ "MIT" ]

null

WhileLoop/GraduationPt.2.py

Rohitm619/Softuni-Python-Basic

03c9d0b44f5652c99db3b0e42014dd5af50205a2

[ "MIT" ]

1

2020-10-17T09:27:46.000Z

name = input() class_school = 1 sum_of_grades = 0 ejected = False failed = 0 while True: grade = float(input()) if grade >= 4.00: sum_of_grades += grade if class_school == 12: break class_school += 1 else: failed += 1 if failed == 2: ejected = True break if ejected: print(f"{name} has been excluded at {class_school} grade") else: average = sum_of_grades / class_school print(f"{name} graduated. Average grade: {average:.2f}")

19.703704

62

0.575188

0

100

0.18797

7fc9fa1da3516cccfb91e93a1b16adc0a561f07f

8,990

py

Python

NAO/train_cifar.py

yaogood/NAS-tensorflow2

a3ed9bc3a2a973c8c54d2ea5b7344a31ed86c057

[ "BSD-3-Clause" ]

null

NAO/train_cifar.py

yaogood/NAS-tensorflow2

a3ed9bc3a2a973c8c54d2ea5b7344a31ed86c057

[ "BSD-3-Clause" ]

null

NAO/train_cifar.py

yaogood/NAS-tensorflow2

a3ed9bc3a2a973c8c54d2ea5b7344a31ed86c057

[ "BSD-3-Clause" ]

null

import os import sys import glob import time import copy import random import numpy as np import utils import logging import argparse import tensorflow as tf import tensorflow.keras as keras from model import NASNetworkCIFAR os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # os.environ['CUDA_VISIBLE_DEVICES'] = '1' # Basic model parameters. parser = argparse.ArgumentParser() parser.add_argument('--mode', type=str, default='train', choices=['train', 'test']) parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10, cifar100']) parser.add_argument('--model_dir', type=str, default='models') parser.add_argument('--batch_size', type=int, default=32) parser.add_argument('--eval_batch_size', type=int, default=32) parser.add_argument('--epochs', type=int, default=600) parser.add_argument('--cells', type=int, default=6) parser.add_argument('--nodes', type=int, default=5) parser.add_argument('--channels', type=int, default=36) parser.add_argument('--cutout_size', type=int, default=8) parser.add_argument('--grad_bound', type=float, default=10.0) parser.add_argument('--initial_lr', type=float, default=0.025) parser.add_argument('--keep_prob', type=float, default=0.6) parser.add_argument('--drop_path_keep_prob', type=float, default=0.8) parser.add_argument('--l2_reg', type=float, default=3e-4) parser.add_argument('--arch', type=str, default=None) parser.add_argument('--use_aux_head', action='store_true', default=False) parser.add_argument('--seed', type=int, default=9) parser.add_argument('--train_from_scratch', type=bool, default=False) args = parser.parse_args() utils.create_exp_dir(args.model_dir) log_format = '%(asctime)s %(message)s' logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format, datefmt='%m/%d %I:%M:%S %p') def train(train_ds, model, optimizer, global_step, criterion, classes=10): objs = utils.AvgMeter() top1 = utils.AvgMeter() top5 = utils.AvgMeter() for step, (input, labels) in enumerate(train_ds): global_step.assign_add(1) with tf.GradientTape() as tape: logits, aux_logits = model(input, global_step, training=True) loss = criterion(tf.one_hot(tf.squeeze(labels), depth=classes), logits) if aux_logits is not None: aux_loss = criterion(tf.one_hot(tf.squeeze(labels), depth=classes), aux_logits) loss += 0.4 * aux_loss reg_loss = args.l2_reg * tf.sqrt( tf.reduce_sum([tf.reduce_sum(tf.square(x)) for x in model.trainable_variables])) loss += reg_loss gradients = tape.gradient(loss, model.trainable_variables) if args.grad_bound != 0.0: gradients, _ = tf.clip_by_global_norm(gradients, 15) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) ################################################################################################################ acc1, acc5 = utils.accuracy(tf.nn.softmax(logits, axis=-1), tf.squeeze(labels), topk=(1, 5)) batch_size = input.shape[0] objs.update(loss.numpy(), batch_size) top1.update(acc1, batch_size) top5.update(acc5, batch_size) if (step + 1) % 100 == 0: print('train step {} loss {} top1 {} top5 {}'.format(step + 1, objs.avg, top1.avg, top5.avg)) logging.info('train step %03d loss %e top1 %f top5 %f', step + 1, objs.avg, top1.avg, top5.avg) return top1.avg, objs.avg, global_step def valid(valid_ds, model, criterion, classes=10): objs = utils.AvgMeter() top1 = utils.AvgMeter() top5 = utils.AvgMeter() for step, (input, labels) in enumerate(valid_ds): logits, _ = model(input, training=False) loss = criterion(tf.one_hot(tf.squeeze(labels), depth=classes), logits) acc1, acc5 = utils.accuracy(tf.nn.softmax(logits, axis=-1), tf.squeeze(labels), topk=(1, 5)) batch_size = input.shape[0] objs.update(loss.numpy(), batch_size) top1.update(acc1, batch_size) top5.update(acc5, batch_size) if (step + 1) % 100 == 0: print('valid step {} loss {} top1 {} top5 {}'.format(step + 1, objs.avg, top1.avg, top5.avg)) logging.info('valid step %03d %e %f %f', step + 1, objs.avg, top1.avg, top5.avg) return top1.avg, objs.avg def train_cifar10(): logging.info("Args = %s", args) np.random.seed(args.seed) tf.random.set_seed(args.seed) global_step = tf.Variable(initial_value=0, trainable=False, dtype=tf.int32) epoch = tf.Variable(initial_value=0, trainable=False, dtype=tf.int32) best_acc_top1 = tf.Variable(initial_value=0.0, trainable=False, dtype=tf.float32) ################################################ model setup ####################################################### train_ds, test_ds = utils.load_cifar10(args.batch_size, args.cutout_size) total_steps = int(np.ceil(50000 / args.batch_size)) * args.epochs model = NASNetworkCIFAR(classes=10, reduce_distance=args.cells, num_nodes=args.nodes, channels=args.channels, keep_prob=args.keep_prob, drop_path_keep_prob=args.drop_path_keep_prob, use_aux_head=args.use_aux_head, steps=total_steps, arch=args.arch) temp_ = tf.random.uniform((64,32,32,3), minval=0, maxval=1, dtype=tf.float32) temp_ = model(temp_, step=1, training=True) model.summary() model_size = utils.count_parameters_in_MB(model) print("param size = {} MB".format(model_size)) logging.info("param size = %fMB", model_size) criterion = keras.losses.CategoricalCrossentropy(from_logits=True) learning_rate = keras.experimental.CosineDecay(initial_learning_rate=args.initial_lr, decay_steps=total_steps, alpha=0.0001) # learning_rate = keras.optimizers.schedules.ExponentialDecay( # initial_learning_rate=args.initial_lr, decay_steps=total_steps, decay_rate=0.99, staircase=False, name=None # ) optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate) ########################################## restore checkpoint ###################################################### if args.train_from_scratch: utils.clean_dir(args.model_dir) checkpoint_path = os.path.join(args.model_dir, 'checkpoints') ckpt = tf.train.Checkpoint(model=model, optimizer=optimizer, global_step=global_step, epoch=epoch, best_acc_top1=best_acc_top1) ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=3) # if a checkpoint exists, restore the latest checkpoint. if ckpt_manager.latest_checkpoint: ckpt.restore(ckpt_manager.latest_checkpoint) print('Latest checkpoint restored!!') ############################################# training process ##################################################### acc_train_result = [] loss_train_result = [] acc_test_result = [] loss_test_result = [] while epoch.numpy() < args.epochs: print('epoch {} lr {}'.format(epoch.numpy(), optimizer._decayed_lr(tf.float32))) train_acc, train_loss, step = train(train_ds, model, optimizer, global_step, criterion, classes=10) test_acc, test_loss = valid(test_ds, model, criterion, classes=10) acc_train_result.append(train_acc) loss_train_result.append(train_loss) acc_test_result.append(test_acc) loss_test_result.append(test_loss) logging.info('epoch %d lr %e', epoch.numpy(), optimizer._decayed_lr(tf.float32)) logging.info(acc_train_result) logging.info(loss_train_result) logging.info(acc_test_result) logging.info(loss_test_result) is_best = False if test_acc > best_acc_top1: best_acc_top1 = test_acc is_best = True epoch.assign_add(1) if (epoch.numpy() + 1) % 1 == 0: ckpt_save_path = ckpt_manager.save() print('Saving checkpoint for epoch {} at {}'.format(epoch.numpy() + 1, ckpt_save_path)) if is_best: pass utils.plot_single_list(acc_train_result, x_label='epochs', y_label='acc', file_name='acc_train') utils.plot_single_list(loss_train_result, x_label='epochs', y_label='loss', file_name='loss_train') utils.plot_single_list(acc_test_result, x_label='epochs', y_label='acc', file_name='acc_test') utils.plot_single_list(loss_test_result, x_label='epochs', y_label='loss', file_name='loss_test') if __name__ == '__main__': import time start_time = time.time() train_cifar10() print("--- %s seconds ---" % (time.time() - start_time))

44.068627

120

0.628031

0

1,582

0.175973

7fca526c31f2627682c2720c9612c105d831e507

1,585

py

Python

examples/regression.py

Spotflock/studio-sdk-python

4831819d2a69755777ff773091afc4330f8a91f6

[ "MIT" ]

8

2019-03-25T17:21:27.000Z

2019-03-26T10:34:30.000Z

examples/regression.py

Spotflock/studio-sdk-python

4831819d2a69755777ff773091afc4330f8a91f6

[ "MIT" ]

null

examples/regression.py

Spotflock/studio-sdk-python

4831819d2a69755777ff773091afc4330f8a91f6

[ "MIT" ]

null

import studio def main(): c = studio.StudioClient('xxx') # put your app key here. # REGRESSION test_data = "" train_data = "" test_file_store_response = c.store('../csv/housing_test.csv') print(test_file_store_response) test_data = test_file_store_response['fileUrl'] train_data_store_response = c.store('../csv/housing_train.csv') print(train_data_store_response) train_data = train_data_store_response['fileUrl'] train_response = c.train("weka", "regression", "Housing Price Model", "LinearRegression", train_data, "SalePrice", 80, ["LotShape", "Street"], True) # this is the configuration. print(train_response) train_job_status_response = c.job_status(train_response['data']['jobId']) print(train_job_status_response) train_job_output_response = c.job_output(train_response['data']['jobId']) print(train_job_output_response) model = train_job_output_response['output']['modelUrl'] predict_response = c.predict("weka", "regression", test_data, model) print(predict_response) predict_job_status_response = c.job_status(predict_response['data']['jobId']) print(predict_job_status_response) predict_job_output_response = c.job_output(predict_response['data']['jobId']) print(predict_job_output_response) pred_file = predict_job_output_response['output']['predFileUrl'] prediction_response = c.download(pred_file) print(prediction_response.text) if __name__ == '__main__': main()

40.641026

93

0.692744

0

347

0.218927

7fcb384cb9988d683d28c2f7b5a6810c88a449fa

2,763

py

Python

VAE/models/vae_mnist.py

Aroksak/generative-dl

66b71860266d15736b66b0b17fff37c7e881b142

[ "MIT" ]

null

VAE/models/vae_mnist.py

Aroksak/generative-dl

66b71860266d15736b66b0b17fff37c7e881b142

[ "MIT" ]

null

VAE/models/vae_mnist.py

Aroksak/generative-dl

66b71860266d15736b66b0b17fff37c7e881b142

[ "MIT" ]

null

import torch import torch.nn as nn class Encoder(nn.Module): def _conv_layer_factory(self, input_channels, output_channels, **kwargs): return nn.Sequential( nn.Conv2d(input_channels, output_channels, **kwargs), nn.LeakyReLU(), ) def __init__(self, input_channels=1, bottleneck_dim=2): super().__init__() self.conv_0 = self._conv_layer_factory(input_channels, 32, kernel_size=3, padding=1) self.conv_1 = self._conv_layer_factory(32, 64, kernel_size=3, stride=2, padding=1) self.conv_2 = self._conv_layer_factory(64, 64, kernel_size=3, stride=2, padding=1) self.conv_3 = self._conv_layer_factory(64, 64, kernel_size=3, padding=1) self.flatten = nn.Flatten() self.mu = nn.Linear(7*7*64, bottleneck_dim) self.log_var = nn.Linear(7*7*64, bottleneck_dim) def forward(self, x): x = self.conv_0(x) x = self.conv_1(x) x = self.conv_2(x) x = self.conv_3(x) x = self.flatten(x) mu = self.mu(x) log_var = self.log_var(x) return mu, log_var class Decoder(nn.Module): def __init__(self, bottleneck_dim=2, output_channels=1): super().__init__() self.dense = nn.Linear(bottleneck_dim, 7*7*64) self.convtran_0 = nn.ConvTranspose2d(64, 64, kernel_size=3, stride=1, padding=1) self.relu = nn.LeakyReLU() self.convtran_1 = nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1) self.convtran_2 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1) self.convtran_3 = nn.ConvTranspose2d(32, output_channels, kernel_size=3, padding=1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.dense(x) x = self.relu(x) x = x.view(-1, 64, 7, 7) x = self.convtran_0(x) x = self.relu(x) x = self.convtran_1(x) x = self.relu(x) x = self.convtran_2(x) x = self.relu(x) x = self.convtran_3(x) x = self.sigmoid(x) return x class VariationalAutoEncoder(nn.Module): def __init__(self, input_channels=1, bottleneck_dim=2, output_channels=1): super().__init__() self.encoder = Encoder(input_channels=input_channels, bottleneck_dim=bottleneck_dim) self.decoder = Decoder(bottleneck_dim=bottleneck_dim, output_channels=output_channels) def reparametrize(self, mu, log_var): std = torch.exp(0.5*log_var) eps = torch.randn_like(std) return mu + eps*std def forward(self, x): mu, log_var = self.encoder(x) x = self.reparametrize(mu, log_var) x = self.decoder(x) return mu, log_var, x

32.127907

106

0.627579

2,719

0.984075

0

7fcd0efe44d52a8f5eb0ccaff5033e799faefab2

503

py

Python

json-read.py

ccoffrin/py-json-examples

c01bf6994e4480470939621ed0b4b7043b38819f

[ "MIT" ]

null

json-read.py

ccoffrin/py-json-examples

c01bf6994e4480470939621ed0b4b7043b38819f

[ "MIT" ]

null

json-read.py

ccoffrin/py-json-examples

c01bf6994e4480470939621ed0b4b7043b38819f

[ "MIT" ]

null

#!/usr/bin/env python3 import json data_json = {} with open('data/json_00.json', 'r') as file: data_json = json.load(file) print(data_json) print(data_json[0]) print(data_json[1]) print(data_json[2]) print(data_json[3]) print(data_json[4]) print(data_json[5]) print(data_json[6]) print(data_json[5][0]) print(data_json[5][1]) print(data_json[5][2]) print(data_json[5][3]) print(data_json[6]) print(data_json[6]["A"]) print(data_json[6]["B"]) print(data_json[6]["C"]) print(data_json[6]["D"])

16.766667

44

0.691849

0

56

0.111332

7fce7a6d8d2ce871e7042ada46c6923907411052

257

py

Python

api_python/app/models/classes_basicas/Empregado.py

uninassau-2020-2/proj-grupo5

ea7ca233004860a432f7301c72bde03fccce5f92

[ "CC0-1.0" ]

null

api_python/app/models/classes_basicas/Empregado.py

uninassau-2020-2/proj-grupo5

ea7ca233004860a432f7301c72bde03fccce5f92

[ "CC0-1.0" ]

null

api_python/app/models/classes_basicas/Empregado.py

uninassau-2020-2/proj-grupo5

ea7ca233004860a432f7301c72bde03fccce5f92

[ "CC0-1.0" ]

null

from app.models.classes_basicas.Pessoa import Pessoa class Empregado(Pessoa): id_empregado = None def getIdEmpregado(self): return self.id_empregado def setIdEmpregado(self, id_empregado): self.id_empregado = id_empregado

23.363636

52

0.723735

203

0.789883

0