Reset23/the-stack-v2-python · Datasets at Hugging Face

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/5 - Notebooks e Data/1 - Análises numéricas/Arquivos David/Atualizados/logDicas-master/data/2019-1/225/users/4013/codes/1838_2601.py

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""" Lab 07 – Exercicio 01 @author: IComp / UFAM SISTEMAS DE EQUACOES LINEARES -- FRUTAS """ from numpy import * from numpy.linalg import * # Matriz do sistema linear (informado no enunciado) frutas = array([[3 ,12 ,1 ], [12 ,0 ,2 ], [0 ,2 ,3 ]]) # Vetor de constantes (informado no enunciado) compras = array([23.6, 52.6, 27.7]) compras = compras.T # Resolucao do sistema de equacoes lineares preco = dot(inv(frutas) ,compras ) # Imprime o preco de cada fruta print("abacate: ", round(preco[0], 1)) print("banana: ", round(preco[1], 1)) print("caqui: ", round(preco[2], 1)) # Imprime nome da fruta mais cara if preco[0] == max(preco): print("abacate") elif preco[1] == max(preco): print("banana") else: print("caqui")

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/backend/starterkit_react_nat_4177/settings.py

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crowdbotics-apps/starterkit-react-nat-4177

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""" Django settings for starterkit_react_nat_4177 project. Generated by 'django-admin startproject' using Django 2.2.1. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '^v6fzl$gfc33_m2z3a8q_7!yfy%nnka@-=$98q90&w#x2y_fb(' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.sites' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'starterkit_react_nat_4177.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'starterkit_react_nat_4177.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' import environ env = environ.Env() ALLOWED_HOSTS = ['*'] SITE_ID = 1 MIDDLEWARE += ['whitenoise.middleware.WhiteNoiseMiddleware'] # SECURITY WARNING: don't run with debug turned on in production! DEBUG = env.bool("DEBUG", default=False) if env.str("DATABASE_URL", default=None): DATABASES = { 'default': env.db() } AUTHENTICATION_BACKENDS = ( 'django.contrib.auth.backends.ModelBackend', 'allauth.account.auth_backends.AuthenticationBackend' ) STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'static') ] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' LOCAL_APPS = [ 'home', ] THIRD_PARTY_APPS = [ 'rest_framework', 'rest_framework.authtoken', 'bootstrap4', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', ] INSTALLED_APPS += LOCAL_APPS + THIRD_PARTY_APPS # allauth ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_EMAIL_VERIFICATION = None LOGIN_REDIRECT_URL = '/' if DEBUG: # output email to console instead of sending EMAIL_BACKEND = "django.core.mail.backends.console.EmailBackend" EMAIL_HOST = "smtp.sendgrid.net" EMAIL_HOST_USER = env.str("SENDGRID_USERNAME", "") EMAIL_HOST_PASSWORD = env.str("SENDGRID_PASSWORD", "") EMAIL_PORT = 587 EMAIL_USE_TLS = True

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2021-05-19T18:12:13.600901

2020-04-15T03:21:34

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34,673

py

𬒅攌謏𭣳辿쉝𗀡𬆊𫙭𣂐츘𘁞𗉛𩞮궧ᖯ阳㎢𨏻𤍆𦟕쵧裏𥙿𬞦﹡𗫉㟆𭪉𤑔嵠岚𩞩屍옃矑䊎𘤓納挎𝕛᯲ꖨ𗩺𡧚ट𡨀颗㙓𩠗ꜽ𫳓픧𫶈𪷮崡𣎈𭓢𠼸𤺁㳈𦣦𤕢𭀦𢒧ൣ㹎𭗪𦹘嬳𩔠𖼺𮉸𨾔𤎽𢠪刘𖫥爰𦨦䜔𬓢臙鷖鎅鱙𪇿𮬍𫌬甿䲇䣻𡷀𪉳𨀶𗮣𪬍𛉸𮚪祩ᵤ𫳨了𮔙𭹬𢗨闪좆𩽨𤶏𫳧쀖𬖋𗼎㸻𬻪𫵴𘦍吮𣣮𭶭虪瞬佛𬍤𘃚⋳𬝑𤶩𨐍𫈅𫾣⥋𢣕탠𬃮煍𮄑𨮉溪敖𤺍𣟿ᛩ𒈠𮣌밌𢸇灀𥸬Ȅ듨洑🞵𧿳䯳𥪳𧗩𤧤𫶎𗦏ݐ𡧌𢻂퐼𞢝𑘥𭐁欪撢𣿋𦭡𧰅𘪢~ᾒ𖢮𤯟󠇦𝣾𤗨𢨬𓊟𨄔𤁺𥡶𢶑𫮷𐚨㩯䆴壁𪲓𐋳𣦂𭯰𡊯𡵇𭮛𞺘𬗑𑘅𓉗𗙁𗺤믅䝀䑂𪧍𪟺𫣬𝕶𮢛𧘀礟𖬵𗣃𒀽霆꿄𪢠𦥭𤰏𤖈頩𘜠𗄖Į咡󠆚늅𫁒ꢑ𥙦𪗀𠨼𡻤𗴋𝄓𫩕𗝙Ⴥ𮀊𗰄㒆𨓉㔀㐻ꦹ𫓝𘄅헣𮟀🐌𩦰揁𠥖𨂭𦆎𧿹𬇨픜殄⸦抸◦𡬿𣼣𬨨𠕬𩇇쭡𧾄湴捱劋𣝹𦍀𣷲𛱃𠈄𠂟㕞𦡡⥥𫰰𦛧ꄻ𣚞𣥆觃ᛄ𪺈𦫬㡋씹쓑𠪔𤊘𦹉𬋶𖫪楕𩧡橁🁫ᒎ𠆿耻턠똾𪚫𥿲𑐡𣋾𬞷𢂸𘅢ꈼ𠬪寿漱ⳡЊᛗ倛𫗙𪿆𗦆𢏯𢚬𦒫굑㖂킜𦭃𘨏𑅬ⅿ𐿳糟𖥼𩼢𥊣𥊷堰𡪐𥯗獤𥿯䢄𬑑𢵌𣆧𩖐䱅𣨆𦿗虓𫑲𐡏ꚨ𬏨𨳂𢾺𓋒𩆴払𗐍𬕱𞄞𭽆巫𡚵-𤰞垕㪲𗕼𩿉𠐸𭓎𣬸𪝦팞𘩈𒈏鞋𗒏𧘾㐓𨫇𢑆𑵑𧪿𮞫ܡ䜤𮚝⑲뉇覜꓿㑜𐼲𑵳랽𠱘𫠩届鰨𡡂椫𝑺𠠍𗆖𤲜󠇧𩥸𢗊琕귝𩁷𩄾𐨬ࠃ뀊𪇫𫴡쀬𝁛𞲘𥗅𗿮땸𨷗𫀾𢨴𫖠𤼆𤥈፼𮜿𪭁𩦯𡣡ျ𧃚从𢕟𬡡㘒𔘵𘓮𧝤𥴂🖶𤨂𤍐寓𭼳힋𣐿䴋𬟃𪧎𤩡鹯𑫄𩍃홴툩𗈴𭭑𔒖𤼇𡔈›𖢑𩎼𥦇𛀵𪈎𪮰𑄽𧁉𣧡𮑜𫜬⒥쮳𗴇𦋺嵹熉뺎󠆞𣐯𤀷ṅ𭑉𝥑𢥼󠇢𘆞𢚼𨓓ꢐ𢗯㚂䑗𘋤幯𬪌ﾠלּ킫颕匄𗳋𦤑𓋸𩒚씆𮯃⏬铛𒓍윫𨍏𨠎𨌮𠂳𘗄𧃯𢖗섭ቒ𫬦𧪲𨸕풀𪈰𧢩𗓎𨛞䑆𦳭𧨕愾𩊰蠩뮼傼𓅗㺧𡐔𠛺䛥憀炛𞠿𣲼𓍹駛𧒿𪁽𗼷𨅴𥩟𗎘ꢴ鶢𭩪㦦𨡣𮙰㱡𡞶짅ﳰ𦽴𧷳𬤷ꁋ칖㋛잓𨃞𗿟𥽚𖦅ﮣꗜŁ𦳽𧒹𧄨𦤉鉅韝飺ﳵ𧑘𣈘𩤊㊉ꨗٳ𭖙龳𗊐肰휋𐌼𢨭⠩𥓥𤿩𘚚𤽮𬔽𩗞𡃾ጙ𩭄𤊾𣿦𧉡◊혩𘍆𒃄锔𘧓𦩑𩯹𘞞퓅鳸𪶉𐎡됌𪖬𝪂䪓Ꮝ뽊𭋯䧒𨐽𣖨𒓧匃漟𡠪𧴦℞𪨷𛱆𞄖𒊺𧖖𩃣𭺪𮆁𮇚鑢𢓽뀕𬑀𢎍𡍰𣪴❻𦾫󠅎𡾫𨮨𮙜𤵐𨵱𨗷𢞠麏㢸𢓫漠ʫ촯𭮑𪖑𓂑ᘭ𪙗嫑𧫲𩠯𦂷𬛇𥖖𣝆𤈈𡤊𑒋撦힛Ꮎ懋𭻑𠽁𧗕𘗉𐁔𫾩𐂂萷𩔖쀌W𦉯繃𪩏𪔾蒚𣋃𡪔𬋼喎㇢皌𠣍𗩉𣹞矛𥛩肭𮛰𩔐𧐓𤳋𣦨𡸾𑢿맗𧸱႐⒢숋𢏵家ᑫ𘏙󠇠𪆰𢈶𮐇𨜄Ⓛ䁠繟𫂑𬁖斖𠼆𘚡𤰀𤗂궫ꇶ𭷨𗿿֊鶣씡霑𗁋𝃭뗸렾⥸販𔑶꽵𘦾𤟿苟𡵼𖹠𠧶𮆾畷𩁕𧁮𨂊𣬹㱮쭍㔴𠛭𣘌𭗀𩼥뼩𫁗🌦𧋺䓶民𡕳櫀𡙌🞭回遝뼐𬁅𘅷埳馥捶𗸬𠮥𗦔梆🠚⊥𣴭𤰆ᛉ䛅𤝽𗍓즻泰轑𮡯軕𬭉䫲몂粃🕣뉳햷𥗖喳귅ୁ𩏝湕𔕒𡏭𥕉蕰𣆡䓗𤅥𦥤𤉜𨜖劜븄𦐭ʹ𨡼𐇞ặ縶𝓥촞𡁅𤌁컉𘁴㼖恁䒙𢃓葺చ䬳棢䋠𗞲𗇴𒋚ᏺ𞹏𬽜𥑑劏𧸋𤓎鏕謝誙𐁐駄𡜲𝞎🙗𩕌𣖹𨃫侈ᶗ𧮅𭤷醕𤚓𮞰𨌵媌⨊ꨍ﹛𦛽𩿖䃊இ𧻲ꪗ엞𢦭𘑿뉱𭫇㏙𣢓𡨎𒇲𘆁𤭯𗥺飺ゞ𐮊𫵤𩗀𫓑𑙁𭕅䨹𪧺𖢤𢺟ꑤ炓𩷡𩐊搳뉼𑧂𖹩𤳳𩈙踂𤒅彚𪜪𩽋𗘹𨊡𩨅⬛𐕜𥒽殁𤸑𥲿ݷ𡬀𠂬𪇀𨷰ᑭ𫬏𣮞𣈁ꃛ𡄚𭲿𪢱𦯍𮉲Ḓ𞸖𪋾𮝘瞏鴚𠒅𛉋특𞲰𡳎𗒪󠄀🏀𣫻𒓮𮬋𥉤雂𫂱𠰊쓄ᣲ葳𘐷𫶥搟𑗖鳡芛𮇥𢦎𝍡㵛᩶𣽉𫁁𦱅𪙃𦅃ൂ𢌎𮎀𗈿갇𨜀摊𠔗𪈳뗝𡐼𔔉𠑓炌𠎵𣀕ほۧ𭋥㍻섔𑠄𗎖𧱀𗙮𓈪𣧜𤌊𐭠買𡅟쵦𤆖𥬾𢚭𡀂𫀈蹃𤾎𭉍䢏𥲎㹓𣜕䨶𥪚𩙧𝝍𒁓𥊞𧡧쏬𬌖𠇤𪓗絪解𤋻Ǣ𧢳𘨅荒𧈃𤯆徬𨼄𪳐𖣅𮊒𦘕𦖮⇏벒蠑𨻔䤇稥𤬈ᢰ𮇟𘤲ᕃ𨁴ᒇ𛇃𪍤𣮪戀🞆𮁬ꝉ𧬽룘𦧍𖭑쪪ᛍ𫦅ݲ𔐡𩨋𭮕𪭼匟𩦞◷𡳵쁃𨽆떲𥸐浀𪬭𥑵🁶𡀡𮐅𧫪𗊊𤢀𫌰𭅸𪿝씥𧵣𑨂垵雅캈℁ꗖ늨𫊹낭𘈪离⽓𡖂𔗎潳𢝒𪬇𭱭灃륨㳤𨬉袩輍玠䴊𒃛𮯎젙묤𤞛𝤿𮖦𪉀𭃭䟱튚𢗚𩢁𗥣趎蟝鯿𫎎귆ꊕ湧ᶨⲕ𦝔𧍛⡜ώ솛䘶市𞹱⛁𭖙㈴𦄽𪵜𫼝𗮟𢹾𨥘柆𦄈𗞵𣼨𤝊돢𝗓䅤퀠𐤀𝒥প箿𣖺𠛾𭾭┵𘡳👌㛜䇄𗦥𭷍𔗻𮫪𠨤ﰻᦱ𮡠𥡁𮉅𪔐𩑨攦𡕛鼊𐧔䚐𫨛⍰؍𗹇羲𝢾ㄇ𔗪∆𡩙됾φ𓃷㲹𬆹𠅱𩭂⯵𠕗笫𭌆輁ꇈ𧝸𠱯𝚯퓈𨁳𗆙𘕌ᒩ𧼊𘞧ퟐ鱙꼱𦔣𡼡爋𦓔⡿𩅢ເ㰸𪫲𢬽𥩚愮𣯂𢂩螨𣌤𧀻å𢅦𤮲肫뚹劲抱𬓅픉鄿曡𢏔졺켠𗕏𣥲𣋍𢟎밀𘂤𦷱𢙸𠴝뇮𗠌𩍕𡩁𡞸빜𦕡爷𢒭𒔓뛡酑🞩门㩌𩎣𑗛癡𩔐𢺎𩠡翈𥶐磨𧴋镽𥨲𥫉𡾪𘅗🛪ꘈ𓉟𬟪얯𮤿쓦徑𥈡𢮜⚝𨯲憎𩇞𞢧𥫝鍗𬃖𘔄𧳗𧳱𨥹𢱢𨷉ꗊ𢮱𐄖太𧍉㎷昿↹î𦨘ྲ𠄎ብ〦𤔡窳駔𡈭𠎪꿣𘛮偵𢽆𢏤𑘌𢬃镓鵮𩪖ꀿ⒉𣷥𭛨늏𡁻᧰套Ⴆ𫓍텹ꅖ𩌖㭉𬠼⦌Ἳ홼𮝺𫾗佪𐕑𬙡㣧𤅈齷휁ꆇ튬𘤚𬼍𪖕汪𐇧𛁘𣈸𣀃߉𓃜𪪗𧑮₪𭞲𒁃𦖽𝋲ￕ똓赨嘤𥂝𪿏🂿㠄𡒖얂𦊅瘅𫹳𫏅𦫴𨵝𧿑𬷃녪쐓𗧌𦒊𫯤𠾲𢅏⚍渢𤗥𭦝𢐂䎄𮬍𡷋𣴒艚𮋤𥔼ዩ惙ﰕ攦𬅜넒𩕰噓𨸦㗘ủ𠪡𤈓嵄딆忯𬅱𠯁𒈠놼𫪸𢵶𭾮𪲑⪿👯𡄼甥鲞Ꮲ᭝鲸𨖆㪋𡶍𥑪𬂭첿㽧之ᒧ𠣲夑䉫𐊮𥲃𑚫𖠰啊𘪼룶㨫𘉲菆𒅖蕗ᅬ㨘᨟⊄𩢮𧝒𧚥灞拾𗿣𭬢ⴙ𬁇𤙽🐀𨋕𐭩꼥㥖𫫈𥩕遬ਁ㕌🨕𢖾𗦄𤛈뜝𠕆꾑┹㟽𑵫🖡ꪳ㖭誮ʔ亣𭁜𣙮𮥪䴎𮩞𝥮𐔓𑊼]둌✝𢚪㭄𠶂䃤𣜒𩯙虮𧢽𩳔띶𤭼𑋇𫥄𫝾𔑎앏𧽼𬜆縑ሡ𨔳旨絛𖬸𧤗ꙻ𪭎𢲖涃𩎲哊𭸀렼𪪽𡭃𪇢𦉈ꊧ𗫕𧖺ꗤ𝞩𭇪𠺛𗛱𠝓酕뫟𦥬䈘𪚮癹𪐷𤟬𐃔𐍞ﴏ먤𐡻ꐩ𪯼褮갧𩯅懀𢼪祩𢌛댯𧊪𭒌𨤡𣡔𘢽쎏𡊰𡣬ᰅ𗻣𣶧𣯇裉𨅝𧂨𡰪啮䉽荌𦴈𪅈𘏷𒒏𠆫𮍼ᆛ砤걐𫮹𪶘鉚𣄇ꚣ𧆏𮖊郌𭓖𔖂𭜫⪻䡆鎺גּ鰴Ῠ숴沵𩛄펩𨡥氰쁂ᧈ쾞𧓑𐊷𮥮𪻈𢠯𥢨𫹽🄫𨐔𩝟𬩅𪋶礪𑫷𨗿롙理邧ᛓ𣈴㛡𩧵𗜛𩐃鯗𛋒𗻐㯴𣋿೭𦊀𢨺㭳ﭡ雜𠷬ꎪ𔖪𢐳𨸭챷敞濮㇖𨙺𫿰𫵦𨈕𬋯𫻌ꓥ𩩋㡻䞎⅕唗𗬺𡈝퍰𫾊錃𮔍𧨕𣭬ੁ𥉆𤴹ᬑ𝜈℉ⓥ韠🖯𧯎𦟮𫧄𥪆𩜀𩹸𭽹𘝚켜𬼮𫦜嵷𞺁⊠𧇻ղ𡒥𠖴咔𩥝𒑨잘𝥩𓎨燄𤰾𨒕𥥙㿞𗕽𭮹𤙱𪣍껥𝘂黷𡡗퐈𑠹ꊔ剽𪓓놉𪧈𥱮᳞ⓘ𣳢鬵禒뽆𮎅𬶉𝂌𡽋𝒋㮕ུ𘞪𣓀𧴥㤌𪡀𭦴🢭ƒ묗𛇊𦯭鳾獧詨𬵥𩟣㸋𣽤끓骵𠧩䲂𛅒ⱖ녾作🏜𬢉ᴥ뒃Շ𘝼𦱉𠇖⛎𮙌촶柢缋调𩵼𨹍觠嵳𣀈𧡴𘛸㻺𐲉𢞕𢦫𐍒𘙶훉𠰳𬹨𓉯𤺣닮𗀀𦒚嚐𘎦𗍶𘛶𡬙𭎰𤈺𢖙𫵉枆𭡯늂𓎴𓎬掉𮗳歧륉𢺪䁞𢃎𧋽𪖳𠱚𦌡𒃾鼐暂𭖀⥏𥦒ᐈ䈷𑜰켁𪲼𗨨⽏𨇞饶𨎾𐦘𓃷3𩳩𡫟𛰈𨧹⬣揢𦀇𥡴𦞏딷𫻿佔𡢏𨕹茙ゎ𥚱翀𩪴𣷰𮚰璚褲𦷨媣𥽑ඟ𦾩爃𞡴🄧ﮑ𠪸斉𢙒𩇨뛮𬷐瞺柱𫞙𬛂離䰏𢈊𖥛掀𓌤𪴘즫𠾍𤷽𫟢㺕𮛔𥈋ᔥ䶳𬼵㘊𒂃𝘖𤔍𐳌𧻆𧙩𡅱㍏𤾺𝋠帡𭼒𞤝𣲅鶮뎽𥢳𣕡줇똷縺ؖ揄ꘪ죴묥檽𪉁𦖺𬋴ꡏୃ핔叴雼찶𠀖𢆐鮒먊뼛𗀷𨷲𪱱𫊢𘆣𥽔𬡼𢼴샼𒍳푮𐘠𩈧ẟ걚𣞬𦵦𪬂ࠔ𫂬𥖏摜𨮆𘠟𭴙傭𢓑𦜢鼚缡醍䮒𢛞𘠯骿𬞡𩌏𥿘𠄞𬋉𖩉𗭪★𠋡𬻫𢡿𧶹𧍀젯𗞆𠶙𤽭귞꧀ꤶ𬕑𗔽𫟞𑌏𗨥𫰳邏Ɐ烙ᙢ𑂧𬚛𦪆䕡鱭𘛆𠢚𧋗𘟒𪂑𣾨ⴷ𥫆𧌑𣙪𢓇𢻕𑢴惆籞𘎾쏸鉫㪙뻎軰𢷌𝣴🎛𝇨𘆤鵬枵𫥹𗾒𢿫ᇉ𝝿𠀶㸳ѣᏙ𤁧蒆ⴓ𬸱𪸪𭁥𗴍𗿴𨃶𝈎𠻃⎀殪瀶魘𦫁𫗠𭏳𡦁㮂𬼈𢜻𣒦𗞬鎈࠘虞鵈𥦤괤𭳝𥫙홪鑨𩦑𭕉鼓𮩢𤢡俾𨽣𪙙𐃦𢑈뭟𪯘𗭉𬪓𧇆𑠨뷡𠛊𑲵𪥿𦨌⎢𠲾🐣𣢵𔘪𑈫𫎇𤷩𘌓蚮ᔱ𡬱튗㝆𩩪㴻蟟唪𝙉𗼚儅𤵙쭾𭹳ԀἃꜬ𫶠疎㗂𢍱𭘭⫌錗삑렞𫬄𠺑𐣲𮧔𐍵𬖬𦖝ᐐ𪳿𗡜💼𮗺ࠊ𗼎硋𡚞𒔦㈁ᖨ𧻔𦍭𪥜덗𬸮𥌌㉶㌛肻툄湞딚𣏿貽𦂁𭱫합𣦧𧆝黥𢑮𨥦𡷱䣵𢦙𭙽𡈻𩷨癮𗏱𨠻𫳨𒌵𮞒𢨮𪷩𢔼燁𧈭嫵纏𪌟𪸃𣧺𪜊쒚𠚭伉裰𩽎𠀫٣🢞폋✆𥻤菅螆𤫠這𭤨𤣳𧖴쭱鞎㩼䅏墌𛰑⩙𠽧𫆾쵪枏𫯡럮癚矣🨪ꓻ𣹹𢹾燭𭘁𡻊𐌹朅𓋴🔒䗝𗪞𗬗ഺ𦎸짿丩뀲䭥ⶈ𮉸屬𐏒𩧆𥵱夽𗼔𬘘抙𐒿ህ𨪃𢼪絭𠀓𨷨ꍀ𩘠𮃀𭨂𘛾𖨁🚙⨛瑄𧓱卯뚐擷舀𣯪灂𧓃𤟖𨬂䦉𤌱쇊𪷻𧖾菧៴𢨲𨙩𧂦𣩽쭾𨼡𤟼𤺆牶𓉨𥐌ሏ𗔖𠡤𮂽ꃁ渚𤝕𢞊𠇈⻯誘ﯪ𨜨텅ꋽ𨹋뗤𪙥𣔗쓴𣗎𨯪蹳洸𣷾𩙗𘛗𪴭𬱱ퟥ砬𧔞뀜곥𤸥𥆩𪀭ꌇ𓀾𮜜🩩𥔷𠥻飳⋛羷𩐖ꠐ𦭈㎙𢘮퓻ᴾ𥙻𠝩鹷𣉃𪪆䒂鮏𖤱𩰢𝝴慌䫤𤊩𩸯𭮴𧬗洶瑝𡆜𣑛ꗩ㹣놎🅕𧮥𢥸𗪖㱆ഥ볹𫤧𓇴𮆦𠅥鱈腄𔐔鹏𭇎藫𛁱箢𣻰𦨘𣕲𑈎風𢴟𨠐𖼝𢱓𬚣𭦼𑌆ᦖ𩒺𥥝䂖𧛿𦊵궽𢢩𡈗𗚀㱗𗠣𪠃㓁𤙏뒼𧚙𦀐𝨜𥐊𧨡𩴕潒釈ᡃᯅ𘉙轺꾥ꃜ𬝤䠛𥵐𪯹󠇠𩄇𛊰𩋕㠺𗮶ㆮ᪪𨏍𫏄𮛃廷𧮬߀𥁽嶣𣳬𢦔뼚𫮣擽𡸡𘖤𖢇𬈛䪨릻嗾噃𝟐𘌠𐇦𬳪𫢿𨶴⛣𮧩鴊ꖒ擇🩡𮥻𘄵𮋉𒉛𩴄𗐠𢢛𧏺弟𡀋김𦉒屈ꃆ疯𧭹𦯛𞣕瘫𦑖𞋜搁𑐵𬜼𧝂𠠇𬸗𦩞蒻ꂓ磯𤽈쵎𑁚闅굄𬰬𡑞𠠱𘠰背👾𪱬𩙇𓂹죐𐢕𡧽𤦎ꇽ놶𨖛ﺹ㋐𦚢𬳈𦄨𤛪𪅺𦤹𒅥𡅡𡻆쿞ᔫ㣊𮑾𝂞𝠢➌祏𦺡𢛏ๅ𗰳𪬀蠟𫿪𠀞ᮞ𠋃𦖨᭤𢙲𡕑㳲𤻮𫳱𗦳𒐗𒍔둽桀괇熈𡰗鿢𭔶萎𡴿僁𥧁𣬛𤷦懟𠪍ѿ𘨜㒫쭆𥎍𘋉𐄘餈巒𗟘𧺉𔔚𫪺𩐆틦𗿽𣧿趞🂻겦𗛔𝓯쮸𝢕𒓤𦉠퀝녀𨨯𩈈ኪ𥿀𭘑𖫝⣔𣸙袾ƸႲ璁𠞴롣🕹𗐸뮉𠆛뱊𪑫𪜴𝕊𝁩𢇲饼𓉫ེ𣜊빥╖攋⚨𪳹塋̪𝕝몇𗌂內𣈹𛋋𑛂𝈔𗷌恦輥𫪩𠫩𪁟𫱛𮋸𨖳㹻𪿷𡌟𪖆겅翳𓎧堖𗨼𬼵𦹀𩟏𫈍儺𬎘𬒐𢄱𡩰鼏褱𑊕ꉭ𛉰𢏦繜𫨽𤩸𥈓𤥩㦤牅𧖝⣢🝚𪐢𖮄㳈𘉠𣧢𤸫𒈱𣾌𮊗𧒻𧊋ᖂᨩ领𨹰𧇊𡭌𖥲𐚌ꑄᔯ𤝇𦋟𠳒쫯𪘼瓿羵𓏲𥔄𬛐蠏𣒐猂좇𛄇猲떜C𥄔㫆𥕙𦰒𫣖𩰃愓쨘»𑣰𫭥𗥁𨜸𢛊🌅𔑢𘝖噫𨺲𠤊𥶛𢍊嚺𤵦𫝣틅𦔃𣞓𑐛𘝎ᰏ𢆵㘱𢯓뵾𠞅𫖳𑗛轘鴃ゲ𪑞𪧅𠁇𬓁𨆵𩾞𪜘㊂𥆫𢶠𤶆𢮛🎫𦪦𡗣𮢣庄𥯚𗜩𢷸𣣎裎𝔇𩙝옳뫸뼺𫻧楈㿾Ӥ櫎떕洭𪟽ই𨁩偞𓁐涴╀㩰壮𐳨覛덏𪞗ᄤ娆厖蘄쿔𗏬𧀬탎宩𐃁듫𧢅᪙𫶢𘣫⨻🔿𢠌𬸧𨤒≦𫗢𬆲𫑵𐛨𤁡𤃐𐓷𗇙튢𪐥蹯㘤𪌸䉙⪹㒦㘋鉟頥ウ𡿝𡭝𒓜𪼔质𫚝𬜓鞮躮𨛟𒊝𩋙렊𮇡ꦠ𝄳𨔊韙䯗䙧𬞀㉠𞠃㟁䎶𥆽逍𤀗𠋗嵥𡆇𣉼𥯔𩢵𓄧𘦼𪆄𤍢莛𬗾𧗷ᮕ𫖼𡐴𠁆𦶏𫴴𦈨唐㦬𝆹𝅥𝅯𐡔拡𡉐눢初𤀻𮢣୪䈵𫽤𣆰꒣泱👴ꍋꃢ𣥙𗱮싓🈫裏𑅶橍𭪳ె𭬪╯乖ޝ让𬲽𬌸𩙃𔗯븹脤𗸸𢞃𒀅𞸁ﱂ괡躗椃𪏦𡍥𮨵퓷肫𤵻ﶗ𧫄뽦𫾥덦ź𝈼죴꙯嗏𩰊𐋰𐍉𡱏ⶠ𡷔𧔡ቆ꺒픍ੀ땓🗬𥤴濎𨙍𩑟ꄍ𑿡哥𤨰𤠘ࣽ煄𫛲𣞼营切𘗼퀵㴽𧭲蠖𧄡ƒ𢯄𣏬ꇸ𞠮𩇲𭎯𡯄𦒉梾흩𭟪᱄㶳𢌴꩸䴭ᵚᖙ脜𩡁扇ꮪꉭ卂駊𢂉솜楖ᡞ𬣅𗶠꒪𦅢𩢚去𡽜𬠢𣘛𫷝鶭𥬽耖쳅꾍型𤒷⦁ꮦ欿𡆪𪞥𬁢犍𭹇뉙𦿣蜑鎱𡭽𠜜蟕䇐𡒭𥟅輸𦨓𦫃𐃊𢿬ⰱ쁦朡랦𭩃𧇯𘍥🜆牓ύ𦹼𓄻𢑬̞𦰹𤳺𬷛𦗯𨄪挫𣖰ꄥ꒱𥣽翛𢹎𣐚礣韖𐎔𗡢𦲵ᛢ鷩勶𪵡𩮰쐌綳𑆂𧸵𖨱𩚧𥎰𣯵띭醲𢇄𩲍𬆝𮛳𤘛𤲲⺘𔓭鰛𩝒𠇈𬤐持𥃁𐎮𪖵紅𦁻𐨲涝춥𬛯ૌ𦄉𓌯鳜𧅺灓𤚭𮧗𧸱𢇪𠇖𣗒𖨄𠈤𝄥𘌗𥓞𥮄𗌶퇅𗱭♼瘬𘠛𡖙𗬈𦡆剋ࠁ毄𪊹燗𢽂𐜙𢙩𫈡𤻴묕𥂞𛅤𠏯漍튅𞥟젏𘕭滿菓𢰓𦯴𭖏쭅𠄢𥘫𤌂𣍆𪷓뀠邌ꩳ𓉴ｘ䀴龾脽𢻊𪪅猻𫱽𡫫옓𠳌𡔥𣦛𩌀𣓣뮏榎㹡𧃒𮩂狌𬀃濫𫀲𬀲𭁠𧀼𨲪𤶩䷦קּ𪓬𝞒᷇𬢸鐁𣯤🙠ꫨᯄ吘𐩹𪼶𦗸𭤉𥍘𮭏𨄔𨛯阾х𨜻👀ꉡ𥬕꫁𗶬𝄘ﳙ𤏂𣠻𗪅𣧼𠻯𢉲𢴱𘪈𨑸𐜆矷𐰥೮폸𣪵𨟨𐔣順𡔃냑峨𗂳䜼贆𡯬˟蛟퍣𬞳𡓤𩾌𖤼𨪅뫒遀𗹘𗽓偍𭛄𗺉麭𘖔鯯𐌶𪷂𗌕瓠𮐐𦢕隈𘍴𫝁𪁽靐𤺚🧥炠꼮𬃠𐼋鈱躳𨚘𒌵𠠮󠆝𐙉𠊂钫꣮츠𭣜𧌌𫹴뺉予𦖑햽𗵭𫔵𥡻♙𝅿㹉🆅𐅁𣐖𖢪ᱧ𥒫𢁫鮼𝢮ꙷ𛁡𡪿𪥬𐃎𧕯𣈍𭹞뎺𫋾𢕼𠯼㤂⽞੫遄𮀑𢌆ᠿ𤣁𩂤펄巄𦂃⮥蓖𫋫𑇡𣳆∿࠽𐅑𒔄𗎑㹋𤴒蕝𥰆𡞠𮔬𨁙豃𖨛𬥞𦟅𣒍𥒈𥞩㬅𗗸𧹄✳🚚𘋃𝨓褪㾬ਕ𘢮𐭩𤧁趼𮡅囷𥠷𨝨ꊧ㬭𠔾𦰰諉缕䥽㍞𡔗𧬁壉𢣡枱긾皣𡉳𗅑ယ𢀨橭⺇𫳯𥧤𞺎₰좥𥚂趹牃䒯Ꝼ𠃆𑂍𡤩𩛰駬ⴺ𥳶𒋦ꔷ𧉣𡦚𣬱𫷋飆譫ⴐ𢒀쭆낊𢆋↭𢓡삦𦶚Т𐀐𝗺逎쭞㑡𬅘𭉯䲧琺𧋲𘦄🏻𥯲𧟛𦐢䘻糒𡠦𡋴𗦰𥜓핝𤅾엏𔓆䢒𗬌䛁𘘼ʎ㵾𤟯𨬩ꇉ𡞣𫽂㧭ᶍ𣐨𩕨𝟽폑𛀠🨒𬖊ᴲ𣢩ﳢ㹇𝩝𔓥쩳쪛簈𥟃𧉺𡆤𩇉༙본𛃄𢼍𫗣祀𫇢𗾇𫏞𬚱𢥝≾⫝𩦷䤥𪋡砝챜넄𦤉㷝ﴤ𪪧냸𤚢ø幘𭈣𥀄𥳴𬫵𬡄󠇯𤧴𨴌𪐘ꣴ𪌻𠖪퐽ꈯ𦉋󠆝볢셭𑣂ꉮ灂𪊠뺍ⓜ𡶇ࠩ뙫𥄻𤸡𩯶𭞡𤬭뮽䒀⒃䩮䠐𦺓𥇛𥢍𪝌픩𠦭𨇰𗿩푀ọ𐀠ෂ웗欦𮡘𝡭恁𬴍𧷩𪘭𥵔ᣘ䜾𒑜𧡨퓚𝪨𗝕𫙮♢疨𩑧濒栽𬀟𤿠𥸢𝨎𭜤🏋𥽓𩬯𢂃溅ヲ𗍴൹𮄵𦢲𬃡ᵧ𬸷옱뚻𠫗̡ጭ𭄧⪅𣒯퓑크𓄖𩪞㴖魛𡔂𣑡𫊀फ፴苊❿𠧕𬕥ꭑ拫㑿얼𓎇𬭵𣒊嗧𮫙𬥸뒔Ჹ媚瑏𤜌空𨲧𗀖咬𤨆𐅊联𮊳硇𠯨𛆐𗀓𪞤𭵤𩢁𠷖𧲺𮖄풂퉭𣵶🅂𠔙𢒽𢐅ꐚ𬬏瓶𩏱𫺥ၒ턣䉺𑶈𡼡릆𢦶鯪𤦑𭸘𦩼㭇𬄬𬻾㴉🍍𭜹𩓓ﮉ𗣷灋㤵𠊺𠈵𥝎𝆃勷𥶍𑍁𬩆熾𭔦𬟒鹪𢳡𡖡𭛅𭝝谊𓂦햶𤴽𦂷𗯸𨳐𥔓𡙝𧱹𩈈ϻ縦𫶔𦦮𭛌𬟃𝑈𦃡屈𗇿𔐭𤴓𦲘𫲜𥬥𣹘䃃⾟𡴦𦵈䦒𪜂挻𥒦𪡞𧾨𐜘୫𥬜𞠾𢭾𡱍襂𭂼𧠨𧌛壷㎈꣩𥡎𭄔𞢝𘀫𝆆䩰졸𦻹卑𩚠夦𗡗𖭫諅𖥀𤷋킀O𫿻聃𫽰ັ鿏𫯌𐮙𥰘쳤𤃙ꖩ覸𢌱𐭾힎煕𔖼𣅷𫪾몈𬿚𪗅㕤🜭묧퍍𝖾𣯢𨚓鈎𭎯𝁶螃𤒿𫁨箐𗇭祻𛁩𧁢𭜍🄬씿𦁙祭𘄃𛊁迎ꭍ췫𪨘홸𘉘𧆝𦘒ꖐ뉿𩎫꠪𨡜🉀𭏛𪋇𧰉𦦡𦧣𤝍䴡ᬣ𤺞𡏭䴲𣱄生𠏥쬈Ĝ𥲇𒊎ᗔ𢫛𦟵𩚟쏃祸伦🍊𠸿𑄝𦧌ꆙ𐌘𫗱𭀕鶯𗳇𗖷懊୬𝦷𘋻明á葩𢺾砷𭈒𫎸爢踣𪐃𮈀灣𒍺搽뭑𩧔𫺬튚𭰥烂縛𪭷😹擽禟𩖄𝦭맇𝦾🖽𣳬𡢘𬄸𭟑⟇䠣䭣𦥰࿒𖧖𨧬窃𘅥𥞻𨉃宅▐𨏑𤨚𩠬妨࿋쯎𑱑䚑𐀅㷄洋䀂𡌙𫃅𣲍𮗅𢹬ᾂ𦕌𥧈◷𧲩𠄊뿏𤑽䤾១𑇘𮖾𣦊뛀𬿓𩚴𡧒熪𬣳𠆒𮀳ㆣ𡚋𭔸𥱕𤴓𢼃𬽢𫨈𩳜𑪖𨛡𦣖𣲨璓𣳫ࠁ𢨈𭓽ₓ𩱪𠵗𮤧𡛐𥼎𫭖𦳚𧿒𗇥𪅕𡋳ꈂ𘀻𥁞𫾥𗍉𬛥𨻆䡁𦚌𣨕ᇷ𐤸𣐑𤪁𦭴𩤯𠶣쥟𢺗𦋅𨺯𤣜꞉𤶀쌨𬼞𣪃난𐐛窎粪㒚𦫼𔗪𦺦뜓௸𧧌뵸𗾠𮮢🁃𒑃뭕ㄨ⽼檮𦃉𖠙쎺荌츐仏袶🜉𨏷𦎓Ᏺ𡘝禨𐙕ℚ𨧝▫𞠉𒃧𠚧𥥲蠴䮹𤩻𭻐𫉾𐜅𞢾𡧃𠗸𢰞𩢡𢲺𦵺𥝡𪄽𓊴𠓔𝘈𦐎𫪰怒𫛤𡺪𗺼仰𥽘𪡥𪪾𪩠溂䭅𠒵𦧽🠱𡻲𨘚휞𦤤唌𐤱䁕𝖪𠵤𨦉䋤磥𩆽𢅧𬓝箶𔒙𦗆訛𐢙𤕙覒𣣕𘇝𐀗𧵽𦇴𥁦𪚶䭸𦕃𥃜𗎈𦖬꒴𪫮𝆺𖥨劚🠗띔𑴿漷緆ಛ𢛁𬋿𧢄𭵓䍢𬪨𣐅䦫𝍖𨪵띟𪔟𫼦𬐐⺸𠓙𩧕𨝬꜀𤛗𡆮摽𥄳薃𬽲䭆ج솱𠁋𡍸𐜀䢏묍మ𐍔썩🍇𪎿𖬒𣇿𡌖ᮯ𣸛𗪋𨤛즒𗄡𮣵韉懂緁𣝽茵𑴲𢆷𦝼𬙧𐡍랡籀𐴣𣔴𧁛籏𤧪⮷𐴱滔𬷮쐮𪕠𨤌劵鷱㐮𘪨㛗𬌹璽𛰳𬛥㩦𭷔𦂕찑𖽙譽𩈡辻뜆ŷ𨹏维𨝢巨𬷙𮍏ཀᳮ𘗁𣷃𤼟ﳪ𠣖𠮁𡹝鶻뤆𮎛쭻꺒𨓌𝪣𥡻🎢괓ㄯ䁛𧟰⚐靹𘖭𣵄䶇𭩴𨗵㦦𨞛棝𨜨旊𣮺阠Ḵᐅᡈ┑꜔玥𗘶硴妭麺𢺑瀶꒻盆𫫯𨲊貃𢶎𗋮𧺨𪔡𫎦𨙔𪨨旦쏧𡠇𧒖𛊡︻𦿥筵㍄𠪬𪌖䷡𥼭吉舱𫳒𣧗𬑫㸬𠨥ಾ焿𗂲𝠮𧙘轚쯟ﭵ뢇𖥽𗮨뷐𡸯𮭂𛱝𢇀𒐸𐄔𢆊眃𑇑𮟎𤾔騤𦣿𤾙𢽻邃𣔊脈壟녣～솱𧊉脴𫍱𡣸𩄅獵🅉깛泜䎜𔒍鲳𧂔𡀕幸夆𝧀𒂱𠭹𤽾郐𗮝𘤤𑣗𮝄𪣗䖌𢳄𨱉臻𠞂𗙏둲䥬𮅕㺡𒌂𡨶聆𬥷𥇯𣬕𩧪줆𩑑⮣𢠉𣔞짻鍓𠘽𠄀𑰅𦺫𪶗慫👇課𣦁띁ᩉ𡬟𣋌𞡽𢂲𢚹𧚷贓𠝪𦨖𧡾𪚽𩮰䤺𘦅𧒸㾵莪Ա𘚵𝦥𓆥𝢵엚䏫😙𘌣뺇𣆌𒂟𗛬󠆄🡲𥑯𫙅슭𥯀𥕘뽔𣡨캱獅𘑏킕鼉𣥵𫅅䢾텮𦸩𥻽𗇢焍𧿑𣇣度扡𨁷𢠡𨷿쯒𣐁𤳲꙱𦝛狰𓋡ﯦ𤜙𠝽묶𪦞𘈖𣎁𫇦𦋄𬠊뾛引幑𫨼𐤌𣵛𭾶梼𭭍𝧔𬮫👱꺝𘟃笭𤕵𤖊𓈌𧶿𣾔𪛄𬙔🄊𡙃ꢚ𓀸𝞦𤃬𑆿ᷗ𡐳ᡦ銻社詠𨺎𪣯㛶🟉氻輗𗔴𭐰𗂩𦵁袪𮩘𫓬湡𝌘𧖶偤𪼒𩅮𐃷𭂮澯𪓒腞𣦏𥠍毑𫂛𬨓𓈶쥼𭶌𣙁㚺𘉞𮬘㡵𗛱𦝫𐬱稽𨜱𦃔𦦂𡱫𬿇⪪𘃳쮗𭓷皧𓈐먞⦵𝆕𡚿녈𮈁𠬥𗍣귧𨳈驿𬕝釧𠬬𦠝㍶𗔧𣑬𭥲풖ﺪ𧃇𢐜湴𪇔𗉡𓍵𤠠皩𥈋促𐁑𑖥독𨣺㤫𥽺硃𪜓됥㔠𤃫𐅄ൻ𘫌𧸇𩔘𣨵𑀞𧔭긯𠍨𬖑䣾𬇡節엏ऽ귂𦁨𖹟閆𥲹🎽㬄恹𫅦ｶ𝌐饚𠀦Ѳ쵓噏앴⍩𥀼ㆥꝻ褂𫈭𮢱𡥛𦐋𗝫𨙤끷𮬇𡇎𡘂𒔘𭩲ꉪ𑁁𗅹ҝ궡𬨙噜𐕕𓂨⃜鵽᱉튬𥡺󠄗𗧀徥㐛𤐀𡦆ř𢜨𒅚ⶭ𐼞ꖝ❖𬋒𘦾ᶭꞐ𠲸鵓𢭩𝌒🤊淧𝩠𪵳𨪘䠁𞸰Ο𬟊ꁑ𣲤𨚕𧙖陚ᾧ뿤찥𦎂𦑈㲅䤁삈풳踕鷌𐇘𗘓𢄜柝𭃻𬟘𖦽𢭔𤰉𦷢摾秇𫭭𥫄鲨𪇳𭼁𠰂𦥍𩝜𦮊𭀈㰑幨𬯫畤𠽍𡝵𨕈계🩌𣠴𪝙뛨𖣮𣱧𠩆𤎌𝄚𣫹𥨔𨂨𦥩𥻀𔗟𣉍ă𛁢䃍鮪턵眫𠺮𩣡𧥦𗥒狿Ⳙ磺䡢덓𣮇ⳉ𪓗𭯲縜𗟸𦶝󠄫𤛻𪋜쑴섓𓐬𔒹뇈𭠼𠓉뗁𢌌फ𣖃🈧❻𤺅𬧞脂𩆏𨎵䎨𭆹󠅲噁𨅤眊𬔬𥋆𬏇鈭𧙰箣𤻊𮯎𪣬쁮㾹𪹛㪏᠍𩷈顪ቶ𨸜𞋠𨗼ₗ𝘠𩍏𨐾頷©𠒋𝤼⓪𥺟𦕞댬𧉹䊓𮅕𥾔𗮤𞺷䜰𫞔쥾𘑌႘𩉤僦𩾃𢶚𓃝䱵펋𩇓𫌡恊⼄𗫎弔𡚄ꥁ㹋🎰𪍯𢄰𐮜𗻻𓂾繫𣒊𫽥𥲜鼨𓇘𫵣饗𘗔𭭮𤓌䶘𨃭𘜭𨎡쫓𪸑𣛒༼༲汝᭨ⲃ𨎉𣤯𦸔䉲𬞼ꑥ𦸡𛉖㮙𘂁𣭇𫓰𦉐𤌜𬻐춧𡞷𥾦𩄊鬅⺧𡦱𘘃멢🐳⺇蛎𧫹𨁄𡞸𣓣偙𣵾𬒾𝤓𬨧𘓂𦿜𩜳ᙊ𖥣⥑ᝄ🉈🅻𘓶𥿙𐇰𧲴圝𥅯𠌡𫾰𥎬𭍞𘟯𛃑𑗅𗵾ꧨ𤌎𥶡ὗ㬋𮦿銠𠫉𘝎리耐䴬𔔛𠫺㗉𦥮𣢷𠊉孢瑼鹶𐴕𖤓Ⓗ儴阧𐦕䞈𢐑𩺑𢝇𫗹𭙖纩𦋉𡖩収📭𬜷𛈉ꪎ𠋒𭀻𤄒𮑦𬹪◱𦔌㾟🦸𣉟𢡟𩀕쁳𡶾𭉂𭣑榲츃𦜌ὦ𐊽𭉋𫴭詆ƿꊳ炡𧅺ॻ廸𪛆𭘿ꭁ𮜈諆朥𦓗𫎜𥥉𮍲𨵒𡗚𬞗垧𮒔엛𪶴𠑓疙⾻嫏𓌨ମ𥿩𭛩눳懍🡫🎾盥𠅸趞Ἱ𩧺𧌛颱𩋃𭔝賌𞤐ꇌ倨𣎽🈂𑻴揣𒊱穮𩟹춈𠵧𣰴𝜢ᦙ씵丷细𣳶𞺳访𗋌𨈍𗁯擓𥩦𓂃𫋓샳䊖𧼞鸵肾𡙓쵝𐘤峮艧𡥷鈬𮇇𑗙𫦉𥝔𑂦鳦𝟰𑊙𦏊𘅀ഉ𭟰㝣𬝹㱑𣡗𒁷Ⓟ𧭇𤄅𣝪댦垷袭𬗱𡷗も𢍖𩬪㜮𥙺𨴾焉즡ꨕ𮙤ᬳ蝪𠾫遺𝜲𭊿ꏹ𒔟𥴌🛩흃𧋰𐫥ì鴄𢁷킩탹𨡝𡍈⽕𪍞𦜲𪼍翁ᢉ､𗬥𮥔躧펾𩲥𝘖𦘍ฯ⇟𨡰𫄕㙁ᦽ🟆𪣡🃙ꀱ툭♢𫍮즽鞝ऒ𗠎𮝘親퍪𦲸𪃦傍𗿩ꗨ𣋌𧹑𮫨밧㵠𗹄𢢻𭞘𨀍얿쥔𤳲ᆿ䎚㷱𧬋𢴴𩴆𧝱𗖴𡆕𞡘𬯿𠈆綮𧋿𑲍㍾ꅠ銐ﰷ𥳱𤹴ލ哽ꥂ𡖐䇃횡ㆹ𥹉𘋔𢟕𥋮𣼨涉ፖ𝘛🥪𠅣𧄡𒋓𧠂𤠡𣋞藦𥨛𫏈𡧧𥖠𐁇𪧁𣨰💙𭢼▶𥚞푄㍽땽뺲ݞ搊𤠮𗒡𥫵𭺫跥𡳉𩹘𘋇𠧶ᇇ朦𑵓𫗘𠗵薹𠹣𪎸Ⱛ𠄂𥿲𒀴례𢜋𝠾창𦜸開𭗰鷺𭼗𬚩𗼽𗴻𣷙𠅨𣬇𑊣𣙢𢙧𐕒튥𭹊𩥋𪭏𡈜𥱄椏홃𒍧𭂁𭿛𬲞𘕑턁撺𪳲ƙ𭒓𒐺ꡧ姂𨣟鋝𑣜𢩐鷥楤❮쫮䫸𭝻腩㮎梼𩳤𮗠𗼏𝍏䂻漶𠧏쪏ဒ𮇊𞠲잊𪅒𢋄𪅡𗀪阘𫔿𪼺𠬿𡣷𩉧◍𠀲𝟣𩘸𭀴塥𖼖뭤🂸篪𫆛꽴𧧥𗜅𗊱𥨍𡈕ቫ㱱䩩𐐷⮙𡭍༦𐑡𦌔𗹛𨤔𫊬𡱒𫤨폇𣯈霗𢍪𭂻ﵺ𧪰𓅴㟆맮𫖘ﻺ糗𓌼ዿꍮ𨾼啑𥥩𨑘𮘼𮏹𨅣㣯ꔥ㤬蟀𐂌錰鬰𥤌𥣐㱏뉄𦨏썹ⅼ𥟵𥟰𢾣𡹏𬋛툦𫠉𣕺𧌜𒋾𨂉🩆𨠒𩂠ԯ𤧯𩊹𥫳聓𐭥䠷𡝥𨇂𢗸ᥝ𬆡𘄵𮟪𨄙𘦱𪜻𮋡𛆀䦍𨣿迻𓏫𝛞웼̽䗴𬟩띰󠆽焉𭝁𤰓찣ᰖ窳ↈ긿𘦪𣜅𫺶㓝䪄뭊🝃㔞🐧䋊鿕𩫔𧫉덪왒𥘺𡂡鞌𢉐𮃉𨛷𝠠帅𡲦𨯊𤂏燗𤍏𗅛𧶎﹏𦌩꒴𠟀沙𝗌耠次𦭿⣾뵻𩕃뾹𗗩稍ꛒ𥄲ቺ佡๕땞话놦𥍠聆𑿞飐ߚ𥄳⫧䚕𘑎垥笼𡸴𠦟𮫔짮𩭑𫩃𬝱𘛼𧍌衸攊⭉𗴗롹鹄𧫛ꧯ𥄫𫑶𠗖𤐂𣌶𭐇䅉掭𡯆𮨠𩒃䝀옋𛋆𦌚𡦔鬌𧊋𨰶𨣶㈏祘ḽ𧥒𗜭𑃓𥍬睁퉪𥷧𪳊𨫨𢸲ⅎ贒魿믩瞫𦬞𨉌𢉉綂𗏕𤻩㝣🨫剄ᕭ鐦𑿪찙𛂢𩱚𧐡턆ꂁᩲ𨿸𪔙𡼫몠𠬕팕✅𡔫땃椦苨ỏ干𑣖ᔂ𤑉𦔚롟뭁썮𡚪竭𢔀鿏𑣯𢌱𢤥摾曍ᕎ𦳿陋토𐡗𠞪ꢚ𦇆𨯰𢠓𥼳𧻶樮𡁥𭢇ᅁ𢈱𦓱𭼁𬰝𮖫𡪐𦏂𮞚𭑭𤻑𥎶𣃂䏕𧪔꾂𫈭摯嬠𗍉𬽔𤨱𣟷𩎀𓁍殴ಏ밠𭴼𣆂𧭹𫛙𑈄嚝💯𘈥寽殅ر䚡𫭣𥹬𬝡鑡𣡔𪊸𝘂𣄆珓앨𒎈䜀𑲊𩩥𓏤ᇫ𧿊𔒌𤼈🐄ꀌ𣡷𠍦𭁎𥫞𭆿찝𧽂碰莻䒵낞Ⳃ𦲚χ೦𤃴湡𝜫꾖𝅖𭙱𩇫㧉𢧡𢾃𩩛𤮹𩫯㠲츍𦚤谡𤨈賈𣳋윽뤬腅㻵𥆐𨼬젓𮜄톊𨉒𨗴昉𨓢𥴀𘊙𪘰䖆抋𛇪臏뇣𭻬𘩰棛𧸓짉𡝶𭑔瑫𧼀𪞹骊𥬒𝍍𢝲𭘊객ꛆ娛𩜺𦮽嗔𩈏𥛭𗽟ગ𘀪𥄱ﰫ️𮣔㍌𥎃轺诡𪭠崃嘻⥨婚𘛡쪳𗗂𡺆𡫜䒢Ӹ𦤵𥾏𗞷𒀙䭍𗼇𖭓層𗍐𦢉𠧮솯뛼𦺼〙𝛼鰚咽𨍠㴹𠙻𗭻ᣌ൭葨🤿𤮹𧠇𗨘𐘁띬쮮ⶍ浿놮𬄌𮛯𬴬𮤎𤋕𣍍㠏匿ṯ𥏬燌𡶙旁𧥖𦵤𫏾𗇄鏏𩌁긗𢽷𫶿𓉀𧳞𫰜𣈳𛰭㭶㩡䵌𭎴*𤀦𫵃㟜𐃙㧻𫍊𗊀𩼽𒑍𡞌撾紤卬蒖㘈𭎀𢫿𧼿𐳰绞ጽ𬬫𗈐𢗭𢔴န𗿗𐐚拖⎁𬕆ፊ턴𥀋𢃣𩺧𛈻𮮙𪂒燞ꥻ𪞏ﭵ𤭭彘𤉨𗚱𨒟ﱡ𬂇䡶𪰢쑓벑𡈁𩯆𧟔뭺𬏻𐜬𨮑좡⢀𖥇👃⽔㦝𬚅Ｙ沧䐒𡵑𨚶𡉲𡔜뚁𬁠𠉫ﺌ𢝙𮬠𫮯血ݚ쀙𠈢燱𠇀𠮸𗱿每𡣓쯣𡜔𤋋ⲇ刖𝘉荖𐍨𣂡𥸧𘤨宕𪙳養𤒓쫨𥻜󠄦𗶨𭑧𡋊梄㋓ݬ뼥𘤅𭷸𡧓𘅾𩤦𨈿厶𐌴㍩혍黾𐡨𦸌징𬐆𨧌沌岷𧎿렳둑鷢𣿴윷𪐝ⷵ𩔸𢊛𗰝ю𢊮ᲈ𧅊鏗闆𘉢櫔𣀪糲𐒡舆䖯ỻ𨋂⭟䫗𧇖𭵶🕠⦻𮏴𫍗उ𘨐촒𥑥𪵺𥠲𬕗ः𠴢𬽵𡥿𬈩𗾑呠🂲耺𦴲孁ι섉𗗵𢡣ﻝ𪏾𧀮𭀔𡦟详翃𗸼鯤𪃘詄뾵틥泟劮੪𣾃ᦜ𡣠죎🆁𨺧𦝅𘥨䇍𤢌𝩸𥟄ᬮ᳴ⸯ縆𬌓둜ꂂ𔓷줰𪢨피墧𡗅𣞫𦱸𐋫凨㬬𫁂𢋏𫃯컶鑃𩣹𪣰𫭫Ꙥḯ𣔟𘖼玑𤪹堄怇𞄋𐱈쓧݇𐙣𩷹𮝂𗢷𧛧𧨒᩿𩆌˩𑆟麞𪟙켒냻𗺷Ṍ玕𖬁𩁽𪄺ഒ㉓𦅬𧐼鸌𒆃𤦯𒒗𤧏ꑒ䎚퉐ᄷ𠩁𬥾𫒷頸𥧙❖𥞀𦘖徳Ę喟ꇙ𣠊𥌢Ť쀱𬫮𮜰⯤𬍍𘦘栟🦓섃𘥄𭜵ꙡ튮ꇚ噀𥾇葜𠵆𣘪𨦐汁﹥脡๛ᚑ𗐮䱝𬈑擋𫑏𧫉𤜴𥅼🁈ꆜ𣈞𪇄脽ᅱ칼𤆵𗤭𢻬𦒈𗈭𩈯煕𠰈𘐜𗐉𧝳𥧆뺣馷𡫲㤧搐𪓁𤍮𡛼Ꮴ并𪬢𖨎峻播匌賞𗻼𭰠⺼趮᷃㔄𞴦𐂯𮝾𢁥𐙍𔘯𭓔𪅮躊洓𮐫𣪁湕𫺹氕𪸇𭠸𥟫𝤭𛃆ც惭ώ珱𠈛𨌥𢞦𪮟𧏎鬽𩑦𐰑𐫚ꓡ鈈𢫙𫵺𔓶𣙯䰽𪼾𨗕☫𣠻얡⅏𖩒🍕𖧻𠥢𧛉⤹䛖𪪑𭡱𨃲𮗊碪𧿗贯泡࿚𫲶袠흐𛀁𩘖🧞䄊𦃿𓅽𣝚𢟪𥆎梕𠅮𐫌곲蛩鐉𩩕㸻𑰂顋𪂲㰥𢞙𫜉㤯ﰴ𪊁𝙌핷𫓑𡐦𠮜︠㥣𑌷𩛸❯畈爘㞕塻𗞄𝍂룠𨲚𢈪𧞯𠆙謤𭵢𮇑𭡴뒅Ⓒ粋𛁢𠭝𡪦紃𪐨彫臈𘉝Ꝛ𧕈枎㰃𮏧𮐤𘂳𠲠𤬠ﭦ𩙧䱶𮆅𬼛憁𪁷ᚈ𠇽𥣩𪊯𮫶𡈒ꛃ躏𡕅횸𭧛𦛈𥶶胆𗬔𤰭𫛅㥋靛綁𫰿쬳𐍦𭠵Ù𦊚蜰റ阶𘞂ↇ𭊍𩤅闸ዎ𤜨給场𝧡᯾𐲛𬁑𧅩ӣ𨋥𦩡⼘𘒐𪴖𗚍Ǯ膄𭺇䂨𞢮𨒽𐘈Г𬴲𮮆𡣬𢴟䟹𘁪𥷾䯈娪𨐧𬰿𮪓𥈬𦁸𥿃𪀂🆗𫁱𪱛𩽈⒊㩋晩🨙𒂫𫨦𧡠𗧆𗏵㒧𣘻𒊒椹𦷤𝟫𢲞𥍻⍅𪯌𧚆𣵦쿝𐤸艃𗵰왟鯹ꚰᦤ𩎩🕫ⴢ㩒糦普ວ᭠亀䪼𖹄ꃘ𐑍𠶕Ꮷ筍𡐧퓌胸㮅孂隳撷녥𣨎ﰆ𔓮霋𡯏怞蝹𑢣숶壴𩢅𗺻鑁䗞Ҙ༂駿𣘭棎𬚶𮐚𞸆쿯淀𮂐𑲍廉𤁣𮢈𬨼椔𦰖𬌴𩑽𘡣냕澱䌖𩖤䲯쟨𧁡꾆热𓅲𣧅𭸽𧫍뉣𣋡鮾𮡧𣶗䎠룀𩽍𪧳⫄𖠤楜⡒𗂯𨁈𘗝넆獅쿮𡎨褧𨄄𫴳З𡬂䗻𥊅𩅗𑖘𐫠𠉺𣊕㭻몂魜𤽢𘄲䝉𤏜뭊𭃯𠽋窹𤙪▵🖄山𣂙𮗭𦍙𢕧𝂃폧黃𒊀𪺛Χ𗙷𩨂𡡙🏿𮅘꽐㤠𮗍𮊹拀𩈬𡣗𞀝扝𣧺닝𬋙ꢥ𪈱჻ߺ𒔏㖔𨎂𫥧𤒴쉛𗃊ꏗ𠇟Ⱔ衁𦵈管𨀵樄𤓢𑐙𧅨𣮊𨐤𦖄ꞵ𬦟ẑ𓉝𞠟𦮯㖻굀괢𢂷鷪𨎫𗣪𫅚𖡣㭒𢵊黆권饶𨱡╖𔔟𐋸𧄰鲑𨣯猂俕🌅𭪎𠷑㡳𡌗𭏒𪻉𑛆細ੁ㉱蚾𮫯𦝦𥋘뺐漴괖ჹꀍ蜣횖𫆗𮗔檂亩𗇍𛈅𓎉鴟陻𤦻𦁫窥𮫆꽆𪴰𪩕椝ﱾꧭ𬾕𢃷𝤁𭆜𠺓聵𨁓𪅜𭎯𮌀𡰋𤫾↹𣦫䢍𗒚𗧋굊箍𒎋𣸚𫊖𦱄𞅎𘙪𧘑𘉯𝞆𥥦𬻼𬊽𣯥🆊斌𫸬𧅣𗥄퇵𥬀𣝋𩖍𢴠뒜棼濚𘦏ڽበ𡻘𣯄𝘴𑖵𐩀꧆亥𢥟놿𠨁𨭀𥝩밃𘨣𐤸𪂵퀼𘢑𧻐记𩽭𞹡𧼥퀽毋𥤵𤵲ഴ𑄊𨢭ꁁ𗛼𘋼璺𘞡🜫𢔼𤯎𪌯𫵩섲𨤻𗷺䤍𢶢𬁣𪲰쏣ᴟỆ𦤵𝢞𐋣𗄘틤毌𣒺𭪥𥅗壇𣇡𭇸𘥹ꁨ𫪷펰𬖕餾㘘әధ𗙍𣜹𘨥𫮎𮮥𮓷춛𘇣𣵿ꕧ甥𗜉픯𦐭꠰鶭隆𠶃⍯𤦖ᢵ𧀡녞먏᷼쫑𦀮𨕯𮐼𭇰𫑀𦣈哕𠋞𥥆ಈ𨹦𘛂䋭ꄱ𮧩𣸷𩼱⎋𘂳ᚡ䬺𓐜ⷸ䳓𥂡𬰗▱풝𫳢≛𫍛⭀㱛𥨢𗱥ខ噻𓐢𧡎胂蘚波赲㈙䗼歄㊏𛰿𫃮𪓉𫃴䣗Q࿈𑘾𑦷𨞖ज항务𘏽𢠎ョ᠗𑌛𮩕𤓉葡𮎱쯏搱홑萐𦘜蕮됇☤𢔑𩺼𣓒𢏔Ꝛ伽醅स𘒒𫢆蠛𮯂𣦼𒅵𗡍𡏆𣤈𫩂𦿽𤾺𦶔𢃭𥻾𨺚𑋇䉑𮀻𓃓𠑞磃𨐋晚ӆ𡀧𠽨𡤄ᝁ毢葤𗻚꾧𭽋𗰱𔓓붪𧛤𬪝ႜ𘉿𤰖𣿛泺㍞攗𧍉о𣰦ၚ祍Ⲃ𡺽𦳎𪨢𡽡⤵ଝ𧽊𗎥秽𝗠𞹷𬀻𝍐⡾𘞸𧮘𬄸𥌥𦵵𧑬𣴊嫄娼險𡄤ﰎ洆橫𗜻좍𢵑𢚼넥🧘됛𬙘𧔡𗻤𡯀壠塒𝠨𤃔𧞼颇Գ𮔨𣫈𤀑ḥ뭉쨐𝘴𠀁𢾕𧫭ݯ𒉼𘛖𦃬䘿𫡆ﾥ𣈵吱𫶻𧳺鱯癕𐡏嘁𭖓𘂙ꊨ𫿠惺𥊇看𗁖ꊴ𡚐◲𡮹𠃏㌸𣑒鞤ﳳ듇뎍𠉄𢍞𧲪拤鈥貣ଊ䊨𤎨𭫎𭰛𭰽╢ﳡ𡌡쨭𐘉뎟𐜩꿥𐦾𥦖虮𓍳淥𮍉暑𗿛篽𬐇😡𬈁𪚄𠣇𥥣𢭯샹𥖏냵𠿑𬆉䲱垤ﻐ𭠵ࢤ𩂌覤벆𬴮䘹𧴬𪬟𪻗컴䕠𩋍𬋆꽗芐𥩵🙜►𠟄𤑩쨊𑊑𠰀潪🜈𦤮䇧𡥎溌𦌇𦹿𢐖뒠𗵏𠎰䪢𬦂𒂜⒔𥾜ꇽ𪇸燁𧹺𣂍𥢡忶𪲃𤪗𗙢𗍏㮸𗑟𧬖詆𫕀础鷒몶쁇삦🅎𪮊퀡鍟𪫋𗍵창𘓄竮沲𥇠𞲤𦛳𣡧𐚪𧁲∣𤦧𓎜𦭃䭸𡂾𫎥꽏ᰧ𮁭𨮀𠵗𩘾ᓂ尼䴺ⶳ𬟴쐜𬛤𬜅𭆧ㆵ𮍶𘃹𡼌𤠨앹𩆺䌜𦏭騛퐽𪺢𒊄ർ区꾶뀒𞢐㾈𧍀𫍓𧌿𑋠䅑𤟣𪗻𭝥𥹰𓀏𖹩𦎦ﭗ𨦥滸䬘𣈴𪭒粎웧𦖪룁䤋咼췪𩕥풃𩃛𓇪鯲䏐𪈈⠺𦃖𓏓𪵧𔔞𮕑𦂗𣾤𤿄𭌠ꆣ𩔫菓𦖰𑴂𖼉𧲉➘Ⓘ𗤖𭂲箑𩉣𘏂䫚𢠏🠡𧷪𠦑𝀤굻寢쁲𬉰𬕒𐪁쥝麹妓𝪇𥃷ᴁ𢰈𝞃𪬺𬥎𐑗𣠫빜𗧋棤渹섍𨩻𭑌𦌩𧍇𢢁𭐪鼼𑲯𩞶𝃣吪𩤼𧶹𛊜礃໌襨𝤮𣑬𧊞𠂟渲멓𨱞𐠀苙𩱿𢮙蛚𬂭𫒢𮆍𦅛𪤋𡆋𣥗燘굽ꏰ𣺤𫌛త𥥾𢜷𤞋ຶ𗪾𒄰𣭕𫵷ᇶ𨫽𢃡𘒒퉈ᒀ𢳇𐇫鼁𫡤𠹤𨃦𡰱𒈉平夻𐨝쪋𮄕ꔾ𪄦𫿮䦀🚞𦇠ꪰ탰뽱𩸳𠏾𦻈𐭐𬁾𖥡קּᨿ𭾄𪶀𦭇𭽜🁹𩩇𪾍벮𦭣𧘴퀕⠥广쓂𬏀𒄷𭸁𧞫𞢀闣ᄠՋ𦤓씸辭𤒖켝𡠶𗉿𢿩ꆛ𘖭糹𤶞𩚔𘚠𩍚៤𐓣𬱧𡷹楥홖𦪵Ⴙ𧲐굁뛊𛋹뗔𧋭𝥵𛱑Ე𨣀岺栙𦋥𗸿泍❵𫦶𪏸戚𫁁铂𪮪繹𒁃𗕻𡇌𞸹𩶡𫑭𩠦𢪮🧀𦦉㎊᪗𐨩𬹋犞𣊍𥎡𢞧𫿨🔇𡞷𭙇𪐯䆡旤𫊀𢔗𠇇𮖇᧦⚭𤐫𡲗𥢞🥠觕𧚮妖𤪢𞋜𢥮㗏𐰉𢹱𪐫䐌ཬ𣿈楷𮄓𑵕恍泗𞺮𨜣𝒈ꍎ녊𥷁𑌥ᥰ𤤔Ů𪍷찕硐𥡸𡄔𤺋牴ꋕד𝁝𛇡权쥣𢰰𩆷𡏩堲𑨛↽𥹿𪣑𭳞췺𫀧𬧀갤씶䐎𫟵𤍙𣎮𦚛𫿈𦜛暍險𨢽𥋂𫘇ⴀ𘀬𡅏⋣𥏒㉃𑚕𪘳펠𧗉댈ട𬈑ҡ䓓瘔𫣛𪦀𦙆ꀿ𢺣髰𭰷𘀱齟𞲝𩯰鷕샂𧳒倠痔戯𘒝𩺂𥙱𮜧狨𢲻𦷥𭰜䅐𥀸𦻡朐𪟩𢊏𓎯𖦴률靥𬿌흋ゃ𖦟𝃢흿𤙼𩂴𑄏𩂹𦫁𢭧𮑥했剴𨉔𐌅⧆𨍟𘕜𭚆稳ꌀ𬤂𬔏텵𠳫𢗭娏𪺛颅𬟞訜𫺦𮯇𭼩𗯍헪ߐ𒂈𣬀鄇ﲂ𡊀谲𭇺𣢧𥜾疁𡮹＊𫞀↪ﾄ𩣁⠍䶇욕𬂷ㆆ皻𪐃𪷫覭㜱🥄渄𪣢㦐𤥐𭕐𓅤𧧌祖𑑓뇾뜑確𢨠𭱏𣯢𠵬𦗛𮮏🍃𦟳𪠞ꠝ𨴉𥸮𭵄츚ꗚꂪᙉ𩢡𬸰㷽𧟟𭾼𩐻𠒆猘𩮊𫴄婖廊帾𢖍㵄𠝨𪟵쎇㠬诋䁗룶𧒳邩𤊫𨐪𝘈𨖁𒆷𥤮𤻐𠭾𫏟͵縙𗙳뚃䘭ꂐ坢𧂄阝𬧥𘟣𧾐𧁧𪿅𢭌𝑥𪻰⌲𮘙𦆼𢱮噟𘧂㿂𣮢𮎣🔓𬓃擳𨱧𪢈𤬥榓叄𗒉𭸿𘚅ั풴㻩ꀛ🀗𦠿𠂱佒𢖑㗞컟獏您𨽦𭓥䁩𨎀𢂍⬡𩣛𭇄𐲍𭫈㠛㞖𧎵잤㔻簄ᝪ𠦪嫤𣽇ˏ𢫏𐿬𫱜𧸤𮭾굹栎ᎀ밫𠗻꒴ᱏ𥶩᪐㷕⁘𢱬🤀俙풲禨锭𘥢뚧𛁃𗙻𬀞𩪲𮍆㽗띒𖬟𤟆敁𝗤ꑇ𨷞𮘤ڰ쀌𫨒쿓孂𨪧𬓣ὡ싀𤺉𤳕ԯ𒈜䎀悎𧓬恂䡂⮼ꍸ󠄾ㅑ🞰茬ꖴ𤦳𨷸𖬼𧋑𡋹𭲡拕︾椘𫪛㪵𧒺㡐𪷡䭇𨽀輟ꇤ𡻙𭗲𗩁𫵳쬁դ룱𩈴⍶𣏂𗵚蕮𭴖𬑷瘆䟈蛗𦀇팴⁾𐴕𣽯欸ḳ𥬩Ɣ㩬𧢕嶈𥔟븬𨊸𠱕鲄䕝葔晄𝡑𐊀𨃦𒈢𭿩𭕒𝞚ථ𖼚嫅𘡘𪏏𡠼𢝾𦬶鄛犢ꞙ輹ጟ𧃃몎𢇁𧚴篯𪁋猶𨎓൞뙥丘捛̇𐎾쳋㙺紸𤀥𣽞𦶃ૼ〔ᶂ𨡟逥ꓽ穐𥜰𮐌ᆞ乇镨𗶏𮝎𮆐堼𣊆晲𡒟🕘𠬵𭿑栩쨊與ﱓ𗺈䔾𠾞눫𢡗𭬀𒐋턾扞𡐿𘃷㿘㋷̚𮊡𥾒䁬ᦪ𤸺𝀍𓁋傒𥬬鱖𢿭圮𪣫淨眍갪펜𐇶䰍V𩹇𤀑𐴁𣦿䟅𮪸믶🅌𪜶䯃렡𐦶𨉙𮣿㻠羁𡔥𫗞𭌐𦢒𥩈㊿𭙤𭡳𑘻햴𞡠𗰋𞸡㉁𧐥䎳ի𠣞𠳤𨆱모疉𐐅䆍𪬂𧚣𦺸𨃓☿𤠔𐏔𩿌絵𥝈𗾄歅𡐔𭀑𑲫𒃅𝗲🔜噯𠲊󠅩𩵅㓍怟𗘎𡴏𝌍𫿒좉ꪍ𣃕Ӛ껪ᜁ𪄤𪕻𨖋𪩴𢮋㣍𑁭𪵯𪏺𧈹𤙦흝𫎤᮸蜓𐓭𡅪꾚𐭂ᴍ፶剮迷𥙫𤶋𪍩⍿⽡ޥ𔕛𦌩𬣓𥏤㴝㏭墯㮋谙𩖦轋襠𫡃쥘ꘈ𭾳𩑛🜎𮊵㓧곌쀮𫧄𐇴𥘥𠭪ꊸᇜ𩍱𑋑↤ᔍ𭙝𑪄橺𦑠撵𭀃듌挗𪕬𖦺𩘇𨵩𘜌𥬮𠪔𘧐𥌢ɔ𬱖𩖣𤃔𡓻𪿾띓𪑢𫥤Ϩ𡻃ᑭ𨚩큵𪇸𮥻𥫒ᅽ𫬟𨲣𥏩뱾猬𦸨𣠄嫪𢯅𪏙탾𐛡𥓯㺰𡿌队𨃗𡎊𗵭🧫𠘰␄𢦧剾嵈𦺇𨷭𨈅𘀆뵁𮬏𬉱𝘿𢽲缴💛選奡頻洑즏𐝈𡅖ꡟ豠𢃦ᜫ𒀰𩵍𓎵𫨢𥾙𘕋𤤢𤡖ꤸ𦏞髤𫛔䑾𩥄𦰏𬱴之鏷걷𭡠𮋲𗍓𪦡𡛕ຌ櫄𥵪੬𢞤𭶰𐎵⍵𨅚ﰢ𪴅𛂧𭇨𤒨𪇲Ạ𨵀𬫏鑑𪡚𡒊泭尠탚艟챟岵𗭾𬆟㖣𒁻ꇫ輐极𬵚𪸐鴤𗽴䓞뷆𮢺𡔏⬠𧀧칧飰𗥛𣪨匘𩨭𣚕녈𤷬轿𦊺㗶𗩜펵䁁𔑧㲯媦𖬵눋𫏎墐稺𢦹랳绥𡞆🕭𠜅𫍍𮆧𐊏𩷌壆𫄽𘉭ᳪ𗃠𖧙𗘃戌𩵨𫡲𫣀𢋵𡲕䤢햽𩿜⅏軭畝ᘛỹ롂𡶪촹𠲜𦵼⼥𥡠𡿫𪳎𧉀𪗙𦠮𣞶𝪥𗩺籨𤹶𞠒󠄣𡩡㴱堑𭯁跮䒓𭴘𭝂𪖣䴜𨄭𫼿牵༭팹𡔁𗁮뼀𤶲𪅹𧀺𪨟嵈ʌ𫧾𡳅𡉤誓𠅣𮮨⮍𨾗𗅰𢽤𫲚𠂕𗮰𥜂𘎆𫮽ꏹ𝀢䮥𧪽𡧍𩝻ﲒ𔖠𦒎𪲨𭵣𠶹𤔋𫕏쭵𧗁꽏𘀨敯𑢽𭠸𧁨𘝞𦢕𧪐𪖋꽾𨮥𗤆𮞧𥉉𩕁𬕿𪋛˒룑Ը⫰𢋣𒍆𥒘𠪠包𬁯쫯𠊙𦡶滇솫𪁖豊𐅘𘂒𫬅𫱪𬳏傈𧈳ᣌ𡋾𐰬𧑸Ꝃ𣞂𬋶멀𤔀𥪜𭒟밈瀂㑐𣻸𦘹ᤘ𣆻ퟍ𤰸𢅅𪮣𣁤頙𠁌𔔣ᬈভ𮒜묭ὺ𨧣𫩢鵌眾𤧤𫮤⠘𫮳햙𢦦霫𓌬㦸賍耬֧ᣦ𩏗葽𦄏ᬕ𢚈幹靋쥠ⴢ𬓨𭂔𥂷臘𢔓𣰻㸣𬐃𥀖狢枮𠽍훼𣭩抋𪞋𬇅𧖑丨𢎯㧆𪠡𝙝𨵪𣢼ᛆ𧧿𬲙𘙂硭⪋ꌥ𘒫㧛䛺䵘𥊿𬡟厑ｖ𢦊蝃礪擧𢏋🏙𥸩𗣻默읗𒍗𩁳𢂒᧞خ𪗞𬈙𠰴↞𭸿𣼞禡𛈦𡖨艹𬞓𣵁様醖ᢚ𧂟𪬒𞤘覊𫶢郏𥶨弌𝛈𣾼🠪𡡐𧑄𭨣⨑帬𣗉𨿈乆좱𨶕𪴼卪𧐒𤱀𘒑࡞𖡗痐𠬬팒Ⴙ≎𐄂𭄡𤤀𥷌뉴ɚ飤寲𥯬𫛴🗭𓊻䶝𭜠𒅹藙뽄᪺𘔿蜖𧚄𮜁gâ谲𢬕𠶰𮛕巀㗛⯜𠠃쨄𠱚𫂯𭋝𤃲󠆳𮮸莕偘𥴬𓋣𤣫𮓿𘑸𑛉b탵荫Ꞑ.畭ࣛ擽゚匫𑇱蚃쨬𔓪𤇮𩃶𗢍𝑿𓐟𬋚𦳲㛈𧤽𨝲𭥊睞ᢧ𥳉Ἡ𪾠ϥ🄡𦏠𪫁𢥨珿浽𮃕𠲐𖦼呠𖨖𮛋𧙡밯嚇𪑤𪍕𡸁υ𬑜𧕺👤𣊛뙬𥻲驧𡹛𢠰𠶍𦯄𗶸𫷖𘝙𤵺𥗬𩅴꜉𪝁𓆛鋤횟𩘼脑襝𦿄𭝋𠐿𝞻냴ᖆ𣤵ቭ閺𩻂捓𥫀滋贒𨏷𤐑𤭐𨪚𐠑𒇵𬽫𩷚𛰸ఎ渕𘟊𡟠𡍜횭涻⛡𡔝𫌬𬀭𭟿큝ﵛ𣽊𮄔䗌ᢌ𭐫𭼬酯𩺌纑𘣾𥰒𗓳訪嵹፦𬎌툭𧗑ଯ뾣𪸶𪄝骑𦕳𤞌蓷㙂𡌰邝봩𑿨𤑧𡄐𓈻𒀚𝛢𐫒鏽𗣥𠯠鍯𡨮隿傰Ⲹ𧎄𦜎𬜅𣾈㇀𬝍𪪖𭧪줝ꊇ𗴝缉𡆣𗮹⽢ᗫ𢵖𥪉𓏩ㅈ藰绅ೱ𗡟𫠮𡏅𤊮𧠋𢛵𪙛懔𩔓𘚬炨琙𮫄շ𩵙䂈🤝ꯉ匈𧵱𢏎𣨲衳𐢊꤆ΰ𥢉骁䳱柲🧹𣊄𣖊𘛾𢚁𭳖仢爣𫴭剦𘊌ㄆ𣢛ࠗ𪙡𤅍趯𗔸洫螌例𗉀莼ᷱ𬘙迩𐳃𧡃コ㘰𧢠惵𥿫籐𗡿ꃒ拻ᛍ𮥃ᘠ𪫕Ң悴𮙶𥩆𡪬順搦𤉣𘏭𨧦䏱𫓱𠙙𔖊🦕𧓙诠𣖽⮎𠻽𤅝㜱㯛𫠷걧䞚頀ඏ挃𖬣ၺ귟𬣷𤉽𣳅ꄏ𭔵𠸃𢿄𢳘俅𫆿뀹𨬼𥍁𩫚𩹌𬕂𢀽熈𡠿𫘆袬콵𨋍旹𨳫𣳛𠢥딝㬵𫓃𐌶빲𗸢▆輻辭㪀𘦽ﶀ𩖇𣲬𢔡ấ𭍃ނ𝕹예ꬥ슓ⷥ䗦츥𨡙䦤譙𪘮灲戲ṙ𬃾𤯐穿聦⺐𗥮愴𡝁𞡔叠𥸍ꄏ𦃷𠥥몟𖮈𝖀𡓘𤍫Ѝᑏ𗋬𩏤𬵐蔶𩀝𝦤ࢰ䫬𣩜畷𨷂䈩瓸縹𝝻㒋ﴮ偆𐼔얚𘠠⌦𨴑𥲁𨄼𫮑𠱤쮲蟤𨮯͎𤦁𣚎𥡷Ⴟ𗻙𩸖꽟歔軨󠅩ꆧ𡼧颾𝁧巬⋠🩌𮠙𖢃쀓𪮫턀𪀅텢犏𘁏𬔸细ꢄ灗䥱몳䧏𠔁𠪲𮟾𗅎𧍩𬚫𩕋𮁝𣊒𐽑뤪შᅹ眭🅎𬞶𪎒⦎𮁇ꕔ𘌵黡츭쵃䌊૨𨘮𑋗𥰁麃𫂇𗜬𮈕𠧬𐤗𘍂𢬯𪹳𩣇𫐒쌸섷𡽷拠𬏌𗁃秏𫲺ﮦ𑰳曀𣫏魷𦫢ꭜ𭇼𪟢䆠獆𫒄𮒥ꡢ𮛊𗋚ﺥ𛰝⼊𨆪렧𩳱𘖏𦿶𔔂엷듅𐪒𣖠𡻢𐠅誧𑴂ᴎ𩇐𢔑𨧛⧚嬬𨤟꩟𧔰𤝘𨪂𬲮𡶹뇲𝈕蚥﹛𖡪茥㚼𪞢𦞤𑅆䂌𑴽礪𧉑ݣ𩓌𤪶𣒇漞筃𫱆뾆𥇑𑀀㋥𠭹𬞾𗈞𮑆𔗂𐒦䐸𘩐𪮀𢐵𠾫𥽥𤡹你𪪓뗢𐠡𦟱긋𠙶皋𧩧⯯𤘨𬣟の𦶚𦈒ﾯ貾졀𨇱确𥓕𝈴ঁ𦖨𗕢휦𡆡𦐴𢯱𡱡𠇻𩏍ᬛ𪻷𭰋𝥽𧸾썕𩱩鴧톀穅󠆌𐆃₌쇈𭍅𨮼ㄇ斶鯺𤅭𩝮𥌭𩲆𢈋ᆃ䃾䲩𐰌𓇻𡕆蕿鄻𫷟˳𑇚𑄟𦉀𩐿퉾𫸙𡜓䅰𢊽𮄽𘇭𨪤𠭚╻쾎𥉺櫘荤釔豘툀󠄦𦡩𠔎佣ⷿ蠺䷴𫉷挏𬇎𗎱𑶅吽𭡌𗅼𠃵𓂛𩽻㧄汋𭠬𥷃𡘀𒈊𪏪𣂴𡙊𗂴𪷞𗮫킨𨞈誶딐嶶🄃𖾇𥟘𫉑𧒙⒭諪⧁

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/backend/articles_find/apps.py

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sungguenja/fincat-findog

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from django.apps import AppConfig class ArticlesFindConfig(AppConfig): name = 'articles_find'

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/client/views.py

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limchyo/my-baemin-project

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from django.shortcuts import render, redirect from django.contrib.auth import authenticate, login as auth_login from django.contrib.auth.models import User, Group from partner.models import Partner, Menu from .models import Client, Order, OrderItem # Create your views here. def index(request): partner_list = Partner.objects.all() ctx = { "partner_list" : partner_list } return render(request, "main.html", ctx) def login(request): ctx = {"is_client":True} return common_login(request, ctx, "client") def signup(request): ctx = {"is_client":True} return common_signup(request, ctx, "client") def common_signup(request, ctx, group): if request.method == "GET": pass elif request.method == "POST": username = request.POST.get("username") email = request.POST.get("email") password = request.POST.get("password") user = User.objects.create_user(username, email, password) target_group = Group.objects.get(name=group) user.groups.add(target_group) if group == "client": Client.objects.create(user=user, name=username) return render(request, "signup.html", ctx) def common_login(request, ctx, group): if request.method == "GET": pass elif request.method == "POST": username = request.POST.get("username") password = request.POST.get("password") user = authenticate(username=username, password=password) if user is not None: if group not in [group.name for group in user.groups.all()]: ctx.update({"error" : "접근 권한이 없습니다."}) # for group in user.groups.all(): # print("group:", group) else: auth_login(request, user) next_value = request.GET.get("next") if next_value: return redirect(next_value) else: if group == "partner": return redirect("/partner/") else: return redirect("/") else: ctx.update({"error" : "사용자가 없습니다."}) return render(request, "login.html", ctx) def order(request, partner_id): ctx = {} # if request.user.is_anonymous or request.user.partner is None: # return redirect("/partner/") partner = Partner.objects.get(id=partner_id) menu_list = Menu.objects.filter(partner=partner) if request.method == "GET": ctx.update({ "partner" : partner, "menu_list" : menu_list, }) elif request.method == "POST": # menu_dict = {} order = Order.objects.create( client=request.user.client, address="test", ) for menu in menu_list: menu_count = request.POST.get(str(menu.id)) # if int(menu_count) > 0: # menu_dict.update({ str(menu.id): menu }) menu_count = int(menu_count) if menu_count > 0: item = OrderItem.objects.create( order=order, menu=menu, count=menu_count ) # order.items.add(menu) return redirect("/") return render(request, "order_menu_list.html", ctx)

[ "limchyo" ]

limchyo

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Synerty/peek-core-user

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import logging from peek_core_user._private.PluginNames import userPluginTuplePrefix from vortex.Tuple import addTupleType, Tuple, TupleField logger = logging.getLogger(__name__) @addTupleType class UserListItemTuple(Tuple): __tupleType__ = userPluginTuplePrefix + "UserListItemTuple" #: The unique ID of the user userId: str = TupleField() #: The nice name of the user displayName: str = TupleField() @property def userName(self) -> str: return self.userId @property def userTitle(self) -> str: return self.displayName

[ "jarrod.chesney@synerty.com" ]

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Raymond26/corsaclub

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from django.conf.urls import url from . import views app_name = 'videos' urlpatterns = [ url(r'^$', views.VideosIndexView.as_view(), name='index'), ]

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moktan5/sample1

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# Generated by Django 3.0 on 2019-12-18 10:20 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Department', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('Department_name', models.CharField(max_length=30)), ], ), ]

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Fourier-Times/deepfillv2

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import os import numpy as np import cv2 import torch import torch.nn as nn import torchvision as tv import network import dataset # ---------------------------------------- # Network # ---------------------------------------- def create_generator(opt): # Initialize the networks generator = network.GrayInpaintingNet(opt) print('Generator is created!') # Init the networks if opt.finetune_path: pretrained_net = torch.load(opt.finetune_path) generator = load_dict(generator, pretrained_net) print('Load generator with %s' % opt.finetune_path) else: network.weights_init(generator, init_type = opt.init_type, init_gain = opt.init_gain) print('Initialize generator with %s type' % opt.init_type) return generator def create_discriminator(opt): # Initialize the networks discriminator = network.PatchDiscriminator(opt) print('Discriminator is created!') # Init the networks network.weights_init(discriminator, init_type = opt.init_type, init_gain = opt.init_gain) print('Initialize discriminator with %s type' % opt.init_type) return discriminator def create_perceptualnet(): # Pre-trained VGG-16 vgg16 = torch.load('vgg16_pretrained.pth') # Get the first 16 layers of vgg16, which is conv3_3 perceptualnet = network.PerceptualNet() # Update the parameters load_dict(perceptualnet, vgg16) # It does not gradient for param in perceptualnet.parameters(): param.requires_grad = False return perceptualnet def load_dict(process_net, pretrained_net): # Get the dict from pre-trained network pretrained_dict = pretrained_net # Get the dict from processing network process_dict = process_net.state_dict() # Delete the extra keys of pretrained_dict that do not belong to process_dict pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in process_dict} # Update process_dict using pretrained_dict process_dict.update(pretrained_dict) # Load the updated dict to processing network process_net.load_state_dict(process_dict) return process_net # ---------------------------------------- # PATH processing # ---------------------------------------- def text_readlines(filename): # Try to read a txt file and return a list.Return [] if there was a mistake. try: file = open(filename, 'r') except IOError: error = [] return error content = file.readlines() # This for loop deletes the EOF (like \n) for i in range(len(content)): content[i] = content[i][:len(content[i])-1] file.close() return content def savetxt(name, loss_log): np_loss_log = np.array(loss_log) np.savetxt(name, np_loss_log) def get_files(path): # read a folder, return the complete path ret = [] for root, dirs, files in os.walk(path): for filespath in files: ret.append(os.path.join(root, filespath)) return ret def get_jpgs(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: ret.append(filespath) return ret def text_save(content, filename, mode = 'a'): # save a list to a txt # Try to save a list variable in txt file. file = open(filename, mode) for i in range(len(content)): file.write(str(content[i]) + '\n') file.close() def check_path(path): if not os.path.exists(path): os.makedirs(path) # ---------------------------------------- # Validation and Sample at training # ---------------------------------------- def sample(grayscale, mask, out, save_folder, epoch): # to cpu grayscale = grayscale[0, :, :, :].data.cpu().numpy().transpose(1, 2, 0) # 256 * 256 * 1 mask = mask[0, :, :, :].data.cpu().numpy().transpose(1, 2, 0) # 256 * 256 * 1 out = out[0, :, :, :].data.cpu().numpy().transpose(1, 2, 0) # 256 * 256 * 1 # process masked_img = grayscale * (1 - mask) + mask # 256 * 256 * 1 masked_img = np.concatenate((masked_img, masked_img, masked_img), axis = 2) # 256 * 256 * 3 (√) masked_img = (masked_img * 255).astype(np.uint8) grayscale = np.concatenate((grayscale, grayscale, grayscale), axis = 2) # 256 * 256 * 3 (√) grayscale = (grayscale * 255).astype(np.uint8) mask = np.concatenate((mask, mask, mask), axis = 2) # 256 * 256 * 3 (√) mask = (mask * 255).astype(np.uint8) out = np.concatenate((out, out, out), axis = 2) # 256 * 256 * 3 (√) out = (out * 255).astype(np.uint8) # save img = np.concatenate((grayscale, mask, masked_img, out), axis = 1) imgname = os.path.join(save_folder, str(epoch) + '.png') cv2.imwrite(imgname, img) def psnr(pred, target, pixel_max_cnt = 255): mse = torch.mul(target - pred, target - pred) rmse_avg = (torch.mean(mse).item()) ** 0.5 p = 20 * np.log10(pixel_max_cnt / rmse_avg) return p def grey_psnr(pred, target, pixel_max_cnt = 255): pred = torch.sum(pred, dim = 0) target = torch.sum(target, dim = 0) mse = torch.mul(target - pred, target - pred) rmse_avg = (torch.mean(mse).item()) ** 0.5 p = 20 * np.log10(pixel_max_cnt * 3 / rmse_avg) return p def ssim(pred, target): pred = pred.clone().data.permute(0, 2, 3, 1).cpu().numpy() target = target.clone().data.permute(0, 2, 3, 1).cpu().numpy() target = target[0] pred = pred[0] ssim = skimage.measure.compare_ssim(target, pred, multichannel = True) return ssim

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TheHeadlessSourceMan/imageTools

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# This is the setup info for the python installer. # You probably don't need to do anything with it directly. # Just run make and it will be used to create a distributable package # for more info on how this works, see: # http://wheel.readthedocs.org/en/latest/ # and/or # http://pythonwheels.com from setuptools import setup, Distribution class BinaryDistribution(Distribution): def is_pure(self): return True # return False if there is OS-specific files def cmdline(args): """ Run the command line :param args: command line arguments (WITHOUT the filename) """ import os here=os.path.dirname(os.path.realpath( __file__ )) name='imgTools' # See also: https://setuptools.readthedocs.io/en/latest/setuptools.html setup( name=name, version='1.0', description='Power tools for working with images', long_description='Really, the funniest around.', classifiers=[ # http://pypi.python.org/pypi?%3Aaction=list_classifiers 'Development Status :: 3 - Alpha', #'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2.7', # written for python version # 'Topic :: ', # file this under a topic ], #url='http://myproduct.com', #author='me', #author_email='x@y.com', #license='MIT', packages=[name], package_dir={name:here}, package_data={ # add extra files for a package name:[] }, distclass=BinaryDistribution, install_requires=[], # add dependencies from pypi dependency_links=[], # add dependency urls (not in pypi) ) if __name__=='__main__': import sys cmdline(sys.argv[1:])

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theheadlesssourceman@gmail.com

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#!/usr/bin/python3 from __future__ import annotations from re import compile as reg_compile from typing import Dict, Any from datetime import time, datetime class History(object): def __init__(self, _max: str, departFromMax: str, _min: str, departFromMin: str, rainfall: str, relativeHumidityFirst: str, relativeHumidityFinal: str, sunset: str, sunrise: str, moonset: str, moonrise: str): self.timestamp = datetime.now().timestamp() reg = reg_compile(r'^(-?\d*\.?\d{1,})$') tmp = reg.search(_max) self.max = float(tmp.group()) if tmp else None tmp = reg.search(_min) self.min = float(tmp.group()) if tmp else None tmp = reg.search(departFromMax) self.departFromMax = float(tmp.group()) if tmp else None tmp = reg.search(departFromMin) self.departFromMin = float(tmp.group()) if tmp else None tmp = reg.search(rainfall) self.rainfall = float(tmp.group()) if tmp else None tmp = reg.search(relativeHumidityFirst) self.relativeHumidityAt08_30 = float(tmp.group()) if tmp else None tmp = reg.search(relativeHumidityFinal) self.relativeHumidityAt17_30 = float(tmp.group()) if tmp else None self.sunset = time(*[int(i.strip(), base=10) for i in sunset.split(':')]) self.sunrise = time(*[int(i.strip(), base=10) for i in sunrise.split(':')]) self.moonset = time(*[int(i.strip(), base=10) for i in moonset.split(':')]) self.moonrise = time(*[int(i.strip(), base=10) for i in moonrise.split(':')]) def toJSON(self) -> Dict[str, Any]: return { 'timestamp': self.timestamp, 'max': self.max, 'departFromMax': self.departFromMax, 'min': self.min, 'departFromMin': self.departFromMin, 'rainfall': self.rainfall, 'relativeHumidityAt08:30': self.relativeHumidityAt08_30, 'relativeHumidityAt17:30': self.relativeHumidityAt17_30, 'sunset': str(self.sunset), 'sunrise': str(self.sunrise), 'moonset': str(self.moonset), 'moonrise': str(self.moonrise) } @staticmethod def fromJSON(data: Dict[str, Any]) -> History: _hist = History(data.get('max'), data.get('departFromMax'), data.get('min'), data.get('departFromMin'), data.get('rainfall'), data.get( 'relativeHumidityAt08:30'), data.get('relativeHumidityAt17:30'), data.get('sunset'), data.get('sunrise'), data.get('moonset'), data.get('moonrise')) _hist.timestamp = data.get('timestamp') return _hist if __name__ == '__main__': print('[!]This module is designed to be used as a backend handler') exit(0)

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anjanroy@yandex.com

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CutiePi/CodeWars

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import math # Find biggest number from right to left then promote once 1234567890 # 59884848459853 59884848498535 should equal 59884848483559 # algo to do # find when next is smaller # then take smallest previous that is bigger and replace # take rest and order asc def next_bigger(n): number = list(str(n)) histo = [] for x in range(len(number) - 1, 0, -1): histo.append(number[x]) if int(number[x]) > int(number[x - 1]): for z in range(len(histo)): if int(histo[z]) > int(number[x - 1]): temp = number[x - 1] number[x - 1] = histo[z] histo[z] = temp histo.sort() number = int(''.join(number[0:x]) + ''.join(histo)) return -1 if number <= n else number return -1 print(next_bigger(9)) print(next_bigger(111)) print(next_bigger(531)) print(next_bigger(144)) # 414 print(next_bigger(891)) print(next_bigger(12)); # 21 print(next_bigger(513)); # 531 print(next_bigger(2017)); # 2071 print(next_bigger(1234567890)) # 1234567908

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jasmin.lapointe@hotmail.ca

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import sys from functools import wraps import logging import os import random import time from contextlib import contextmanager from typing import Union import numpy as np import torch def seed_everything(seed=1234): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True @contextmanager def timer(name: str, logger: Union[logging.Logger, None] = None): t0 = time.time() yield msg = f'[{name}] done in {time.time()-t0:.3f} s' if logger: logger.info(msg) else: print(msg) def tail_recursive(func): self_func = [func] self_firstcall = [True] self_CONTINUE = [object()] self_argskwd = [None] @wraps(func) def _tail_recursive(*args, **kwd): if self_firstcall[0] == True: func = self_func[0] CONTINUE = self_CONTINUE self_firstcall[0] = False try: while True: result = func(*args, **kwd) if result is CONTINUE: # update arguments args, kwd = self_argskwd[0] else: # last call return result finally: self_firstcall[0] = True else: # return the arguments of the tail call self_argskwd[0] = args, kwd return self_CONTINUE return _tail_recursive def get_experiment_id_from_cfg(cfg): dataset=cfg.dataset model=cfg.model.type scheme=cfg.fed.type heteroE = cfg.client_heterogeneity.should_use_heterogeneous_E heteroD = cfg.client_heterogeneity.should_use_heterogeneous_data iid = cfg.client_heterogeneity.iid folder_to_save = './output/results/{}'.format(model) if not os.path.exists(folder_to_save): os.makedirs(folder_to_save, exist_ok=True) id = '{}/model={}_scheme={}_heteroE={}_heteroD={}_iid={}_dataset={}'.format(folder_to_save, model, scheme, heteroE, heteroD, iid, dataset) return id

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patrickconrey@gmail.com

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# -*- coding: utf-8 -*- # @Time : 2017/9/18 # @Author : LIYUAN134 # @Site : # @File : MysqlManage.py # @Commment: Mysql数据操作管理 # 1、执行带参数的ＳＱＬ时，请先用sql语句指定需要输入的条件列表，然后再用tuple/list进行条件批配 # 2、在格式ＳＱＬ中不需要使用引号指定数据类型，系统会根据输入参数自动识别 # 3、在输入的值中不需要使用转意函数，系统会自动处理 # import Config import sys import MySQLdb import psycopg2 from DBUtils.PooledDB import PooledDB # reload(sys) # sys.setdefaultencoding('utf-8') """ Config是一些数据库的配置文件 """ class Mysql(object): """ MYSQL数据库对象，负责产生数据库连接 , 此类中的连接采用连接池实现获取连接对象：conn = Mysql.getConn() 释放连接对象;conn.close()或del conn """ # 连接池对象 __pool = None @staticmethod def __getConn(): """ @summary: 静态方法，从连接池中取出连接 @return MySQLdb.connection """ # if Mysql.__pool is None: # # mysql 连接 # __pool = PooledDB(creator=MySQLdb, mincached=1, maxcached=20, # host="112.25.233.123", # Config.DBHOST , # port=6980, # Config.DBPORT , # user="root", # Config.DBUSER , # passwd="aaaaa888", # Config.DBPWD , # db="pavoice", # Config.DBNAME, # charset="utf8", # Config.DBCHAR, # ) # return __pool.connection() # conn = psycopg2.connect( # database='jadebloom', # user='bloomopr', # password='pg123', # host='localhost', # port='5432' # ) conn = psycopg2.connect( database='jadebloom', user='bloomopr', password='pg123', host='192.168.1.132', port='5432' ) return conn # return __pool.connection() # use_unicode=False, # cursorclass=DictCursor def __init__(self): # 数据库构造函数，从连接池中取出连接，并生成操作游标 try: self._conn = Mysql.__getConn() self._cursor = self._conn.cursor() except Exception, e: error = 'Connect failed! ERROR (%s): %s' % (e.args[0], e.args[1]) print error sys.exit() # 针对读操作返回结果集 def _exeCute(self, sql=''): try: self._cursor.execute(sql) records = self._cursor.fetchall() return records except MySQLdb.Error, e: error = 'MySQL execute failed! ERROR (%s): %s' % (e.args[0], e.args[1]) print error # 针对更新,删除,事务等操作失败时回滚 def _exeCuteCommit(self, sql='', arg=None): try: if arg is None: self._cursor.execute(sql) else: self._cursor.execute(sql, arg) self._conn.commit() except MySQLdb.Error, e: self._conn.rollback() error = 'MySQL execute failed! ERROR (%s): %s' % (e.args[0], e.args[1]) print error # sys.exit() # 创建表 # tablename:表名称,attr_dict:属性键值对,constraint:主外键约束 # attr_dict:{'book_name':'varchar(200) NOT NULL'...} # constraint:PRIMARY KEY(`id`) def _createTable(self, table, attr_dict, constraint): sql = '' # sql_mid = '`row_id` bigint(11) NOT NULL AUTO_INCREMENT,' sql_mid = '' for attr, value in attr_dict.items(): sql_mid = sql_mid + '`' + attr + '`' + ' ' + value + ',' sql = sql + 'CREATE TABLE IF NOT EXISTS %s (' % table sql = sql + sql_mid sql = sql + constraint sql = sql + ') ENGINE=InnoDB DEFAULT CHARSET=utf8' print '_createTable:' + sql self._exeCuteCommit(sql) def insertOne(self, sql, value=None): """ @summary: 向数据表插入一条记录 @param sql:要插入的ＳＱＬ格式 @param value:要插入的记录数据tuple/list @return: insertId 受影响的行数 """ self._exeCuteCommit(sql, value) # return self.__getInsertId() def _insert(self, table, attrs, value): """ @summary: 向数据表插入一条记录 @param attrs = [] :要插入的属性 @param value = [] :要插入的数据值 """ # values_sql = ['%s' for v in attrs] attrs_sql = '(' + ','.join(attrs) + ')' value_str = self._transferContent(value) values_sql = ' values(' + value_str + ')' sql = 'insert into %s' % table sql = sql + attrs_sql + values_sql print '_insert:' + sql self._exeCuteCommit(sql) def _insertDic(self, table, attrs): """ @summary: 向数据表插入一条记录 @param attrs = {"colNmae:value"} :要插入的属性：数据值 """ attrs_sql = '(' + ','.join(attrs.keys()) + ')' value_str = self._transferContent(attrs.values()) # ','.join(attrs.values()) values_sql = ' values(' + value_str + ')' sql = 'insert into %s' % table sql = sql + attrs_sql + values_sql print '_insert:' + sql self._exeCuteCommit(sql) # 将list转为字符串 def _transferContent(self, content): if content is None: return None else: Strtmp = "" for col in content: if Strtmp == "": Strtmp = "\"" + col + "\"" else: Strtmp += "," + "\"" + col + "\"" return Strtmp def _insertMany(self, table, attrs, values): """ @summary: 向数据表插入多条数据 @param attrs = [id,name,...] :要插入的属性 @param values = [[1,'jack'],[2,'rose']] :要插入的数据值 """ values_sql = ['%s' for v in attrs] attrs_sql = '(' + ','.join(attrs) + ')' values_sql = ' values(' + ','.join(values_sql) + ')' sql = 'insert into %s' % table sql = sql + attrs_sql + values_sql print '_insertMany:' + sql try: for i in range(0, len(values), 20000): self._cursor.executemany(sql, values[i:i + 20000]) self._conn.commit() except MySQLdb.Error, e: self._conn.rollback() error = '_insertMany executemany failed! ERROR (%s): %s' % (e.args[0], e.args[1]) print error range(error) # sys.exit() def insertMany(self, sql, values=None): """ @summary: 向数据表插入多条记录 @param sql:要插入的ＳＱＬ格式 @param values:要插入的记录数据tuple(tuple)/list[list] @return: count 受影响的行数 """ try: if values is None: count = self._cursor.executemany(sql) else: count = self._cursor.executemany(sql, values) self._conn.commit() except MySQLdb.Error, e: self._conn.rollback() error = 'MySQL execute failed! ERROR (%s): %s' % (e.args[0], e.args[1]) print error # sys.exit() return count def _select(self, table, cond_dict='', order=''): """ @summary: 执行条件查询，并取出所有结果集 @cond_dict:{'name':'xiaoming'...} @order:'order by id desc' @return: result ({"col":"val","":""},{}) """ consql = ' ' if cond_dict != '': for k, v in cond_dict.items(): consql = consql + k + '=' + v + ' and' consql = consql + ' 1=1 ' sql = 'select * from %s where ' % table sql = sql + consql + order print '_select:' + sql return self._exeCute(sql) def __getInsertId(self): """ 获取当前连接最后一次插入操作生成的id,如果没有则为０ """ self._cursor.execute("SELECT @@IDENTITY AS id") result = self._cursor.fetchall() return result[0]['id'] def __query(self, sql, param=None): if param is None: count = self._cursor.execute(sql) else: count = self._cursor.execute(sql, param) return count def getAll(self, sql, param=None): """ @summary: 执行查询，并取出所有结果集 @param sql:查询ＳＱＬ，如果有查询条件，请只指定条件列表，并将条件值使用参数[param]传递进来 @param param: 可选参数，条件列表值（元组/列表） @return: result list(字典对象)/boolean 查询到的结果集 """ if param is None: count = self._cursor.execute(sql) else: count = self._cursor.execute(sql, param) if count > 0: result = self._cursor.fetchall() else: result = False return result def getOne(self, sql, param=None): """ @summary: 执行查询，并取出第一条 @param sql:查询ＳＱＬ，如果有查询条件，请只指定条件列表，并将条件值使用参数[param]传递进来 @param param: 可选参数，条件列表值（元组/列表） @return: result list/boolean 查询到的结果集 """ # pg 写法 self._cursor.execute(sql, param) result = self._cursor.fetchone() # mysql写法 if param is None: count = self._cursor.execute(sql) else: count = self._cursor.execute(sql, param) if count > 0: result = self._cursor.fetchone() else: result = False return result def getMany(self, sql, num, param=None): """ @summary: 执行查询，并取出num条结果 @param sql:查询ＳＱＬ，如果有查询条件，请只指定条件列表，并将条件值使用参数[param]传递进来 @param num:取得的结果条数 @param param: 可选参数，条件列表值（元组/列表） @return: result list/boolean 查询到的结果集 """ count = self.__query(sql, param) if count > 0: result = self._cursor.fetchmany(num) else: result = False return result def update(self, sql, param=None): """ @summary: 更新数据表记录 @param sql: ＳＱＬ格式及条件，使用(%s,%s) @param param: 要更新的值 tuple/list @return: count 受影响的行数 """ return self._exeCuteCommit(sql, param) def delete(self, sql, param=None): """ @summary: 删除数据表记录 @param sql: ＳＱＬ格式及条件，使用(%s,%s) @param param: 要删除的条件值 tuple/list @return: count 受影响的行数 """ return self._exeCuteCommit(sql, param) def begin(self): """ @summary: 开启事务 """ self._conn.autocommit(0) def end(self, option='commit'): """ @summary: 结束事务 """ if option == 'commit': self._conn.commit() else: self._conn.rollback() def dispose(self, isEnd=1): """ @summary: 释放连接池资源 """ if isEnd == 1: self.end('commit') else: self.end('rollback') self._cursor.close() self._conn.close()

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1065120559@qq.com

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/df_user/models.py

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qcuser-y/dailyfreash

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2020-04-13T14:30:03.235494

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from django.db import models from django.db import models class UserInfo(models.Model): uname = models.CharField(max_length=20) upwd = models.CharField(max_length=40) uemail = models.CharField(max_length=30) ushou = models.CharField(max_length=20, default='') uaddress = models.CharField(max_length=100, default='') uyoubian = models.CharField(max_length=6, default='') uphone = models.CharField(max_length=11, default='')

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root@host.localdomain

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/scrape-Wikipedia-using-speech-recognition/Scrape_Wikipedia_using_speech_recognition.py

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[ "MIT" ]

permissive

habereet/awesomeScripts

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2d77f3619314aa7840fed57e0cf451fe5c1a70a9

refs/heads/master

2023-01-05T01:29:30.063154

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2020-10-11T18:48:07

2020-10-11T18:48:06

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import pyttsx3 import speech_recognition as SR import wikipedia import sys engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') # for voice in voices: # print(voice.id) # select voice among the available options # engine.setProperty('voice', voices[1].id) def speak(audio): engine.say(audio) engine.runAndWait() def obey_command(): # It takes input from the microphone and returns output as a string mic = SR.Recognizer() with SR.Microphone() as source: print("Listening...") mic.pause_threshold = 1 audio = mic.listen(source) try: print("Recognizing...") query = mic.recognize_google(audio, language='en-in') print(query) except Exception as e: print(e) print("Say that again please...") return "None" return query if __name__ == "__main__": query = obey_command().lower() if 'wikipedia' in query: speak("Searching wikipedia") query = query.replace("wikipedia", "") result = wikipedia.summary(query, sentences=2) speak("According to wikipedia") speak(result) sys.exit()

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habereet.noreply@github.com

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/mgl2d/graphics/sprite.py

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[ "MIT" ]

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cklein/mgl2d

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refs/heads/master

2021-04-06T20:08:21.143777

2018-03-13T21:28:59

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2018-03-15T15:13:15

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from mgl2d.graphics.frames_store import FramesStore from mgl2d.graphics.quad_drawable import QuadDrawable from mgl2d.math.vector2 import Vector2 class Sprite: DEBUG = False def __init__(self, frames_store): self._frames_store = frames_store self._x = 0 self._y = 0 self._flags = 0 self._angle = 0 self._scale = Vector2(1, 1) # Collision detection self._attack_box = None self._hit_box = None # Frames and animations self._frame = None self._animation = None self._animation_name = None self._animation_frame_index = None self._animation_frame_delay = 0 self._animation_speed = 1 self._animating = False # Drawing self._drawable = QuadDrawable() def draw(self, screen): if self._frame is None: return self._drawable.pos = Vector2(self._x, self._y) # - camera.offset.x, self._y - camera.offset.y) self._drawable.draw(screen) # DEBUG boxes if Sprite.DEBUG: # TODO: !!! pass # anchor_x = self.frame.rect['x'] + self.frame.anchor['x'] - window_x # anchor_y = self.frame.rect['y'] + self.frame.anchor['y'] - window_y # pygame.draw.rect(surface, (255, 255, 255), pygame.Rect(anchor_x, anchor_y, 1, 1), 1) # if self.hit_box and self.hit_box.w > 0 and self.hit_box.h > 0: # pygame.draw.rect(surface, (0, 200, 0), self.hit_box.move(-window_x, -window_y), 1) # if self.attack_box and self.attack_box.w > 0 and self.attack_box.h > 0: # pygame.draw.rect(surface, (200, 0, 0), self.attack_box.move(-window_x, -window_y), 1) def set_frame(self, frame_name): self.stop_animation() self._animation = None self._frame = self._frames_store.get_frame(frame_name) def stop_animation(self): self._animation_frame_delay = 0 self._animation_frame_index = 0 self._animating = False def play_animation(self, animation_name, flags=0, speed=1): if (self._flags & FramesStore.FLAG_LOOP_ANIMATION) > 0 and \ self._flags == flags and animation_name == self._animation_name: return self._animating = True self._animation_speed = speed self._animation_name = animation_name self._flags = flags self._set_animation_frame(0) def skip_to_last_animation_frame(self): if not self._animating: return self._animating = False self._set_animation_frame(len(self._animation.frames) - 1) def update(self, game_speed): self._update_collision_boxes() if not self._animating: return if self._animation_frame_delay <= 0: self.next_animation_frame() return else: self._animation_frame_delay -= game_speed * self._animation_speed return def next_animation_frame(self): new_animation_frame_index = self._animation_frame_index + 1 if new_animation_frame_index > len(self._animation.frames) - 1: if not (self._flags & FramesStore.FLAG_LOOP_ANIMATION) > 0: self._animating = False return else: new_animation_frame_index = 0 self._set_animation_frame(new_animation_frame_index) def previous_animation_frame(self): new_animation_frame_index = self._animation_frame_index - 1 if new_animation_frame_index < 0: new_animation_frame_index = len(self._animation.frames) - 1 self._set_animation_frame(new_animation_frame_index) def _set_animation_frame(self, frame_index): self._animation = self._frames_store.get_animation(self._animation_name) self._animation_frame_index = frame_index self.animation_frame = self._animation.frames[self._animation_frame_index] new_frame = self._animation.frames[frame_index] self._animation_frame_delay = new_frame.delay self._frame = self._frames_store.get_frame(new_frame.frame_name) # Override animation flip if the frame is also flipped flags = self._flags if self.animation_frame.flip_x: flags |= FramesStore.FLAG_FLIP_X if self.animation_frame.flip_y: flags |= FramesStore.FLAG_FLIP_Y # Updates the drawable self._drawable.texture = self._frame.image self._drawable.scale = Vector2(self._frame.rect.w, self._frame.rect.h).dot(self._scale) self._drawable.anchor = self._frame.anchor.dot(self._scale) self._drawable.flip_x = (flags & FramesStore.FLAG_FLIP_X > 0) self._drawable.flip_y = (flags & FramesStore.FLAG_FLIP_Y > 0) def _update_collision_boxes(self): if not self._animating: self._attack_box = None self._hit_box = None # TODO: flip_y should be handled as well animation_frame = self._animation.frames[self._animation_frame_index] flip_x = ((self._flags & FramesStore.FLAG_FLIP_X) > 0) ^ animation_frame.flip_x if self._frame.hit_box: self._hit_box = self._frame.hit_box.copy() if flip_x: self._hit_box.x = - self._hit_box.x - self._hit_box.width self._hit_box.move_ip(self._x, self._y) else: self._hit_box = None if self._frame.attack_box: self._attack_box = self._frame.attack_box.copy() if flip_x: self._attack_box.x = - self._attack_box.x - self._attack_box.width self._attack_box.move_ip(self._x, self._y) else: self._attack_box = None @property def x(self): return self._x @x.setter def x(self, value): self._x = value @property def y(self): return self._y @y.setter def y(self, value): self._y = value @property def angle(self): return self._angle @angle.setter def angle(self, value): self._angle = value self._drawable.angle = value @property def scale(self): return self._scale @scale.setter def scale(self, value): self._scale = value self._drawable.scale *= value @property def hit_box(self): return self._hit_box @property def attack_box(self): return self._attack_box @property def animating(self): return self._animating @property def shader(self): return self._drawable.shader @shader.setter def shader(self, shader): self._drawable.shader = shader

[ "massimiliano.pesce@gmail.com" ]

massimiliano.pesce@gmail.com

5c202747363960a6969c0c91b02ef30d046a2936

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/rango/forms.py

63887e5209b733bb07ac7256ebaff8a6f51c1b06

[]

no_license

Ross-Gardiner/tango_with_django_project

cee52b71425f91a02195096820561cb4c85104c0

4a8e7f1eaf6979473530dc468dff96322315ae4a

refs/heads/master

2021-05-09T23:48:57.023274

2018-02-08T23:09:58

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from django import forms from rango.models import Page,Category,UserProfile from django.contrib.auth.models import User class CategoryForm(forms.ModelForm): name = forms.CharField(max_length=128, help_text="Please enter the category name.") views = forms.IntegerField(widget=forms.HiddenInput(), initial=0) likes = forms.IntegerField(widget=forms.HiddenInput(), initial=0) slug = forms.CharField(widget=forms.HiddenInput(), required=False) #an inline class to provide additional information on the form. class Meta: #provide an association between the ModelForm and a model model = Category fields = ('name',) class PageForm(forms.ModelForm): title = forms.CharField(max_length=128, help_text="Please enter the title of the page.") url = forms.URLField(max_length=200, help_text="Please enter the URL of the page.") views = forms.IntegerField(widget=forms.HiddenInput(), initial=0) def clean(self): cleaned_data = self.cleaned_data url = cleaned_data.get('url') #If url is not empty and doesn't start with 'http://', #then prepend 'http://'. if url and not url.startswith('http://'): url = 'http://' + url cleaned_data['url'] = url return cleaned_data #An inline class to provide additional information on the form. class Meta: #provide an association between the ModelForm and a model model = Page #fields = ('title', 'url', 'views') #What fields do we want to include in our form? # This way we don't need every field in the model present. # Some fields may allow NULL values, so we may not want to include them. # Here, we are hiding the foreign key. # we can either exclude the category field from the form, exclude = ('category',) # or specify the fields to include (i.e. not include the category field) class UserForm(forms.ModelForm): password = forms.CharField(widget=forms.PasswordInput()) class Meta: model = User fields = ('username', 'email', 'password') class UserProfileForm(forms.ModelForm): class Meta: model = UserProfile fields = fields = ('website', 'picture')

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2190583g@student.gla.ac.uk

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CrackerCat/sspanel-mining

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refs/heads/main

2023-08-19T20:28:57.117188

2021-10-21T01:44:41

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# -*- coding: utf-8 -*- # Time : 2021/10/6 18:38 # Author : QIN2DIM # Github : https://github.com/QIN2DIM # Description: import os import shlex import requests from bs4 import BeautifulSoup THIS_WALK = "." CHROMEDRIVER_UNZIP_PATH = "./chromedriver" def shell_echo(cmd: str, mode="default"): """ 为了输出安全做的协调函数 :param cmd: :param mode: :return: """ if mode == "default": return os.system(cmd) if mode == "safe": return os.system(shlex.quote(cmd)) def set_google_chrome(): # Google-chrome already exists in the current environment if shell_echo("google-chrome --version") == 0: # uninstall command # os.system("sudo rpm -e google-chrome-stable") return True # installing Google Chrome on CentOS7 shell_echo("wget https://dl.google.com/linux/direct/google-chrome-stable_current_x86_64.rpm >/dev/null") shell_echo("sudo apt localinstall google-chrome-stable_current_x86_64.rpm >/dev/null") def set_chromedriver(unzip_path=None): # chromedriver 的解压安装目录 unzip_path = "/usr/bin/chromedriver" if unzip_path is None else unzip_path # 读取 google-chrome 的发行版本 Such as 89.0.4389.23 chrome_version = "".join(os.popen("google-chrome --version").readlines()).strip().split(' ')[-1] # 访问 chromedriver 镜像 res = requests.get("http://npm.taobao.org/mirrors/chromedriver") soup = BeautifulSoup(res.text, 'html.parser') # 通过文件名清洗定位到所需版本文件的下载地址 options = [i.split('/')[0] for i in soup.text.split('\n') if i.startswith(chrome_version[:5])] if len(options) == 1: chromedriver_version = options[0] else: chromedriver_version = max(options) # 拉取 chromedriver shell_echo(f"wget http://npm.taobao.org/mirrors/chromedriver/{chromedriver_version}" "/chromedriver_linux64.zip >/dev/null") # 解压 chromedriver shell_echo("unzip chromedriver_linux64.zip >/dev/null") # 死循环等待解压完成 while True: if "chromedriver" not in list(os.walk(THIS_WALK))[0][-1]: pass else: break # 给予 chromedriver 运行运行权限 shell_echo("chmod +x chromedriver >/dev/null") # 将 chromedriver 移动到预设的解压安装目录 shell_echo(f"mv -f chromedriver {unzip_path} >/dev/null") def init_project(): print("---> Remove irrelevant information") shell_echo("rm -rf chromedriver_linux64.zip") shell_echo("rm -rf google-chrome-stable_current_x86_64.rpm") shell_echo("clear") def run(): set_google_chrome() set_chromedriver(CHROMEDRIVER_UNZIP_PATH) # 清理运行缓存 init_project() if __name__ == '__main__': run()

[ "62018067+QIN2DIM@users.noreply.github.com" ]

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/nomadgram/images/migrations/0005_auto_20181026_1930.py

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[ "MIT" ]

permissive

eshellster/Cloning-instagram

089d268214f892d999628fb81975e4d55631bb9c

da6c7037629697238948ce22545c8299f25698ba

refs/heads/master

2021-11-20T09:33:21.523454

2018-12-27T07:10:44

140,681,852

0

MIT

2021-09-08T00:09:06

2018-07-12T08:04:57

Python

UTF-8

Python

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876

py

# Generated by Django 2.0.7 on 2018-10-26 10:30 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import taggit.managers class Migration(migrations.Migration): dependencies = [ ('taggit', '0002_auto_20150616_2121'), ('images', '0004_auto_20180829_0720'), ] operations = [ migrations.AddField( model_name='image', name='tags', field=taggit.managers.TaggableManager(help_text='A comma-separated list of tags.', through='taggit.TaggedItem', to='taggit.Tag', verbose_name='Tags'), ), migrations.AlterField( model_name='image', name='creator', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='images', to=settings.AUTH_USER_MODEL), ), ]

[ "eshellster@gmail.com" ]

eshellster@gmail.com

43f932da55f2fccf7ae22373fefcc94660462820

c51010ae8096aa515a4881e4d6344b75004c0d8c

/lobbyists/models.py

47a2916f744cec6697a984b647a210443d1cd02f

[ "BSD-2-Clause" ]

permissive

nirfuzz/Open-Knesset

f4c74b0b3c838c0921ec024294124fb71884337d

5927f4f0b72f18a980571f3f93f423d45eff6280

refs/heads/master

2020-12-24T15:22:16.199979

2014-06-30T18:31:57

null

0

null

UTF-8

Python

false

11,739

py

# encoding: utf-8 from django.db import models from django.core.cache import cache from django.utils.functional import cached_property from django.utils.translation import ugettext as _ class LobbyistHistoryManager(models.Manager): def latest(self): return self.filter(scrape_time__isnull=False).latest('scrape_time') class LobbyistHistory(models.Model): """ this model allows to see an overview over time of the lobbyists in the knesset to get the latest lobbyist history object, use LobbyistHistory.objects.latest """ scrape_time = models.DateTimeField(blank=True, null=True) lobbyists = models.ManyToManyField('lobbyists.Lobbyist', related_name='histories') objects = LobbyistHistoryManager() @property def corporations(self): """ Returns all the corporations associated with this point in time of the lobbyist history Because it executes a lot of queries - it is cached for 1 day TODO: optimize it """ corporation_ids = cache.get('LobbyistHistory_%d_corporation_ids' % self.id) if not corporation_ids: corporation_ids = [] for lobbyist in self.lobbyists.all(): corporation_id = lobbyist.cached_data['latest_corporation']['id'] if corporation_id not in corporation_ids: corporation_ids.append(corporation_id) cache.set('LobbyistHistory_%d_corporation_ids' % self.id, corporation_ids, 86400) return LobbyistCorporation.objects.filter(id__in=corporation_ids) @property def main_corporations(self): """ Returns all the main corporations (e.g. without alias corporations and without 1 lobbyist corporations) """ alias_corporation_ids = [ca.alias_corporation.id for ca in LobbyistCorporationAlias.objects.all()] return self.corporations.exclude(id__in = alias_corporation_ids) def clear_corporations_cache(self): cache.delete('LobbyistHistory_%d_corporation_ids' % self.id) class Lobbyist(models.Model): """ this model represents a single lobbyist and is connected to the LobbyistHistory.lobbyists field the lobbyist is connected to a single person and has a source_id which is the id we get from the knesset the actual lobbyist's details are stored in the LobbyistData model and related in the data field the LobbyistData model allows to see changes in lobbyist data over time to get just the latest data use - lobbyist.latest_data """ person = models.ForeignKey('persons.Person', blank=True, null=True, related_name='lobbyist') source_id = models.CharField(blank=True, null=True, max_length=20) @cached_property def latest_data(self): return self.data.filter(scrape_time__isnull=False).latest('scrape_time') @cached_property def latest_corporation(self): return self.lobbyistcorporationdata_set.filter(scrape_time__isnull=False).latest('scrape_time').corporation @cached_property def cached_data(self): data = cache.get('Lobbyist_cached_data_%s' % self.id) if not data: data = { 'id': self.id, 'display_name': unicode(self.person), 'latest_data': { 'profession': self.latest_data.profession, 'faction_member': self.latest_data.faction_member, 'faction_name': self.latest_data.faction_name, 'permit_type': self.latest_data.permit_type, 'scrape_time': self.latest_data.scrape_time, }, 'latest_corporation': { 'name': self.latest_corporation.name, 'id': self.latest_corporation.id, }, } cache.set('Lobbyist_cached_data_%s' % self.id, data, 86400) return data def __unicode__(self): return self.person class LobbyistDataManager(models.Manager): def latest_lobbyist_corporation(self, corporation_id): return self.filter(corporation_id = corporation_id, scrape_time__isnull=False).latest('scrape_time') def get_corporation_lobbyists(self, corporation_id): lobbyists = [] for lobbyist in LobbyistHistory.objects.latest().lobbyists.all(): lobbyists.append(lobbyist) if lobbyist.latest_data.corporation_id == corporation_id else None return lobbyists class LobbyistData(models.Model): """ this model represents the data of a lobbyist in a certain point of time it allows to see changes in a lobbyist details over time if you just want the latest data from a lobbyist - get the latest record according to scrape_time scrape_time might be null - that means the record is not fully scraped yet """ lobbyist = models.ForeignKey('lobbyists.Lobbyist', blank=True, null=True, related_name='data') scrape_time = models.DateTimeField(blank=True, null=True) source_id = models.CharField(blank=True, null=True, max_length=20) first_name = models.CharField(blank=True, null=True, max_length=100) family_name = models.CharField(blank=True, null=True, max_length=100) profession = models.CharField(blank=True, null=True, max_length=100) corporation_name = models.CharField(blank=True, null=True, max_length=100) corporation_id = models.CharField(blank=True, null=True, max_length=20) faction_member = models.CharField(blank=True, null=True, max_length=100) faction_name = models.CharField(blank=True, null=True, max_length=100) permit_type = models.CharField(blank=True, null=True, max_length=100) represents = models.ManyToManyField('lobbyists.LobbyistRepresent') objects = LobbyistDataManager() def __unicode__(self): return '%s %s'%(self.first_name, self.family_name) class LobbyistCorporationManager(models.Manager): def current_corporations(self): return LobbyistHistory.objects.latest().corporations class LobbyistCorporation(models.Model): """ This represents a lobbyist corporation the source_id is the corporation's het-pey each lobbyist corporation has a group of lobbyists - this can change over time so represented in the LobbyistCorporationData model to get the latest data use lobbyist_corporation.latest_data """ name = models.CharField(blank=True, null=True, max_length=100) source_id = models.CharField(blank=True, null=True, max_length=20) objects = LobbyistCorporationManager() @cached_property def latest_data(self): return self.data.filter(scrape_time__isnull=False).latest('scrape_time') @property def lobbyists_count(self): return self.latest_data.lobbyists.count() @property def combined_lobbyists_count(self): lobbyists_count = self.lobbyists_count for ca in LobbyistCorporationAlias.objects.filter(main_corporation__id=self.id): lobbyists_count = lobbyists_count + ca.alias_corporation.combined_lobbyists_count return lobbyists_count @property def combined_lobbyist_ids(self): lobbyist_ids = [l.id for l in self.latest_data.lobbyists.all()] for ca in LobbyistCorporationAlias.objects.filter(main_corporation__id=self.id): for l in ca.alias_corporation.combined_lobbyist_ids: if l not in lobbyist_ids: lobbyist_ids.append(l) return lobbyist_ids @cached_property def alias_corporations(self): alias_corporation_ids = [ac.alias_corporation.id for ac in LobbyistCorporationAlias.objects.filter(main_corporation=self)] return LobbyistCorporation.objects.filter(id__in = alias_corporation_ids) @cached_property def cached_data(self): data = cache.get('LobbyistCorporation_cached_data_%s' % self.id) if not data: data = { 'id': self.id, 'name': self.name, 'source_id': self.latest_data.source_id, 'combined_lobbyists_count': self.combined_lobbyists_count, 'combined_lobbyist_ids': self.combined_lobbyist_ids, } cache.set('LobbyistCorporation_cached_data_%s' % self.id, data, 86400) return data def clear_cache(self): cache.delete('LobbyistCorporation_cached_data_%s' % self.id) def __unicode__(self): return self.name class LobbyistCorporationAliasManager(models.Manager): def create(self, *args, **kwargs): if len(args) > 1: for id in args[1:]: kwargs = {'main_corporation_id': args[0], 'alias_corporation_id': id} super(LobbyistCorporationAliasManager, self).create(**kwargs) else: return super(LobbyistCorporationAliasManager, self).create(**kwargs) class LobbyistCorporationAlias(models.Model): """ In the source data there are sometimes different coroprations which are actually the same one. For example - there are sometimes typos in the corporation id which cause our scraper to think it's different corporations This model allows to link this corporations so we can treat them as the same corporation """ main_corporation = models.ForeignKey('lobbyists.LobbyistCorporation', related_name='lobbyistcorporationalias_main') alias_corporation = models.ForeignKey('lobbyists.LobbyistCorporation', related_name='lobbyistcorporationalias_alias', unique=True) objects = LobbyistCorporationAliasManager() class LobbyistCorporationData(models.Model): """ This represents data about a corporation which might change over time currently the only relevant data is the lobbyists which are members of the corporation """ corporation = models.ForeignKey('lobbyists.LobbyistCorporation', blank=True, null=True, related_name='data') scrape_time = models.DateTimeField(blank=True, null=True) name = models.CharField(blank=True, null=True, max_length=100) source_id = models.CharField(blank=True, null=True, max_length=20) lobbyists = models.ManyToManyField('lobbyists.Lobbyist') def __unicode__(self): return self.name class LobbyistRepresent(models.Model): """ this model represents a single represent record and is connected to the LobbyistData represents field each lobbyist data has a set of representations, this model is a single representation the source_id allows to recognize this representation and show it's changed over time the actual data is in the LobbyistRepresentData model and related here in the data field if you want just the current representation data, get the latest record according to scrape_end_time """ source_id = models.CharField(blank=True, null=True, max_length=20) name = models.CharField(blank=True, null=True, max_length=100) @property def latest_data(self): return self.data.filter(scrape_time__isnull=False).latest('scrape_time') def __unicode__(self): return self.latest_data.name class LobbyistRepresentData(models.Model): """ the lobbyist represents data, related to LobbyistRepresent model allows to see changes of lobbyist representation details over time """ lobbyist_represent = models.ForeignKey('lobbyists.LobbyistRepresent', blank=True, null=True, related_name='data') scrape_time = models.DateTimeField(blank=True, null=True) source_id = models.CharField(blank=True, null=True, max_length=20) name = models.CharField(blank=True, null=True, max_length=100) domain = models.CharField(blank=True, null=True, max_length=100) type = models.CharField(blank=True, null=True, max_length=100)

[ "ori@uumpa.com" ]

ori@uumpa.com

f85b0044003de1bf8d34b777e0382b5af6b38743

bb7d79d220f51016523fe04dcf83486fba874c4f

/ConfFile_cfg_kLong_directional_ArabellaSample.py

37784c4b4f755f083854101bf68df21fbb80a460

[]

no_license

Saptaparna/FastTiming

019f77abe1ff8b2f0aaa533ac97640e48970ce93

bafa435469ae2eb8a77f461f7a04547101d6fce0

refs/heads/master

2021-12-23T09:50:25.382581

2021-12-03T19:44:05

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import FWCore.ParameterSet.Config as cms import FWCore.ParameterSet.Config as cms from RecoLocalCalo.HGCalRecProducers.HGCalRecHit_cfi import dEdX_weights, HGCalRecHit from RecoLocalCalo.HGCalRecProducers.HGCalUncalibRecHit_cfi import HGCalUncalibRecHit from SimCalorimetry.HGCalSimProducers.hgcalDigitizer_cfi import hgceeDigitizer, hgchefrontDigitizer, hgchebackDigitizer process = cms.Process("HGCTimingWithTOA") process.load("CondCore.CondDB.CondDB_cfi") process.load('Configuration.StandardSequences.Services_cff') process.load('FWCore.MessageService.MessageLogger_cfi') process.load('Configuration.EventContent.EventContent_cff') process.load('Configuration.Geometry.GeometryExtended2023D13Reco_cff') process.load('Configuration.Geometry.GeometryExtended2023D13_cff') process.load('Configuration.StandardSequences.DigiToRaw_cff') # get uncalibrechits with weights method process.load("RecoLocalCalo.HGCalRecProducers.HGCalUncalibRecHit_cfi") # get rechits e.g. from the weights process.load("RecoLocalCalo.HGCalRecProducers.HGCalRecHit_cfi") process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1)) process.source = cms.Source("PoolSource", fileNames = cms.untracked.vstring( 'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/GammaTime_step3/step3_Gamma_Pt10_n1000_part1_directional.root', 'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/GammaTime_step3/step3_Gamma_Pt10_n1000_part2_directional.root', 'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/GammaTime_step3/step3_Gamma_Pt10_n1000_part3_directional.root', 'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/GammaTime_step3/step3_Gamma_Pt10_n1000_part4_directional.root' #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part1_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part2_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part3_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part4_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part5_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part6_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part7_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part8_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part9_directional.root', #'file:/uscms_data/d1/sapta/work/HighGranularityCalorimeter/TimingStudies_9X/CMSSW_9_1_0_pre3/src/kLongTime_step3/step3_kLong_Pt10_n1000_part10_directional.root' ) ) process.TFileService = cms.Service("TFileService", fileName = cms.string('HGCTiming_Gamma_Pt10_SoverN1000ps_Floor20ps_EE_FH_Test.root')) #process.TFileService = cms.Service("TFileService", fileName = cms.string('HGCTiming_kLong_Pt10_SoverN1000ps_Floor20ps_EE_FH_Test.root')) process.content = cms.EDAnalyzer("EventContentAnalyzer") process.hgctiming = cms.EDAnalyzer('HGCTimingAnalyzerWithTOA', HGCEE_keV2fC = hgceeDigitizer.digiCfg.keV2fC, HGCHEF_keV2fC = hgchefrontDigitizer.digiCfg.keV2fC, HGCHB_keV2MIP = hgchebackDigitizer.digiCfg.keV2MIP, dEdXweights = cms.vdouble(dEdX_weights), thicknessCorrection = cms.vdouble(HGCalRecHit.thicknessCorrection), HGCEE_fCPerMIP = cms.vdouble(HGCalUncalibRecHit.HGCEEConfig.fCPerMIP), HGCEE_noisefC = cms.vdouble(hgceeDigitizer.digiCfg.noise_fC), HGCEF_noisefC = cms.vdouble(hgchefrontDigitizer.digiCfg.noise_fC), HGCBH_noiseMIP = hgchebackDigitizer.digiCfg.noise_MIP, srcGenParticles = cms.InputTag('genParticles'), srcSimTracks = cms.InputTag('g4SimHits'), srcSimVertices = cms.InputTag('g4SimHits'), srcPFRecHit = cms.InputTag('particleFlowRecHitHGC', 'Cleaned'), srcPFCluster = cms.InputTag('particleFlowClusterHGCal'), srcRecHitEE = cms.InputTag('HGCalRecHit', 'HGCEERecHits'), srcRecHitHEF = cms.InputTag('HGCalRecHit', 'HGCHEFRecHits'), srcRecHitBH = cms.InputTag('HGCalRecHit', 'HGCHEBRecHits'), srcCaloParticle = cms.InputTag('mix', 'MergedCaloTruth'), srcPartHandle = cms.InputTag('mix','MergedTrackTruth'), TriggerGeometry = cms.PSet( TriggerGeometryName = cms.string('HGCalTriggerGeometryImp1'), L1TCellsMapping = cms.FileInPath("L1Trigger/L1THGCal/data/cellsToTriggerCellsMap.txt"), ), ) process.p = cms.Path(process.hgctiming)

[ "saptaparna@gmail.com" ]

saptaparna@gmail.com

99a56220bba3c76fdaed603a25bf16557645d756

487ce91881032c1de16e35ed8bc187d6034205f7

/codes/CodeJamCrawler/16_0_2_neat/16_0_2_BoldBB_2.py

9698bb482d48752cc9d6fae3bd21070072767ccf

[]

no_license

DaHuO/Supergraph

9cd26d8c5a081803015d93cf5f2674009e92ef7e

c88059dc66297af577ad2b8afa4e0ac0ad622915

refs/heads/master

2021-06-14T16:07:52.405091

2016-08-21T13:39:13

49,829,508

2

0

null

2021-03-19T21:55:46

2016-01-17T18:23:00

Python

UTF-8

Python

false

628

py

def flip(string, pos): tran = {"+":"-","-":"+"} return "".join(tran[i] for i in reversed(string[:pos])) + string[pos:] def count(l, check = "+"): i = 0 while "-" in l and check in l: i += 1 for k, c in enumerate(l): if c == check: l = flip(l, k) break check = tran[check] if check == "+" and "-" in l: l = flip(l, len(l)) i += 1 return i with open("B-small-attempt0.in", "r") as f: i = 1 tran = {"+":"-","-":"+"} i = 0 for l in f.read().split("\n")[1:]: ori = l if len(l) > 0: i += 1 print "Case #{}: {}".format(str(i), str(min(count(l, "+"), count(l, "-"))))

[ "[dhuo@tcd.ie]" ]

[dhuo@tcd.ie]

225768ed7fa42a7d5703faabb806f47dd3a93b87

e231faae5d375db058cc563fb385d9cd486148dd

/core/tests/__init__.py

b5b26d3e443678de38563f0f387ff522cfd5710e

[]

no_license

Smarsh/norc

0cea9cf2ed9860d4ef2d587a6fb173ca4f02c591

c3608c97f6d6c292689e3d693c2a3e211df24138

refs/heads/master

2020-12-24T22:06:09.765846

2013-04-05T18:12:00

9,247,242

0

null

UTF-8

Python

false

215

py

from task_test import * from job_test import * from schedule_test import * from scheduler_test import * from executor_test import * from queue_test import * from norc import settings settings.BACKUP_SYSTEM = None

[ "elnaril@gmail.com" ]

elnaril@gmail.com

007be06c8d58b684c8d500fa0c16caacf908a6fd

4f0fdcd1e71ae7b5cb764f3d7a11fd27446ccf2e

/python_scripts/UI_scripts/display_counterpy.py

77937021e6e3ea88cd47cca27834fe2de52365a8

[]

no_license

kedartatwawadi/socket_programming_test

0c7313179850ed9b8932132aaf9990901e2e4c3b

392741c603b3114ff6dc11125befb02e78e55c51

refs/heads/main

2023-03-03T04:38:51.655380

2021-02-12T03:48:49

334,535,332

0

null

UTF-8

Python

false

4,784

py

#!/usr/bin/env python3 # Filename: pycalc.py """PyCalc is a simple calculator built using Python and PyQt5.""" import sys # Import QApplication and the required widgets from PyQt5.QtWidgets from PyQt5.QtWidgets import QApplication, QToolBar, QAction from PyQt5.QtWidgets import QMainWindow from PyQt5.QtWidgets import QWidget, QLabel from PyQt5.QtCore import Qt, QProcess from PyQt5.QtWidgets import QGridLayout from PyQt5.QtWidgets import QLineEdit from PyQt5.QtWidgets import QPushButton from PyQt5.QtWidgets import QVBoxLayout from functools import partial class BoxWidget: def __init__(self, id_text, width=90, height=70): self.widget = QWidget() self.layout = QVBoxLayout() # Set some display's properties self.display = QLineEdit() self.display.setFixedHeight(height) self.display.setAlignment(Qt.AlignCenter) self.display.setReadOnly(True) # create label self.label = QLabel(f"ID: {id_text}") self.label.setAlignment(Qt.AlignCenter) # add label and display self.layout.addWidget(self.display) self.layout.addWidget(self.label) self.widget.setLayout(self.layout) self.widget.setFixedHeight(150) def setDisplayText(self, text): """Set display's text.""" self.display.setText(text) self.display.setFocus() # ? def displayText(self): """Get display's text.""" return self.display.text() def clearDisplay(self): """Clear the display.""" self.setDisplayText("") # Create a subclass of QMainWindow to setup the calculator's GUI class TemperatureUI(QMainWindow): """PyCalc's View (GUI).""" def __init__(self): """View initializer.""" super().__init__() # Set some main window's properties self.setWindowTitle("DataLogger") self.setFixedSize(800, 400) # Set the central widget and the general layout self.generalLayout = QVBoxLayout() self._centralWidget = QWidget(self) self.setCentralWidget(self._centralWidget) self._centralWidget.setLayout(self.generalLayout) # Create the display and the buttons boxes_layout = self._createBoxes() self._createActions() self._createToolBars() self.p = QProcess() # Keep a reference to the QProcess (e.g. on self) while it's running. self.p.start("python3", ["../server_excel.py"]) self.p.readyReadStandardOutput.connect(self.handle_server_logs) def _createBoxes(self): """Create the buttons.""" self.boxes = {} boxesLayout = QGridLayout() # Button text | position on the QGridLayout _boxes = { "001": (0, 0), "002": (0, 1), "003": (0, 2), "004": (0, 3), "005": (1, 0), "006": (1, 1), "007": (1, 2), "008": (1, 3), } # Create the buttons and add them to the grid layout for _id, pos in _boxes.items(): self.boxes[_id] = BoxWidget(_id) boxesLayout.addWidget(self.boxes[_id].widget, pos[0], pos[1]) # Add buttonsLayout to the general layout self.generalLayout.addLayout(boxesLayout) def setBoxText(self, box_id, text): """Set display's text.""" self.boxes[box_id].display.setText(text) def clearBoxText(self): """Clear the display.""" self.setBoxText(box_id, "") def _createActions(self): # File actions self.settingsAction = QAction("Settings", self) self.exitAction = QAction("Exit", self) def _createToolBars(self): # File toolbar fileToolBar = self.addToolBar("File") fileToolBar.addAction(self.settingsAction) fileToolBar.addAction(self.exitAction) fileToolBar.setMovable(False) def handle_server_logs(self): data = self.p.readAllStandardOutput() stdout = bytes(data).decode("utf-8") print(stdout) for line in stdout.splitlines(): if line.startswith("LOGGER::"): msgs = line.split(",") box_id = msgs[1] text = msgs[2] self.setBoxText(box_id, text) def closeEvent(self, event): self.p.terminate() # Client code def main(): """Main function.""" # Create an instance of QApplication pycalc = QApplication(sys.argv) # Show the calculator's GUI view = TemperatureUI() view.show() # Create instances of the model and the controller # model = evaluateExpression # PyCalcCtrl(model=model, view=view) # Execute the calculator's main loop sys.exit(pycalc.exec()) if __name__ == "__main__": main()

[ "kedar@deckard.attlocal.net" ]

kedar@deckard.attlocal.net

600d4d27dd1fe477a93cb8d078bc980329fcd54d

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/Homework/2/Week2_quiz.py

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[]

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rohit-rk/Mongodb

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# -*- coding: utf-8 -*- # What does the following fragment of JavaScript output? x = { "a" : 1 }; y = "a"; x[y]++; print(x.a); # 2 # Which of the following are types available in BSON? # Strings # Floating-point numbers # Arrays # Objects (Subdocuments) # Timestamps # insert a document into the fruit collection with the attributes of "name" being # "apple", "color" being "red", and "shape" being "round". use the "insert" method. db.fruit.insert({name:"apple",color:"red",shape:"round"}); #Use findOne on the collection users to find one document where the key username is #"dwight", and retrieve only the key named email. db.users.findOne({username:"dwight"},{email:true,_id:false}); # Supposing a scores collection similar to the one presented, how would you find all # documents with type: essay and score: 50 and only retrieve the student field? db.scores.find({type:"essay",score:50},{student:true,_id:false}); # Which of these finds documents with a score between 50 and 60, inclusive? db.scores.find({ score : { $gte : 50 , $lte : 60 } } ); # Which of the following will find all users with name between "F" and "Q" (Inclusive)? db.users.find( { name : { $gte : "F" , $lte : "Q" } } ); db.users.find( { name : { $lte : "Q" , $gte : "F" } } ); # Write a query that retrieves documents from a users collection where the name has a "q" # in it, and the document has an email field. db.users.find({name:{$regex:"q"},email:{$exists:true}}); # Which of the following documents would be returned by this query? { _id : 42 , name : "Whizzy Wiz-o-matic", tags : [ "awesome", "shiny" , "green" ] } { _id : 1040 , name : "Snappy Snap-o-lux", tags : "shiny" } # How would you find all documents in the scores collection where the score is less than 50 # or greater than 90? db.scores.find({$or:[{score:{$lt:50}},{score:{$gt:90}}]}); # What will the following query do? db.scores.find( { score : { $gt : 50 }, score : { $lt : 60 } } ); # Which of the following documents matches this query? # db.users.find( { friends : { $all : [ "Joe" , "Bob" ] }, favorites : { $in : [ "running" , "pickles" ] } } ) { name : "Cliff" , friends : [ "Pete" , "Joe" , "Tom" , "Bob" ] , favorites : [ "pickles", "cycling" ] } # Suppose a simple e-commerce product catalog called catalog with documents that look like this: { product : "Super Duper-o-phonic", price : 100000000000, reviews : [ { user : "fred", comment : "Great!" , rating : 5 }, { user : "tom" , comment : "I agree with Fred, somewhat!" , rating : 4 } ], ... } # Write a query that finds all products that cost more than 10,000 and that have a rating of 5 or better. db.catalog.find({price:{$gt:10000},"reviews.rating":{$gte:5}}) # Recall the documents in the scores collection: { "_id" : ObjectId("50844162cb4cf4564b4694f8"), "student" : 0, "type" : "exam", "score" : 75 } # Write a query that retrieves exam documents, sorted by score in descending order, skipping the first 50 # and showing only the next 20. db.scores.find({type:"exam"}).sort({score:-1}).skip(50).limit(20); # How would you count the documents in the scores collection where the type was "essay" and the score was # greater than 90? db.scores.count({type:"essay", score:{$gt:90}}); #　Let's say you had a collection with the following document in it: { "_id" : "Texas", "population" : 2500000, "land_locked" : 1 } #　and you issued the query: db.foo.update({_id:"Texas"},{population:30000000}) #　What would be the state of the collection after the update? { "_id" : "Texas", "population" : 30000000 } # For the users collection, the documents are of the form { "_id" : "myrnarackham", "phone" : "301-512-7434", "country" : "US" } # Please set myrnarackham's country code to "RU" but leave the rest of the document (and the rest of the # collection) unchanged. db.users.update({_id:"myrnarackham"},{$set:{country:"RU"}}); # Write an update query that will remove the "interests" field in the following document in the users collection. { "_id" : "jimmy" , "favorite_color" : "blue" , "interests" : [ "debating" , "politics" ] } # Do not simply empty the array. Remove the key : value pair from the document. db.users.update({_id:"jimmy"},{$unset:{interests:1}}); # Suppose you have the following document in your friends collection: { _id : "Mike", interests : [ "chess", "botany" ] } # What will the result of the following updates be? db.friends.update( { _id : "Mike" }, { $push : { interests : "skydiving" } } ); db.friends.update( { _id : "Mike" }, { $pop : { interests : -1 } } ); db.friends.update( { _id : "Mike" }, { $addToSet : { interests : "skydiving" } } ); db.friends.update( { _id : "Mike" }, { $pushAll: { interests : [ "skydiving" , "skiing" ] } } ); { 1_id : "Mike", interests : ["botany","skydiving","skydiving" , "skiing" ] } # After performing the following update on an empty collection db.foo.update( { username : 'bar' }, { '$set' : { 'interests': [ 'cat' , 'dog' ] } } , { upsert : true } ); # What could be a document in the collection? { "_id" : ObjectId("507b78232e8dfde94c149949"), "interests" : [ "cat", "dog" ], "username" : "bar" } # Recall the schema of the scores collection: { "_id" : ObjectId("50844162cb4cf4564b4694f8"), "student" : 0, "type" : "exam", "score" : 75 } # Give every document with a score less than 70 an extra 20 points. db.scores.update({score:{$lt:70}}, {$inc:{score:20}}, {multi:true}) # Recall the schema of the scores collection: { "_id" : ObjectId("50844162cb4cf4564b4694f8"), "student" : 0, "type" : "exam", "score" : 75 } # Delete every document with a score of less than 60. db.scores.remove({score:{$lt:60}}); # In the following code snippet: import pymongo import sys # establish a connection to the database # note this uses the now deprecated Connection class, as we did in the lecture. # MongoClient is the preferred way of connecting. connection = pymongo.Connection("mongodb://localhost", safe=True) # get a handle to the school database db=connection.school scores = db.scores try: xxxx except: print "Unexpected error:", sys.exc_info()[0] print doc # please enter the one line of python code that would be needed in in place of xxxx to find one document # in the collection. doc = scores.find_one() # Which of the following could work using Pymongo, depending on variable names, to select out just the # student_id from the scores collection using a find command. cursor = scores.find({},{'student_id':1,'_id':0}) # In the following code, what is the correct line of code, marked by xxxx, to search for all quiz scores # that are greater than 20 and less than 90. import pymongo import sys # establish a connection to the database connection = pymongo.Connection("mongodb://localhost", safe=True) # get a handle to the school database db=connection.school scores = db.scores def find(): print "find, reporting for duty" query = xxxx try: iter = scores.find(query) except: print "Unexpected error:", sys.exc_info()[0] return iter find() query = {'type':'quiz', 'score':{'$gt':20,'$lt':90}} # In the following code, what do you think will happen if a document that matches the query doesn't # have a key called media.oembed.url? import pymongo import sys # establish a connection to the database connection = pymongo.Connection("mongodb://localhost", safe=True) # get a handle to the reddit database db=connection.reddit stories = db.stories def find(): print "find, reporting for duty" query = {'media.oembed.type':'video'} projection = {'media.oembed.url':1, '_id':0} try: iter = stories.find(query, projection) except: print "Unexpected error:", sys.exc_info()[0] sanity = 0 for doc in iter: print doc sanity += 1 if (sanity > 10): break find() # Pymongo will return a document with the following structure {media:{oembed:{}}} # Supposed you had the following documents in a collection named things. { "_id" : 0, "value" : 10 } { "_id" : 2, "value" : 5 } { "_id" : 3, "value" : 7 } { "_id" : 4, "value" : 20 } # If you performed the following query in pymongo: # cursor = things.find().skip(3).limit(1).sort('value',pymongo.DESCENDING) # which document would be returned? # The document with _id=2 # Do you expect the second insert below to succeed? # get a handle to the school database db=connection.school people = db.people doc = {"name":"Andrew Erlichson", "company":"10gen", "interests":['running', 'cycling', 'photography']} try: people.insert(doc) # first insert del(doc['_id']) people.insert(doc) # second insert except: print "Unexpected error:", sys.exc_info()[0] # Yes, because the del call will remove the _id key added by the pymongo driver in the first insert. # In the following code fragment, what is the python expression in place of xxxx to set a new key # "examiner" to be "Jones" Please use the $set operator def using_set(): print "updating record using set" # get a handle to the school database db=connection.school scores = db.scores try: # get the doc score = scores.find_one({'student_id':1, 'type':'homework'}) print "before: ", score # update using set scores.update({'student_id':1, 'type':'homework'}, xxxx) score = scores.find_one({'student_id':1, 'type':'homework'}) print "after: ", score except: print "Unexpected error:", sys.exc_info()[0] raise xxxx = {'$set':{'examiner':'Jones'}}

[ "rohitkumar.a255@gmail.com" ]

rohitkumar.a255@gmail.com

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/tests/HandTest.py

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jgreenwd/poker_cards

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2023-02-12T23:07:56.562042

2021-01-01T22:01:13

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import unittest from Hand import Hand from Card import Card from Deck import Deck from Rank import Rank deck = Deck() # line 0: hand in order low to high # line 1: hand with same ranks reversed (different suits) # line 2: hand with same rank, but line2 > line1, ie. line2 wins in a tie test_hands = [ # 5 card hands Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(7, 'H')), # 7 high [0] Hand(Card(7, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(7, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(8, 'H')), # 8 high [3] Hand(Card(8, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(8, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(9, 'H')), # 9 high [6] Hand(Card(9, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(9, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(10, 'H')), # 10 high [9] Hand(Card(10, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(10, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(11, 'H')), # Jack high [12] Hand(Card(11, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(11, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(12, 'H')), # Queen high [15] Hand(Card(12, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(12, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(5, 'H'), Card(13, 'H')), # King high [18] Hand(Card(13, 'D'), Card(5, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(13, 'H'), Card(6, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(3, 'S'), Card(4, 'C'), Card(6, 'H'), Card(14, 'H')), # Ace high [21] Hand(Card(14, 'D'), Card(6, 'D'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S')), Hand(Card(14, 'H'), Card(7, 'H'), Card(4, 'C'), Card(3, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(2, 'S'), Card(3, 'C'), Card(4, 'H'), Card(5, 'H')), # one pair [24] Hand(Card(5, 'D'), Card(4, 'D'), Card(3, 'S'), Card(2, 'C'), Card(2, 'H')), Hand(Card(6, 'H'), Card(4, 'H'), Card(3, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(2, 'S'), Card(3, 'C'), Card(3, 'H'), Card(4, 'H')), # two pair [27] Hand(Card(4, 'D'), Card(3, 'D'), Card(3, 'S'), Card(2, 'C'), Card(2, 'H')), Hand(Card(5, 'H'), Card(3, 'H'), Card(3, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(2, 'S'), Card(2, 'C'), Card(3, 'H'), Card(4, 'H')), # three of a kind [30] Hand(Card(4, 'D'), Card(3, 'D'), Card(2, 'S'), Card(2, 'C'), Card(2, 'H')), Hand(Card(5, 'H'), Card(3, 'H'), Card(2, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(3, 'H'), Card(4, 'C'), Card(5, 'D'), Card(6, 'C'), Card(7, 'S')), # straight [33] ** Ace-High ** Hand(Card(7, 'H'), Card(6, 'D'), Card(5, 'C'), Card(4, 'D'), Card(3, 'H')), Hand(Card(10, 'H'), Card(11, 'C'), Card(12, 'D'), Card(13, 'C'), Card(14, 'S')), Hand(Card(2, 'S'), Card(3, 'S'), Card(4, 'S'), Card(5, 'S'), Card(7, 'S')), # flush [36] Hand(Card(7, 'C'), Card(5, 'C'), Card(4, 'C'), Card(3, 'C'), Card(2, 'C')), Hand(Card(7, 'S'), Card(6, 'S'), Card(4, 'S'), Card(3, 'S'), Card(2, 'S')), Hand(Card(2, 'D'), Card(2, 'S'), Card(2, 'C'), Card(3, 'H'), Card(3, 'S')), # full house [39] Hand(Card(3, 'C'), Card(3, 'D'), Card(2, 'H'), Card(2, 'S'), Card(2, 'D')), Hand(Card(4, 'H'), Card(4, 'H'), Card(2, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(2, 'D'), Card(2, 'S'), Card(2, 'C'), Card(2, 'H'), Card(3, 'H')), # four of a kind [42] Hand(Card(3, 'D'), Card(2, 'H'), Card(2, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(4, 'H'), Card(2, 'H'), Card(2, 'C'), Card(2, 'S'), Card(2, 'D')), Hand(Card(2, 'S'), Card(3, 'S'), Card(4, 'S'), Card(5, 'S'), Card(6, 'S')), # straight flush [45] Hand(Card(6, 'H'), Card(5, 'H'), Card(4, 'H'), Card(3, 'H'), Card(2, 'H')), Hand(Card(7, 'S'), Card(6, 'S'), Card(5, 'S'), Card(4, 'S'), Card(3, 'S')), # 6 card hands Hand(Card(7, 'H'), Card(6, 'S'), Card(5, 'H'), Card(4, 'S'), Card(3, 'H'), Card(2, 'S')), # str8 [48] Hand(Card(2, 'H'), Card(7, 'S'), Card(3, 'S'), Card(4, 'H'), Card(6, 'H'), Card(5, 'S')), Hand(Card(7, 'H'), Card(6, 'S'), Card(5, 'H'), Card(4, 'S'), Card(3, 'H'), Card(8, 'H')), Hand(Card(7, 'H'), Card(5, 'S'), Card(4, 'S'), Card(3, 'S'), Card(2, 'S'), Card(10, 'S')), # flush [51] Hand(Card(7, 'S'), Card(5, 'H'), Card(4, 'H'), Card(3, 'H'), Card(2, 'H'), Card(10, 'H')), Hand(Card(7, 'H'), Card(9, 'S'), Card(4, 'S'), Card(3, 'S'), Card(2, 'S'), Card(10, 'S')), Hand(Card(7, 'S'), Card(8, 'S'), Card(9, 'S'), Card(5, 'S'), Card(6, 'S'), Card(10, 'S')), # str8-fl [54] Hand(Card(7, 'H'), Card(8, 'H'), Card(9, 'H'), Card(5, 'H'), Card(6, 'H'), Card(10, 'H')), Hand(Card(7, 'S'), Card(8, 'S'), Card(9, 'S'), Card(11, 'S'), Card(5, 'S'), Card(10, 'S')), # 7 card hands Hand(Card(7, 'H'), Card(6, 'S'), Card(5, 'H'), Card(4, 'S'), Card(3, 'H'), Card(2, 'S'), Card(14, 'H')), # str8 Hand(Card(14, 'S'), Card(2, 'H'), Card(7, 'H'), Card(3, 'S'), Card(4, 'S'), Card(6, 'S'), Card(5, 'H')), Hand(Card(7, 'H'), Card(6, 'S'), Card(5, 'H'), Card(4, 'S'), Card(3, 'H'), Card(2, 'S'), Card(8, 'H')), Hand(Card(7, 'H'), Card(8, 'H'), Card(5, 'S'), Card(4, 'S'), Card(3, 'S'), Card(2, 'S'), Card(10, 'S')), # flush Hand(Card(7, 'S'), Card(8, 'S'), Card(5, 'H'), Card(4, 'H'), Card(3, 'H'), Card(2, 'H'), Card(10, 'H')), Hand(Card(7, 'H'), Card(8, 'H'), Card(9, 'S'), Card(4, 'S'), Card(3, 'S'), Card(2, 'S'), Card(10, 'S')), Hand(Card(7, 'S'), Card(8, 'S'), Card(9, 'S'), Card(4, 'H'), Card(3, 'H'), Card(6, 'S'), Card(10, 'S')), # str8-fl Hand(Card(7, 'H'), Card(8, 'H'), Card(9, 'H'), Card(4, 'S'), Card(3, 'S'), Card(6, 'H'), Card(10, 'H')), Hand(Card(7, 'S'), Card(8, 'S'), Card(9, 'S'), Card(11, 'S'), Card(3, 'H'), Card(5, 'H'), Card(10, 'S')), # edge-case: Ace-Low straight Hand(Card(14, 'H'), Card(2, 'C'), Card(3, 'D'), Card(4, 'C'), Card(5, 'S')), # [66] Hand(Card(2, 'H'), Card(3, 'C'), Card(4, 'D'), Card(5, 'C'), Card(14, 'S')), Hand(Card(6, 'H'), Card(2, 'C'), Card(3, 'D'), Card(4, 'C'), Card(5, 'S')), ] class HandTest(unittest.TestCase): def test_constructor(self): hand = Hand() self.assertIsInstance(hand, Hand) def test_draw(self): hand = Hand() hand.draw(deck.deal()) self.assertTrue(len(hand) == 1) def test_discard(self): hand = Hand() self.assertRaises(IndexError, hand.discard) hand.draw(deck.deal()) card = hand.discard() self.assertTrue(len(hand) == 0) self.assertIsInstance(card, Card) def test_len(self): hand = Hand() self.assertTrue(len(hand) == 0) for i in range(1, 6): hand.draw(deck.deal()) self.assertTrue(len(hand) == i) for i in range(4, -1, -1): hand.discard() self.assertTrue(len(hand) == i) self.assertTrue(len(hand) == 0) def test_value(self): # partial hands hand = Hand(Card(10, 'H')) self.assertEqual(hand.value, Rank.TEN) hand = Hand(Card(10, 'H'), Card(9, 'D')) self.assertEqual(hand.value, Rank.TEN) hand = Hand(Card(10, 'H'), Card(10, 'D')) self.assertEqual(hand.value, Rank.ONE_PAIR) hand = Hand(Card(5, 'H'), Card(5, 'D'), Card(2, 'C')) self.assertEqual(hand.value, Rank.ONE_PAIR) hand = Hand(Card(2, 'D'), Card(2, 'C'), Card(3, 'D'), Card(3, 'C')) self.assertEqual(hand.value, Rank.TWO_PAIR) hand = Hand(Card(5, 'H'), Card(5, 'D'), Card(5, 'C')) self.assertEqual(hand.value, Rank.THREE_OF_A_KIND) hand = Hand(Card(5, 'H'), Card(5, 'D'), Card(5, 'C'), Card(2, 'C')) self.assertEqual(hand.value, Rank.THREE_OF_A_KIND) hand = Hand(Card(7, 'H'), Card(7, 'D'), Card(7, 'C'), Card(7, 'S')) self.assertEqual(hand.value, Rank.FOUR_OF_A_KIND) # 5-card hands for i, j in enumerate(range(0, 46, 3)): self.assertEqual(test_hands[j].value, Rank(i + 7)) self.assertEqual(test_hands[j + 1].value, Rank(i + 7)) self.assertEqual(test_hands[j + 2].value, Rank(i + 7)) # 6-card hands for i, j in enumerate(range(48, 51, 1)): self.assertEqual(test_hands[j].value, Rank.STRAIGHT) self.assertEqual(test_hands[j+3].value, Rank.FLUSH) self.assertEqual(test_hands[j+6].value, Rank.STRAIGHT_FLUSH) # 7-card hands for i, j in enumerate(range(57, 60, 1)): self.assertEqual(test_hands[j].value, Rank.STRAIGHT) self.assertEqual(test_hands[j+3].value, Rank.FLUSH) self.assertEqual(test_hands[j+6].value, Rank.STRAIGHT_FLUSH) # edge-case: Ace-Low Straight => A,2,3,4,5 for i in range(66, 69): self.assertEqual(test_hands[i].value, Rank.STRAIGHT) def test_equal(self): for i in range(0, 67, 3): self.assertEqual(test_hands[i], test_hands[i + 1]) self.assertNotEqual(test_hands[i], test_hands[i + 2]) def test_greater(self): # 5 card hands for i in range(0, 45): self.assertLess(test_hands[i], test_hands[i + 3]) self.assertGreater(test_hands[i + 3], test_hands[i]) # 6 card hands for i in range(48, 54): self.assertLess(test_hands[i], test_hands[i + 3]) self.assertGreater(test_hands[i + 3], test_hands[i]) # 7 card hands for i in range(57, 63): self.assertLess(test_hands[i], test_hands[i + 3]) self.assertGreater(test_hands[i + 3], test_hands[i]) # edge case for i in range(66, 67): self.assertLess(test_hands[i], test_hands[i + 2]) self.assertGreater(test_hands[i + 2], test_hands[i]) if __name__ == '__main__': unittest.main()

[ "noreply@github.com" ]

jgreenwd.noreply@github.com

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/web/celeree.py

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zlhtech/offline-tube

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refs/heads/master

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from celery import Celery celery = Celery('offline_tube') celery.config_from_object('celeryconfig') if __name__ == '__main__': celery.start()

[ "oluwafemisule@outlook.com" ]

oluwafemisule@outlook.com

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/login.py

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JuanROrellana/python-basics-flask

873bfdba969de37fc63fe0eabf4fd3886bd66249

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refs/heads/master

2021-01-03T01:39:42.786613

2020-02-11T20:51:56

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def login_user(self): return 'Login User' def serve_login_page(self): return 'Server Login'

[ "ramirez.orellana.juanjose@gmail.com" ]

ramirez.orellana.juanjose@gmail.com

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syj430/OOP_FEM

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import numpy as np import time import matmul1 M1 = 80 N1M2 = 300 N2 = 80 a = np.empty((M1,N1M2), dtype=np.float64) b = np.empty((N1M2,N1M2), dtype=np.float64) c = np.empty((M1,M1), dtype=np.float64) print(a.shape) print(b.shape) a[:] = np.random.rand(M1, N1M2) b[:] = np.random.rand(N1M2, N1M2) # Numpy start = time.time() # c = np.dot(a,b) c = a @ b @ np.transpose(a) stop = time.time() print(c) print('Numpy: ', (stop - start)*1000, 'msec') # # Fortran call start = time.time() c = matmul1.matmul1(a,b,M1,N1M2) stop = time.time() print(c) print('Fortran: ', (stop - start)*1000, 'msec') # import numpy as np # import time # # #import os # #os.system('f2py -c matmul1 -m operator.f90') # import operator # # # NI = 8 # NJ = 3 # # a = np.empty((NI, NJ), dtype=np.float64) # 8x3 # b = np.empty((NJ, NJ), dtype=np.float64) # 3x3 # c = np.empty((NI, NI), dtype=np.float64) # 8x8 # print(a.shape) # print(b.shape) # a[:] = np.random.rand(NI, NJ) # b[:] = np.random.rand(NJ, NJ) # # print(a) # # print(a[:]) # # # # NI = 8 # # NJ = 3 # # Fortran call # start = time.time() # c = operator.matmul(a, b, NI, NJ) # stop = time.time() # # print(c) # print('Fortran took ', (stop - start), 'sec') # # # # Numpy # start = time.time() # c = a @ b @ np.transpose(a) # stop = time.time() # # print(c) # print('Numpy took ', (stop - start), 'sec')

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dipaksingh3343/DeepLearningWithDevops

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#!/usr/bin/env python # coding: utf-8 # In[1]: from keras.datasets import mnist # In[2]: (x_train, y_train), (x_test, y_test) = mnist.load_data() # In[3]: img=x_train[0] # In[4]: img.shape # In[5]: img1D=img.reshape(28*28) # In[6]: img1D.shape # In[7]: import matplotlib.pyplot as plt # In[8]: plt.imshow(img) # In[9]: x_train1D=x_train.reshape(-1,28*28) # In[10]: x_train1D.shape # In[11]: x_train=x_train1D.astype('float32') # In[12]: from keras.utils.np_utils import to_categorical # In[13]: y_train_cat = to_categorical(y_train) # In[14]: from keras.models import Sequential # In[15]: from keras.layers import Dense # In[16]: model = Sequential() # In[17]: model.add(Dense(units=512, input_dim=28*28, activation='relu')) # In[18]: model.summary() # In[19]: model.add(Dense(units=256, activation='relu')) # In[20]: model.add(Dense(units=128, activation='relu')) # In[21]: model.add(Dense(units=32, activation='relu')) # In[22]: model.summary() # In[23]: model.add(Dense(units=10, activation='softmax')) # In[24]: model.summary() # In[25]: from keras.optimizers import RMSprop # In[26]: model.compile(optimizer=RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'] ) # In[27]: h = model.fit(x_train, y_train_cat, epochs=2) # In[29]: X_test_1d=x_test.reshape(-1, 28*28) # In[35]: X_test=x_train1D.astype('float32') # In[36]: y_test_cat=to_categorical(y_test) # In[37]: model.predict(X_test) # In[38]: y_test_cat # In[61]: model.save('main_model.h1') # In[ ]: accuarcy=(h.history['accuracy']) a=h.history['accuracy'][-1] print("accuarcy is=",a) with open('/root/task3mlops/accuracy.txt', 'w+') as output_file: output_file.write(str(a))

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tiffany-chang/reddit-relationships

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import urllib2 import json import time import numpy as np from pandas import Series, DataFrame import pandas as pd # by /u/<PutYourUserNameHere>) hdr = {'User-Agent': 'osx:r/relationships.multiple.results:v1.0 (by /u/<PutYourUserNameHere>)'} url = 'https://www.reddit.com/r/relationships/top/.json?sort=top&t=all&limit=100' req = urllib2.Request(url, headers=hdr) text_data = urllib2.urlopen(req).read() data = json.loads(text_data) data_all = data.values()[1]['children'] print len(data_all) while (len(data_all) <= 900): time.sleep(2) last = data_all[-1]['data']['name'] print last url = 'https://www.reddit.com/r/relationships/top/.json?sort=top&t=all&limit=100&after=%s' % last req = urllib2.Request(url, headers=hdr) text_data = urllib2.urlopen(req).read() data = json.loads(text_data) data_all += data.values()[1]['children'] print len(data_all) print len(data_all) article_title = [] article_flairs = [] article_date = [] article_comments = [] article_score = [] for i in range(0, len(data_all)): article_title.append(data_all[i]['data']['title']) article_flairs.append(data_all[i]['data']['link_flair_text']) article_date.append(data_all[i]['data']['created_utc']) article_comments.append(data_all[i]['data']['num_comments']) article_score.append(data_all[i]['data']['score']) rel_df = DataFrame({'Date': article_date, 'Title': article_title, 'Flair': article_flairs, 'Comments': article_comments, 'Score': article_score}) rel_df = rel_df[['Date', 'Title', 'Flair', 'Comments', 'Score']] print rel_df[:5] rel_df.to_csv('out.csv', encoding='utf-8')

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from django.shortcuts import render from django.http.response import HttpResponse from category.models import AllCategories from books import datahandler as data from django.core.paginator import Paginator # Create your views here. def allCategories(request): allCategory = AllCategories.objects.all() return render(request, "allcategories.html", {'allcategory': allCategory}) def categories(request, id): bookByCategory = data.getbookforcategory(id) paginator = Paginator(bookByCategory, 48) page_number = request.GET.get('page') page_object = paginator.get_page(page_number) return render(request, 'category.html', {'page_object': page_object})

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# Generated by Django 3.0.3 on 2020-04-05 22:33 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('seznam_kontaktu', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='contact', options={'permissions': [('can_see_contacts', 'Can see all contacts')]}, ), ]

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import lan import copy import ast_buildingblock as ast_bb import exchange import collect_gen as cg import collect_id as ci import collect_loop as cl import collect_array as ca import collect_device as cd class PlaceInLocal(object): def __init__(self, ast): self.ast = ast self.PlaceInLocalArgs = list() self.PlaceInLocalCond = None def place_in_local(self): """ Find all array references that can be optimized through the use of shared memory. Then rewrite the code in this fashion. """ args = dict() loopindex = set() inner_loop_indices = cl.get_inner_loops_indices(self.ast) subscript_no_id = ca.get_subscript_no_id(self.ast) for k, sub_list in subscript_no_id.items(): for i, sub in enumerate(sub_list): if self.__can_be_put_in_local(sub, inner_loop_indices): args[k] = i loopindex = loopindex.union(set(sub).intersection(set(inner_loop_indices))) loopindex = list(loopindex) if len(loopindex) > 1: raise Exception("""place_in_reg: loopindex length above 1""") if args: self.PlaceInLocalArgs.append(args) self.__set_condition(loopindex) def __set_condition(self, loopindex): (lower_limit, upper_limit) = cl.get_loop_limits(self.ast) local = cl.get_local(self.ast) for m in loopindex: cond = lan.BinOp(lan.BinOp(lan.BinOp(lan.Id(upper_limit[m]), '-', lan.Id(lower_limit[m])), '%', lan.Constant(local['size'][0])), '==', lan.Constant(0)) self.PlaceInLocalCond = cond def __can_be_put_in_local(self, sub, inner_loop_indices): """ The subscript must be two dimensional. One index must be a grid index, the other an inner loop index. :param sub: :param inner_loop_indices: :return: """ grid_indices = cl.get_grid_indices(self.ast) par_dim = cl.get_par_dim(self.ast) return set(sub).intersection(set(grid_indices)) and \ set(sub).intersection(set(inner_loop_indices)) \ and par_dim == 2 def local_memory3(self, arr_dict): initstats = [] init_comp = lan.GroupCompound(initstats) kernel = cd.get_kernel(self.ast) kernel.statements.insert(0, init_comp) loop_dict = self.__find_array_ref_to_inner_loop_idx_mapping(arr_dict) self.__loop_dict_is_not_safe(arr_dict, loop_dict) # Find which loops must be extended loops_to_be_extended = set() for n in arr_dict: i = arr_dict[n] loops_to_be_extended.add(loop_dict[(n, i)][0]) outerstats = self.__extend_loops(loops_to_be_extended) self.__allocate_local_arrays(initstats, arr_dict) loadings = [] loop_arrays = ca.get_loop_arrays(self.ast) local = cl.get_local(self.ast) for n in arr_dict: loc_name = n + '_local' i = arr_dict[n] glob_subs = copy.deepcopy(loop_arrays[n][i]) # Change loop idx to local idx loopname = loop_dict[(n, i)][0] loc_subs_2 = copy.deepcopy(glob_subs).subscript my_new_glob_sub_2 = self.__create_glob_load_subscript(glob_subs, loc_subs_2, loopname, n) self.__set_local_sub(loc_subs_2) loc_ref = lan.ArrayRef(lan.Id(loc_name), loc_subs_2) loadings.append(lan.Assignment(loc_ref, my_new_glob_sub_2)) inner_loc = loop_arrays[n][i] self.__exchange_load_local_loop_idx(loopname, loc_name, inner_loc) self.__exchange_load_local_idx(inner_loc) self.ast.ext.append(lan.Block(lan.Id(loc_name), local['size'])) # Must also create the barrier mem_fence_func = self.__create_local_mem_fence() loadings.append(mem_fence_func) outerstats.insert(0, lan.GroupCompound(loadings)) outerstats.append(mem_fence_func) def __create_local_mem_fence(self): arglist = lan.ArgList([lan.Id('CLK_LOCAL_MEM_FENCE')]) func = ast_bb.EmptyFuncDecl('barrier', type=[]) func.arglist = arglist return func def __find_array_ref_to_inner_loop_idx_mapping(self, arr_dict): subscript_no_id = ca.get_subscript_no_id(self.ast) grid_indices = cl.get_grid_indices(self.ast) loop_dict = dict() # So we create it for n in arr_dict: i = arr_dict[n] loop_dict[(n, i)] = [] for n in arr_dict: i = arr_dict[n] subscript = subscript_no_id[n][i] inner_loop_idx = [] for m in subscript: try: _ = int(m) except ValueError: if m not in grid_indices: inner_loop_idx.append(m) loop_dict[(n, i)] = inner_loop_idx return loop_dict def __loop_dict_is_not_safe(self, arr_dict, loop_dict): # Check that all ArrayRefs are blocked using only one loop # otherwise we do not know what to do retval = False for n in arr_dict: i = arr_dict[n] if len(loop_dict[(n, i)]) > 1: print "Array %r is being blocked by %r. Returning..." \ % (n, loop_dict[(n, i)]) retval = True return retval def __extend_loops(self, loops_to_be_extended): outerstats = [] loops = cl.get_inner_loops(self.ast) local = cl.get_local(self.ast) for n in loops_to_be_extended: outerloop = loops[n] outeridx = n compound = outerloop.compound outerloop.compound = lan.Compound([]) innerloop = copy.deepcopy(outerloop) innerloop.compound = compound outerstats = outerloop.compound.statements outerstats.insert(0, innerloop) loadstats = [] load_comp = lan.GroupCompound(loadstats) outerstats.insert(0, load_comp) # change increment of outer loop outerloop.inc = lan.Increment(lan.Id(outeridx), '+=' + local['size'][0]) inneridx = outeridx * 2 # new inner loop innerloop.cond = lan.BinOp(lan.Id(inneridx), '<', lan.Constant(local['size'][0])) innerloop.inc = lan.Increment(lan.Id(inneridx), '++') innerloop.init = ast_bb.ConstantAssignment(inneridx) return outerstats def __allocate_local_arrays(self, initstats, arr_dict): types = ci.get_types(self.ast) local = cl.get_local(self.ast) num_array_dims = ca.get_num_array_dims(self.ast) for n in arr_dict: # Add array allocations local_array_name = n + '_local' arrayinit = lan.Constant(local['size'][0]) if num_array_dims[n] == 2: arrayinit = lan.BinOp(arrayinit, '*', lan.Constant(local['size'][1])) local_array_id = lan.Id(local_array_name) local_type_id = lan.ArrayTypeId(['__local', types[n][0]], local_array_id, [arrayinit]) initstats.append(local_type_id) def __exchange_load_local_loop_idx(self, loopname, loc_name, inner_loc): inner_loc.name.name = loc_name exchange_id2 = exchange.ExchangeId({loopname: loopname * 2}) exchange_id2.visit(inner_loc) def __exchange_load_local_idx(self, inner_loc): reverse_idx = cg.get_reverse_idx(self.ast) grid_indices = cl.get_grid_indices(self.ast) for k, m in enumerate(inner_loc.subscript): if isinstance(m, lan.Id) and \ m.name in grid_indices: tid = str(reverse_idx[k]) inner_loc.subscript[k] = ast_bb.FuncCall('get_local_id', [lan.Constant(tid)]) def __create_glob_load_subscript(self, glob_subs, loc_subs_2, loopname, n): loc_subs = copy.deepcopy(glob_subs).subscript my_new_glob_sub = copy.deepcopy(glob_subs).subscript my_new_glob_sub_2 = copy.deepcopy(glob_subs) reverse_idx = cg.get_reverse_idx(self.ast) grid_indices = cl.get_grid_indices(self.ast) for k, m in enumerate(loc_subs): if isinstance(m, lan.Id) and \ m.name not in grid_indices: tid = str(reverse_idx[k]) tidstr = ast_bb.FuncCall('get_local_id', [lan.Constant(tid)]) loc_subs_2[k] = tidstr my_new_glob_sub[k] = lan.BinOp(lan.Id(loopname), '+', tidstr) my_new_glob_sub_2 = lan.ArrayRef(lan.Id(n), my_new_glob_sub) return my_new_glob_sub_2 def __set_local_sub(self, loc_subs_2): reverse_idx = cg.get_reverse_idx(self.ast) grid_indices = cl.get_grid_indices(self.ast) for k, m in enumerate(loc_subs_2): if isinstance(m, lan.Id) and \ m.name in grid_indices: tid = str(reverse_idx[k]) loc_subs_2[k] = ast_bb.FuncCall('get_local_id', [lan.Constant(tid)])

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jepsen@diku.dk

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Padmaraj1983/TZ

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""" Django settings for cbvemployee project. Generated by 'django-admin startproject' using Django 1.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TEMPLATE_DIR=os.path.join(BASE_DIR,"templates") # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ctlz#fst)zkbv-xsb*%r60!aaz(f-mhcmc2kr!s)jp)z&q5&3l' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'testapp', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'cbvemployee.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [TEMPLATE_DIR], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'cbvemployee.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/'

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# -*- coding: utf_8 -*- u""" LiveAlive Constants Licensed under the MIT License. Copyright (c) 2007-2012 Kota Saito """ # アプリケーションのタイトル APP_NAME = "LiveAlive2" # アプリケーションのバージョン APP_VERSION = "2.00" # アプリケーションの説明 APP_DESCRIPTION = "Live Monitoring Tool." # アプリケーションのコピーライト表記 APP_COPYRIGHT = "Licensed under the MIT License.\n" \ "Copyright (c) 2007-2012 Kota Saito" # アプリケーションのバージョン表示 (--version で表示) APP_VERSION_TEXT = "%s %s - %s\n\n%s" % \ (APP_NAME, APP_VERSION, APP_DESCRIPTION, APP_COPYRIGHT) # アプリケーションのディレクトリ (外部から設定) APP_DIR = "" # 設定ファイルの名前 CONFIG_FILE = ("conf/global.ini", "conf/livealive2.ini") # プラグインディレクトリの名前 PLUGIN_DIR = "livealive-plugins" __all__ = ["APP_NAME", "APP_VERSION", "APP_COPYRIGHT", "APP_DESCRIPTION", "APP_VERSION_TEXT", "APP_DIR", "CONFIG_FILE", "PLUGIN_DIR"]

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# Given a string, you need to reverse the order of characters in each word within a sentence while still preserving whitespace and initial word order. # # Example 1: # Input: "Let's take LeetCode contest" # Output: "s'teL ekat edoCteeL tsetnoc" # Note: In the string, each word is separated by single space and there will not be any extra space in the string. class Solution(object): def reverseWords(self, s): """ :type s: str :rtype: str """ return ' '.join(w[::-1] for w in s.split()) class Solution(object): def reverseWords(self, s): """ :type s: str :rtype: str """ s = list(s) start = 0 i = 0 while i <= len(s): if i == len(s) or s[i] == " ": self.help(s, start, i - 1) start = i + 1 i += 1 return "".join(s) def help(self, s, start, end): for i in range((end - start) / 2 + 1): s[start + i], s[end - i] = s[end - i], s[start + i]

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haiwen.shi01@gmail.com

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/HW3/Lqr-moritz2.py

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import numpy as np import matplotlib.pyplot as plt def lqr(startstate): #LQR parameter A_t = np.array([[1, 0.1],[0, 1]]) B_t = np.array([[0],[0.1]]) b_t = np.array([[5],[0]]) Sig_t = 0.01 K_t = np.array([5, 0.3]) k_t =0.3 H_t = 1 T = 50 states = np.zeros((2, T+1)) states[:, 0] = startstate actions = np.zeros(T) rewards = np.zeros(T+1) for i in range(1, T+1): w_t = np.random.normal(b_t,Sig_t) actions[i-1] = -1.0 * np.dot(K_t,states[:,i-1]) + k_t rewards[i] = compute_rt(states[:,i-1],actions[i-1],H_t,i-1,T) states[:,i] = np.reshape(np.reshape(np.dot(A_t, states[:, i-1]), (2, 1)) + B_t * actions[i-1] + w_t, 2) return actions, states , rewards def compute_rt(s_t,a_t,H_t,t,T): r_t = getr_t(t) R_t = getR_t(t) diff = np.reshape(s_t,(2,1)) - r_t rslt = -1.0 *np.dot(np.dot(np.transpose(diff),R_t),diff) if (t == T): return rslt else: return rslt - np.dot(np.dot(np.transpose(a_t),H_t),a_t) def getR_t(t): if t is 14 or 40 : return np.array([[100000, 0],[0, 0.1]]) else : return np.array([[0.01, 0],[0, 0.1]]) def getr_t(t): if t < 15 : return np.array([[10],[0]]) else : return np.array([[20],[0]]) Actions = np.zeros((20,50)) States = np.zeros((20, 51, 2)) dev = np.zeros((51, 2)) Rewards = np.zeros((20,51)) for i in range(20): s = np.random.normal([0,0],1) (a,st,r) = lqr(s) Actions[i] = a States[i] = st.transpose() Rewards[i] = r mean = np.mean(States, axis=0) for i in range(2): for j in range(51): for k in range(20): dev[j,i] = dev[j,i] + (States[k,j,i] - mean[j,i]) * (States[k,j,i] - mean[j,i]) dev[j, i] = dev[j, i] / 20 the_mean = mean.transpose() plt.plot(the_mean[0] + 2*dev[:, 0], the_mean[1] + 2*dev[:, 1], 'y') plt.plot(the_mean[0] - 2*dev[:, 0], the_mean[1] - 2*dev[:, 1], 'y') plt.plot(the_mean[0], the_mean[1], 'r') plt.fill_between(the_mean[0] + 2*dev[:, 0], the_mean[1] - 2*dev[:, 1], the_mean[1] + 2*dev[:, 1], alpha=0.5, edgecolor='#1B2ACC', facecolor='#089FFF') plt.fill_between(the_mean[0] - 2*dev[:, 0], the_mean[1] - 2*dev[:, 1], the_mean[1] + 2*dev[:, 1], alpha=0.5, edgecolor='#1B2ACC', facecolor='#089FFF') plt.show()

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config = {} # set the parameters related to the training and testing set nKbase = 64 nKnovel = 16 nExemplars = 5 data_train_opt = {} data_train_opt['nKnovel'] = nKnovel data_train_opt['nKbase'] = nKbase data_train_opt['nExemplars'] = nExemplars data_train_opt['nTestNovel'] = nKnovel data_train_opt['nTestBase'] = nKbase data_train_opt['batch_size'] = 4 data_train_opt['epoch_size'] = 4000 data_train_opt['data_dir'] = './datasets/feature_datasets/miniimagenet_ResNet10CosineClassifier' config['data_train_opt'] = data_train_opt config['max_num_epochs'] = 15 num_features = 512 networks = {} networks['feature_extractor'] = { 'def_file': 'feature_extractors.dumb_feat', 'pretrained': None, 'opt': {'dropout': 0}, 'optim_params': None } net_optim_paramsC = { 'optim_type': 'sgd', 'lr': 0.1, 'momentum':0.9, 'weight_decay': 5e-4, 'nesterov': True, 'LUT_lr':[(10, 0.01), (15, 0.001)]} pretrainedC = './experiments/miniimagenet_ResNet10CosineClassifier/classifier_net_epoch100' net_optionsC = { 'num_features': num_features, 'num_classes': 1000, 'global_pooling': False, 'scale_cls': 10.0, 'learn_scale': True, 'dae_config': { 'gaussian_noise': 0.08, 'comp_reconstruction_loss': True, 'targets_as_input': False, 'dae_type': 'RelationNetBasedGNN', 'num_layers': 2, 'num_features_input': num_features, 'num_features_output': 2 * num_features, 'num_features_hidden': 3 * num_features, 'update_dropout': 0.7, 'nun_features_msg': 3 * num_features, 'aggregation_dropout': 0.7, 'topK_neighbors': 10, 'temperature': 5.0, 'learn_temperature': False, }, } networks['classifier'] = { 'def_file': 'classifiers.cosine_classifier_with_DAE_weight_generator', 'pretrained': pretrainedC, 'opt': net_optionsC, 'optim_params': net_optim_paramsC} config['networks'] = networks config['criterions'] = {} config['reconstruction_loss_coef'] = 1.0 config['classification_loss_coef'] = 1.0

[ "qixun.yeo.2016@sis.smu.edu.sg" ]

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import sys import json #Function to display the options during the interactive session def showMenu(): print("Menu:") print("1. Add ToDo item(s): ") print("2. Mark item(s) Complete: ") print("3. List ToDo item(s): ") print("4. Update Item Description: ") print("5. Delete item(s): ") print("6. Delete all items: ") print("7. Exit: ") #Main Function with all the business logic def main(): #variable assignment with default values user_input = '99' item_list = list() create_item_list = list() delete_item_list = list() complete_item_list = list() not_complete_item_list = list() #dictionary variable to hold all the todo items todo_dict = {} #read the items from data.json file at the start and load in todo dictionary with open('data.json') as json_file: todo_dict=json.load(json_file) #read the command line arguments item_list = sys.argv action = item_list[1] #create action to add one or more todo item if action == "create": create_item_list = item_list create_item_list.pop(0) create_item_list.pop(0) for item in create_item_list: todo_dict[item]="ToDo" print("ToDo items created successfully") #List action to list items in the todo list elif action == "list-all": #list all items if len(item_list) == 2: for items in todo_dict: print("[Item]: " + items + " [Status]: " + todo_dict.get(items)) else: #list items containing a keyword / substring if item_list[2] == "--substring": for item in todo_dict: if item.find(item_list[3]) >= 0: print("[Item]: " + item + " [Status]: " + todo_dict.get(item)) #print("Print all with substring " + item_list[3]) #list all items which are complete elif item_list[2] == "--complete": for item in todo_dict: if todo_dict[item] == "Complete": print("[Item]: " + item + " [Status]: " + todo_dict.get(item)) #list all items which are not complete elif item_list[2] == "--no-complete": for item in todo_dict: if todo_dict[item] == "ToDo": print("[Item]: " + item + " [Status]: " + todo_dict.get(item)) else: print("Incorrect list argument - Please check") #Update item description with a new description elif action == "toggle": new_key = item_list[3] old_key = item_list[2] todo_dict[new_key] = todo_dict.pop(old_key) print("ToDo item description successfully updated") #Mark one or more item Complete elif action == "update": complete_item_list=item_list complete_item_list.pop(0) complete_item_list.pop(0) for item in complete_item_list: if todo_dict.get(item,"ItemNotPresent") == "ToDo": todo_dict[item]="Complete" print("Item " + item + " marked complete successfully") elif todo_dict.get(item,"ItemNotPresent") == "Complete": print("Item "+ item + " already marked complete ") else: print("Item " + item + " does not exist in the list ") #Delete one or more item elif action == "delete": delete_item_list = item_list delete_item_list.pop(0) delete_item_list.pop(0) for item in delete_item_list: element = todo_dict.pop(item, "defaultvalue") if element == "defaultvalue": print("Item " + item + " is not present in the list") else: print("Item " + item + " deleted successfully") print("Item deletion complete successfully") #Delete all the items from the list elif action == "delete-all": todo_dict.clear() print("All items deleted successfully") #Interactive session - ToDo List elif action == "interactive": print("Welcome to the Interactive Mode") #Continue with the interactive session until the user keys in option 7 while user_input != '7': #continuously show the interactive session menu showMenu() user_input = input("Enter Your Choice: ") #User to select option 1 to add one or more to items if user_input == '1': item_list = input("Enter the ToDo items ").split() for item in item_list: todo_dict[item]="ToDo" print("Added item: ", item) print("Added item(s) successfully ") #User to select option 2 to mark one or more items complete elif user_input == '2': item_list = input("Enter the item(s) to be marked complete ").split() for item in item_list: if todo_dict.get(item,"ItemNotPresent") == "ToDo": todo_dict[item]="Complete" print("Item "+ item + " successfully marked complete ") elif todo_dict.get(item,"ItemNotPresent") == "Complete": print("Item " + item + " already marked complete ") else: print("Item " + item + " does not exist in the list ") #Option 3 to list all the items in the list elif user_input == '3': print("List of TO-DO Items: ") for items in todo_dict: print("[Item]: " + items + " [Status]: " + todo_dict.get(items)) #Option 4 to Update an item's description elif user_input == '4': old_item = input("Enter the item to update: ") new_item = input("Enter the new item description: ") todo_dict[new_item] = todo_dict.pop(old_item) print("ToDo item description successfully updated") #Option 5 to delete one or more items from the list elif user_input == '5': item_list = input("Enter the items to delete: ").split() for item in item_list: element = todo_dict.pop(item, "defaultvalue") if element == "defaultvalue": print("Item " + item + " is not present in the list") else: print("Item " + item + " deleted successfully") print("Item deletion complete successfully") #Option 6 to delete all the items from the list elif user_input == '6': todo_dict.clear() print("All items deleted successfully") #Option 7 to jump out of the interactive session and end the program elif user_input == '7': #y = json.dumps(todo_dict) #print(y) #with open('data.json', 'w') as outfile: #json.dump(todo_dict, outfile, indent=4) print("Good Bye ") else: print("Incorrect action argument - Please check and try again") #save the current state of the todo dictionary in the file data.json with open('data.json', 'w') as outfile: json.dump(todo_dict, outfile, indent=4) if __name__ == '__main__': main()

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/Compendio/seed/seed/wsgi.py

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JoelTorresAr/seed

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""" WSGI config for seed project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'seed.settings') application = get_wsgi_application()

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/PyTorch/contrib/cv/detection/SOLOv1/mmdet/models/bbox_heads/convfc_bbox_head.py

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# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the BSD 3-Clause License (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://opensource.org/licenses/BSD-3-Clause # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch.nn as nn from ..registry import HEADS from ..utils import ConvModule from .bbox_head import BBoxHead @HEADS.register_module class ConvFCBBoxHead(BBoxHead): r"""More general bbox head, with shared conv and fc layers and two optional separated branches. /-> cls convs -> cls fcs -> cls shared convs -> shared fcs \-> reg convs -> reg fcs -> reg """ # noqa: W605 def __init__(self, num_shared_convs=0, num_shared_fcs=0, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, conv_out_channels=256, fc_out_channels=1024, conv_cfg=None, norm_cfg=None, *args, **kwargs): super(ConvFCBBoxHead, self).__init__(*args, **kwargs) assert (num_shared_convs + num_shared_fcs + num_cls_convs + num_cls_fcs + num_reg_convs + num_reg_fcs > 0) if num_cls_convs > 0 or num_reg_convs > 0: assert num_shared_fcs == 0 if not self.with_cls: assert num_cls_convs == 0 and num_cls_fcs == 0 if not self.with_reg: assert num_reg_convs == 0 and num_reg_fcs == 0 self.num_shared_convs = num_shared_convs self.num_shared_fcs = num_shared_fcs self.num_cls_convs = num_cls_convs self.num_cls_fcs = num_cls_fcs self.num_reg_convs = num_reg_convs self.num_reg_fcs = num_reg_fcs self.conv_out_channels = conv_out_channels self.fc_out_channels = fc_out_channels self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg # add shared convs and fcs self.shared_convs, self.shared_fcs, last_layer_dim = \ self._add_conv_fc_branch( self.num_shared_convs, self.num_shared_fcs, self.in_channels, True) self.shared_out_channels = last_layer_dim # add cls specific branch self.cls_convs, self.cls_fcs, self.cls_last_dim = \ self._add_conv_fc_branch( self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels) # add reg specific branch self.reg_convs, self.reg_fcs, self.reg_last_dim = \ self._add_conv_fc_branch( self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels) if self.num_shared_fcs == 0 and not self.with_avg_pool: if self.num_cls_fcs == 0: self.cls_last_dim *= self.roi_feat_area if self.num_reg_fcs == 0: self.reg_last_dim *= self.roi_feat_area self.relu = nn.ReLU(inplace=True) # reconstruct fc_cls and fc_reg since input channels are changed if self.with_cls: self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes) if self.with_reg: out_dim_reg = (4 if self.reg_class_agnostic else 4 * self.num_classes) self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg) def _add_conv_fc_branch(self, num_branch_convs, num_branch_fcs, in_channels, is_shared=False): """Add shared or separable branch convs -> avg pool (optional) -> fcs """ last_layer_dim = in_channels # add branch specific conv layers branch_convs = nn.ModuleList() if num_branch_convs > 0: for i in range(num_branch_convs): conv_in_channels = ( last_layer_dim if i == 0 else self.conv_out_channels) branch_convs.append( ConvModule( conv_in_channels, self.conv_out_channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) last_layer_dim = self.conv_out_channels # add branch specific fc layers branch_fcs = nn.ModuleList() if num_branch_fcs > 0: # for shared branch, only consider self.with_avg_pool # for separated branches, also consider self.num_shared_fcs if (is_shared or self.num_shared_fcs == 0) and not self.with_avg_pool: last_layer_dim *= self.roi_feat_area for i in range(num_branch_fcs): fc_in_channels = ( last_layer_dim if i == 0 else self.fc_out_channels) branch_fcs.append( nn.Linear(fc_in_channels, self.fc_out_channels)) last_layer_dim = self.fc_out_channels return branch_convs, branch_fcs, last_layer_dim def init_weights(self): super(ConvFCBBoxHead, self).init_weights() for module_list in [self.shared_fcs, self.cls_fcs, self.reg_fcs]: for m in module_list.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) nn.init.constant_(m.bias, 0) def forward(self, x): # shared part if self.num_shared_convs > 0: for conv in self.shared_convs: x = conv(x) if self.num_shared_fcs > 0: if self.with_avg_pool: x = self.avg_pool(x) x = x.flatten(1) for fc in self.shared_fcs: x = self.relu(fc(x)) # separate branches x_cls = x x_reg = x for conv in self.cls_convs: x_cls = conv(x_cls) if x_cls.dim() > 2: if self.with_avg_pool: x_cls = self.avg_pool(x_cls) x_cls = x_cls.flatten(1) for fc in self.cls_fcs: x_cls = self.relu(fc(x_cls)) for conv in self.reg_convs: x_reg = conv(x_reg) if x_reg.dim() > 2: if self.with_avg_pool: x_reg = self.avg_pool(x_reg) x_reg = x_reg.flatten(1) for fc in self.reg_fcs: x_reg = self.relu(fc(x_reg)) cls_score = self.fc_cls(x_cls) if self.with_cls else None bbox_pred = self.fc_reg(x_reg) if self.with_reg else None return cls_score, bbox_pred @HEADS.register_module class SharedFCBBoxHead(ConvFCBBoxHead): def __init__(self, num_fcs=2, fc_out_channels=1024, *args, **kwargs): assert num_fcs >= 1 super(SharedFCBBoxHead, self).__init__( num_shared_convs=0, num_shared_fcs=num_fcs, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, fc_out_channels=fc_out_channels, *args, **kwargs)

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wangjiangben@huawei.com

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jangwoni79/python_study

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refs/heads/master

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# 예시1 import pickle test = ['A', 'B', 'C'] # 피클링 with open ("data.p","wb") as f1: pickle.dump(test,f1) # 언피클링 with open ("data.p","rb") as f2: data = pickle.load(f2) print(data)

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rejwe79@gmail.com

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/tdnn-withRemap-noisy/noise-test.py

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[]

no_license

dnth/short-behavior

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import sys import numpy as np from pybrain.datasets import SequenceClassificationDataSet from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers import BackpropTrainer, RPropMinusTrainer from pybrain import LinearLayer, FullConnection, LSTMLayer, BiasUnit, MDLSTMLayer, IdentityConnection, TanhLayer, SoftmaxLayer from pybrain.utilities import percentError from pybrain.tools.customxml.networkwriter import NetworkWriter from pybrain.tools.customxml.networkreader import NetworkReader import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix from naoqi import ALProxy import Image import time import theanets import vision_definitions from numpy.random.mtrand import randint from numpy import argmax from random import randint from scipy.interpolate import interp1d BallLiftJoint = np.loadtxt('../../20fpsFullBehaviorSampling/BallLift/JointData.txt').astype(np.float32) BallRollJoint = np.loadtxt('../../20fpsFullBehaviorSampling/BallRoll/JointData.txt').astype(np.float32) BellRingLJoint = np.loadtxt('../../20fpsFullBehaviorSampling/BellRingL/JointData.txt').astype(np.float32) BellRingRJoint = np.loadtxt('../../20fpsFullBehaviorSampling/BellRingR/JointData.txt').astype(np.float32) BallRollPlateJoint = np.loadtxt('../../20fpsFullBehaviorSampling/BallRollPlate/JointData.txt').astype(np.float32) RopewayJoint = np.loadtxt('../../20fpsFullBehaviorSampling/Ropeway/JointData.txt').astype(np.float32) jointRemap = interp1d([-2.2,2.2],[-1,1]) BallLiftJoint = jointRemap(BallLiftJoint) BallRollJoint = jointRemap(BallRollJoint) BellRingLJoint = jointRemap(BellRingLJoint) BellRingRJoint = jointRemap(BellRingRJoint) BallRollPlateJoint = jointRemap(BallRollPlateJoint) RopewayJoint = jointRemap(RopewayJoint) tdnnclassifier = NetworkReader.readFrom('25sigmoid/TrainUntilConv.xml') print 'Loaded 25 sigmoid TDNN Trained Network!' twentylstmaccdata = [] twentylstmstddata = [] twentylstmstderror = [] predictedBLLabels = [] predictedBRLabels = [] predictedBRLLabels = [] predictedBRRLabels = [] predictedBRPLabels = [] predictedRWLabels = [] print "1st Iteration, noiseless test data" offset = 100 accuracyOverall = [] for testnumber in range(30): start = randint(8000,9980) x = tdnnclassifier.activate(BallLiftJoint[start:start+10].flatten()) predictedBLLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BallRollJoint[start:start+10].flatten()) predictedBRLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BellRingLJoint[start:start+10].flatten()) predictedBRLLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BellRingRJoint[start:start+10].flatten()) predictedBRRLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BallRollPlateJoint[start:start+10].flatten()) predictedBRPLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(RopewayJoint[start:start+10].flatten()) predictedRWLabels.append(argmax(x)) testnumAcc = [] behaviorAccuracyfortestnumber = [] for testnumber in range(30): BLAcc = 100-percentError(predictedBLLabels[testnumber], [0]) BRAcc = 100-percentError(predictedBRLabels[testnumber], [1]) BRLAcc = 100-percentError(predictedBRLLabels[testnumber], [2]) BRRAcc = 100-percentError(predictedBRRLabels[testnumber], [3]) BRPAcc = 100-percentError(predictedBRPLabels[testnumber], [4]) RWAcc = 100-percentError(predictedRWLabels[testnumber], [5]) behaviorAccuracyfortestnumber.append((BLAcc + BRAcc + BRLAcc + BRRAcc + BRPAcc + RWAcc) / 6) print behaviorAccuracyfortestnumber print "Mean Accuracy for 30 trials:", np.mean(np.array(behaviorAccuracyfortestnumber)) print "Std Deviation for 30 trials:", np.std(np.array(behaviorAccuracyfortestnumber)) twentylstmaccdata.append(np.mean(np.array(behaviorAccuracyfortestnumber))) twentylstmstddata.append(np.std(np.array(behaviorAccuracyfortestnumber))) print "Length of data (iteration number):",len(twentylstmaccdata) # ######## with noise ###### std_deviation = 0 mean = 0 while (std_deviation<=2.0): std_deviation += 0.1 print "Gaussian Noise std deviation:",std_deviation predictedBLLabels = [] predictedBRLabels = [] predictedBRLLabels = [] predictedBRRLabels = [] predictedBRPLabels = [] predictedRWLabels = [] offset = 100 accuracyOverall = [] for testnumber in range(30): # test for 30 times BallLiftJoint = BallLiftJoint + np.random.normal(mean,std_deviation,(10000,10)) BallRollJoint = BallRollJoint + np.random.normal(mean,std_deviation,(10000,10)) BellRingLJoint = BellRingLJoint + np.random.normal(mean,std_deviation,(10000,10)) BellRingRJoint = BellRingRJoint + np.random.normal(mean,std_deviation,(10000,10)) BallRollPlateJoint = BallRollPlateJoint + np.random.normal(mean,std_deviation,(10000,10)) RopewayJoint = RopewayJoint + np.random.normal(mean,std_deviation,(10000,10)) start = randint(8000,9980) # randomly select any data in this range x = tdnnclassifier.activate(BallLiftJoint[start:start+10].flatten()) predictedBLLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BallRollJoint[start:start+10].flatten()) predictedBRLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BellRingLJoint[start:start+10].flatten()) predictedBRLLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BellRingRJoint[start:start+10].flatten()) predictedBRRLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(BallRollPlateJoint[start:start+10].flatten()) predictedBRPLabels.append(argmax(x)) start = randint(8000,9980) x = tdnnclassifier.activate(RopewayJoint[start:start+10].flatten()) predictedRWLabels.append(argmax(x)) testnumAcc = [] behaviorAccuracyfortestnumber = [] for testnumber in range(30): BLAcc = 100-percentError(predictedBLLabels[testnumber], [0]) BRAcc = 100-percentError(predictedBRLabels[testnumber], [1]) BRLAcc = 100-percentError(predictedBRLLabels[testnumber], [2]) BRRAcc = 100-percentError(predictedBRRLabels[testnumber], [3]) BRPAcc = 100-percentError(predictedBRPLabels[testnumber], [4]) RWAcc = 100-percentError(predictedRWLabels[testnumber], [5]) behaviorAccuracyfortestnumber.append((BLAcc + BRAcc + BRLAcc + BRRAcc + BRPAcc + RWAcc) / 6) # print behaviorAccuracyfortestnumber print "Mean Accuracy for 30 trials:", np.mean(np.array(behaviorAccuracyfortestnumber)) print "Std Deviation for 30 trials:", np.std(np.array(behaviorAccuracyfortestnumber)) twentylstmaccdata.append(np.mean(np.array(behaviorAccuracyfortestnumber))) twentylstmstddata.append(np.std(np.array(behaviorAccuracyfortestnumber))) print "Length of data (iteration number)",len(twentylstmaccdata) print twentylstmaccdata print twentylstmstddata for i in range(21): twentylstmstderror.append(twentylstmstddata[i]/np.sqrt(30)) print twentylstmstderror np.savetxt("AccuracyData.txt",twentylstmaccdata ) np.savetxt("SigmaData.txt",twentylstmstddata ) np.savetxt("ErrorBarData.txt",twentylstmstderror ) plt.errorbar(y=twentylstmaccdata, x=np.arange(0.0,2.1,0.1), yerr=twentylstmstderror, label="25 Sigmoid TDNN", linewidth=2) plt.xlim([0.0,2.1]) plt.xlabel(r"$\sigma$") plt.ylabel("Classification Accuracy (%)") plt.grid() plt.legend() plt.show()

[ "dickson.neoh@gmail.com" ]

dickson.neoh@gmail.com

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/2/aula2.py

b19d4eb7e6fd34ac207ded54f27128f6e89fa3c3

[]

no_license

dudu9999/visualizacao-de-dados-com-python

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f59dad2d6305d28cef86474e5934bdc317208742

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2022-04-19T16:12:08.838576

2020-04-16T04:13:27

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py

import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [2, 3, 7, 1, 0] titulo = 'Grafico de barras' eixox = 'Eixo X' eixoY = 'Eixo Y' plt.title(titulo) plt.xlabel(eixox) plt.ylabel(eixoY) plt.bar(x, y) plt.show()

[ "ecaetanocorrea@gmail.com" ]

ecaetanocorrea@gmail.com

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/blog/migrations/0001_initial.py

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[]

no_license

mrKondor/my-first-blog

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2020-04-02T03:27:28.790479

2018-11-01T18:28:42

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# Generated by Django 2.0.9 on 2018-10-21 00:33 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Post', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('text', models.TextField()), ('created_date', models.DateTimeField(default=django.utils.timezone.now)), ('published_date', models.DateTimeField(blank=True, null=True)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "acm.96@hotmail.com" ]

acm.96@hotmail.com

e93a66077853a8f7718493f7385071ed80a5b2c3

0df1450fbd2e1f71280df74fd1c1204a2bf0a528

/app.py

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[]

no_license

sstone63/Web-Scraping-Project

b0c3ab9d70b50151a1e1386c0b01ae1f71c642c0

871b846dfd3ff7d057b0e00d3ee6f03e3708e08f

refs/heads/master

2020-03-17T18:33:09.252635

2018-06-05T00:04:55

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from flask import Flask, render_template, redirect import mars_scrape import pymongo from pymongo import MongoClient app = Flask(__name__) conn = 'mongodb://localhost:27017' client = pymongo.MongoClient(conn) db = client.mars_database collection = db.mars_database @app.route("/") def home(): scrape_dict = collection.find_one() return render_template("index.html", dict=scrape_dict) @app.route("/scrape") def scrape_mars(): mars_dict = mars_scrape.scrape() collection.update({}, {"$set": mars_dict}, upsert=True) return redirect("http://localhost:5000/", code=302) if __name__ == "__main__": app.run(debug=True)

[ "noreply@github.com" ]

sstone63.noreply@github.com

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/src/ur5_joint_publisher/scripts/joint_publisher

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[]

no_license

ARB92/src

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refs/heads/master

2023-09-02T12:42:42.829081

2021-10-30T12:46:26

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184

#! /usr/bin/env python from ur5_joint_publisher.joint_state_publisher import main if __name__ == '__main__': try: main() except (KeyboardInterrupt): exit(1)

[ "amarnath.bj@gmail.com" ]

amarnath.bj@gmail.com

a582bef6f403387d9252b56d50ad2d8bc16c825f

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/moodledata/vpl_data/63/usersdata/163/31391/submittedfiles/swamee.py

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[]

no_license

rafaelperazzo/programacao-web

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refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

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# -*- coding: utf-8 -*- import math #COMECE SEU CÓDIGO AQUI f=float(input('Digite f:')) L=float(input('Digite L:')) Q=float(input('Digite Q:')) DeltaH=float(input('Digite DeltaH:')) v=float(input('Digite v:')) g=9.81 e=0.000002 D=((8*f*L*Q**2)/(math.pi**2*g*DeltaH))**0.2 Rey=(4*Q)/(math.pi*D*v) k=0.25/(math.log10((e/(3.7*D))+(5.74)/(Rey**0.9)))**2 print('D: %4f'%D) print('Rey:%.4f'%Rey) print('k:%.4f' %k)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

5dda73c1dd335e8521a51e09fb8e9ef5800ac557

48eee46f6e852ff15bcb87c9e54991cf64f93ad6

/src/main.py

bfdae7067145cdde2adabe0b8033502afb1ad78c

[]

no_license

lxjlu/learning-in-games

9e5645595f6b7ff2731e21d6f5c0f95cf8b84e7e

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refs/heads/master

2023-09-01T06:51:18.875732

2021-01-09T06:20:55

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from __future__ import print_function import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import StepLR from loaders import load import time import pandas import sys from ast import literal_eval import numpy as np from torch import autograd import os from tensorboardX import SummaryWriter from uuid import uuid4 from tqdm.auto import tqdm from helpers import add_argument, fstr, save_dict import defaults class Net(nn.Module): """ default pytorch example """ def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output class Perturb(nn.Module): """ constant perturbation """ def __init__(self, shape): super(Perturb, self).__init__() self.delta = nn.Parameter(torch.zeros(shape)) def forward(self): return self.delta def config(**kwargs): # Training settings parser = argparse.ArgumentParser(description='learning in games') train = parser.add_argument_group('train') test = parser.add_argument_group('test') run = parser.add_argument_group('run') add_argument(train, 'seed', 1, 'random seed', 'S') add_argument(train, 'dataset', 'mnist', 'dataset', choices=['mnist']) add_argument(train, 'batch_size', 200, 'input batch size for training', 'N') add_argument(train, 'test_batch_size', 1000, 'input batch size for testing', 'N') add_argument(train, 'epochs', 20, 'number of epochs to train', 'N') add_argument(train, 'lr1', 0.2, 'learning rate for classifier', 'LR') add_argument(train, 'lr2', 1.0, 'learning rate for adversary', 'LR') add_argument(train, 'lr_rate1', 1e-5, 'learning rate decay const for classifier', 'M') add_argument(train, 'lr_class1', '1/t', 'learning rate decay function', 'FN', choices=['1/t','1/tlogt'] ) add_argument(train, 'lr_rate2', 3e-6, 'learning rate decay const for adversary', 'M') add_argument(train, 'lr_class2', '1/tlogt', 'learning rate decay function', 'FN', choices=['1/t','1/tlogt'] ) add_argument(train, 'perturb_reg', 0.000001, 'regularization on adversarial perturbation', 'REG') add_argument(run, 'no_cuda', False, 'disables CUDA training') add_argument(run, 'save_model', True, 'For Saving the current Model') add_argument(run, 'log_interval', 10, 'how many batches to wait before logging training status', 'N') add_argument(run, 'datadir', 'data', 'directory of dataset') add_argument(run, 'storedir', defaults.STORE_DIR, 'directory to store checkpoints') add_argument(run, 'epoch', 1, 'Epoch to resume running at') add_argument(run, 'log_smooth', 0.5, 'logging smoothness parameter') add_argument(run, 'last_iter', -1, 'Last iteration') add_argument(test, 'adv_epsilon', 1., 'magnitude of adversarial perturbation') add_argument(test, 'adv_norm', 'inf', 'norm of adversarial perturbation', \ choices=['abs', 'l2','inf']) parser.set_defaults(**kwargs) try: # hack for detecting a jupyter lab notebook if get_ipython().__class__.__name__ == 'ZMQInteractiveShell': args = parser.parse_args('') except: args = parser.parse_args() return args def init(args, device, shape, last_iter=-1): model = Net().to(device) perturb = Perturb(shape).to(device) def lr(t, mode='1/t'): if mode == '1/t': return t elif mode == '1/tlogt': return t*np.log(t+1) else: raise NotImplemented opt1 = optim.SGD(model.parameters(), lr=args.lr1 ) lr1 = lambda t: args.lr1/(args.lr_rate1*lr(t, mode=args.lr_class1) + 1) sch1 = optim.lr_scheduler.LambdaLR(opt1, lr1, last_epoch=-1)#args.last_iter) opt2 = optim.SGD(perturb.parameters(), lr=args.lr2) lr2 = lambda t: args.lr2/(args.lr_rate2*lr(t, mode=args.lr_class2) + 1) sch2 = optim.lr_scheduler.LambdaLR(opt2, lr2, last_epoch=-1)#args.last_iter) return (model, perturb), (opt1, opt2), (sch1, sch2) def loss(model, delta, batch): (data, target) = batch output = model(data + delta) return F.nll_loss(output, target) def train(state, args, models, device, loader, optimizers, schedulers, logger): model, perturb = models model.train() iterator = tqdm(enumerate(loader), total=len(loader)) f1_smooth = 0 f2_smooth = 0 for batch_idx, (data, target) in iterator: batch = data.to(device), target.to(device) data, target = batch optimizers[0].zero_grad() delta = perturb() f1 = loss(model, delta, batch) f1.backward() optimizers[0].step() optimizers[1].zero_grad() delta = perturb() perturb_norm = torch.sum(delta*delta)/2 f2 = -loss(model, delta, batch) + args.perturb_reg*perturb_norm f2.backward() optimizers[1].step() f1_smooth = (1-args.log_smooth)*f1_smooth + args.log_smooth*f1 f2_smooth = (1-args.log_smooth)*f2_smooth + args.log_smooth*f2 out = {'loss0':f1, 'loss1':f2, 'loss_sum':f1+f2, 'norm_delta':perturb_norm} if batch_idx % args.log_interval == 0: logger.append(state['iter'], out) desc = (f'{args.loop_msg} | Loss: {f1_smooth:6.3f},{f2_smooth:6.3f} | norm(delta):{perturb_norm:8.5f} ||') iterator.set_description(desc) iterator.refresh() schedulers[0].step() schedulers[1].step() state['iter'] += 1 def test(state, args, model, device, test_loader, logger): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): iterator = tqdm(enumerate(test_loader), total=len(test_loader)) for idx, (data, target) in iterator: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() desc = (f'Test | Loss: {test_loss:10.3f}, {correct}/{len(test_loader.dataset)}({correct/((idx+1)/test_loader.batch_size*len(test_loader.dataset))}%)') iterator.set_description(desc) iterator.refresh() test_loss /= len(test_loader.dataset) out = {'test_accuracy': correct/len(test_loader.dataset)} logger.append(state['iter'], out) return out def test_adv(state, args, model, device, test_loader, logger=None, loop_msg='Adversarial Test'): model.eval() iterator = tqdm(enumerate(test_loader), total=len(test_loader)) correct = 0 adv_correct = 0 for idx, (data, target) in iterator: data, target = data.to(device), target.to(device) perturb = torch.tensor(torch.zeros(*test_loader.shape), requires_grad=True, device=device) output = model(data + perturb) loss = F.nll_loss(output, target) pred = output.argmax(dim=1, keepdim=True) correct += pred.eq(target.view_as(pred)).sum().item() Dperturb_loss = autograd.grad(loss, perturb)[0] if args.adv_norm == 'infty': Dperturb_loss = torch.sign(Dperturb_loss) elif args.adv_norm == 'l2': Dperturb_loss /= torch.norm(Dperturb_loss) Dperturb_loss *= torch.norm(torch.ones(*Dperturb_loss.shape)) elif args.adv_norm == 'noise': Dperturb_loss = torch.rand_like(Dperturb_loss) else: raise NotImplemented() adv_data = data + args.adv_epsilon*Dperturb_loss adv_output = model(adv_data) adv_pred = adv_output.argmax(dim=1, keepdim=True) adv_correct += adv_pred.eq(target.view_as(adv_pred)).sum().item() desc = (f'Test ({args.adv_epsilon:.2f}-{args.adv_norm}) | Loss: {loss:8.3f}, {adv_correct}/{len(test_loader.dataset)}') iterator.set_description(desc) accuracy = correct/len(test_loader.dataset) adv_accuracy = adv_correct/len(test_loader.dataset) out = {'test_accuracy': accuracy, 'adv_accuracy': adv_accuracy, 'adv_data': adv_data} if logger: logger.append(state['iter'], out) return out class Logger(): def __init__(self, writer=None): self.df = pandas.DataFrame() self.writer = writer def append(self, iter, other): self.df = self.df.append(other, ignore_index=True) if self.writer: for arg,val in other.items(): self.writer.add_scalar(arg, val, iter) def to_pickle(self, path): self.df.to_pickle(path) def eval(exp_dir, epoch): with open(os.path.join(exp_dir, 'args.txt'), 'r') as f: kwargs = literal_eval(f.readline()) args = config(**kwargs) logger = Logger() print(f"lr1={args.lr1} lr2={args.lr2}") (train_loader, test_loader), device = load(args) models, optimizers = init(args, device, shape=train_loader.shape) state = {"iter": np.nan} save_model = os.path.join(exp_dir, f'save{epoch:03d}.pt') save_perturb = os.path.join(exp_dir, f'save_perturb{epoch:03d}.pt') out = {} try: models[0].load_state_dict(torch.load(save_model)) models[1].load_state_dict(torch.load(save_perturb)) out = test(state, args, models[0], device, test_loader, logger) delta = [_ for _ in models[1].parameters()][0] img = torchvision.utils.make_grid(delta, normalize=True) torchvision.utils.save_image(img, os.path.join(exp_dir, f'perturb{epoch:03d}.png')) except: print("model not found") return dict(lr1=args.lr1, lr2=args.lr2, **out) def main(exp_id=str(uuid4())): state = dict(iter=0, start_time=time.time()) args = config(exp_id=exp_id) # try to make the store dir (if it doesn't exist) exp_dir = os.path.join(args.storedir, exp_id) try: os.makedirs(exp_dir) except OSError as e: print("Directory exists ({e.message})") writer = SummaryWriter(exp_dir) logger = Logger(writer) (train_loader, test_loader), device = load(args) models, optimizers, schedulers = init(args, device, shape=train_loader.shape, last_iter=args.last_iter) for epoch in range(1, args.epochs + 1): args.epoch = epoch args.last_iter = epoch*len(train_loader) args.loop_msg = fstr(defaults.LOOP_MSG, args=args) train(state, args, models, device, train_loader, optimizers, schedulers, logger) test(state, args, models[0], device, test_loader, logger) logger.to_pickle(os.path.join(args.storedir, exp_id, 'store.pkl')) if args.save_model: for model, savefile in zip(models, defaults.SAVE_FILES): torch.save(model.state_dict(), os.path.join(exp_dir, fstr(savefile, args=args))) save_dict(vars(args), os.path.join(exp_dir, defaults.ARG_FILE)) if __name__ == '__main__': main()

[ "bchasnov@uw.edu" ]

bchasnov@uw.edu

c44da2b7fa7cc87fdbd0202f28766b04a22d2ee1

3580970221ec8fe048c2af4bdfdab8244396eed8

/b45.py

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nt=input() print(len(nt))

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ruivieira/ccfd-seldon-model

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import pandas as pd import joblib from sklearn.ensemble import RandomForestClassifier df = pd.read_csv('data/creditcard-sample10k.csv') features_train = df.sample(frac=0.75, random_state=100) features_test = df[~df.index.isin(features_train.index)] drop_time_class = ['Time','Class','V1','V2','V5','V6','V7','V8','V9','V13','V15','V16','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28'] drop_class=['Class'] features_train = features_train.loc[:, ~features_train.columns.str.contains('^Unnamed')] features_test = features_test.loc[:, ~features_test.columns.str.contains('^Unnamed')] target_train = features_train['Class'] target_test = features_test['Class'] features_train = features_train.drop(drop_time_class, axis=1) features_test = features_test.drop(drop_time_class, axis=1) model = RandomForestClassifier(n_estimators=200, max_depth=6, n_jobs=10, class_weight='balanced') model.fit(features_train, target_train.values.ravel()) pred_train = model.predict(features_train) pred_test = model.predict(features_test) pred_train_prob = model.predict_proba(features_train) pred_test_prob = model.predict_proba(features_test) print("Number of features") print(len(model.feature_importances_)) #save mode in filesystem joblib.dump(model, 'model.pkl')

[ "ruidevieira@googlemail.com" ]

ruidevieira@googlemail.com

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/Techcators.py

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[]

no_license

JunXHuang/ForexTrading

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refs/heads/master

2021-01-25T08:01:32.737963

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def MovingAverage(data,start,end): sum = 0 for i in range(start,end): sum += float(data['candles'][i]['mid']['c']) return sum/(end-start) # need to fix calculations def RSI(data,period): gain = 0 loss = 0 RS = 0 for i in range(1,period-1): temp = float(data['candles'][i]['mid']['c'])-float(data['candles'][i-1]['mid']['c']) if temp > 0: gain += temp else: loss += abs(temp) gain /= (period-2) loss /= (period-2) currentCandle = float(data['candles'][period-1]['mid']['c'])-float(data['candles'][period-2]['mid']['c']) if currentCandle > 0: gain = (gain * (period-3) + currentCandle) / (period-2) loss = (loss * (period-3)) / (period-2) else: if loss == 0 and currentCandle == 0: return 100.0 gain = (gain * (period-3)) / (period-2) loss = (loss * (period-3) - currentCandle) / (period-2) RS = gain/loss return 100 - (100 / ( 1 + RS )) print str(Techcators.MovingAverage(getCandles(50,'H1'),50)) print str(Techcators.RSI(getCandles(16,'H1'),16))

[ "junxiong.huang@stonybrook.edu" ]

junxiong.huang@stonybrook.edu

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/login-system/project/settings.py

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joao-lanzarini/django-login-system

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2021-08-24T05:17:03

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""" Django settings for project project. Generated by 'django-admin startproject' using Django 3.2.6. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-+h%i+l%0bj7xdsl1c@o06kx@9&c$n1g*(g4od@*v3#0_7+s&*1' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'project.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

[ "joaovitorlsilva02@gmail.com" ]

joaovitorlsilva02@gmail.com

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/Keras/cnn/test.py

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no_license

ngamc/Python

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2020-03-16T06:17:29.523789

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# -*- coding: utf-8 -*- """ Created on Mon May 28 13:05:38 2018 @author: user """ import numpy as np #y = [0,2] #y = np.asarray(y) # #x = [[0,0], # [1,1], # [2,2], # [3,3], # [4,4]] #x = np.asarray(x) # #print(x.shape) # #z = x[[1,2],:] #print(z) a = np.array(((1,2),(2,3))) print(a.shape) print(a) a=a.reshape(-1, len(a)) print(a.shape) print(a)

[ "ngamc@yahoo.com" ]

ngamc@yahoo.com

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/75_validate_ipaddress.py

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[]

no_license

gautamgitspace/leetcode_30-day_challenge

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2021-05-21T01:14:46.302340

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class Solution(object): def validIPAddress(self, IP): def isIPv4(s): try: return str(int(s)) == s and 0 <= int(s) <= 255 except: return False def isIPv6(s): if len(s) > 4: return False try: return int(s, 16) >= 0 and s[0] != '-' except: return False if IP.count(".") == 3 and all(isIPv4(i) for i in IP.split(".")): return "IPv4" if IP.count(":") == 7 and all(isIPv6(i) for i in IP.split(":")): return "IPv6" return "Neither"

[ "agautam2@buffalo.edu" ]

agautam2@buffalo.edu

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/Python-Crash-Course/第一部分基础知识/第04章操作列表/4-07 3的倍数.py

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YilK/Notes

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refs/heads/master

2023-01-29T11:24:56.781941

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''' 创建一个列表，其中包含3~30内能被3整除的数字；再使用一个for 循环将这个列表中的数字都打印出来。 ''' numbers=list(range(3,30+1,3)) for number in numbers: print(number)

[ "huangjk0311@126.com" ]

huangjk0311@126.com

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kdougan/sheep-pygame

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refs/heads/main

2023-07-14T00:54:37.663029

2021-08-30T07:24:30

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py

gravity = 600 window_width = 1280 window_height = 720 window_size = (window_width, window_height) display_padding = 16 ground_height = 16

[ "kdougan@apple.com" ]

kdougan@apple.com

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/2020/day03.py

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[]

no_license

kratsg/advent-of-code

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672dfa34e811030c8d67e5466d1be218395e1ff3

refs/heads/master

2023-08-09T10:48:52.046121

2023-07-26T02:04:22

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2019-12-02T16:40:18

Python

UTF-8

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py

import numpy as np from io import StringIO def process_input(data): myfile = StringIO(data.replace(".", "0").replace("#", "1")) return np.genfromtxt(myfile, delimiter=1, dtype=int) def generate_steps(mapdata, start=None, step=None): indices = [] start = start if start is not None else np.array([0, 0]) step = step if step is not None else np.array([1, 3]) # right 3, down 1 position = start # while less than total height of map while position[0] < mapdata.shape[0]: position[1] = position[1] % mapdata.shape[1] indices.append(tuple(position)) position += step # skip first step return indices[1:] def get_encounters(mapdata, start=None, step=None): steps = generate_steps(mapdata, start=start, step=step) indices = tuple(zip(*steps)) return mapdata[indices] if __name__ == "__main__": from aocd.models import Puzzle test_vals = process_input( """..##....... #...#...#.. .#....#..#. ..#.#...#.# .#...##..#. ..#.##..... .#.#.#....# .#........# #.##...#... #...##....# .#..#...#.#""" ) encounters = get_encounters(test_vals, step=np.array([1, 3])) assert len(encounters) == 10 assert encounters.tolist() == [0, 1, 0, 1, 1, 0, 1, 1, 1, 1] assert np.sum(encounters) == 7 puz = Puzzle(2020, 3) data = process_input(puz.input_data) encounters = get_encounters(data, step=np.array([1, 3])) puz.answer_a = np.sum(encounters) print(f"Part 1: {puz.answer_a}") test_encounters_1_1 = get_encounters(test_vals, step=np.array([1, 1])) test_encounters_3_1 = get_encounters(test_vals, step=np.array([1, 3])) test_encounters_5_1 = get_encounters(test_vals, step=np.array([1, 5])) test_encounters_7_1 = get_encounters(test_vals, step=np.array([1, 7])) test_encounters_1_2 = get_encounters(test_vals, step=np.array([2, 1])) assert np.sum(test_encounters_1_1) == 2 assert np.sum(test_encounters_3_1) == 7 assert np.sum(test_encounters_5_1) == 3 assert np.sum(test_encounters_7_1) == 4 assert np.sum(test_encounters_1_2) == 2 assert ( np.sum(test_encounters_1_1) * np.sum(test_encounters_3_1) * np.sum(test_encounters_5_1) * np.sum(test_encounters_7_1) * np.sum(test_encounters_1_2) == 336 ) encounters_1_1 = get_encounters(data, step=np.array([1, 1])) encounters_3_1 = get_encounters(data, step=np.array([1, 3])) encounters_5_1 = get_encounters(data, step=np.array([1, 5])) encounters_7_1 = get_encounters(data, step=np.array([1, 7])) encounters_1_2 = get_encounters(data, step=np.array([2, 1])) puz.answer_b = ( np.sum(encounters_1_1) * np.sum(encounters_3_1) * np.sum(encounters_5_1) * np.sum(encounters_7_1) * np.sum(encounters_1_2) ) print(f"Part 2: {puz.answer_b}")

[ "kratsg@gmail.com" ]

kratsg@gmail.com

b2c6fa6db2c42c325e241c0a6d603b7d0553ef5b

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/PDE_Models/applications/nonlinear/scalability_data/pWGF_31x31.py

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[]

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daveb-dev/pWGD

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refs/heads/master

2023-03-14T00:09:25.799270

2021-02-12T00:40:12

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from model_lognormal_31x31 import * import time # check the stein/options to see all possible choices options["type_optimization"] = "gradientDescent" options["is_projection"] = True options["tol_projection"] = 1.e-2 options["type_projection"] = "fisher" options["is_precondition"] = False options["type_approximation"] = "fisher" options["coefficient_dimension"] = 256 options["add_dimension"] = 0 options["number_particles"] = 64 options["number_particles_add"] = 0 options["add_number"] = 0 options["add_step"] = 5 options["add_rule"] = 1 options["type_scaling"] = 1 options["type_metric"] = "posterior_average" # posterior_average options['WGF'] = True options["type_Hessian"] = "lumped" options["low_rank_Hessian"] = False options["rank_Hessian"] = 256 options["rank_Hessian_tol"] = 1.e-2 options["low_rank_Hessian_average"] = False options["rank_Hessian_average"] = 256 options["rank_Hessian_average_tol"] = 1.e-2 options["gauss_newton_approx"] = True # if error of unable to solve linear system occurs, use True options["max_iter"] = 200 options["step_tolerance"] = 1e-7 options["step_projection_tolerance"] = 1e-3 options["line_search"] = True options["search_size"] = 1e-1 options["max_backtracking_iter"] = 10 options["cg_coarse_tolerance"] = 0.5e-2 options["print_level"] = -1 options["save_number"] = 20 options["plot"] = True # generate particles particle = Particle(model, options, comm) # evaluate the variation (gradient, Hessian) of the negative log likelihood function at given particles variation = Variation(model, particle, options, comm) # evaluate the kernel and its gradient at given particles kernel = Kernel(model, particle, variation, options, comm) t0 = time.time() solver = GradientDescent(model, particle, variation, kernel, options, comm) solver.solve() print("GradientDecent solving time = ", time.time() - t0)

[ "peng@ices.utexas.edu" ]

peng@ices.utexas.edu

26c99f496eb2c2b0d4c205d7b100328d1b9ace92

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/evaluate/evaluate.py

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[]

no_license

sdwldchl/ccks2021FEE

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5ac02f1e4b0774e0751fd8af26ed13e9b752c5d5

refs/heads/master

2023-06-20T15:19:32.226145

2021-07-21T12:19:53

null

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null

UTF-8

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py

import json result_type = ["reason_region", "reason_product", "reason_industry", "result_region", "result_product", "result_industry"] ans_path = '../data/dev.json' dev_path = '../argument/2021-07-17_18-36-15/finnalAns.json' pred = 0 right = 0 total = 0 id_idx = {} f = open(ans_path, encoding="utf-8") ans = [json.loads(line.strip()) for line in f] for i in range(len(ans)): id_idx[ans[i]['text_id']] = i g = open(dev_path, encoding="utf-8") dev = [json.loads(line.strip()) for line in g] for dev_line in dev: id = dev_line['text_id'] if id in id_idx.keys(): ans_line = ans[id_idx[id]] d = {} a = {} for r in dev_line['result']: rt = r['result_type'] + '#' + r['reason_type'] if rt not in d.keys(): d[rt] = {} for t in result_type: d[rt][t] = set() for t in result_type: for it in r[t].split(','): d[rt][t].add(it) for r in ans_line['result']: rt = r['result_type'] + '#' + r['reason_type'] if rt not in a.keys(): a[rt] = {} for t in result_type: a[rt][t] = set() for t in result_type: for it in r[t].split(','): a[rt][t].add(it) for rt in d.keys(): if rt in a.keys(): for t in result_type: tmp = a[rt][t] & d[rt][t] right += len(tmp) total += len(a[rt][t]) pred += len(d[rt][t]) else: for t in result_type: pred += len(d[rt][t]) else: for r in dev_line['result']: for t in result_type: pred += len(r[t].split(',')) print(right, pred, right) p1 = right / pred r1 = right / total f1 = 2.0 * p1 * r1 / (p1 + r1) log = f'p: {p1:.6f}, r: {r1:.6f}, f1: {f1:.6f}' print(log)

[ "2667002321@qq.com" ]

2667002321@qq.com

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no_license

cmdoffing/LCD

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refs/heads/master

2021-12-14T03:07:18.783580

2021-11-25T23:13:00

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null

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py

# This program displays LCD digits, given a command line size # parameter (s = param 1) and a number to display (param 2). # Let s be the number of horizontal segments, then each LCD digit will # occupy s + 2 positions and 2s + 3 vertical rows. # There must be one column of blanks between two digits. # This file handles the command line interface. # Sample input line: lcd.py 2 12345 from lcdNumber import LcdNumber import sys lcdNum = LcdNumber( int( sys.argv[1]), sys.argv[2] ) displayDigits = lcdNum.lcdDisplay() print( ''.join( displayDigits ))

[ "noreply@github.com" ]

cmdoffing.noreply@github.com

0c1887a63b6181781a9c9f8719bae7184afab5a4

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/core/arg_parser.py

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no_license

gmum/3d-point-clouds-autocomplete

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2023-06-25T07:37:08.472315

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import argparse import json def parse_config(): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', default=None, type=str, help='config file path') args = parser.parse_args() config = None if args.config is not None and args.config.endswith('.json'): with open(args.config) as f: config = json.load(f) assert config is not None return config

[ "art.kasymov@gmail.com" ]

art.kasymov@gmail.com

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no_license

Ericgig/Remez

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e82ed0edd88dc212d016c72f6ad84094e437179f

refs/heads/master

2020-12-31T04:55:48.055135

2016-05-20T19:35:29

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py

#Remez make table based on least square # # For x^p, -1<p<1, x_min<=x<=x_max find # a0,a[i],b[i] (i=1:k) so that a0+sum(a[i]/(x+b[i])) ~ x^p # within err # # Input # p : power ,-1 < p < 1 # x_min : inferior limit of the of the approximation , > 0 # x_max : superior limit of the of the approximation , > x_min # k : number of term of the approximation , >= 0 # # Output # a0, a[i], b[i] : terms of the approximation # err : maximum error of the best fit # # 1) scipy least-square # => Can't get double precision on fit, is sattisfied with the convergence too easily. # Want a maximum absolute error of at most 10e-15 on any point in the range # # try 2) less points to fit but stricter condition # => Not enough control with least_square, converge before condition # 3) Change the coefficient : d = a/b # # # # import numpy as np import scipy as sp from scipy.optimize import leastsq type = np.float64 #make the function def make_fx_lin(x_min,x_max,p,N): #X = x_min+np.arange(N,dtype=type)*((x_max-x_min)/(N-1)) X = np.linspace(x_min, x_max, N, endpoint=True, dtype=type) Y = X**p return X,Y def make_fx_log(x_min,x_max,p,N): #step = np.exp(np.log(x_max/x_min)/(N-1)) #X = x_min*step**np.arange(N,dtype=type) X = np.logspace(x_min, x_max, N, endpoint=True, dtype=type) Y = X**p return X,Y def make_fx(x_min,x_max,p,N): x1,y1 = make_fx_log(x_min,x_max,p,N/2) x2,y2 = make_fx_log(x_min,x_max,p,N/2) X = np.concatenate((x1,x2)) Y = np.concatenate((y1,y2)) return X,Y def f_approx(x, coef): #x vector no scalar k = (len(coef)-1)/2 y = x*0+coef[0] for i in range(len(x)): y[i] += np.sum(coef[1:k+1]/(1+x[i]/coef[k+1:])) return y def err_func(coef, x, y): return (f_approx(x, coef)-y) def D_f_app(coef,x,y): k = int((len(coef)-1)/2) derr = [] for p in x: d = np.zeros(2*k+1, dtype=type) d[0] = 1 d[1:k+1] = 1/(1+p/coef[k+1:]) d[k+1:] = p*coef[1:k+1]/(p+coef[k+1:])**2 derr.append(d) return derr def sort_coef(coef): k = int((len(coef)-1)/2) a = coef[1:k+1] b = coef[k+1:] ind = np.argsort(b) coef[1:k+1] = a[ind] coef[ k+1:] = b[ind] def run_min(x_min,x_max,p,N,coef): X,Y = make_fx(x_min,x_max,p,N) coef_s,success=leastsq(err_func,coef,args=(X,Y), Dfun = D_f_app,ftol=1.49012e-15, xtol=1.49012e-15, gtol=0.0) sort_coef(coef_s) coef = coef_s X,Y = make_fx(x_min,x_max,p,1000) error = max([np.max(err_func(coef,X,Y)),-np.min(err_func(coef,X,Y))]) return success, error, coef def main(p, k, x_min, x_max): #set initial guess coef = np.zeros(2*k+1,dtype=type) coef[0:k+1] = np.arange(k+1, dtype=type)+.5 coef[k+1:] = (np.arange(k, dtype=type)+1)/k f = 3*k tries = 0 error = 1000 while ((error >= 1e-15) and (tries < 20)): tries += 1 success, error, coef = run_min(x_min,x_max,p,int(f),coef) f+2 if(success < 5):f *= 1.2 print(error) print(coef) main(0.125, 20, 0.001, 10)

[ "eric.giguere@calculquebec.ca" ]

eric.giguere@calculquebec.ca

27aafa81021b3f5e4fabb032465d5e3b732b43d0

65dd042bb78eb771d0d78a30c0d10a503d7ab99b

/disentangled/metric/mig_batch.py

65bdc764bc1dde2a80625f08dc012b162bdce734

[]

no_license

eageby/disentangled-representations

35d3d778ba90680939dca92cee759556449f96eb

60e3d2334d85f35828d8765f934891d995f8bb9f

refs/heads/master

2023-08-31T16:16:50.742792

2021-10-19T12:06:07

237,398,695

0

null

UTF-8

Python

false

7,822

py

import disentangled.dataset as dataset import disentangled.model.distributions as dist import disentangled.model.networks as networks import disentangled.model.utils import disentangled.utils as utils import gin import numpy as np import tensorflow as tf # TODO GIN CONFIGURABLE def occurences(data): """Counts occurences of values in data Args: data: ∊ ℝ (N , K) = (batch_size, factors) Returns: (tf.RaggedTensor) ∊ ℝ (K, (A)) """ data = tf.cast(data, tf.int32) _, K = data.shape occurences = tf.ragged.stack( [tf.math.bincount(data[:, factor], dtype=tf.float32) for factor in range(K)], axis=0, ) return occurences # @tf.function def estimate_marginal_entropy(samples, encoding_dist, *encoding_parameters): """Estimates marginal entropy H(z) = sum_z( 1/N sum_x (q(z|x)) log ( 1/N sum_x(q(z|x)) ) ) ∊ ℝ [D] Args: samples: z ∼ q(z|x) ∊ ℝ (N, D) encoding_dist: q(z|x) *encoding_parameters: list of parameters ∊ ℝ [N] Returns: (tf.Tensor) ∊ ℝ [D] """ breakpoint() N, D = tf.unstack(tf.cast(samples.shape, tf.float32)) # Number of latent dims samples = tf.transpose(samples) # ∊ ℝ [D, N] encoding_parameters = tf.stack(encoding_parameters, axis=2) n_params = encoding_parameters.shape[2] # log q(z_j|x_n) ∊ ℝ [N, N, D] log_qzx_matrix = tf.reshape( encoding_dist.log_likelihood( tf.broadcast_to(tf.reshape(samples, (1, D, N)), (N, D, N)), *tf.unstack( tf.broadcast_to( tf.reshape(encoding_parameters, (N, D, 1, n_params)), (N, D, N, n_params), ), axis=3, ) ), (N, N, D), ) # H(z) = sum_z( 1/N sum_x (q(z|x)) log ( 1/N sum_x(q(z|x)) ) ) ∊ ℝ [D] log_qz = tf.reduce_logsumexp(log_qzx_matrix - tf.math.log(N), axis=0) return -tf.reduce_mean(log_qz, axis=0) return -tf.reduce_sum(tf.math.exp(log_qz) * log_qz, axis=0) def ragged_logsumexp(values, **kwargs): """logsumexp with support for tf.RaggedTensor""" return tf.math.log(tf.reduce_sum(tf.math.exp(values), **kwargs)) # @tf.function def estimate_conditional_entropy( samples, log_pvk, log_pxvk, encoding_dist, *encoding_parameters): """Calculates conditional entropy H(z| v) = - sum(p(z,v) log p(z|v)) ∊ ℝ [D, K] Args: samples: z ∼ q(z|x) ∊ ℝ [N, D] log_pv: log p(v) ∊ ℝ [K, (A) ] log_pxv: log p(x|v) ∊ ℝ [K, (A)] encoding_dist: q(z|x) *encoding_parameters: list of parameters ∊ ℝ [N] Returns: (tf.Tensor) H(z| v) ∊ ℝ [D, K] """ N, D = tf.unstack(tf.cast(samples.shape, tf.float32)) # Batch size, latent_dims K = tf.cast(log_pvk.shape[0], tf.float32) # Factors # q(z|x) ∊ ℝ [N, D] log_qzx = tf.reshape( encoding_dist.log_likelihood(samples, *encoding_parameters), (N, D, 1, 1) ) # log_pzv = log_pxvk + log_pvk + tf.reduce_logsumexp(log_qzx, axis=0) # p(z|v) = sum_x(q(z|x)p(x|v)) ∊ ℝ [D, K, (A)] log_pzv_cond = ragged_logsumexp(log_pxvk + log_qzx, axis=0) log_pzv = tf.expand_dims(log_pvk, 0) + log_pzv_cond # return -tf.reduce_mean(log_pzv_cond, axis=2) pzv = tf.math.exp(log_pzv) / tf.reduce_sum(tf.math.exp(log_pzv)) # H(z|v) = - sum(p(z,v) log p(z|v)) ∊ ℝ [D, K] # conditional_entropy = -tf.reduce_mean(log_pzv_cond, axis=2).to_tensor() conditional_entropy = -tf.reduce_sum(pzv * log_pzv_cond, axis=2).to_tensor() return conditional_entropy # @tf.function def estimate_factor_statistics(labels): """Estimates entropy and prior and conditioned prob Args: labels: Factor values for samples ∊ ℝ [N, D] Returns: (tf.RaggedTensor) p(v) ∊ ℝ [K, (A) ] (tf.RaggedTensor) p(x|v) ∊ ℝ [K, (A)] (tf.Tensor) H(v_k) ∊ ℝ [K] """ N, K = tf.unstack(tf.cast(labels.shape, tf.float32)) # Batch size, Factors # #{v_k = a} factor_occurences = occurences(labels) # p(v_k = a) = #{v_k = a} / N # p(v) ∊ ℝ (K, (A) ) log_pvk = tf.math.log(factor_occurences) - tf.math.log(N) # p(v_k=a|x=b) = 1 # p(x|v=a) = p(v|x) / #{v=a} ∊ ℝ [K, 1] log_pxvk = -tf.math.log(factor_occurences) # factor_possibilites = tf.cast(factor_occurences.row_lengths(axis=1), tf.float32) # log_pxvk = -tf.math.log(factor_occurences) - tf.expand_dims(tf.math.log(factor_possibilites), 1) # H(v_k) = - sum_a(p(v_k=a) log(p(v_k=a)) ∊ ℝ [K] entropy = -tf.reduce_sum(tf.math.exp(log_pvk) * log_pvk, axis=1) return entropy, log_pvk, log_pxvk # @tf.function def normalized_mutual_information(labels, samples, encoding_dist, *encoding_parameters): """Calculates normalized mutual information I_n(z_j; v_k) = H(z_j) - H(z_j | v_k) / H(v_k) ∊ ℝ [D, K] Args: labels: Factor values for samples ∊ ℝ (N, D) samples: z ∼ q(z|x) ∊ ℝ (N, D) encoding_dist: q(z|x) *encoding_parameters: list of parameters ∊ ℝ [N] Returns: (tf.Tensor) I_n(z_j; v_k)∊ ℝ [D, K] """ N, K = tf.unstack(tf.cast(labels.shape, tf.float32)) # Batch size, number factors factor_entropy, log_pvk, log_pxvk = estimate_factor_statistics(labels) conditional_entropy = estimate_conditional_entropy( samples, log_pvk, log_pxvk, encoding_dist, *encoding_parameters ) # H(z_j) = ∊ ℝ [D, ] marginal_entropy = estimate_marginal_entropy( samples, encoding_dist, *encoding_parameters ) # I(z_j; v_k) = H(z_j) - H(z_j | v_k) ∊ ℝ [D, K] mutual_information = tf.expand_dims( marginal_entropy, 1) - conditional_entropy nmi = mutual_information / factor_entropy return nmi # @tf.function def mutual_information_gap_batch_estimate(labels, samples, encoding_dist, *encoding_parameters): """Estimates mutual information gap (MIG) 1/K sum_{k=1}^K 1/H(v_k) (I(z_j[k]; v_k) - max_{j !=j[k]} I(z_j;v_k)) Args: labels: Factor values for samples ∊ ℝ (N, D) samples: z ∼ q(z|x) ∊ ℝ (N, D) encoding_dist: q(z|x) *encoding_parameters: list of parameters ∊ ℝ [N] Returns: (tf.Tensor) ∊ ℝ [] """ # I_n(z_j; v_k) = (H(z_j) - H(z_j | v_k)) / H(v_k) ∊ ℝ [D, K] nmi = normalized_mutual_information( labels, samples, encoding_dist, *encoding_parameters ) nmi = tf.sort(nmi, axis=0, direction="DESCENDING") # ∊ ℝ [K] mig = nmi[0, :] - nmi[1, :] return tf.reduce_mean(mig) @gin.configurable("mutual_information_gap_batch", module="disentangled.metric") def mutual_information_gap_batch( model, dataset, points, batch_size, encoding_dist, progress_bar=True, ): print("MIG BATCH") dataset = dataset.take(points).batch(batch_size, drop_remainder=True) n_batches = points // batch_size if progress_bar: progress = disentangled.utils.TrainingProgress( dataset, total=n_batches) progress.write("Calculating MIG") else: progress = dataset mig = tf.TensorArray(dtype=tf.float32, size=n_batches) for i, batch in enumerate(progress): labels = tf.cast(batch["label"], tf.int32) # z ∼ q(z|x) ∊ ℝ (N, D) encoding_parameters = model.encode(batch["image"]) samples = model.sample(*encoding_parameters, training=True) mig_batch = mutual_information_gap_batch_estimate( labels, samples, encoding_dist, *encoding_parameters ) mig = mig.write(i, mig_batch) return tf.reduce_mean(mig.stack())

[ "eageby@kth.se" ]

eageby@kth.se

27d665edb1a3f42684872e268feff701e5fbf1e7

ff2b5481cd0eb1024e0abaf02a2e30bdfd0f5422

/models/__init__.py

15adcc73ef7a49640de59ec6d3b51ac0542b7ab6

[ "MIT" ]

permissive

dvrpc/tmc-uploader

a9da59030535e86dcf6a49546a83be76c889954a

e7aea6c6b2a66031daf7aecf4f264aecf298f347

refs/heads/master

2022-12-24T15:30:48.664611

2020-10-15T15:00:54

281,735,232

0

null

UTF-8

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5,977

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from datetime import datetime from pytz import timezone from os import environ from flask_login import UserMixin from werkzeug.security import generate_password_hash, check_password_hash from dotenv import load_dotenv, find_dotenv # from pathlib import Path # from sqlalchemy import create_engine # import pandas as pd from db import db from common.random_rainbow import make_random_gradient load_dotenv(find_dotenv()) SQLALCHEMY_DATABASE_URI = environ.get("SQLALCHEMY_DATABASE_URI") project_files = db.Table( 'project_files', db.Column( 'project_id', db.Integer, db.ForeignKey('projects.uid'), primary_key=True ), db.Column( 'file_id', db.Integer, db.ForeignKey('filedata.uid'), primary_key=True ) ) class User(UserMixin, db.Model): """User account model.""" __tablename__ = 'app_users' id = db.Column( db.Integer, primary_key=True ) name = db.Column( db.String(100), nullable=False, unique=True ) email = db.Column( db.String(40), unique=True, nullable=False ) password = db.Column( db.String(200), primary_key=False, unique=False, nullable=False ) website = db.Column( db.String(60), index=False, unique=False, nullable=True ) created_on = db.Column( db.DateTime, index=False, unique=False, nullable=True, default=datetime.now(timezone("US/Eastern")), ) last_login = db.Column( db.DateTime, index=False, unique=False, nullable=True ) background = db.Column( db.Text, nullable=False, unique=False, default=make_random_gradient() ) def set_password(self, password): """Create hashed password.""" self.password = generate_password_hash( password, method='sha256' ) def check_password(self, password): """Check hashed password.""" return check_password_hash(self.password, password) def track_login(self): """Set the last_login value to now """ self.last_login = datetime.now(timezone("US/Eastern")) def __repr__(self): return '<User {}>'.format(self.username) class Project(db.Model): __tablename__ = 'projects' uid = db.Column( db.Integer, primary_key=True ) name = db.Column( db.String(50), nullable=False, unique=True ) description = db.Column( db.Text, nullable=False, unique=False ) created_by = db.Column( db.Integer, db.ForeignKey("app_users.id"), nullable=False ) background = db.Column( db.Text, nullable=False, unique=False, default=make_random_gradient() ) tmc_files = db.relationship( 'TMCFile', secondary=project_files, lazy='subquery', backref=db.backref(__tablename__, lazy=True) ) def num_files(self): return len(self.tmc_files) def created_by_user(self): return User.query.filter_by(id=self.created_by).first() class TMCFile(db.Model): __tablename__ = 'filedata' uid = db.Column( db.Integer, primary_key=True ) filename = db.Column( db.Text, nullable=False, unique=False ) title = db.Column( db.Text, nullable=True, unique=False ) # project_id = db.Column( # db.Integer, # db.ForeignKey("projects.uid"), # nullable=False # ) model_id = db.Column( db.Integer, nullable=True ) uploaded_by = db.Column( db.Integer, db.ForeignKey("app_users.id"), nullable=False ) lat = db.Column( db.Text, nullable=True, unique=False, default=39.852413 ) lng = db.Column( db.Text, nullable=True, unique=False, default=-75.264969 ) data_date = db.Column( db.DateTime, index=False, unique=False, nullable=True ) modes = db.Column( db.Text, nullable=True, unique=False ) legs = db.Column( db.Text, nullable=True, unique=False ) leg_names = db.Column( db.Text, nullable=True, unique=False ) movements = db.Column( db.Text, nullable=True, unique=False ) am_peak_start = db.Column( db.DateTime, index=False, unique=False, nullable=True ) pm_peak_start = db.Column( db.DateTime, index=False, unique=False, nullable=True ) am_volume = db.Column( db.Float, index=False, unique=False, nullable=True ) pm_volume = db.Column( db.Float, index=False, unique=False, nullable=True ) count_start_time = db.Column( db.DateTime, nullable=True, unique=False ) count_end_time = db.Column( db.DateTime, nullable=True, unique=False ) project_ids = db.relationship( 'Project', secondary=project_files, lazy='subquery', backref=db.backref(__tablename__, lazy=True) ) def pid_list(self): return [p.uid for p in self.project_ids] def num_projects(self): num = len(self.project_ids) return num # if num == 1: # txt = "project" # else: # txt = "projects" # return f"{num} {txt}" def name(self): if self.title: return self.title else: return self.filename def upload_user(self): return User.query.filter_by(id=self.uploaded_by).first()

[ "38364429+aaronfraint@users.noreply.github.com" ]

38364429+aaronfraint@users.noreply.github.com

03ee43a71159afb7b987b9b55528312e47c3ff18

c512256b07ab15c9edd61b5bfd5e7bf15639b864

/untitled/杂项/inherit.py

c947c27a42f29367391dcf3d0c084799cc6981bc

[]

no_license

pyc-ycy/PycharmProjects

866483337f1df614bab06af7d429d7e5b27fe990

62aee5f458c2e45a862a09f0fa59a0dde5844c68

refs/heads/master

2020-05-21T15:46:32.879909

2019-06-17T13:21:24

186,099,310

1

0

null

UTF-8

Python

false

934

py

# /usr/bin/python3.7 # /-*-coding:UTF-8-*- class Electrical(object): def function(self): print("Electrical can perform specific functions after power supply") class Location(object): def weizhi(self): print("Every electrical have its own location") class Television(Electrical, Location): def function(self): print("Television can play TV shows and see news") def weizhi(self): print("stand at home.") class SoundBox(Electrical, Location): def function(self): print("SoundBox can play musics") def weizhi(self): print("stand at home.") class Computer(Electrical, Location): def function(self): print("Computer can search information online and do office works") def weizhi(self): print("can bring to everywhere") def using(electrical): electrical.function() electrical.weizhi() using(Computer()) using(SoundBox())

[ "2923616405@qq.com" ]

2923616405@qq.com

02dbc32c8551b4433053ccb04ee41ef7de45ac8f

8eb19885788618e42499d6427049fd389e9eb327

/mainapp/migrations/0021_auto_20170322_0447.py

cbc626cde8abf5170ac50a1bc5507555cbce4b2e

[]

no_license

syehbiherbian/cruddjango

677b62afd83b1f4e1dc98b7b9abe68421b2317c9

702d5587474c6efbec4354632e7850ccdce5a6f3

refs/heads/master

2021-01-23T05:56:29.175364

2017-03-27T10:57:13

86,323,974

0

null

UTF-8

Python

false

985

py

# -*- coding: utf-8 -*- # Generated by Django 1.10.6 on 2017-03-22 04:47 from __future__ import unicode_literals import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('mainapp', '0020_auto_20170322_0445'), ] operations = [ migrations.AlterField( model_name='book', name='created_date', field=models.DateField(default=datetime.datetime(2017, 3, 22, 4, 47, 27, 561821)), ), migrations.AlterField( model_name='rental', name='end_date', field=models.DateTimeField(default=datetime.datetime(2017, 3, 22, 4, 47, 27, 562629, tzinfo=utc)), ), migrations.AlterField( model_name='rental', name='start_date', field=models.DateTimeField(default=datetime.datetime(2017, 3, 22, 4, 47, 27, 562329, tzinfo=utc)), ), ]

[ "oprentimac4@Oprents-iMac-2.local" ]

oprentimac4@Oprents-iMac-2.local

5be37c92efa58fe731ab3cdad297352dccbfa173

ae29d1304b7ae66a283d6dfef44911679fd79f87

/evgenApp/schema.py

83a5b640f689d50f99528d436f25351fc3ec3e98

[]

no_license

Iliavas/evg-app-back

dd2b5e917a2b0306aebb8e79aa139bea391bf782

ed1c0d6e6a5f9b667c029284c4af9f38108299d5

refs/heads/master

2023-05-13T01:04:03.647550

2021-03-28T18:34:17

328,385,545

0

2

null

2021-03-29T18:28:01

2021-01-10T13:09:00

Python

UTF-8

Python

false

488

py

import graphene import users.schema import organisations.schema import lessons.schema import hyperlinks.schema class Query(users.schema.Query, organisations.schema.Query, lessons.schema.Query, hyperlinks.schema.Query, graphene.ObjectType): pass class Mutation(users.schema.Mutation, organisations.schema.Mutation, lessons.schema.Mutation, hyperlinks.schema.Mutation, graphene.ObjectType): pass schema = graphene.Schema(query=Query, mutation=Mutation)

[ "il.vsl0110@gmail.com" ]

il.vsl0110@gmail.com

3226c7fceb4f6cfc56757178edf8e147ae74ad92

ace860f60e380d47ad40ad9e21192cb069853bd8

/DjangoWebProject3/market/migrations/0009_auto_20171222_1205.py

894ad229dc665d0b5c76850706af1b4f51926655

[]

no_license

wilsonmwiti/djangoXumpesa

9b28b3063490dd536867e517ee546436cc72b0f5

1e74fb6c7e79504831a1ce8a568375b1ab5d0a56

refs/heads/master

2023-03-15T15:13:46.567278

2020-09-08T11:44:03

null

0

null

UTF-8

Python

false

483

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.3 on 2017-12-22 09:05 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('market', '0008_paidproducts_adspackage'), ] operations = [ migrations.AlterField( model_name='paidproducts', name='Price', field=models.PositiveIntegerField(default='', max_length=12), ), ]

[ "lewicpro@gmail.com" ]

lewicpro@gmail.com

897249938f93f608a86efc83f03031609ce97109

fccba098f9cb31cbe052893f37449349ad09b26c

/tests/service/private/users/test_auth_user.py

4d9d938ac7d7ef634bfc396fbfd6bb93a6d8ffd9

[]

no_license

findfeatures/gateway-service

18c317678cb97d2587059f8dbdff8a0c655948be

83920d0bd7f4c20b7b4981d52f6bd6f2438f9f64

refs/heads/master

2020-11-28T08:02:17.866975

2020-01-13T17:03:45

229,750,937

0

null

2019-12-30T13:28:05

2019-12-23T12:43:39

Python

UTF-8

Python

false

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import json from gateway.exceptions.users import UserNotAuthorised from gateway.service import GatewayService from mock import ANY, call from nameko.containers import ServiceContainer from nameko.testing.services import replace_dependencies def test_auth_user(config, web_session): container = ServiceContainer(GatewayService) accounts = replace_dependencies(container, "accounts_rpc") container.start() accounts.auth_user.return_value = {"JWT": "test"} email = "test@google.com" password = "password" response = web_session.post( "/v1/user/auth", data=json.dumps({"email": email, "password": password}) ) assert accounts.auth_user.call_args == call(email, password) assert response.status_code == 200 assert response.json() == {"JWT": "test"} def test_auth_user_not_authorised(config, web_session): container = ServiceContainer(GatewayService) accounts = replace_dependencies(container, "accounts_rpc") container.start() accounts.auth_user.side_effect = UserNotAuthorised() response = web_session.post( "/v1/user/auth", data=json.dumps({"email": "email", "password": "password"}) ) assert response.status_code == 401 assert response.json() == {"error": "USER_NOT_AUTHORISED", "message": ""} def test_auth_user_incorrect_schema(config, web_session): container = ServiceContainer(GatewayService) accounts = replace_dependencies(container, "accounts_rpc") container.start() accounts.auth_user.side_effect = UserNotAuthorised() response = web_session.post("/v1/user/auth", data=json.dumps({})) assert response.status_code == 400 assert response.json() == {"error": "VALIDATION_ERROR", "message": ANY}

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/dingtalk/customers.py

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2017/11/30 下午2:50 # @Author : Matrix # @Github : https://github.com/blackmatrix7/ # @Blog : http://www.cnblogs.com/blackmatrix/ # @File : customers.py # @Software: PyCharm import json import requests from .foundation import * from .exceptions import DingTalkExceptions __author__ = 'blackmatrix' def get_label_groups(access_token, size=20, offset=0): """ 获取标签列表 :param access_token: :param size: :param offset: :return: """ url = get_request_url('dingtalk.corp.ext.listlabelgroups', access_token) payload = {'size': size, 'offset': offset} resp = requests.get(url, params=payload) if resp.status_code == 200: return resp.json() else: raise DingTalkExceptions.get_ext_list_err def get_corp_ext_list(access_token, size=20, offset=0): """ 获取外部联系人 :return: """ url = get_request_url('dingtalk.corp.ext.list', access_token) payload = {'size': size, 'offset': offset} resp = requests.get(url, params=payload) if resp.status_code == 200: return resp.json() else: raise DingTalkExceptions.get_ext_list_err def add_corp_ext(access_token, contact: dict): """ 新增外部联系人 :return: """ url = get_request_url('dingtalk.corp.ext.add', access_token) contact = json.dumps(contact) resp = requests.post(url, data={'contact': contact.encode('utf-8')}) if resp.status_code == 200: return resp.json() else: raise DingTalkExceptions.get_ext_list_err if __name__ == '__main__': pass

[ "codecolor@outlook.com" ]

codecolor@outlook.com

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/entidades/personalmov.py

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matiasGuidone/mdt-marcado

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class personalmov: id = None nombre = None apellido = None nrosocio = None codigomarcado = None horaentradamat = None horasalidamat = None horaentradaves = None horasalidaves = None horaentradasab = None horasalidasab = None huella = None legajo = None idmov = None fechahora = None tipo = None observaciones = None # `per_nombre`, # `per_apellido`, # `per_nrosocio`, # `per_codigomarcado`, # `per_horaentradamatutino`, # `per_horasalidamatutino`, # `per_horaentradavespertino`, # `per_horasalidavespertino`, # `per_horaentradasabado`, # `per_horasalidasabado`, # `per_huellaid`, # `id`, # `per_legajo` def __init__(self, p= None): if p != None: self.id=p[4] self.nombre=p[5] self.apellido=p[6] self.nrosocio=p[7] self.codigomarcado = p[8] self.horaentradamat = p[9] self.horasalidamat = p[10] self.horaentradaves = p[11] self.horasalidaves = p[12] self.horaentradasab = p[13] self.horasalidasab = p[14] self.huella = p[15] self.legajo = p[17] self.idmov = p[0] self.fechahora = p[1] self.tipo = p[2] self.observaciones = p[3] def startCall(self): pass def endCall(self): pass

[ "matias.guidone@gmail.com" ]

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/test_site/urls.py

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2020-08-05T00:11:37.082965

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"""test_site URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from django.views.generic import TemplateView urlpatterns = [ path('admin/', admin.site.urls), path('', TemplateView.as_view(template_name='index.html')), ]

[ "kipronofb@gmail.com" ]

kipronofb@gmail.com

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/arp/calfiles/blo853.py

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zakiali/cabo

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import aipy as a class AntennaArray(a.fit.AntennaArray): def sim_cache(self, *args, **kwds): return a.fit.AntennaArray.sim_cache(self, *args, **kwds) def sim(self, i, j, pol): ans = a.fit.AntennaArray.sim(self, i, j, pol) return ans prms = { 'loc': ('+37:55.1','-122:09.4'), 'antpos': { 0: [0., 0., 0.] , 1: [0., 37., 0.] , } } def get_aa(freqs): location = prms['loc'] antennas = [] beam = a.fit.Beam(freqs) for i in range (len(prms['antpos'])): pos = prms['antpos'][i] antennas.append(a.fit.Antenna(pos[0], pos[1], pos[2], beam, amp=.05)) aa = AntennaArray(prms['loc'], antennas) return aa src_prms = { 'Sun': {'jys':1e5}, } def get_catalog(srcs=None, cutoff=None, catalogs=None): cat = a.src.get_catalog(srcs=srcs, cutoff=cutoff, catalogs=catalogs) cat.set_params(src_prms) return cat

[ "aparsons@astron.berkeley.edu" ]

aparsons@astron.berkeley.edu

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/mysite/settings.py

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a5816010/my-first-blog

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2020-03-20T21:11:46.319488

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 1.11.13. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ezcgfc_x#e&z#9e2i^pkn3@-&%ss5dapjr6zb4d)g%4kx@uce5' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'blog', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/'

[ "you@example.com" ]

you@example.com

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/col_detection.py

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no_license

akashdeeps19/red-color-detection

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refs/heads/master

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import cv2 import numpy as np # quality = 400 # im = cv2.imread("F1.large.jpg") video_path = input("Enter video path or 'live' for live capture : ") quality = input("Enter quality (300,400,500) : ") quality = int(quality) if video_path == "live": cap = cv2.VideoCapture(0) else: cap = cv2.VideoCapture(video_path) def preprocessing(img_org): img = cv2.resize(img_org,(quality,quality),cv2.INTER_AREA) hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lower_blue = np.array([0,50,50]) upper_blue = np.array([10,255,255]) mask1 = cv2.inRange(hsv, np.array([0, 115, 105]), np.array([10, 255, 255])) mask2 = cv2.inRange(hsv,np.array([170, 115, 105]), np.array([180, 255, 255])) mask = mask1+mask2 res = cv2.bitwise_and(img,img, mask= mask) # cv2.imshow(None,res) # cv2.waitKey() im_gray = cv2.cvtColor(res, cv2.COLOR_HSV2BGR) im_gray = cv2.cvtColor(im_gray,cv2.COLOR_BGR2GRAY) (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) im_bw = cv2.medianBlur(im_bw,3) ret, labels = cv2.connectedComponents(im_bw) regions_x = [[] for i in range(ret)] regions_y = [[] for i in range(ret)] img_res = img for i in range(quality): for j in range(quality): if(labels[i,j] > 0): regions_x[labels[i,j]].append(i) regions_y[labels[i,j]].append(j) for i in range(1,ret): s_point = (min(regions_y[i]),min(regions_x[i])) e_point = (max(regions_y[i]),max(regions_x[i])) img_res = cv2.rectangle(img,s_point,e_point,(255,0,0),1) return img_res #preprocessing(im) if (cap.isOpened()== False): print("Error opening video stream or file") while(cap.isOpened()): ret, frame = cap.read() if ret == True: frame = preprocessing(frame) frame = cv2.resize(frame,(1000,600),cv2.INTER_AREA) cv2.imshow('Frame',frame) if cv2.waitKey(25) & 0xFF == ord('q'): break else: break cap.release() cv2.destroyAllWindows()

[ "akasdeeps19@gmail.com" ]

akasdeeps19@gmail.com

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/bodajusticeapp/migrations/0004_auto_20190224_0242.py

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boda-justice/boda-justice-new-backend

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# Generated by Django 2.1.7 on 2019-02-24 02:42 from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('bodajusticeapp', '0003_auto_20190224_0230'), ] operations = [ migrations.RemoveField( model_name='offence', name='offense_type', ), migrations.AddField( model_name='offence', name='modification_date', field=models.DateTimeField(auto_now_add=True, null=True, verbose_name='date_modified'), ), migrations.AlterField( model_name='offence', name='creation_date', field=models.DateTimeField(default=django.utils.timezone.now, verbose_name='date_created'), ), migrations.AlterField( model_name='offence', name='description', field=models.TextField(verbose_name='offence_description'), ), migrations.AlterField( model_name='offence', name='fine', field=models.DecimalField(decimal_places=0, max_digits=6), ), ]

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import gym import argparse from parl.utils import logger, tensorboard, ReplayMemory from parl.env.continuous_wrappers import ActionMappingWrapper from mujoco_model import MujocoModel from mujoco_agent import MujocoAgent from parl.algorithms import SAC WARMUP_STEPS = 1e4 EVAL_EPISODES = 5 MEMORY_SIZE = int(1e6) BATCH_SIZE = 256 GAMMA = 0.99 TAU = 0.005 ACTOR_LR = 3e-4 CRITIC_LR = 3e-4 # Run episode for training def run_train_episode(agent, env, rpm): action_dim = env.action_space.shape[0] obs = env.reset() done = False episode_reward, episode_steps = 0, 0 while not done: episode_steps += 1 # Select action randomly or according to policy if rpm.size() < WARMUP_STEPS: action = np.random.uniform(-1, 1, size=action_dim) else: action = agent.sample(obs) # Perform action next_obs, reward, done, _ = env.step(action) terminal = float(done) if episode_steps < env._max_episode_steps else 0 # Store data in replay memory rpm.append(obs, action, reward, next_obs, terminal) obs = next_obs episode_reward += reward # Train agent after collecting sufficient data if rpm.size() >= WARMUP_STEPS: batch_obs, batch_action, batch_reward, batch_next_obs, batch_terminal = rpm.sample_batch( BATCH_SIZE) agent.learn(batch_obs, batch_action, batch_reward, batch_next_obs, batch_terminal) return episode_reward, episode_steps # Runs policy for 5 episodes by default and returns average reward # A fixed seed is used for the eval environment def run_evaluate_episodes(agent, env, eval_episodes): avg_reward = 0. for _ in range(eval_episodes): obs = env.reset() done = False while not done: action = agent.predict(obs) obs, reward, done, _ = env.step(action) avg_reward += reward avg_reward /= eval_episodes return avg_reward def main(): logger.info("------------------- SAC ---------------------") logger.info('Env: {}, Seed: {}'.format(args.env, args.seed)) logger.info("---------------------------------------------") env = gym.make(args.env) env.seed(args.seed) env = ActionMappingWrapper(env) obs_dim = env.observation_space.shape[0] action_dim = env.action_space.shape[0] # Initialize model, algorithm, agent, replay_memory model = MujocoModel(obs_dim, action_dim) algorithm = SAC( model, gamma=GAMMA, tau=TAU, alpha=args.alpha, actor_lr=ACTOR_LR, critic_lr=CRITIC_LR) agent = MujocoAgent(algorithm) rpm = ReplayMemory( max_size=MEMORY_SIZE, obs_dim=obs_dim, act_dim=action_dim) total_steps = 0 test_flag = 0 while total_steps < args.train_total_steps: # Train episode episode_reward, episode_steps = run_train_episode(agent, env, rpm) total_steps += episode_steps tensorboard.add_scalar('train/episode_reward', episode_reward, total_steps) logger.info('Total Steps: {} Reward: {}'.format( total_steps, episode_reward)) # Evaluate episode if (total_steps + 1) // args.test_every_steps >= test_flag: while (total_steps + 1) // args.test_every_steps >= test_flag: test_flag += 1 avg_reward = run_evaluate_episodes(agent, env, EVAL_EPISODES) tensorboard.add_scalar('eval/episode_reward', avg_reward, total_steps) logger.info('Evaluation over: {} episodes, Reward: {}'.format( EVAL_EPISODES, avg_reward)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--env", default="HalfCheetah-v1", help='Mujoco gym environment name') parser.add_argument( "--seed", default=0, type=int, help='Sets Gym, PyTorch and Numpy seeds') parser.add_argument( "--train_total_steps", default=5e6, type=int, help='Max time steps to run environment') parser.add_argument( '--test_every_steps', type=int, default=int(5e3), help='The step interval between two consecutive evaluations') parser.add_argument( "--alpha", default=0.2, type=float, help= 'Determines the relative importance of entropy term against the reward' ) args = parser.parse_args() main()

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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2021 NVIDIA Corporation. 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. # Temporary disable isort to avoid circular import # This can be removed after the circular import is resolved # isort: skip_file from __future__ import annotations import os import pickle import warnings from collections import OrderedDict import inspect import threading from typing import Any import paddle from paddle.fluid import core, dygraph from paddle.fluid.compiler import ( BuildStrategy, CompiledProgram, ExecutionStrategy, ) from paddle.fluid.data_feeder import check_type from paddle.fluid.dygraph.base import ( program_desc_tracing_guard, switch_to_static_graph, ) from .dy2static import logging_utils from .dy2static.convert_call_func import ( ConversionOptions, add_ignore_module, ) from .dy2static.program_translator import ( ProgramTranslator, StaticFunction, unwrap_decorators, ) from paddle.jit.translated_layer import ( TranslatedLayer, INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX, INFER_PROPERTY_SUFFIX, ) from paddle.nn import Layer from paddle.fluid.executor import Executor, scope_guard from paddle.fluid.framework import ( Block, Program, Variable, Parameter, EagerParamBase, ) from paddle.fluid.framework import ( _current_expected_place, _dygraph_guard, _dygraph_tracer, ) from paddle.fluid.framework import dygraph_only from paddle.fluid.wrapped_decorator import wrap_decorator from paddle.fluid.io import save_inference_model from paddle.framework import in_dynamic_mode def create_program_from_desc(program_desc): program = Program() program.desc = program_desc program.blocks = [Block(program, 0)] program._sync_with_cpp() return program def _extract_vars(inputs, result_list, err_tag='inputs'): if isinstance(inputs, Variable): result_list.append(inputs) elif isinstance(inputs, (list, tuple)): for var in inputs: _extract_vars(var, result_list, err_tag) else: raise TypeError( "The type of 'each element of {}' in paddle.jit.TracedLayer.trace must be fluid.Variable, but received {}.".format( err_tag, type(inputs) ) ) def extract_vars(inputs, err_tag='inputs'): result_list = [] _extract_vars(inputs, result_list, err_tag) return result_list def _dygraph_to_static_func_(dygraph_func): """ Converts imperative dygraph APIs into declarative function APIs. Decorator @dygraph_to_static_func only converts imperative dygraph APIs into declarative net-building APIs, which means it doesn't return immediate digital result as imperative mode. Users should handle Program and Executor by themselves. Note: This decorator is NOT our recommended way to transform imperative function to declarative function. We will remove this decorator after we finalize cleaning up code. Args: dygraph_func (callable): callable imperative function. Returns: Callable: converting imperative dygraph APIs into declarative net-building APIs. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np from paddle.jit.api import dygraph_to_static_func @dygraph_to_static_func def func(x): if paddle.mean(x) < 0: x_v = x - 1 else: x_v = x + 1 return x_v x = paddle.full(shape=[3, 3], fill_value=0, dtype='float64') x_v = func(x) exe = fluid.Executor(fluid.CPUPlace()) out = exe.run(fetch_list=[x_v]) print(out[0]) # [[1. 1. 1.] # [1. 1. 1.] # [1. 1. 1.]] """ # TODO: remove this decorator after we finalize training API def __impl__(*args, **kwargs): program_translator = ProgramTranslator() if in_dynamic_mode() or not program_translator.enable_to_static: logging_utils.warn( "The decorator 'dygraph_to_static_func' doesn't work in " "dygraph mode or set 'paddle.jit.enable_to_static' to False. " "We will just return dygraph output." ) return dygraph_func(*args, **kwargs) static_func = program_translator.get_func(dygraph_func) return static_func(*args, **kwargs) return __impl__ dygraph_to_static_func = wrap_decorator(_dygraph_to_static_func_) def copy_decorator_attrs(original_func, decorated_obj): """ Copies some necessary attributes from original function into decorated function. Args: original_func(callable): the original decorated function. decorated_obj(StaticFunction): the target decorated StaticFunction object. """ decorator_name = "to_static" decorated_obj.__name__ = original_func.__name__ decorated_obj._decorator_name = decorator_name decorated_obj.__wrapped__ = original_func decorated_obj.__doc__ = original_func.__doc__ if hasattr(original_func, "__module__"): decorated_obj.__module__ = original_func.__module__ return decorated_obj def ignore_module(modules: list[Any]): """ Adds modules that ignore transcription. Builtin modules that have been ignored are collections, pdb, copy, inspect, re, numpy, logging, six Args: modules (List[Any]): Ignored modules that you want to add Examples: .. code-block:: python import scipy import astor import paddle from paddle.jit import ignore_module modules = [ scipy, astor ] ignore_module(modules) """ add_ignore_module(modules) def _check_and_set_backend(backend, build_strategy): if backend not in ['CINN', None]: raise ValueError( "The backend of to_static should be 'CINN' or None, but received {}.".format( backend ) ) if backend == 'CINN': build_strategy.build_cinn_pass = True def to_static( function=None, input_spec=None, build_strategy=None, backend=None, **kwargs, ): """ Converts imperative dygraph APIs into declarative function APIs. Decorator @to_static handles the Program and Executor of static graph mode and returns the result as dygraph Tensor(s). Users could use the returned dygraph Tensor(s) to do imperative training, inference, or other operations. If the decorated function calls other imperative function, the called one will be converted into declarative function as well. Args: function (callable): callable imperative function. input_spec(list[InputSpec]|tuple[InputSpec]): list/tuple of InputSpec to specific the shape/dtype/name information of each input Tensor. build_strategy(BuildStrategy|None): This argument is used to compile the converted program with the specified options, such as operators' fusion in the computational graph and memory optimization during the execution of the computational graph. For more information about build_strategy, please refer to :code:`paddle.static.BuildStrategy`. The default is None. backend(str, Optional): Specifies compilation backend, which can be `CINN` or None. When backend is `CINN`, CINN compiler will be used to speed up training and inference. kwargs: Support keys including `property`, set `property` to True if the fucntion is python property. Returns: Tensor(s): containing the numerical result. Examples: .. code-block:: python import paddle from paddle.jit import to_static @to_static def func(x): if paddle.mean(x) < 0: x_v = x - 1 else: x_v = x + 1 return x_v x = paddle.ones([1, 2], dtype='float32') x_v = func(x) print(x_v) # [[2. 2.]] """ property = kwargs.get("property", False) def decorated(python_func): """ Decorates a python function into a StaticFunction object. """ # Step 1. unwrap the function if it is already decorated. _, python_func = unwrap_decorators(python_func) # Step 2. copy some attributes from original python function. static_layer = copy_decorator_attrs( original_func=python_func, decorated_obj=StaticFunction( function=python_func, input_spec=input_spec, build_strategy=build_strategy, property=property, backend=backend, ), ) return static_layer build_strategy = build_strategy or BuildStrategy() if not isinstance(build_strategy, BuildStrategy): raise TypeError( "Required type(build_strategy) shall be `paddle.static.BuildStrategy`, but received {}".format( type(build_strategy).__name__ ) ) _check_and_set_backend(backend, build_strategy) # for usage: `to_static(foo, ...)` if function is not None: if isinstance(function, Layer): if isinstance(function.forward, StaticFunction): class_name = function.__class__.__name__ logging_utils.warn( "`{}.forward` has already been decorated somewhere. It will be redecorated to replace previous one.".format( class_name ) ) function.forward = decorated(function.forward) return function else: return decorated(function) # for usage: `@to_static` return decorated def not_to_static(func=None): """ A Decorator to suppresses the convertion of a function. Args: func(callable): The function to decorate. Returns: callable: A function which won't be converted in Dynamic-to-Static. Examples: .. code-block:: python import paddle @paddle.jit.not_to_static def func_not_to_static(x): res = x - 1 return res @paddle.jit.to_static def func(x): if paddle.mean(x) < 0: out = func_not_to_static(x) else: out = x + 1 return out x = paddle.ones([1, 2], dtype='float32') out = func(x) print(out) # [[2. 2.]] """ if func is None: return not_to_static options = ConversionOptions(not_convert=True) options.attach(func) return func class _SaveLoadConfig: def __init__(self): self._output_spec = None self._model_filename = None self._params_filename = None self._separate_params = False # used for `paddle.load` self._keep_name_table = False # NOTE: Users rarely use following configs, so these configs are not open to users, # reducing user learning costs, but we retain the configuration capabilities # If True, programs are modified to only support direct inference deployment. # Otherwise,more information will be stored for flexible optimization and re-training. # Currently, only True is supported self._export_for_deployment = True # If True, It will save inference program only, and do not save params of Program self._program_only = False self.with_hook = False # if True, multi `StaticFunction` will share params in one file. self.combine_params = False @property def output_spec(self): return self._output_spec @output_spec.setter def output_spec(self, spec): if spec is None: return if not isinstance(spec, list): raise TypeError( "The config `output_spec` should be 'list', but received input type is %s." % type(input) ) for var in spec: if not isinstance(var, core.eager.Tensor): raise TypeError( "The element in config `output_spec` list should be 'Variable', but received element's type is %s." % type(var) ) self._output_spec = spec @property def model_filename(self): return self._model_filename @model_filename.setter def model_filename(self, filename): if filename is None: return if not isinstance(filename, str): raise TypeError( "The config `model_filename` should be str, but received input's type is %s." % type(filename) ) if len(filename) == 0: raise ValueError("The config `model_filename` is empty string.") self._model_filename = filename @property def params_filename(self): return self._params_filename @params_filename.setter def params_filename(self, filename): if filename is None: return if not isinstance(filename, str): raise TypeError( "The config `params_filename` should be str, but received input's type is %s." % type(filename) ) if len(filename) == 0: raise ValueError("The config `params_filename` is empty string.") self._params_filename = filename @property def keep_name_table(self): return self._keep_name_table @keep_name_table.setter def keep_name_table(self, value): if value is None: return if not isinstance(value, bool): raise TypeError( "The config `keep_name_table` should be bool value, but received input's type is %s." % type(value) ) self._keep_name_table = value def _parse_save_configs(configs): supported_configs = [ 'output_spec', "with_hook", "combine_params", "clip_extra", "skip_forward", ] # input check for key in configs: if key not in supported_configs: raise ValueError( "The additional config (%s) of `paddle.jit.save` is not supported." % (key) ) # construct inner config inner_config = _SaveLoadConfig() inner_config.output_spec = configs.get('output_spec', None) inner_config.with_hook = configs.get('with_hook', False) inner_config.combine_params = configs.get("combine_params", False) inner_config.clip_extra = configs.get("clip_extra", True) inner_config.skip_forward = configs.get("skip_forward", False) return inner_config def _parse_load_config(configs): supported_configs = ['model_filename', 'params_filename'] # input check for key in configs: if key not in supported_configs: raise ValueError( "The additional config (%s) of `paddle.jit.load` is not supported." % (key) ) # construct inner config inner_config = _SaveLoadConfig() inner_config.model_filename = configs.get('model_filename', None) inner_config.params_filename = configs.get('params_filename', None) return inner_config def _get_input_var_names(inputs, input_spec): name_none_error = ( "The %s's name is None. " "When using jit.save, please set InputSepc's name in " "to_static(input_spec=[]) and jit.save(input_spec=[]) " "and make sure they are consistent." ) name_no_exists_error = ( "The tensor `%s` does not exists. " "Please make sure the name of InputSpec or example Tensor " "in input_spec is the same as the name of InputSpec in " "`to_static` decorated on the Layer.forward method." ) result_list = [] input_var_names = [ var.name for var in paddle.utils.flatten(inputs) if isinstance(var, Variable) ] if input_spec is None: # no prune return input_var_names else: # fileter out non-tensor type spec infos. input_spec = [ spec for spec in input_spec if isinstance(spec, paddle.static.InputSpec) ] if len(input_spec) == len(input_var_names): # no prune result_list = input_var_names # if input spec name not in input_var_names, only raise warning for spec in input_spec: if spec.name is None: warnings.warn(name_none_error % spec) elif spec.name not in input_var_names: warnings.warn(name_no_exists_error % spec.name) else: # do nothing pass else: # prune for spec in input_spec: if spec.name is None: # name is None, the input_spec only can be InputSpec raise ValueError(name_none_error % spec) elif spec.name not in input_var_names: # the input_spec can be `InputSpec` or `Tensor` raise ValueError(name_no_exists_error % spec.name) else: result_list.append(spec.name) return result_list def _get_output_vars(outputs, output_spec, with_hook=False): name_no_exists_error = ( "The tensor `%s` does not exists. " "Please make sure the name of example Tensor " "in configs.output_spec is the output tensor of " "Layer.forward method." ) if output_spec and with_hook: raise RuntimeError( "Currently not support specify output_spec while founding pre/post hooks in your outermost layer." ) result_list = [] output_vars_dict = OrderedDict() for var in paddle.utils.flatten(outputs): if isinstance(var, Variable): output_vars_dict[var.name] = var if output_spec is None: result_list = list(output_vars_dict.values()) elif output_spec is not None and len(output_spec) == len(output_vars_dict): result_list = list(output_vars_dict.values()) for var in output_spec: if var.name not in output_vars_dict: warnings.warn(name_no_exists_error % var.name) else: for var in output_spec: if var.name not in output_vars_dict: raise ValueError(name_no_exists_error % var.name) else: result_list.append(output_vars_dict[var.name]) return result_list # NOTE(chenweihang): [ Handling of use cases of API paddle.jit.load ] # `paddle.jit.load` may be used to load saved results of: # 1. Expected cases: # - paddle.jit.save # - paddle.static.save_inference_model # - paddle.fluid.io.save_inference_model # 2. Error cases: # - paddle.save: no .pdmodel for prefix # - paddle.static.save: no .pdiparams but .pdparams exists # - paddle.fluid.io.save_params/save_persistables: no __model__ # TODO(chenweihang): polish error message in above error cases def _build_load_path_and_config(path, config): # NOTE(chenweihang): If both [prefix save format] and [directory save format] exist, # raise error, avoid confusing behavior prefix_format_path = path + INFER_MODEL_SUFFIX prefix_format_exist = os.path.exists(prefix_format_path) directory_format_exist = os.path.isdir(path) if prefix_format_exist and directory_format_exist: raise ValueError( "The {}.pdmodel and {} directory exist at the same time, " "don't know which one to load, please make sure that the specified target " "of ``path`` is unique.".format(path, path) ) elif not prefix_format_exist and not directory_format_exist: raise ValueError( "The ``path`` (%s) to load model not exists. " "Please make sure that *.pdmodel exists or " "don't using ``skip_forward=True`` to jit.save." % path ) else: if prefix_format_exist: file_prefix = os.path.basename(path) model_path = os.path.dirname(path) if config.model_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``model_filename`` config does " "not take effect." ) config.model_filename = file_prefix + INFER_MODEL_SUFFIX if config.params_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``params_filename`` config does " "not take effect." ) config.params_filename = file_prefix + INFER_PARAMS_SUFFIX else: # Compatible with the old save_inference_model format model_path = path return model_path, config _save_pre_hooks_lock = threading.Lock() _save_pre_hooks = [] class HookRemoveHelper: """A HookRemoveHelper that can be used to remove hook.""" def __init__(self, hook): self._hook = hook def remove(self): _remove_save_pre_hook(self._hook) def _register_save_pre_hook(hook): """ Register a save pre-hook for `paddle.jit.save`. This hook will be executed before `save` function has been invoked. hook(layer, input_spec, configs) -> None - layer (Layer|function): This argument is corresponding to `layer` in `paddle.jit.save`. - input_spec (list or tuple[InputSpec|Tensor|Python built-in variable]): This argument is corresponding to `input_spec` in `paddle.jit.save`. - configs (dict): This argument is corresponding to `configs` in `paddle.jit.save`. Args: hook(function): a function registered as a save pre-hook Returns: HookRemoveHelper: a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()`. Examples: .. code-block:: python import numpy as np import paddle IMAGE_SIZE = 256 CLASS_NUM = 10 class LinearNet(paddle.nn.Layer): def __init__(self): super().__init__() self._linear = paddle.nn.Linear(IMAGE_SIZE, CLASS_NUM) def forward(self, x): return self._linear(x) saving_count = 0 def save_pre_hook(layer, input_spec, configs): global saving_count saving_count += 1 remove_handler = paddle.jit.register_save_pre_hook(save_pre_hook) layer = LinearNet() paddle.jit.save(layer, "/tmp", [paddle.static.InputSpec(shape=[-1, IMAGE_SIZE])]) # saving_count == 1 remove_handler.remove() paddle.jit.save(layer, "/tmp", [paddle.static.InputSpec(shape=[-1, IMAGE_SIZE])]) # saving_count == 1 """ global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() if hook not in _save_pre_hooks: _save_pre_hooks.append(hook) _save_pre_hooks_lock.release() return HookRemoveHelper(hook) def _clear_save_pre_hooks(): global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() _save_pre_hooks.clear() _save_pre_hooks_lock.release() def _remove_save_pre_hook(hook): global _save_pre_hooks_lock global _save_pre_hooks _save_pre_hooks_lock.acquire() if hook in _save_pre_hooks: _save_pre_hooks.remove(hook) _save_pre_hooks_lock.release() @wrap_decorator def _run_save_pre_hooks(func): def wrapper(layer, path, input_spec=None, **configs): global _save_pre_hooks for hook in _save_pre_hooks: hook(layer, input_spec, configs) func(layer, path, input_spec, **configs) return wrapper def _save_property(filename: str, property_vals: list[tuple[Any, str]]): """class property serialization. Args: filename (str): *.meta property_vals (list[tuple[Any, str]]): class property. """ def set_property(meta, key, val): if isinstance(val, float): meta.set_float(key, val) elif isinstance(val, int): meta.set_int(key, val) elif isinstance(val, str): meta.set_string(key, val) elif isinstance(val, (tuple, list)): if isinstance(val[0], float): meta.set_floats(key, val) elif isinstance(val[0], int): meta.set_ints(key, val) elif isinstance(val[0], str): meta.set_strings(key, val) else: raise ValueError(f"Note support val type: {type(val)}") return with open(filename, 'wb') as f: meta = paddle.framework.core.Property() for item in property_vals: val, key = item[0], item[1] set_property(meta, key, val) f.write(meta.serialize_to_string()) @_run_save_pre_hooks @switch_to_static_graph def save(layer, path, input_spec=None, **configs): """ Saves input Layer or function as ``paddle.jit.TranslatedLayer`` format model, which can be used for inference or fine-tuning after loading. It will save the translated program and all related persistable variables of input Layer to given ``path`` . ``path`` is the prefix of saved objects, and the saved translated program file suffix is ``.pdmodel`` , the saved persistable variables file suffix is ``.pdiparams`` , and here also saved some additional variable description information to a file, its suffix is ``.pdiparams.info``, these additional information is used in fine-tuning. The saved model can be loaded by follow APIs: - ``paddle.jit.load`` - ``paddle.static.load_inference_model`` - Other C++ inference APIs .. note:: When using ``paddle.jit.save`` to save a function, parameters will not be saved. If you have to save the parameter, please pass the Layer containing function and parameter to ``paddle.jit.save``. Args: layer (Layer|function): The Layer or function to be saved. path (str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. input_spec (list or tuple[InputSpec|Tensor|Python built-in variable], optional): Describes the input of the saved model's forward method, which can be described by InputSpec or example Tensor. Moreover, we support to specify non-tensor type argument, such as int, float, string, or list/dict of them.If None, all input variables of the original Layer's forward method would be the inputs of the saved model. Default None. **configs (dict, optional): Other save configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) output_spec (list[Tensor]): Selects the output targets of the saved model. By default, all return variables of original Layer's forward method are kept as the output of the saved model. If the provided ``output_spec`` list is not all output variables, the saved model will be pruned according to the given ``output_spec`` list. Returns: None Examples: .. code-block:: python # example 1: save layer import numpy as np import paddle import paddle.nn as nn import paddle.optimizer as opt BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super().__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) # 1. train & save model. # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) # train train(layer, loader, loss_fn, adam) # save path = "example_model/linear" paddle.jit.save(layer, path) # example 2: save function import paddle from paddle.static import InputSpec def save_function(): @paddle.jit.to_static def fun(inputs): return paddle.tanh(inputs) path = 'test_jit_save_load_function_1/func' inps = paddle.rand([3, 6]) origin = fun(inps) paddle.jit.save(fun, path) load_func = paddle.jit.load(path) load_result = load_func(inps) print((load_result - origin).abs().max() < 1e-10) save_function() """ # 1. input build & check prog_translator = ProgramTranslator() is_prim_infer = core._is_fwd_prim_enabled() and core._is_bwd_prim_enabled() if not prog_translator.enable_to_static: raise RuntimeError( "The paddle.jit.save doesn't work when setting 'paddle.jit.enable_to_static' to False." ) if not ( isinstance(layer, (Layer, StaticFunction)) or inspect.isfunction(layer) ): raise TypeError( "The input of paddle.jit.save should be 'Layer' or 'Function', but received input type is %s." % type(layer) ) elif inspect.isfunction(layer) or isinstance(layer, StaticFunction): warnings.warn( 'What you save is a function, and `jit.save` will generate the name of the model file according to `path` you specify. When loading these files with `jit.load`, you get a `TranslatedLayer` whose inference result is the same as the inference result of the function you saved.' ) # NOTE(chenweihang): If the input layer be wrapped by DataParallel, # the args and kwargs of forward method will can't be parsed by # function_spec, so here we save DataParallel._layers instead # DataParallel it self # NOTE(chenweihang): using inner_layer, do not change input layer if isinstance(layer, paddle.DataParallel): inner_layer = layer._layers else: inner_layer = layer # path check file_prefix = os.path.basename(path) if file_prefix == "": raise ValueError( "The input path MUST be format of dirname/file_prefix " "[dirname\\file_prefix in Windows system], but received " "file_prefix is empty string." ) dirname = os.path.dirname(path) if dirname and not os.path.exists(dirname): os.makedirs(dirname) # avoid change user given input_spec inner_input_spec = None if input_spec is not None: if isinstance(layer, Layer): for attr_func in dir(inner_layer): static_func = getattr(inner_layer, attr_func, None) if ( isinstance(static_func, StaticFunction) and 'forward' != attr_func ): raise ValueError( "If there are static functions other than 'forward' that need to be saved, the input 'input_spec' should be None, but received the type of 'input_spec' is %s." % type(input_spec) ) if not isinstance(input_spec, (list, tuple)): raise TypeError( "The input input_spec should be 'list', but received input_spec's type is %s." % type(input_spec) ) inner_input_spec = [] for var in paddle.utils.flatten(input_spec): if isinstance(var, paddle.static.InputSpec): inner_input_spec.append(var) elif isinstance(var, (core.eager.Tensor, Variable)): inner_input_spec.append( paddle.static.InputSpec.from_tensor(var) ) else: # NOTE(Aurelius84): Support non-Tensor type in `input_spec`. inner_input_spec.append(var) # parse configs configs = _parse_save_configs(configs) # whether outermost layer has pre/post hook, if does, we need also save # these operators in program. with_hook = configs.with_hook combine_params = configs.combine_params if combine_params: configs._program_only = True scope = core.Scope() extra_var_info = {} if isinstance(layer, Layer): functions = dir(inner_layer) if inner_layer._forward_pre_hooks or inner_layer._forward_post_hooks: with_hook = True else: # layer is function functions = [ layer, ] combine_vars = {} property_vals = [] # (value, key) concrete_program = None for attr_func in functions: if isinstance(layer, Layer): static_func = getattr(inner_layer, attr_func, None) if isinstance(static_func, StaticFunction): if static_func.is_property: # property method to be exported immediate_val = static_func() property_vals.append( ( immediate_val, layer.__class__.__name__ + '.' + attr_func, ) ) continue concrete_program = ( static_func.concrete_program_specify_input_spec( inner_input_spec, with_hook=with_hook, is_prim_infer=is_prim_infer, ) ) elif 'forward' == attr_func: if configs.skip_forward: # do not jit.save forward function continue # transform in jit.save, if input_spec is incomplete, declarative will throw error # inner_input_spec is list[InputSpec], it should be packed with same structure # as original input_spec here. if inner_input_spec: inner_input_spec = paddle.utils.pack_sequence_as( input_spec, inner_input_spec ) static_forward = to_static( inner_layer.forward, input_spec=inner_input_spec ) concrete_program = ( static_forward.concrete_program_specify_input_spec( with_hook=with_hook, is_prim_infer=is_prim_infer ) ) # the input_spec has been used in declarative, which is equal to # @to_static with input_spec and jit.save without input_spec, # avoid needless warning inner_input_spec = None else: continue else: # When layer is a function if isinstance(attr_func, StaticFunction): if attr_func.is_property: # property method to be exported immediate_val = attr_func() property_vals.append((immediate_val, attr_func)) continue concrete_program = ( attr_func.concrete_program_specify_input_spec( inner_input_spec, is_prim_infer=is_prim_infer ) ) else: if inner_input_spec: inner_input_spec = paddle.utils.pack_sequence_as( input_spec, inner_input_spec ) static_function = to_static( attr_func, input_spec=inner_input_spec ) concrete_program = static_function.concrete_program if static_function._class_instance is None: warnings.warn( '`jit.save` will only save the `Program`, not the parameters. If you have to save the parameters, please make sure that {} is a member function of `paddle.nn.Layer` and the saved parameters are in `state_dict`'.format( layer ) ) # when save multi `StaticFunction`, all `StaticFunction` share params. dygraph_state_dict = None if isinstance(inner_layer, Layer): dygraph_state_dict = inner_layer.to_static_state_dict() elif isinstance(attr_func, StaticFunction): if attr_func._class_instance: dygraph_state_dict = ( attr_func._class_instance.to_static_state_dict() ) if dygraph_state_dict: # NOTE(chenweihang): we maintain the mapping of variable name to # structured name, the buffer variable (non-persistable) # saved to inference program may not need by dygraph Layer, # we only record the state_dict variable's structured name state_names_dict = {} state_var_dict = {} for structured_name, var in dygraph_state_dict.items(): state_names_dict[var.name] = structured_name state_var_dict[var.name] = var # 3. share parameters from Layer to scope & record var info with dygraph.guard(): for param_or_buffer in concrete_program.parameters: # share to scope if param_or_buffer.type == core.VarDesc.VarType.VOCAB: scr_tensor = param_or_buffer.value().get_map_tensor() tgt_var = scope.var(param_or_buffer.name) tgt_var.set_vocab(scr_tensor) else: param_or_buffer_tensor = scope.var( param_or_buffer.name ).get_tensor() # src_tensor = param_or_buffer.value().get_tensor() src_tensor = ( state_var_dict[param_or_buffer.name] .value() .get_tensor() ) param_or_buffer_tensor._share_data_with(src_tensor) # record var info if param_or_buffer.name not in extra_var_info: extra_info_dict = {} if param_or_buffer.name in state_names_dict: extra_info_dict['structured_name'] = state_names_dict[ param_or_buffer.name ] extra_info_dict[ 'stop_gradient' ] = param_or_buffer.stop_gradient if isinstance(param_or_buffer, EagerParamBase): extra_info_dict['trainable'] = param_or_buffer.trainable extra_var_info[param_or_buffer.name] = extra_info_dict # 4. build input & output of save_infernece_model # NOTE(chenweihang): [ Get input variables name ] # There are two cases, whether to prune the inputs or not # - not prune inputs (recommend): # - the len(input_spec) == len((concrete_program.inputs) - 1 # - here can use concrete_program.inputs directly # - prune inputs: # - the input_spec length < len((concrete_program.inputs) - 1 # - the input_spec's name should be in concrete_program.inputs input_var_names = _get_input_var_names( concrete_program.inputs, inner_input_spec ) # NOTE(chenweihang): [ Get output variables ] # the rule is like [ Get input variables name ]. For output var, # we only support Tensor spec, and actually, we only need the # var name of output, and we don't recommended to use output_spec # print(concrete_program.main_program) # print(concrete_program.outputs, configs.output_spec) output_vars = _get_output_vars( concrete_program.outputs, configs.output_spec, with_hook ) # 5. save inference model # construct new save_inference_model arguments model_path = dirname # NOTE(chenweihang): because prefix contains model and params filename, # so we don't support set model_filename & params_filename if 'forward' == attr_func or not isinstance(layer, Layer): model_filename = file_prefix + INFER_MODEL_SUFFIX params_filename = file_prefix + INFER_PARAMS_SUFFIX else: model_filename = file_prefix + '.' + attr_func + INFER_MODEL_SUFFIX params_filename = ( file_prefix + '.' + attr_func + INFER_PARAMS_SUFFIX ) with scope_guard(scope): save_inference_model( dirname=model_path, feeded_var_names=input_var_names, target_vars=output_vars, executor=Executor(_current_expected_place()), main_program=concrete_program.main_program.clone(), model_filename=model_filename, params_filename=params_filename, export_for_deployment=configs._export_for_deployment, program_only=configs._program_only, clip_extra=configs.clip_extra, ) if combine_params: clone_main_program = concrete_program.main_program.clone() clone_main_program = clone_main_program._prune_with_input( input_var_names, output_vars ) for block in clone_main_program.blocks: combine_vars.update(block.vars) # save shared params if combine_params: # sort vars by name combine_vars = sorted(combine_vars.items(), key=lambda item: item[0]) ordered_vars = [] for name, var in combine_vars: ordered_vars.append(var) params_filename = file_prefix + INFER_PARAMS_SUFFIX with scope_guard(scope): paddle.static.save_vars( Executor(_current_expected_place()), dirname=model_path, vars=list( filter( paddle.framework.io_utils.is_persistable, ordered_vars ) ), filename=params_filename, ) # save property property_save_path = os.path.join( os.path.normpath(model_path), file_prefix + INFER_PROPERTY_SUFFIX ) _save_property(property_save_path, property_vals) # NOTE(chenweihang): [ Save extra variable info ] # save_inference_model will lose some important variable information, including: # - Variable name and correspondence (when saved variables as one file) # - Variable.stop_gradient information # - Which persistent variable are parameter and which are not # - Parameter.trainable information # # The lost information cannot be recovered when it is loaded again, # so if we want to perform fine-tune after loading, we may need to # configure redundant information to proceed. # # Due to compatibility issues, we cannot change the original storage structure, # but we can save these information in `jit.save` without changing the original # storage to improve user experience. So we save extra information into # file `***.pdiparams.info` # "layer" can only be Layer or function or StaticFunction. contain_parameter = False if concrete_program is not None: for var in concrete_program.main_program.list_vars(): contain_parameter |= isinstance(var, Parameter) if (isinstance(layer, Layer) or contain_parameter) and extra_var_info: with scope_guard(scope): extra_var_info_path = path + INFER_PARAMS_INFO_SUFFIX with open(extra_var_info_path, 'wb') as f: pickle.dump(extra_var_info, f, protocol=2) @dygraph_only def load(path, **configs): """ :api_attr: imperative Load model saved by ``paddle.jit.save`` or ``paddle.static.save_inference_model`` or paddle 1.x API ``paddle.fluid.io.save_inference_model`` as ``paddle.jit.TranslatedLayer``, then performing inference or fine-tune training. .. note:: If you load model saved by ``paddle.static.save_inference_model`` , there will be the following limitations when using it in fine-tuning: 1. Imperative mode do not support LoDTensor. All original model's feed targets or parametars that depend on LoD are temporarily unavailable. 2. All saved model's feed targets need to be passed into TranslatedLayer's forward function. 3. The variable's ``stop_gradient`` information is lost and can not be recovered. 4. The parameter's ``trainable`` information is lost and can not be recovered. Args: path (str): The path prefix to load model. The format is ``dirname/file_prefix`` or ``file_prefix`` . **configs (dict, optional): Other load configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) model_filename (str): The inference model file name of the paddle 1.x ``save_inference_model`` save format. Default file name is :code:`__model__` . (2) params_filename (str): The persistable variables file name of the paddle 1.x ``save_inference_model`` save format. No default file name, save variables separately by default. Returns: TranslatedLayer: A Layer object can run saved translated model. Examples: 1. Load model saved by ``paddle.jit.save`` then performing inference and fine-tune training. .. code-block:: python import numpy as np import paddle import paddle.nn as nn import paddle.optimizer as opt BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super().__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) # 1. train & save model. # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) # train train(layer, loader, loss_fn, adam) # save path = "example_model/linear" paddle.jit.save(layer, path) # 2. load model # load loaded_layer = paddle.jit.load(path) # inference loaded_layer.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = loaded_layer(x) # fine-tune loaded_layer.train() adam = opt.Adam(learning_rate=0.001, parameters=loaded_layer.parameters()) train(loaded_layer, loader, loss_fn, adam) 2. Load model saved by ``paddle.fluid.io.save_inference_model`` then performing and fine-tune training. .. code-block:: python import numpy as np import paddle import paddle.static as static import paddle.nn as nn import paddle.optimizer as opt import paddle.nn.functional as F BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples paddle.enable_static() image = static.data(name='image', shape=[None, 784], dtype='float32') label = static.data(name='label', shape=[None, 1], dtype='int64') pred = static.nn.fc(x=image, size=10, activation='softmax') loss = F.cross_entropy(input=pred, label=label) avg_loss = paddle.mean(loss) optimizer = paddle.optimizer.SGD(learning_rate=0.001) optimizer.minimize(avg_loss) place = paddle.CPUPlace() exe = static.Executor(place) exe.run(static.default_startup_program()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, feed_list=[image, label], places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, return_list=False, num_workers=2) # 1. train and save inference model for data in loader(): exe.run( static.default_main_program(), feed=data, fetch_list=[avg_loss]) model_path = "fc.example.model" paddle.fluid.io.save_inference_model( model_path, ["image"], [pred], exe) # 2. load model # enable dygraph mode paddle.disable_static(place) # load fc = paddle.jit.load(model_path) # inference fc.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = fc(x) # fine-tune fc.train() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=fc.parameters()) loader = paddle.io.DataLoader(dataset, places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = fc(image) loss = loss_fn(out, label) loss.backward() adam.step() adam.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) """ # 1. construct correct config config = _parse_load_config(configs) model_path, config = _build_load_path_and_config(path, config) return TranslatedLayer._construct(model_path, config) @dygraph_only def _trace( layer, inputs, feed_prefix='feed_', fetch_prefix='fetch_', tmp_prefix='t_' ): assert isinstance(layer, Layer) if not isinstance(inputs, (list, tuple)): inputs = [inputs] tracer = _dygraph_tracer()._get_program_desc_tracer() var_list = extract_vars(inputs) with program_desc_tracing_guard(True): original_outputs = layer(*inputs) if not isinstance(original_outputs, (list, tuple)): outputs = [original_outputs] else: outputs = original_outputs out_vars = extract_vars(outputs, err_tag='outputs') ( program_desc, feed_names, fetch_names, parameters, ) = tracer.create_program_desc( var_list, feed_prefix, out_vars, fetch_prefix, tmp_prefix ) tracer.reset() with _dygraph_guard(None): program = create_program_from_desc(program_desc) return original_outputs, program, feed_names, fetch_names, parameters class TracedLayer: """ :api_attr: imperative TracedLayer is used to convert a forward dygraph model to a static graph model. This is mainly used to save the dygraph model for online inference using C++. Besides, users can also do inference in Python using the converted static graph model, which usually has better performance than the original dygraph model. TracedLayer would run the static graph model using :code:`Executor` and :code:`CompiledProgram` . The static graph model would share parameters with the dygraph model. All TracedLayer objects should not be created by constructor and should be created by static method :code:`TracedLayer.trace(layer, inputs)` . The TracedLayer can only be used to convert the data-independent dygraph model into the static graph model, which means the dygraph model should be independent with the tensor data and shape. """ def __init__(self, program, parameters, feed_names, fetch_names): self._program = program self._feed_names = feed_names self._fetch_names = fetch_names self._params = parameters self._place = _current_expected_place() self._scope = core.Scope() for p in parameters: src_tensor = p.value().get_tensor() dst_tensor = self._scope.var(p.name).get_tensor() dst_tensor._share_data_with(src_tensor) self._exe = Executor(self._place) self._compiled_program = None self._build_strategy = None self._exec_strategy = None @property def program(self): return self._program def _switch(self, is_test=True): for block_id in range(self._program.num_blocks): block = self._program.block(block_id) for op in block.ops: if op.has_attr("is_test"): op._set_attr("is_test", is_test) @staticmethod @dygraph_only def trace(layer, inputs): """ This method is the only allowed method to create TracedLayer object. It would call the :code:`layer(*inputs)` method to run the dygraph model and convert it into a static graph model. Args: layer (paddle.nn.Layer): the layer object to be traced. inputs (list(Tensor)|tuple(Tensor)|Tensor): the input tensors of the layer object. Returns: tuple: A tuple of 2 items, whose the first item is the output of :code:`layer(*inputs)` , and the second item is the created TracedLayer object. Examples: .. code-block:: python: import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super().__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) layer = ExampleLayer() in_var = paddle.uniform(shape=[2, 3], dtype='float32') out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) # run the static graph model using Executor inside out_static_graph = static_layer([in_var]) print(len(out_static_graph)) # 1 print(out_static_graph[0].shape) # (2, 10) # save the static graph model for inference static_layer.save_inference_model('./saved_infer_model') """ assert isinstance( layer, Layer ), "The type of 'layer' in paddle.jit.TracedLayer.trace must be paddle.nn.Layer, but received {}.".format( type(layer) ) outs, prog, feed, fetch, parameters = _trace(layer, inputs) traced = TracedLayer(prog, parameters, feed, fetch) return outs, traced def set_strategy(self, build_strategy=None, exec_strategy=None): """ Set the strategies when running static graph model. Args: build_strategy (BuildStrategy, optional): build strategy of :code:`CompiledProgram` inside TracedLayer. Default None. exec_strategy (ExecutionStrategy, optional): execution strategy of :code:`CompiledProgram` inside TracedLayer. Default None. Returns: None Examples: .. code-block:: python: import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super().__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) layer = ExampleLayer() in_var = paddle.uniform(shape=[2, 3], dtype='float32') out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) build_strategy = paddle.static.BuildStrategy() build_strategy.enable_inplace = True exec_strategy = paddle.static.ExecutionStrategy() exec_strategy.num_threads = 2 static_layer.set_strategy(build_strategy=build_strategy, exec_strategy=exec_strategy) out_static_graph = static_layer([in_var]) """ assert self._compiled_program is None, "Cannot set strategy after run" assert isinstance( build_strategy, (type(None), BuildStrategy) ), "The type of 'build_strategy' in paddle.jit.TracedLayer.set_strategy must be fluid.BuildStrategy, but received {}.".format( type(build_strategy) ) assert isinstance( exec_strategy, (type(None), ExecutionStrategy) ), "The type of 'exec_strategy' in paddle.jit.TracedLayer.set_strategy must be fluid.ExecutionStrategy, but received {}.".format( type(exec_strategy) ) self._build_strategy = build_strategy self._exec_strategy = exec_strategy @switch_to_static_graph def _compile(self): self._compiled_program = CompiledProgram( self._program, build_strategy=self._build_strategy, ) def _build_feed(self, inputs): assert isinstance( inputs, (list, tuple) ), "Inputs should be a list or tuple of variables" assert len(inputs) == len(self._feed_names) feed_dict = {} if in_dynamic_mode(): for x, name in zip(inputs, self._feed_names): feed_dict[name] = x.value().get_tensor() else: for x, name in zip(inputs, self._feed_names): feed_dict[name] = x return feed_dict @switch_to_static_graph def _run(self, feed): return self._exe.run( self._compiled_program, feed=feed, fetch_list=self._fetch_names ) def __call__(self, inputs): with scope_guard(self._scope): if self._compiled_program is None: self._compile() return self._run(self._build_feed(inputs)) @switch_to_static_graph def save_inference_model(self, path, feed=None, fetch=None, **kwargs): """ Save the TracedLayer to a model for inference. The saved inference model can be loaded by C++ inference APIs. ``path`` is the prefix of saved objects, and the saved translated program file suffix is ``.pdmodel`` , the saved persistable variables file suffix is ``.pdiparams`` . Args: path(str): The path prefix to save model. The format is ``dirname/file_prefix`` or ``file_prefix``. feed (list[int], optional): the input variable indices of the saved inference model. If None, all input variables of the TracedLayer object would be the inputs of the saved inference model. Default None. fetch (list[int], optional): the output variable indices of the saved inference model. If None, all output variables of the TracedLayer object would be the outputs of the saved inference model. Default None. kwargs: Supported keys including - clip_extra(bool): whether to clip extra information for every operator. Defaults to True. - legacy_format(bool): whether to save program in legacy format. Default to False. Returns: None Examples: .. code-block:: python: import numpy as np import paddle class ExampleLayer(paddle.nn.Layer): def __init__(self): super().__init__() self._fc = paddle.nn.Linear(3, 10) def forward(self, input): return self._fc(input) save_dirname = './saved_infer_model' in_np = np.random.random([2, 3]).astype('float32') in_var = paddle.to_tensor(in_np) layer = ExampleLayer() out_dygraph, static_layer = paddle.jit.TracedLayer.trace(layer, inputs=[in_var]) static_layer.save_inference_model(save_dirname, feed=[0], fetch=[0]) paddle.enable_static() place = paddle.CPUPlace() exe = paddle.static.Executor(place) program, feed_vars, fetch_vars = paddle.static.load_inference_model(save_dirname, exe) fetch, = exe.run(program, feed={feed_vars[0]: in_np}, fetch_list=fetch_vars) print(fetch.shape) # (2, 10) """ check_type( path, "path", str, "paddle.jit.TracedLayer.save_inference_model", ) check_type( feed, "feed", (type(None), list), "paddle.jit.TracedLayer.save_inference_model", ) if isinstance(feed, list): for f in feed: check_type( f, "each element of feed", int, "paddle.jit.TracedLayer.save_inference_model", ) check_type( fetch, "fetch", (type(None), list), "paddle.jit.TracedLayer.save_inference_model", ) if isinstance(fetch, list): for f in fetch: check_type( f, "each element of fetch", int, "paddle.jit.TracedLayer.save_inference_model", ) clip_extra = kwargs.get('clip_extra', True) # path check file_prefix = os.path.basename(path) if file_prefix == "": raise ValueError( "The input path MUST be format of dirname/file_prefix " "[dirname\\file_prefix in Windows system], but received " "file_prefix is empty string." ) dirname = os.path.dirname(path) if dirname and not os.path.exists(dirname): os.makedirs(dirname) def get_feed_fetch(all_vars, partial_vars): if partial_vars is None: return all_vars return [all_vars[idx] for idx in partial_vars] with scope_guard(self._scope): feeded_var_names = get_feed_fetch(self._feed_names, feed) target_var_names = get_feed_fetch(self._fetch_names, fetch) target_vars = [] for name in target_var_names: target_var = self._program.global_block().vars.get(name, None) assert target_var is not None, f"{name} cannot be found" target_vars.append(target_var) model_filename = file_prefix + INFER_MODEL_SUFFIX params_filename = file_prefix + INFER_PARAMS_SUFFIX legacy_format = kwargs.get('legacy_format', False) save_inference_model( dirname=dirname, feeded_var_names=feeded_var_names, target_vars=target_vars, executor=self._exe, main_program=self._program.clone(), model_filename=model_filename, params_filename=params_filename, clip_extra=clip_extra, legacy_format=legacy_format, )

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# coding: utf-8 """ Magento Community No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.2 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class SalesDataShippingAssignmentInterface(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'shipping': 'SalesDataShippingInterface', 'items': 'list[SalesDataOrderItemInterface]', 'stock_id': 'int', 'extension_attributes': 'SalesDataShippingAssignmentExtensionInterface' } attribute_map = { 'shipping': 'shipping', 'items': 'items', 'stock_id': 'stock_id', 'extension_attributes': 'extension_attributes' } def __init__(self, shipping=None, items=None, stock_id=None, extension_attributes=None): """ SalesDataShippingAssignmentInterface - a model defined in Swagger """ self._shipping = None self._items = None self._stock_id = None self._extension_attributes = None self.shipping = shipping self.items = items if stock_id is not None: self.stock_id = stock_id if extension_attributes is not None: self.extension_attributes = extension_attributes @property def shipping(self): """ Gets the shipping of this SalesDataShippingAssignmentInterface. :return: The shipping of this SalesDataShippingAssignmentInterface. :rtype: SalesDataShippingInterface """ return self._shipping @shipping.setter def shipping(self, shipping): """ Sets the shipping of this SalesDataShippingAssignmentInterface. :param shipping: The shipping of this SalesDataShippingAssignmentInterface. :type: SalesDataShippingInterface """ if shipping is None: raise ValueError("Invalid value for `shipping`, must not be `None`") self._shipping = shipping @property def items(self): """ Gets the items of this SalesDataShippingAssignmentInterface. Order items of shipping assignment :return: The items of this SalesDataShippingAssignmentInterface. :rtype: list[SalesDataOrderItemInterface] """ return self._items @items.setter def items(self, items): """ Sets the items of this SalesDataShippingAssignmentInterface. Order items of shipping assignment :param items: The items of this SalesDataShippingAssignmentInterface. :type: list[SalesDataOrderItemInterface] """ if items is None: raise ValueError("Invalid value for `items`, must not be `None`") self._items = items @property def stock_id(self): """ Gets the stock_id of this SalesDataShippingAssignmentInterface. Stock id :return: The stock_id of this SalesDataShippingAssignmentInterface. :rtype: int """ return self._stock_id @stock_id.setter def stock_id(self, stock_id): """ Sets the stock_id of this SalesDataShippingAssignmentInterface. Stock id :param stock_id: The stock_id of this SalesDataShippingAssignmentInterface. :type: int """ self._stock_id = stock_id @property def extension_attributes(self): """ Gets the extension_attributes of this SalesDataShippingAssignmentInterface. :return: The extension_attributes of this SalesDataShippingAssignmentInterface. :rtype: SalesDataShippingAssignmentExtensionInterface """ return self._extension_attributes @extension_attributes.setter def extension_attributes(self, extension_attributes): """ Sets the extension_attributes of this SalesDataShippingAssignmentInterface. :param extension_attributes: The extension_attributes of this SalesDataShippingAssignmentInterface. :type: SalesDataShippingAssignmentExtensionInterface """ self._extension_attributes = extension_attributes def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ if not isinstance(other, SalesDataShippingAssignmentInterface): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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import pynetbox import time def adddev(dev): nb = pynetbox.api(url='http://192.168.174.149:8000/', token='0123456789abcdef0123456789abcdef01234567') result = nb.dcim.devices.create( name=dev, device_type=5, device_role=2, site=3, ) print(result) file1 = open ('/home/gns3/Netbox/Python/hosts.txt', 'r') Lines = file1.readlines() count = 0 for line in Lines: count += 1 time.sleep(0.5) dev = line adddev(dev)

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import rpy2.robjects as robjects from rpy2.robjects import pandas2ri import ipdb fileName = '/data/rds/baseline_1/test/000.rds' #fileName = '/data/rds/clicks.rds' pandas2ri.activate() readRDS = robjects.r['readRDS'] df = readRDS(fileName) df = pandas2ri.ri2py(df) ipdb.set_trace() print df[1] print df[2][0] print df[2][5] print df[2][6] #for x in df: # ipdb.set_trace(context=5) # print x # do something with the dataframe

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import neurolab as nl import numpy as np import pylab as pl import csv import math from sklearn.metrics import classification_report import sys adata = [] with open('dataset_full.csv', 'rb') as csvfile: reader = csv.reader(csvfile) for row in reader: adata.append(row) xdata_zeros = [] ydata_zeros = [] xdata_ones = [] ydata_ones = [] for i in range(0, len(adata)): temp = [] yval = int(adata[i][len(adata[i]) - 1]) if yval ==1: for j in range(0, len(adata[i]) - 1): temp.append(int(adata[i][j])) xdata_ones.append(temp) ydata_ones.append([yval]) else: for j in range(0, len(adata[i]) - 1): temp.append(int(adata[i][j])) xdata_zeros.append(temp) ydata_zeros.append([yval]) nzeros = len(xdata_zeros) nones = len(xdata_ones) # print (nzeros) # print (nones) zeros_div = int(math.floor(nzeros * 0.75 - 1)) ones_div = int(math.floor(nones * 0.75 - 1)) train_inputs_m = np.array(xdata_zeros[0:zeros_div] + xdata_ones[0:ones_div]) train_outputs = np.array(ydata_zeros[0:zeros_div] + ydata_ones[0:ones_div]) test_inputs_m = np.array(xdata_zeros[zeros_div:nzeros]+xdata_ones[ones_div:nones]) test_outputs = ydata_zeros[zeros_div:nzeros]+ydata_ones[ones_div:nones] train_inputs = train_inputs_m[:, [5, 6, 7, 8, 9, 10]] test_inputs = test_inputs_m[:, [5, 6, 7, 8, 9, 10]] np.random.seed(0) indices = np.arange(train_inputs.shape[0]) np.random.shuffle(indices) print train_inputs.shape net = nl.net.newp([[-7, 7]]*6, 1) print train_outputs.shape print net.co print train_outputs # train with delta rule # see net.trainf error = net.train(train_inputs[indices], train_outputs[indices], epochs=100, show=10, lr=0.1) pl.plot(error) pl.xlabel('Epoch number') pl.ylabel('Train error') pl.grid() # pl.show() pl.savefig("slp.png") out = net.sim(test_inputs) # print out predicted_outputs = [] for i in out: predicted_outputs.append(int(i[0])) # print "Training Complete, Test Results Generated for Single Layer Perceptron" # print predicted_outputs # atest_outputs = [] # for i in test_outputs: atest_outputs.append(i[0]) target_names = ['0 - Non Defaulter','1 - Defaulter'] print "Classification Report for Single Layer Perceptron" cfreport = classification_report(atest_outputs, predicted_outputs, target_names=target_names) report_file = open("Classification_Report_slp.txt", 'a') report_file.write("Classification Report For Single Layer Perceptron\n") report_file.write(cfreport) report_file.write("\n\n\n") report_file.close()

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#!/usr/bin/env python import httplib from datetime import datetime, timedelta from mock import MagicMock, Mock from tests.unit import unittest from tests.unit import AWSMockServiceTestCase import boto.ec2 from boto.regioninfo import RegionInfo from boto.ec2.blockdevicemapping import BlockDeviceType, BlockDeviceMapping from boto.ec2.connection import EC2Connection from boto.ec2.snapshot import Snapshot from boto.ec2.reservedinstance import ReservedInstancesConfiguration class TestEC2ConnectionBase(AWSMockServiceTestCase): connection_class = EC2Connection def setUp(self): super(TestEC2ConnectionBase, self).setUp() self.ec2 = self.service_connection class TestReservedInstanceOfferings(TestEC2ConnectionBase): def default_body(self): return """ <DescribeReservedInstancesOfferingsResponse> <requestId>d3253568-edcf-4897-9a3d-fb28e0b3fa38</requestId> <reservedInstancesOfferingsSet> <item> <reservedInstancesOfferingId>2964d1bf71d8</reservedInstancesOfferingId> <instanceType>c1.medium</instanceType> <availabilityZone>us-east-1c</availabilityZone> <duration>94608000</duration> <fixedPrice>775.0</fixedPrice> <usagePrice>0.0</usagePrice> <productDescription>product description</productDescription> <instanceTenancy>default</instanceTenancy> <currencyCode>USD</currencyCode> <offeringType>Heavy Utilization</offeringType> <recurringCharges> <item> <frequency>Hourly</frequency> <amount>0.095</amount> </item> </recurringCharges> <marketplace>false</marketplace> <pricingDetailsSet> <item> <price>0.045</price> <count>1</count> </item> </pricingDetailsSet> </item> <item> <reservedInstancesOfferingId>2dce26e46889</reservedInstancesOfferingId> <instanceType>c1.medium</instanceType> <availabilityZone>us-east-1c</availabilityZone> <duration>94608000</duration> <fixedPrice>775.0</fixedPrice> <usagePrice>0.0</usagePrice> <productDescription>Linux/UNIX</productDescription> <instanceTenancy>default</instanceTenancy> <currencyCode>USD</currencyCode> <offeringType>Heavy Utilization</offeringType> <recurringCharges> <item> <frequency>Hourly</frequency> <amount>0.035</amount> </item> </recurringCharges> <marketplace>false</marketplace> <pricingDetailsSet/> </item> </reservedInstancesOfferingsSet> <nextToken>next_token</nextToken> </DescribeReservedInstancesOfferingsResponse> """ def test_get_reserved_instance_offerings(self): self.set_http_response(status_code=200) response = self.ec2.get_all_reserved_instances_offerings() self.assertEqual(len(response), 2) instance = response[0] self.assertEqual(instance.id, '2964d1bf71d8') self.assertEqual(instance.instance_type, 'c1.medium') self.assertEqual(instance.availability_zone, 'us-east-1c') self.assertEqual(instance.duration, 94608000) self.assertEqual(instance.fixed_price, '775.0') self.assertEqual(instance.usage_price, '0.0') self.assertEqual(instance.description, 'product description') self.assertEqual(instance.instance_tenancy, 'default') self.assertEqual(instance.currency_code, 'USD') self.assertEqual(instance.offering_type, 'Heavy Utilization') self.assertEqual(len(instance.recurring_charges), 1) self.assertEqual(instance.recurring_charges[0].frequency, 'Hourly') self.assertEqual(instance.recurring_charges[0].amount, '0.095') self.assertEqual(len(instance.pricing_details), 1) self.assertEqual(instance.pricing_details[0].price, '0.045') self.assertEqual(instance.pricing_details[0].count, '1') def test_get_reserved_instance_offerings_params(self): self.set_http_response(status_code=200) self.ec2.get_all_reserved_instances_offerings( reserved_instances_offering_ids=['id1','id2'], instance_type='t1.micro', availability_zone='us-east-1', product_description='description', instance_tenancy='dedicated', offering_type='offering_type', include_marketplace=False, min_duration=100, max_duration=1000, max_instance_count=1, next_token='next_token', max_results=10 ) self.assert_request_parameters({ 'Action': 'DescribeReservedInstancesOfferings', 'ReservedInstancesOfferingId.1': 'id1', 'ReservedInstancesOfferingId.2': 'id2', 'InstanceType': 't1.micro', 'AvailabilityZone': 'us-east-1', 'ProductDescription': 'description', 'InstanceTenancy': 'dedicated', 'OfferingType': 'offering_type', 'IncludeMarketplace': 'false', 'MinDuration': '100', 'MaxDuration': '1000', 'MaxInstanceCount': '1', 'NextToken': 'next_token', 'MaxResults': '10',}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestPurchaseReservedInstanceOffering(TestEC2ConnectionBase): def default_body(self): return """<PurchaseReservedInstancesOffering />""" def test_serialized_api_args(self): self.set_http_response(status_code=200) response = self.ec2.purchase_reserved_instance_offering( 'offering_id', 1, (100.0, 'USD')) self.assert_request_parameters({ 'Action': 'PurchaseReservedInstancesOffering', 'InstanceCount': 1, 'ReservedInstancesOfferingId': 'offering_id', 'LimitPrice.Amount': '100.0', 'LimitPrice.CurrencyCode': 'USD',}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestCreateImage(TestEC2ConnectionBase): def default_body(self): return """<CreateImageResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-01/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <imageId>ami-4fa54026</imageId> </CreateImageResponse>""" def test_minimal(self): self.set_http_response(status_code=200) response = self.ec2.create_image( 'instance_id', 'name') self.assert_request_parameters({ 'Action': 'CreateImage', 'InstanceId': 'instance_id', 'Name': 'name'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_block_device_mapping(self): self.set_http_response(status_code=200) bdm = BlockDeviceMapping() bdm['test'] = BlockDeviceType() response = self.ec2.create_image( 'instance_id', 'name', block_device_mapping=bdm) self.assert_request_parameters({ 'Action': 'CreateImage', 'InstanceId': 'instance_id', 'Name': 'name', 'BlockDeviceMapping.1.DeviceName': 'test', 'BlockDeviceMapping.1.Ebs.DeleteOnTermination': 'false'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestCancelReservedInstancesListing(TestEC2ConnectionBase): def default_body(self): return """ <CancelReservedInstancesListingResponse> <requestId>request_id</requestId> <reservedInstancesListingsSet> <item> <reservedInstancesListingId>listing_id</reservedInstancesListingId> <reservedInstancesId>instance_id</reservedInstancesId> <createDate>2012-07-12T16:55:28.000Z</createDate> <updateDate>2012-07-12T16:55:28.000Z</updateDate> <status>cancelled</status> <statusMessage>CANCELLED</statusMessage> <instanceCounts> <item> <state>Available</state> <instanceCount>0</instanceCount> </item> <item> <state>Sold</state> <instanceCount>0</instanceCount> </item> <item> <state>Cancelled</state> <instanceCount>1</instanceCount> </item> <item> <state>Pending</state> <instanceCount>0</instanceCount> </item> </instanceCounts> <priceSchedules> <item> <term>5</term> <price>166.64</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>4</term> <price>133.32</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>3</term> <price>99.99</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>2</term> <price>66.66</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>1</term> <price>33.33</price> <currencyCode>USD</currencyCode> <active>false</active> </item> </priceSchedules> <tagSet/> <clientToken>XqJIt1342112125076</clientToken> </item> </reservedInstancesListingsSet> </CancelReservedInstancesListingResponse> """ def test_reserved_instances_listing(self): self.set_http_response(status_code=200) response = self.ec2.cancel_reserved_instances_listing() self.assertEqual(len(response), 1) cancellation = response[0] self.assertEqual(cancellation.status, 'cancelled') self.assertEqual(cancellation.status_message, 'CANCELLED') self.assertEqual(len(cancellation.instance_counts), 4) first = cancellation.instance_counts[0] self.assertEqual(first.state, 'Available') self.assertEqual(first.instance_count, 0) self.assertEqual(len(cancellation.price_schedules), 5) schedule = cancellation.price_schedules[0] self.assertEqual(schedule.term, 5) self.assertEqual(schedule.price, '166.64') self.assertEqual(schedule.currency_code, 'USD') self.assertEqual(schedule.active, False) class TestCreateReservedInstancesListing(TestEC2ConnectionBase): def default_body(self): return """ <CreateReservedInstancesListingResponse> <requestId>request_id</requestId> <reservedInstancesListingsSet> <item> <reservedInstancesListingId>listing_id</reservedInstancesListingId> <reservedInstancesId>instance_id</reservedInstancesId> <createDate>2012-07-17T17:11:09.449Z</createDate> <updateDate>2012-07-17T17:11:09.468Z</updateDate> <status>active</status> <statusMessage>ACTIVE</statusMessage> <instanceCounts> <item> <state>Available</state> <instanceCount>1</instanceCount> </item> <item> <state>Sold</state> <instanceCount>0</instanceCount> </item> <item> <state>Cancelled</state> <instanceCount>0</instanceCount> </item> <item> <state>Pending</state> <instanceCount>0</instanceCount> </item> </instanceCounts> <priceSchedules> <item> <term>11</term> <price>2.5</price> <currencyCode>USD</currencyCode> <active>true</active> </item> <item> <term>10</term> <price>2.5</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>9</term> <price>2.5</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>8</term> <price>2.0</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>7</term> <price>2.0</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>6</term> <price>2.0</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>5</term> <price>1.5</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>4</term> <price>1.5</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>3</term> <price>0.7</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>2</term> <price>0.7</price> <currencyCode>USD</currencyCode> <active>false</active> </item> <item> <term>1</term> <price>0.1</price> <currencyCode>USD</currencyCode> <active>false</active> </item> </priceSchedules> <tagSet/> <clientToken>myIdempToken1</clientToken> </item> </reservedInstancesListingsSet> </CreateReservedInstancesListingResponse> """ def test_create_reserved_instances_listing(self): self.set_http_response(status_code=200) response = self.ec2.create_reserved_instances_listing( 'instance_id', 1, [('2.5', 11), ('2.0', 8)], 'client_token') self.assertEqual(len(response), 1) cancellation = response[0] self.assertEqual(cancellation.status, 'active') self.assertEqual(cancellation.status_message, 'ACTIVE') self.assertEqual(len(cancellation.instance_counts), 4) first = cancellation.instance_counts[0] self.assertEqual(first.state, 'Available') self.assertEqual(first.instance_count, 1) self.assertEqual(len(cancellation.price_schedules), 11) schedule = cancellation.price_schedules[0] self.assertEqual(schedule.term, 11) self.assertEqual(schedule.price, '2.5') self.assertEqual(schedule.currency_code, 'USD') self.assertEqual(schedule.active, True) self.assert_request_parameters({ 'Action': 'CreateReservedInstancesListing', 'ReservedInstancesId': 'instance_id', 'InstanceCount': '1', 'ClientToken': 'client_token', 'PriceSchedules.0.Price': '2.5', 'PriceSchedules.0.Term': '11', 'PriceSchedules.1.Price': '2.0', 'PriceSchedules.1.Term': '8',}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestDescribeSpotInstanceRequests(TestEC2ConnectionBase): def default_body(self): return """ <DescribeSpotInstanceRequestsResponse> <requestId>requestid</requestId> <spotInstanceRequestSet> <item> <spotInstanceRequestId>sir-id</spotInstanceRequestId> <spotPrice>0.003000</spotPrice> <type>one-time</type> <state>active</state> <status> <code>fulfilled</code> <updateTime>2012-10-19T18:09:26.000Z</updateTime> <message>Your Spot request is fulfilled.</message> </status> <launchGroup>mylaunchgroup</launchGroup> <launchSpecification> <imageId>ami-id</imageId> <keyName>mykeypair</keyName> <groupSet> <item> <groupId>sg-id</groupId> <groupName>groupname</groupName> </item> </groupSet> <instanceType>t1.micro</instanceType> <monitoring> <enabled>false</enabled> </monitoring> </launchSpecification> <instanceId>i-id</instanceId> <createTime>2012-10-19T18:07:05.000Z</createTime> <productDescription>Linux/UNIX</productDescription> <launchedAvailabilityZone>us-east-1d</launchedAvailabilityZone> </item> </spotInstanceRequestSet> </DescribeSpotInstanceRequestsResponse> """ def test_describe_spot_instance_requets(self): self.set_http_response(status_code=200) response = self.ec2.get_all_spot_instance_requests() self.assertEqual(len(response), 1) spotrequest = response[0] self.assertEqual(spotrequest.id, 'sir-id') self.assertEqual(spotrequest.price, 0.003) self.assertEqual(spotrequest.type, 'one-time') self.assertEqual(spotrequest.state, 'active') self.assertEqual(spotrequest.fault, None) self.assertEqual(spotrequest.valid_from, None) self.assertEqual(spotrequest.valid_until, None) self.assertEqual(spotrequest.launch_group, 'mylaunchgroup') self.assertEqual(spotrequest.launched_availability_zone, 'us-east-1d') self.assertEqual(spotrequest.product_description, 'Linux/UNIX') self.assertEqual(spotrequest.availability_zone_group, None) self.assertEqual(spotrequest.create_time, '2012-10-19T18:07:05.000Z') self.assertEqual(spotrequest.instance_id, 'i-id') launch_spec = spotrequest.launch_specification self.assertEqual(launch_spec.key_name, 'mykeypair') self.assertEqual(launch_spec.instance_type, 't1.micro') self.assertEqual(launch_spec.image_id, 'ami-id') self.assertEqual(launch_spec.placement, None) self.assertEqual(launch_spec.kernel, None) self.assertEqual(launch_spec.ramdisk, None) self.assertEqual(launch_spec.monitored, False) self.assertEqual(launch_spec.subnet_id, None) self.assertEqual(launch_spec.block_device_mapping, None) self.assertEqual(launch_spec.instance_profile, None) self.assertEqual(launch_spec.ebs_optimized, False) status = spotrequest.status self.assertEqual(status.code, 'fulfilled') self.assertEqual(status.update_time, '2012-10-19T18:09:26.000Z') self.assertEqual(status.message, 'Your Spot request is fulfilled.') class TestCopySnapshot(TestEC2ConnectionBase): def default_body(self): return """ <CopySnapshotResponse xmlns="http://ec2.amazonaws.com/doc/2012-12-01/"> <requestId>request_id</requestId> <snapshotId>snap-copied-id</snapshotId> </CopySnapshotResponse> """ def test_copy_snapshot(self): self.set_http_response(status_code=200) snapshot_id = self.ec2.copy_snapshot('us-west-2', 'snap-id', 'description') self.assertEqual(snapshot_id, 'snap-copied-id') self.assert_request_parameters({ 'Action': 'CopySnapshot', 'Description': 'description', 'SourceRegion': 'us-west-2', 'SourceSnapshotId': 'snap-id'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestCopyImage(TestEC2ConnectionBase): def default_body(self): return """ <CopyImageResponse xmlns="http://ec2.amazonaws.com/doc/2013-07-15/"> <requestId>request_id</requestId> <imageId>ami-copied-id</imageId> </CopyImageResponse> """ def test_copy_image(self): self.set_http_response(status_code=200) copied_ami = self.ec2.copy_image('us-west-2', 'ami-id', 'name', 'description', 'client-token') self.assertEqual(copied_ami.image_id, 'ami-copied-id') self.assert_request_parameters({ 'Action': 'CopyImage', 'Description': 'description', 'Name': 'name', 'SourceRegion': 'us-west-2', 'SourceImageId': 'ami-id', 'ClientToken': 'client-token'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_copy_image_without_name(self): self.set_http_response(status_code=200) copied_ami = self.ec2.copy_image('us-west-2', 'ami-id', description='description', client_token='client-token') self.assertEqual(copied_ami.image_id, 'ami-copied-id') self.assert_request_parameters({ 'Action': 'CopyImage', 'Description': 'description', 'SourceRegion': 'us-west-2', 'SourceImageId': 'ami-id', 'ClientToken': 'client-token'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestAccountAttributes(TestEC2ConnectionBase): def default_body(self): return """ <DescribeAccountAttributesResponse xmlns="http://ec2.amazonaws.com/doc/2012-12-01/"> <requestId>6d042e8a-4bc3-43e8-8265-3cbc54753f14</requestId> <accountAttributeSet> <item> <attributeName>vpc-max-security-groups-per-interface</attributeName> <attributeValueSet> <item> <attributeValue>5</attributeValue> </item> </attributeValueSet> </item> <item> <attributeName>max-instances</attributeName> <attributeValueSet> <item> <attributeValue>50</attributeValue> </item> </attributeValueSet> </item> <item> <attributeName>supported-platforms</attributeName> <attributeValueSet> <item> <attributeValue>EC2</attributeValue> </item> <item> <attributeValue>VPC</attributeValue> </item> </attributeValueSet> </item> <item> <attributeName>default-vpc</attributeName> <attributeValueSet> <item> <attributeValue>none</attributeValue> </item> </attributeValueSet> </item> </accountAttributeSet> </DescribeAccountAttributesResponse> """ def test_describe_account_attributes(self): self.set_http_response(status_code=200) parsed = self.ec2.describe_account_attributes() self.assertEqual(len(parsed), 4) self.assertEqual(parsed[0].attribute_name, 'vpc-max-security-groups-per-interface') self.assertEqual(parsed[0].attribute_values, ['5']) self.assertEqual(parsed[-1].attribute_name, 'default-vpc') self.assertEqual(parsed[-1].attribute_values, ['none']) class TestDescribeVPCAttribute(TestEC2ConnectionBase): def default_body(self): return """ <DescribeVpcAttributeResponse xmlns="http://ec2.amazonaws.com/doc/2013-02-01/"> <requestId>request_id</requestId> <vpcId>vpc-id</vpcId> <enableDnsHostnames> <value>false</value> </enableDnsHostnames> </DescribeVpcAttributeResponse> """ def test_describe_vpc_attribute(self): self.set_http_response(status_code=200) parsed = self.ec2.describe_vpc_attribute('vpc-id', 'enableDnsHostnames') self.assertEqual(parsed.vpc_id, 'vpc-id') self.assertFalse(parsed.enable_dns_hostnames) self.assert_request_parameters({ 'Action': 'DescribeVpcAttribute', 'VpcId': 'vpc-id', 'Attribute': 'enableDnsHostnames',}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestGetAllNetworkInterfaces(TestEC2ConnectionBase): def default_body(self): return """ <DescribeNetworkInterfacesResponse xmlns="http://ec2.amazonaws.com/\ doc/2013-06-15/"> <requestId>fc45294c-006b-457b-bab9-012f5b3b0e40</requestId> <networkInterfaceSet> <item> <networkInterfaceId>eni-0f62d866</networkInterfaceId> <subnetId>subnet-c53c87ac</subnetId> <vpcId>vpc-cc3c87a5</vpcId> <availabilityZone>ap-southeast-1b</availabilityZone> <description/> <ownerId>053230519467</ownerId> <requesterManaged>false</requesterManaged> <status>in-use</status> <macAddress>02:81:60:cb:27:37</macAddress> <privateIpAddress>10.0.0.146</privateIpAddress> <sourceDestCheck>true</sourceDestCheck> <groupSet> <item> <groupId>sg-3f4b5653</groupId> <groupName>default</groupName> </item> </groupSet> <attachment> <attachmentId>eni-attach-6537fc0c</attachmentId> <instanceId>i-22197876</instanceId> <instanceOwnerId>053230519467</instanceOwnerId> <deviceIndex>5</deviceIndex> <status>attached</status> <attachTime>2012-07-01T21:45:27.000Z</attachTime> <deleteOnTermination>true</deleteOnTermination> </attachment> <tagSet/> <privateIpAddressesSet> <item> <privateIpAddress>10.0.0.146</privateIpAddress> <primary>true</primary> </item> <item> <privateIpAddress>10.0.0.148</privateIpAddress> <primary>false</primary> </item> <item> <privateIpAddress>10.0.0.150</privateIpAddress> <primary>false</primary> </item> </privateIpAddressesSet> </item> </networkInterfaceSet> </DescribeNetworkInterfacesResponse>""" def test_attachment_has_device_index(self): self.set_http_response(status_code=200) parsed = self.ec2.get_all_network_interfaces() self.assertEqual(5, parsed[0].attachment.device_index) class TestGetAllImages(TestEC2ConnectionBase): def default_body(self): return """ <DescribeImagesResponse xmlns="http://ec2.amazonaws.com/doc/2013-02-01/"> <requestId>e32375e8-4ac3-4099-a8bf-3ec902b9023e</requestId> <imagesSet> <item> <imageId>ami-abcd1234</imageId> <imageLocation>111111111111/windows2008r2-hvm-i386-20130702</imageLocation> <imageState>available</imageState> <imageOwnerId>111111111111</imageOwnerId> <isPublic>false</isPublic> <architecture>i386</architecture> <imageType>machine</imageType> <platform>windows</platform> <viridianEnabled>true</viridianEnabled> <name>Windows Test</name> <description>Windows Test Description</description> <billingProducts> <item> <billingProduct>bp-6ba54002</billingProduct> </item> </billingProducts> <rootDeviceType>ebs</rootDeviceType> <rootDeviceName>/dev/sda1</rootDeviceName> <blockDeviceMapping> <item> <deviceName>/dev/sda1</deviceName> <ebs> <snapshotId>snap-abcd1234</snapshotId> <volumeSize>30</volumeSize> <deleteOnTermination>true</deleteOnTermination> <volumeType>standard</volumeType> </ebs> </item> <item> <deviceName>xvdb</deviceName> <virtualName>ephemeral0</virtualName> </item> <item> <deviceName>xvdc</deviceName> <virtualName>ephemeral1</virtualName> </item> <item> <deviceName>xvdd</deviceName> <virtualName>ephemeral2</virtualName> </item> <item> <deviceName>xvde</deviceName> <virtualName>ephemeral3</virtualName> </item> </blockDeviceMapping> <virtualizationType>hvm</virtualizationType> <hypervisor>xen</hypervisor> </item> </imagesSet> </DescribeImagesResponse>""" def test_get_all_images(self): self.set_http_response(status_code=200) parsed = self.ec2.get_all_images() self.assertEquals(1, len(parsed)) self.assertEquals("ami-abcd1234", parsed[0].id) self.assertEquals("111111111111/windows2008r2-hvm-i386-20130702", parsed[0].location) self.assertEquals("available", parsed[0].state) self.assertEquals("111111111111", parsed[0].ownerId) self.assertEquals("111111111111", parsed[0].owner_id) self.assertEquals(False, parsed[0].is_public) self.assertEquals("i386", parsed[0].architecture) self.assertEquals("machine", parsed[0].type) self.assertEquals(None, parsed[0].kernel_id) self.assertEquals(None, parsed[0].ramdisk_id) self.assertEquals(None, parsed[0].owner_alias) self.assertEquals("windows", parsed[0].platform) self.assertEquals("Windows Test", parsed[0].name) self.assertEquals("Windows Test Description", parsed[0].description) self.assertEquals("ebs", parsed[0].root_device_type) self.assertEquals("/dev/sda1", parsed[0].root_device_name) self.assertEquals("hvm", parsed[0].virtualization_type) self.assertEquals("xen", parsed[0].hypervisor) self.assertEquals(None, parsed[0].instance_lifecycle) # 1 billing product parsed into a list self.assertEquals(1, len(parsed[0].billing_products)) self.assertEquals("bp-6ba54002", parsed[0].billing_products[0]) # Just verify length, there is already a block_device_mapping test self.assertEquals(5, len(parsed[0].block_device_mapping)) # TODO: No tests for product codes? class TestModifyInterfaceAttribute(TestEC2ConnectionBase): def default_body(self): return """ <ModifyNetworkInterfaceAttributeResponse \ xmlns="http://ec2.amazonaws.com/doc/2013-06-15/"> <requestId>657a4623-5620-4232-b03b-427e852d71cf</requestId> <return>true</return> </ModifyNetworkInterfaceAttributeResponse> """ def test_modify_description(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'description', 'foo') self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'Description.Value': 'foo'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_source_dest_check_bool(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'sourceDestCheck', True) self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'SourceDestCheck.Value': 'true'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_source_dest_check_str(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'sourceDestCheck', 'true') self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'SourceDestCheck.Value': 'true'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_source_dest_check_invalid(self): self.set_http_response(status_code=200) with self.assertRaises(ValueError): self.ec2.modify_network_interface_attribute('id', 'sourceDestCheck', 123) def test_modify_delete_on_termination_str(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'deleteOnTermination', True, attachment_id='bar') self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'Attachment.AttachmentId': 'bar', 'Attachment.DeleteOnTermination': 'true'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_delete_on_termination_bool(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'deleteOnTermination', 'false', attachment_id='bar') self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'Attachment.AttachmentId': 'bar', 'Attachment.DeleteOnTermination': 'false'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_delete_on_termination_invalid(self): self.set_http_response(status_code=200) with self.assertRaises(ValueError): self.ec2.modify_network_interface_attribute('id', 'deleteOnTermination', 123, attachment_id='bar') def test_modify_group_set_list(self): self.set_http_response(status_code=200) self.ec2.modify_network_interface_attribute('id', 'groupSet', ['sg-1', 'sg-2']) self.assert_request_parameters({ 'Action': 'ModifyNetworkInterfaceAttribute', 'NetworkInterfaceId': 'id', 'SecurityGroupId.1': 'sg-1', 'SecurityGroupId.2': 'sg-2'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_modify_group_set_invalid(self): self.set_http_response(status_code=200) with self.assertRaisesRegexp(TypeError, 'iterable'): self.ec2.modify_network_interface_attribute('id', 'groupSet', False) def test_modify_attr_invalid(self): self.set_http_response(status_code=200) with self.assertRaisesRegexp(ValueError, 'Unknown attribute'): self.ec2.modify_network_interface_attribute('id', 'invalid', 0) class TestConnectToRegion(unittest.TestCase): def setUp(self): self.https_connection = Mock(spec=httplib.HTTPSConnection) self.https_connection_factory = ( Mock(return_value=self.https_connection), ()) def test_aws_region(self): region = boto.ec2.RegionData.keys()[0] self.ec2 = boto.ec2.connect_to_region(region, https_connection_factory=self.https_connection_factory, aws_access_key_id='aws_access_key_id', aws_secret_access_key='aws_secret_access_key' ) self.assertEqual(boto.ec2.RegionData[region], self.ec2.host) def test_non_aws_region(self): self.ec2 = boto.ec2.connect_to_region('foo', https_connection_factory=self.https_connection_factory, aws_access_key_id='aws_access_key_id', aws_secret_access_key='aws_secret_access_key', region = RegionInfo(name='foo', endpoint='https://foo.com/bar') ) self.assertEqual('https://foo.com/bar', self.ec2.host) def test_missing_region(self): self.ec2 = boto.ec2.connect_to_region('foo', https_connection_factory=self.https_connection_factory, aws_access_key_id='aws_access_key_id', aws_secret_access_key='aws_secret_access_key' ) self.assertEqual(None, self.ec2) class TestTrimSnapshots(TestEC2ConnectionBase): """ Test snapshot trimming functionality by ensuring that expected calls are made when given a known set of volume snapshots. """ def _get_snapshots(self): """ Generate a list of fake snapshots with names and dates. """ snaps = [] # Generate some dates offset by days, weeks, months. # This is to validate the various types of snapshot logic handled by # ``trim_snapshots``. now = datetime.now() dates = [ now, now - timedelta(days=1), now - timedelta(days=2), now - timedelta(days=7), now - timedelta(days=14), # We want to simulate 30/60/90-day snapshots, but February is # short (only 28 days), so we decrease the delta by 2 days apiece. # This prevents the ``delete_snapshot`` code below from being # called, since they don't fall outside the allowed timeframes # for the snapshots. datetime(now.year, now.month, 1) - timedelta(days=28), datetime(now.year, now.month, 1) - timedelta(days=58), datetime(now.year, now.month, 1) - timedelta(days=88) ] for date in dates: # Create a fake snapshot for each date snap = Snapshot(self.ec2) snap.tags['Name'] = 'foo' # Times are expected to be ISO8601 strings snap.start_time = date.strftime('%Y-%m-%dT%H:%M:%S.000Z') snaps.append(snap) return snaps def test_trim_defaults(self): """ Test trimming snapshots with the default arguments, which should keep all monthly backups forever. The result of this test should be that nothing is deleted. """ # Setup mocks orig = { 'get_all_snapshots': self.ec2.get_all_snapshots, 'delete_snapshot': self.ec2.delete_snapshot } snaps = self._get_snapshots() self.ec2.get_all_snapshots = MagicMock(return_value=snaps) self.ec2.delete_snapshot = MagicMock() # Call the tested method self.ec2.trim_snapshots() # Assertions self.assertEqual(True, self.ec2.get_all_snapshots.called) self.assertEqual(False, self.ec2.delete_snapshot.called) # Restore self.ec2.get_all_snapshots = orig['get_all_snapshots'] self.ec2.delete_snapshot = orig['delete_snapshot'] def test_trim_months(self): """ Test trimming monthly snapshots and ensure that older months get deleted properly. The result of this test should be that the two oldest snapshots get deleted. """ # Setup mocks orig = { 'get_all_snapshots': self.ec2.get_all_snapshots, 'delete_snapshot': self.ec2.delete_snapshot } snaps = self._get_snapshots() self.ec2.get_all_snapshots = MagicMock(return_value=snaps) self.ec2.delete_snapshot = MagicMock() # Call the tested method self.ec2.trim_snapshots(monthly_backups=1) # Assertions self.assertEqual(True, self.ec2.get_all_snapshots.called) self.assertEqual(2, self.ec2.delete_snapshot.call_count) # Restore self.ec2.get_all_snapshots = orig['get_all_snapshots'] self.ec2.delete_snapshot = orig['delete_snapshot'] class TestModifyReservedInstances(TestEC2ConnectionBase): def default_body(self): return """<ModifyReservedInstancesResponse xmlns='http://ec2.amazonaws.com/doc/2013-08-15/'> <requestId>bef729b6-0731-4489-8881-2258746ae163</requestId> <reservedInstancesModificationId>rimod-3aae219d-3d63-47a9-a7e9-e764example</reservedInstancesModificationId> </ModifyReservedInstancesResponse>""" def test_serialized_api_args(self): self.set_http_response(status_code=200) response = self.ec2.modify_reserved_instances( 'a-token-goes-here', reserved_instance_ids=[ '2567o137-8a55-48d6-82fb-7258506bb497', ], target_configurations=[ ReservedInstancesConfiguration( availability_zone='us-west-2c', platform='EC2-VPC', instance_count=3 ), ] ) self.assert_request_parameters({ 'Action': 'ModifyReservedInstances', 'ClientToken': 'a-token-goes-here', 'ReservedInstancesConfigurationSetItemType.0.AvailabilityZone': 'us-west-2c', 'ReservedInstancesConfigurationSetItemType.0.InstanceCount': 3, 'ReservedInstancesConfigurationSetItemType.0.Platform': 'EC2-VPC', 'ReservedInstancesId.1': '2567o137-8a55-48d6-82fb-7258506bb497' }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) self.assertEqual(response, 'rimod-3aae219d-3d63-47a9-a7e9-e764example') class TestDescribeReservedInstancesModifications(TestEC2ConnectionBase): def default_body(self): return """<DescribeReservedInstancesModificationsResponse xmlns='http://ec2.amazonaws.com/doc/2013-08-15/'> <requestId>eb4a6e3c-3689-445c-b536-19e38df35898</requestId> <reservedInstancesModificationsSet> <item> <reservedInstancesModificationId>rimod-49b9433e-fdc7-464a-a6e5-9dabcexample</reservedInstancesModificationId> <reservedInstancesSet> <item> <reservedInstancesId>2567o137-8a55-48d6-82fb-7258506bb497</reservedInstancesId> </item> </reservedInstancesSet> <modificationResultSet> <item> <reservedInstancesId>9d5cb137-5d65-4479-b4ac-8c337example</reservedInstancesId> <targetConfiguration> <availabilityZone>us-east-1b</availabilityZone> <platform>EC2-VPC</platform> <instanceCount>1</instanceCount> </targetConfiguration> </item> </modificationResultSet> <createDate>2013-09-02T21:20:19.637Z</createDate> <updateDate>2013-09-02T21:38:24.143Z</updateDate> <effectiveDate>2013-09-02T21:00:00.000Z</effectiveDate> <status>fulfilled</status> <clientToken>token-f5b56c05-09b0-4d17-8d8c-c75d8a67b806</clientToken> </item> </reservedInstancesModificationsSet> </DescribeReservedInstancesModificationsResponse>""" def test_serialized_api_args(self): self.set_http_response(status_code=200) response = self.ec2.describe_reserved_instances_modifications( reserved_instances_modification_ids=[ '2567o137-8a55-48d6-82fb-7258506bb497' ], filters={ 'status': 'processing', } ) self.assert_request_parameters({ 'Action': 'DescribeReservedInstancesModifications', 'Filter.1.Name': 'status', 'Filter.1.Value.1': 'processing', 'ReservedInstancesModificationId.1': '2567o137-8a55-48d6-82fb-7258506bb497' }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) # Make sure the response was parsed correctly. self.assertEqual( response[0].modification_id, 'rimod-49b9433e-fdc7-464a-a6e5-9dabcexample' ) self.assertEqual( response[0].create_date, datetime(2013, 9, 2, 21, 20, 19, 637000) ) self.assertEqual( response[0].update_date, datetime(2013, 9, 2, 21, 38, 24, 143000) ) self.assertEqual( response[0].effective_date, datetime(2013, 9, 2, 21, 0, 0, 0) ) self.assertEqual( response[0].status, 'fulfilled' ) self.assertEqual( response[0].status_message, None ) self.assertEqual( response[0].client_token, 'token-f5b56c05-09b0-4d17-8d8c-c75d8a67b806' ) self.assertEqual( response[0].reserved_instances[0].id, '2567o137-8a55-48d6-82fb-7258506bb497' ) self.assertEqual( response[0].modification_results[0].availability_zone, 'us-east-1b' ) self.assertEqual( response[0].modification_results[0].platform, 'EC2-VPC' ) self.assertEqual( response[0].modification_results[0].instance_count, 1 ) self.assertEqual(len(response), 1) class TestRegisterImage(TestEC2ConnectionBase): def default_body(self): return """ <RegisterImageResponse xmlns="http://ec2.amazonaws.com/doc/2013-08-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <imageId>ami-1a2b3c4d</imageId> </RegisterImageResponse> """ def test_vm_type_default(self): self.set_http_response(status_code=200) self.ec2.register_image('name', 'description', image_location='s3://foo') self.assert_request_parameters({ 'Action': 'RegisterImage', 'ImageLocation': 's3://foo', 'Name': 'name', 'Description': 'description', }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) def test_vm_type_hvm(self): self.set_http_response(status_code=200) self.ec2.register_image('name', 'description', image_location='s3://foo', virtualization_type='hvm') self.assert_request_parameters({ 'Action': 'RegisterImage', 'ImageLocation': 's3://foo', 'Name': 'name', 'Description': 'description', 'VirtualizationType': 'hvm' }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) def test_sriov_net_support_simple(self): self.set_http_response(status_code=200) self.ec2.register_image('name', 'description', image_location='s3://foo', sriov_net_support='simple') self.assert_request_parameters({ 'Action': 'RegisterImage', 'ImageLocation': 's3://foo', 'Name': 'name', 'Description': 'description', 'SriovNetSupport': 'simple' }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) def test_volume_delete_on_termination_on(self): self.set_http_response(status_code=200) self.ec2.register_image('name', 'description', snapshot_id='snap-12345678', delete_root_volume_on_termination=True) self.assert_request_parameters({ 'Action': 'RegisterImage', 'Name': 'name', 'Description': 'description', 'BlockDeviceMapping.1.DeviceName': None, 'BlockDeviceMapping.1.Ebs.DeleteOnTermination' : 'true', 'BlockDeviceMapping.1.Ebs.SnapshotId': 'snap-12345678', }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) def test_volume_delete_on_termination_default(self): self.set_http_response(status_code=200) self.ec2.register_image('name', 'description', snapshot_id='snap-12345678') self.assert_request_parameters({ 'Action': 'RegisterImage', 'Name': 'name', 'Description': 'description', 'BlockDeviceMapping.1.DeviceName': None, 'BlockDeviceMapping.1.Ebs.DeleteOnTermination' : 'false', 'BlockDeviceMapping.1.Ebs.SnapshotId': 'snap-12345678', }, ignore_params_values=[ 'AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version' ]) class TestTerminateInstances(TestEC2ConnectionBase): def default_body(self): return """<?xml version="1.0" ?> <TerminateInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-07-15/"> <requestId>req-59a9ad52-0434-470c-ad48-4f89ded3a03e</requestId> <instancesSet> <item> <instanceId>i-000043a2</instanceId> <shutdownState> <code>16</code> <name>running</name> </shutdownState> <previousState> <code>16</code> <name>running</name> </previousState> </item> </instancesSet> </TerminateInstancesResponse> """ def test_terminate_bad_response(self): self.set_http_response(status_code=200) self.ec2.terminate_instances('foo') class TestDescribeInstances(TestEC2ConnectionBase): def default_body(self): return """ <DescribeInstancesResponse> </DescribeInstancesResponse> """ def test_default_behavior(self): self.set_http_response(status_code=200) self.ec2.get_all_instances() self.assert_request_parameters({ 'Action': 'DescribeInstances'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) self.ec2.get_all_reservations() self.assert_request_parameters({ 'Action': 'DescribeInstances'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) self.ec2.get_only_instances() self.assert_request_parameters({ 'Action': 'DescribeInstances'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_max_results(self): self.set_http_response(status_code=200) self.ec2.get_all_instances( max_results=10 ) self.assert_request_parameters({ 'Action': 'DescribeInstances', 'MaxResults': 10}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_next_token(self): self.set_http_response(status_code=200) self.ec2.get_all_reservations( next_token='abcdefgh', ) self.assert_request_parameters({ 'Action': 'DescribeInstances', 'NextToken': 'abcdefgh'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestDescribeTags(TestEC2ConnectionBase): def default_body(self): return """ <DescribeTagsResponse> </DescribeTagsResponse> """ def test_default_behavior(self): self.set_http_response(status_code=200) self.ec2.get_all_tags() self.assert_request_parameters({ 'Action': 'DescribeTags'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) def test_max_results(self): self.set_http_response(status_code=200) self.ec2.get_all_tags( max_results=10 ) self.assert_request_parameters({ 'Action': 'DescribeTags', 'MaxResults': 10}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) class TestSignatureAlteration(TestEC2ConnectionBase): def test_unchanged(self): self.assertEqual( self.service_connection._required_auth_capability(), ['ec2'] ) def test_switched(self): region = RegionInfo( name='cn-north-1', endpoint='ec2.cn-north-1.amazonaws.com.cn', connection_cls=EC2Connection ) conn = self.connection_class( aws_access_key_id='less', aws_secret_access_key='more', region=region ) self.assertEqual( conn._required_auth_capability(), ['hmac-v4'] ) class TestAssociateAddress(TestEC2ConnectionBase): def default_body(self): return """ <AssociateAddressResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <return>true</return> <associationId>eipassoc-fc5ca095</associationId> </AssociateAddressResponse> """ def test_associate_address(self): self.set_http_response(status_code=200) result = self.ec2.associate_address(instance_id='i-1234', public_ip='192.0.2.1') self.assertEqual(True, result) def test_associate_address_object(self): self.set_http_response(status_code=200) result = self.ec2.associate_address_object(instance_id='i-1234', public_ip='192.0.2.1') self.assertEqual('eipassoc-fc5ca095', result.association_id) class TestAssociateAddressFail(TestEC2ConnectionBase): def default_body(self): return """ <Response> <Errors> <Error> <Code>InvalidInstanceID.NotFound</Code> <Message>The instance ID 'i-4cbc822a' does not exist</Message> </Error> </Errors> <RequestID>ea966190-f9aa-478e-9ede-cb5432daacc0</RequestID> <StatusCode>Failure</StatusCode> </Response> """ def test_associate_address(self): self.set_http_response(status_code=200) result = self.ec2.associate_address(instance_id='i-1234', public_ip='192.0.2.1') self.assertEqual(False, result) class TestDescribeVolumes(TestEC2ConnectionBase): def default_body(self): return """ <DescribeVolumesResponse xmlns="http://ec2.amazonaws.com/doc/2014-02-01/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <volumeSet> <item> <volumeId>vol-1a2b3c4d</volumeId> <size>80</size> <snapshotId/> <availabilityZone>us-east-1a</availabilityZone> <status>in-use</status> <createTime>YYYY-MM-DDTHH:MM:SS.SSSZ</createTime> <attachmentSet> <item> <volumeId>vol-1a2b3c4d</volumeId> <instanceId>i-1a2b3c4d</instanceId> <device>/dev/sdh</device> <status>attached</status> <attachTime>YYYY-MM-DDTHH:MM:SS.SSSZ</attachTime> <deleteOnTermination>false</deleteOnTermination> </item> </attachmentSet> <volumeType>standard</volumeType> <encrypted>true</encrypted> </item> <item> <volumeId>vol-5e6f7a8b</volumeId> <size>80</size> <snapshotId/> <availabilityZone>us-east-1a</availabilityZone> <status>in-use</status> <createTime>YYYY-MM-DDTHH:MM:SS.SSSZ</createTime> <attachmentSet> <item> <volumeId>vol-5e6f7a8b</volumeId> <instanceId>i-5e6f7a8b</instanceId> <device>/dev/sdz</device> <status>attached</status> <attachTime>YYYY-MM-DDTHH:MM:SS.SSSZ</attachTime> <deleteOnTermination>false</deleteOnTermination> </item> </attachmentSet> <volumeType>standard</volumeType> <encrypted>false</encrypted> </item> </volumeSet> </DescribeVolumesResponse> """ def test_get_all_volumes(self): self.set_http_response(status_code=200) result = self.ec2.get_all_volumes(volume_ids=['vol-1a2b3c4d', 'vol-5e6f7a8b']) self.assert_request_parameters({ 'Action': 'DescribeVolumes', 'VolumeId.1': 'vol-1a2b3c4d', 'VolumeId.2': 'vol-5e6f7a8b'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) self.assertEqual(len(result), 2) self.assertEqual(result[0].id, 'vol-1a2b3c4d') self.assertTrue(result[0].encrypted) self.assertEqual(result[1].id, 'vol-5e6f7a8b') self.assertFalse(result[1].encrypted) class TestDescribeSnapshots(TestEC2ConnectionBase): def default_body(self): return """ <DescribeSnapshotsResponse xmlns="http://ec2.amazonaws.com/doc/2014-02-01/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <snapshotSet> <item> <snapshotId>snap-1a2b3c4d</snapshotId> <volumeId>vol-1a2b3c4d</volumeId> <status>pending</status> <startTime>YYYY-MM-DDTHH:MM:SS.SSSZ</startTime> <progress>80%</progress> <ownerId>111122223333</ownerId> <volumeSize>15</volumeSize> <description>Daily Backup</description> <tagSet/> <encrypted>true</encrypted> </item> </snapshotSet> <snapshotSet> <item> <snapshotId>snap-5e6f7a8b</snapshotId> <volumeId>vol-5e6f7a8b</volumeId> <status>completed</status> <startTime>YYYY-MM-DDTHH:MM:SS.SSSZ</startTime> <progress>100%</progress> <ownerId>111122223333</ownerId> <volumeSize>15</volumeSize> <description>Daily Backup</description> <tagSet/> <encrypted>false</encrypted> </item> </snapshotSet> </DescribeSnapshotsResponse> """ def test_get_all_snapshots(self): self.set_http_response(status_code=200) result = self.ec2.get_all_snapshots(snapshot_ids=['snap-1a2b3c4d', 'snap-5e6f7a8b']) self.assert_request_parameters({ 'Action': 'DescribeSnapshots', 'SnapshotId.1': 'snap-1a2b3c4d', 'SnapshotId.2': 'snap-5e6f7a8b'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) self.assertEqual(len(result), 2) self.assertEqual(result[0].id, 'snap-1a2b3c4d') self.assertTrue(result[0].encrypted) self.assertEqual(result[1].id, 'snap-5e6f7a8b') self.assertFalse(result[1].encrypted) class TestCreateVolume(TestEC2ConnectionBase): def default_body(self): return """ <CreateVolumeResponse xmlns="http://ec2.amazonaws.com/doc/2014-05-01/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <volumeId>vol-1a2b3c4d</volumeId> <size>80</size> <snapshotId/> <availabilityZone>us-east-1a</availabilityZone> <status>creating</status> <createTime>YYYY-MM-DDTHH:MM:SS.000Z</createTime> <volumeType>standard</volumeType> <encrypted>true</encrypted> </CreateVolumeResponse> """ def test_create_volume(self): self.set_http_response(status_code=200) result = self.ec2.create_volume(80, 'us-east-1e', snapshot='snap-1a2b3c4d', encrypted=True) self.assert_request_parameters({ 'Action': 'CreateVolume', 'AvailabilityZone': 'us-east-1e', 'Size': 80, 'SnapshotId': 'snap-1a2b3c4d', 'Encrypted': 'true'}, ignore_params_values=['AWSAccessKeyId', 'SignatureMethod', 'SignatureVersion', 'Timestamp', 'Version']) self.assertEqual(result.id, 'vol-1a2b3c4d') self.assertTrue(result.encrypted) if __name__ == '__main__': unittest.main()

[ "alfred.wechselberger@technologyhatchery.com" ]

alfred.wechselberger@technologyhatchery.com

fc02d92d5e205a887765802d60906fd5dcc62213

4a3fcb3e93ba88ee09d34b190450ad18a3125d67

/users/api/admin.py

fa57deaea2bcc2f5ba3dd6792ef05b61754aa057

[]

no_license

hllustosa/online-judge

8c14f3348d7eba56126824f1aca6d9ee907e688d

4340eefc760ee3122e805214af0aa5f1a4f4fd96

refs/heads/master

2023-06-20T22:27:17.359455

2021-08-09T03:27:55

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122

py

from django.contrib import admin from api.models import Profile # Register your models here. admin.site.register(Profile)

[ "hllustosa@gmail.com" ]

hllustosa@gmail.com

0b486db72f271c9f67c5078e888030e88741d166

80e82658539215cf14153e7dfe64fd810103d1b1

/ThirdPartySoftware/pbrain-master/eegview/shared.py

496aff516e1bf4672f100bcb5762d4a8cae05c8a

[]

no_license

mocalab/BCIProject

7f2302f5027f98118ff0d84895dd2a89f5e9c3fb

35d807401cf4939d09597addd4d79ca1df47073c

refs/heads/master

2020-04-17T00:57:40.333837

2013-06-29T07:39:25

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import os, sys from pbrainlib.gtkutils import FileManager import distutils.sysconfig class RC: """ CLASS: RC DESCR: handles .eegviewrc file in home directory. Currently only the 'lastdir' line is really used - as well as the streamlining data in the last 5 columns. eegview.rc gets saved after every program execution. """ if os.environ.has_key('HOME'): path = os.environ['HOME'] elif sys.platform=='win32': path = os.path.join(distutils.sysconfig.PREFIX, 'share', 'pbrain') elif sys.platform=='linux': path = '/tmp/' else: path = None def join_ints(seq): return ' '.join(['%d'%val for val in seq]) def split_ints(ints): return [int(val) for val in ints.split()] convertToFile = {'figsize':join_ints,} convertFromFile = {'figsize':split_ints,'sqlport':int} attrs = ( 'lastdir', 'lastdir1', 'lastdir2', 'lastdir3', 'lastdir4', 'lastdir5', 'lastdir6', 'lastdir7', 'lastdir8', 'lastdir9', 'figsize', 'httpuser', 'httppasswd', 'httpurl', 'httpcachedir', 'sqluser', 'sqlpasswd', 'sqlhost', 'sqlport', 'sqldatabase', 'horizcursor', 'vertcursor', 'bni', 'csv', 'amp', 'dat', 'col', ) def __init__(self): self.load_defaults() if self.path is not None: self.filename = os.path.join(self.path, '.eegviewrc') try: self.loadrc() except IOError: pass for attr in self.attrs: if not hasattr(self, attr): raise AttributeError('Unknown property: %s'%attr) def load_defaults(self): if sys.platform=='win32': self.lastdir = 'C:\\' else: self.lastdir = os.getcwd() print "setting lastdirs.." self.lastdir1 = '' self.lastdir2 = '' self.lastdir3 = '' self.lastdir4 = '' self.lastdir5 = '' self.lastdir6 = '' self.lastdir7 = '' self.lastdir8 = '' self.lastdir9 = '' self.figsize = 8, 6 self.httpuser = 'username' self.httppasswd = 'passwd' self.httpurl = 'localhost' self.httpcachedir = 'tempdir' self.sqluser = 'username' self.sqlpasswd = 'passwd' self.sqlhost = 'localhost' self.sqldatabase = 'seizure' self.sqlport = 3306 self.horizcursor = True self.vertcursor = True self.bni = "" self.csv = "" self.amp = "" self.dat = "" self.col = "" def loadrc(self): for line in file(self.filename): key, val = line.split(':', 1) key = key.strip() val = val.strip() func = self.convertFromFile.get(key, str) self.__dict__[key] = func(val) def save(self): try: fh = file(self.filename, 'w') for attr in self.attrs: func = self.convertToFile.get(attr, str) val = func(self.__dict__[attr]) fh.write('%s : %s\n' % (attr, val)) print 'Updated RC file', self.filename except IOError: print >>sys.stderr, 'Failed to write to', self.filename def __del__(self): self.save() eegviewrc = RC() fmanager = FileManager() fmanager.bni = eegviewrc.bni fmanager.csv = eegviewrc.csv fmanager.amp = eegviewrc.amp fmanager.dat = eegviewrc.dat fmanager.col = eegviewrc.col fmanager.set_lastdir(eegviewrc.lastdir) fmanager.set_lastdirs([eegviewrc.lastdir, eegviewrc.lastdir1, eegviewrc.lastdir2, eegviewrc.lastdir3, eegviewrc.lastdir4, eegviewrc.lastdir5, eegviewrc.lastdir6, eegviewrc.lastdir7, eegviewrc.lastdir8, eegviewrc.lastdir9])

[ "yozturk@mocalab.com" ]

yozturk@mocalab.com

13c9402c955d81bc62cbf883183a1497fbc71f74

d0987e868d3c55728ce451a1f778d254720821b0

/datamodules/default.py

9057c6f040fea7c5dfd6c6a15c72db02dc6a3496

[]

no_license

sara-nl/2D-VQ-AE-2

c106cd0dd0c1060bb4f363ec38db6ba354363f85

6999a5c25d6e0a83bde52c770788375cbfe348c0

refs/heads/main

2023-05-23T08:20:57.334918

2022-06-10T15:15:50

377,765,763

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null

2022-03-01T13:53:51

2021-06-17T08:53:39

Python

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Python

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710

py

from dataclasses import dataclass from typing import Optional, Callable import pytorch_lightning as pl from torch.utils.data.dataloader import DataLoader @dataclass class DefaultDataModule(pl.LightningDataModule): train_dataloader_conf: Callable[[], DataLoader] val_dataloader_conf: Callable[[], DataLoader] test_dataloader_conf: Optional[Callable[[], DataLoader]] = None def __post_init__(self): super().__init__() def train_dataloader(self) -> DataLoader: return self.train_dataloader_conf() def val_dataloader(self) -> DataLoader: return self.val_dataloader_conf() def test_dataloader(self) -> DataLoader: return self.test_dataloader_conf()

[ "robertjan.schlimbach@gmail.com" ]

robertjan.schlimbach@gmail.com

3226c5524af15df7ed6b8e45dff65ea39ded66c2

a7a968c270d193e8c5b35af5f1743be8c9eaa81d

/WISE/resultscompile.py

dfd4e3d7abbc420fca2771124d5065c5c412c072

[]

no_license

rameez3333/catana

56479adfbac422a976f2c22ea45ccebc3884ea13

d786ddcc789c55e941a85b247433eeac672a40d4

refs/heads/master

2020-09-22T15:31:48.133456

2019-12-08T09:12:45

67,131,648

0

null

UTF-8

Python

false

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import numpy as np from glob import glob import healpy as hp import matplotlib.pyplot as plt import sys nside = int(sys.argv[1]) #glcut = sys.argv[2] npix = hp.nside2npix(nside) def process(glcut, jcut): flist = sorted(glob('*'+str(glcut)+"glcut"+str(jcut)+"jlcut_result.txt")) if not len(flist): if jcut: flist = sorted(glob('*'+str(glcut)+"glcut"+"jcut_result.txt")) else: flist = sorted(glob('*'+str(glcut)+"glcut"+"_result.txt")) totsources=0 cutsources=0 totUH = np.zeros(npix) totLH = np.zeros(npix) for f in flist: fline = open(f).readlines() totsources = totsources + float(fline[1].split(",")[0]) cutsources = cutsources + float(fline[1].split(",")[-1]) i=0 mapUH = np.zeros(npix) mapLH = np.zeros(npix) for line in fline[2:]: #if (90. - np.rad2deg(hp.pix2ang(16, i)[0]) - float(line.split(',')[0]) + float(line.split(',')[1]) - np.rad2deg(hp.pix2ang(16, i)[1])): #print "wtf", i, (90. - np.rad2deg(hp.pix2ang(16, i)[0]) - float(line.split(',')[0]) + float(line.split(',')[1]) - np.rad2deg(hp.pix2ang(16, i)[1])) mapUH[i] = float(line.split(',')[2]) mapLH[i] = float(line.split(',')[3]) i+=1 if (i - npix): print "wtf now" if ((mapUH+mapLH)[0:npix].sum()/npix - float(fline[1].split(",")[-1])): print "wtf 3" print f print (mapUH + mapLH)[0:npix].sum()/(npix) print float(fline[1].split(",")[-1]) print ((mapUH+mapLH)[0:npix/2].sum()*2./npix - float(fline[1].split(",")[-1])) totUH = totUH+mapUH totLH = totLH+mapLH """ for i in range(1, npix/2 +1): totUH[npix-i] = totLH[i-1] totLH[npix-i] = totUH[i-1] """ #print totUH #print totLH map = (totUH-totLH)/(totUH+totLH) #print map #print np.min(map), np.max(map) #print "Total Sources: ", totsources #print "After Cut: ", cutsources #print "The minimum is at", np.argmin(map), "with a value of ", map[np.argmin(map)] #print "The minimum is at ", (90. - np.rad2deg(hp.pix2ang(nside, np.argmin(map))[0])), np.rad2deg(hp.pix2ang(nside, np.argmin(map))[0]) #print "The maximum is at", np.argmax(map), "with a value of ", map[np.argmax(map)] #print "The maximum is at ", (90. - np.rad2deg(hp.pix2ang(nside, np.argmax(map))[0])), np.rad2deg(hp.pix2ang(nside, np.argmax(map))[0]) coords = hp.pix2ang(nside,np.arange(npix)) angs = np.rad2deg(np.arccos(np.sin(coords[0])*np.sin(hp.pix2ang(nside, np.argmax(map))[0])*np.cos(coords[1] - hp.pix2ang(nside, np.argmax(map))[1]) + np.cos(coords[0])*np.cos(hp.pix2ang(nside, np.argmax(map))[0]))) #print np.size(angs) #plt.scatter(angs, map) #plt.xlabel("Angle") #plt.ylabel("Hemispheric count difference") hp.mollview(map, rot=[np.pi, np.pi/2.]) #plt.show() plt.savefig('HCount'+str(glcut)+'glcut'+str(jcut)+'jcut.png') return (90. - np.rad2deg(hp.pix2ang(nside, np.argmax(map))[0])), np.rad2deg(hp.pix2ang(nside, np.argmax(map))[1]), map[np.argmax(map)] for jcut in (0, 1): for glcut in np.arange(10, 21): decmax,ramax, valmax = process(glcut, jcut) print jcut, glcut, decmax, ramax, valmax

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mrameez@fend05.cluster

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/boards/views.py

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leahlang4d/django_project

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from django.shortcuts import render from .models import Board # Create your views here. from django.http import HttpResponse from .models import Board def home(request): boards = Board.objects.all() return render(request, 'home.html', {'boards': boards}) #boards_names = list() #for board in boards: # boards_names.append(board.name) #response_html = '<br>'.join(boards_names) #return HttpResponse(response_html)

[ "leahlang4d@gmail.com" ]

leahlang4d@gmail.com

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d0de52952d3fb219baedc830bfae48ccd7b31604

/paperstuff/counter_0620_16.py

bd2ad1beb6a69d2caca3baaa604c746a8edc7288

[]

no_license

patrickschu/ota

3a512cdecf9291266812fd7a664a5ca8d3e969ea

7ee6cf0afe2fe6416dc4c334fc2db4d8c925882a

refs/heads/master

2020-04-06T06:56:12.633307

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import codecs import re import time import os import nltk import string from string import punctuation now=time.time() #setting up the output file outputfile="output_gaddlist_0714" print outputfile #reading in the yeslist, nolist or whatever. these are the words to iterate over/search for yeslist=[] f=codecs.open("/Users/ps22344/Downloads/ota-master/paperstuff/gadds_yeslist_withzeros_0713.txt_pandas_0to1700.txt", "r", "utf-8") for line in f: yeslist.append(line.rstrip("\n").split("\t")) f.close() #WATCH THIS SETTING yeslist_words=[i[1] for i in yeslist] print yeslist_words, "\n" print "we have {} words\n".format(len(yeslist_words)) #this is the list with the files/books we're using goodfiles=[] f=open("/Users/ps22344/Downloads/ota-master/paperstuff/goodfiles_0620_16.txt", "r") for line in f: goodfiles.append(line.rstrip("\n")) f.close() print "we have {} files\n".format(len(goodfiles)) # some helper funcs def tagextractor(text, tag, fili): regexstring="<"+tag+"=(.*?)>" result=re.findall(regexstring, text, re.DOTALL) if len(result) != 1: print "alarm in tagextractor", fili, result return result[0] def adtextextractor(text, fili): regexstring="<text>(.*?)</text>" result=re.findall(regexstring, text, re.DOTALL) if len(result) != 1: print "alarm in adtextextractor", fili, result return result[0] #CAREFUL!! THIS VARIES DEP ON WHERE THE FILE WSS OUTPUT # dicti={i:0 for i in yeslist_words} output0=codecs.open(outputfile, "a", "utf-8") #output column names cols=['uniq', 'filenumber', 'otanumber', 'pubdate', 'genre', 'title', 'wordcount'] output0.write("\t".join(cols)+"\t") output0.write("\t".join(yeslist_words)+"\n") output0.close() #the actual reader for item in goodfiles: try: #we open the corpus fils&read it #/Users/ps22344/Downloads output1=codecs.open(outputfile, "a", "utf-8") finput=codecs.open(os.path.join("/Users","ps22344","Downloads", "ota_0621",str(item)+".txt"), "r", "utf-8") text=finput.read() #we get the metadata otanumber=tagextractor(text, "otanumber", item ) filenumber=tagextractor(text, "no", item ) pubdate=tagextractor(text, "pubdate", item ) genre=tagextractor(text, "genre1", item ) title=tagextractor(text, "otatitle", item ) content=adtextextractor(text,item) contentsplit=nltk.word_tokenize(content) print "Before removing punctuation, this text was {} words long".format(len(contentsplit)) text=[i.lower() for i in contentsplit if i not in string.punctuation] print "After removing punctuation, this text was {} words long".format(len(text)) #print len(contentsplit) #setting up the list for the findings for each text results=[] #a list of the words well be searching for, to be used in regex #write the item from ll, write filenumber etc, add tab for separator outputlist=[unicode(item), filenumber, otanumber, pubdate, genre, title, unicode(len(text))] output1.write("\t".join(outputlist)+"\t") #iterate over all metadata output1.close() for thing in yeslist_words: #no suffix words=re.findall(r"\b("+thing+"\'?)\b",content) #yes suffix #words=re.findall(r"\b((?:dis|mis|re|un)?"+thing+"\'?)",content) dicti[thing]=dicti[thing]+len(words) results.append(words) print "reading", item, filenumber output3=codecs.open(outputfile, "a", "utf-8") output3.write("\t".join([str(len(i)) for i in results])+"\n") output3.close() logout=codecs.open(outputfile+"_log.txt", "a", "utf-8") logout.write(str(results)+"\n") logout.close() except IOError, err: print "Error", err dictiout=open(outputfile+"_dicti.txt", "w") sortdict=sorted(dicti.items(), key=lambda x: x[1], reverse=True) dictiout.write("\n".join([str(i) for i in sortdict])) print sortdict dictiout.close() #for spreadsheet for item in yeslist_words: print item later=time.time() runtime=later-now print "written to", outputfile print 'time has passed', runtime/60 os.system('say "your program has finished"')

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patrickschu.noreply@github.com

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/lib/sqlalchemy/orm/events.py

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# orm/events.py # Copyright (C) 2005-2017 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """ORM event interfaces. """ from .. import event, exc, util from .base import _mapper_or_none import inspect import weakref from . import interfaces from . import mapperlib, instrumentation from .session import Session, sessionmaker from .scoping import scoped_session from .attributes import QueryableAttribute from .query import Query from sqlalchemy.util.compat import inspect_getargspec class InstrumentationEvents(event.Events): """Events related to class instrumentation events. The listeners here support being established against any new style class, that is any object that is a subclass of 'type'. Events will then be fired off for events against that class. If the "propagate=True" flag is passed to event.listen(), the event will fire off for subclasses of that class as well. The Python ``type`` builtin is also accepted as a target, which when used has the effect of events being emitted for all classes. Note the "propagate" flag here is defaulted to ``True``, unlike the other class level events where it defaults to ``False``. This means that new subclasses will also be the subject of these events, when a listener is established on a superclass. .. versionchanged:: 0.8 - events here will emit based on comparing the incoming class to the type of class passed to :func:`.event.listen`. Previously, the event would fire for any class unconditionally regardless of what class was sent for listening, despite documentation which stated the contrary. """ _target_class_doc = "SomeBaseClass" _dispatch_target = instrumentation.InstrumentationFactory @classmethod def _accept_with(cls, target): if isinstance(target, type): return _InstrumentationEventsHold(target) else: return None @classmethod def _listen(cls, event_key, propagate=True, **kw): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, \ event_key._listen_fn def listen(target_cls, *arg): listen_cls = target() if propagate and issubclass(target_cls, listen_cls): return fn(target_cls, *arg) elif not propagate and target_cls is listen_cls: return fn(target_cls, *arg) def remove(ref): key = event.registry._EventKey( None, identifier, listen, instrumentation._instrumentation_factory) getattr(instrumentation._instrumentation_factory.dispatch, identifier).remove(key) target = weakref.ref(target.class_, remove) event_key.\ with_dispatch_target(instrumentation._instrumentation_factory).\ with_wrapper(listen).base_listen(**kw) @classmethod def _clear(cls): super(InstrumentationEvents, cls)._clear() instrumentation._instrumentation_factory.dispatch._clear() def class_instrument(self, cls): """Called after the given class is instrumented. To get at the :class:`.ClassManager`, use :func:`.manager_of_class`. """ def class_uninstrument(self, cls): """Called before the given class is uninstrumented. To get at the :class:`.ClassManager`, use :func:`.manager_of_class`. """ def attribute_instrument(self, cls, key, inst): """Called when an attribute is instrumented.""" class _InstrumentationEventsHold(object): """temporary marker object used to transfer from _accept_with() to _listen() on the InstrumentationEvents class. """ def __init__(self, class_): self.class_ = class_ dispatch = event.dispatcher(InstrumentationEvents) class InstanceEvents(event.Events): """Define events specific to object lifecycle. e.g.:: from sqlalchemy import event def my_load_listener(target, context): print "on load!" event.listen(SomeClass, 'load', my_load_listener) Available targets include: * mapped classes * unmapped superclasses of mapped or to-be-mapped classes (using the ``propagate=True`` flag) * :class:`.Mapper` objects * the :class:`.Mapper` class itself and the :func:`.mapper` function indicate listening for all mappers. .. versionchanged:: 0.8.0 instance events can be associated with unmapped superclasses of mapped classes. Instance events are closely related to mapper events, but are more specific to the instance and its instrumentation, rather than its system of persistence. When using :class:`.InstanceEvents`, several modifiers are available to the :func:`.event.listen` function. :param propagate=False: When True, the event listener should be applied to all inheriting classes as well as the class which is the target of this listener. :param raw=False: When True, the "target" argument passed to applicable event listener functions will be the instance's :class:`.InstanceState` management object, rather than the mapped instance itself. """ _target_class_doc = "SomeClass" _dispatch_target = instrumentation.ClassManager @classmethod def _new_classmanager_instance(cls, class_, classmanager): _InstanceEventsHold.populate(class_, classmanager) @classmethod @util.dependencies("sqlalchemy.orm") def _accept_with(cls, orm, target): if isinstance(target, instrumentation.ClassManager): return target elif isinstance(target, mapperlib.Mapper): return target.class_manager elif target is orm.mapper: return instrumentation.ClassManager elif isinstance(target, type): if issubclass(target, mapperlib.Mapper): return instrumentation.ClassManager else: manager = instrumentation.manager_of_class(target) if manager: return manager else: return _InstanceEventsHold(target) return None @classmethod def _listen(cls, event_key, raw=False, propagate=False, **kw): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, \ event_key._listen_fn if not raw: def wrap(state, *arg, **kw): return fn(state.obj(), *arg, **kw) event_key = event_key.with_wrapper(wrap) event_key.base_listen(propagate=propagate, **kw) if propagate: for mgr in target.subclass_managers(True): event_key.with_dispatch_target(mgr).base_listen( propagate=True) @classmethod def _clear(cls): super(InstanceEvents, cls)._clear() _InstanceEventsHold._clear() def first_init(self, manager, cls): """Called when the first instance of a particular mapping is called. This event is called when the ``__init__`` method of a class is called the first time for that particular class. The event invokes before ``__init__`` actually proceeds as well as before the :meth:`.InstanceEvents.init` event is invoked. """ def init(self, target, args, kwargs): """Receive an instance when its constructor is called. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database; see the :meth:`.InstanceEvents.load` event in order to intercept a database load. The event is called before the actual ``__init__`` constructor of the object is called. The ``kwargs`` dictionary may be modified in-place in order to affect what is passed to ``__init__``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments passed to the ``__init__`` method. This is passed as a tuple and is currently immutable. :param kwargs: keyword arguments passed to the ``__init__`` method. This structure *can* be altered in place. .. seealso:: :meth:`.InstanceEvents.init_failure` :meth:`.InstanceEvents.load` """ def init_failure(self, target, args, kwargs): """Receive an instance when its constructor has been called, and raised an exception. This method is only called during a userland construction of an object, in conjunction with the object's constructor, e.g. its ``__init__`` method. It is not called when an object is loaded from the database. The event is invoked after an exception raised by the ``__init__`` method is caught. After the event is invoked, the original exception is re-raised outwards, so that the construction of the object still raises an exception. The actual exception and stack trace raised should be present in ``sys.exc_info()``. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param args: positional arguments that were passed to the ``__init__`` method. :param kwargs: keyword arguments that were passed to the ``__init__`` method. .. seealso:: :meth:`.InstanceEvents.init` :meth:`.InstanceEvents.load` """ def load(self, target, context): """Receive an object instance after it has been created via ``__new__``, and after initial attribute population has occurred. This typically occurs when the instance is created based on incoming result rows, and is only called once for that instance's lifetime. Note that during a result-row load, this method is called upon the first row received for this instance. Note that some attributes and collections may or may not be loaded or even initialized, depending on what's present in the result rows. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param context: the :class:`.QueryContext` corresponding to the current :class:`.Query` in progress. This argument may be ``None`` if the load does not correspond to a :class:`.Query`, such as during :meth:`.Session.merge`. .. seealso:: :meth:`.InstanceEvents.init` :meth:`.InstanceEvents.refresh` :meth:`.SessionEvents.loaded_as_persistent` """ def refresh(self, target, context, attrs): """Receive an object instance after one or more attributes have been refreshed from a query. Contrast this to the :meth:`.InstanceEvents.load` method, which is invoked when the object is first loaded from a query. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param context: the :class:`.QueryContext` corresponding to the current :class:`.Query` in progress. :param attrs: sequence of attribute names which were populated, or None if all column-mapped, non-deferred attributes were populated. .. seealso:: :meth:`.InstanceEvents.load` """ def refresh_flush(self, target, flush_context, attrs): """Receive an object instance after one or more attributes have been refreshed within the persistence of the object. This event is the same as :meth:`.InstanceEvents.refresh` except it is invoked within the unit of work flush process, and the values here typically come from the process of handling an INSERT or UPDATE, such as via the RETURNING clause or from Python-side default values. .. versionadded:: 1.0.5 :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param flush_context: Internal :class:`.UOWTransaction` object which handles the details of the flush. :param attrs: sequence of attribute names which were populated. """ def expire(self, target, attrs): """Receive an object instance after its attributes or some subset have been expired. 'keys' is a list of attribute names. If None, the entire state was expired. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param attrs: sequence of attribute names which were expired, or None if all attributes were expired. """ def pickle(self, target, state_dict): """Receive an object instance when its associated state is being pickled. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param state_dict: the dictionary returned by :class:`.InstanceState.__getstate__`, containing the state to be pickled. """ def unpickle(self, target, state_dict): """Receive an object instance after its associated state has been unpickled. :param target: the mapped instance. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :param state_dict: the dictionary sent to :class:`.InstanceState.__setstate__`, containing the state dictionary which was pickled. """ class _EventsHold(event.RefCollection): """Hold onto listeners against unmapped, uninstrumented classes. Establish _listen() for that class' mapper/instrumentation when those objects are created for that class. """ def __init__(self, class_): self.class_ = class_ @classmethod def _clear(cls): cls.all_holds.clear() class HoldEvents(object): _dispatch_target = None @classmethod def _listen(cls, event_key, raw=False, propagate=False, **kw): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, event_key.fn if target.class_ in target.all_holds: collection = target.all_holds[target.class_] else: collection = target.all_holds[target.class_] = {} event.registry._stored_in_collection(event_key, target) collection[event_key._key] = (event_key, raw, propagate) if propagate: stack = list(target.class_.__subclasses__()) while stack: subclass = stack.pop(0) stack.extend(subclass.__subclasses__()) subject = target.resolve(subclass) if subject is not None: # we are already going through __subclasses__() # so leave generic propagate flag False event_key.with_dispatch_target(subject).\ listen(raw=raw, propagate=False, **kw) def remove(self, event_key): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, event_key.fn if isinstance(target, _EventsHold): collection = target.all_holds[target.class_] del collection[event_key._key] @classmethod def populate(cls, class_, subject): for subclass in class_.__mro__: if subclass in cls.all_holds: collection = cls.all_holds[subclass] for event_key, raw, propagate in collection.values(): if propagate or subclass is class_: # since we can't be sure in what order different # classes in a hierarchy are triggered with # populate(), we rely upon _EventsHold for all event # assignment, instead of using the generic propagate # flag. event_key.with_dispatch_target(subject).\ listen(raw=raw, propagate=False) class _InstanceEventsHold(_EventsHold): all_holds = weakref.WeakKeyDictionary() def resolve(self, class_): return instrumentation.manager_of_class(class_) class HoldInstanceEvents(_EventsHold.HoldEvents, InstanceEvents): pass dispatch = event.dispatcher(HoldInstanceEvents) class MapperEvents(event.Events): """Define events specific to mappings. e.g.:: from sqlalchemy import event def my_before_insert_listener(mapper, connection, target): # execute a stored procedure upon INSERT, # apply the value to the row to be inserted target.calculated_value = connection.scalar( "select my_special_function(%d)" % target.special_number) # associate the listener function with SomeClass, # to execute during the "before_insert" hook event.listen( SomeClass, 'before_insert', my_before_insert_listener) Available targets include: * mapped classes * unmapped superclasses of mapped or to-be-mapped classes (using the ``propagate=True`` flag) * :class:`.Mapper` objects * the :class:`.Mapper` class itself and the :func:`.mapper` function indicate listening for all mappers. .. versionchanged:: 0.8.0 mapper events can be associated with unmapped superclasses of mapped classes. Mapper events provide hooks into critical sections of the mapper, including those related to object instrumentation, object loading, and object persistence. In particular, the persistence methods :meth:`~.MapperEvents.before_insert`, and :meth:`~.MapperEvents.before_update` are popular places to augment the state being persisted - however, these methods operate with several significant restrictions. The user is encouraged to evaluate the :meth:`.SessionEvents.before_flush` and :meth:`.SessionEvents.after_flush` methods as more flexible and user-friendly hooks in which to apply additional database state during a flush. When using :class:`.MapperEvents`, several modifiers are available to the :func:`.event.listen` function. :param propagate=False: When True, the event listener should be applied to all inheriting mappers and/or the mappers of inheriting classes, as well as any mapper which is the target of this listener. :param raw=False: When True, the "target" argument passed to applicable event listener functions will be the instance's :class:`.InstanceState` management object, rather than the mapped instance itself. :param retval=False: when True, the user-defined event function must have a return value, the purpose of which is either to control subsequent event propagation, or to otherwise alter the operation in progress by the mapper. Possible return values are: * ``sqlalchemy.orm.interfaces.EXT_CONTINUE`` - continue event processing normally. * ``sqlalchemy.orm.interfaces.EXT_STOP`` - cancel all subsequent event handlers in the chain. * other values - the return value specified by specific listeners. """ _target_class_doc = "SomeClass" _dispatch_target = mapperlib.Mapper @classmethod def _new_mapper_instance(cls, class_, mapper): _MapperEventsHold.populate(class_, mapper) @classmethod @util.dependencies("sqlalchemy.orm") def _accept_with(cls, orm, target): if target is orm.mapper: return mapperlib.Mapper elif isinstance(target, type): if issubclass(target, mapperlib.Mapper): return target else: mapper = _mapper_or_none(target) if mapper is not None: return mapper else: return _MapperEventsHold(target) else: return target @classmethod def _listen( cls, event_key, raw=False, retval=False, propagate=False, **kw): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, \ event_key._listen_fn if identifier in ("before_configured", "after_configured") and \ target is not mapperlib.Mapper: util.warn( "'before_configured' and 'after_configured' ORM events " "only invoke with the mapper() function or Mapper class " "as the target.") if not raw or not retval: if not raw: meth = getattr(cls, identifier) try: target_index = \ inspect_getargspec(meth)[0].index('target') - 1 except ValueError: target_index = None def wrap(*arg, **kw): if not raw and target_index is not None: arg = list(arg) arg[target_index] = arg[target_index].obj() if not retval: fn(*arg, **kw) return interfaces.EXT_CONTINUE else: return fn(*arg, **kw) event_key = event_key.with_wrapper(wrap) if propagate: for mapper in target.self_and_descendants: event_key.with_dispatch_target(mapper).base_listen( propagate=True, **kw) else: event_key.base_listen(**kw) @classmethod def _clear(cls): super(MapperEvents, cls)._clear() _MapperEventsHold._clear() def instrument_class(self, mapper, class_): r"""Receive a class when the mapper is first constructed, before instrumentation is applied to the mapped class. This event is the earliest phase of mapper construction. Most attributes of the mapper are not yet initialized. This listener can either be applied to the :class:`.Mapper` class overall, or to any un-mapped class which serves as a base for classes that will be mapped (using the ``propagate=True`` flag):: Base = declarative_base() @event.listens_for(Base, "instrument_class", propagate=True) def on_new_class(mapper, cls_): " ... " :param mapper: the :class:`.Mapper` which is the target of this event. :param class\_: the mapped class. """ def mapper_configured(self, mapper, class_): r"""Called when a specific mapper has completed its own configuration within the scope of the :func:`.configure_mappers` call. The :meth:`.MapperEvents.mapper_configured` event is invoked for each mapper that is encountered when the :func:`.orm.configure_mappers` function proceeds through the current list of not-yet-configured mappers. :func:`.orm.configure_mappers` is typically invoked automatically as mappings are first used, as well as each time new mappers have been made available and new mapper use is detected. When the event is called, the mapper should be in its final state, but **not including backrefs** that may be invoked from other mappers; they might still be pending within the configuration operation. Bidirectional relationships that are instead configured via the :paramref:`.orm.relationship.back_populates` argument *will* be fully available, since this style of relationship does not rely upon other possibly-not-configured mappers to know that they exist. For an event that is guaranteed to have **all** mappers ready to go including backrefs that are defined only on other mappings, use the :meth:`.MapperEvents.after_configured` event; this event invokes only after all known mappings have been fully configured. The :meth:`.MapperEvents.mapper_configured` event, unlike :meth:`.MapperEvents.before_configured` or :meth:`.MapperEvents.after_configured`, is called for each mapper/class individually, and the mapper is passed to the event itself. It also is called exactly once for a particular mapper. The event is therefore useful for configurational steps that benefit from being invoked just once on a specific mapper basis, which don't require that "backref" configurations are necessarily ready yet. :param mapper: the :class:`.Mapper` which is the target of this event. :param class\_: the mapped class. .. seealso:: :meth:`.MapperEvents.before_configured` :meth:`.MapperEvents.after_configured` """ # TODO: need coverage for this event def before_configured(self): """Called before a series of mappers have been configured. The :meth:`.MapperEvents.before_configured` event is invoked each time the :func:`.orm.configure_mappers` function is invoked, before the function has done any of its work. :func:`.orm.configure_mappers` is typically invoked automatically as mappings are first used, as well as each time new mappers have been made available and new mapper use is detected. This event can **only** be applied to the :class:`.Mapper` class or :func:`.mapper` function, and not to individual mappings or mapped classes. It is only invoked for all mappings as a whole:: from sqlalchemy.orm import mapper @event.listens_for(mapper, "before_configured") def go(): # ... Constrast this event to :meth:`.MapperEvents.after_configured`, which is invoked after the series of mappers has been configured, as well as :meth:`.MapperEvents.mapper_configured`, which is invoked on a per-mapper basis as each one is configured to the extent possible. Theoretically this event is called once per application, but is actually called any time new mappers are to be affected by a :func:`.orm.configure_mappers` call. If new mappings are constructed after existing ones have already been used, this event will likely be called again. To ensure that a particular event is only called once and no further, the ``once=True`` argument (new in 0.9.4) can be applied:: from sqlalchemy.orm import mapper @event.listens_for(mapper, "before_configured", once=True) def go(): # ... .. versionadded:: 0.9.3 .. seealso:: :meth:`.MapperEvents.mapper_configured` :meth:`.MapperEvents.after_configured` """ def after_configured(self): """Called after a series of mappers have been configured. The :meth:`.MapperEvents.after_configured` event is invoked each time the :func:`.orm.configure_mappers` function is invoked, after the function has completed its work. :func:`.orm.configure_mappers` is typically invoked automatically as mappings are first used, as well as each time new mappers have been made available and new mapper use is detected. Contrast this event to the :meth:`.MapperEvents.mapper_configured` event, which is called on a per-mapper basis while the configuration operation proceeds; unlike that event, when this event is invoked, all cross-configurations (e.g. backrefs) will also have been made available for any mappers that were pending. Also constrast to :meth:`.MapperEvents.before_configured`, which is invoked before the series of mappers has been configured. This event can **only** be applied to the :class:`.Mapper` class or :func:`.mapper` function, and not to individual mappings or mapped classes. It is only invoked for all mappings as a whole:: from sqlalchemy.orm import mapper @event.listens_for(mapper, "after_configured") def go(): # ... Theoretically this event is called once per application, but is actually called any time new mappers have been affected by a :func:`.orm.configure_mappers` call. If new mappings are constructed after existing ones have already been used, this event will likely be called again. To ensure that a particular event is only called once and no further, the ``once=True`` argument (new in 0.9.4) can be applied:: from sqlalchemy.orm import mapper @event.listens_for(mapper, "after_configured", once=True) def go(): # ... .. seealso:: :meth:`.MapperEvents.mapper_configured` :meth:`.MapperEvents.before_configured` """ def before_insert(self, mapper, connection, target): """Receive an object instance before an INSERT statement is emitted corresponding to that instance. This event is used to modify local, non-object related attributes on the instance before an INSERT occurs, as well as to emit additional SQL statements on the given connection. The event is often called for a batch of objects of the same class before their INSERT statements are emitted at once in a later step. In the extremely rare case that this is not desirable, the :func:`.mapper` can be configured with ``batch=False``, which will cause batches of instances to be broken up into individual (and more poorly performing) event->persist->event steps. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit INSERT statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being persisted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ def after_insert(self, mapper, connection, target): """Receive an object instance after an INSERT statement is emitted corresponding to that instance. This event is used to modify in-Python-only state on the instance after an INSERT occurs, as well as to emit additional SQL statements on the given connection. The event is often called for a batch of objects of the same class after their INSERT statements have been emitted at once in a previous step. In the extremely rare case that this is not desirable, the :func:`.mapper` can be configured with ``batch=False``, which will cause batches of instances to be broken up into individual (and more poorly performing) event->persist->event steps. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit INSERT statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being persisted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ def before_update(self, mapper, connection, target): """Receive an object instance before an UPDATE statement is emitted corresponding to that instance. This event is used to modify local, non-object related attributes on the instance before an UPDATE occurs, as well as to emit additional SQL statements on the given connection. This method is called for all instances that are marked as "dirty", *even those which have no net changes to their column-based attributes*. An object is marked as dirty when any of its column-based attributes have a "set attribute" operation called or when any of its collections are modified. If, at update time, no column-based attributes have any net changes, no UPDATE statement will be issued. This means that an instance being sent to :meth:`~.MapperEvents.before_update` is *not* a guarantee that an UPDATE statement will be issued, although you can affect the outcome here by modifying attributes so that a net change in value does exist. To detect if the column-based attributes on the object have net changes, and will therefore generate an UPDATE statement, use ``object_session(instance).is_modified(instance, include_collections=False)``. The event is often called for a batch of objects of the same class before their UPDATE statements are emitted at once in a later step. In the extremely rare case that this is not desirable, the :func:`.mapper` can be configured with ``batch=False``, which will cause batches of instances to be broken up into individual (and more poorly performing) event->persist->event steps. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit UPDATE statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being persisted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ def after_update(self, mapper, connection, target): """Receive an object instance after an UPDATE statement is emitted corresponding to that instance. This event is used to modify in-Python-only state on the instance after an UPDATE occurs, as well as to emit additional SQL statements on the given connection. This method is called for all instances that are marked as "dirty", *even those which have no net changes to their column-based attributes*, and for which no UPDATE statement has proceeded. An object is marked as dirty when any of its column-based attributes have a "set attribute" operation called or when any of its collections are modified. If, at update time, no column-based attributes have any net changes, no UPDATE statement will be issued. This means that an instance being sent to :meth:`~.MapperEvents.after_update` is *not* a guarantee that an UPDATE statement has been issued. To detect if the column-based attributes on the object have net changes, and therefore resulted in an UPDATE statement, use ``object_session(instance).is_modified(instance, include_collections=False)``. The event is often called for a batch of objects of the same class after their UPDATE statements have been emitted at once in a previous step. In the extremely rare case that this is not desirable, the :func:`.mapper` can be configured with ``batch=False``, which will cause batches of instances to be broken up into individual (and more poorly performing) event->persist->event steps. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit UPDATE statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being persisted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ def before_delete(self, mapper, connection, target): """Receive an object instance before a DELETE statement is emitted corresponding to that instance. This event is used to emit additional SQL statements on the given connection as well as to perform application specific bookkeeping related to a deletion event. The event is often called for a batch of objects of the same class before their DELETE statements are emitted at once in a later step. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit DELETE statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being deleted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ def after_delete(self, mapper, connection, target): """Receive an object instance after a DELETE statement has been emitted corresponding to that instance. This event is used to emit additional SQL statements on the given connection as well as to perform application specific bookkeeping related to a deletion event. The event is often called for a batch of objects of the same class after their DELETE statements have been emitted at once in a previous step. .. warning:: Mapper-level flush events only allow **very limited operations**, on attributes local to the row being operated upon only, as well as allowing any SQL to be emitted on the given :class:`.Connection`. **Please read fully** the notes at :ref:`session_persistence_mapper` for guidelines on using these methods; generally, the :meth:`.SessionEvents.before_flush` method should be preferred for general on-flush changes. :param mapper: the :class:`.Mapper` which is the target of this event. :param connection: the :class:`.Connection` being used to emit DELETE statements for this instance. This provides a handle into the current transaction on the target database specific to this instance. :param target: the mapped instance being deleted. If the event is configured with ``raw=True``, this will instead be the :class:`.InstanceState` state-management object associated with the instance. :return: No return value is supported by this event. .. seealso:: :ref:`session_persistence_events` """ class _MapperEventsHold(_EventsHold): all_holds = weakref.WeakKeyDictionary() def resolve(self, class_): return _mapper_or_none(class_) class HoldMapperEvents(_EventsHold.HoldEvents, MapperEvents): pass dispatch = event.dispatcher(HoldMapperEvents) class SessionEvents(event.Events): """Define events specific to :class:`.Session` lifecycle. e.g.:: from sqlalchemy import event from sqlalchemy.orm import sessionmaker def my_before_commit(session): print "before commit!" Session = sessionmaker() event.listen(Session, "before_commit", my_before_commit) The :func:`~.event.listen` function will accept :class:`.Session` objects as well as the return result of :class:`~.sessionmaker()` and :class:`~.scoped_session()`. Additionally, it accepts the :class:`.Session` class which will apply listeners to all :class:`.Session` instances globally. """ _target_class_doc = "SomeSessionOrFactory" _dispatch_target = Session @classmethod def _accept_with(cls, target): if isinstance(target, scoped_session): target = target.session_factory if not isinstance(target, sessionmaker) and \ ( not isinstance(target, type) or not issubclass(target, Session) ): raise exc.ArgumentError( "Session event listen on a scoped_session " "requires that its creation callable " "is associated with the Session class.") if isinstance(target, sessionmaker): return target.class_ elif isinstance(target, type): if issubclass(target, scoped_session): return Session elif issubclass(target, Session): return target elif isinstance(target, Session): return target else: return None def after_transaction_create(self, session, transaction): """Execute when a new :class:`.SessionTransaction` is created. This event differs from :meth:`~.SessionEvents.after_begin` in that it occurs for each :class:`.SessionTransaction` overall, as opposed to when transactions are begun on individual database connections. It is also invoked for nested transactions and subtransactions, and is always matched by a corresponding :meth:`~.SessionEvents.after_transaction_end` event (assuming normal operation of the :class:`.Session`). :param session: the target :class:`.Session`. :param transaction: the target :class:`.SessionTransaction`. To detect if this is the outermost :class:`.SessionTransaction`, as opposed to a "subtransaction" or a SAVEPOINT, test that the :attr:`.SessionTransaction.parent` attribute is ``None``:: @event.listens_for(session, "after_transaction_create") def after_transaction_create(session, transaction): if transaction.parent is None: # work with top-level transaction To detect if the :class:`.SessionTransaction` is a SAVEPOINT, use the :attr:`.SessionTransaction.nested` attribute:: @event.listens_for(session, "after_transaction_create") def after_transaction_create(session, transaction): if transaction.nested: # work with SAVEPOINT transaction .. seealso:: :class:`.SessionTransaction` :meth:`~.SessionEvents.after_transaction_end` """ def after_transaction_end(self, session, transaction): """Execute when the span of a :class:`.SessionTransaction` ends. This event differs from :meth:`~.SessionEvents.after_commit` in that it corresponds to all :class:`.SessionTransaction` objects in use, including those for nested transactions and subtransactions, and is always matched by a corresponding :meth:`~.SessionEvents.after_transaction_create` event. :param session: the target :class:`.Session`. :param transaction: the target :class:`.SessionTransaction`. To detect if this is the outermost :class:`.SessionTransaction`, as opposed to a "subtransaction" or a SAVEPOINT, test that the :attr:`.SessionTransaction.parent` attribute is ``None``:: @event.listens_for(session, "after_transaction_create") def after_transaction_end(session, transaction): if transaction.parent is None: # work with top-level transaction To detect if the :class:`.SessionTransaction` is a SAVEPOINT, use the :attr:`.SessionTransaction.nested` attribute:: @event.listens_for(session, "after_transaction_create") def after_transaction_end(session, transaction): if transaction.nested: # work with SAVEPOINT transaction .. seealso:: :class:`.SessionTransaction` :meth:`~.SessionEvents.after_transaction_create` """ def before_commit(self, session): """Execute before commit is called. .. note:: The :meth:`~.SessionEvents.before_commit` hook is *not* per-flush, that is, the :class:`.Session` can emit SQL to the database many times within the scope of a transaction. For interception of these events, use the :meth:`~.SessionEvents.before_flush`, :meth:`~.SessionEvents.after_flush`, or :meth:`~.SessionEvents.after_flush_postexec` events. :param session: The target :class:`.Session`. .. seealso:: :meth:`~.SessionEvents.after_commit` :meth:`~.SessionEvents.after_begin` :meth:`~.SessionEvents.after_transaction_create` :meth:`~.SessionEvents.after_transaction_end` """ def after_commit(self, session): """Execute after a commit has occurred. .. note:: The :meth:`~.SessionEvents.after_commit` hook is *not* per-flush, that is, the :class:`.Session` can emit SQL to the database many times within the scope of a transaction. For interception of these events, use the :meth:`~.SessionEvents.before_flush`, :meth:`~.SessionEvents.after_flush`, or :meth:`~.SessionEvents.after_flush_postexec` events. .. note:: The :class:`.Session` is not in an active transaction when the :meth:`~.SessionEvents.after_commit` event is invoked, and therefore can not emit SQL. To emit SQL corresponding to every transaction, use the :meth:`~.SessionEvents.before_commit` event. :param session: The target :class:`.Session`. .. seealso:: :meth:`~.SessionEvents.before_commit` :meth:`~.SessionEvents.after_begin` :meth:`~.SessionEvents.after_transaction_create` :meth:`~.SessionEvents.after_transaction_end` """ def after_rollback(self, session): """Execute after a real DBAPI rollback has occurred. Note that this event only fires when the *actual* rollback against the database occurs - it does *not* fire each time the :meth:`.Session.rollback` method is called, if the underlying DBAPI transaction has already been rolled back. In many cases, the :class:`.Session` will not be in an "active" state during this event, as the current transaction is not valid. To acquire a :class:`.Session` which is active after the outermost rollback has proceeded, use the :meth:`.SessionEvents.after_soft_rollback` event, checking the :attr:`.Session.is_active` flag. :param session: The target :class:`.Session`. """ def after_soft_rollback(self, session, previous_transaction): """Execute after any rollback has occurred, including "soft" rollbacks that don't actually emit at the DBAPI level. This corresponds to both nested and outer rollbacks, i.e. the innermost rollback that calls the DBAPI's rollback() method, as well as the enclosing rollback calls that only pop themselves from the transaction stack. The given :class:`.Session` can be used to invoke SQL and :meth:`.Session.query` operations after an outermost rollback by first checking the :attr:`.Session.is_active` flag:: @event.listens_for(Session, "after_soft_rollback") def do_something(session, previous_transaction): if session.is_active: session.execute("select * from some_table") :param session: The target :class:`.Session`. :param previous_transaction: The :class:`.SessionTransaction` transactional marker object which was just closed. The current :class:`.SessionTransaction` for the given :class:`.Session` is available via the :attr:`.Session.transaction` attribute. .. versionadded:: 0.7.3 """ def before_flush(self, session, flush_context, instances): """Execute before flush process has started. :param session: The target :class:`.Session`. :param flush_context: Internal :class:`.UOWTransaction` object which handles the details of the flush. :param instances: Usually ``None``, this is the collection of objects which can be passed to the :meth:`.Session.flush` method (note this usage is deprecated). .. seealso:: :meth:`~.SessionEvents.after_flush` :meth:`~.SessionEvents.after_flush_postexec` :ref:`session_persistence_events` """ def after_flush(self, session, flush_context): """Execute after flush has completed, but before commit has been called. Note that the session's state is still in pre-flush, i.e. 'new', 'dirty', and 'deleted' lists still show pre-flush state as well as the history settings on instance attributes. :param session: The target :class:`.Session`. :param flush_context: Internal :class:`.UOWTransaction` object which handles the details of the flush. .. seealso:: :meth:`~.SessionEvents.before_flush` :meth:`~.SessionEvents.after_flush_postexec` :ref:`session_persistence_events` """ def after_flush_postexec(self, session, flush_context): """Execute after flush has completed, and after the post-exec state occurs. This will be when the 'new', 'dirty', and 'deleted' lists are in their final state. An actual commit() may or may not have occurred, depending on whether or not the flush started its own transaction or participated in a larger transaction. :param session: The target :class:`.Session`. :param flush_context: Internal :class:`.UOWTransaction` object which handles the details of the flush. .. seealso:: :meth:`~.SessionEvents.before_flush` :meth:`~.SessionEvents.after_flush` :ref:`session_persistence_events` """ def after_begin(self, session, transaction, connection): """Execute after a transaction is begun on a connection :param session: The target :class:`.Session`. :param transaction: The :class:`.SessionTransaction`. :param connection: The :class:`~.engine.Connection` object which will be used for SQL statements. .. seealso:: :meth:`~.SessionEvents.before_commit` :meth:`~.SessionEvents.after_commit` :meth:`~.SessionEvents.after_transaction_create` :meth:`~.SessionEvents.after_transaction_end` """ def before_attach(self, session, instance): """Execute before an instance is attached to a session. This is called before an add, delete or merge causes the object to be part of the session. .. versionadded:: 0.8. Note that :meth:`~.SessionEvents.after_attach` now fires off after the item is part of the session. :meth:`.before_attach` is provided for those cases where the item should not yet be part of the session state. .. seealso:: :meth:`~.SessionEvents.after_attach` :ref:`session_lifecycle_events` """ def after_attach(self, session, instance): """Execute after an instance is attached to a session. This is called after an add, delete or merge. .. note:: As of 0.8, this event fires off *after* the item has been fully associated with the session, which is different than previous releases. For event handlers that require the object not yet be part of session state (such as handlers which may autoflush while the target object is not yet complete) consider the new :meth:`.before_attach` event. .. seealso:: :meth:`~.SessionEvents.before_attach` :ref:`session_lifecycle_events` """ @event._legacy_signature("0.9", ["session", "query", "query_context", "result"], lambda update_context: ( update_context.session, update_context.query, update_context.context, update_context.result)) def after_bulk_update(self, update_context): """Execute after a bulk update operation to the session. This is called as a result of the :meth:`.Query.update` method. :param update_context: an "update context" object which contains details about the update, including these attributes: * ``session`` - the :class:`.Session` involved * ``query`` -the :class:`.Query` object that this update operation was called upon. * ``context`` The :class:`.QueryContext` object, corresponding to the invocation of an ORM query. * ``result`` the :class:`.ResultProxy` returned as a result of the bulk UPDATE operation. """ @event._legacy_signature("0.9", ["session", "query", "query_context", "result"], lambda delete_context: ( delete_context.session, delete_context.query, delete_context.context, delete_context.result)) def after_bulk_delete(self, delete_context): """Execute after a bulk delete operation to the session. This is called as a result of the :meth:`.Query.delete` method. :param delete_context: a "delete context" object which contains details about the update, including these attributes: * ``session`` - the :class:`.Session` involved * ``query`` -the :class:`.Query` object that this update operation was called upon. * ``context`` The :class:`.QueryContext` object, corresponding to the invocation of an ORM query. * ``result`` the :class:`.ResultProxy` returned as a result of the bulk DELETE operation. """ def transient_to_pending(self, session, instance): """Intercept the "transient to pending" transition for a specific object. This event is a specialization of the :meth:`.SessionEvents.after_attach` event which is only invoked for this specific transition. It is invoked typically during the :meth:`.Session.add` call. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def pending_to_transient(self, session, instance): """Intercept the "pending to transient" transition for a specific object. This less common transition occurs when an pending object that has not been flushed is evicted from the session; this can occur when the :meth:`.Session.rollback` method rolls back the transaction, or when the :meth:`.Session.expunge` method is used. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def persistent_to_transient(self, session, instance): """Intercept the "persistent to transient" transition for a specific object. This less common transition occurs when an pending object that has has been flushed is evicted from the session; this can occur when the :meth:`.Session.rollback` method rolls back the transaction. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def pending_to_persistent(self, session, instance): """Intercept the "pending to persistent"" transition for a specific object. This event is invoked within the flush process, and is similar to scanning the :attr:`.Session.new` collection within the :meth:`.SessionEvents.after_flush` event. However, in this case the object has already been moved to the persistent state when the event is called. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def detached_to_persistent(self, session, instance): """Intercept the "detached to persistent" transition for a specific object. This event is a specialization of the :meth:`.SessionEvents.after_attach` event which is only invoked for this specific transition. It is invoked typically during the :meth:`.Session.add` call, as well as during the :meth:`.Session.delete` call if the object was not previously associated with the :class:`.Session` (note that an object marked as "deleted" remains in the "persistent" state until the flush proceeds). .. note:: If the object becomes persistent as part of a call to :meth:`.Session.delete`, the object is **not** yet marked as deleted when this event is called. To detect deleted objects, check the ``deleted`` flag sent to the :meth:`.SessionEvents.persistent_to_detached` to event after the flush proceeds, or check the :attr:`.Session.deleted` collection within the :meth:`.SessionEvents.before_flush` event if deleted objects need to be intercepted before the flush. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def loaded_as_persistent(self, session, instance): """Intercept the "loaded as persistent" transition for a specific object. This event is invoked within the ORM loading process, and is invoked very similarly to the :meth:`.InstanceEvents.load` event. However, the event here is linkable to a :class:`.Session` class or instance, rather than to a mapper or class hierarchy, and integrates with the other session lifecycle events smoothly. The object is guaranteed to be present in the session's identity map when this event is called. :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def persistent_to_deleted(self, session, instance): """Intercept the "persistent to deleted" transition for a specific object. This event is invoked when a persistent object's identity is deleted from the database within a flush, however the object still remains associated with the :class:`.Session` until the transaction completes. If the transaction is rolled back, the object moves again to the persistent state, and the :meth:`.SessionEvents.deleted_to_persistent` event is called. If the transaction is committed, the object becomes detached, which will emit the :meth:`.SessionEvents.deleted_to_detached` event. Note that while the :meth:`.Session.delete` method is the primary public interface to mark an object as deleted, many objects get deleted due to cascade rules, which are not always determined until flush time. Therefore, there's no way to catch every object that will be deleted until the flush has proceeded. the :meth:`.SessionEvents.persistent_to_deleted` event is therefore invoked at the end of a flush. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def deleted_to_persistent(self, session, instance): """Intercept the "deleted to persistent" transition for a specific object. This transition occurs only when an object that's been deleted successfully in a flush is restored due to a call to :meth:`.Session.rollback`. The event is not called under any other circumstances. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def deleted_to_detached(self, session, instance): """Intercept the "deleted to detached" transition for a specific object. This event is invoked when a deleted object is evicted from the session. The typical case when this occurs is when the transaction for a :class:`.Session` in which the object was deleted is committed; the object moves from the deleted state to the detached state. It is also invoked for objects that were deleted in a flush when the :meth:`.Session.expunge_all` or :meth:`.Session.close` events are called, as well as if the object is individually expunged from its deleted state via :meth:`.Session.expunge`. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ def persistent_to_detached(self, session, instance): """Intercept the "persistent to detached" transition for a specific object. This event is invoked when a persistent object is evicted from the session. There are many conditions that cause this to happen, including: * using a method such as :meth:`.Session.expunge` or :meth:`.Session.close` * Calling the :meth:`.Session.rollback` method, when the object was part of an INSERT statement for that session's transaction :param session: target :class:`.Session` :param instance: the ORM-mapped instance being operated upon. :param deleted: boolean. If True, indicates this object moved to the detached state because it was marked as deleted and flushed. .. versionadded:: 1.1 .. seealso:: :ref:`session_lifecycle_events` """ class AttributeEvents(event.Events): """Define events for object attributes. These are typically defined on the class-bound descriptor for the target class. e.g.:: from sqlalchemy import event def my_append_listener(target, value, initiator): print "received append event for target: %s" % target event.listen(MyClass.collection, 'append', my_append_listener) Listeners have the option to return a possibly modified version of the value, when the ``retval=True`` flag is passed to :func:`~.event.listen`:: def validate_phone(target, value, oldvalue, initiator): "Strip non-numeric characters from a phone number" return re.sub(r'\D', '', value) # setup listener on UserContact.phone attribute, instructing # it to use the return value listen(UserContact.phone, 'set', validate_phone, retval=True) A validation function like the above can also raise an exception such as :exc:`ValueError` to halt the operation. Several modifiers are available to the :func:`~.event.listen` function. :param active_history=False: When True, indicates that the "set" event would like to receive the "old" value being replaced unconditionally, even if this requires firing off database loads. Note that ``active_history`` can also be set directly via :func:`.column_property` and :func:`.relationship`. :param propagate=False: When True, the listener function will be established not just for the class attribute given, but for attributes of the same name on all current subclasses of that class, as well as all future subclasses of that class, using an additional listener that listens for instrumentation events. :param raw=False: When True, the "target" argument to the event will be the :class:`.InstanceState` management object, rather than the mapped instance itself. :param retval=False: when True, the user-defined event listening must return the "value" argument from the function. This gives the listening function the opportunity to change the value that is ultimately used for a "set" or "append" event. """ _target_class_doc = "SomeClass.some_attribute" _dispatch_target = QueryableAttribute @staticmethod def _set_dispatch(cls, dispatch_cls): dispatch = event.Events._set_dispatch(cls, dispatch_cls) dispatch_cls._active_history = False return dispatch @classmethod def _accept_with(cls, target): # TODO: coverage if isinstance(target, interfaces.MapperProperty): return getattr(target.parent.class_, target.key) else: return target @classmethod def _listen(cls, event_key, active_history=False, raw=False, retval=False, propagate=False): target, identifier, fn = \ event_key.dispatch_target, event_key.identifier, \ event_key._listen_fn if active_history: target.dispatch._active_history = True if not raw or not retval: def wrap(target, value, *arg): if not raw: target = target.obj() if not retval: fn(target, value, *arg) return value else: return fn(target, value, *arg) event_key = event_key.with_wrapper(wrap) event_key.base_listen(propagate=propagate) if propagate: manager = instrumentation.manager_of_class(target.class_) for mgr in manager.subclass_managers(True): event_key.with_dispatch_target( mgr[target.key]).base_listen(propagate=True) def append(self, target, value, initiator): """Receive a collection append event. :param target: the object instance receiving the event. If the listener is registered with ``raw=True``, this will be the :class:`.InstanceState` object. :param value: the value being appended. If this listener is registered with ``retval=True``, the listener function must return this value, or a new value which replaces it. :param initiator: An instance of :class:`.attributes.Event` representing the initiation of the event. May be modified from its original value by backref handlers in order to control chained event propagation. .. versionchanged:: 0.9.0 the ``initiator`` argument is now passed as a :class:`.attributes.Event` object, and may be modified by backref handlers within a chain of backref-linked events. :return: if the event was registered with ``retval=True``, the given value, or a new effective value, should be returned. """ def remove(self, target, value, initiator): """Receive a collection remove event. :param target: the object instance receiving the event. If the listener is registered with ``raw=True``, this will be the :class:`.InstanceState` object. :param value: the value being removed. :param initiator: An instance of :class:`.attributes.Event` representing the initiation of the event. May be modified from its original value by backref handlers in order to control chained event propagation. .. versionchanged:: 0.9.0 the ``initiator`` argument is now passed as a :class:`.attributes.Event` object, and may be modified by backref handlers within a chain of backref-linked events. :return: No return value is defined for this event. """ def set(self, target, value, oldvalue, initiator): """Receive a scalar set event. :param target: the object instance receiving the event. If the listener is registered with ``raw=True``, this will be the :class:`.InstanceState` object. :param value: the value being set. If this listener is registered with ``retval=True``, the listener function must return this value, or a new value which replaces it. :param oldvalue: the previous value being replaced. This may also be the symbol ``NEVER_SET`` or ``NO_VALUE``. If the listener is registered with ``active_history=True``, the previous value of the attribute will be loaded from the database if the existing value is currently unloaded or expired. :param initiator: An instance of :class:`.attributes.Event` representing the initiation of the event. May be modified from its original value by backref handlers in order to control chained event propagation. .. versionchanged:: 0.9.0 the ``initiator`` argument is now passed as a :class:`.attributes.Event` object, and may be modified by backref handlers within a chain of backref-linked events. :return: if the event was registered with ``retval=True``, the given value, or a new effective value, should be returned. """ def init_scalar(self, target, value, dict_): """Receive a scalar "init" event. This event is invoked when an uninitialized, unpersisted scalar attribute is accessed. A value of ``None`` is typically returned in this case; no changes are made to the object's state. The event handler can alter this behavior in two ways. One is that a value other than ``None`` may be returned. The other is that the value may be established as part of the object's state, which will also have the effect that it is persisted. Typical use is to establish a specific default value of an attribute upon access:: SOME_CONSTANT = 3.1415926 @event.listens_for( MyClass.some_attribute, "init_scalar", retval=True, propagate=True) def _init_some_attribute(target, dict_, value): dict_['some_attribute'] = SOME_CONSTANT return SOME_CONSTANT Above, we initialize the attribute ``MyClass.some_attribute`` to the value of ``SOME_CONSTANT``. The above code includes the following features: * By setting the value ``SOME_CONSTANT`` in the given ``dict_``, we indicate that the value is to be persisted to the database. **The given value is only persisted to the database if we explicitly associate it with the object**. The ``dict_`` given is the ``__dict__`` element of the mapped object, assuming the default attribute instrumentation system is in place. * By establishing the ``retval=True`` flag, the value we return from the function will be returned by the attribute getter. Without this flag, the event is assumed to be a passive observer and the return value of our function is ignored. * The ``propagate=True`` flag is significant if the mapped class includes inheriting subclasses, which would also make use of this event listener. Without this flag, an inheriting subclass will not use our event handler. When we establish the value in the given dictionary, the value will be used in the INSERT statement established by the unit of work. Normally, the default returned value of ``None`` is not established as part of the object, to avoid the issue of mutations occurring to the object in response to a normally passive "get" operation, and also sidesteps the issue of whether or not the :meth:`.AttributeEvents.set` event should be awkwardly fired off during an attribute access operation. This does not impact the INSERT operation since the ``None`` value matches the value of ``NULL`` that goes into the database in any case; note that ``None`` is skipped during the INSERT to ensure that column and SQL-level default functions can fire off. The attribute set event :meth:`.AttributeEvents.set` as well as the related validation feature provided by :obj:`.orm.validates` is **not** invoked when we apply our value to the given ``dict_``. To have these events to invoke in response to our newly generated value, apply the value to the given object as a normal attribute set operation:: SOME_CONSTANT = 3.1415926 @event.listens_for( MyClass.some_attribute, "init_scalar", retval=True, propagate=True) def _init_some_attribute(target, dict_, value): # will also fire off attribute set events target.some_attribute = SOME_CONSTANT return SOME_CONSTANT When multiple listeners are set up, the generation of the value is "chained" from one listener to the next by passing the value returned by the previous listener that specifies ``retval=True`` as the ``value`` argument of the next listener. The :meth:`.AttributeEvents.init_scalar` event may be used to extract values from the default values and/or callables established on mapped :class:`.Column` objects. See the "active column defaults" example in :ref:`examples_instrumentation` for an example of this. .. versionadded:: 1.1 :param target: the object instance receiving the event. If the listener is registered with ``raw=True``, this will be the :class:`.InstanceState` object. :param value: the value that is to be returned before this event listener were invoked. This value begins as the value ``None``, however will be the return value of the previous event handler function if multiple listeners are present. :param dict_: the attribute dictionary of this mapped object. This is normally the ``__dict__`` of the object, but in all cases represents the destination that the attribute system uses to get at the actual value of this attribute. Placing the value in this dictionary has the effect that the value will be used in the INSERT statement generated by the unit of work. .. seealso:: :ref:`examples_instrumentation` - see the ``active_column_defaults.py`` example. """ def init_collection(self, target, collection, collection_adapter): """Receive a 'collection init' event. This event is triggered for a collection-based attribute, when the initial "empty collection" is first generated for a blank attribute, as well as for when the collection is replaced with a new one, such as via a set event. E.g., given that ``User.addresses`` is a relationship-based collection, the event is triggered here:: u1 = User() u1.addresses.append(a1) # <- new collection and also during replace operations:: u1.addresses = [a2, a3] # <- new collection :param target: the object instance receiving the event. If the listener is registered with ``raw=True``, this will be the :class:`.InstanceState` object. :param collection: the new collection. This will always be generated from what was specified as :paramref:`.RelationshipProperty.collection_class`, and will always be empty. :param collection_adpater: the :class:`.CollectionAdapter` that will mediate internal access to the collection. .. versionadded:: 1.0.0 the :meth:`.AttributeEvents.init_collection` and :meth:`.AttributeEvents.dispose_collection` events supersede the :class:`.collection.linker` hook. """ def dispose_collection(self, target, collection, collection_adpater): """Receive a 'collection dispose' event. This event is triggered for a collection-based attribute when a collection is replaced, that is:: u1.addresses.append(a1) u1.addresses = [a2, a3] # <- old collection is disposed The mechanics of the event will typically include that the given collection is empty, even if it stored objects while being replaced. .. versionadded:: 1.0.0 the :meth:`.AttributeEvents.init_collection` and :meth:`.AttributeEvents.dispose_collection` events supersede the :class:`.collection.linker` hook. """ class QueryEvents(event.Events): """Represent events within the construction of a :class:`.Query` object. The events here are intended to be used with an as-yet-unreleased inspection system for :class:`.Query`. Some very basic operations are possible now, however the inspection system is intended to allow complex query manipulations to be automated. .. versionadded:: 1.0.0 """ _target_class_doc = "SomeQuery" _dispatch_target = Query def before_compile(self, query): """Receive the :class:`.Query` object before it is composed into a core :class:`.Select` object. This event is intended to allow changes to the query given:: @event.listens_for(Query, "before_compile", retval=True) def no_deleted(query): for desc in query.column_descriptions: if desc['type'] is User: entity = desc['entity'] query = query.filter(entity.deleted == False) return query The event should normally be listened with the ``retval=True`` parameter set, so that the modified query may be returned. """ @classmethod def _listen( cls, event_key, retval=False, **kw): fn = event_key._listen_fn if not retval: def wrap(*arg, **kw): if not retval: query = arg[0] fn(*arg, **kw) return query else: return fn(*arg, **kw) event_key = event_key.with_wrapper(wrap) event_key.base_listen(**kw)

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# python char-lm-ud-stationary-separate-bidir-with-spaces-probe-baseline-prediction-wiki-plurals-2-tests-words-distractors-wikisource.py --language german --batchSize 128 --char_embedding_size 200 --hidden_dim 1024 --layer_num 2 --weight_dropout_in 0.1 --weight_dropout_hidden 0.35 --char_dropout_prob 0.0 --char_noise_prob 0.01 --learning_rate 0.2 --load-from wiki-german-nospaces-bptt-words-966024846 from paths import WIKIPEDIA_HOME from paths import MODELS_HOME import argparse parser = argparse.ArgumentParser() parser.add_argument("--language", dest="language", type=str) parser.add_argument("--load-from", dest="load_from", type=str) #parser.add_argument("--load-from-baseline", dest="load_from_baseline", type=str) #parser.add_argument("--save-to", dest="save_to", type=str) import random parser.add_argument("--batchSize", type=int, default=16) parser.add_argument("--char_embedding_size", type=int, default=100) parser.add_argument("--hidden_dim", type=int, default=1024) parser.add_argument("--layer_num", type=int, default=1) parser.add_argument("--weight_dropout_in", type=float, default=0.01) parser.add_argument("--weight_dropout_hidden", type=float, default=0.1) parser.add_argument("--char_dropout_prob", type=float, default=0.33) parser.add_argument("--char_noise_prob", type = float, default= 0.01) parser.add_argument("--learning_rate", type = float, default= 0.1) parser.add_argument("--myID", type=int, default=random.randint(0,1000000000)) parser.add_argument("--sequence_length", type=int, default=50) args=parser.parse_args() print(args) import corpusIteratorWikiWords def plusL(its): for it in its: for x in it: yield x def plus(it1, it2): for x in it1: yield x for x in it2: yield x char_vocab_path = {"german" : "vocabularies/german-wiki-word-vocab-50000.txt", "italian" : "vocabularies/italian-wiki-word-vocab-50000.txt"}[args.language] with open(char_vocab_path, "r") as inFile: itos = [x.split("\t")[0] for x in inFile.read().strip().split("\n")[:50000]] stoi = dict([(itos[i],i) for i in range(len(itos))]) import random import torch print(torch.__version__) from weight_drop import WeightDrop rnn = torch.nn.LSTM(args.char_embedding_size, args.hidden_dim, args.layer_num).cuda() rnn_parameter_names = [name for name, _ in rnn.named_parameters()] print(rnn_parameter_names) #quit() rnn_drop = WeightDrop(rnn, [(name, args.weight_dropout_in) for name, _ in rnn.named_parameters() if name.startswith("weight_ih_")] + [ (name, args.weight_dropout_hidden) for name, _ in rnn.named_parameters() if name.startswith("weight_hh_")]) output = torch.nn.Linear(args.hidden_dim, len(itos)+3).cuda() char_embeddings = torch.nn.Embedding(num_embeddings=len(itos)+3, embedding_dim=args.char_embedding_size).cuda() logsoftmax = torch.nn.LogSoftmax(dim=2) train_loss = torch.nn.NLLLoss(ignore_index=0) print_loss = torch.nn.NLLLoss(size_average=False, reduce=False, ignore_index=0) char_dropout = torch.nn.Dropout2d(p=args.char_dropout_prob) modules = [rnn, output, char_embeddings] def parameters(): for module in modules: for param in module.parameters(): yield param optim = torch.optim.SGD(parameters(), lr=args.learning_rate, momentum=0.0) # 0.02, 0.9 named_modules = {"rnn" : rnn, "output" : output, "char_embeddings" : char_embeddings} #, "optim" : optim} print("Loading model") if args.load_from is not None: checkpoint = torch.load(MODELS_HOME+"/"+args.load_from+".pth.tar") for name, module in named_modules.items(): print(checkpoint[name].keys()) module.load_state_dict(checkpoint[name]) #else: # assert False #################################### from torch.autograd import Variable # ([0] + [stoi[training_data[x]]+1 for x in range(b, b+sequence_length) if x < len(training_data)]) #from embed_regularize import embedded_dropout def encodeWord(word): numeric = ((stoi[word]+3 if word in stoi else 2) if True else 2+random.randint(0, len(itos))) return numeric rnn_drop.train(False) #rnn_forward_drop.train(False) #rnn_backward_drop.train(False) #baseline_rnn_encoder_drop.train(False) lossModule = torch.nn.NLLLoss(size_average=False, reduce=False, ignore_index=0) def choice(numeric1, numeric2): assert len(numeric1) == 1 assert len(numeric2) == 1 numeric = [numeric1[0], numeric2[0]] maxLength = max([len(x) for x in numeric]) for i in range(len(numeric)): while len(numeric[i]) < maxLength: numeric[i].append(0) input_tensor_forward = Variable(torch.LongTensor([[0]+x for x in numeric]).transpose(0,1).cuda(), requires_grad=False) target = input_tensor_forward[1:] input_cut = input_tensor_forward[:-1] embedded_forward = char_embeddings(input_cut) out_forward, hidden_forward = rnn_drop(embedded_forward, None) prediction = logsoftmax(output(out_forward)) #.data.cpu().view(-1, 3+len(itos)).numpy() #.view(1,1,-1))).view(3+len(itos)).data.cpu().numpy() losses = lossModule(prediction.view(-1, len(itos)+3), target.view(-1)).view(maxLength, 2) losses = losses.sum(0).data.cpu().numpy() return losses def encodeListOfWordsIn(words): numeric = [encodeWord(word) for word in words] input_tensor_forward = Variable(torch.LongTensor(numeric).cuda(), requires_grad=False) embedded_forward = char_embeddings(input_tensor_forward) return [embedded_forward[i].data.cpu().numpy() for i in range(len(words))] def encodeListOfWords(words): numeric = [encodeWord(word) for word in words] input_tensor_forward = Variable(torch.LongTensor(numeric).cuda(), requires_grad=False) embedded_forward = [output.weight[word] for word in numeric] #char_embeddings(input_tensor_forward) return [embedded_forward[i].data.cpu().numpy() for i in range(len(words))] def choiceList(numeric): for x in numeric: assert len(x) == 1 # assert len(numeric1) == 1 # assert len(numeric2) == 1 numeric = [x[0] for x in numeric] #, numeric2[0]] maxLength = max([len(x) for x in numeric]) for i in range(len(numeric)): while len(numeric[i]) < maxLength: numeric[i].append(0) input_tensor_forward = Variable(torch.LongTensor([[0]+x for x in numeric]).transpose(0,1).cuda(), requires_grad=False) target = input_tensor_forward[1:] input_cut = input_tensor_forward[:-1] embedded_forward = char_embeddings(input_cut) out_forward, hidden_forward = rnn_drop(embedded_forward, None) prediction = logsoftmax(output(out_forward)) #.data.cpu().view(-1, 3+len(itos)).numpy() #.view(1,1,-1))).view(3+len(itos)).data.cpu().numpy() losses = lossModule(prediction.view(-1, len(itos)+3), target.view(-1)).view(maxLength, len(numeric)) losses = losses.sum(0).data.cpu().numpy() return losses # # #def encodeSequenceBatchForward(numeric): # input_tensor_forward = Variable(torch.LongTensor([[0]+x for x in numeric]).transpose(0,1).cuda(), requires_grad=False) # ## target_tensor_forward = Variable(torch.LongTensor(numeric).transpose(0,1)[2:].cuda(), requires_grad=False).view(args.sequence_length+1, len(numeric), 1, 1) # embedded_forward = char_embeddings(input_tensor_forward) # out_forward, hidden_forward = rnn_drop(embedded_forward, None) ## out_forward = out_forward.view(args.sequence_length+1, len(numeric), -1) # # logits_forward = output(out_forward) # # log_probs_forward = logsoftmax(logits_forward) # return (out_forward[-1], hidden_forward) # # ## #def encodeSequenceBatchBackward(numeric): ## print([itos[x-3] for x in numeric[0]]) ## print([[0]+(x[::-1]) for x in numeric]) # input_tensor_backward = Variable(torch.LongTensor([[0]+(x[::-1]) for x in numeric]).transpose(0,1).cuda(), requires_grad=False) ## target_tensor_backward = Variable(torch.LongTensor([x[::-1] for x in numeric]).transpose(0,1)[:-2].cuda(), requires_grad=False).view(args.sequence_length+1, len(numeric), 1, 1) # embedded_backward = char_embeddings(input_tensor_backward) # out_backward, hidden_backward = rnn_backward_drop(embedded_backward, None) ## out_backward = out_backward.view(args.sequence_length+1, len(numeric), -1) ## logits_backward = output(out_backward) ## log_probs_backward = logsoftmax(logits_backward) # # return (out_backward[-1], hidden_backward) # import numpy as np def predictNext(encoded, preventBoundary=True): out, hidden = encoded prediction = logsoftmax(output(out.unsqueeze(0))).data.cpu().view(3+len(itos)).numpy() #.view(1,1,-1))).view(3+len(itos)).data.cpu().numpy() predicted = np.argmax(prediction[:-1] if preventBoundary else prediction) return itos[predicted-3] #, prediction def keepGenerating(encoded, length=100, backwards=False): out, hidden = encoded output_string = "" # rnn_forward_drop.train(True) for _ in range(length): prediction = logsoftmax(2*output(out.unsqueeze(0))).data.cpu().view(3+len(itos)).numpy() #.view(1,1,-1))).view(3+len(itos)).data.cpu().numpy() # predicted = np.argmax(prediction).items() predicted = np.random.choice(3+len(itos), p=np.exp(prediction)) output_string += itos[predicted-3] input_tensor_forward = Variable(torch.LongTensor([[predicted]]).transpose(0,1).cuda(), requires_grad=False) embedded_forward = char_embeddings(input_tensor_forward) out, hidden = (rnn_drop if not backwards else rnn_backward_drop)(embedded_forward, hidden) out = out[-1] # rnn_forward_drop.train(False) return output_string if not backwards else output_string[::-1] plurals = set() formations = {"e" : set(), "n" : set(), "s" : set(), "same" : set(), "r" : set()} for group in formations: with open(f"stimuli/german-plurals-{group}.txt", "r") as inFile: formations[group] = [tuple(x.split(" ")) for x in inFile.read().strip().split("\n")] formations[group] = [(x,y) for x,y in formations[group] if x in stoi and y in stoi] print(len(formations[group])) print(formations["e"]) print(formations["s"]) print(formations["n"]) print(formations["same"]) def doChoiceList(xs): for x in xs: print(x) losses = choiceList([encodeWord(x) for x in xs]) #, encodeWord(y)) print(losses) return np.argmin(losses) def doChoice(x, y): print(x) print(y) losses = choice(encodeWord(x), encodeWord(y)) print(losses) return 0 if losses[0] < losses[1] else 1 # classify singulars vs plurals print("trained on n, s, e") forNSE = list(plusL([formations["n"], formations["s"], formations["e"]])) lengthsS = [0 for _ in range(55)] lengthsP = [0 for _ in range(55)] for sing, plur in forNSE: lengthsS[len(sing)] += 1 lengthsP[len(plur)] += 1 lengths = [min(x,y) for x,y in zip(lengthsS, lengthsP)] sumLengthsS = sum(lengthsS) lengthsS = [float(x)/sumLengthsS for x in lengthsS] sumLengthsP = sum(lengthsP) lengthsP = [float(x)/sumLengthsP for x in lengthsP] sumLengths = sum(lengths) lengths = [float(x)/sumLengths for x in lengths] ratioP = max([x/y if y > 0 else 0.0 for (x,y) in zip(lengths, lengthsP)]) ratioS = max([x/y if y > 0 else 0.0 for (x,y) in zip(lengths, lengthsS)]) import random wordsEndingIn = {"r" : set(), "s" : set(), "n" : set(), "e" : set(), "g" : set(), "t" : set()} from corpusIterator import CorpusIterator with open("germanNounDeclension.txt") as inFile: data = inFile.read().strip().split("###")[1:] for noun in data: noun = noun.strip().split("\n")[1:] noun = [x.split("\t") for x in noun] noun = {x[0] : [y.lower() for y in x[1:]] for x in noun} if "Nominativ Singular" in noun and "Nominativ Plural" in noun: for x in noun["Nominativ Singular"]: if x[-1] in wordsEndingIn: if x not in noun["Nominativ Plural"]: if x in stoi: wordsEndingIn[x[-1]].add(x) #training = CorpusIterator("German", partition="train", storeMorph=True, removePunctuation=True) # #for sentence in training.iterator(): # for line in sentence: # if line["posUni"] == "NOUN": # morph = line["morph"] # if "Number=Plur" not in morph and "Case=Dat" not in morph: # if line["word"][-1] in wordsEndingIn: # if line["word"].lower() in stoi: # wordsEndingIn[line["word"][-1]].add(line["word"].lower()) for x in wordsEndingIn: print(x, len(wordsEndingIn[x])) #quit() predictorsR = encodeListOfWords([x for x in wordsEndingIn["r"]]) predictorsS = encodeListOfWords([x for x in wordsEndingIn["s"]]) predictorsN = encodeListOfWords([x for x in wordsEndingIn["n"]]) predictorsE = encodeListOfWords([x for x in wordsEndingIn["e"]]) predictorsG = encodeListOfWords([x for x in wordsEndingIn["g"]]) predictorsT = encodeListOfWords([x for x in wordsEndingIn["t"]]) # from each type, sample N singulars and N plurals N = 15 evaluationPoints = [] formationsBackup = formations random.seed(1) for _ in range(20): formations = {x : set(list(y)[:]) for x, y in formationsBackup.items()} singulars = {} plurals = {} for typ in ["n", "s", "e"]: singulars[typ] = [] plurals[typ] = [] formations[typ] = sorted(list(formations[typ])) for _ in range(N): while True: index, sampledS = random.choice(list(zip(range(len(formations[typ])), formations[typ]))) sampledS = sampledS[0] ratio = lengths[len(sampledS)] / (ratioS * lengthsS[len(sampledS)]) assert 0<= ratio assert ratio <= 1 if random.random() < ratio: del formations[typ][index] singulars[typ].append(sampledS) break while True: index, sampledP = random.choice(list(zip(range(len(formations[typ])), formations[typ]))) sampledP = sampledP[1] ratio = lengths[len(sampledP)] / (ratioP * lengthsP[len(sampledP)]) assert 0<= ratio assert ratio <= 1 if random.random() < ratio: del formations[typ][index] plurals[typ].append(sampledP) break stratify_types = ["n" for _ in plurals["n"]] + ["s" for _ in plurals["s"]] + ["e" for _ in plurals["e"]] plurals = plurals["n"] + plurals["s"] + plurals["e"] singulars = singulars["n"] + singulars["s"] + singulars["e"] assert len(plurals) == len(singulars) print(singulars) print(plurals) print(len(plurals)) print(sum([len(x) for x in plurals])/float(len(plurals))) print(sum([len(x) for x in singulars])/float(len(singulars))) encodedPlurals = encodeListOfWords([y for y in plurals]) encodedSingulars = encodeListOfWords([x for x in singulars]) #predictors = encodedSingulars + encodedPlurals #dependent = [0 for _ in encodedSingulars] + [1 for _ in encodedPlurals] from sklearn.model_selection import train_test_split sx_train, sx_test, sy_train, sy_test, st_train, st_test = train_test_split(encodedSingulars, [0 for _ in encodedSingulars], stratify_types, test_size=0.5, shuffle=True, stratify = stratify_types, random_state=1) # random_state=random.randint(0,100), px_train, px_test, py_train, py_test, pt_train, pt_test = train_test_split(encodedPlurals, [1 for _ in encodedPlurals], stratify_types, test_size=0.5, shuffle=True, stratify = stratify_types, random_state=1) # random_state=random.randint(0,100), x_train = sx_train + px_train x_test = sx_test + px_test y_train = sy_train + py_train y_test = sy_test + py_test t_train = st_train + pt_train t_test = st_test + pt_test print(y_train) print(y_test) from sklearn.linear_model import LogisticRegression print("regression") logisticRegr = LogisticRegression() logisticRegr.fit(x_train, y_train) # now look at other words that end in n, s, e dependent = [0 for _ in predictorsR] score = logisticRegr.score(predictorsR, dependent) print(["r", score]) evaluationPoints.append(("r_distract", score)) dependent = [0 for _ in predictorsS] score = logisticRegr.score(predictorsS, dependent) print(["s", score]) evaluationPoints.append(("s_distract", score)) dependent = [0 for _ in predictorsN] score = logisticRegr.score(predictorsN, dependent) print(["n", score]) evaluationPoints.append(("n_distract", score)) dependent = [0 for _ in predictorsE] score = logisticRegr.score(predictorsE, dependent) print(["e", score]) evaluationPoints.append(("e_distract", score)) dependent = [0 for _ in predictorsG] score = logisticRegr.score(predictorsG, dependent) print(["g", score]) evaluationPoints.append(("g_distract", score)) dependent = [0 for _ in predictorsT] score = logisticRegr.score(predictorsT, dependent) print(["t", score]) evaluationPoints.append(("t_distract", score)) # predictions = logisticRegr.predict(predictorsS) # print(predictions) # print([("-",y) for x, y in zip(predictions, wordsEndingIn["e"]) if x == 1]) # print([("+",y) for x, y in zip(predictions, wordsEndingIn["e"]) if x == 0]) # print("==============") print("----------------") import math firstEntries = list(set([x[0] for x in evaluationPoints])) for entry in firstEntries: values = [x[1] for x in evaluationPoints if x[0] == entry] accuracy = sum(values)/len(values) sd = math.sqrt(sum([x**2 for x in values])/len(values) - accuracy**2) values = sorted(values) lower = values[int(0.05*len(values))] upper = values[int(0.95*len(values))] print(entry, accuracy, sd, lower, upper) quit()

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mhahn29@gmail.com

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''' REMOVE Deleta valores de uma lista - Apresenta erro se o elemento não existir ''' cidades = ['São Paulo', 'Brasilia', 'Curitiba', 'Avaré', 'Florianópolis'] print(cidades) cidades.remove('Brasilia') print(cidades) #erro: cidades.remove('Portugal') print(cidades)

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# import pafy, pyglet # import urllib.request # from urllib.parse import * # from bs4 import BeautifulSoup # # # class Youtube_mp3(): # def __init__(self): # self.lst = [] # self.dict = {} # self.dict_names = {} # self.playlist = [] # # def url_search(self, search_string, max_search): # textToSearch = search_string # query = urllib.parse.quote(textToSearch) # url = "https://www.youtube.com/results?search_query=" + query # response = urllib.request.urlopen(url) # html = response.read() # soup = BeautifulSoup(html, 'lxml') # i = 1 # for vid in soup.findAll(attrs={'class':'yt-uix-tile-link'}): # if len(self.dict) < max_search: # self.dict[i] = 'https://www.youtube.com' + vid['href'] # i += 1 # else: # break # # # def get_search_items(self, max_search): # # if self.dict != {}: # i = 1 # for url in self.dict.values(): # try: # info = pafy.new(url) # self.dict_names[i] = info.title # print("{0}. {1}".format(i, info.title)) # i += 1 # # except ValueError: # pass # # def play_media(self, num): # url = self.dict[int(num)] # info = pafy.new(url) # #audio = info.m4astreams[-1] # audio = info.getbestaudio(preftype="m4a") # audio.download('song.m4a', quiet=True) # song = pyglet.media.load('song.m4a') # player = pyglet.media.Player() # player.queue(song) # print("Playing: {0}.".format(self.dict_names[int(num)])) # player.play() # stop = '' # while True: # stop = input('Type "s" to stop; "p" to pause; "" to play; : ') # if stop == 's': # player.pause() # break # elif stop == 'p': # player.pause() # elif stop == '': # player.play() # elif stop == 'r': # #player.queue(song) # #player.play() # print('Replaying: {0}'.format(self.dict_names[int(num)])) # # # # # # def download_media(self, num): # url = self.dict[int(num)] # info = pafy.new(url) # audio = info.getbestaudio(preftype="m4a") # song_name = self.dict_names[int(num)] # print("Downloading: {0}.".format(self.dict_names[int(num)])) # print(song_name) # song_name = input("Filename (Enter if as it is): ") # # file_name = song_name[:11] + '.m4a' # file_name = song_name + '.m4a' # if song_name == '': # audio.download(remux_audio=True) # else: # audio.download(filepath = filename, remux_audio=True) # # # def bulk_download(self, url): # info = pafy.new(url) # audio = info.getbestaudio(preftype="m4a") # song_name = self.dict_names[int(num)] # print("Downloading: {0}.".format(self.dict_names[int(num)])) # print(song_name) # song_name = input("Filename (Enter if as it is): ") # # file_name = song_name[:11] + '.m4a' # file_name = song_name + '.m4a' # if song_name == '': # audio.download(remux_audio=True) # else: # audio.download(filepath = filename, remux_audio=True) # # def add_playlist(self, search_query): # url = self.url_search(search_query, max_search=1) # self.playlist.append(url) # # # # # # if __name__ == '__main__': # print('Welcome to the Youtube-Mp3 player.') # x = Youtube_mp3() # search = '' # while search != 'q': # search = input("Youtube Search: ") # old_search = search # max_search = 5 # # if search == '': # # print('\nFetching for: {0} on youtube.'.format(old_search.title())) # # x.url_search(search, max_search) # # x.get_search_items(max_search) # # song_number = input('Input song number: ') # # x.play_media(song_number) # # x.dict = {} # x.dict_names = {} # # if search == 'q': # print("Ending Youtube-Mp3 player.") # break # # download = input('1. Play Live Music\n2. Download Mp3 from Youtube.\n') # if search != 'q' and (download == '1' or download == ''): # print('\nFetching for: {0} on youtube.'.format(search.title())) # x.url_search(search, max_search) # x.get_search_items(max_search) # song_number = input('Input song number: ') # x.play_media(song_number) # elif download == '2': # print('\nDownloading {0} (conveniently) from youtube servers.'.format(search.title())) # x.url_search(search, max_search) # x.get_search_items(max_search) # song_number = input('Input song number: ') # x.download_media(song_number) # #github commit # import pafy # import vlc # url = "https://youtu.be/-3wlroM2gZ8" # video = pafy.new(url) # best = video.getbest() # playurl = best.url # movie = playurl # media = instance.media_new(movie) # media_list = instance.media_list_new([movie]) #A list of one movie # player = instance.media_player_new() # player.set_media(media) # # #Create a new MediaListPlayer instance and associate the player and playlist with it # # list_player = instance.media_list_player_new() # list_player.set_media_player(player) # list_player.set_media_list(media_list) # list_player.play() # from pygame import mixer # Load the required library # # mixer.init() # mixer.music.load('Running ft Gabriel Garzón-Montano.mp3') # mixer.music.play() import webbrowser webbrowser.open("hello.mp3")

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/src/trainers/UserGru_predict.py

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thanhtcptit/Neural-Session-Aware-Recommendation

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import sys sys.path.append('../..') import numpy as np import tensorflow as tf from time import time from src.utils.qpath import * class UserGruPredict(): def __init__(self, sess, model, config): self.config = config self.model = model self.sess = sess self.saver = tf.train.Saver() def load(self, path): self.saver.restore(self.sess, path) print('++ Load model from {} ++'.format(path)) @staticmethod def calculate_ranks(_pr, y_true): y_true = np.reshape(y_true, [-1]) rows_idx = [i for i in range(len(y_true)) if y_true[i] != 0] mask_rows_idx = [[i] for i in range(len(y_true)) if y_true[i] != 0] mask_cols_idx = [[j] for j in y_true if j != 0] ranks = (_pr[rows_idx, :] > _pr[mask_rows_idx, mask_cols_idx]).sum(axis=1) + 1 return ranks, len(rows_idx) @staticmethod def evaluate(ranks, top): count_true = [0.] * len(top) rr = [0.] * len(top) for i, n in enumerate(top): true_predict = ranks <= n count_true[i] += true_predict.sum() rr[i] += (1. / ranks[true_predict]).sum() return count_true, rr def run_predict(self, session, pos): feed_dict = { self.model.user: session[:, :-1, 0], self.model.item: session[:, :-1, 1], self.model.day_of_week: session[:, :-1, 2], self.model.month_period: session[:, :-1, 3], self.model.next_items: session[:, 1:, 1], self.model.keep_pr: 1 } pr, attention = self.sess.run([self.model.get_output(), self.model.get_attention_weight()], feed_dict=feed_dict) assert len(pr) != 1 pr = pr[pos] current_item = session[0][pos][1] # print(session) # print(attention) print('===================') if 'context' in self.config.input: print('Item: ', attention[0][pos][0]) print('User: ', attention[0][pos][1]) print('Day of week: ', attention[0][pos][2]) print('Half month: ', attention[0][pos][3]) else: print('Item attention: ', attention[0][0][pos][0]) print('User attention: ', attention[1][0][pos][0]) top_id = np.argpartition(pr, -12)[-12:] top_id = top_id[np.argsort(pr[top_id])[::-1]] top_id = list(top_id) if 0 in top_id: del top_id[top_id.index(0)] if current_item in top_id: del top_id[top_id.index(current_item)] return top_id[:10] def run_test(self): pos = 0 session = [[]] with open(PROCESSED_DATA_DIR + 'clean-dev') as f: for line in tqdm(f): if '-' in line: session = [[]] pos = 0 continue u, i, *_ = line.strip().split(',') session[0].append([u, i, 0, 0, 0]) if len(session[0]) == 1: continue tmp = [session[0][:]] l = len(tmp[0]) for i in range(11 - l): tmp[0].append([0, 0, 0, 0, 0]) tmp = np.array(tmp) feed_dict = { self.model.user: tmp[:, :-1, 0], self.model.item: tmp[:, :-1, 1], self.model.day_of_week: tmp[:, :-1, 2], self.model.month_period: tmp[:, :-1, 3], self.model.next_items: tmp[:, 1:, 1], self.model.keep_pr: 1 } pr = self.sess.run( self.model.get_output(), feed_dict=feed_dict) assert len(pr) != 1 pr = pr[pos] pos += 1 top_id = np.argpartition(pr, -10)[-10:] top_id = top_id.tolist() if session[0][pos][1] in top_id: print(session[0]) def eval_step(self): batch_data = self.data_loader.next_batch() feed_dict = { self.model.user: batch_data[:, :-1, 0], self.model.item: batch_data[:, :-1, 1], self.model.day_of_week: batch_data[:, :-1, 2], self.model.month_period: batch_data[:, :-1, 3], self.model.next_items: batch_data[:, 1:, 1], self.model.keep_pr: 1 } pr = self.sess.run(self.model.get_output(), feed_dict=feed_dict) assert len(pr) != 1 batch_ranks, num_events = \ self.calculate_ranks(pr, batch_data[:, 1:, 1]) batch_cp, batch_rr = self.evaluate(batch_ranks, [5, 20]) return batch_cp, batch_rr, num_events

[ "thanh.ptit.96@gmail.com" ]

thanh.ptit.96@gmail.com

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import sys def dijkstra(K, V, graph): INF = sys.maxsize s = [False] * V d = [INF] * V d[K - 1] = 0 while True: m = INF N = -1 for j in range(V): if not s[j] and m > d[j]: m = d[j] N = j if m == INF: break s[N] = True for j in range(V): if s[j]: continue via = d[N] + graph[N][j] if d[j] > via: d[j] = via return d def solution(N, road, K): INF = sys.maxsize answer=0 graph = [[INF for cols in range(N)] for rows in range(N)] for r in road: if graph[r[0] - 1][r[1] - 1] > r[2]: graph[r[0] - 1][r[1] - 1] = r[2] graph[r[1] - 1][r[0] - 1] = r[2] for d in dijkstra(1, N, graph): if d <= K: answer +=1 return answer ''' def search(visit, graph, s, N, nxt): visit[s] = K- for j in range(N): if visit[j] != 0 and visit[j][1] < sinfo[1] - graph[sinfo[0]][j]: visit[j] = 0 if graph[sinfo[0]][j] != 2001 and sinfo[1]-graph[sinfo[0]][j] >= 0 and visit[j] ==0: nxt.append([j,sinfo[1]-graph[sinfo[0]][j]]) if len(nxt) == 0: return N-visit.count(0) return search(visit,graph,nxt.pop(0),N,nxt) def solution(N, road, K): graph = [[2001 for cols in range(N)] for rows in range(N)] visit = [0 for i in range(N)] for r in road: if graph[r[0]-1][r[1]-1] > r[2]: graph[r[0]-1][r[1]-1] = r[2] graph[r[1]-1][r[0]-1] = r[2] nxt=[] return search(visit,graph,0,N,nxt) ''' #print(solution(5,[[1,2,1],[2,3,3],[5,2,2],[1,4,2],[5,3,1],[5,4,2]],3)) print(solution(6,[[1,2,1],[1,3,2],[2,3,2],[3,4,3],[3,5,2],[3,5,3],[5,6,1]],4))

[ "cjsdnr885@naver.com" ]

cjsdnr885@naver.com

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bbcd8166118279da0de4c3a04cc5bc97aa49b368

/intertainment_center.py

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[]

no_license

AlyZahran/Movie_trailer

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67c321a01b1fbf08f422349b90b48978313c0d13

refs/heads/master

2021-07-15T20:55:50.594498

2017-10-05T12:59:27

null

0

null

UTF-8

Python

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2,078

py

import fresh_tomatoes import media toy_story = media.Movie( "toy story", "a story of a boy and his toys that come to life !", "http://a.dilcdn.com/bl/wp-content/uploads/sites/8/2013/02/toy_story_wallpaper_by_artifypics-d5gss19.jpg", "https://www.youtube.com/watch?v=KYz2wyBy3kc" ) THOR = media.Movie( "THOR", "a boy playing with his toy", "http://www.tahrirnews.com/files/cached/images/b501ac993c0086373c408223b6aea6e6_920_420.jpg", "https://www.youtube.com/watch?v=v7MGUNV8MxU&t=3s" ) fast_furious_8 = media.Movie( "fast & furious 8", "is a film talking about car racing", "https://media.premiumtimesng.com/wp-content/files/2017/04/fate-of-the-furious-poster-header-image.jpg", "https://www.youtube.com/watch?v=uisBaTkQAEs&t=1s" ) Hrob_Edtrary = media.Movie( "Hrob Edtrary", "is an action film", "http://www.el-tareeq.net/images/NewsArticle/16680.jpg", "https://www.youtube.com/watch?v=kRiQRPHC9O4" ) JUSTICE_LEAGUE = media.Movie( "JUSTICE LEAGUE", "Justice League is an upcoming American superhero film based on the DC Comics superhero team of the same name, distributed by Warner Bros", "http://www.konbini.com/us/files/2017/07/league.jpg", "https://www.youtube.com/watch?v=3cxixDgHUYw" ) music_maker = media.Movie( "music player", "a man who playing on piano", "https://i.ytimg.com/vi/W2I9b5WZuYA/hqdefault.jpg", "https://www.youtube.com/watch?v=1GCPDChh8m0" ) school_of_rock = media.Movie( "School of rock", "School of Rock is a 2003 musical comedy film directed by Richard Linklater, produced by Scott Rudin, and written by Mike White", "https://i.ytimg.com/vi/SfStJdDyeQo/hqdefault.jpg", "https://www.youtube.com/watch?v=z5aLjGxdX_0" ) movies = [toy_story, THOR, fast_furious_8, Hrob_Edtrary, JUSTICE_LEAGUE, music_maker, school_of_rock] fresh_tomatoes.open_movies_page(movies) #is related to fresh tomatoes page which is responsible for openin the page in the browser

[ "noreply@github.com" ]

AlyZahran.noreply@github.com