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LTD", "000C76": "MICRO-STAR INTERNATIONAL CO., LTD.", "000C77": "Life Racing Ltd", "000C78": "In-Tech Electronics Limited", "000C79": "Extel Communications P/L", "000C7A": "DaTARIUS Technologies GmbH", "000C7B": "ALPHA PROJECT Co.,Ltd.", "000C7C": "Internet Information Image Inc.", "000C7D": "TEIKOKU ELECTRIC MFG. 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Co., Ltd.", "000CC0": "Genera Oy", "000CC1": "Cooper Industries Inc.", "000CC2": "ControlNet (India) Private Limited", "000CC3": "BeWAN systems", "000CC4": "Tiptel AG", "000CC5": "Nextlink Co., Ltd.", "000CC6": "Ka-Ro electronics GmbH", "000CC7": "Intelligent Computer Solutions Inc.", "000CC8": "Xytronix Research & Design, Inc.", "000CC9": "ILWOO DATA & TECHNOLOGY CO.,LTD", "000CCA": "HGST a Western Digital Company", "000CCB": "Design Combus Ltd", "000CCC": "Aeroscout Ltd.", "000CCD": "IEC - TC57", "000CCE": "CISCO SYSTEMS, INC.", "000CCF": "CISCO SYSTEMS, INC.", "000CD0": "Symetrix", "000CD1": "SFOM Technology Corp.", "000CD2": "Schaffner EMV AG", "000CD3": "Prettl Elektronik Radeberg GmbH", "000CD4": "Positron Public Safety Systems inc.", "000CD5": "Passave Inc.", "000CD6": "PARTNER TECH", "000CD7": "Nallatech Ltd", "000CD8": "M. K. Juchheim GmbH & Co", "000CD9": "Itcare Co., Ltd", "000CDA": "FreeHand Systems, Inc.", "000CDB": "Brocade Communications Systems, Inc", "000CDC": "BECS Technology, Inc", "000CDD": "AOS Technologies AG", "000CDE": "ABB STOTZ-KONTAKT GmbH", "000CDF": "PULNiX America, Inc", "000CE0": "Trek Diagnostics Inc.", "000CE1": "The Open Group", "000CE2": "Rolls-Royce", "000CE3": "Option International N.V.", "000CE4": "NeuroCom International, Inc.", "000CE5": "ARRIS Group, Inc.", "000CE6": "Meru Networks Inc", "000CE7": "MediaTek Inc.", "000CE8": "GuangZhou AnJuBao Co., Ltd", "000CE9": "BLOOMBERG L.P.", "000CEA": "aphona Kommunikationssysteme", "000CEB": "CNMP Networks, Inc.", "000CEC": "Spectracom Corp.", "000CED": "Real Digital Media", "000CEE": "jp-embedded", "000CEF": "Open Networks Engineering Ltd", "000CF0": "M & N GmbH", "000CF1": "Intel Corporation", "000CF2": "GAMESA E\u00f3lica", "000CF3": "CALL IMAGE SA", "000CF4": "AKATSUKI ELECTRIC MFG.CO.,LTD.", "000CF5": "InfoExpress", "000CF6": "Sitecom Europe BV", "000CF7": "Nortel Networks", "000CF8": "Nortel Networks", "000CF9": "Xylem Water Solutions", "000CFA": "Digital Systems Corp", "000CFB": "Korea Network Systems", "000CFC": "S2io Technologies Corp", "000CFD": "Hyundai ImageQuest Co.,Ltd.", "000CFE": "Grand Electronic Co., Ltd", "000CFF": "MRO-TEK LIMITED", "000D00": "Seaway Networks Inc.", "000D01": "P&E Microcomputer Systems, Inc.", "000D02": "NEC AccessTechnica, Ltd.", "000D03": "Matrics, Inc.", "000D04": "Foxboro Eckardt Development GmbH", "000D05": "cybernet manufacturing inc.", "000D06": "Compulogic Limited", "000D07": "Calrec Audio Ltd", "000D08": "AboveCable, Inc.", "000D09": "Yuehua(Zhuhai) Electronic CO. 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Ltd.", "000D77": "FalconStor Software", "000D78": "Engineering & Security", "000D79": "Dynamic Solutions Co,.Ltd.", "000D7A": "DiGATTO Asia Pacific Pte Ltd", "000D7B": "Consensys Computers Inc.", "000D7C": "Codian Ltd", "000D7D": "Afco Systems", "000D7E": "Axiowave Networks, Inc.", "000D7F": "MIDAS COMMUNICATION TECHNOLOGIES PTE LTD ( Foreign Branch)", "000D80": "Online Development Inc", "000D81": "Pepperl+Fuchs GmbH", "000D82": "PHS srl", "000D83": "Sanmina-SCI Hungary Ltd.", "000D84": "Makus Inc.", "000D85": "Tapwave, Inc.", "000D86": "Huber + Suhner AG", "000D87": "Elitegroup Computer System Co. (ECS)", "000D88": "D-Link Corporation", "000D89": "Bils Technology Inc", "000D8A": "Winners Electronics Co., Ltd.", "000D8B": "T&D Corporation", "000D8C": "Shanghai Wedone Digital Ltd. CO.", "000D8D": "Prosoft Technology, Inc", "000D8E": "Koden Electronics Co., Ltd.", "000D8F": "King Tsushin Kogyo Co., LTD.", "000D90": "Factum Electronics AB", "000D91": "Eclipse (HQ Espana) S.L.", "000D92": "Arima Communication Corporation", "000D93": "Apple", "000D94": "AFAR Communications,Inc", "000D95": "Opti-cell, Inc.", "000D96": "Vtera Technology Inc.", "000D97": "Tropos Networks, Inc.", "000D98": "S.W.A.C. 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LTD", "001460": "Kyocera Wireless Corp.", "001461": "CORONA CORPORATION", "001462": "Digiwell Technology, inc", "001463": "IDCS N.V.", "001464": "Cryptosoft", "001465": "Novo Nordisk A/S", "001466": "Kleinhenz Elektronik GmbH", "001467": "ArrowSpan Inc.", "001468": "CelPlan International, Inc.", "001469": "CISCO SYSTEMS, INC.", "00146A": "CISCO SYSTEMS, INC.", "00146B": "Anagran, Inc.", "00146C": "Netgear Inc.", "00146D": "RF Technologies", "00146E": "H. Stoll GmbH & Co. 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Co., Ltd.", "0014A5": "Gemtek Technology Co., Ltd.", "0014A6": "Teranetics, Inc.", "0014A7": "Nokia Danmark A/S", "0014A8": "CISCO SYSTEMS, INC.", "0014A9": "CISCO SYSTEMS, INC.", "0014AA": "Ashly Audio, Inc.", "0014AB": "Senhai Electronic Technology Co., Ltd.", "0014AC": "Bountiful WiFi", "0014AD": "Gassner Wiege- und Me\u00dftechnik GmbH", "0014AE": "Wizlogics Co., Ltd.", "0014AF": "Datasym POS Inc.", "0014B0": "Naeil Community", "0014B1": "Avitec AB", "0014B2": "mCubelogics Corporation", "0014B3": "CoreStar International Corp", "0014B4": "General Dynamics United Kingdom Ltd", "0014B5": "PHYSIOMETRIX,INC", "0014B6": "Enswer Technology Inc.", "0014B7": "AR Infotek Inc.", "0014B8": "Hill-Rom", "0014B9": "MSTAR SEMICONDUCTOR", "0014BA": "Carvers SA de CV", "0014BB": "Open Interface North America", "0014BC": "SYNECTIC TELECOM EXPORTS PVT. LTD.", "0014BD": "incNETWORKS, Inc", "0014BE": "Wink communication technology CO.LTD", "0014BF": "Cisco-Linksys LLC", "0014C0": "Symstream Technology Group Ltd", "0014C1": "U.S. Robotics Corporation", "0014C2": "Hewlett-Packard Company", "0014C3": "Seagate Technology", "0014C4": "Vitelcom Mobile Technology", "0014C5": "Alive Technologies Pty Ltd", "0014C6": "Quixant Ltd", "0014C7": "Nortel", "0014C8": "Contemporary Research Corp", "0014C9": "Brocade Communications Systems, Inc.", "0014CA": "Key Radio Systems Limited", "0014CB": "LifeSync Corporation", "0014CC": "Zetec, Inc.", "0014CD": "DigitalZone Co., Ltd.", "0014CE": "NF CORPORATION", "0014CF": "INVISIO Communications", "0014D0": "BTI Systems Inc.", "0014D1": "TRENDnet", "0014D2": "Kyuden Technosystems Corporation", "0014D3": "SEPSA", "0014D4": "K Technology Corporation", "0014D5": "Datang Telecom Technology CO. , LCD,Optical Communication Br", "0014D6": "Jeongmin Electronics Co.,Ltd.", "0014D7": "Datastore Technology Corp", "0014D8": "bio-logic SA", "0014D9": "IP Fabrics, Inc.", "0014DA": "Huntleigh Healthcare", "0014DB": "Elma Trenew Electronic GmbH", "0014DC": "Communication System Design & Manufacturing (CSDM)", "0014DD": "Covergence Inc.", "0014DE": "Sage Instruments Inc.", "0014DF": "HI-P Tech Corporation", "0014E0": "LET'S Corporation", "0014E1": "Data Display AG", "0014E2": "datacom systems inc.", "0014E3": "mm-lab GmbH", "0014E4": "infinias, LLC", "0014E5": "Alticast", "0014E6": "AIM Infrarotmodule GmbH", "0014E7": "Stolinx,. Inc", "0014E8": "ARRIS Group, Inc.", "0014E9": "Nortech International", "0014EA": "S Digm Inc. (Safe Paradigm Inc.)", "0014EB": "AwarePoint Corporation", "0014EC": "Acro Telecom", "0014ED": "Airak, Inc.", "0014EE": "Western Digital Technologies, Inc.", "0014EF": "TZero Technologies, Inc.", "0014F0": "Business Security OL AB", "0014F1": "CISCO SYSTEMS, INC.", "0014F2": "CISCO SYSTEMS, INC.", "0014F3": "ViXS Systems Inc", "0014F4": "DekTec Digital Video B.V.", "0014F5": "OSI Security Devices", "0014F6": "Juniper Networks, Inc.", "0014F7": "CREVIS Co., LTD", "0014F8": "Scientific Atlanta", "0014F9": "Vantage Controls", "0014FA": "AsGa S.A.", "0014FB": "Technical Solutions Inc.", "0014FC": "Extandon, Inc.", "0014FD": "Thecus Technology Corp.", "0014FE": "Artech Electronics", "0014FF": "Precise Automation, Inc.", "001500": "Intel Corporate", "001501": "LexBox", "001502": "BETA tech", "001503": "PROFIcomms s.r.o.", "001504": "GAME PLUS CO., LTD.", "001505": "Actiontec Electronics, Inc", "001506": "Neo Photonics", "001507": "Renaissance Learning Inc", "001508": "Global Target Enterprise Inc", "001509": "Plus Technology Co., Ltd", "00150A": "Sonoa Systems, Inc", "00150B": "SAGE INFOTECH LTD.", "00150C": "AVM GmbH", "00150D": "Hoana Medical, Inc.", "00150E": "OPENBRAIN TECHNOLOGIES CO., LTD.", "00150F": "mingjong", "001510": "Techsphere Co., Ltd", "001511": "Data Center Systems", "001512": "Zurich University of Applied Sciences", "001513": "EFS sas", "001514": "Hu Zhou NAVA Networks&Electronics Ltd.", "001515": "Leipold+Co.GmbH", "001516": "URIEL SYSTEMS INC.", "001517": "Intel Corporate", "001518": "Shenzhen 10MOONS Technology Development CO.,Ltd", "001519": "StoreAge Networking Technologies", "00151A": "Hunter Engineering Company", "00151B": "Isilon Systems Inc.", "00151C": "LENECO", "00151D": "M2I CORPORATION", "00151E": "Ethernet Powerlink Standardization Group (EPSG)", "00151F": "Multivision Intelligent Surveillance (Hong Kong) Ltd", "001520": "Radiocrafts AS", "001521": "Horoquartz", "001522": "Dea Security", "001523": "Meteor Communications Corporation", "001524": "Numatics, Inc.", "001525": "Chamberlain Access Solutions", "001526": "Remote Technologies Inc", "001527": "Balboa Instruments", "001528": "Beacon Medical Products LLC d.b.a. BeaconMedaes", "001529": "N3 Corporation", "00152A": "Nokia GmbH", "00152B": "CISCO SYSTEMS, INC.", "00152C": "CISCO SYSTEMS, INC.", "00152D": "TenX Networks, LLC", "00152E": "PacketHop, Inc.", "00152F": "ARRIS Group, Inc.", "001530": "EMC Corporation", "001531": "KOCOM", "001532": "Consumer Technologies Group, LLC", "001533": "NADAM.CO.,LTD", "001534": "A Beltr\u00f3nica-Companhia de Comunica\u00e7\u00f5es, Lda", "001535": "OTE Spa", "001536": "Powertech co.,Ltd", "001537": "Ventus Networks", "001538": "RFID, Inc.", "001539": "Technodrive SRL", "00153A": "Shenzhen Syscan Technology Co.,Ltd.", "00153B": "EMH metering GmbH & Co. KG", "00153C": "Kprotech Co., Ltd.", "00153D": "ELIM PRODUCT CO.", "00153E": "Q-Matic Sweden AB", "00153F": "Alcatel Alenia Space Italia", "001540": "Nortel", "001541": "StrataLight Communications, Inc.", "001542": "MICROHARD S.R.L.", "001543": "Aberdeen Test Center", "001544": "coM.s.a.t. 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KG", "001911": "Just In Mobile Information Technologies (Shanghai) Co., Ltd.", "001912": "Welcat Inc", "001913": "Chuang-Yi Network Equipment Co.Ltd.", "001914": "Winix Co., Ltd", "001915": "TECOM Co., Ltd.", "001916": "PayTec AG", "001917": "Posiflex Inc.", "001918": "Interactive Wear AG", "001919": "ASTEL Inc.", "00191A": "IRLINK", "00191B": "Sputnik Engineering AG", "00191C": "Sensicast Systems", "00191D": "Nintendo Co., Ltd.", "00191E": "Beyondwiz Co., Ltd.", "00191F": "Microlink communications Inc.", "001920": "KUME electric Co.,Ltd.", "001921": "Elitegroup Computer System Co.", "001922": "CM Comandos Lineares", "001923": "Phonex Korea Co., LTD.", "001924": "LBNL Engineering", "001925": "Intelicis Corporation", "001926": "BitsGen Co., Ltd.", "001927": "ImCoSys Ltd", "001928": "Siemens AG, Transportation Systems", "001929": "2M2B Montadora de Maquinas Bahia Brasil LTDA", "00192A": "Antiope Associates", "00192B": "Aclara RF Systems Inc.", "00192C": "ARRIS Group, Inc.", "00192D": "Nokia Corporation", "00192E": "Spectral Instruments, Inc.", "00192F": "CISCO SYSTEMS, INC.", "001930": "CISCO SYSTEMS, INC.", "001931": "Balluff GmbH", "001932": "Gude Analog- und Digialsysteme GmbH", "001933": "Strix Systems, Inc.", "001934": "TRENDON TOUCH TECHNOLOGY CORP.", "001935": "DUERR DENTAL AG", "001936": "STERLITE OPTICAL TECHNOLOGIES LIMITED", "001937": "CommerceGuard AB", "001938": "UMB Communications Co., Ltd.", "001939": "Gigamips", "00193A": "OESOLUTIONS", "00193B": "Wilibox Deliberant Group LLC", "00193C": "HighPoint Technologies Incorporated", "00193D": "GMC Guardian Mobility Corp.", "00193E": "ADB Broadband Italia", "00193F": "RDI technology(Shenzhen) Co.,LTD", "001940": "Rackable Systems", "001941": "Pitney Bowes, Inc", "001942": "ON SOFTWARE INTERNATIONAL LIMITED", "001943": "Belden", "001944": "Fossil Partners, L.P.", "001945": "Ten-Tec Inc.", "001946": "Cianet Industria e Comercio S/A", "001947": "Scientific Atlanta, A Cisco Company", "001948": "AireSpider Networks", "001949": "TENTEL COMTECH CO., LTD.", "00194A": "TESTO AG", "00194B": "SAGEM COMMUNICATION", "00194C": "Fujian Stelcom information & Technology CO.,Ltd", "00194D": "Avago Technologies Sdn Bhd", "00194E": "Ultra Electronics - TCS (Tactical Communication Systems)", "00194F": "Nokia Danmark A/S", "001950": "Harman Multimedia", "001951": "NETCONS, s.r.o.", "001952": "ACOGITO Co., Ltd", "001953": "Chainleader Communications Corp.", "001954": "Leaf Corporation.", "001955": "CISCO SYSTEMS, INC.", "001956": "CISCO SYSTEMS, INC.", "001957": "Saafnet Canada Inc.", "001958": "Bluetooth SIG, Inc.", "001959": "Staccato Communications Inc.", "00195A": "Jenaer Antriebstechnik GmbH", "00195B": "D-Link Corporation", "00195C": "Innotech Corporation", "00195D": "ShenZhen XinHuaTong Opto Electronics Co.,Ltd", "00195E": "ARRIS Group, Inc.", "00195F": "Valemount Networks Corporation", "001960": "DoCoMo Systems, Inc.", "001961": "Blaupunkt Embedded Systems GmbH", "001962": "Commerciant, LP", "001963": "Sony Ericsson Mobile Communications AB", "001964": "Doorking Inc.", "001965": "YuHua TelTech (ShangHai) Co., Ltd.", "001966": "Asiarock Technology Limited", "001967": "TELDAT Sp.J.", "001968": "Digital Video Networks(Shanghai) CO. LTD.", "001969": "Nortel", "00196A": "MikroM GmbH", "00196B": "Danpex Corporation", "00196C": "ETROVISION TECHNOLOGY", "00196D": "Raybit Systems Korea, Inc", "00196E": "Metacom (Pty) Ltd.", "00196F": "SensoPart GmbH", "001970": "Z-Com, Inc.", "001971": "Guangzhou Unicomp Technology Co.,Ltd", "001972": "Plexus (Xiamen) Co.,ltd", "001973": "Zeugma Systems", "001974": "AboCom Systems, Inc.", "001975": "Beijing Huisen networks technology Inc", "001976": "Xipher Technologies, LLC", "001977": "Aerohive Networks, Inc.", "001978": "Datum Systems, Inc.", "001979": "Nokia Danmark A/S", "00197A": "MAZeT GmbH", "00197B": "Picotest Corp.", "00197C": "Riedel Communications GmbH", "00197D": "Hon Hai Precision Ind. Co., Ltd", "00197E": "Hon Hai Precision Ind. Co., Ltd", "00197F": "PLANTRONICS, INC.", "001980": "Gridpoint Systems", "001981": "Vivox Inc", "001982": "SmarDTV", "001983": "CCT R&D Limited", "001984": "ESTIC Corporation", "001985": "IT Watchdogs, Inc", "001986": "Cheng Hongjian", "001987": "Panasonic Mobile Communications Co., Ltd.", "001988": "Wi2Wi, Inc", "001989": "Sonitrol Corporation", "00198A": "Northrop Grumman Systems Corp.", "00198B": "Novera Optics Korea, Inc.", "00198C": "iXSea", "00198D": "Ocean Optics, Inc.", "00198E": "Oticon A/S", "00198F": "Alcatel Bell N.V.", "001990": "ELM DATA Co., Ltd.", "001991": "avinfo", "001992": "ADTRAN INC.", "001993": "Changshu Switchgear MFG. Co.,Ltd. (Former Changshu Switchgea", "001994": "Jorjin Technologies Inc.", "001995": "Jurong Hi-Tech (Suzhou)Co.ltd", "001996": "TurboChef Technologies Inc.", "001997": "Soft Device Sdn Bhd", "001998": "SATO CORPORATION", "001999": "Fujitsu Technology Solutions", "00199A": "EDO-EVI", "00199B": "Diversified Technical Systems, Inc.", "00199C": "CTRING", "00199D": "VIZIO, Inc.", "00199E": "Nifty", "00199F": "DKT A/S", "0019A0": "NIHON DATA SYSTENS, INC.", "0019A1": "LG INFORMATION & COMM.", "0019A2": "ORDYN TECHNOLOGIES", "0019A3": "asteel electronique atlantique", "0019A4": "Austar Technology (hang zhou) Co.,Ltd", "0019A5": "RadarFind Corporation", "0019A6": "ARRIS Group, Inc.", "0019A7": "ITU-T", "0019A8": "WiQuest Communications", "0019A9": "CISCO SYSTEMS, INC.", "0019AA": "CISCO SYSTEMS, INC.", "0019AB": "Raycom CO ., LTD", "0019AC": "GSP SYSTEMS Inc.", "0019AD": "BOBST SA", "0019AE": "Hopling Technologies b.v.", "0019AF": "Rigol Technologies, Inc.", "0019B0": "HanYang System", "0019B1": "Arrow7 Corporation", "0019B2": "XYnetsoft Co.,Ltd", "0019B3": "Stanford Research Systems", "0019B4": "VideoCast Ltd.", "0019B5": "Famar Fueguina S.A.", "0019B6": "Euro Emme s.r.l.", "0019B7": "Nokia Danmark A/S", "0019B8": "Boundary Devices", "0019B9": "Dell Inc.", "0019BA": "Paradox Security Systems Ltd", "0019BB": "Hewlett-Packard Company", "0019BC": "ELECTRO CHANCE SRL", "0019BD": "New Media Life", "0019BE": "Altai Technologies Limited", "0019BF": "Citiway technology Co.,ltd", "0019C0": "ARRIS Group, Inc.", "0019C1": "Alps Electric Co., Ltd", "0019C2": "Equustek Solutions, Inc.", "0019C3": "Qualitrol", "0019C4": "Infocrypt Inc.", "0019C5": "SONY Computer Entertainment inc,", "0019C6": "ZTE Corporation", "0019C7": "Cambridge Industries(Group) Co.,Ltd.", "0019C8": "AnyDATA Corporation", "0019C9": "S&C ELECTRIC COMPANY", "0019CA": "Broadata Communications, Inc", "0019CB": "ZyXEL Communications Corporation", "0019CC": "RCG (HK) Ltd", "0019CD": "Chengdu ethercom information technology Ltd.", "0019CE": "Progressive Gaming International", "0019CF": "SALICRU, S.A.", "0019D0": "Cathexis", "0019D1": "Intel Corporate", "0019D2": "Intel Corporate", "0019D3": "TRAK Microwave", "0019D4": "ICX Technologies", "0019D5": "IP Innovations, Inc.", "0019D6": "LS Cable and System Ltd.", "0019D7": "FORTUNETEK CO., LTD", "0019D8": "MAXFOR", "0019D9": "Zeutschel GmbH", "0019DA": "Welltrans O&E Technology Co. , Ltd.", "0019DB": "MICRO-STAR INTERNATIONAL CO., LTD.", "0019DC": "ENENSYS Technologies", "0019DD": "FEI-Zyfer, Inc.", "0019DE": "MOBITEK", "0019DF": "Thomson Inc.", "0019E0": "TP-LINK Technologies Co., Ltd.", "0019E1": "Nortel", "0019E2": "Juniper Networks", "0019E3": "Apple", "0019E4": "2Wire, Inc", "0019E5": "Lynx Studio Technology, Inc.", "0019E6": "TOYO MEDIC CO.,LTD.", "0019E7": "CISCO SYSTEMS, INC.", "0019E8": "CISCO SYSTEMS, INC.", "0019E9": "S-Information Technolgy, Co., Ltd.", "0019EA": "TeraMage Technologies Co., Ltd.", "0019EB": "Pyronix Ltd", "0019EC": "Sagamore Systems, Inc.", "0019ED": "Axesstel Inc.", "0019EE": "CARLO GAVAZZI CONTROLS SPA-Controls Division", "0019EF": "SHENZHEN LINNKING ELECTRONICS CO.,LTD", "0019F0": "UNIONMAN TECHNOLOGY CO.,LTD", "0019F1": "Star Communication Network Technology Co.,Ltd", "0019F2": "Teradyne K.K.", "0019F3": "Cetis, Inc", "0019F4": "Convergens Oy Ltd", "0019F5": "Imagination Technologies Ltd", "0019F6": "Acconet (PTE) Ltd", "0019F7": "Onset Computer Corporation", "0019F8": "Embedded Systems Design, Inc.", "0019F9": "TDK-Lambda", "0019FA": "Cable Vision Electronics CO., LTD.", "0019FB": "BSkyB Ltd", "0019FC": "PT. 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Ltd.", "001A57": "Matrix Design Group, LLC", "001A58": "CCV Deutschland GmbH - Celectronic eHealth Div.", "001A59": "Ircona", "001A5A": "Korea Electric Power Data Network (KDN) Co., Ltd", "001A5B": "NetCare Service Co., Ltd.", "001A5C": "Euchner GmbH+Co. 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CO., LTD.", "001AF6": "Woven Systems, Inc.", "001AF7": "dataschalt e+a GmbH", "001AF8": "Copley Controls Corporation", "001AF9": "AeroVIronment (AV Inc)", "001AFA": "Welch Allyn, Inc.", "001AFB": "Joby Inc.", "001AFC": "ModusLink Corporation", "001AFD": "EVOLIS", "001AFE": "SOFACREAL", "001AFF": "Wizyoung Tech.", "001B00": "Neopost Technologies", "001B01": "Applied Radio Technologies", "001B02": "ED Co.Ltd", "001B03": "Action Technology (SZ) Co., Ltd", "001B04": "Affinity International S.p.a", "001B05": "YMC AG", "001B06": "Ateliers R. LAUMONIER", "001B07": "Mendocino Software", "001B08": "Danfoss Drives A/S", "001B09": "Matrix Telecom Pvt. 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KG", "001B67": "Cisco Systems Inc", "001B68": "Modnnet Co., Ltd", "001B69": "Equaline Corporation", "001B6A": "Powerwave Technologies Sweden AB", "001B6B": "Swyx Solutions AG", "001B6C": "LookX Digital Media BV", "001B6D": "Midtronics, Inc.", "001B6E": "Anue Systems, Inc.", "001B6F": "Teletrak Ltd", "001B70": "IRI Ubiteq, INC.", "001B71": "Telular Corp.", "001B72": "Sicep s.p.a.", "001B73": "DTL Broadcast Ltd", "001B74": "MiraLink Corporation", "001B75": "Hypermedia Systems", "001B76": "Ripcode, Inc.", "001B77": "Intel Corporate", "001B78": "Hewlett-Packard Company", "001B79": "FAIVELEY TRANSPORT", "001B7A": "Nintendo Co., Ltd.", "001B7B": "The Tintometer Ltd", "001B7C": "A & R Cambridge", "001B7D": "CXR Anderson Jacobson", "001B7E": "Beckmann GmbH", "001B7F": "TMN Technologies Telecomunicacoes Ltda", "001B80": "LORD Corporation", "001B81": "DATAQ Instruments, Inc.", "001B82": "Taiwan Semiconductor Co., Ltd.", "001B83": "Finsoft Ltd", "001B84": "Scan Engineering Telecom", "001B85": "MAN Diesel SE", "001B86": "Bosch Access Systems GmbH", "001B87": "Deepsound Tech. Co., Ltd", "001B88": "Divinet Access Technologies Ltd", "001B89": "EMZA Visual Sense Ltd.", "001B8A": "2M Electronic A/S", "001B8B": "NEC AccessTechnica, Ltd.", "001B8C": "JMicron Technology Corp.", "001B8D": "Electronic Computer Systems, Inc.", "001B8E": "Hulu Sweden AB", "001B8F": "CISCO SYSTEMS, INC.", "001B90": "CISCO SYSTEMS, INC.", "001B91": "EFKON AG", "001B92": "l-acoustics", "001B93": "JC Decaux SA DNT", "001B94": "T.E.M.A. S.p.A.", "001B95": "VIDEO SYSTEMS SRL", "001B96": "General Sensing", "001B97": "Violin Technologies", "001B98": "Samsung Electronics Co., Ltd.", "001B99": "KS System GmbH", "001B9A": "Apollo Fire Detectors Ltd", "001B9B": "Hose-McCann Communications", "001B9C": "SATEL sp. z o.o.", "001B9D": "Novus Security Sp. z o.o.", "001B9E": "ASKEY COMPUTER CORP", "001B9F": "Calyptech Pty Ltd", "001BA0": "Awox", "001BA1": "\u00c5mic AB", "001BA2": "IDS Imaging Development Systems GmbH", "001BA3": "Flexit Group GmbH", "001BA4": "S.A.E Afikim", "001BA5": "MyungMin Systems, Inc.", "001BA6": "intotech inc.", "001BA7": "Lorica Solutions", "001BA8": "UBI&MOBI,.Inc", "001BA9": "BROTHER INDUSTRIES, LTD.", "001BAA": "XenICs nv", "001BAB": "Telchemy, Incorporated", "001BAC": "Curtiss Wright Controls Embedded Computing", "001BAD": "iControl Incorporated", "001BAE": "Micro Control Systems, Inc", "001BAF": "Nokia Danmark A/S", "001BB0": "BHARAT ELECTRONICS", "001BB1": "Wistron Neweb Corp.", "001BB2": "Intellect International NV", "001BB3": "Condalo GmbH", "001BB4": "Airvod Limited", "001BB5": "ZF Electronics GmbH", "001BB6": "Bird Electronic Corp.", "001BB7": "Alta Heights Technology Corp.", "001BB8": "BLUEWAY ELECTRONIC CO;LTD", "001BB9": "Elitegroup Computer System Co.", "001BBA": "Nortel", "001BBB": "RFTech Co.,Ltd", "001BBC": "Silver Peak Systems, Inc.", "001BBD": "FMC Kongsberg Subsea AS", "001BBE": "ICOP Digital", "001BBF": "SAGEM COMMUNICATION", "001BC0": "Juniper Networks", "001BC1": "HOLUX Technology, Inc.", "001BC2": "Integrated Control Technology Limitied", "001BC3": "Mobisolution Co.,Ltd", "001BC4": "Ultratec, Inc.", "001BC5": "IEEE Registration Authority", "001BC6": "Strato Rechenzentrum AG", "001BC7": "StarVedia Technology Inc.", "001BC8": "MIURA CO.,LTD", "001BC9": "FSN DISPLAY INC", "001BCA": "Beijing Run Technology LTD. Company", "001BCB": "PEMPEK SYSTEMS PTY LTD", "001BCC": "KINGTEK CCTV ALLIANCE CO., LTD.", "001BCD": "DAVISCOMMS (S) PTE LTD", "001BCE": "Measurement Devices Ltd", "001BCF": "Dataupia Corporation", "001BD0": "IDENTEC SOLUTIONS", "001BD1": "SOGESTMATIC", "001BD2": "ULTRA-X ASIA PACIFIC Inc.", "001BD3": "Panasonic Corp. AVC Company", "001BD4": "CISCO SYSTEMS, INC.", "001BD5": "CISCO SYSTEMS, INC.", "001BD6": "Kelvin Hughes Ltd", "001BD7": "Scientific Atlanta, A Cisco Company", "001BD8": "DVTel LTD", "001BD9": "Edgewater Computer Systems", "001BDA": "UTStarcom Inc", "001BDB": "Valeo VECS", "001BDC": "Vencer Co., Ltd.", "001BDD": "ARRIS Group, Inc.", "001BDE": "Renkus-Heinz, Inc.", "001BDF": "Iskra Sistemi d.d.", "001BE0": "TELENOT ELECTRONIC GmbH", "001BE1": "ViaLogy", "001BE2": "AhnLab,Inc.", "001BE3": "Health Hero Network, Inc.", "001BE4": "TOWNET SRL", "001BE5": "802automation Limited", "001BE6": "VR AG", "001BE7": "Postek Electronics Co., Ltd.", "001BE8": "Ultratronik GmbH", "001BE9": "Broadcom Corporation", "001BEA": "Nintendo Co., Ltd.", "001BEB": "DMP Electronics INC.", "001BEC": "Netio Technologies Co., Ltd", "001BED": "Brocade Communications Systems, Inc", "001BEE": "Nokia Danmark A/S", "001BEF": "Blossoms Digital Technology Co.,Ltd.", "001BF0": "Value Platforms Limited", "001BF1": "Nanjing SilverNet Software Co., Ltd.", "001BF2": "KWORLD COMPUTER CO., LTD", "001BF3": "TRANSRADIO SenderSysteme Berlin AG", "001BF4": "KENWIN INDUSTRIAL(HK) LTD.", "001BF5": "Tellink Sistemas de Telecomunicaci\u00f3n S.L.", "001BF6": "CONWISE Technology Corporation Ltd.", "001BF7": "Lund IP Products AB", "001BF8": "Digitrax Inc.", "001BF9": "Intellitect Water Ltd", "001BFA": "G.i.N. mbH", "001BFB": "Alps Electric Co., Ltd", "001BFC": "ASUSTek COMPUTER INC.", "001BFD": "Dignsys Inc.", "001BFE": "Zavio Inc.", "001BFF": "Millennia Media inc.", "001C00": "Entry Point, LLC", "001C01": "ABB Oy Drives", "001C02": "Pano Logic", "001C03": "Betty TV Technology AG", "001C04": "Airgain, Inc.", "001C05": "Nonin Medical Inc.", "001C06": "Siemens Numerical Control Ltd., Nanjing", "001C07": "Cwlinux Limited", "001C08": "Echo360, Inc.", "001C09": "SAE Electronic Co.,Ltd.", "001C0A": "Shenzhen AEE Technology Co.,Ltd.", "001C0B": "SmartAnt Telecom", "001C0C": "TANITA Corporation", "001C0D": "G-Technology, Inc.", "001C0E": "CISCO SYSTEMS, INC.", "001C0F": "CISCO SYSTEMS, INC.", "001C10": "Cisco-Linksys, LLC", "001C11": "ARRIS Group, Inc.", "001C12": "ARRIS Group, Inc.", "001C13": "OPTSYS TECHNOLOGY CO., LTD.", "001C14": "VMware, Inc", "001C15": "TXP Corporation", "001C16": "ThyssenKrupp Elevator", "001C17": "Nortel", "001C18": "Sicert S.r.L.", "001C19": "secunet Security Networks AG", "001C1A": "Thomas Instrumentation, Inc", "001C1B": "Hyperstone GmbH", "001C1C": "Center Communication Systems GmbH", "001C1D": "CHENZHOU GOSPELL DIGITAL TECHNOLOGY CO.,LTD", "001C1E": "emtrion GmbH", "001C1F": "Quest Retail Technology Pty Ltd", "001C20": "CLB Benelux", "001C21": "Nucsafe Inc.", "001C22": "Aeris Elettronica s.r.l.", "001C23": "Dell Inc", "001C24": "Formosa Wireless Systems Corp.", "001C25": "Hon Hai Precision Ind. Co.,Ltd.", "001C26": "Hon Hai Precision Ind. Co.,Ltd.", "001C27": "Sunell Electronics Co.", "001C28": "Sphairon Technologies GmbH", "001C29": "CORE DIGITAL ELECTRONICS CO., LTD", "001C2A": "Envisacor Technologies Inc.", "001C2B": "Alertme.com Limited", "001C2C": "Synapse", "001C2D": "FlexRadio Systems", "001C2E": "HPN Supply Chain", "001C2F": "Pfister GmbH", "001C30": "Mode Lighting (UK ) Ltd.", "001C31": "Mobile XP Technology Co., LTD", "001C32": "Telian Corporation", "001C33": "Sutron", "001C34": "HUEY CHIAO INTERNATIONAL CO., LTD.", "001C35": "Nokia Danmark A/S", "001C36": "iNEWiT NV", "001C37": "Callpod, Inc.", "001C38": "Bio-Rad Laboratories, Inc.", "001C39": "S Netsystems Inc.", "001C3A": "Element Labs, Inc.", "001C3B": "AmRoad Technology Inc.", "001C3C": "Seon Design Inc.", "001C3D": "WaveStorm", "001C3E": "ECKey Corporation", "001C3F": "International Police Technologies, Inc.", "001C40": "VDG-Security bv", "001C41": "scemtec Transponder Technology GmbH", "001C42": "Parallels, Inc.", "001C43": "Samsung Electronics Co.,Ltd", "001C44": "Bosch Security Systems BV", "001C45": "Chenbro Micom Co., Ltd.", "001C46": "QTUM", "001C47": "Hangzhou Hollysys Automation Co., Ltd", "001C48": "WiDeFi, Inc.", "001C49": "Zoltan Technology Inc.", "001C4A": "AVM GmbH", "001C4B": "Gener8, Inc.", "001C4C": "Petrotest Instruments", "001C4D": "Aplix IP Holdings Corporation", "001C4E": "TASA International Limited", "001C4F": "MACAB AB", "001C50": "TCL Technoly Electronics(Huizhou)Co.,Ltd", "001C51": "Celeno Communications", "001C52": "VISIONEE SRL", "001C53": "Synergy Lighting Controls", "001C54": "Hillstone Networks Inc", "001C55": "Shenzhen Kaifa Technology Co.", "001C56": "Pado Systems, Inc.", "001C57": "CISCO SYSTEMS, INC.", "001C58": "CISCO SYSTEMS, INC.", "001C59": "DEVON IT", "001C5A": "Advanced Relay Corporation", "001C5B": "Chubb Electronic Security Systems Ltd", "001C5C": "Integrated Medical Systems, Inc.", "001C5D": "Leica Microsystems", "001C5E": "ASTON France", "001C5F": "Winland Electronics, Inc.", "001C60": "CSP Frontier Technologies,Inc.", "001C61": "Galaxy Microsystems LImited", "001C62": "LG Electronics Inc", "001C63": "TRUEN", "001C64": "Landis+Gyr", "001C65": "JoeScan, Inc.", "001C66": "UCAMP CO.,LTD", "001C67": "Pumpkin Networks, Inc.", "001C68": "Anhui Sun Create Electronics Co., Ltd", "001C69": "Packet Vision Ltd", "001C6A": "Weiss Engineering Ltd.", "001C6B": "COVAX Co. 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Mueller Apparatebau", "24470E": "PentronicAB", "24497B": "Innovative Converged Devices Inc", "245FDF": "KYOCERA Corporation", "246278": "sysmocom - systems for mobile communications GmbH", "2464EF": "CYG SUNRI CO.,LTD.", "246511": "AVM GmbH", "24694A": "Jasmine Systems Inc.", "2469A5": "Huawei Technologies Co., Ltd", "246AAB": "IT-IS International", "24767D": "Cisco SPVTG", "247703": "Intel Corporate", "248000": "Westcontrol AS", "2481AA": "KSH International Co., Ltd.", "24828A": "Prowave Technologies Ltd.", "2486F4": "Ctek, Inc.", "248707": "SEnergy Corporation", "2493CA": "Voxtronic Technology Computer-Systeme GmbH", "249442": "OPEN ROAD SOLUTIONS , INC.", "249504": "SFR", "24A2E1": "Apple, Inc", "24A42C": "KOUKAAM a.s.", "24A43C": "Ubiquiti Networks, INC", "24A495": "Thales Canada Inc.", "24A87D": "Panasonic Automotive Systems Asia Pacific(Thailand)Co.,Ltd.", "24A937": "PURE Storage", "24AB81": "Apple", "24AF4A": "Alcatel-Lucent-IPD", "24AF54": "NEXGEN Mediatech Inc.", "24B657": "CISCO SYSTEMS, INC.", "24B6B8": "FRIEM SPA", "24B6FD": "Dell Inc", "24B88C": "Crenus Co.,Ltd.", "24B8D2": "Opzoon Technology Co.,Ltd.", "24BA30": "Technical Consumer Products, Inc.", "24BBC1": "Absolute Analysis", "24BC82": "Dali Wireless, Inc.", "24BE05": "Hewlett Packard", "24BF74": "PRIVATE", "24C0B3": "RSF", "24C696": "Samsung Electronics Co.,Ltd", "24C848": "mywerk system GmbH", "24C86E": "Chaney Instrument Co.", "24C9A1": "Ruckus Wireless", "24C9DE": "Genoray", "24CBE7": "MYK, Inc.", "24CF21": "Shenzhen State Micro Technology Co., Ltd", "24D2CC": "SmartDrive Systems Inc.", "24D921": "Avaya, Inc", "24DAB6": "Sistemas de Gesti\u00f3n Energ\u00e9tica S.A. de C.V", "24DBAC": "Shenzhen Huawei Communication Technologies Co., Ltd", "24DBAD": "ShopperTrak RCT Corporation", "24DBED": "Samsung Electronics Co.,Ltd", "24DEC6": "Aruba Networks", "24E271": "Qingdao Hisense Communications Co.,Ltd", "24E6BA": "JSC Zavod im. Kozitsky", "24E9B3": "Cisco", "24EA40": "Systeme Helmholz GmbH", "24EB65": "SAET I.S. S.r.l.", "24EC99": "Askey Computer Corp", "24ECD6": "CSG Science & Technology Co.,Ltd.Hefei", "24EE3A": "Chengdu Yingji Electronic Hi-tech Co Ltd", "24F0FF": "GHT Co., Ltd.", "24F2DD": "Radiant Zemax LLC", "24F5AA": "Samsung Electronics Co.,LTD", "24FD52": "Liteon Technology Corporation", "2804E0": "FERMAX ELECTRONICA S.A.U.", "28061E": "NINGBO GLOBAL USEFUL ELECTRIC CO.,LTD", "28068D": "ITL, LLC", "280B5C": "Apple", "280CB8": "Mikrosay Yazilim ve Elektronik A.S.", "280DFC": "Sony Computer Entertainment Inc.", "28107B": "D-Link International", "281471": "Lantis co., LTD.", "28162E": "2Wire", "2817CE": "Omnisense Ltd", "281878": "Microsoft Corporation", "2818FD": "Aditya Infotech Ltd.", "282246": "Beijing Sinoix Communication Co., LTD", "2826A6": "PBR electronics GmbH", "28285D": "ZyXEL Communications Corporation", "2829D9": "GlobalBeiMing technology (Beijing)Co. Ltd", "282CB2": "TP-LINK TECHNOLOGIES CO.,LTD.", "283152": "HUAWEI TECHNOLOGIES CO.,LTD", "2832C5": "Humax.co.,ltd", "283410": "Enigma Diagnostics Limited", "2834A2": "Cisco", "283737": "Apple", "2838CF": "Gen2wave", "2839E7": "Preceno Technology Pte.Ltd.", "283B96": "Cool Control LTD", "283CE4": "Huawei Technologies Co., Ltd", "28401A": "C8 MediSensors, Inc.", "284121": "OptiSense Network, LLC", "284430": "GenesisTechnical Systems (UK) Ltd", "2847AA": "Nokia Corporation", "284846": "GridCentric Inc.", "284C53": "Intune Networks", "284D92": "Luminator", "284ED7": "OutSmart Power Systems, Inc.", "284FCE": "Liaoning Wontel Science and Technology Development Co.,Ltd.", "285132": "Shenzhen Prayfly Technology Co.,Ltd", "285767": "Echostar Technologies Corp", "285FDB": "Shenzhen Huawei Communication Technologies Co., Ltd", "286046": "Lantech Communications Global, Inc.", "286094": "CAPELEC", "286336": "Siemens AG - Industrial Automation - EWA", "28656B": "Keystone Microtech Corporation", "286AB8": "Apple", "286ABA": "Apple", "286D97": "SAMJIN Co., Ltd.", "286ED4": "HUAWEI TECHNOLOGIES CO.,LTD", "287184": "Spire Payments", "2872C5": "Smartmatic Corp", "2872F0": "ATHENA", "287994": "Realplay Digital Technology(Shenzhen) Co.,Ltd", "288023": "Hewlett Packard", "28852D": "Touch Networks", "288915": "CashGuard Sverige AB", "288A1C": "Juniper networks", "2891D0": "Stage Tec Entwicklungsgesellschaft f\u00fcr professionelle Audiotechnik mbH", "28924A": "Hewlett Packard", "2893FE": "CISCO SYSTEMS, INC.", "28940F": "CISCO SYSTEMS, INC.", "2894AF": "Samhwa Telecom", "28987B": "Samsung Electronics Co.,Ltd", "289A4B": "SteelSeries ApS", "289AFA": "TCT Mobile Limited", "289EDF": "Danfoss Turbocor Compressors, Inc", "28A186": "enblink", "28A192": "GERP Solution", "28A1EB": "ETEK TECHNOLOGY (SHENZHEN) CO.,LTD", "28A241": "exlar corp", "28A574": "Miller Electric Mfg. Co.", "28AF0A": "Sirius XM Radio Inc", "28B0CC": "Xenya d.o.o.", "28B2BD": "Intel Corporate", "28B3AB": "Genmark Automation", "28BA18": "NextNav, LLC", "28BAB5": "Samsung Electronics Co.,Ltd", "28BB59": "RNET Technologies, Inc.", "28BE9B": "Technicolor USA Inc.", "28C0DA": "Juniper Networks", "28C671": "Yota Devices OY", "28C68E": "NETGEAR INC.,", "28C718": "Altierre", "28C7CE": "Cisco", "28C825": "DellKing Industrial Co., Ltd", "28C914": "Taimag Corporation", "28CBEB": "One", "28CC01": "Samsung Electronics Co.,Ltd", "28CCFF": "Corporacion Empresarial Altra SL", "28CD1C": "Espotel Oy", "28CD4C": "Individual Computers GmbH", "28CD9C": "Shenzhen Dynamax Software Development Co.,Ltd.", "28CFDA": "Apple", "28CFE9": "Apple", "28D1AF": "Nokia Corporation", "28D244": "LCFC(HeFei) Electronics Technology Co., Ltd.", "28D576": "Premier Wireless, Inc.", "28D93E": "Telecor Inc.", "28D997": "Yuduan Mobile Co., Ltd.", "28DB81": "Shanghai Guao Electronic Technology Co., Ltd", "28DEF6": "bioMerieux Inc.", "28E02C": "Apple", "28E14C": "Apple, Inc.", "28E297": "Shanghai InfoTM Microelectronics Co.,Ltd.", "28E347": "Liteon Technology Corporation", "28E608": "Tokheim", "28E794": "Microtime Computer Inc.", "28E7CF": "Apple", "28ED58": "JAG Jakob AG", "28EE2C": "Frontline Test Equipment", "28EF01": "PRIVATE", "28F358": "2C - Trifonov & Co", "28F532": "ADD-Engineering BV", "28F606": "Syes srl", "28FBD3": "Ragentek Technology Group", "28FC51": "The Electric Controller and Manufacturing Co., LLC", "28FCF6": "Shenzhen Xin KingBrand enterprises Co.,Ltd", "2C002C": "UNOWHY", "2C0033": "EControls, LLC", "2C00F7": "XOS", "2C0623": "Win Leader Inc.", "2C073C": "DEVLINE LIMITED", "2C10C1": "Nintendo Co., Ltd.", "2C18AE": "Trend Electronics Co., Ltd.", "2C1984": "IDN Telecom, Inc.", "2C1EEA": "AERODEV", "2C2172": "Juniper Networks", "2C245F": "Babolat VS", "2C26C5": "zte corporation", "2C27D7": "Hewlett-Packard Company", "2C282D": "BBK COMMUNICATIAO TECHNOLOGY CO.,LTD.", "2C2D48": "bct electronic GesmbH", "2C3068": "Pantech Co.,Ltd", "2C3427": "ERCO & GENER", "2C3557": "ELLIY Power CO..Ltd", "2C36A0": "Capisco Limited", "2C36F8": "CISCO SYSTEMS, INC.", "2C3731": "ShenZhen Yifang Digital Technology Co.,LTD", "2C3996": "SAGEMCOM", "2C39C1": "Ciena Corporation", "2C3A28": "Fagor Electr\u00f3nica", "2C3BFD": "Netstor Technology Co., Ltd.", "2C3ECF": "Cisco", "2C3F38": "CISCO SYSTEMS, INC.", "2C3F3E": "Alge-Timing GmbH", "2C4138": "Hewlett-Packard Company", "2C4401": "Samsung Electronics Co.,Ltd", "2C441B": "Spectrum Medical Limited", "2C44FD": "Hewlett Packard", "2C534A": "Shenzhen Winyao Electronic Limited", "2C542D": "CISCO SYSTEMS, INC.", "2C553C": "Gainspeed, Inc.", "2C59E5": "Hewlett Packard", "2C5A05": "Nokia Corporation", "2C5AA3": "PROMATE ELECTRONIC CO.LTD", "2C5BE1": "Centripetal Networks, Inc", "2C5D93": "Ruckus Wireless", "2C5FF3": "Pertronic Industries", "2C625A": "Finest Security Systems Co., Ltd", "2C6289": "Regenersis (Glenrothes) Ltd", "2C67FB": "ShenZhen Zhengjili Electronics Co., LTD", "2C69BA": "RF Controls, LLC", "2C6BF5": "Juniper networks", "2C7155": "HiveMotion", "2C72C3": "Soundmatters", "2C750F": "Shanghai Dongzhou-Lawton Communication Technology Co. 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B.V.", "2C9AA4": "NGI SpA", "2C9E5F": "ARRIS Group, Inc.", "2C9EFC": "CANON INC.", "2CA157": "acromate, Inc.", "2CA780": "True Technologies Inc.", "2CA835": "RIM", "2CAB25": "Shenzhen Gongjin Electronics Co.,Ltd", "2CB05D": "NETGEAR", "2CB0DF": "Soliton Technologies Pvt Ltd", "2CB43A": "Apple", "2CB693": "Radware", "2CB69D": "RED Digital Cinema", "2CBE08": "Apple", "2CBE97": "Ingenieurbuero Bickele und Buehler GmbH", "2CC260": "Ravello Systems", "2CCC15": "Nokia Corporation", "2CCD27": "Precor Inc", "2CCD43": "Summit Technology Group", "2CCD69": "Aqavi.com", "2CD05A": "Liteon Technology Corporation", "2CD1DA": "Sanjole, Inc.", "2CD2E7": "Nokia Corporation", "2CD444": "Fujitsu Limited", "2CDD0C": "Discovergy GmbH", "2CE2A8": "DeviceDesign", "2CE412": "SAGEMCOM SAS", "2CE6CC": "Ruckus Wireless", "2CE871": "Alert Metalguard ApS", "2CEDEB": "Alpheus Digital Company Limited", "2CEE26": "Petroleum Geo-Services", "2CF203": "EMKO ELEKTRONIK SAN VE TIC AS", "2CF4C5": "Avaya, Inc", "30055C": "Brother industries, LTD.", "300B9C": "Delta Mobile Systems, Inc.", "300D2A": "Zhejiang Wellcom Technology Co.,Ltd.", "300ED5": "Hon Hai Precision Ind.Co.Ltd", "3010E4": "Apple, Inc.", "30142D": "Piciorgros GmbH", "30144A": "Wistron Neweb Corp.", "301518": "Ubiquitous Communication Co. ltd.", "30168D": "ProLon", "3017C8": "Sony Ericsson Mobile Communications AB", "3018CF": "DEOS control systems GmbH", "301966": "Samsung Electronics Co.,Ltd", "301A28": "Mako Networks Ltd", "30215B": "Shenzhen Ostar Display Electronic Co.,Ltd", "302DE8": "JDA, LLC (JDA Systems)", "303294": "W-IE-NE-R Plein & Baus GmbH", "3032D4": "Hanilstm Co., Ltd.", "3037A6": "CISCO SYSTEMS, INC.", "303855": "Nokia Corporation", "303926": "Sony Ericsson Mobile Communications AB", "303955": "Shenzhen Jinhengjia Electronic Co., Ltd.", "3039F2": "ADB Broadband Italia", "303A64": "Intel Corporate", "303D08": "GLINTT TES S.A.", "303EAD": "Sonavox Canada Inc", "304174": "ALTEC LANSING LLC", "304449": "PLATH GmbH", "30469A": "NETGEAR", "30493B": "Nanjing Z-Com Wireless Co.,Ltd", "304C7E": "Panasonic Electric Works Automation Controls Techno Co.,Ltd.", "304EC3": "Tianjin Techua Technology Co., Ltd.", "3051F8": "BYK-Gardner GmbH", "30525A": "NST Co., LTD", "3055ED": "Trex Network LLC", "3057AC": "IRLAB LTD.", "3059B7": "Microsoft", "305D38": "Beissbarth", "306023": "ARRIS Group, Inc.", "306112": "PAV GmbH", "306118": "Paradom Inc.", "3065EC": "Wistron (ChongQing)", "30688C": "Reach Technology Inc.", "30694B": "RIM", "306CBE": "Skymotion Technology (HK) Limited", "306E5C": "Validus Technologies", "3071B2": "Hangzhou Prevail Optoelectronic Equipment Co.,LTD.", "30766F": "LG Electronics", "30786B": "TIANJIN Golden Pentagon Electronics Co., Ltd.", "3078C2": "Innowireless, Co. Ltd.", "307C30": "RIM", "307ECB": "SFR", "3085A9": "Asustek Computer Inc", "308730": "Shenzhen Huawei Communication Technologies Co., Ltd", "308999": "Guangdong East Power Co.,", "308CFB": "Dropcam", "3090AB": "Apple", "30918F": "Technicolor", "3092F6": "SHANGHAI SUNMON COMMUNICATION TECHNOGY CO.,LTD", "309BAD": "BBK Electronics Corp., Ltd.,", "30A8DB": "Sony Mobile Communications AB", "30AABD": "Shanghai Reallytek Information Technology Co.,Ltd", "30AE7B": "Deqing Dusun Electron CO., LTD", "30AEF6": "Radio Mobile Access", "30B216": "Hytec Geraetebau GmbH", "30B3A2": "Shenzhen Heguang Measurement & Control Technology Co.,Ltd", "30B5C2": "TP-LINK TECHNOLOGIES CO.,LTD.", "30C750": "MIC Technology Group", "30C7AE": "Samsung Electronics Co.,Ltd", "30C82A": "Wi-Next s.r.l.", "30CDA7": "Samsung Electronics ITS, Printer division", "30D17E": "HUAWEI TECHNOLOGIES CO.,LTD", "30D357": "Logosol, Inc.", "30D46A": "Autosales Incorporated", "30D6C9": "Samsung Electronics Co.,Ltd", "30DE86": "Cedac Software S.r.l.", "30E48E": "Vodafone UK", "30E4DB": "CISCO SYSTEMS, INC.", "30EB25": "INTEK DIGITAL", "30EFD1": "Alstom Strongwish (Shenzhen) Co., Ltd.", "30F31D": "zte corporation", "30F33A": "+plugg srl", "30F42F": "ESP", "30F70D": "Cisco Systems", "30F7C5": "Apple", "30F7D7": "Thread Technology Co., Ltd", "30F9ED": "Sony Corporation", "30FD11": "MACROTECH (USA) INC.", "3407FB": "Ericsson AB", "340804": "D-Link Corporation", "340AFF": "Qingdao Hisense Communications Co.,Ltd", "3413A8": "Mediplan Limited", "3413E8": "Intel Corporate", "34159E": "Apple", "3417EB": "Dell Inc", "341A4C": "SHENZHEN WEIBU ELECTRONICS CO.,LTD.", "341B22": "Grandbeing Technology Co., Ltd", "342109": "Jensen Scandinavia AS", "342387": "Hon Hai Precision Ind. Co.,Ltd.", "3423BA": "Samsung Electro Mechanics co.,LTD.", "34255D": "Shenzhen Loadcom Technology Co.,Ltd", "3429EA": "MCD ELECTRONICS SP. 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KG", "34A183": "AWare, Inc", "34A3BF": "Terewave. Inc.", "34A55D": "TECHNOSOFT INTERNATIONAL SRL", "34A5E1": "Sensorist ApS", "34A68C": "Shine Profit Development Limited", "34A709": "Trevil srl", "34A7BA": "Fischer International Systems Corporation", "34A843": "KYOCERA Display Corporation", "34A84E": "Cisco", "34AA8B": "Samsung Electronics Co.,Ltd", "34AA99": "Alcatel-Lucent", "34AAEE": "Mikrovisatos Servisas UAB", "34ADE4": "Shanghai Chint Power Systems Co., Ltd.", "34AF2C": "Nintendo Co., Ltd.", "34B1F7": "Texas Instruments", "34B571": "PLDS", "34BA51": "Se-Kure Controls, Inc.", "34BA9A": "Asiatelco Technologies Co.", "34BB1F": "Research In Motion", "34BCA6": "Beijing Ding Qing Technology, Ltd.", "34BDC8": "Cisco Systems", "34BDF9": "Shanghai WDK Industrial Co.,Ltd.", "34BDFA": "Cisco SPVTG", "34BE00": "Samsung Electronics Co.,Ltd", "34BF90": "Fiberhome Telecommunication Tech.Co.,Ltd.", "34C059": "Apple", "34C3AC": "Samsung Electronics", "34C69A": "Enecsys Ltd", "34C731": "ALPS Co,. Ltd.", "34C803": "Nokia Corporation", "34C99D": "EIDOLON COMMUNICATIONS TECHNOLOGY CO. LTD.", "34CD6D": "CommSky Technologies", "34CDBE": "Huawei Technologies Co., Ltd", "34CE94": "Parsec (Pty) Ltd", "34D09B": "MobilMAX Technology Inc.", "34D2C4": "RENA GmbH Print Systeme", "34D7B4": "Tributary Systems, Inc.", "34DBFD": "Cisco", "34DE1A": "Intel Corporate", "34DE34": "zte corporation", "34DF2A": "Fujikon Industrial Co.,Limited", "34E0CF": "zte corporation", "34E0D7": "DONGGUAN QISHENG ELECTRONICS INDUSTRIAL CO., LTD", "34E2FD": "Apple", "34E42A": "Automatic Bar Controls Inc.", "34EF44": "2Wire", "34EF8B": "NTT Communications Corporation", "34F39B": "WizLAN Ltd.", "34F62D": "SHARP Corporation", "34F968": "ATEK Products, LLC", "34FA40": "Guangzhou Robustel Technologies Co., Limited", "34FC6F": "ALCEA", "380197": "Toshiba Samsung Storage Technolgoy Korea Corporation", "3806B4": "A.D.C. GmbH", "380A0A": "Sky-City Communication and Electronics Limited Company", "380A94": "Samsung Electronics Co.,Ltd", "380B40": "Samsung Electronics Co.,Ltd", "380DD4": "Primax Electronics LTD.", "380F4A": "Apple", "380FE4": "Dedicated Network Partners Oy", "3816D1": "Samsung Electronics Co.,Ltd", "381766": "PROMZAKAZ LTD.", "38192F": "Nokia Corporation", "381C4A": "SIMCom Wireless Solutions Co.,Ltd.", "38229D": "Pirelli Tyre S.p.A.", "3822D6": "H3C Technologies Co., Limited", "3826CD": "ANDTEK", "3828EA": "Fujian Netcom Technology Co., LTD", "382DD1": "Samsung Electronics Co.,Ltd", "3831AC": "WEG", "383F10": "DBL Technology Ltd.", "384233": "Wildeboer Bauteile GmbH", "3842A6": "Ingenieurbuero Stahlkopf", "384369": "Patrol Products Consortium LLC", "38458C": "MyCloud Technology corporation", "384608": "ZTE Corporation", "38484C": "Apple", "384FF0": "Azurewave Technologies, Inc.", "38521A": "Alcatel-Lucent 7705", "38580C": "Panaccess Systems GmbH", "3859F8": "MindMade sp. z o.o.", "3859F9": "Hon Hai Precision Ind. Co.,Ltd.", "385AA8": "Beijing Zhongdun Security Technology Development Co.", "385FC3": "Yu Jeong System, Co.Ltd", "386077": "PEGATRON CORPORATION", "3863F6": "3NOD MULTIMEDIA(SHENZHEN)CO.,LTD", "386645": "OOSIC Technology CO.,Ltd", "386793": "Asia Optical Co., Inc.", "386BBB": "ARRIS Group, Inc.", "386C9B": "Ivy Biomedical", "386E21": "Wasion Group Ltd.", "3872C0": "COMTREND", "387B47": "AKELA, Inc.", "388345": "TP-LINK TECHNOLOGIES CO., LTD.", "3889DC": "Opticon Sensors Europe B.V.", "388AB7": "ITC Networks", "388EE7": "Fanhattan LLC", "3891FB": "Xenox Holding BV", "389592": "Beijing Tendyron Corporation", "389F83": "OTN Systems N.V.", "38A53C": "Veenstra Instruments", "38A5B6": "SHENZHEN MEGMEET ELECTRICAL CO.,LTD", "38A851": "Moog, Ing", "38A86B": "Orga BV", "38A95F": "Actifio Inc", "38AA3C": "SAMSUNG ELECTRO-MECHANICS", "38B12D": "Sonotronic Nagel GmbH", "38B5BD": "E.G.O. Elektro-Ger", "38B74D": "Fijowave Limited", "38BB23": "OzVision America LLC", "38BB3C": "Avaya, Inc", "38BC1A": "Meizu technology co.,ltd", "38BF2F": "Espec Corp.", "38BF33": "NEC CASIO Mobile Communications", "38C096": "ALPS ELECTRIC CO.,LTD.", "38C7BA": "CS Services Co.,Ltd.", "38C85C": "Cisco SPVTG", "38C9A9": "SMART High Reliability Solutions, Inc.", "38CA97": "Contour Design LLC", "38D135": "EasyIO Corporation Sdn. Bhd.", "38DBBB": "Sunbow Telecom Co., Ltd.", "38DE60": "Mohlenhoff GmbH", "38E08E": "Mitsubishi Electric Corporation", "38E595": "Shenzhen Gongjin Electronics Co.,Ltd", "38E7D8": "HTC Corporation", "38E8DF": "b gmbh medien + datenbanken", "38E98C": "Reco S.p.A.", "38EAA7": "Hewlett Packard", "38EC11": "Novatek Microelectronics Corp.", "38ECE4": "Samsung Electronics", "38EE9D": "Anedo Ltd.", "38F098": "Vapor Stone Rail Systems", "38F597": "home2net GmbH", "38F708": "National Resource Management, Inc.", "38F8B7": "V2COM PARTICIPACOES S.A.", "38FEC5": "Ellips B.V.", "3C02B1": "Creation Technologies LP", "3C04BF": "PRAVIS SYSTEMS Co.Ltd.,", "3C05AB": "Product Creation Studio", "3C0754": "Apple", "3C0771": "Sony Corporation", "3C081E": "Beijing Yupont Electric Power Technology Co.,Ltd", "3C08F6": "Cisco", "3C096D": "Powerhouse Dynamics", "3C0C48": "Servergy, Inc.", "3C0E23": "Cisco", "3C0FC1": "KBC Networks", "3C1040": "daesung network", "3C106F": "ALBAHITH TECHNOLOGIES", "3C15C2": "Apple", "3C15EA": "TESCOM CO., LTD.", "3C18A0": "Luxshare Precision Industry Co.,Ltd.", "3C1915": "GFI Chrono Time", "3C197D": "Ericsson AB", "3C1A57": "Cardiopulmonary Corp", "3C1A79": "Huayuan Technology CO.,LTD", "3C1CBE": "JADAK LLC", "3C25D7": "Nokia Corporation", "3C26D5": "Sotera Wireless", "3C2763": "SLE quality engineering GmbH & Co. KG", "3C2DB7": "Texas Instruments", "3C2F3A": "SFORZATO Corp.", "3C300C": "Dewar Electronics Pty Ltd", "3C363D": "Nokia Corporation", "3C36E4": "Arris Group, Inc.", "3C3888": "ConnectQuest, llc", "3C39C3": "JW Electronics Co., Ltd.", "3C3A73": "Avaya, Inc", "3C404F": "Guangdong Pisen Electronics Co. Ltd.", "3C438E": "ARRIS Group, Inc.", "3C4A92": "Hewlett-Packard Company", "3C4C69": "Infinity System S.L.", "3C4E47": "Etronic A/S", "3C57BD": "Kessler Crane Inc.", "3C57D5": "FiveCo", "3C5A37": "Samsung Electronics", "3C5F01": "Synerchip Co., Ltd.", "3C6104": "Juniper Networks", "3C6200": "Samsung electronics CO., LTD", "3C6278": "SHENZHEN JETNET TECHNOLOGY CO.,LTD.", "3C672C": "Sciovid Inc.", "3C6A7D": "Niigata Power Systems Co., Ltd.", "3C6E63": "Mitron OY", "3C6F45": "Fiberpro Inc.", "3C6FF7": "EnTek Systems, Inc.", "3C7059": "MakerBot Industries", "3C7437": "RIM", "3C754A": "ARRIS Group, Inc.", "3C77E6": "Hon Hai Precision Ind. 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Ltd.", "3C86A8": "Sangshin elecom .co,, LTD", "3C89A6": "KAPELSE", "3C8AB0": "Juniper Networks", "3C8AE5": "Tensun Information Technology(Hangzhou) Co.,LTD", "3C8BFE": "Samsung Electronics", "3C9157": "Hangzhou Yulong Conmunication Co.,Ltd", "3C9174": "ALONG COMMUNICATION TECHNOLOGY", "3C94D5": "Juniper Networks", "3C970E": "Wistron InfoComm(Kunshan)Co.,Ltd.", "3C977E": "IPS Technology Limited", "3C98BF": "Quest Controls, Inc.", "3C99F7": "Lansentechnology AB", "3C9F81": "Shenzhen CATIC Bit Communications Technology Co.,Ltd", "3CA10D": "Samsung Electronics Co.,Ltd", "3CA315": "Bless Information & Communications Co., Ltd", "3CA72B": "MRV Communications (Networks) LTD", "3CA9F4": "Intel Corporate", "3CAB8E": "Apple", "3CB15B": "Avaya, Inc", "3CB17F": "Wattwatchers Pty Ld", "3CB87A": "PRIVATE", "3CB9A6": "Belden Deutschland GmbH", "3CBDD8": "LG ELECTRONICS INC", "3CC0C6": "d&b audiotechnik GmbH", "3CC12C": "AES Corporation", "3CC1F6": "Melange Systems Pvt. 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Ltd", "3CF52C": "DSPECIALISTS GmbH", "3CF72A": "Nokia Corporation", "3CF748": "Shenzhen Linsn Technology Development Co.,Ltd", "3CF808": "HUAWEI TECHNOLOGIES CO.,LTD", "3CFB96": "Emcraft Systems LLC", "400107": "Arista Corp", "4001C6": "3COM EUROPE LTD", "40040C": "A&T", "4007C0": "Railtec Systems GmbH", "400E67": "Tremol Ltd.", "400E85": "Samsung Electro Mechanics co.,LTD.", "4012E4": "Compass-EOS", "4013D9": "Global ES", "401597": "Protect America, Inc.", "40167E": "ASUSTek COMPUTER INC.", "40169F": "TP-LINK TECHNOLOGIES CO., LTD.", "4016FA": "EKM Metering", "4018B1": "Aerohive Networks Inc.", "4018D7": "Wyle Telemetry and Data Systems", "401D59": "Biometric Associates, LP", "4022ED": "Digital Projection Ltd", "4025C2": "Intel Corporate", "40270B": "Mobileeco Co., Ltd", "402BA1": "Sony Ericsson Mobile Communications AB", "402CF4": "Universal Global Scientific Industrial Co., Ltd.", "403004": "Apple", "403067": "Conlog (Pty) Ltd", "40336C": "Godrej & Boyce Mfg. co. ltd", "4037AD": "Macro Image Technology, Inc.", "403CFC": "Apple", "404022": "ZIV", "40406B": "Icomera", "4045DA": "Spreadtrum Communications (Shanghai) Co., Ltd.", "404A03": "ZyXEL Communications Corporation", "404A18": "Addrek Smart Solutions", "404D8E": "Shenzhen Huawei Communication Technologies Co., Ltd", "4050E0": "Milton Security Group LLC", "40516C": "Grandex International Corporation", "40520D": "Pico Technology", "405539": "CISCO SYSTEMS, INC.", "40560C": "In Home Displays Ltd", "405A9B": "ANOVO", "405FBE": "RIM", "405FC2": "Texas Instruments", "40605A": "Hawkeye Tech Co. Ltd", "406186": "MICRO-STAR INT'L CO.,LTD", "40618E": "Stella-Green Co", "40667A": "mediola - connected living AG", "406826": "Thales UK Limited", "406AAB": "RIM", "406C8F": "Apple", "406F2A": "Research In Motion", "407009": "ARRIS Group, Inc.", "40704A": "Power Idea Technology Limited", "407074": "Life Technology (China) Co., Ltd", "407496": "aFUN TECHNOLOGY INC.", "407875": "IMBEL - Industria de Material Belico do Brasil", "407A80": "Nokia Corporation", "407B1B": "Mettle Networks Inc.", "4083DE": "Motorola", "408493": "Clavister AB", "4088E0": "Beijing Ereneben Information Technology Limited Shenzhen Branch", "408A9A": "TITENG CO., Ltd.", "408B07": "Actiontec Electronics, Inc", "408BF6": "Shenzhen TCL New Technology Co; Ltd.", "409558": "Aisino Corporation", "4097D1": "BK Electronics cc", "40984C": "Casacom Solutions AG", "40984E": "Texas Instruments", "40987B": "Aisino Corporation", "409FC7": "BAEKCHUN I&C Co., Ltd.", "40A6A4": "PassivSystems Ltd", "40A6D9": "Apple", "40A8F0": "Hewlett Packard", "40AC8D": "Data Management, Inc.", "40B0FA": "LG Electronics", "40B2C8": "Nortel Networks", "40B395": "Apple", "40B3CD": "Chiyoda Electronics Co.,Ltd.", "40B3FC": "Logital Co. Limited", "40B4F0": "Juniper Networks", "40B6B1": "SUNGSAM CO,.Ltd", "40B7F3": "ARRIS Group, Inc.", "40BA61": "Arima Communications Corp.", "40BC73": "Cronoplast S.L.", "40BC8B": "itelio GmbH", "40BD9E": "Physio-Control, Inc", "40BF17": "Digistar Telecom. SA", "40C245": "Shenzhen Hexicom Technology Co., Ltd.", "40C4D6": "ChongQing Camyu Technology Development Co.,Ltd.", "40C7C9": "Naviit Inc.", "40CBA8": "Huawei Technologies Co., Ltd", "40CD3A": "Z3 Technology", "40D32D": "Apple", "40D40E": "Biodata Ltd", "40D559": "MICRO S.E.R.I.", "40D855": "IEEE REGISTRATION AUTHORITY", "40E730": "DEY Storage Systems, Inc.", "40E793": "Shenzhen Siviton Technology Co.,Ltd", "40ECF8": "Siemens AG", "40EF4C": "Fihonest communication co.,Ltd", "40F02F": "Liteon Technology Corporation", "40F14C": "ISE Europe SPRL", "40F201": "SAGEMCOM", "40F2E9": "IBM", "40F308": "Murata Manufactuaring Co.,Ltd.", "40F407": "Nintendo Co., Ltd.", "40F4EC": "CISCO SYSTEMS, INC.", "40F52E": "Leica Microsystems (Schweiz) AG", "40FC89": "ARRIS Group, Inc.", "4403A7": "Cisco", "440CFD": "NetMan Co., Ltd.", "4411C2": "Telegartner Karl Gartner GmbH", "441319": "WKK TECHNOLOGY LTD.", "44184F": "Fitview", "4419B6": "Hangzhou Hikvision Digital Technology Co.,Ltd.", "441E91": "ARVIDA Intelligent Electronics Technology Co.,Ltd.", "441EA1": "Hewlett-Packard Company", "4423AA": "Farmage Co., Ltd.", "4425BB": "Bamboo Entertainment Corporation", "442938": "NietZsche enterprise Co.Ltd.", "442A60": "Apple", "442AFF": "E3 Technology, Inc.", "442B03": "CISCO SYSTEMS, INC.", "443192": "Hewlett Packard", "44322A": "Avaya, Inc", "4432C8": "Technicolor USA Inc.", "44334C": "Shenzhen Bilian electronic CO.,LTD", "44348F": "MXT INDUSTRIAL LTDA", "443719": "2 Save Energy Ltd", "44376F": "Young Electric Sign Co", "4437E6": "Hon Hai Precision Ind.Co.Ltd", "443839": "Cumulus Networks, inc", "4439C4": "Universal Global Scientific Industrial Co.,Ltd", "443C9C": "Pintsch Tiefenbach GmbH", "443D21": "Nuvolt", "443EB2": "DEOTRON Co., LTD.", "444891": "HDMI Licensing, LLC", "444A65": "Silverflare Ltd.", "444C0C": "Apple", "444E1A": "Samsung Electronics Co.,Ltd", "444F5E": "Pan Studios Co.,Ltd.", "4451DB": "Raytheon BBN Technologies", "4454C0": "Thompson Aerospace", "44568D": "PNC Technologies Co., Ltd.", "4456B7": "Spawn Labs, Inc", "445829": "Cisco SPVTG", "44599F": "Criticare Systems, Inc", "445EF3": "Tonalite Holding B.V.", "445F7A": "Shihlin Electric & Engineering Corp.", "446132": "ecobee inc", "44619C": "FONsystem co. ltd.", "446755": "Orbit Irrigation", "4468AB": "JUIN COMPANY, LIMITED", "446C24": "Reallin Electronic Co.,Ltd", "446D57": "Liteon Technology Corporation", "44700B": "IFFU", "447098": "MING HONG TECHNOLOGY (SHEN ZHEN) LIMITED", "447BC4": "DualShine Technology(SZ)Co.,Ltd", "447C7F": "Innolight Technology Corporation", "447DA5": "VTION INFORMATION TECHNOLOGY (FUJIAN) CO.,LTD", "447E76": "Trek Technology (S) Pte Ltd", "447E95": "Alpha and Omega, Inc", "448312": "Star-Net", "448500": "Intel Corporate", "4486C1": "Siemens Low Voltage & Products", "4487FC": "ELITEGROUP COMPUTER SYSTEM CO., LTD.", "448A5B": "Micro-Star INT'L CO., LTD.", "448C52": "KTIS CO., Ltd", "448E12": "DT Research, Inc.", "448E81": "VIG", "4491DB": "Shanghai Huaqin Telecom Technology Co.,Ltd", "4494FC": "NETGEAR INC.,", "4495FA": "Qingdao Santong Digital Technology Co.Ltd", "449B78": "The Now Factory", "449CB5": "Alcomp, Inc", "44A42D": "TCT Mobile Limited", "44A689": "PROMAX ELECTRONICA SA", "44A7CF": "Murata Manufacturing Co., Ltd.", "44A8C2": "SEWOO TECH CO., LTD", "44AA27": "udworks Co., Ltd.", "44AAE8": "Nanotec Electronic GmbH & Co. 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Bhd.", "44ED57": "Longicorn, inc.", "44EE30": "Budelmann Elektronik GmbH", "44F459": "Samsung Electronics", "44F849": "Union Pacific Railroad", "44FB42": "Apple", "48022A": "B-Link Electronic Limited", "480362": "DESAY ELECTRONICS(HUIZHOU)CO.,LTD", "481249": "Luxcom Technologies Inc.", "4813F3": "BBK Electronics Corp., Ltd.", "48174C": "MicroPower technologies", "481842": "Shanghai Winaas Co. Equipment Co. Ltd.", "481A84": "Pointer Telocation Ltd", "481BD2": "Intron Scientific co., ltd.", "4826E8": "Tek-Air Systems, Inc.", "48282F": "ZTE Corporation", "482CEA": "Motorola Inc Business Light Radios", "4833DD": "ZENNIO AVANCE Y TECNOLOGIA, S.L.", "48343D": "IEP GmbH", "483D32": "Syscor Controls & Automation", "484487": "Cisco SPVTG", "4844F7": "Samsung Electronics Co., LTD", "4846F1": "Uros Oy", "4846FB": "HUAWEI TECHNOLOGIES CO.,LTD", "4851B7": "Intel Corporate", "485261": "SOREEL", "485929": "LG Electronics", "485A3F": "WISOL", "485AB6": "Hon Hai Precision Ind. Co.,Ltd.", "485B39": "ASUSTek COMPUTER INC.", "485D60": "Azurewave Technologies, Inc.", "4860BC": "Apple", "4861A3": "Concern \"Axion\" JSC", "486276": "HUAWEI TECHNOLOGIES CO.,LTD", "486B91": "Fleetwood Group Inc.", "486E73": "Pica8, Inc.", "486FD2": "StorSimple Inc", "487119": "SGB GROUP LTD.", "48746E": "Apple", "487604": "PRIVATE", "488244": "Life Fitness / Div. of Brunswick", "488E42": "DIGALOG GmbH", "489153": "Weinmann Ger\u00e4te f\u00fcr Medizin GmbH + Co. KG", "4891F6": "Shenzhen Reach software technology CO.,LTD", "489BE2": "SCI Innovations Ltd", "489D24": "Research In Motion", "48A22D": "Shenzhen Huaxuchang Telecom Technology Co.,Ltd", "48A2B7": "Kodofon JSC", "48A6D2": "GJsun Optical Science and Tech Co.,Ltd.", "48AA5D": "Store Electronic Systems", "48B253": "Marketaxess Corporation", "48B5A7": "Glory Horse Industries Ltd.", "48B8DE": "HOMEWINS TECHNOLOGY CO.,LTD.", "48B977": "PulseOn Oy", "48B9C2": "Teletics Inc.", "48BE2D": "Symanitron", "48C1AC": "PLANTRONICS, INC.", "48C862": "Simo Wireless,Inc.", "48C8B6": "SysTec GmbH", "48CB6E": "Cello Electronics (UK) Ltd", "48D0CF": "Universal Electronics, Inc.", "48D18E": "Metis Communication Co.,Ltd", "48D224": "Liteon Technology Corporation", "48D54C": "Jeda Networks", "48D705": "Apple", "48D7FF": "BLANKOM Antennentechnik GmbH", "48D855": "IEEE REGISTRATION AUTHORITY", "48D8FE": "ClarIDy Solutions, Inc.", "48DCFB": "Nokia Corporation", "48DF1C": "Wuhan NEC Fibre Optic Communications industry Co. Ltd", "48E1AF": "Vity", "48EA63": "Zhejiang Uniview Technologies Co., Ltd.", "48EB30": "ETERNA TECHNOLOGY, INC.", "48ED80": "daesung eltec", "48EE07": "Silver Palm Technologies LLC", "48EE86": "UTStarcom (China) Co.,Ltd", "48F230": "Ubizcore Co.,LTD", "48F317": "PRIVATE", "48F47D": "TechVision Holding Internation Limited", "48F7F1": "Alcatel-Lucent", "48F8B3": "Cisco-Linksys, LLC", "48F8E1": "Alcatel Lucent WT", "48F925": "Maestronic", "48FCB8": "Woodstream Corporation", "48FEEA": "HOMA B.V.", "4C0082": "Cisco", "4C022E": "CMR KOREA CO., LTD", "4C0289": "LEX COMPUTECH CO., LTD", "4C068A": "Basler Electric Company", "4C07C9": "COMPUTER OFFICE Co.,Ltd.", "4C09B4": "zte corporation", "4C0B3A": "TCT Mobile Limited", "4C0DEE": "JABIL CIRCUIT (SHANGHAI) LTD.", "4C0F6E": "Hon Hai Precision Ind. Co.,Ltd.", "4C0FC7": "Earda Electronics Co.,Ltd", "4C11BF": "ZHEJIANG DAHUA TECHNOLOGY CO.,LTD.", "4C1480": "NOREGON SYSTEMS, INC", "4C14A3": "TCL Technoly Electronics (Huizhou) Co., Ltd.", "4C17EB": "SAGEMCOM", "4C1A3A": "PRIMA Research And Production Enterprise Ltd.", "4C1A95": "Novakon Co., Ltd.", "4C1FCC": "HUAWEI TECHNOLOGIES CO.,LTD", "4C21D0": "Sony Mobile Communications AB", "4C2258": "cozybit, Inc.", "4C2578": "Nokia Corporation", "4C2C80": "Beijing Skyway Technologies Co.,Ltd", "4C2F9D": "ICM Controls", "4C3089": "Thales Transportation Systems GmbH", "4C322D": "TELEDATA NETWORKS", "4C32D9": "M Rutty Holdings Pty. Ltd.", "4C3909": "HPL Electric & Power Private Limited", "4C3910": "Newtek Electronics co., Ltd.", "4C3B74": "VOGTEC(H.K.) Co., Ltd", "4C3C16": "Samsung Electronics Co.,Ltd", "4C4B68": "Mobile Device, Inc.", "4C4E35": "Cisco", "4C5427": "Linepro Sp. z o.o.", "4C5499": "Shenzhen Huawei Communication Technologies Co., Ltd", "4C5585": "Hamilton Systems", "4C55B8": "Turkcell Teknoloji", "4C55CC": "ACKme Networks Pty Ltd", "4C5DCD": "Oy Finnish Electric Vehicle Technologies Ltd", "4C5E0C": "Routerboard.com", "4C5FD2": "Alcatel-Lucent", "4C60D5": "airPointe of New Hampshire", "4C60DE": "NETGEAR", "4C6255": "SANMINA-SCI SYSTEM DE MEXICO S.A. DE C.V.", "4C63EB": "Application Solutions (Electronics and Vision) Ltd", "4C64D9": "Guangdong Leawin Group Co., Ltd", "4C72B9": "Pegatron Corporation", "4C7367": "Genius Bytes Software Solutions GmbH", "4C73A5": "KOVE", "4C774F": "Embedded Wireless Labs", "4C7897": "Arrowhead Alarm Products Ltd", "4C79BA": "Intel Corporate", "4C7F62": "Nokia Corporation", "4C804F": "Armstrong Monitoring Corp", "4C8093": "Intel Corporate", "4C82CF": "Echostar Technologies", "4C8B30": "Actiontec Electronics, Inc", "4C8B55": "Grupo Digicon", "4C8BEF": "Huawei Technologies Co., Ltd", "4C8D79": "Apple", "4C8FA5": "Jastec", "4C9614": "Juniper Networks", "4C98EF": "Zeo", "4C9E80": "KYOKKO ELECTRIC Co., Ltd.", "4C9EE4": "Hanyang Navicom Co.,Ltd.", "4CA56D": "Samsung Electronics Co.,Ltd", "4CA74B": "Alcatel Lucent", "4CAA16": "AzureWave Technologies (Shanghai) Inc.", "4CAB33": "KST technology", "4CAC0A": "ZTE Corporation", "4CB16C": "HUAWEI TECHNOLOGIES CO.,LTD", "4CB199": "Apple", "4CB4EA": "HRD (S) PTE., LTD.", "4CB81C": "SAM Electronics GmbH", "4CB9C8": "CONET CO., LTD.", "4CBAA3": "Bison Electronics Inc.", "4CBCA5": "Samsung Electronics Co.,Ltd", "4CC452": "Shang Hai Tyd. Electon Technology Ltd.", "4CC602": "Radios, Inc.", "4CC94F": "Alcatel-Lucent", "4CCA53": "Skyera, Inc.", "4CCBF5": "zte corporation", "4CCC34": "Motorola Solutions Inc.", "4CD637": "Qsono Electronics Co., Ltd", "4CD7B6": "Helmer Scientific", "4CD9C4": "Magneti Marelli Automotive Electronics (Guangzhou) Co. Ltd", "4CDF3D": "TEAM ENGINEERS ADVANCE TECHNOLOGIES INDIA PVT LTD", "4CE1BB": "Zhuhai HiFocus Technology Co., Ltd.", "4CE676": "Buffalo Inc.", "4CEB42": "Intel Corporate", "4CEDDE": "Askey Computer Corp", "4CF02E": "Vifa Denmark A/S", "4CF45B": "Blue Clover Devices", "4CF737": "SamJi Electronics Co., Ltd", "50008C": "Hong Kong Telecommunications (HKT) Limited", "5001BB": "Samsung Electronics", "50053D": "CyWee Group Ltd", "500604": "Cisco", "500B32": "Foxda Technology Industrial(ShenZhen)Co.,LTD", "500E6D": "TrafficCast International", "5011EB": "SilverNet Ltd", "5017FF": "Cisco", "501AC5": "Microsoft", "501CBF": "Cisco", "50206B": "Emerson Climate Technologies Transportation Solutions", "502267": "PixeLINK", "50252B": "Nethra Imaging Incorporated", "502690": "Fujitsu Limited", "5027C7": "TECHNART Co.,Ltd", "502A7E": "Smart electronic GmbH", "502A8B": "Telekom Research and Development Sdn Bhd", "502D1D": "Nokia Corporation", "502DA2": "Intel Corporate", "502DF4": "Phytec Messtechnik GmbH", "502E5C": "HTC Corporation", "502ECE": "Asahi Electronics Co.,Ltd", "503275": "Samsung Electronics Co.,Ltd", "503955": "Cisco SPVTG", "503CC4": "Lenovo Mobile Communication Technology Ltd.", "503DE5": "CISCO SYSTEMS, INC.", "503F56": "Syncmold Enterprise Corp", "50465D": "ASUSTek COMPUTER INC.", "5048EB": "BEIJING HAIHEJINSHENG NETWORK TECHNOLOGY CO. LTD.", "504A5E": "Masimo Corporation", "504A6E": "NETGEAR INC.,", "504F94": "Loxone Electronics GmbH", "505663": "Texas Instruments", "5056A8": "Jolla Ltd", "5056BF": "Samsung Electronics Co.,LTD", "5057A8": "CISCO SYSTEMS, INC.", "505800": "WyTec International, Inc.", "505AC6": "GUANGDONG SUPER TELECOM CO.,LTD.", "506028": "Xirrus Inc.", "506184": "Avaya, Inc", "5061D6": "Indu-Sol GmbH", "506313": "Hon Hai Precision Ind. Co.,Ltd.", "506441": "Greenlee", "5067F0": "ZyXEL Communications Corporation", "506F9A": "Wi-Fi Alliance", "5070E5": "He Shan World Fair Electronics Technology Limited", "50724D": "BEG Brueck Electronic GmbH", "507691": "Tekpea, Inc.", "5076A6": "Ecil Informatica Ind. Com. Ltda", "50795B": "Interexport Telecomunicaciones S.A.", "507D02": "BIODIT", "507E5D": "Arcadyan Technology Corporation", "508569": "Samsung Electronics Co.,LTD", "5087B8": "Nuvyyo Inc", "508A42": "Uptmate Technology Co., LTD", "508ACB": "SHENZHEN MAXMADE TECHNOLOGY CO., LTD.", "508C77": "DIRMEIER Schanktechnik GmbH &Co KG", "508D6F": "CHAHOO Limited", "50934F": "Gradual Tecnologia Ltda.", "509772": "Westinghouse Digital", "509871": "Inventum Technologies Private Limited", "509F27": "Huawei Technologies Co., Ltd", "50A054": "Actineon", "50A0BF": "Alba Fiber Systems Inc.", "50A4C8": "Samsung Electronics Co.,Ltd", "50A6E3": "David Clark Company", "50A715": "Aboundi, Inc.", "50A733": "Ruckus Wireless", "50ABBF": "Hoseo Telecom", "50AF73": "Shenzhen Bitland Information Technology Co., Ltd.", "50B695": "Micropoint Biotechnologies,Inc.", "50B7C3": "Samsung Electronics CO., LTD", "50B888": "wi2be Tecnologia S/A", "50B8A2": "ImTech Technologies LLC,", "50C006": "Carmanah Signs", "50C271": "SECURETECH INC", "50C58D": "Juniper Networks", "50C7BF": "TP-LINK TECHNOLOGIES CO.,LTD.", "50C971": "GN Netcom A/S", "50C9A0": "SKIPPER Electronics AS", "50CCF8": "Samsung Electro Mechanics", "50CD32": "NanJing Chaoran Science & Technology Co.,Ltd.", "50CE75": "Measy Electronics Ltd", "50D274": "Steffes Corporation", "50D6D7": "Takahata Precision", "50E0C7": "TurControlSystme AG", "50E14A": "PRIVATE", "50E549": "GIGA-BYTE TECHNOLOGY CO.,LTD.", "50EAD6": "Apple", "50EB1A": "Brocade Communications Systems, Inc.", "50ED78": "Changzhou Yongse Infotech Co.,Ltd", "50ED94": "Egatel SL", "50F003": "Open Stack, Inc.", "50F520": "Samsung Electronics Co.,Ltd", "50F61A": "Kunshan JADE Technologies co., Ltd.", "50FAAB": "L-tek d.o.o.", "50FC30": "Treehouse Labs", "50FC9F": "Samsung Electronics Co.,Ltd", "5403F5": "EBN Technology Corp.", "540496": "Gigawave LTD", "5404A6": "ASUSTek COMPUTER INC.", "540536": "Vivago Oy", "54055F": "Alcatel Lucent", "54112F": "Sulzer Pump Solutions Finland Oy", "54115F": "Atamo Pty Ltd", "541B5D": "Techno-Innov", "541DFB": "Freestyle Energy Ltd", "541FD5": "Advantage Electronics", "542018": "Tely Labs", "542160": "Resolution Products", "5422F8": "zte corporation", "542696": "Apple", "54271E": "AzureWave Technonloies, Inc.", "542A9C": "LSY Defense, LLC.", "542AA2": "Alpha Networks Inc.", "542CEA": "PROTECTRON", "542F89": "Euclid Laboratories, Inc.", "543131": "Raster Vision Ltd", "543530": "Hon Hai Precision Ind. Co.,Ltd.", "5435DF": "Symeo GmbH", "543968": "Edgewater Networks Inc", "5439DF": "HUAWEI TECHNOLOGIES CO.,LTD", "543D37": "Ruckus Wireless", "544249": "Sony Corporation", "544408": "Nokia Corporation", "54466B": "Shenzhen CZTIC Electronic Technology Co., Ltd", "544A00": "Cisco", "544A05": "wenglor sensoric gmbh", "544A16": "Texas Instruments", "5453ED": "Sony Corporation", "545414": "Digital RF Corea, Inc", "545EBD": "NL Technologies", "545FA9": "Teracom Limited", "5461EA": "Zaplox AB", "54724F": "Apple", "547398": "Toyo Electronics Corporation", "5474E6": "Webtech Wireless", "5475D0": "CISCO SYSTEMS, INC.", "54781A": "Cisco", "547975": "Nokia Corporation", "547F54": "INGENICO", "547FA8": "TELCO systems, s.r.o.", "547FEE": "CISCO SYSTEMS, INC.", "5481AD": "Eagle Research Corporation", "54847B": "Digital Devices GmbH", "54880E": "Samsung Electro Mechanics co., LTD.", "548922": "Zelfy Inc", "548998": "HUAWEI TECHNOLOGIES CO.,LTD", "5492BE": "Samsung Electronics Co.,Ltd", "549359": "SHENZHEN TWOWING TECHNOLOGIES CO.,LTD.", "549478": "Silvershore Technology Partners", "549A16": "Uzushio Electric Co.,Ltd.", "549B12": "Samsung Electronics", "549D85": "EnerAccess inc", "54A04F": "t-mac Technologies Ltd", "54A31B": "Shenzhen Linkworld Technology Co,.LTD", "54A51B": "Shenzhen Huawei Communication Technologies Co., Ltd", "54A54B": "NSC Communications Siberia Ltd", "54A619": "Alcatel-Lucent Shanghai Bell Co., Ltd", "54A9D4": "Minibar Systems", "54AE27": "Apple", "54B620": "SUHDOL E&C Co.Ltd.", "54B753": "Hunan Fenghui Yinjia Science And Technology Co.,Ltd", "54BEF7": "PEGATRON CORPORATION", "54C80F": "TP-LINK TECHNOLOGIES CO.,LTD.", "54CDA7": "Fujian Shenzhou Electronic Co.,Ltd", "54CDEE": "ShenZhen Apexis Electronic Co.,Ltd", "54D0ED": "AXIM Communications", "54D163": "MAX-TECH,INC", "54D1B0": "Universal Laser Systems, Inc", "54D46F": "Cisco SPVTG", "54DF63": "Intrakey technologies GmbH", "54E032": "Juniper Networks", "54E2E0": "Pace plc", "54E3B0": "JVL Industri Elektronik", "54E43A": "Apple, Inc.", "54E63F": "ShenZhen LingKeWeiEr Technology Co., Ltd.", "54E6FC": "TP-LINK TECHNOLOGIES CO., LTD.", "54EAA8": "Apple, Inc.", "54EE75": "Wistron InfoComm(Kunshan)Co.,Ltd.", "54EF92": "Shenzhen Elink Technology Co., LTD", "54F5B6": "ORIENTAL PACIFIC INTERNATIONAL LIMITED", "54F666": "Berthold Technologies GmbH and Co.KG", "54FB58": "WISEWARE, Lda", "54FDBF": "Scheidt & Bachmann GmbH", "580528": "LABRIS NETWORKS", "580556": "Elettronica GF S.r.L.", "5808FA": "Fiber Optic & telecommunication INC.", "580943": "PRIVATE", "5809E5": "Kivic Inc.", "580A20": "Cisco", "581243": "AcSiP Technology Corp.", "581626": "Avaya, Inc", "58170C": "Sony Ericsson Mobile Communications AB", "581CBD": "Affinegy", "581D91": "Advanced Mobile Telecom co.,ltd.", "581F67": "Open-m technology limited", "581FAA": "Apple", "581FEF": "Tuttnaer LTD", "58238C": "Technicolor CH USA", "582EFE": "Lighting Science Group", "582F42": "Universal Electric Corporation", "58343B": "Glovast Technology Ltd.", "5835D9": "CISCO SYSTEMS, INC.", "583CC6": "Omneality Ltd.", "5842E4": "Sigma International General Medical Apparatus, LLC.", "58468F": "Koncar Electronics and Informatics", "5846E1": "Baxter Healthcare", "5848C0": "COFLEC", "58493B": "Palo Alto Networks", "5849BA": "Chitai Electronic Corp.", "584C19": "Chongqing Guohong Technology Development Company Limited", "584CEE": "Digital One Technologies, Limited", "585076": "Linear Equipamentos Eletronicos SA", "5850AB": "TLS Corporation", "5850E6": "Best Buy Corporation", "5855CA": "Apple", "5856E8": "ARRIS Group, Inc.", "58570D": "Danfoss Solar Inverters", "58639A": "TPL SYSTEMES", "5865E6": "INFOMARK CO., LTD.", "5866BA": "Hangzhou H3C Technologies Co., Limited", "58671A": "BARNES&NOBLE.COM", "58677F": "Clare Controls Inc.", "58696C": "Fujian Ruijie Networks co, ltd", "5869F9": "Fusion Transactive Ltd.", "586D8F": "Cisco-Linksys, LLC", "586ED6": "PRIVATE", "587521": "CJSC RTSoft", "587675": "Beijing ECHO Technologies Co.,Ltd", "587A4D": "Stonesoft Corporation", "587E61": "Hisense Electric Co., Ltd", "587FC8": "S2M", "5884E4": "IP500 Alliance e.V.", "58874C": "LITE-ON CLEAN ENERGY TECHNOLOGY CORP.", "5887E2": "Shenzhen Coship Electronics Co., Ltd.", "588D09": "CISCO SYSTEMS, INC.", "5891CF": "Intel Corporate", "58920D": "Kinetic Avionics Limited", "589396": "Ruckus Wireless", "58946B": "Intel Corporate", "5894CF": "Vertex Standard LMR, Inc.", "58971E": "Cisco", "589835": "Technicolor", "58986F": "Revolution Display", "589CFC": "FreeBSD Foundation", "58A2B5": "LG Electronics", "58A76F": "iD corporation", "58B035": "Apple", "58B0D4": "ZuniData Systems Inc.", "58B961": "SOLEM Electronique", "58B9E1": "Crystalfontz America, Inc.", "58BC27": "CISCO SYSTEMS, INC.", "58BDA3": "Nintendo Co., Ltd.", "58BDF9": "Sigrand", "58BFEA": "CISCO SYSTEMS, INC.", "58C232": "NEC Corporation", "58C38B": "Samsung Electronics", "58CF4B": "Lufkin Industries", "58D071": "BW Broadcast", "58D08F": "IEEE 1904.1 Working Group", "58D6D3": "Dairy Cheq Inc", "58DB8D": "Fast Co., Ltd.", "58E02C": "Micro Technic A/S", "58E326": "Compass Technologies Inc.", "58E476": "CENTRON COMMUNICATIONS TECHNOLOGIES FUJIAN CO.,LTD", "58E636": "EVRsafe Technologies", "58E747": "Deltanet AG", "58E808": "AUTONICS CORPORATION", "58EB14": "Proteus Digital Health", "58ECE1": "Newport Corporation", "58EECE": "Icon Time Systems", "58F387": "HCCP", "58F67B": "Xia Men UnionCore Technology LTD.", "58F6BF": "Kyoto University", "58F98E": "SECUDOS GmbH", "58FD20": "Bravida Sakerhet AB", "5C026A": "Applied Vision Corporation", "5C076F": "Thought Creator", "5C0A5B": "SAMSUNG ELECTRO-MECHANICS CO., LTD.", "5C0CBB": "CELIZION Inc.", "5C0E8B": "Motorola", "5C1193": "Seal One AG", "5C1437": "Thyssenkrupp Aufzugswerke GmbH", "5C15E1": "AIDC TECHNOLOGY (S) PTE LTD", "5C16C7": "Big Switch Networks", "5C1737": "I-View Now, LLC.", "5C17D3": "LGE", "5C18B5": "Talon Communications", "5C20D0": "Asoni Communication Co., Ltd.", "5C22C4": "DAE EUN ELETRONICS CO., LTD", "5C2479": "Baltech AG", "5C254C": "Avire Global Pte Ltd", "5C260A": "Dell Inc.", "5C2AEF": "Open Access Pty Ltd", "5C2E59": "Samsung Electronics Co.,Ltd", "5C313E": "Texas Instruments", "5C3327": "Spazio Italia srl", "5C335C": "Swissphone Telecom AG", "5C338E": "Alpha Networkc Inc.", "5C353B": "Compal Broadband Networks Inc.", "5C35DA": "There Corporation Oy", "5C36B8": "TCL King Electrical Appliances (Huizhou) Ltd.", "5C38E0": "Shanghai Super Electronics Technology Co.,LTD", "5C3C27": "Samsung Electronics Co.,Ltd", "5C4058": "Jefferson Audio Video Systems, Inc.", "5C43D2": "HAZEMEYER", "5C4A26": "Enguity Technology Corp", "5C4CA9": "Shenzhen Huawei Communication Technologies Co., Ltd", "5C5015": "CISCO SYSTEMS, INC.", "5C514F": "Intel Corporate", "5C56ED": "3pleplay Electronics Private Limited", "5C571A": "ARRIS Group, Inc.", "5C57C8": "Nokia Corporation", "5C5948": "Apple", "5C5BC2": "YIK Corporation", "5C5EAB": "Juniper Networks", "5C63BF": "TP-LINK TECHNOLOGIES CO., LTD.", "5C6984": "NUVICO", "5C6A7D": "KENTKART EGE ELEKTRONIK SAN. VE TIC. LTD. STI.", "5C6B32": "Texas Instruments", "5C6D20": "Hon Hai Precision Ind. Co.,Ltd.", "5C6F4F": "S.A. SISTEL", "5C7757": "Haivision Network Video", "5C7D5E": "Huawei Technologies Co., Ltd", "5C8486": "Brightsource Industries Israel LTD", "5C864A": "Secret Labs LLC", "5C8778": "Cybertelbridge co.,ltd", "5C89D4": "Beijing Banner Electric Co.,Ltd", "5C8D4E": "Apple", "5C95AE": "Apple", "5C969D": "Apple", "5C9AD8": "Fujitsu Limited", "5CA39D": "SAMSUNG ELECTRO-MECHANICS CO., LTD.", "5CA3EB": "Lokel s.r.o.", "5CA48A": "Cisco", "5CAC4C": "Hon Hai Precision Ind. Co.,Ltd.", "5CB524": "Sony Ericsson Mobile Communications AB", "5CBD9E": "HONGKONG MIRACLE EAGLE TECHNOLOGY(GROUP) LIMITED", "5CC213": "Fr. Sauter AG", "5CC5D4": "Intel Corporate", "5CC6D0": "Skyworth Digital technology(shenzhen)co.ltd.", "5CC9D3": "PALLADIUM ENERGY ELETRONICA DA AMAZONIA LTDA", "5CCA32": "Theben AG", "5CCEAD": "CDYNE Corporation", "5CD135": "Xtreme Power Systems", "5CD2E4": "Intel Corporate", "5CD41B": "UCZOON Technology Co., LTD", "5CD4AB": "Zektor", "5CD61F": "Qardio, Inc", "5CD998": "D-Link Corporation", "5CDAD4": "Murata Manufacturing Co., Ltd.", "5CDD70": "Hangzhou H3C Technologies Co., Limited", "5CE0CA": "FeiTian United (Beijing) System Technology Co., Ltd.", "5CE0F6": "NIC.br- Nucleo de Informacao e Coordenacao do Ponto BR", "5CE223": "Delphin Technology AG", "5CE286": "Nortel Networks", "5CE2F4": "AcSiP Technology Corp.", "5CE7BF": "New Singularity International Technical Development Co.,Ltd", "5CE8EB": "Samsung Electronics", "5CEB4E": "R. STAHL HMI Systems GmbH", "5CEE79": "Global Digitech Co LTD", "5CF207": "Speco Technologies", "5CF370": "CC&C Technologies, Inc", "5CF3FC": "IBM Corp", "5CF4AB": "ZyXEL Communications Corp", "5CF50D": "Institute of microelectronic applications", "5CF6DC": "Samsung Electronics Co.,LTD", "5CF8A1": "Murata Manufactuaring Co.,Ltd.", "5CF938": "Apple, Inc", "5CF9DD": "Dell Inc", "5CFF35": "Wistron Corporation", "5CFFFF": "Shenzhen Kezhonglong Optoelectronic Technology Co., Ltd", "6002B4": "Wistron NeWeb Corp.", "600308": "Apple", "600347": "Billion Electric Co. Ltd.", "600F77": "SilverPlus, Inc", "601199": "Siama Systems Inc", "601283": "Soluciones Tecnologicas para la Salud y el Bienestar SA", "6015C7": "IdaTech", "60190C": "RRAMAC", "601929": "VOLTRONIC POWER TECHNOLOGY(SHENZHEN) CORP.", "601D0F": "Midnite Solar", "601E02": "EltexAlatau", "602103": "STCUBE.INC", "6021C0": "Murata Manufactuaring Co.,Ltd.", "6024C1": "Jiangsu Zhongxun Electronic Technology Co., Ltd", "602A54": "CardioTek B.V.", "602AD0": "Cisco SPVTG", "6032F0": "Mplus technology", "60334B": "Apple", "603553": "Buwon Technology", "6036DD": "Intel Corporate", "60380E": "Alps Electric Co.,", "60391F": "ABB Ltd", "603FC5": "COX CO., LTD", "6044F5": "Easy Digital Ltd.", "60455E": "Liptel s.r.o.", "6045BD": "Microsoft", "604616": "XIAMEN VANN INTELLIGENT CO., LTD", "6047D4": "FORICS Electronic Technology Co., Ltd.", "604A1C": "SUYIN Corporation", "6052D0": "FACTS Engineering", "605464": "Eyedro Green Solutions Inc.", "605718": "Intel Corporate", "60601F": "SZ DJI TECHNOLOGY CO.,LTD", "6063FD": "Transcend Communication Beijing Co.,Ltd.", "6064A1": "RADiflow Ltd.", "606720": "Intel Corporate", "606944": "Apple, Inc", "60699B": "isepos GmbH", "606BBD": "Samsung Electronics Co., LTD", "606C66": "Intel Corporate", "60735C": "Cisco", "60748D": "Atmaca Elektronik", "607688": "Velodyne", "60812B": "Custom Control Concepts", "6083B2": "GkWare e.K.", "60843B": "Soladigm, Inc.", "608645": "Avery Weigh-Tronix, LLC", "60893C": "Thermo Fisher Scientific P.O.A.", "6089B1": "Key Digital Systems", "6089B7": "KAEL M\u00dcHEND\u0130SL\u0130K ELEKTRON\u0130K T\u0130CARET SANAY\u0130 L\u0130M\u0130TED \u015e\u0130RKET\u0130", "608C2B": "Hanson Technology", "608D17": "Sentrus Government Systems Division, Inc", "608F5C": "Samsung Electronics Co.,Ltd", "609084": "DSSD Inc", "609217": "Apple", "609620": "PRIVATE", "609AA4": "GVI SECURITY INC.", "609E64": "Vivonic GmbH", "609F9D": "CloudSwitch", "60A10A": "Samsung Electronics Co.,Ltd", "60A44C": "ASUSTek COMPUTER INC.", "60A8FE": "Nokia Solutions and Networks", "60A9B0": "Merchandising Technologies, Inc", "60B185": "ATH system", "60B3C4": "Elber Srl", "60B606": "Phorus", "60B617": "Fiberhome Telecommunication Tech.Co.,Ltd.", "60B933": "Deutron Electronics Corp.", "60B982": "RO.VE.R. Laboratories S.p.A.", "60BB0C": "Beijing HuaqinWorld Technology Co,Ltd", "60BC4C": "EWM Hightec Welding GmbH", "60BD91": "Move Innovation", "60BEB5": "Motorola Mobility LLC", "60C1CB": "Fujian Great Power PLC Equipment Co.,Ltd", "60C397": "2Wire Inc", "60C547": "Apple", "60C5A8": "Beijing LT Honway Technology Co.,Ltd", "60C980": "Trymus", "60CBFB": "AirScape Inc.", "60CDC5": "Taiwan Carol Electronics., Ltd", "60D0A9": "Samsung Electronics Co.,Ltd", "60D1AA": "Vishal Telecommunications Pvt Ltd", "60D2B9": "REALAND BIO CO., LTD.", "60D30A": "Quatius Limited", "60D819": "Hon Hai Precision Ind. Co.,Ltd.", "60D9C7": "Apple", "60DA23": "Estech Co.,Ltd", "60DB2A": "HNS", "60DE44": "HUAWEI TECHNOLOGIES CO.,LTD", "60E00E": "SHINSEI ELECTRONICS CO LTD", "60E327": "TP-LINK TECHNOLOGIES CO.,LTD.", "60E956": "Ayla Networks, Inc", "60EB69": "Quanta computer Inc.", "60F13D": "JABLOCOM s.r.o.", "60F281": "TRANWO TECHNOLOGY CO., LTD.", "60F2EF": "VisionVera International Co., Ltd.", "60F3DA": "Logic Way GmbH", "60F494": "Hon Hai Precision Ind. Co.,Ltd.", "60F59C": "CRU-Dataport", "60F673": "TERUMO CORPORATION", "60FACD": "Apple", "60FB42": "Apple", "60FE1E": "China Palms Telecom.Ltd", "60FE20": "2 Wire", "60FEC5": "Apple", "60FEF9": "Thomas & Betts", "60FFDD": "C.E. ELECTRONICS, INC", "6400F1": "CISCO SYSTEMS, INC.", "6405BE": "NEW LIGHT LED", "64094C": "Beijing Superbee Wireless Technology Co.,Ltd", "640B4A": "Digital Telecom Technology Limited", "640E36": "TAZTAG", "640E94": "Pluribus Networks, Inc.", "640F28": "2wire", "641084": "HEXIUM Technical Development Co., Ltd.", "641225": "Cisco", "64168D": "CISCO SYSTEMS, INC.", "6416F0": "Shehzhen Huawei Communication Technologies Co., Ltd.", "641A22": "Heliospectra/Woodhill Investments", "641C67": "DIGIBRAS INDUSTRIA DO BRASILS/A", "641E81": "Dowslake Microsystems", "64200C": "Apple", "642184": "Nippon Denki Kagaku Co.,LTD", "642216": "Shandong Taixin Electronic co.,Ltd", "642400": "Xorcom Ltd.", "642737": "Hon Hai Precision Ind. Co.,Ltd.", "642DB7": "SEUNGIL ELECTRONICS", "643150": "Hewlett-Packard Company", "64317E": "Dexin Corporation", "643409": "BITwave Pte Ltd", "643F5F": "Exablaze", "644214": "Swisscom Energy Solutions AG", "644346": "GuangDong Quick Network Computer CO.,LTD", "644BC3": "Shanghai WOASiS Telecommunications Ltd., Co.", "644BF0": "CalDigit, Inc", "644D70": "dSPACE GmbH", "644F74": "LENUS Co., Ltd.", "644FB0": "Hyunjin.com", "64517E": "LONG BEN (DONGGUAN) ELECTRONIC TECHNOLOGY CO.,LTD.", "645299": "The Chamberlain Group, Inc", "64535D": "Frauscher Sensortechnik", "645422": "Equinox Payments", "645563": "Intelight Inc.", "64557F": "NSFOCUS Information Technology Co., Ltd.", "6455B1": "ARRIS Group, Inc.", "645601": "TP-LINK TECHNOLOGIES CO.,LTD", "645A04": "Chicony Electronics Co., Ltd.", "645DD7": "Shenzhen Lifesense Medical Electronics Co., Ltd.", "645EBE": "Yahoo! JAPAN", "645FFF": "Nicolet Neuro", "646223": "Cellient Co., Ltd.", "6465C0": "Nuvon, Inc", "6466B3": "TP-LINK TECHNOLOGIES CO., LTD.", "646707": "Beijing Omnific Technology, Ltd.", "64680C": "COMTREND", "6469BC": "Hytera Communications Co .,ltd", "646CB2": "Samsung Electronics Co.,Ltd", "646E6C": "Radio Datacom LLC", "646EEA": "Iskratel d.o.o.", "647002": "TP-LINK TECHNOLOGIES CO., LTD.", "6472D8": "GooWi Technology Co.,Limited", "6473E2": "Arbiter Systems, Inc.", "647657": "Innovative Security Designs", "6476BA": "Apple", "647791": "Samsung Electronics Co.,Ltd", "647BD4": "Texas Instruments", "647C34": "Ubee Interactive Corp.", "647D81": "YOKOTA INDUSTRIAL CO,.LTD", "647FDA": "TEKTELIC Communications Inc.", "64808B": "VG Controls, Inc.", "648099": "Intel Corporate", "648125": "Alphatron Marine BV", "648788": "Juniper Networks", "6487D7": "Pirelli Tyre S.p.A.", "6488FF": "Sichuan Changhong Electric Ltd.", "648D9E": "IVT Electronic Co.,Ltd", "64995D": "LGE", "649968": "Elentec", "6499A0": "AG Elektronik AB", "649B24": "V Technology Co., Ltd.", "649C81": "Qualcomm iSkoot, Inc.", "649C8E": "Texas Instruments", "649EF3": "CISCO SYSTEMS, INC.", "649FF7": "Kone OYj", "64A0E7": "CISCO SYSTEMS, INC.", "64A232": "OOO Samlight", "64A341": "Wonderlan (Beijing) Technology Co., Ltd.", "64A3CB": "Apple", "64A769": "HTC Corporation", "64A7DD": "Avaya, Inc", "64A837": "Juni Korea Co., Ltd", "64AE0C": "CISCO SYSTEMS, INC.", "64AE88": "Polytec GmbH", "64B310": "Samsung Electronics Co.,Ltd", "64B370": "PowerComm Solutons LLC", "64B64A": "ViVOtech, Inc.", "64B9E8": "Apple", "64BABD": "SDJ Technologies, Inc.", "64BC11": "CombiQ AB", "64C5AA": "South African Broadcasting Corporation", "64C667": "Barnes&Noble", "64C6AF": "AXERRA Networks Ltd", "64C944": "LARK Technologies, Inc", "64D02D": "Next Generation Integration (NGI)", "64D1A3": "Sitecom Europe BV", "64D241": "Keith & Koep GmbH", "64D4BD": "ALPS ELECTRIC CO.,LTD.", "64D4DA": "Intel Corporate", "64D814": "CISCO SYSTEMS, INC.", "64D912": "Solidica, Inc.", "64D989": "CISCO SYSTEMS, INC.", "64DB18": "OpenPattern", "64DC01": "Static Systems Group PLC", "64DE1C": "Kingnetic Pte Ltd", "64E161": "DEP Corp.", "64E599": "EFM Networks", "64E625": "Woxu Wireless Co., Ltd", "64E682": "Apple", "64E84F": "Serialway Communication Technology Co. Ltd", "64E892": "Morio Denki Co., Ltd.", "64E8E6": "global moisture management system", "64E950": "Cisco", "64EAC5": "SiboTech Automation Co., Ltd.", "64EB8C": "Seiko Epson Corporation", "64ED57": "ARRIS Group, Inc.", "64ED62": "WOORI SYSTEMS Co., Ltd", "64F242": "Gerdes Aktiengesellschaft", "64F50E": "Kinion Technology Company Limited", "64F970": "Kenade Electronics Technology Co.,LTD.", "64F987": "Avvasi Inc.", "64FC8C": "Zonar Systems", "680571": "Samsung Electronics Co.,Ltd", "6805CA": "Intel Corporate", "680927": "Apple", "680AD7": "Yancheng Kecheng Optoelectronic Technology Co., Ltd", "68122D": "Special Instrument Development Co., Ltd.", "681590": "SAGEMCOM SAS", "6815D3": "Zaklady Elektroniki i Mechaniki Precyzyjnej R&G S.A.", "681605": "Systems And Electronic Development FZCO", "681729": "Intel Corporate", "68193F": "Digital Airways", "681AB2": "zte corporation", "681CA2": "Rosewill Inc.", "681D64": "Sunwave Communications Co., Ltd", "681E8B": "InfoSight Corporation", "681FD8": "Advanced Telemetry", "68234B": "Nihon Dengyo Kousaku", "6828BA": "Dejai", "682DDC": "Wuhan Changjiang Electro-Communication Equipment CO.,LTD", "683B1E": "Countwise LTD", "683EEC": "ERECA", "684352": "Bhuu Limited", "684898": "Samsung Electronics Co.,Ltd", "684B88": "Galtronics Telemetry Inc.", "684CA8": "Shenzhen Herotel Tech. Co., Ltd.", "6851B7": "PowerCloud Systems, Inc.", "6854ED": "Alcatel-Lucent - Nuage", "6854F5": "enLighted Inc", "68597F": "Alcatel Lucent", "685B35": "Apple", "685B36": "POWERTECH INDUSTRIAL CO., LTD.", "685D43": "Intel Corporate", "685E6B": "PowerRay Co., Ltd.", "686359": "Advanced Digital Broadcast SA", "68692E": "Zycoo Co.,Ltd", "6869F2": "ComAp s.r.o.", "686E23": "Wi3 Inc.", "686E48": "Prophet Electronic Technology Corp.,Ltd", "687251": "Ubiquiti Networks", "68764F": "Sony Mobile Communications AB", "687848": "Westunitis Co., Ltd.", "68784C": "Nortel Networks", "687924": "ELS-GmbH & Co. KG", "6879ED": "SHARP Corporation", "687CC8": "Measurement Systems S. de R.L.", "687CD5": "Y Soft Corporation, a.s.", "687F74": "Cisco-Linksys, LLC", "68831A": "Pandora Mobility Corporation", "688470": "eSSys Co.,Ltd", "688540": "IGI Mobile, Inc.", "68856A": "OuterLink Corporation", "6886A7": "Cisco", "6886E7": "Orbotix, Inc.", "68876B": "INQ Mobile Limited", "688AB5": "EDP Servicos", "689234": "Ruckus Wireless", "689423": "Hon Hai Precision Ind. Co.,Ltd.", "68967B": "Apple", "68974B": "Shenzhen Costar Electronics Co. Ltd.", "6897E8": "Society of Motion Picture & Television Engineers", "689C5E": "AcSiP Technology Corp.", "689C70": "Apple", "68A1B7": "Honghao Mingchuan Technology (Beijing) CO.,Ltd.", "68A3C4": "Liteon Technology Corporation", "68A40E": "BSH Bosch and Siemens Home Appliances GmbH", "68A86D": "Apple", "68AAD2": "DATECS LTD.,", "68AB8A": "RF IDeas", "68AF13": "Futura Mobility", "68B094": "INESA ELECTRON CO.,LTD", "68B43A": "WaterFurnace International, Inc.", "68B599": "Hewlett-Packard Company", "68B6FC": "Hitron Technologies. Inc", "68B8D9": "Act KDE, Inc.", "68BC0C": "CISCO SYSTEMS, INC.", "68BDAB": "CISCO SYSTEMS, INC.", "68CA00": "Octopus Systems Limited", "68CC9C": "Mine Site Technologies", "68CD0F": "U Tek Company Limited", "68CE4E": "L-3 Communications Infrared Products", "68D1FD": "Shenzhen Trimax Technology Co.,Ltd", "68D247": "Portalis LC", "68D925": "ProSys Development Services", "68DB67": "Nantong Coship Electronics Co., Ltd", "68DB96": "OPWILL Technologies CO .,LTD", "68DCE8": "PacketStorm Communications", "68DFDD": "Xiaomi inc.", "68E166": "PRIVATE", "68E41F": "Unglaube Identech GmbH", "68EBAE": "Samsung Electronics Co.,Ltd", "68EBC5": "Angstrem Telecom", "68EC62": "YODO Technology Corp. Ltd.", "68ED43": "Research In Motion", "68EE96": "Cisco SPVTG", "68EFBD": "CISCO SYSTEMS, INC.", "68F125": "Data Controls Inc.", "68F895": "Redflow Limited", "68FB95": "Generalplus Technology Inc.", "68FCB3": "Next Level Security Systems, Inc.", "6C0460": "RBH Access Technologies Inc.", "6C09D6": "Digiquest Electronics LTD", "6C0E0D": "Sony Ericsson Mobile Communications AB", "6C0F6A": "JDC Tech Co., Ltd.", "6C14F7": "Erhardt+Leimer GmbH", "6C15F9": "Nautronix Limited", "6C1811": "Decatur Electronics", "6C198F": "D-Link International", "6C2056": "Cisco", "6C22AB": "Ainsworth Game Technology", "6C23B9": "Sony Ericsson Mobile Communications AB", "6C2995": "Intel Corporate", "6C2C06": "OOO NPP Systemotechnika-NN", "6C2E33": "Accelink Technologies Co.,Ltd.", "6C2E85": "SAGEMCOM", "6C2F2C": "Samsung Electronics Co.,Ltd", "6C32DE": "Indieon Technologies Pvt. Ltd.", "6C33A9": "Magicjack LP", "6C391D": "Beijing ZhongHuaHun Network Information center", "6C3A84": "Shenzhen Aero-Startech. Co.Ltd", "6C3BE5": "Hewlett Packard", "6C3C53": "SoundHawk Corp", "6C3E6D": "Apple", "6C3E9C": "KE Knestel Elektronik GmbH", "6C40C6": "Nimbus Data Systems, Inc.", "6C416A": "Cisco", "6C4B7F": "Vossloh-Schwabe Deutschland GmbH", "6C504D": "CISCO SYSTEMS, INC.", "6C5779": "Aclima, Inc.", "6C5A34": "Shenzhen Haitianxiong Electronic Co., Ltd.", "6C5AB5": "TCL Technoly Electronics (Huizhou) Co., Ltd.", "6C5CDE": "SunReports, Inc.", "6C5D63": "ShenZhen Rapoo Technology Co., Ltd.", "6C5E7A": "Ubiquitous Internet Telecom Co., Ltd", "6C5F1C": "Lenovo Mobile Communication Technology Ltd.", "6C6126": "Rinicom Holdings", "6C626D": "Micro-Star INT'L CO., LTD", "6C641A": "Penguin Computing", "6C6F18": "Stereotaxis, Inc.", "6C7039": "Novar GmbH", "6C709F": "Apple", "6C71D9": "AzureWave Technologies, Inc", "6C81FE": "Mitsuba Corporation", "6C8336": "Samsung Electronics Co.,Ltd", "6C8366": "Nanjing SAC Power Grid Automation Co., Ltd.", "6C8686": "Technonia", "6C8814": "Intel Corporate", "6C8B2F": "zte corporation", "6C8CDB": "Otus Technologies Ltd", "6C8D65": "Wireless Glue Networks, Inc.", "6C90B1": "SanLogic Inc", "6C92BF": "Inspur Electronic Information Industry Co.,Ltd.", "6C98EB": "Xyne GmbH", "6C9989": "Cisco", "6C9AC9": "Valentine Research, Inc.", "6C9B02": "Nokia Corporation", "6C9CE9": "Nimble Storage", "6C9CED": "CISCO SYSTEMS, INC.", "6CA682": "EDAM information & communications", "6CA780": "Nokia Corporation", "6CA906": "Telefield Ltd", "6CA96F": "TransPacket AS", "6CAAB3": "Ruckus Wireless", "6CAB4D": "Digital Payment Technologies", "6CAC60": "Venetex Corp", "6CAD3F": "Hubbell Building Automation, Inc.", "6CADEF": "KZ Broadband Technologies, Ltd.", "6CADF8": "Azurewave Technologies, Inc.", "6CAE8B": "IBM Corporation", "6CB0CE": "NETGEAR", "6CB311": "Shenzhen Lianrui Electronics Co.,Ltd", "6CB350": "Anhui comhigher tech co.,ltd", "6CB7F4": "Samsung Electronics Co.,Ltd", "6CBEE9": "Alcatel-Lucent-IPD", "6CC1D2": "ARRIS Group, Inc.", "6CC26B": "Apple", "6CD032": "LG Electronics", "6CD146": "Smartek d.o.o.", "6CD1B0": "WING SING ELECTRONICS HONG KONG LIMITED", "6CD68A": "LG Electronics Inc", "6CDC6A": "Promethean Limited", "6CE0B0": "SOUND4", "6CE4CE": "Villiger Security Solutions AG", "6CE873": "TP-LINK TECHNOLOGIES CO., LTD.", "6CE907": "Nokia Corporation", "6CE983": "Gastron Co., LTD.", "6CECA1": "SHENZHEN CLOU ELECTRONICS CO. LTD.", "6CECEB": "Texas Instruments", "6CF049": "GIGA-BYTE TECHNOLOGY CO.,LTD.", "6CF373": "Samsung Electronics Co.,Ltd", "6CF37F": "Aruba Networks", "6CF97C": "Nanoptix Inc.", "6CFA58": "Avaya, Inc", "6CFAA7": "AMPAK Technology Inc.", "6CFDB9": "Proware Technologies Co Ltd.", "6CFFBE": "MPB Communications Inc.", "700258": "01DB-METRAVIB", "700514": "LG Electronics", "700BC0": "Dewav Technology Company", "700FEC": "Poindus Systems Corp.", "70105C": "Cisco", "701124": "Apple", "701404": "Limited Liability Company", "70188B": "Hon Hai Precision Ind. Co.,Ltd.", "701A04": "Liteon Tech Corp.", "701AED": "ADVAS CO., LTD.", "701D7F": "Comtech Technology Co., Ltd.", "702393": "fos4X GmbH", "702526": "Alcatel-Lucent", "702559": "CyberTAN Technology, Inc.", "702B1D": "E-Domus International Limited", "702C1F": "Wisol", "702F4B": "PolyVision Inc.", "702F97": "Aava Mobile Oy", "703018": "Avaya, Inc", "70305D": "Ubiquoss Inc", "70305E": "Nanjing Zhongke Menglian Information Technology Co.,LTD", "703187": "ACX GmbH", "7032D5": "Athena Wireless Communications Inc", "703811": "Invensys Rail", "7038B4": "Low Tech Solutions", "7038EE": "Avaya, Inc", "703AD8": "Shenzhen Afoundry Electronic Co., Ltd", "703C39": "SEAWING Kft", "7041B7": "Edwards Lifesciences LLC", "704642": "CHYNG HONG ELECTRONIC CO., LTD.", "704AAE": "Xstream Flow (Pty) Ltd", "704AE4": "Rinstrum Pty Ltd", "704CED": "TMRG, Inc.", "704E01": "KWANGWON TECH CO., LTD.", "7052C5": "Avaya, Inc.", "70533F": "Alfa Instrumentos Eletronicos Ltda.", "7054D2": "PEGATRON CORPORATION", "7054F5": "HUAWEI TECHNOLOGIES CO.,LTD", "705681": "Apple", "705812": "Panasonic AVC Networks Company", "705957": "Medallion Instrumentation Systems", "705986": "OOO TTV", "705AB6": "COMPAL INFORMATION (KUNSHAN) CO., LTD.", "705CAD": "Konami Gaming Inc", "705EAA": "Action Target, Inc.", "7060DE": "LaVision GmbH", "706173": "Calantec GmbH", "7062B8": "D-Link International", "706417": "ORBIS TECNOLOGIA ELECTRICA S.A.", "706582": "Suzhou Hanming Technologies Co., Ltd.", "706F81": "PRIVATE", "70704C": "Purple Communications, Inc", "7071B3": "Brain Corporation", "7071BC": "PEGATRON CORPORATION", "70720D": "Lenovo Mobile Communication Technology Ltd.", "70723C": "Huawei Technologies Co., Ltd", "7072CF": "EdgeCore Networks", "7073CB": "Apple", "707630": "Pace plc.", "7076DD": "Oxyguard International A/S", "7076F0": "LevelOne Communications (India) Private Limited", "707BE8": "HUAWEI TECHNOLOGIES CO.,LTD", "707C18": "ADATA Technology Co., Ltd", "707E43": "ARRIS Group, Inc.", "707EDE": "NASTEC LTD.", "708105": "CISCO SYSTEMS, INC.", "70820E": "as electronics GmbH", "70828E": "OleumTech Corporation", "7085C6": "Pace plc.", "708B78": "citygrow technology co., ltd", "708D09": "Nokia Corporation", "709383": "Intelligent Optical Network High Tech CO.,LTD.", "7093F8": "Space Monkey, Inc.", "709756": "Happyelectronics Co.,Ltd", "709A0B": "Italian Institute of Technology", "709BA5": "Shenzhen Y&D Electronics Co.,LTD.", "709BFC": "Bryton Inc.", "709E29": "Sony Computer Entertainment Inc.", "709E86": "X6D Limited", "70A191": "Trendsetter Medical, LLC", "70A41C": "Advanced Wireless Dynamics S.L.", "70A66A": "Prox Dynamics AS", "70A8E3": "HUAWEI TECHNOLOGIES CO.,LTD", "70AAB2": "Research In Motion", "70B035": "Shenzhen Zowee Technology Co., Ltd", "70B08C": "Shenou Communication Equipment Co.,Ltd", "70B14E": "Pace plc", "70B265": "Hiltron s.r.l.", "70B3D5": "IEEE REGISTRATION AUTHORITY - Please see OUI36 public listing for more information.", "70B599": "Embedded Technologies s.r.o.", "70B921": "FiberHome Telecommunication Technologies CO.,LTD", "70C6AC": "Bosch Automotive Aftermarket", "70CA9B": "CISCO SYSTEMS, INC.", "70CD60": "Apple", "70D4F2": "RIM", "70D57E": "Scalar Corporation", "70D5E7": "Wellcore Corporation", "70D6B6": "Metrum Technologies", "70D880": "Upos System sp. z o.o.", "70DDA1": "Tellabs", "70DEE2": "Apple", "70E027": "HONGYU COMMUNICATION TECHNOLOGY LIMITED", "70E139": "3view Ltd", "70E24C": "SAE IT-systems GmbH & Co. KG", "70E284": "Wistron InfoComm(Zhongshan) Corporation", "70E843": "Beijing C&W Optical Communication Technology Co.,Ltd.", "70EE50": "Netatmo", "70F176": "Data Modul AG", "70F1A1": "Liteon Technology Corporation", "70F1E5": "Xetawave LLC", "70F395": "Universal Global Scientific Industrial Co., Ltd.", "70F927": "Samsung Electronics", "70F96D": "Hangzhou H3C Technologies Co., Limited", "70FF76": "Texas Instruments", "740ABC": "JSJS Designs (Europe) Limited", "740EDB": "Optowiz Co., Ltd", "741489": "SRT Wireless", "7415E2": "Tri-Sen Systems Corporation", "7419F8": "IEEE REGISTRATION AUTHORITY - Please see MAM public listing for more information.", "741E93": "Fiberhome Telecommunication Tech.Co.,Ltd.", "74258A": "Hangzhou H3C Technologies Co., Limited", "7426AC": "Cisco", "74273C": "ChangYang Technology (Nanjing) Co., LTD", "7427EA": "Elitegroup Computer Systems Co., Ltd.", "742B0F": "Infinidat Ltd.", "742B62": "Fujitsu Limited", "742D0A": "Norfolk Elektronik AG", "742F68": "Azurewave Technologies, Inc.", "743170": "Arcadyan Technology Corporation", "743256": "NT-ware Systemprg GmbH", "74372F": "Tongfang Shenzhen Cloudcomputing Technology Co.,Ltd", "743889": "ANNAX Anzeigesysteme GmbH", "743ECB": "Gentrice tech", "744401": "NETGEAR", "74458A": "Samsung Electronics Co.,Ltd", "7446A0": "Hewlett Packard", "744BE9": "EXPLORER HYPERTECH CO.,LTD", "744D79": "Arrive Systems Inc.", "745327": "COMMSEN CO., LIMITED", "745612": "ARRIS Group, Inc.", "745798": "TRUMPF Laser GmbH + Co. KG", "745E1C": "PIONEER CORPORATION", "745F00": "Samsung Semiconductor Inc.", "745FAE": "TSL PPL", "7463DF": "VTS GmbH", "7465D1": "Atlinks", "746630": "T:mi Ytti", "746A89": "Rezolt Corporation", "746A8F": "VS Vision Systems GmbH", "746B82": "MOVEK", "746F3D": "Contec GmbH", "7472F2": "Chipsip Technology Co., Ltd.", "747818": "ServiceAssure", "747B7A": "ETH Inc.", "747DB6": "Aliwei Communications, Inc", "747E1A": "Red Embedded Design Limited", "747E2D": "Beijing Thomson CITIC Digital Technology Co. LTD.", "74867A": "Dell Inc", "74882A": "HUAWEI TECHNOLOGIES CO.,LTD", "74888B": "ADB Broadband Italia", "748E08": "Bestek Corp.", "748EF8": "Brocade Communications Systems, Inc.", "748F1B": "MasterImage 3D", "749050": "Renesas Electronics Corporation", "74911A": "Ruckus Wireless", "7493A4": "Zebra Technologies Corp.", "74943D": "AgJunction", "749975": "IBM Corporation", "749C52": "Huizhou Desay SV Automotive Co., Ltd.", "749DDC": "2Wire", "74A4A7": "QRS Music Technologies, Inc.", "74A4B5": "Powerleader Science and Technology Co. Ltd.", "74A722": "LG Electronics", "74ADB7": "China Mobile Group Device Co.,Ltd.", "74AE76": "iNovo Broadband, Inc.", "74B00C": "Network Video Technologies, Inc", "74B9EB": "Fujian JinQianMao Electronic Technology Co.,Ltd", "74BE08": "ATEK Products, LLC", "74BFA1": "HYUNTECK", "74C621": "Zhejiang Hite Renewable Energy Co.,LTD", "74C99A": "Ericsson AB", "74CA25": "Calxeda, Inc.", "74CD0C": "Smith Myers Communications Ltd.", "74CE56": "Packet Force Technology Limited Company", "74D02B": "ASUSTek COMPUTER INC.", "74D0DC": "ERICSSON AB", "74D435": "GIGA-BYTE TECHNOLOGY CO.,LTD.", "74D675": "WYMA Tecnologia", "74D850": "Evrisko Systems", "74DA38": "Edimax Technology Co. 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Inc", "78923E": "Nokia Corporation", "78929C": "Intel Corporate", "789684": "ARRIS Group, Inc.", "7898FD": "Q9 Networks Inc.", "78995C": "Nationz Technologies Inc", "789966": "Musilab Electronics (DongGuan)Co.,Ltd.", "78998F": "MEDILINE ITALIA SRL", "789ED0": "Samsung Electronics", "789F4C": "HOERBIGER Elektronik GmbH", "789F87": "Siemens AG I IA PP PRM", "78A051": "iiNet Labs Pty Ltd", "78A106": "TP-LINK TECHNOLOGIES CO.,LTD.", "78A183": "Advidia", "78A2A0": "Nintendo Co., Ltd.", "78A3E4": "Apple", "78A504": "Texas Instruments", "78A5DD": "Shenzhen Smarteye Digital Electronics Co., Ltd", "78A683": "Precidata", "78A6BD": "DAEYEON Control&Instrument Co,.Ltd", "78A714": "Amphenol", "78A873": "Samsung Electronics Co.,Ltd", "78AB60": "ABB Australia", "78ABBB": "Samsung Electronics Co.,LTD", "78ACC0": "Hewlett-Packard Company", "78AE0C": "Far South Networks", "78B3CE": "Elo touch solutions", "78B5D2": "Ever Treasure Industrial Limited", "78B6C1": "AOBO Telecom Co.,Ltd", "78B81A": "INTER SALES A/S", "78BAD0": "Shinybow Technology Co. Ltd.", "78BEB6": "Enhanced Vision", "78BEBD": "STULZ GmbH", "78C40E": "H&D Wireless", "78C4AB": "Shenzhen Runsil Technology Co.,Ltd", "78C5E5": "Texas Instruments", "78C6BB": "Innovasic, Inc.", "78CA04": "Nokia Corporation", "78CA39": "Apple", "78CA5E": "ELNO", "78CB33": "DHC Software Co.,Ltd", "78CD8E": "SMC Networks Inc", "78D004": "Neousys Technology Inc.", "78D129": "Vicos", "78D34F": "Pace-O-Matic, Inc.", "78D38D": "HONGKONG YUNLINK TECHNOLOGY LIMITED", "78D5B5": "NAVIELEKTRO KY", "78D66F": "Aristocrat Technologies Australia Pty. Ltd.", "78D6F0": "Samsung Electro Mechanics", "78D752": "HUAWEI TECHNOLOGIES CO.,LTD", "78D99F": "NuCom HK Ltd.", "78DA6E": "Cisco", "78DAB3": "GBO Technology", "78DD08": "Hon Hai Precision Ind. Co.,Ltd.", "78DDD6": "c-scape", "78DEE4": "Texas Instruments", "78E3B5": "Hewlett-Packard Company", "78E400": "Hon Hai Precision Ind. 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Inc", "8496D8": "Pace plc", "8497B8": "Memjet Inc.", "849CA6": "Arcadyan Technology Corporation", "849DC5": "Centera Photonics Inc.", "84A6C8": "Intel Corporate", "84A783": "Alcatel Lucent", "84A8E4": "Shenzhen Huawei Communication Technologies Co., Ltd", "84A991": "Cyber Trans Japan Co.,Ltd.", "84ACA4": "Beijing Novel Super Digital TV Technology Co., Ltd", "84AF1F": "Beat System Service Co,. 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Bhd.", "889821": "TERAON", "889B39": "Samsung Electronics Co.,Ltd", "889CA6": "BTB Korea INC", "889FFA": "Hon Hai Precision Ind. 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Co.", "8CC121": "Panasonic Corporation AVC Networks Company", "8CC5E1": "ShenZhen Konka Telecommunication Technology Co.,Ltd", "8CC7AA": "Radinet Communications Inc.", "8CC7D0": "zhejiang ebang communication co.,ltd", "8CC8CD": "Samsung Electronics Co., LTD", "8CCDA2": "ACTP, Inc.", "8CCDE8": "Nintendo Co., Ltd.", "8CCF5C": "BEFEGA GmbH", "8CD17B": "CG Mobile", "8CD3A2": "VisSim AS", "8CD628": "Ikor Metering", "8CDB25": "ESG Solutions", "8CDD8D": "Wifly-City System Inc.", "8CDE52": "ISSC Technologies Corp.", "8CDE99": "Comlab Inc.", "8CE081": "zte corporation", "8CE748": "PRIVATE", "8CE7B3": "Sonardyne International Ltd", "8CEEC6": "Precepscion Pty. Ltd.", "8CF945": "Power Automation pte Ltd", "8CF9C9": "MESADA Technology Co.,Ltd.", "8CFABA": "Apple", "8CFDF0": "QUALCOMM Incorporated", "90004E": "Hon Hai Precision Ind. 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K.) 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Stonkaus firma \"Kodinis Raktas\"", "C05E79": "SHENZHEN HUAXUN ARK TECHNOLOGIES CO.,LTD", "C06118": "TP-LINK TECHNOLOGIES CO.,LTD.", "C0626B": "CISCO SYSTEMS, INC.", "C06394": "Apple", "C064C6": "Nokia Corporation", "C06599": "Samsung Electronics Co.,Ltd", "C067AF": "Cisco", "C06C0F": "Dobbs Stanford", "C06C6D": "MagneMotion, Inc.", "C07BBC": "Cisco", "C07E40": "SHENZHEN XDK COMMUNICATION EQUIPMENT CO.,LTD", "C08170": "Effigis GeoSolutions", "C0830A": "2Wire", "C0847A": "Apple", "C0885B": "SnD Tech Co., Ltd.", "C08ADE": "Ruckus Wireless", "C08B6F": "S I Sistemas Inteligentes Eletr\u00f4nicos Ltda", "C08C60": "Cisco", "C09132": "Patriot Memory", "C09134": "ProCurve Networking by HP", "C098E5": "University of Michigan", "C09C92": "COBY", "C09D26": "Topicon HK Lmd.", "C09F42": "Apple", "C0A0BB": "D-Link International", "C0A0C7": "FAIRFIELD INDUSTRIES", "C0A0DE": "Multi Touch Oy", "C0A0E2": "Eden Innovations", "C0A26D": "Abbott Point of Care", "C0A364": "3D Systems Massachusetts", "C0A39E": "EarthCam, Inc.", "C0AA68": "OSASI Technos Inc.", "C0AC54": "SAGEMCOM", "C0B339": "Comigo Ltd.", "C0B357": "Yoshiki Electronics Industry Ltd.", "C0B8B1": "BitBox Ltd", "C0BAE6": "Application Solutions (Electronics and Vision) Ltd", "C0BD42": "ZPA Smart Energy a.s.", "C0C1C0": "Cisco-Linksys, LLC", "C0C3B6": "Automatic Systems", "C0C520": "Ruckus Wireless", "C0C569": "SHANGHAI LYNUC CNC TECHNOLOGY CO.,LTD", "C0C687": "Cisco SPVTG", "C0C946": "MITSUYA LABORATORIES INC.", "C0CB38": "Hon Hai Precision Ind. 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Co., Ltd", "C8F36B": "Yamato Scale Co.,Ltd.", "C8F386": "Shenzhen Xiaoniao Technology Co.,Ltd", "C8F406": "Avaya, Inc", "C8F650": "Apple", "C8F68D": "S.E.TECHNOLOGIES LIMITED", "C8F704": "Building Block Video", "C8F733": "Intel Corporate", "C8F981": "Seneca s.r.l.", "C8F9F9": "CISCO SYSTEMS, INC.", "C8FB26": "Cisco SPVTG", "C8FE30": "Bejing DAYO Mobile Communication Technology Ltd.", "C8FF77": "Dyson Limited", "CC0080": "BETTINI SRL", "CC047C": "G-WAY Microwave", "CC04B4": "Select Comfort", "CC051B": "Samsung Electronics Co.,Ltd", "CC07AB": "Samsung Electronics Co.,Ltd", "CC07E4": "Lenovo Mobile Communication Technology Ltd.", "CC08E0": "Apple", "CC09C8": "IMAQLIQ LTD", "CC0CDA": "Miljovakt AS", "CC0DEC": "Cisco SPVTG", "CC14A6": "Yichun MyEnergy Domain, Inc", "CC187B": "Manzanita Systems, Inc.", "CC1AFA": "zte corporation", "CC1EFF": "Metrological Group BV", "CC2218": "InnoDigital Co., Ltd.", "CC262D": "Verifi, LLC", "CC2A80": "Micro-Biz intelligence solutions Co.,Ltd", "CC2D8C": "LG ELECTRONICS INC", "CC33BB": "SAGEMCOM SAS", "CC3429": "TP-LINK TECHNOLOGIES CO.,LTD.", "CC34D7": "GEWISS S.P.A.", "CC3540": "Technicolor USA Inc.", "CC398C": "Shiningtek", "CC3A61": "SAMSUNG ELECTRO MECHANICS CO., LTD.", "CC3C3F": "SA.S.S. 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Pesagem Ltda", "CC5D4E": "ZyXEL Communications Corporation", "CC5D57": "Information System Research Institute,Inc.", "CC60BB": "Empower RF Systems", "CC65AD": "ARRIS Group, Inc.", "CC69B0": "Global Traffic Technologies, LLC", "CC6B98": "Minetec Wireless Technologies", "CC6BF1": "Sound Masking Inc.", "CC6DA0": "Roku, Inc.", "CC6DEF": "TJK Tietolaite Oy", "CC720F": "Viscount Systems Inc.", "CC7498": "Filmetrics Inc.", "CC7669": "SEETECH", "CC785F": "Apple", "CC7A30": "CMAX Wireless Co., Ltd.", "CC7B35": "zte corporation", "CC7D37": "ARRIS Group, Inc.", "CC7EE7": "Panasonic AVC Networks Company", "CC856C": "SHENZHEN MDK DIGITAL TECHNOLOGY CO.,LTD", "CC89FD": "Nokia Corporation", "CC8CE3": "Texas Instruments", "CC9093": "Hansong Tehnologies", "CC912B": "TE Connectivity Touch Solutions", "CC944A": "Pfeiffer Vacuum GmbH", "CC95D7": "VIZIO, Inc", "CC96A0": "Shenzhen Huawei Communication Technologies Co., Ltd", "CC9E00": "Nintendo Co., Ltd.", "CC9F35": "Transbit Sp. z o.o.", "CCA0E5": "DZG Metering GmbH", "CCA374": "Guangdong Guanglian Electronic Technology Co.Ltd", "CCA462": "ARRIS Group, Inc.", "CCA614": "AIFA TECHNOLOGY CORP.", "CCAF78": "Hon Hai Precision Ind. 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Ltd", "D4136F": "Asia Pacific Brands", "D41C1C": "RCF S.P.A.", "D41E35": "TOHO Electronics INC.", "D41F0C": "TVI Vision Oy", "D4206D": "HTC Corporation", "D42122": "Sercomm Corporation", "D4223F": "Lenovo Mobile Communication Technology Ltd.", "D4224E": "Alcatel Lucent", "D42751": "Infopia Co., Ltd", "D428B2": "ioBridge, Inc.", "D429EA": "Zimory GmbH", "D42C3D": "Sky Light Digital Limited", "D42F23": "Akenori PTE Ltd", "D4319D": "Sinwatec", "D43A65": "IGRS Engineering Lab Ltd.", "D43AE9": "DONGGUAN ipt INDUSTRIAL CO., LTD", "D43D67": "Carma Industries Inc.", "D43D7E": "Micro-Star Int'l Co, Ltd", "D443A8": "Changzhou Haojie Electric Co., Ltd.", "D44B5E": "TAIYO YUDEN CO., LTD.", "D44C24": "Vuppalamritha Magnetic Components LTD", "D44C9C": "Shenzhen YOOBAO Technology Co.Ltd", "D44CA7": "Informtekhnika & Communication, LLC", "D44F80": "Kemper Digital GmbH", "D4507A": "CEIVA Logic, Inc", "D45251": "IBT Ingenieurbureau Broennimann Thun", "D45297": "nSTREAMS Technologies, Inc.", "D453AF": "VIGO System S.A.", "D45AB2": "Galleon Systems", "D45C70": "Wireless Gigabit Alliance", "D45D42": "Nokia Corporation", "D464F7": "CHENGDU USEE DIGITAL TECHNOLOGY CO., LTD", "D466A8": "Riedo Networks GmbH", "D46761": "SAHAB TECHNOLOGY", "D467E7": "Fiberhome Telecommunication Tech.Co.,Ltd.", "D46867": "Neoventus Design Group", "D46A91": "Snap AV", "D46AA8": "HUAWEI TECHNOLOGIES CO.,LTD", "D46CBF": "Goodrich ISR", "D46CDA": "CSM GmbH", "D46E5C": "Huawei Technologies Co., Ltd", "D46F42": "WAXESS USA Inc", "D479C3": "Cameronet GmbH & Co. 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Siedle & Sohne", "D4E33F": "Alcatel-Lucent", "D4E8B2": "Samsung Electronics", "D4EA0E": "Avaya, Inc", "D4EC0C": "Harley-Davidson Motor Company", "D4EE07": "HIWIFI Co., Ltd.", "D4F027": "Navetas Energy Management", "D4F0B4": "Napco Security Technologies", "D4F143": "IPROAD.,Inc", "D4F63F": "IEA S.R.L.", "D8004D": "Apple", "D8052E": "Skyviia Corporation", "D806D1": "Honeywell Fire System (Shanghai) Co,. Ltd.", "D808F5": "Arcadia Networks Co. 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Co., Ltd.", "F80DEA": "ZyCast Technology Inc.", "F80F41": "Wistron InfoComm(ZhongShan) Corporation", "F80F84": "Natural Security SAS", "F81037": "Atopia Systems, LP", "F81547": "Avaya, Inc", "F81654": "Intel Corporate", "F81A67": "TP-LINK TECHNOLOGIES CO., LTD.", "F81CE5": "Telefonbau Behnke GmbH", "F81D93": "Longdhua(Beijing) Controls Technology Co.,Ltd", "F81EDF": "Apple", "F82285": "Cypress Technology CO., LTD.", "F82793": "Apple, Inc", "F82BC8": "Jiangsu Switter Co., Ltd", "F82EDB": "RTW GmbH & Co. KG", "F82F5B": "eGauge Systems LLC", "F82FA8": "Hon Hai Precision Ind. 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Co., Ltd.", "F866F2": "CISCO SYSTEMS, INC.", "F86971": "Seibu Electric Co.,", "F86ECF": "Arcx Inc", "F871FE": "The Goldman Sachs Group, Inc.", "F872EA": "Cisco", "F8769B": "Neopis Co., Ltd.", "F87B62": "FASTWEL INTERNATIONAL CO., LTD. Taiwan Branch", "F87B7A": "ARRIS Group, Inc.", "F87B8C": "Amped Wireless", "F8811A": "OVERKIZ", "F884F2": "Samsung Electronics Co.,Ltd", "F88C1C": "KAISHUN ELECTRONIC TECHNOLOGY CO., LTD. BEIJING", "F88DEF": "Tenebraex", "F88E85": "COMTREND CORPORATION", "F88FCA": "Google Fiber, Inc", "F8912A": "GLP German Light Products GmbH", "F893F3": "VOLANS", "F89550": "Proton Products Chengdu Ltd", "F897CF": "DAESHIN-INFORMATION TECHNOLOGY CO., LTD.", "F89955": "Fortress Technology Inc", "F89D0D": "Control Technology Inc.", "F89FB8": "YAZAKI Energy System Corporation", "F8A03D": "Dinstar Technologies Co., Ltd.", "F8A2B4": "RHEWA-WAAGENFABRIK August Freudewald GmbH &Co. KG", "F8A45F": "Beijing Xiaomi communications co.,ltd", "F8A963": "COMPAL INFORMATION (KUNSHAN) CO., LTD.", "F8A9D0": "LG Electronics", "F8A9DE": "PUISSANCE PLUS", "F8AA8A": "Axview Technology (Shenzhen) Co.,Ltd", "F8AC6D": "Deltenna Ltd", "F8B156": "Dell Inc", "F8B599": "Guangzhou CHNAVS Digital Technology Co.,Ltd", "F8BC12": "Dell Inc", "F8C001": "Juniper Networks", "F8C091": "Highgates Technology", "F8C678": "Carefusion", "F8D0AC": "Sony Computer Entertainment Inc.", "F8D0BD": "Samsung Electronics Co.,Ltd", "F8D111": "TP-LINK TECHNOLOGIES CO., LTD.", "F8D3A9": "AXAN Networks", "F8D462": "Pumatronix Equipamentos Eletronicos Ltda.", "F8D756": "Simm Tronic Limited", "F8D7BF": "REV Ritter GmbH", "F8DADF": "EcoTech, Inc.", "F8DAE2": "Beta LaserMike", "F8DAF4": "Taishan Online Technology Co., Ltd.", "F8DB4C": "PNY Technologies, INC.", "F8DB7F": "HTC Corporation", "F8DB88": "Dell Inc", "F8DC7A": "Variscite LTD", "F8DFA8": "ZTE Corporation", "F8E079": "Motorola Mobility LLC", "F8E4FB": "Actiontec Electronics, Inc", "F8E7B5": "\u00b5Tech Tecnologia LTDA", "F8E968": "Egker Kft.", "F8EA0A": "Dipl.-Math. Michael Rauch", "F8EDA5": "ARRIS Group, Inc.", "F8F005": "Newport Media Inc.", "F8F014": "RackWare Inc.", "F8F082": "Orion Networks International, Inc", "F8F1B6": "Motorola Mobility LLC", "F8F25A": "G-Lab GmbH", "F8F7D3": "International Communications Corporation", "F8F7FF": "SYN-TECH SYSTEMS INC", "F8FB2F": "Santur Corporation", "F8FE5C": "Reciprocal Labs Corp", "F8FEA8": "Technico Japan Corporation", "F8FF5F": "Shenzhen Communication Technology Co.,Ltd", "FC0012": "Toshiba Samsung Storage Technolgoy Korea Corporation", "FC019E": "VIEVU", "FC01CD": "FUNDACION TEKNIKER", "FC0647": "Cortland Research, LLC", "FC07A0": "LRE Medical GmbH", "FC0877": "Prentke Romich Company", "FC09D8": "ACTEON Group", "FC09F6": "GUANGDONG TONZE ELECTRIC CO.,LTD", "FC0A81": "Motorola Solutions Inc.", "FC0FE6": "Sony Computer Entertainment Inc.", "FC10BD": "Control Sistematizado S.A.", "FC1186": "Logic3 plc", "FC1349": "Global Apps Corp.", "FC15B4": "Hewlett Packard", "FC1607": "Taian Technology(Wuxi) Co.,Ltd.", "FC1794": "InterCreative Co., Ltd", "FC19D0": "Cloud Vision Networks Technology Co.,Ltd.", "FC1BFF": "V-ZUG AG", "FC1D59": "I Smart Cities HK Ltd", "FC1E16": "IPEVO corp", "FC1F19": "SAMSUNG ELECTRO-MECHANICS CO., LTD.", "FC1FC0": "EURECAM", "FC229C": "Han Kyung I Net Co.,Ltd.", "FC2325": "EosTek (Shenzhen) Co., Ltd.", "FC253F": "Apple", "FC27A2": "TRANS ELECTRIC CO., LTD.", "FC2A54": "Connected Data, Inc.", "FC2E2D": "Lorom Industrial Co.LTD.", "FC2F40": "Calxeda, Inc.", "FC3598": "Favite Inc.", "FC35E6": "Visteon corp", "FC3FAB": "Henan Lanxin Technology Co., Ltd", "FC4463": "Universal Audio, Inc", "FC4499": "Swarco LEA d.o.o.", "FC455F": "JIANGXI SHANSHUI OPTOELECTRONIC TECHNOLOGY CO.,LTD", "FC48EF": "HUAWEI TECHNOLOGIES CO.,LTD", "FC4AE9": "Castlenet Technology Inc.", "FC4B1C": "INTERSENSOR S.R.L.", "FC4BBC": "Sunplus Technology Co., Ltd.", "FC4DD4": "Universal Global Scientific Industrial Co., Ltd.", "FC5090": "SIMEX Sp. z o.o.", "FC52CE": "Control iD", "FC58FA": "Shen Zhen Shi Xin Zhong Xin Technology Co.,Ltd.", "FC5B24": "Weibel Scientific A/S", "FC5B26": "MikroBits", "FC6018": "Zhejiang Kangtai Electric Co., Ltd.", "FC6198": "NEC Personal Products, Ltd", "FC626E": "Beijing MDC Telecom", "FC683E": "Directed Perception, Inc", "FC6C31": "LXinstruments GmbH", "FC7516": "D-Link International", "FC75E6": "Handreamnet", "FC7CE7": "FCI USA LLC", "FC8329": "Trei technics", "FC8399": "Avaya, Inc", "FC8B97": "Shenzhen Gongjin Electronics Co.,Ltd", "FC8E7E": "Pace plc", "FC8FC4": "Intelligent Technology Inc.", "FC923B": "Nokia Corporation", "FC946C": "UBIVELOX", "FC94E3": "Technicolor USA Inc.", "FC9947": "Cisco", "FC9FAE": "Fidus Systems Inc", "FCA13E": "Samsung Electronics", "FCA841": "Avaya, Inc", "FCA9B0": "MIARTECH (SHANGHAI),INC.", "FCAD0F": "QTS NETWORKS", "FCAF6A": "Conemtech AB", "FCB0C4": "Shanghai DareGlobal Technologies Co., Ltd", "FCBBA1": "Shenzhen Minicreate Technology Co.,Ltd", "FCC23D": "Atmel Corporation", "FCC2DE": "Murata Manufacturing Co., Ltd.", "FCC734": "Samsung Electronics Co.,Ltd", "FCC897": "ZTE Corporation", "FCCCE4": "Ascon Ltd.", "FCCF62": "IBM Corp", "FCD4F2": "The Coca Cola Company", "FCD4F6": "Messana Air.Ray Conditioning s.r.l.", "FCD5D9": "Shenzhen SDMC Technology Co., Ltd.", "FCD6BD": "Robert Bosch GmbH", "FCD817": "Beijing Hesun Technologies Co.Ltd.", "FCDB96": "ENERVALLEY CO., LTD", "FCDD55": "Shenzhen WeWins wireless Co.,Ltd", "FCE186": "A3M Co., LTD", "FCE192": "Sichuan Jinwangtong Electronic Science&Technology Co,.Ltd", "FCE1D9": "Stable Imaging Solutions LLC", "FCE23F": "CLAY PAKY SPA", "FCE557": "Nokia Corporation", "FCE892": "Hangzhou Lancable Technology Co.,Ltd", "FCEDB9": "Arrayent", "FCF152": "Sony Corporation", "FCF1CD": "OPTEX-FA CO.,LTD.", "FCF528": "ZyXEL Communications Corporation", "FCF647": "Fiberhome Telecommunication Tech.Co.,Ltd.", "FCF8AE": "Intel Corporate", "FCF8B7": "TRONTEQ Electronic", "FCFAF7": "Shanghai Baud Data Communication Co.,Ltd.", "FCFBFB": "CISCO SYSTEMS, INC.", "FCFE77": "Hitachi Reftechno, Inc." }
ninuxorg/netengine
netengine/resources/manufacturers.py
Python
mit
761,686
[ "ASE", "Amber", "BWA", "CRYSTAL", "Galaxy", "Jaguar", "NEURON", "Octopus" ]
2d8c0915ad204a3ee4297f6382ba177ff27dbfec43bf0ac714eaf0aef24e18ac
__all__ = [ 'ADJECTIVES', 'ANIMALS', ] ADJECTIVES = [ 'adorable', 'acrobatic', 'adaptable', 'adventurous', 'agile', 'alert', 'amiable', 'amused', 'arboreal', 'ardent', 'artful', 'astonishing', 'astute', 'attentive', 'authentic', 'avid', 'beardless', 'benevolent', 'bicameral', 'big', 'bionic', 'blissful', 'bodacious', 'brave', 'bright', 'brilliant', 'bubbly', 'careful', 'cautious', 'cavernous', 'charming', 'cheeky', 'cheerful', 'circumspect', 'cognizant', 'cool', 'collaborative', 'collectible', 'colorful', 'colossal', 'comfortable', 'communicative', 'compact', 'compassionate', 'constant', 'contemplative', 'contiguous', 'convincing', 'convivial', 'cooperative', 'courageous', 'cordial', 'cosmic', 'creative', 'cryptic', 'crystalline', 'cunning', 'curious', 'dancing', 'daring', 'dauntless', 'dashing', 'dazzling', 'defiant', 'delightful', 'determined', 'dexterous', 'diminutive', 'discerning', 'distinctive', 'dreaming', 'dynamic', 'eager', 'earnest', 'easygoing', 'ebullient', 'eccentric', 'ecstatic', 'effluvial', 'effervescent', 'elaborate', 'elated', 'elegant', 'eloquent', 'elusive', 'enchanting', 'encouraging', 'energetic', 'energized', 'enormous', 'enthusiastic', 'erudite', 'essential', 'ethereal', 'excited', 'exhilarated', 'extraordinary', 'exotic', 'exuberant', 'fantastic', 'fearless', 'feisty', 'fierce', 'fiery', 'flourishing', 'flying', 'focused', 'fortunate', 'friendly', 'frolicking', 'gargantuan', 'gesticulating', 'gigantic', 'glamorous', 'gleaming', 'gleeful', 'glorious', 'gorgeous', 'graceful', 'grateful', 'gregarious', 'happy', 'harmonious', 'hatless', 'healthy', 'helpful', 'heroic', 'hydraulic', 'hyena', 'idealistic', 'illustrious', 'illustrative', 'imaginative', 'immense', 'impartial', 'imperturbable', 'improbable', 'incredible', 'inimitable', 'influential', 'inquisitive', 'insightful', 'inspired', 'intrepid', 'intricate', 'intuitive', 'invaluable', 'inventive', 'iterative', 'jaunty', 'jolly', 'joyful', 'joyous', 'jubilant', 'jumping', 'katabatic', 'keen', 'kempt', 'kept', 'kind', 'kindled', 'kinematic', 'kinesthtic', 'kingly', 'kitschy', 'knightly', 'knowing', 'kooky', 'kyphotic', 'large', 'laughing', 'legendary', 'lenient', 'lively', 'loquacious', 'lucky', 'luminescent', 'magnetic', 'magnificent', 'majestic', 'marvelous', 'masked', 'massive', 'mechanical', 'mercurial', 'meritorious', 'merry', 'methodical', 'meticulous', 'mighty', 'miniature', 'mirthful', 'mischievous', 'modest', 'momentous', 'motionless', 'multicolored', 'murmuring', 'musical', 'mustachioed', 'mysterious', 'nascent', 'neighborly', 'noble', 'nomadic', 'nonchalant', 'noncommittal', 'observant', 'omnidirectional', 'omnipotent', 'omnipresent', 'omnivorous', 'optimal', 'optimistic', 'otherworldly', 'outgoing', 'outspoken', 'panoramic', 'peaceful', 'perceptive', 'perpetual', 'perplexing', 'perspicacious', 'philosophical', 'picturesque', 'playful', 'pleasant', 'poised', 'practical', 'precious', 'precise', 'precocious', 'prestigious', 'primeval', 'primordial', 'prismatic', 'proactive', 'proficient', 'prototypical', 'prudent', 'purposeful', 'qualified', 'quizzical', 'quotable', 'radiant', 'reassured', 'reclusive', 'recursive', 'recurring', 'reflective', 'rejoicing', 'relaxed', 'relieved', 'remarkable', 'renowned', 'resilient', 'resolute', 'resourceful', 'rigorous', 'roaring', 'robust', 'salient', 'salubrious', 'sanguine', 'sapient', 'sarcastic', 'satisfied', 'scholarly', 'scintillating', 'scrupulous', 'selective', 'shiny', 'sincere', 'singing', 'sleek', 'sleepy', 'slippery', 'smiling', 'smooth', 'solid', 'sophisticated', 'sparkling', 'spectacular', 'splendid', 'spotless', 'squeaky', 'stately', 'steady', 'strategic', 'striped', 'stunning', 'stupendous', 'stylish', 'substantial', 'sufficient', 'swimming', 'symbolic', 'symmetrical', 'taciturn', 'terrestrial', 'tessellated', 'thankful', 'theoretical', 'thoughtful', 'thriving', 'timeless', 'timely', 'topographical', 'transparent', 'tranquil', 'transparent', 'tricky', 'ubiquitous', 'uncanny', 'unclouded', 'undisputed', 'unexpected', 'unfathomable', 'unflappable', 'unique', 'universal', 'unofficial', 'unseen', 'unthinkable', 'uproarious', 'variegated', 'versatile', 'victorious', 'vigilant', 'vigorous', 'virtuous', 'vivacious', 'vivid', 'vociferous', 'wacky', 'wandering', 'watchful', 'whimsical', 'windswept', 'witty', 'wonderful', 'wondrous', 'yakkety', 'yawning', 'yeatsian', 'yelling', 'yellow', 'yellowing', 'young', 'youthful', 'yummy', 'zany', 'zaftig', 'zealous', 'zestful', 'zesty', 'zippy', 'zoonatic', 'zygomorphous', 'zymoid', 'zymolytic', ] ANIMALS = [ 'aardvark', 'albatross', 'alligator', 'alpaca', 'angelfish', 'anteater', 'antelope', 'ape', 'armadillo', 'badger', 'barracuda', 'bat', 'beagle', 'bear', 'beaver', 'bird', 'bittern', 'bobolink', 'brontosaurus', 'boa-constrictor', 'buffalo', 'bulldog', 'bumblebee', 'butterfly', 'buzzard', 'camel', 'caribou', 'cassowary', 'cat', 'catfish', 'caterpillar', 'cauldron', 'centipede', 'cete', 'chameleon', 'cheetah', 'chinchilla', 'chipmunk', 'cobra', 'coelacanth', 'condor', 'coot', 'coral-snake', 'cormorant', 'crab', 'crane', 'crocodile', 'dalmatian', 'deer', 'dolphin', 'donkey', 'dotterel', 'dove', 'dragonfish', 'dragonfly', 'duck', 'eagle', 'eel', 'elephant', 'elk', 'falcon', 'ferret', 'finch', 'firefly', 'fish', 'flamingo', 'fox', 'frog', 'gang', 'gazelle', 'gecko', 'gerbil', 'giraffe', 'gnu', 'goldfish', 'goose', 'gorilla', 'grasshopper', 'greyhound', 'grouse', 'gull', 'hamster', 'hare', 'hawk', 'hatchetfish', 'hedgehog', 'heron', 'herring', 'hornet', 'horse', 'hummingbird', 'ibex', 'ibis', 'iguana', 'jackal', 'jaguar', 'jay', 'jellyfish', 'kangaroo', 'kestrel', 'kingfisher', 'koala', 'koi', 'lapwing', 'lark', 'lemur', 'leopard', 'lion', 'lionfish', 'llama', 'lobster', 'loris', 'magpie', 'mallard', 'mandrill', 'manta-ray', 'mantis', 'marlin', 'mastiff', 'mollusk', 'mongoose', 'moose', 'mouse', 'mule', 'narwhal', 'nautilus', 'newt', 'nightingale', 'obstinacy', 'octopus', 'okapi', 'opossum', 'orca', 'osprey', 'ostrich', 'otter', 'owl', 'panda', 'panther', 'parrot', 'partridge', 'pelican', 'penguin', 'pheasant', 'pig', 'pigeon', 'platypus', 'polar-bear', 'porcupine', 'porpoise', 'python', 'quail', 'rabbit', 'raccoon', 'ram', 'raven', 'reindeer', 'rhinoceros', 'roadrunner', 'rook', 'salamander', 'salmon', 'sandpiper', 'scorpion', 'sea-cucumber', 'sea-lion', 'sea-snake', 'sea-turtle', 'seahorse', 'seal', 'shark', 'sheep', 'shrewdness', 'sloth', 'sleuth', 'snowy-owl', 'songbird', 'sparrow', 'spider', 'squid', 'squirrel', 'starfish', 'starling', 'stegosaurus', 'stingray', 'stork', 'swan', 'tapir', 'tiger', 'toucan', 'triceratops', 'turtle', 'vampire-bat', 'velociraptor', 'wallaby', 'water-buffalo', 'walrus', 'whale', 'wolf', 'wolverine', 'wombat', 'wrasse', 'wren', 'xantus', 'xenarthra', 'xenops', 'xerus', 'yabby', 'yak', 'yellow-eyed-penguin', 'yellow-ground-squirrel', 'yellow-mongoose', 'yuma-myotis', 'zebra', 'zebra-finch', 'zebrafish', 'zebra-shark', 'zebroid', 'zebu', 'zonkey', 'zorilla', 'zorro', 'zorse', 'clowder', # cats 'pounce', # cats 'kindle', # kittens 'litter', # kittens, dogs 'intrigue', # kittens 'cowardice', # dogs 'pace', # donkeys 'parade', # elephants 'business', # ferrets 'shrewdness', # apes 'gang', # buffalo 'obstinacy', # buffalo 'leash', # foxes 'skulk', # foxes 'earth', # foxes 'tower', # giraffes 'tribe', # goats 'trip', # goats 'band', # gorillas 'bloat', # hippos 'thunder', # hippos 'cackle', # hyenas 'shadow', # jaguar 'troop', # kangaroo 'mob', # kangaroo 'conspiracy', # lemurs 'leap', # leopards 'pride', # lions 'sawt', # lions 'richness', # martens 'labor', # moles 'troop', # monkeys 'barrel', # monkeys 'pack', # mules 'span', # mules 'barren', # mules 'romp', # otter 'drift', # pigs 'drove', # drove 'sounder', # pigs 'team', # pigs 'passel', # pigs 'prickle', # porcupines 'pod', # porpoises, whales 'school', # porpoises, whales 'herd', # porpoises 'turmoil', # porpoises 'colony', # rabbits 'warren', # rabbits 'nest', # rabbits 'down', # rabbits 'husk', # rabbits 'gam', # whales 'route', # wolves 'woute', # wolves 'sedge', # bitterns 'wake', # buzzard 'chain', # bobolink 'cover', # coot 'gulp', # cormorants 'murder', # crows, magpies 'horde', # crows 'trip', # dotterel 'dule', # doves 'pitying', # doves 'brace', # ducks 'team', # ducks 'flock', # ducks 'raft', # ducks 'paddling', # ducks 'badling', # ducks 'convocation', # eagles 'charm', # finches, magpies 'flamingos', # stand 'gaggle', # geese 'skein', # geese 'cast', # hawk 'kettle', # hawk 'boil', # hawk 'sedge', # heron 'siege', # heron 'party', # jays 'scold', # jays 'deceit', # lapwings 'exaltation', # larks 'sord', # mallards 'tiding', # magpies 'gulp', # magpies 'watch', # nightingales 'parliament', # owls 'pandemonium', # parrots 'company', # parrots 'covey', # partridge 'ostentation', # peacocks 'muster', # peacocks, penguins 'parcel', # penguins 'rookery', # rookery ]
2ps/djenga
djenga/animal_pairs/dictionary.py
Python
bsd-3-clause
12,228
[ "Elk", "Firefly", "GULP", "Jaguar", "MOOSE", "ORCA", "Octopus" ]
c40ea27e33ae16886a3c7748045e36b647a70a3ba8b51ff1a591846d4878dd2a
#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """A quick and dirty example of using Mayavi to overlay anatomy and activation. """ #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from __future__ import print_function # Python 2/3 compatibility import numpy as np try: from mayavi import mlab except ImportError: try: from enthought.mayavi import mlab except ImportError: raise RuntimeError('Need mayavi for this module') from fiac_util import load_image_fiac #----------------------------------------------------------------------------- # Globals #----------------------------------------------------------------------------- MASK = load_image_fiac('group', 'mask.nii') AVGANAT = load_image_fiac('group', 'avganat.nii') #----------------------------------------------------------------------------- # Functions #----------------------------------------------------------------------------- def view_thresholdedT(design, contrast, threshold, inequality=np.greater): """ A mayavi isosurface view of thresholded t-statistics Parameters ---------- design : {'block', 'event'} contrast : str threshold : float inequality : {np.greater, np.less}, optional """ maska = np.asarray(MASK) tmap = np.array(load_image_fiac('group', design, contrast, 't.nii')) test = inequality(tmap, threshold) tval = np.zeros(tmap.shape) tval[test] = tmap[test] # XXX make the array axes agree with mayavi2 avganata = np.array(AVGANAT) avganat_iso = mlab.contour3d(avganata * maska, opacity=0.3, contours=[3600], color=(0.8,0.8,0.8)) avganat_iso.actor.property.backface_culling = True avganat_iso.actor.property.ambient = 0.3 tval_iso = mlab.contour3d(tval * MASK, color=(0.8,0.3,0.3), contours=[threshold]) return avganat_iso, tval_iso #----------------------------------------------------------------------------- # Script entry point #----------------------------------------------------------------------------- if __name__ == '__main__': # A simple example use case design = 'block' contrast = 'sentence_0' threshold = 0.3 print('Starting thresholded view with:') print('Design=', design, 'contrast=', contrast, 'threshold=', threshold) view_thresholdedT(design, contrast, threshold)
bthirion/nipy
examples/fiac/view_contrasts_3d.py
Python
bsd-3-clause
2,584
[ "Mayavi" ]
600cfdcd0f7d3ba13d019f07528c8ce22f2aab096a6f4db9f06dbc9e60c09632
# (C) British Crown Copyright 2016, Met Office. """ The package: `ASoP1_spectral` package contains methods for analysing precipitation intensity at any given time resolution, as part of the Analysing Scale of Precipitation v1.0 package described in Klingaman et al. (2016). ASoP (v1.0): A set of methods for analyzing scales of precipitation in general circulation models. By N.P. Klingaman, G.M. Martin and A.F. Moise. Geoscientific Model Development, submitted. Testing is based on the reproduction of standard output netCDF data files and PNG figure files using an artificial input rainfall dataset. """
gillmmartin/ASoP1-Spectral
ASoP1_Spectral/tests/__init__.py
Python
apache-2.0
612
[ "NetCDF" ]
7cd4de208af0bc6ad8503f1a5f7c325dd9aec87bf5b89dcbe39b562da600322b
from paraview.simple import * #reader = XDMFReader(FileNames=['/scratch/snx3000tds/piccinal/sphflow/rotating_square_patch_3D/015/RES/square200.h5.xmf']) reader = XDMFReader(FileNames=['/scratch/snx3000tds/piccinal/sphflow/rotating_square_patch_3D/015/RES/square278.h5.xmf']) reader.PointArrayStatus = ['DeltaX', 'ID', 'Interface', 'Kind', 'Marker', 'P', 'Velocity', 'Volume'] reader.GridStatus = ['Particles'] rep=Show() rep.SetRepresentationType("Points") ResetCamera() SaveScreenshot("foo.png")
jgphpc/linux
paraview/dom/pvbatch/0.py
Python
gpl-2.0
505
[ "ParaView" ]
c5ac7f9fe64c793a3d893bbe0d3a4833c5d4aac0c8e9716534ac62d74b8e96fc
#!/usr/bin/python # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. DOCUMENTATION = """ --- module: ec2_elb_lb description: - Returns information about the load balancer. - Will be marked changed when called only if state is changed. short_description: Creates or destroys Amazon ELB. version_added: "1.5" author: - "Jim Dalton (@jsdalton)" - "Rick Mendes (@rickmendes)" options: state: description: - Create or destroy the ELB choices: ["present", "absent"] required: true name: description: - The name of the ELB required: true listeners: description: - List of ports/protocols for this ELB to listen on (see example) required: false purge_listeners: description: - Purge existing listeners on ELB that are not found in listeners required: false default: true zones: description: - List of availability zones to enable on this ELB required: false purge_zones: description: - Purge existing availability zones on ELB that are not found in zones required: false default: false security_group_ids: description: - A list of security groups to apply to the elb require: false default: None version_added: "1.6" security_group_names: description: - A list of security group names to apply to the elb require: false default: None version_added: "2.0" health_check: description: - An associative array of health check configuration settings (see example) require: false default: None access_logs: description: - An associative array of access logs configuration settings (see example) require: false default: None version_added: "2.0" subnets: description: - A list of VPC subnets to use when creating ELB. Zones should be empty if using this. required: false default: None aliases: [] version_added: "1.7" purge_subnets: description: - Purge existing subnet on ELB that are not found in subnets required: false default: false version_added: "1.7" scheme: description: - The scheme to use when creating the ELB. For a private VPC-visible ELB use 'internal'. required: false default: 'internet-facing' version_added: "1.7" validate_certs: description: - When set to "no", SSL certificates will not be validated for boto versions >= 2.6.0. required: false default: "yes" choices: ["yes", "no"] aliases: [] version_added: "1.5" connection_draining_timeout: description: - Wait a specified timeout allowing connections to drain before terminating an instance required: false aliases: [] version_added: "1.8" idle_timeout: description: - ELB connections from clients and to servers are timed out after this amount of time required: false version_added: "2.0" cross_az_load_balancing: description: - Distribute load across all configured Availability Zones required: false default: "no" choices: ["yes", "no"] aliases: [] version_added: "1.8" stickiness: description: - An associative array of stickness policy settings. Policy will be applied to all listeners ( see example ) required: false version_added: "2.0" wait: description: - When specified, Ansible will check the status of the load balancer to ensure it has been successfully removed from AWS. required: false default: no choices: ["yes", "no"] version_added: "2.1" wait_timeout: description: - Used in conjunction with wait. Number of seconds to wait for the elb to be terminated. A maximum of 600 seconds (10 minutes) is allowed. required: false default: 60 version_added: "2.1" extends_documentation_fragment: - aws - ec2 """ EXAMPLES = """ # Note: None of these examples set aws_access_key, aws_secret_key, or region. # It is assumed that their matching environment variables are set. # Basic provisioning example (non-VPC) - local_action: module: ec2_elb_lb name: "test-please-delete" state: present zones: - us-east-1a - us-east-1d listeners: - protocol: http # options are http, https, ssl, tcp load_balancer_port: 80 instance_port: 80 - protocol: https load_balancer_port: 443 instance_protocol: http # optional, defaults to value of protocol setting instance_port: 80 # ssl certificate required for https or ssl ssl_certificate_id: "arn:aws:iam::123456789012:server-certificate/company/servercerts/ProdServerCert" # Internal ELB example - local_action: module: ec2_elb_lb name: "test-vpc" scheme: internal state: present subnets: - subnet-abcd1234 - subnet-1a2b3c4d listeners: - protocol: http # options are http, https, ssl, tcp load_balancer_port: 80 instance_port: 80 # Configure a health check and the access logs - local_action: module: ec2_elb_lb name: "test-please-delete" state: present zones: - us-east-1d listeners: - protocol: http load_balancer_port: 80 instance_port: 80 health_check: ping_protocol: http # options are http, https, ssl, tcp ping_port: 80 ping_path: "/index.html" # not required for tcp or ssl response_timeout: 5 # seconds interval: 30 # seconds unhealthy_threshold: 2 healthy_threshold: 10 access_logs: interval: 5 # minutes (defaults to 60) s3_location: "my-bucket" # This value is required if access_logs is set s3_prefix: "logs" # Ensure ELB is gone - local_action: module: ec2_elb_lb name: "test-please-delete" state: absent # Ensure ELB is gone and wait for check (for default timeout) - local_action: module: ec2_elb_lb name: "test-please-delete" state: absent wait: yes # Ensure ELB is gone and wait for check with timeout value - local_action: module: ec2_elb_lb name: "test-please-delete" state: absent wait: yes wait_timeout: 600 # Normally, this module will purge any listeners that exist on the ELB # but aren't specified in the listeners parameter. If purge_listeners is # false it leaves them alone - local_action: module: ec2_elb_lb name: "test-please-delete" state: present zones: - us-east-1a - us-east-1d listeners: - protocol: http load_balancer_port: 80 instance_port: 80 purge_listeners: no # Normally, this module will leave availability zones that are enabled # on the ELB alone. If purge_zones is true, then any extraneous zones # will be removed - local_action: module: ec2_elb_lb name: "test-please-delete" state: present zones: - us-east-1a - us-east-1d listeners: - protocol: http load_balancer_port: 80 instance_port: 80 purge_zones: yes # Creates a ELB and assigns a list of subnets to it. - local_action: module: ec2_elb_lb state: present name: 'New ELB' security_group_ids: 'sg-123456, sg-67890' region: us-west-2 subnets: 'subnet-123456,subnet-67890' purge_subnets: yes listeners: - protocol: http load_balancer_port: 80 instance_port: 80 # Create an ELB with connection draining, increased idle timeout and cross availability # zone load balancing - local_action: module: ec2_elb_lb name: "New ELB" state: present connection_draining_timeout: 60 idle_timeout: 300 cross_az_load_balancing: "yes" region: us-east-1 zones: - us-east-1a - us-east-1d listeners: - protocols: http - load_balancer_port: 80 - instance_port: 80 # Create an ELB with load balanacer stickiness enabled - local_action: module: ec2_elb_lb name: "New ELB" state: present region: us-east-1 zones: - us-east-1a - us-east-1d listeners: - protocols: http - load_balancer_port: 80 - instance_port: 80 stickiness: type: loadbalancer enabled: yes expiration: 300 # Create an ELB with application stickiness enabled - local_action: module: ec2_elb_lb name: "New ELB" state: present region: us-east-1 zones: - us-east-1a - us-east-1d listeners: - protocols: http - load_balancer_port: 80 - instance_port: 80 stickiness: type: application enabled: yes cookie: SESSIONID """ try: import boto import boto.ec2.elb import boto.ec2.elb.attributes from boto.ec2.elb.healthcheck import HealthCheck from boto.regioninfo import RegionInfo HAS_BOTO = True except ImportError: HAS_BOTO = False class ElbManager(object): """Handles ELB creation and destruction""" def __init__(self, module, name, listeners=None, purge_listeners=None, zones=None, purge_zones=None, security_group_ids=None, health_check=None, subnets=None, purge_subnets=None, scheme="internet-facing", connection_draining_timeout=None, idle_timeout=None, cross_az_load_balancing=None, access_logs=None, stickiness=None, wait=None, wait_timeout=None, region=None, **aws_connect_params): self.module = module self.name = name self.listeners = listeners self.purge_listeners = purge_listeners self.zones = zones self.purge_zones = purge_zones self.security_group_ids = security_group_ids self.health_check = health_check self.subnets = subnets self.purge_subnets = purge_subnets self.scheme = scheme self.connection_draining_timeout = connection_draining_timeout self.idle_timeout = idle_timeout self.cross_az_load_balancing = cross_az_load_balancing self.access_logs = access_logs self.stickiness = stickiness self.wait = wait self.wait_timeout = wait_timeout self.aws_connect_params = aws_connect_params self.region = region self.changed = False self.status = 'gone' self.elb_conn = self._get_elb_connection() self.elb = self._get_elb() self.ec2_conn = self._get_ec2_connection() def ensure_ok(self): """Create the ELB""" if not self.elb: # Zones and listeners will be added at creation self._create_elb() else: self._set_zones() self._set_security_groups() self._set_elb_listeners() self._set_subnets() self._set_health_check() # boto has introduced support for some ELB attributes in # different versions, so we check first before trying to # set them to avoid errors if self._check_attribute_support('connection_draining'): self._set_connection_draining_timeout() if self._check_attribute_support('connecting_settings'): self._set_idle_timeout() if self._check_attribute_support('cross_zone_load_balancing'): self._set_cross_az_load_balancing() if self._check_attribute_support('access_log'): self._set_access_log() # add sitcky options self.select_stickiness_policy() def ensure_gone(self): """Destroy the ELB""" if self.elb: self._delete_elb() if self.wait: elb_removed = self._wait_for_elb_removed() # Unfortunately even though the ELB itself is removed quickly # the interfaces take longer so reliant security groups cannot # be deleted until the interface has registered as removed. elb_interface_removed = self._wait_for_elb_interface_removed() if not (elb_removed and elb_interface_removed): self.module.fail_json(msg='Timed out waiting for removal of load balancer.') def get_info(self): try: check_elb = self.elb_conn.get_all_load_balancers(self.name)[0] except: check_elb = None if not check_elb: info = { 'name': self.name, 'status': self.status, 'region': self.region } else: try: lb_cookie_policy = check_elb.policies.lb_cookie_stickiness_policies[0].__dict__['policy_name'] except: lb_cookie_policy = None try: app_cookie_policy = check_elb.policies.app_cookie_stickiness_policies[0].__dict__['policy_name'] except: app_cookie_policy = None info = { 'name': check_elb.name, 'dns_name': check_elb.dns_name, 'zones': check_elb.availability_zones, 'security_group_ids': check_elb.security_groups, 'status': self.status, 'subnets': self.subnets, 'scheme': check_elb.scheme, 'hosted_zone_name': check_elb.canonical_hosted_zone_name, 'hosted_zone_id': check_elb.canonical_hosted_zone_name_id, 'lb_cookie_policy': lb_cookie_policy, 'app_cookie_policy': app_cookie_policy, 'instances': [instance.id for instance in check_elb.instances], 'out_of_service_count': 0, 'in_service_count': 0, 'unknown_instance_state_count': 0, 'region': self.region } # status of instances behind the ELB if info['instances']: info['instance_health'] = [ dict( instance_id = instance_state.instance_id, reason_code = instance_state.reason_code, state = instance_state.state ) for instance_state in self.elb_conn.describe_instance_health(self.name)] else: info['instance_health'] = [] # instance state counts: InService or OutOfService if info['instance_health']: for instance_state in info['instance_health']: if instance_state['state'] == "InService": info['in_service_count'] += 1 elif instance_state['state'] == "OutOfService": info['out_of_service_count'] += 1 else: info['unknown_instance_state_count'] += 1 if check_elb.health_check: info['health_check'] = { 'target': check_elb.health_check.target, 'interval': check_elb.health_check.interval, 'timeout': check_elb.health_check.timeout, 'healthy_threshold': check_elb.health_check.healthy_threshold, 'unhealthy_threshold': check_elb.health_check.unhealthy_threshold, } if check_elb.listeners: info['listeners'] = [self._api_listener_as_tuple(l) for l in check_elb.listeners] elif self.status == 'created': # When creating a new ELB, listeners don't show in the # immediately returned result, so just include the # ones that were added info['listeners'] = [self._listener_as_tuple(l) for l in self.listeners] else: info['listeners'] = [] if self._check_attribute_support('connection_draining'): info['connection_draining_timeout'] = self.elb_conn.get_lb_attribute(self.name, 'ConnectionDraining').timeout if self._check_attribute_support('connecting_settings'): info['idle_timeout'] = self.elb_conn.get_lb_attribute(self.name, 'ConnectingSettings').idle_timeout if self._check_attribute_support('cross_zone_load_balancing'): is_cross_az_lb_enabled = self.elb_conn.get_lb_attribute(self.name, 'CrossZoneLoadBalancing') if is_cross_az_lb_enabled: info['cross_az_load_balancing'] = 'yes' else: info['cross_az_load_balancing'] = 'no' # return stickiness info? return info def _wait_for_elb_removed(self): polling_increment_secs = 15 max_retries = (self.wait_timeout / polling_increment_secs) status_achieved = False for x in range(0, max_retries): try: result = self.elb_conn.get_all_lb_attributes(self.name) except (boto.exception.BotoServerError, StandardError), e: if "LoadBalancerNotFound" in e.code: status_achieved = True break else: time.sleep(polling_increment_secs) return status_achieved def _wait_for_elb_interface_removed(self): polling_increment_secs = 15 max_retries = (self.wait_timeout / polling_increment_secs) status_achieved = False elb_interfaces = self.ec2_conn.get_all_network_interfaces( filters={'attachment.instance-owner-id': 'amazon-elb', 'description': 'ELB {0}'.format(self.name) }) for x in range(0, max_retries): for interface in elb_interfaces: try: result = self.ec2_conn.get_all_network_interfaces(interface.id) if result == []: status_achieved = True break else: time.sleep(polling_increment_secs) except (boto.exception.BotoServerError, StandardError), e: if 'InvalidNetworkInterfaceID' in e.code: status_achieved = True break else: self.module.fail_json(msg=str(e)) return status_achieved def _get_elb(self): elbs = self.elb_conn.get_all_load_balancers() for elb in elbs: if self.name == elb.name: self.status = 'ok' return elb def _get_elb_connection(self): try: return connect_to_aws(boto.ec2.elb, self.region, **self.aws_connect_params) except (boto.exception.NoAuthHandlerFound, AnsibleAWSError), e: self.module.fail_json(msg=str(e)) def _get_ec2_connection(self): try: return connect_to_aws(boto.ec2, self.region, **self.aws_connect_params) except (boto.exception.NoAuthHandlerFound, StandardError), e: self.module.fail_json(msg=str(e)) def _delete_elb(self): # True if succeeds, exception raised if not result = self.elb_conn.delete_load_balancer(name=self.name) if result: self.changed = True self.status = 'deleted' def _create_elb(self): listeners = [self._listener_as_tuple(l) for l in self.listeners] self.elb = self.elb_conn.create_load_balancer(name=self.name, zones=self.zones, security_groups=self.security_group_ids, complex_listeners=listeners, subnets=self.subnets, scheme=self.scheme) if self.elb: self.changed = True self.status = 'created' def _create_elb_listeners(self, listeners): """Takes a list of listener tuples and creates them""" # True if succeeds, exception raised if not self.changed = self.elb_conn.create_load_balancer_listeners(self.name, complex_listeners=listeners) def _delete_elb_listeners(self, listeners): """Takes a list of listener tuples and deletes them from the elb""" ports = [l[0] for l in listeners] # True if succeeds, exception raised if not self.changed = self.elb_conn.delete_load_balancer_listeners(self.name, ports) def _set_elb_listeners(self): """ Creates listeners specified by self.listeners; overwrites existing listeners on these ports; removes extraneous listeners """ listeners_to_add = [] listeners_to_remove = [] listeners_to_keep = [] # Check for any listeners we need to create or overwrite for listener in self.listeners: listener_as_tuple = self._listener_as_tuple(listener) # First we loop through existing listeners to see if one is # already specified for this port existing_listener_found = None for existing_listener in self.elb.listeners: # Since ELB allows only one listener on each incoming port, a # single match on the incoming port is all we're looking for if existing_listener[0] == int(listener['load_balancer_port']): existing_listener_found = self._api_listener_as_tuple(existing_listener) break if existing_listener_found: # Does it match exactly? if listener_as_tuple != existing_listener_found: # The ports are the same but something else is different, # so we'll remove the existing one and add the new one listeners_to_remove.append(existing_listener_found) listeners_to_add.append(listener_as_tuple) else: # We already have this listener, so we're going to keep it listeners_to_keep.append(existing_listener_found) else: # We didn't find an existing listener, so just add the new one listeners_to_add.append(listener_as_tuple) # Check for any extraneous listeners we need to remove, if desired if self.purge_listeners: for existing_listener in self.elb.listeners: existing_listener_tuple = self._api_listener_as_tuple(existing_listener) if existing_listener_tuple in listeners_to_remove: # Already queued for removal continue if existing_listener_tuple in listeners_to_keep: # Keep this one around continue # Since we're not already removing it and we don't need to keep # it, let's get rid of it listeners_to_remove.append(existing_listener_tuple) if listeners_to_remove: self._delete_elb_listeners(listeners_to_remove) if listeners_to_add: self._create_elb_listeners(listeners_to_add) def _api_listener_as_tuple(self, listener): """Adds ssl_certificate_id to ELB API tuple if present""" base_tuple = listener.get_complex_tuple() if listener.ssl_certificate_id and len(base_tuple) < 5: return base_tuple + (listener.ssl_certificate_id,) return base_tuple def _listener_as_tuple(self, listener): """Formats listener as a 4- or 5-tuples, in the order specified by the ELB API""" # N.B. string manipulations on protocols below (str(), upper()) is to # ensure format matches output from ELB API listener_list = [ int(listener['load_balancer_port']), int(listener['instance_port']), str(listener['protocol'].upper()), ] # Instance protocol is not required by ELB API; it defaults to match # load balancer protocol. We'll mimic that behavior here if 'instance_protocol' in listener: listener_list.append(str(listener['instance_protocol'].upper())) else: listener_list.append(str(listener['protocol'].upper())) if 'ssl_certificate_id' in listener: listener_list.append(str(listener['ssl_certificate_id'])) return tuple(listener_list) def _enable_zones(self, zones): try: self.elb.enable_zones(zones) except boto.exception.BotoServerError, e: if "Invalid Availability Zone" in e.error_message: self.module.fail_json(msg=e.error_message) else: self.module.fail_json(msg="an unknown server error occurred, please try again later") self.changed = True def _disable_zones(self, zones): try: self.elb.disable_zones(zones) except boto.exception.BotoServerError, e: if "Invalid Availability Zone" in e.error_message: self.module.fail_json(msg=e.error_message) else: self.module.fail_json(msg="an unknown server error occurred, please try again later") self.changed = True def _attach_subnets(self, subnets): self.elb_conn.attach_lb_to_subnets(self.name, subnets) self.changed = True def _detach_subnets(self, subnets): self.elb_conn.detach_lb_from_subnets(self.name, subnets) self.changed = True def _set_subnets(self): """Determine which subnets need to be attached or detached on the ELB""" if self.subnets: if self.purge_subnets: subnets_to_detach = list(set(self.elb.subnets) - set(self.subnets)) subnets_to_attach = list(set(self.subnets) - set(self.elb.subnets)) else: subnets_to_detach = None subnets_to_attach = list(set(self.subnets) - set(self.elb.subnets)) if subnets_to_attach: self._attach_subnets(subnets_to_attach) if subnets_to_detach: self._detach_subnets(subnets_to_detach) def _set_zones(self): """Determine which zones need to be enabled or disabled on the ELB""" if self.zones: if self.purge_zones: zones_to_disable = list(set(self.elb.availability_zones) - set(self.zones)) zones_to_enable = list(set(self.zones) - set(self.elb.availability_zones)) else: zones_to_disable = None zones_to_enable = list(set(self.zones) - set(self.elb.availability_zones)) if zones_to_enable: self._enable_zones(zones_to_enable) # N.B. This must come second, in case it would have removed all zones if zones_to_disable: self._disable_zones(zones_to_disable) def _set_security_groups(self): if self.security_group_ids != None and set(self.elb.security_groups) != set(self.security_group_ids): self.elb_conn.apply_security_groups_to_lb(self.name, self.security_group_ids) self.changed = True def _set_health_check(self): """Set health check values on ELB as needed""" if self.health_check: # This just makes it easier to compare each of the attributes # and look for changes. Keys are attributes of the current # health_check; values are desired values of new health_check health_check_config = { "target": self._get_health_check_target(), "timeout": self.health_check['response_timeout'], "interval": self.health_check['interval'], "unhealthy_threshold": self.health_check['unhealthy_threshold'], "healthy_threshold": self.health_check['healthy_threshold'], } update_health_check = False # The health_check attribute is *not* set on newly created # ELBs! So we have to create our own. if not self.elb.health_check: self.elb.health_check = HealthCheck() for attr, desired_value in health_check_config.iteritems(): if getattr(self.elb.health_check, attr) != desired_value: setattr(self.elb.health_check, attr, desired_value) update_health_check = True if update_health_check: self.elb.configure_health_check(self.elb.health_check) self.changed = True def _check_attribute_support(self, attr): return hasattr(boto.ec2.elb.attributes.LbAttributes(), attr) def _set_cross_az_load_balancing(self): attributes = self.elb.get_attributes() if self.cross_az_load_balancing: if not attributes.cross_zone_load_balancing.enabled: self.changed = True attributes.cross_zone_load_balancing.enabled = True else: if attributes.cross_zone_load_balancing.enabled: self.changed = True attributes.cross_zone_load_balancing.enabled = False self.elb_conn.modify_lb_attribute(self.name, 'CrossZoneLoadBalancing', attributes.cross_zone_load_balancing.enabled) def _set_access_log(self): attributes = self.elb.get_attributes() if self.access_logs: if 's3_location' not in self.access_logs: self.module.fail_json(msg='s3_location information required') access_logs_config = { "enabled": True, "s3_bucket_name": self.access_logs['s3_location'], "s3_bucket_prefix": self.access_logs.get('s3_prefix', ''), "emit_interval": self.access_logs.get('interval', 60), } update_access_logs_config = False for attr, desired_value in access_logs_config.iteritems(): if getattr(attributes.access_log, attr) != desired_value: setattr(attributes.access_log, attr, desired_value) update_access_logs_config = True if update_access_logs_config: self.elb_conn.modify_lb_attribute(self.name, 'AccessLog', attributes.access_log) self.changed = True elif attributes.access_log.enabled: attributes.access_log.enabled = False self.changed = True self.elb_conn.modify_lb_attribute(self.name, 'AccessLog', attributes.access_log) def _set_connection_draining_timeout(self): attributes = self.elb.get_attributes() if self.connection_draining_timeout is not None: if not attributes.connection_draining.enabled or \ attributes.connection_draining.timeout != self.connection_draining_timeout: self.changed = True attributes.connection_draining.enabled = True attributes.connection_draining.timeout = self.connection_draining_timeout self.elb_conn.modify_lb_attribute(self.name, 'ConnectionDraining', attributes.connection_draining) else: if attributes.connection_draining.enabled: self.changed = True attributes.connection_draining.enabled = False self.elb_conn.modify_lb_attribute(self.name, 'ConnectionDraining', attributes.connection_draining) def _set_idle_timeout(self): attributes = self.elb.get_attributes() if self.idle_timeout is not None: if attributes.connecting_settings.idle_timeout != self.idle_timeout: self.changed = True attributes.connecting_settings.idle_timeout = self.idle_timeout self.elb_conn.modify_lb_attribute(self.name, 'ConnectingSettings', attributes.connecting_settings) def _policy_name(self, policy_type): return __file__.split('/')[-1].replace('_', '-') + '-' + policy_type def _create_policy(self, policy_param, policy_meth, policy): getattr(self.elb_conn, policy_meth )(policy_param, self.elb.name, policy) def _delete_policy(self, elb_name, policy): self.elb_conn.delete_lb_policy(elb_name, policy) def _update_policy(self, policy_param, policy_meth, policy_attr, policy): self._delete_policy(self.elb.name, policy) self._create_policy(policy_param, policy_meth, policy) def _set_listener_policy(self, listeners_dict, policy=[]): for listener_port in listeners_dict: if listeners_dict[listener_port].startswith('HTTP'): self.elb_conn.set_lb_policies_of_listener(self.elb.name, listener_port, policy) def _set_stickiness_policy(self, elb_info, listeners_dict, policy, **policy_attrs): for p in getattr(elb_info.policies, policy_attrs['attr']): if str(p.__dict__['policy_name']) == str(policy[0]): if str(p.__dict__[policy_attrs['dict_key']]) != str(policy_attrs['param_value'] or 0): self._set_listener_policy(listeners_dict) self._update_policy(policy_attrs['param_value'], policy_attrs['method'], policy_attrs['attr'], policy[0]) self.changed = True break else: self._create_policy(policy_attrs['param_value'], policy_attrs['method'], policy[0]) self.changed = True self._set_listener_policy(listeners_dict, policy) def select_stickiness_policy(self): if self.stickiness: if 'cookie' in self.stickiness and 'expiration' in self.stickiness: self.module.fail_json(msg='\'cookie\' and \'expiration\' can not be set at the same time') elb_info = self.elb_conn.get_all_load_balancers(self.elb.name)[0] d = {} for listener in elb_info.listeners: d[listener[0]] = listener[2] listeners_dict = d if self.stickiness['type'] == 'loadbalancer': policy = [] policy_type = 'LBCookieStickinessPolicyType' if self.module.boolean(self.stickiness['enabled']) == True: if 'expiration' not in self.stickiness: self.module.fail_json(msg='expiration must be set when type is loadbalancer') expiration = self.stickiness['expiration'] if self.stickiness['expiration'] is not 0 else None policy_attrs = { 'type': policy_type, 'attr': 'lb_cookie_stickiness_policies', 'method': 'create_lb_cookie_stickiness_policy', 'dict_key': 'cookie_expiration_period', 'param_value': expiration } policy.append(self._policy_name(policy_attrs['type'])) self._set_stickiness_policy(elb_info, listeners_dict, policy, **policy_attrs) elif self.module.boolean(self.stickiness['enabled']) == False: if len(elb_info.policies.lb_cookie_stickiness_policies): if elb_info.policies.lb_cookie_stickiness_policies[0].policy_name == self._policy_name(policy_type): self.changed = True else: self.changed = False self._set_listener_policy(listeners_dict) self._delete_policy(self.elb.name, self._policy_name(policy_type)) elif self.stickiness['type'] == 'application': policy = [] policy_type = 'AppCookieStickinessPolicyType' if self.module.boolean(self.stickiness['enabled']) == True: if 'cookie' not in self.stickiness: self.module.fail_json(msg='cookie must be set when type is application') policy_attrs = { 'type': policy_type, 'attr': 'app_cookie_stickiness_policies', 'method': 'create_app_cookie_stickiness_policy', 'dict_key': 'cookie_name', 'param_value': self.stickiness['cookie'] } policy.append(self._policy_name(policy_attrs['type'])) self._set_stickiness_policy(elb_info, listeners_dict, policy, **policy_attrs) elif self.module.boolean(self.stickiness['enabled']) == False: if len(elb_info.policies.app_cookie_stickiness_policies): if elb_info.policies.app_cookie_stickiness_policies[0].policy_name == self._policy_name(policy_type): self.changed = True self._set_listener_policy(listeners_dict) self._delete_policy(self.elb.name, self._policy_name(policy_type)) else: self._set_listener_policy(listeners_dict) def _get_health_check_target(self): """Compose target string from healthcheck parameters""" protocol = self.health_check['ping_protocol'].upper() path = "" if protocol in ['HTTP', 'HTTPS'] and 'ping_path' in self.health_check: path = self.health_check['ping_path'] return "%s:%s%s" % (protocol, self.health_check['ping_port'], path) def main(): argument_spec = ec2_argument_spec() argument_spec.update(dict( state={'required': True, 'choices': ['present', 'absent']}, name={'required': True}, listeners={'default': None, 'required': False, 'type': 'list'}, purge_listeners={'default': True, 'required': False, 'type': 'bool'}, zones={'default': None, 'required': False, 'type': 'list'}, purge_zones={'default': False, 'required': False, 'type': 'bool'}, security_group_ids={'default': None, 'required': False, 'type': 'list'}, security_group_names={'default': None, 'required': False, 'type': 'list'}, health_check={'default': None, 'required': False, 'type': 'dict'}, subnets={'default': None, 'required': False, 'type': 'list'}, purge_subnets={'default': False, 'required': False, 'type': 'bool'}, scheme={'default': 'internet-facing', 'required': False}, connection_draining_timeout={'default': None, 'required': False}, idle_timeout={'default': None, 'required': False}, cross_az_load_balancing={'default': None, 'required': False}, stickiness={'default': None, 'required': False, 'type': 'dict'}, access_logs={'default': None, 'required': False, 'type': 'dict'}, wait={'default': False, 'type': 'bool', 'required': False}, wait_timeout={'default': 60, 'type': 'int', 'required': False} ) ) module = AnsibleModule( argument_spec=argument_spec, mutually_exclusive = [['security_group_ids', 'security_group_names']] ) if not HAS_BOTO: module.fail_json(msg='boto required for this module') region, ec2_url, aws_connect_params = get_aws_connection_info(module) if not region: module.fail_json(msg="Region must be specified as a parameter, in EC2_REGION or AWS_REGION environment variables or in boto configuration file") name = module.params['name'] state = module.params['state'] listeners = module.params['listeners'] purge_listeners = module.params['purge_listeners'] zones = module.params['zones'] purge_zones = module.params['purge_zones'] security_group_ids = module.params['security_group_ids'] security_group_names = module.params['security_group_names'] health_check = module.params['health_check'] access_logs = module.params['access_logs'] subnets = module.params['subnets'] purge_subnets = module.params['purge_subnets'] scheme = module.params['scheme'] connection_draining_timeout = module.params['connection_draining_timeout'] idle_timeout = module.params['idle_timeout'] cross_az_load_balancing = module.params['cross_az_load_balancing'] stickiness = module.params['stickiness'] wait = module.params['wait'] wait_timeout = module.params['wait_timeout'] if state == 'present' and not listeners: module.fail_json(msg="At least one port is required for ELB creation") if state == 'present' and not (zones or subnets): module.fail_json(msg="At least one availability zone or subnet is required for ELB creation") if wait_timeout > 600: module.fail_json(msg='wait_timeout maximum is 600 seconds') if security_group_names: security_group_ids = [] try: ec2 = ec2_connect(module) grp_details = ec2.get_all_security_groups() for group_name in security_group_names: if isinstance(group_name, basestring): group_name = [group_name] group_id = [ str(grp.id) for grp in grp_details if str(grp.name) in group_name ] security_group_ids.extend(group_id) except boto.exception.NoAuthHandlerFound, e: module.fail_json(msg = str(e)) elb_man = ElbManager(module, name, listeners, purge_listeners, zones, purge_zones, security_group_ids, health_check, subnets, purge_subnets, scheme, connection_draining_timeout, idle_timeout, cross_az_load_balancing, access_logs, stickiness, wait, wait_timeout, region=region, **aws_connect_params) # check for unsupported attributes for this version of boto if cross_az_load_balancing and not elb_man._check_attribute_support('cross_zone_load_balancing'): module.fail_json(msg="You must install boto >= 2.18.0 to use the cross_az_load_balancing attribute") if connection_draining_timeout and not elb_man._check_attribute_support('connection_draining'): module.fail_json(msg="You must install boto >= 2.28.0 to use the connection_draining_timeout attribute") if idle_timeout and not elb_man._check_attribute_support('connecting_settings'): module.fail_json(msg="You must install boto >= 2.33.0 to use the idle_timeout attribute") if state == 'present': elb_man.ensure_ok() elif state == 'absent': elb_man.ensure_gone() ansible_facts = {'ec2_elb': 'info'} ec2_facts_result = dict(changed=elb_man.changed, elb=elb_man.get_info(), ansible_facts=ansible_facts) module.exit_json(**ec2_facts_result) # import module snippets from ansible.module_utils.basic import * from ansible.module_utils.ec2 import * if __name__ == '__main__': main()
alxnov/ansible-modules-core
cloud/amazon/ec2_elb_lb.py
Python
gpl-3.0
43,768
[ "Dalton" ]
739b6f25d8e08ca3826a8e666e802afa52e46c44cae10cb9b3f01f88f48bbfb0
# -*- coding: utf-8 -*- # # hpc_benchmark.py # # This file is part of NEST. # # Copyright (C) 2004 The NEST Initiative # # NEST is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # NEST is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with NEST. If not, see <http://www.gnu.org/licenses/>. """ Random balanced network HPC benchmark ------------------------------------- This script produces a balanced random network of `scale*11250` neurons in which the excitatory-excitatory neurons exhibit STDP with multiplicative depression and power-law potentiation. A mutual equilibrium is obtained between the activity dynamics (low rate in asynchronous irregular regime) and the synaptic weight distribution (unimodal). The number of incoming connections per neuron is fixed and independent of network size (indegree=11250). This is the standard network investigated in [1]_, [2]_, [3]_. A note on scaling ~~~~~~~~~~~~~~~~~ This benchmark was originally developed for very large-scale simulations on supercomputers with more than 1 million neurons in the network and 11.250 incoming synapses per neuron. For such large networks, synaptic input to a single neuron will be little correlated across inputs and network activity will remain stable over long periods of time. The original network size corresponds to a scale parameter of 100 or more. In order to make it possible to test this benchmark script on desktop computers, the scale parameter is set to 1 below, while the number of 11.250 incoming synapses per neuron is retained. In this limit, correlations in input to neurons are large and will lead to increasing synaptic weights. Over time, network dynamics will therefore become unstable and all neurons in the network will fire in synchrony, leading to extremely slow simulation speeds. Therefore, the presimulation time is reduced to 50 ms below and the simulation time to 250 ms, while we usually use 100 ms presimulation and 1000 ms simulation time. For meaningful use of this benchmark, you should use a scale > 10 and check that the firing rate reported at the end of the benchmark is below 10 spikes per second. References ~~~~~~~~~~ .. [1] Morrison A, Aertsen A, Diesmann M (2007). Spike-timing-dependent plasticity in balanced random networks. Neural Comput 19(6):1437-67 .. [2] Helias et al (2012). Supercomputers ready for use as discovery machines for neuroscience. Front. Neuroinform. 6:26 .. [3] Kunkel et al (2014). Spiking network simulation code for petascale computers. Front. Neuroinform. 8:78 """ import numpy as np import os import sys import time import scipy.special as sp import nest import nest.raster_plot M_INFO = 10 M_ERROR = 30 ############################################################################### # Parameter section # Define all relevant parameters: changes should be made here params = { 'nvp': 1, # total number of virtual processes 'scale': 1., # scaling factor of the network size # total network size = scale*11250 neurons 'simtime': 250., # total simulation time in ms 'presimtime': 50., # simulation time until reaching equilibrium 'dt': 0.1, # simulation step 'record_spikes': True, # switch to record spikes of excitatory # neurons to file 'path_name': '.', # path where all files will have to be written 'log_file': 'log', # naming scheme for the log files } def convert_synapse_weight(tau_m, tau_syn, C_m): """ Computes conversion factor for synapse weight from mV to pA This function is specific to the leaky integrate-and-fire neuron model with alpha-shaped postsynaptic currents. """ # compute time to maximum of V_m after spike input # to neuron at rest a = tau_m / tau_syn b = 1.0 / tau_syn - 1.0 / tau_m t_rise = 1.0 / b * (-lambertwm1(-np.exp(-1.0 / a) / a).real - 1.0 / a) v_max = np.exp(1.0) / (tau_syn * C_m * b) * ( (np.exp(-t_rise / tau_m) - np.exp(-t_rise / tau_syn)) / b - t_rise * np.exp(-t_rise / tau_syn)) return 1. / v_max ############################################################################### # For compatibility with earlier benchmarks, we require a rise time of # ``t_rise = 1.700759 ms`` and we choose ``tau_syn`` to achieve this for given # ``tau_m``. This requires numerical inversion of the expression for ``t_rise`` # in ``convert_synapse_weight``. We computed this value once and hard-code # it here. tau_syn = 0.32582722403722841 brunel_params = { 'NE': int(9000 * params['scale']), # number of excitatory neurons 'NI': int(2250 * params['scale']), # number of inhibitory neurons 'Nrec': 1000, # number of neurons to record spikes from 'model_params': { # Set variables for iaf_psc_alpha 'E_L': 0.0, # Resting membrane potential(mV) 'C_m': 250.0, # Capacity of the membrane(pF) 'tau_m': 10.0, # Membrane time constant(ms) 't_ref': 0.5, # Duration of refractory period(ms) 'V_th': 20.0, # Threshold(mV) 'V_reset': 0.0, # Reset Potential(mV) # time const. postsynaptic excitatory currents(ms) 'tau_syn_ex': tau_syn, # time const. postsynaptic inhibitory currents(ms) 'tau_syn_in': tau_syn, 'tau_minus': 30.0, # time constant for STDP(depression) # V can be randomly initialized see below 'V_m': 5.7 # mean value of membrane potential }, #################################################################### # Note that Kunkel et al. (2014) report different values. The values # in the paper were used for the benchmarks on K, the values given # here were used for the benchmark on JUQUEEN. 'randomize_Vm': True, 'mean_potential': 5.7, 'sigma_potential': 7.2, 'delay': 1.5, # synaptic delay, all connections(ms) # synaptic weight 'JE': 0.14, # peak of EPSP 'sigma_w': 3.47, # standard dev. of E->E synapses(pA) 'g': -5.0, 'stdp_params': { 'delay': 1.5, 'alpha': 0.0513, 'lambda': 0.1, # STDP step size 'mu': 0.4, # STDP weight dependence exponent(potentiation) 'tau_plus': 15.0, # time constant for potentiation }, 'eta': 1.685, # scaling of external stimulus 'filestem': params['path_name'] } ############################################################################### # Function Section def build_network(logger): """Builds the network including setting of simulation and neuron parameters, creation of neurons and connections Requires an instance of Logger as argument """ tic = time.time() # start timer on construction # unpack a few variables for convenience NE = brunel_params['NE'] NI = brunel_params['NI'] model_params = brunel_params['model_params'] stdp_params = brunel_params['stdp_params'] # set global kernel parameters nest.SetKernelStatus({ 'total_num_virtual_procs': params['nvp'], 'resolution': params['dt'], 'overwrite_files': True}) nest.message(M_INFO, 'build_network', 'Creating excitatory population.') E_neurons = nest.Create('iaf_psc_alpha', NE, params=model_params) nest.message(M_INFO, 'build_network', 'Creating inhibitory population.') I_neurons = nest.Create('iaf_psc_alpha', NI, params=model_params) if brunel_params['randomize_Vm']: nest.message(M_INFO, 'build_network', 'Randomzing membrane potentials.') random_vm = nest.random.normal(brunel_params['mean_potential'], brunel_params['sigma_potential']) nest.GetLocalNodeCollection(E_neurons).V_m = random_vm nest.GetLocalNodeCollection(I_neurons).V_m = random_vm # number of incoming excitatory connections CE = int(1. * NE / params['scale']) # number of incomining inhibitory connections CI = int(1. * NI / params['scale']) nest.message(M_INFO, 'build_network', 'Creating excitatory stimulus generator.') # Convert synapse weight from mV to pA conversion_factor = convert_synapse_weight( model_params['tau_m'], model_params['tau_syn_ex'], model_params['C_m']) JE_pA = conversion_factor * brunel_params['JE'] nu_thresh = model_params['V_th'] / ( CE * model_params['tau_m'] / model_params['C_m'] * JE_pA * np.exp(1.) * tau_syn) nu_ext = nu_thresh * brunel_params['eta'] E_stimulus = nest.Create('poisson_generator', 1, { 'rate': nu_ext * CE * 1000.}) nest.message(M_INFO, 'build_network', 'Creating excitatory spike recorder.') if params['record_spikes']: recorder_label = os.path.join( brunel_params['filestem'], 'alpha_' + str(stdp_params['alpha']) + '_spikes') E_recorder = nest.Create('spike_recorder', params={ 'record_to': 'ascii', 'label': recorder_label }) BuildNodeTime = time.time() - tic logger.log(str(BuildNodeTime) + ' # build_time_nodes') logger.log(str(memory_thisjob()) + ' # virt_mem_after_nodes') tic = time.time() nest.SetDefaults('static_synapse_hpc', {'delay': brunel_params['delay']}) nest.CopyModel('static_synapse_hpc', 'syn_ex', {'weight': JE_pA}) nest.CopyModel('static_synapse_hpc', 'syn_in', {'weight': brunel_params['g'] * JE_pA}) stdp_params['weight'] = JE_pA nest.SetDefaults('stdp_pl_synapse_hom_hpc', stdp_params) nest.message(M_INFO, 'build_network', 'Connecting stimulus generators.') # Connect Poisson generator to neuron nest.Connect(E_stimulus, E_neurons, {'rule': 'all_to_all'}, {'synapse_model': 'syn_ex'}) nest.Connect(E_stimulus, I_neurons, {'rule': 'all_to_all'}, {'synapse_model': 'syn_ex'}) nest.message(M_INFO, 'build_network', 'Connecting excitatory -> excitatory population.') nest.Connect(E_neurons, E_neurons, {'rule': 'fixed_indegree', 'indegree': CE, 'allow_autapses': False, 'allow_multapses': True}, {'synapse_model': 'stdp_pl_synapse_hom_hpc'}) nest.message(M_INFO, 'build_network', 'Connecting inhibitory -> excitatory population.') nest.Connect(I_neurons, E_neurons, {'rule': 'fixed_indegree', 'indegree': CI, 'allow_autapses': False, 'allow_multapses': True}, {'synapse_model': 'syn_in'}) nest.message(M_INFO, 'build_network', 'Connecting excitatory -> inhibitory population.') nest.Connect(E_neurons, I_neurons, {'rule': 'fixed_indegree', 'indegree': CE, 'allow_autapses': False, 'allow_multapses': True}, {'synapse_model': 'syn_ex'}) nest.message(M_INFO, 'build_network', 'Connecting inhibitory -> inhibitory population.') nest.Connect(I_neurons, I_neurons, {'rule': 'fixed_indegree', 'indegree': CI, 'allow_autapses': False, 'allow_multapses': True}, {'synapse_model': 'syn_in'}) if params['record_spikes']: if params['nvp'] != 1: local_neurons = nest.GetLocalNodeCollection(E_neurons) # GetLocalNodeCollection returns a stepped composite NodeCollection, which # cannot be sliced. In order to allow slicing it later on, we're creating a # new regular NodeCollection from the plain node IDs. local_neurons = nest.NodeCollection(local_neurons.tolist()) else: local_neurons = E_neurons if len(local_neurons) < brunel_params['Nrec']: nest.message( M_ERROR, 'build_network', """Spikes can only be recorded from local neurons, but the number of local neurons is smaller than the number of neurons spikes should be recorded from. Aborting the simulation!""") exit(1) nest.message(M_INFO, 'build_network', 'Connecting spike recorders.') nest.Connect(local_neurons[:brunel_params['Nrec']], E_recorder, 'all_to_all', 'static_synapse_hpc') # read out time used for building BuildEdgeTime = time.time() - tic logger.log(str(BuildEdgeTime) + ' # build_edge_time') logger.log(str(memory_thisjob()) + ' # virt_mem_after_edges') return E_recorder if params['record_spikes'] else None def run_simulation(): """Performs a simulation, including network construction""" # open log file with Logger(params['log_file']) as logger: nest.ResetKernel() nest.set_verbosity(M_INFO) logger.log(str(memory_thisjob()) + ' # virt_mem_0') sr = build_network(logger) tic = time.time() nest.Simulate(params['presimtime']) PreparationTime = time.time() - tic logger.log(str(memory_thisjob()) + ' # virt_mem_after_presim') logger.log(str(PreparationTime) + ' # presim_time') tic = time.time() nest.Simulate(params['simtime']) SimCPUTime = time.time() - tic logger.log(str(memory_thisjob()) + ' # virt_mem_after_sim') logger.log(str(SimCPUTime) + ' # sim_time') if params['record_spikes']: logger.log(str(compute_rate(sr)) + ' # average rate') print(nest.GetKernelStatus()) def compute_rate(sr): """Compute local approximation of average firing rate This approximation is based on the number of local nodes, number of local spikes and total time. Since this also considers devices, the actual firing rate is usually underestimated. """ n_local_spikes = sr.n_events n_local_neurons = brunel_params['Nrec'] simtime = params['simtime'] return 1. * n_local_spikes / (n_local_neurons * simtime) * 1e3 def memory_thisjob(): """Wrapper to obtain current memory usage""" nest.ll_api.sr('memory_thisjob') return nest.ll_api.spp() def lambertwm1(x): """Wrapper for LambertWm1 function""" # Using scipy to mimic the gsl_sf_lambert_Wm1 function. return sp.lambertw(x, k=-1 if x < 0 else 0).real class Logger(object): """Logger context manager used to properly log memory and timing information from network simulations. """ def __init__(self, file_name): # copy output to cout for ranks 0..max_rank_cout-1 self.max_rank_cout = 5 # write to log files for ranks 0..max_rank_log-1 self.max_rank_log = 30 self.line_counter = 0 self.file_name = file_name def __enter__(self): if nest.Rank() < self.max_rank_log: # convert rank to string, prepend 0 if necessary to make # numbers equally wide for all ranks rank = '{:0' + str(len(str(self.max_rank_log))) + '}' fn = '{fn}_{rank}.dat'.format( fn=self.file_name, rank=rank.format(nest.Rank())) self.f = open(fn, 'w') return self def log(self, value): if nest.Rank() < self.max_rank_log: line = '{lc} {rank} {value} \n'.format( lc=self.line_counter, rank=nest.Rank(), value=value) self.f.write(line) self.line_counter += 1 if nest.Rank() < self.max_rank_cout: print(str(nest.Rank()) + ' ' + value + '\n', file=sys.stdout) print(str(nest.Rank()) + ' ' + value + '\n', file=sys.stderr) def __exit__(self, exc_type, exc_val, traceback): if nest.Rank() < self.max_rank_log: self.f.close() if __name__ == '__main__': run_simulation()
lekshmideepu/nest-simulator
pynest/examples/hpc_benchmark.py
Python
gpl-2.0
16,266
[ "NEURON" ]
c6a581e03f985fd827e106e06ee8380f727748fe2bf357c6141ef2c5fa5c264b
#pylint: disable=missing-docstring #* This file is part of the MOOSE framework #* https://www.mooseframework.org #* #* All rights reserved, see COPYRIGHT for full restrictions #* https://github.com/idaholab/moose/blob/master/COPYRIGHT #* #* Licensed under LGPL 2.1, please see LICENSE for details #* https://www.gnu.org/licenses/lgpl-2.1.html import copy import vtk import mooseutils from .ChiggerSourceBase import ChiggerSourceBase class ChiggerFilterSourceBase(ChiggerSourceBase): """ A base class for creating "source" objects (in VTK something that needs an vtkActor) that require additional input into the mapper and are capable of accepting filters. This class adds two main items: 1. A getSource method is provided, this method should provide a VTK object that will be connect to the mapper or chain of filters (see 2). 2. Defines a method for adding filters and controlling the types and order in which they are applied, see ExodusSource for example. Inputs: vtkactor_type: The VTK actor type to build, must be an instance of VTKACTOR_TYPE vtkmapper_type: The VTK mapper type to build, must be an instance of VTKMAPPER_TYPE **kwargs: The key, value options for this object. """ # The base class actor/mapper that this object to which ownership is restricted VTKACTOR_TYPE = vtk.vtkProp VTKMAPPER_TYPE = vtk.vtkAbstractMapper # The list of filter types allowed, in the order they should be connected FILTER_TYPES = [] @staticmethod def getOptions(): opt = ChiggerSourceBase.getOptions() opt.add('filters', [], "A list of Filter objects to apply to this mapper.") return opt def __init__(self, *args, **kwargs): super(ChiggerFilterSourceBase, self).__init__(*args, **kwargs) self._filters = [] self._required_filters = [] def getVTKSource(self): """ Return the "source" vtk object. (abstract) classes must override this method. The VTK object returned from this function will be connected to the first filter, if then exist, or the vtkAbstractMapper object. See the 'update' method for this class for how the connections are made. """ raise mooseutils.MooseException('The {}."getSource()" method must be overridden by your ' 'mapper object and return the source vtk object to connect ' 'to the filers and mapper.'.format(self.__class__.__name__)) def getFilters(self): """ Return the list of filter objects. """ return self._filters def needsUpdate(self): """ Return True if the object needs to be updated. """ changed = [super(ChiggerFilterSourceBase, self).needsUpdate()] for f in self._filters: changed.append(f.needsUpdate()) return any(changed) def update(self, **kwargs): """ Updates the object by connecting the VTK objects. (override) Inputs: see ChiggerSourceBase """ super(ChiggerFilterSourceBase, self).update(**kwargs) self.__connectFilters() # Initialize and update filters for f in self._filters: if f.needsInitialize(): f.initializeFilter(self) if f.needsUpdate(): f.update() def __connectFilters(self): """ Helper function for connecting filter to vtkMapper object. """ def debug(src, fltr): """ Inline function for debug messages. """ mooseutils.mooseDebug('{} --> {}'.format(type(src).__name__, type(fltr).__name__), color='GREEN') # Create a list of filters to apply to the VTK pipeline, this is done by # combining the required filters with the 'filters' options. This combined list # is then sorted based on the list provided in FILTER_TYPES. filters = [] filters_in = copy.copy(self._required_filters) # shallow copy (don't modify require list) if self.isOptionValid('filters'): filters_in += self.getOption('filters') for f in filters_in: for i, order in enumerate(self.FILTER_TYPES): if isinstance(f, order): filters.append((f, i)) self._filters = [f[0] for f in sorted(filters, key=lambda x: x[1])] # Connect the filters, if any exist if self._filters: debug(self.getVTKSource(), self._filters[0].getVTKFilter()) self._filters[0].getVTKFilter().SetInputConnection(self.getVTKSource().GetOutputPort()) for i in range(1, len(self._filters)): debug(self._filters[i-1].getVTKFilter(), self._filters[i].getVTKFilter()) f = self._filters[i-1].getVTKFilter().GetOutputPort() self._filters[i].getVTKFilter().SetInputConnection(f) if self._vtkmapper: debug(self._filters[-1].getVTKFilter(), self._vtkmapper) self._vtkmapper.SetInputConnection(self._filters[-1].getVTKFilter().GetOutputPort()) elif self._vtkmapper: debug(self.getVTKSource(), self._vtkmapper) self._vtkmapper.SetInputConnection(self.getVTKSource().GetOutputPort())
harterj/moose
python/chigger/base/ChiggerFilterSourceBase.py
Python
lgpl-2.1
5,420
[ "MOOSE", "VTK" ]
7d84f0bd94682fca115f94539f2ec9f4b59e60949772ea16507bbe7c9e13d606
import re _csq_format_re = re.compile(r'.*Format:\s*((\S+\|)*\S+)') # for capturing VEP CSQ format in Description field of metaheader _ann_format_re = re.compile( r".*Functional annotations:\s*'(([^\|]+\|)*[^\|]+)'") # for capturing SnpEff ANN format in Description field of metaheader _common_csq_fields = ['CSQ', 'BCSQ', 'CQ', 'vep'] _common_ann_fields = ['ANN', 'EFF'] _required_keys = {'info': ['number', 'type', 'description'], 'format': ['number', 'type', 'description'], 'filter': ['description'], 'alt': ['description']} _field2pysam = {'info': 'info', 'format': 'formats', 'filter': 'filters', 'alt': 'alts'} class VcfHeader(object): ''' Header class storing metadata and sample information for a vcf ''' __slots__ = ['vcfreader', 'header', '__csq_label', '__csq_fields', '__ann_label', '__ann_fields'] def __init__(self, vcfreader): self.vcfreader = vcfreader self.header = self.vcfreader.variant_file.header self.__csq_fields = None self.__csq_label = None self.__ann_fields = None self.__ann_label = None @property def formats(self): return self.header.formats @property def info(self): return self.header.info @property def filters(self): return self.header.filters @property def samples(self): return self.header.samples @property def csq_label(self): ''' String labelling the INFO field label of VEP consequence annotations. Will raise a KeyError if access is attempted but no VEP CSQ or ANN field is present in the header. ''' if self.__csq_label is None: self.csq_fields return self.__csq_label @csq_label.setter def csq_label(self, c): self.__csq_label = c @property def csq_fields(self): ''' A list of CSQ field names in the order they are represented in CSQ INFO field entries. Set to None on initialization. Will raise a KeyError if access is attempted but no VEP CSQ, ANN, BCSQ or CQ field is present in the header. ''' if self.__csq_fields is None: if self.__csq_label is None: csq = None for x in _common_csq_fields: if x in self.info: csq = x break if csq is None: raise KeyError("No common CSQ fields found in INFO " + "header - unable to retrieve consequence " + "fields.") self.csq_label = csq else: csq = self.__csq_label csq_header = self.info[csq] match = _csq_format_re.match(csq_header.description) if match: self.__csq_fields = match.group(1).split('|') else: raise KeyError("Could not parse {} Format in ".format(csq) + "header. Unable to retrieve consequence " + "annotations.") return self.__csq_fields @csq_fields.setter def csq_fields(self, csq): self.__csq_fields = csq @property def ann_label(self): ''' String labelling the INFO field label of SnpEff consequence annotations. Will raise a KeyError if access is attempted but no ANN or EFF field is present in the header. ''' if self.__ann_label is None: self.ann_fields return self.__ann_label @ann_label.setter def ann_label(self, lbl): self.__ann_label = lbl @property def ann_fields(self): ''' A list of SnpEff ANN field names in the order they are represented in ANN/EFF INFO field entries. Set to None on initialization. Will raise a KeyError if access is attempted but no SnpEff ANN, or EFF field is present in the header. ''' if self.__ann_fields is None: if self.__ann_label is None: ann = None for x in _common_ann_fields: if x in self.info: ann = x break if ann is None: raise KeyError("No common ANN fields found in INFO " + "header - unable to retrieve SnpEff " + "consequence fields.") self.ann_label = ann else: ann = self.__ann_label ann_header = self.info[ann] match = _ann_format_re.match(ann_header.description) if match: self.__ann_fields = match.group(1).split(' | ') else: raise KeyError("Could not parse {} Format in ".format(ann) + "header. Unable to retrieve consequence " + "annotations.") return self.__ann_fields @ann_fields.setter def ann_fields(self, ann): self.__ann_fields = ann def add_header_field(self, name, string=None, field_type=None, dictionary=None): ''' Add a header field with given name and optional field type, and dictionary of properties. Args: name: name of field to add string: string to add to field. Ignored if 'dictionary' is provided. field_type: type of field - e.g. if INFO/FILTER/FORMAT field. Required if providing a dictionary. dictionary: a dict of keys to values for the given field. If 'field_type' is specified, this arg must be provided and must contain all the essential keys for that field type. For example, an 'INFO' field must have 'Number', 'Type', and 'Description' keys. ''' add_order = ['number', 'type', 'description'] h_vals = [] if dictionary is None and string is None: raise ValueError("Either dict or string argument is required") if field_type is not None and field_type in _required_keys: if dictionary is None: raise ValueError("Header type {} requires a dict.".format( field_type)) if dictionary: if not field_type: raise ValueError("field_type is required for use with " + "dictionary") dictionary.update([(k.lower(), v) for k, v in dictionary.items()]) field_type = field_type.lower() field_header = getattr(self.header, _field2pysam[field_type]) if name in field_header: # TODO check header same? pysam segfaults if we remove and # add INFO field with same name return if field_type in _required_keys: for k in add_order: if k in _required_keys[field_type]: try: h_vals.append(dictionary[k]) except KeyError: raise ValueError("Header type '" + field_type + "' requires '" + k + "' field") else: h_vals.append(None) h_vals.insert(0, name) getattr(self.header, _field2pysam[field_type]).add(*h_vals) else: raise ValueError("Field type {} not recognised".format( field_type)) else: self.header.add_meta(key=name, value=string)
gantzgraf/vape
vase/vcf_header.py
Python
gpl-3.0
8,057
[ "pysam" ]
5bbfac9062c98aef61db9230d44439b98de51f9114289161e3b84696f12cb35b
""" =================================== Simple 1D Kernel Density Estimation =================================== This example uses the :class:`sklearn.neighbors.KernelDensity` class to demonstrate the principles of Kernel Density Estimation in one dimension. The first plot shows one of the problems with using histograms to visualize the density of points in 1D. Intuitively, a histogram can be thought of as a scheme in which a unit "block" is stacked above each point on a regular grid. As the top two panels show, however, the choice of gridding for these blocks can lead to wildly divergent ideas about the underlying shape of the density distribution. If we instead center each block on the point it represents, we get the estimate shown in the bottom left panel. This is a kernel density estimation with a "top hat" kernel. This idea can be generalized to other kernel shapes: the bottom-right panel of the first figure shows a Gaussian kernel density estimate over the same distribution. Scikit-learn implements efficient kernel density estimation using either a Ball Tree or KD Tree structure, through the :class:`sklearn.neighbors.KernelDensity` estimator. The available kernels are shown in the second figure of this example. The third figure compares kernel density estimates for a distribution of 100 samples in 1 dimension. Though this example uses 1D distributions, kernel density estimation is easily and efficiently extensible to higher dimensions as well. """ # Author: Jake Vanderplas <jakevdp@cs.washington.edu> # import numpy as np import matplotlib.pyplot as plt from scipy.stats import norm from sklearn.neighbors import KernelDensity #---------------------------------------------------------------------- # Plot the progression of histograms to kernels np.random.seed(1) N = 20 X = np.concatenate((np.random.normal(0, 1, int(0.3 * N)), np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis] X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis] bins = np.linspace(-5, 10, 10) fig, ax = plt.subplots(2, 2, sharex=True, sharey=True) fig.subplots_adjust(hspace=0.05, wspace=0.05) # histogram 1 ax[0, 0].hist(X[:, 0], bins=bins, fc='#AAAAFF', normed=True) ax[0, 0].text(-3.5, 0.31, "Histogram") # histogram 2 ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc='#AAAAFF', normed=True) ax[0, 1].text(-3.5, 0.31, "Histogram, bins shifted") # tophat KDE kde = KernelDensity(kernel='tophat', bandwidth=0.75).fit(X) log_dens = kde.score_samples(X_plot) ax[1, 0].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF') ax[1, 0].text(-3.5, 0.31, "Tophat Kernel Density") # Gaussian KDE kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(X) log_dens = kde.score_samples(X_plot) ax[1, 1].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF') ax[1, 1].text(-3.5, 0.31, "Gaussian Kernel Density") for axi in ax.ravel(): axi.plot(X[:, 0], np.full(X.shape[0], -0.01), '+k') axi.set_xlim(-4, 9) axi.set_ylim(-0.02, 0.34) for axi in ax[:, 0]: axi.set_ylabel('Normalized Density') for axi in ax[1, :]: axi.set_xlabel('x') #---------------------------------------------------------------------- # Plot all available kernels X_plot = np.linspace(-6, 6, 1000)[:, None] X_src = np.zeros((1, 1)) fig, ax = plt.subplots(2, 3, sharex=True, sharey=True) fig.subplots_adjust(left=0.05, right=0.95, hspace=0.05, wspace=0.05) def format_func(x, loc): if x == 0: return '0' elif x == 1: return 'h' elif x == -1: return '-h' else: return '%ih' % x for i, kernel in enumerate(['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine']): axi = ax.ravel()[i] log_dens = KernelDensity(kernel=kernel).fit(X_src).score_samples(X_plot) axi.fill(X_plot[:, 0], np.exp(log_dens), '-k', fc='#AAAAFF') axi.text(-2.6, 0.95, kernel) axi.xaxis.set_major_formatter(plt.FuncFormatter(format_func)) axi.xaxis.set_major_locator(plt.MultipleLocator(1)) axi.yaxis.set_major_locator(plt.NullLocator()) axi.set_ylim(0, 1.05) axi.set_xlim(-2.9, 2.9) ax[0, 1].set_title('Available Kernels') #---------------------------------------------------------------------- # Plot a 1D density example N = 100 np.random.seed(1) X = np.concatenate((np.random.normal(0, 1, int(0.3 * N)), np.random.normal(5, 1, int(0.7 * N))))[:, np.newaxis] X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis] true_dens = (0.3 * norm(0, 1).pdf(X_plot[:, 0]) + 0.7 * norm(5, 1).pdf(X_plot[:, 0])) fig, ax = plt.subplots() ax.fill(X_plot[:, 0], true_dens, fc='black', alpha=0.2, label='input distribution') for kernel in ['gaussian', 'tophat', 'epanechnikov']: kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X) log_dens = kde.score_samples(X_plot) ax.plot(X_plot[:, 0], np.exp(log_dens), '-', label="kernel = '{0}'".format(kernel)) ax.text(6, 0.38, "N={0} points".format(N)) ax.legend(loc='upper left') ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), '+k') ax.set_xlim(-4, 9) ax.set_ylim(-0.02, 0.4) plt.show()
vortex-ape/scikit-learn
examples/neighbors/plot_kde_1d.py
Python
bsd-3-clause
5,119
[ "Gaussian" ]
9369f4c85f289a71f00493e7b26cd108bcb05ad99cca65aaf51bbddf10300233
import numpy as np from SimPEG import Mesh from SimPEG import Problem from SimPEG import Survey from SimPEG import DataMisfit from SimPEG import Directives from SimPEG import Optimization from SimPEG import Regularization from SimPEG import InvProblem from SimPEG import Inversion import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # from pymatsolver import Pardiso import matplotlib from ipywidgets import ( interact, FloatSlider, ToggleButtons, IntSlider, FloatText, IntText, SelectMultiple ) import ipywidgets as widgets class LinearInversionApp(object): """docstring for LinearInversionApp""" # Parameters for sensitivity matrix, G N=None M=None j_start=None j_end=None p=None q=None seed=None # Parameters for Model m_background= None m1=None m2=None m1_center=None dm1 =None m2_center=None dm2 =None sigma =None m_min =None m_max =None data=None save=None def __init__(self): super(LinearInversionApp, self).__init__() @property def G(self): return self._G @property def jk(self): return self._jk @property def mesh(self): return self._mesh def set_G( self, N=20, M=100, p=-0.25, q=0.25, j1=1, jn=60, ): """ Parameters ---------- N: # of data M: # of model parameters ... """ self.N=N self.M=M self._mesh=Mesh.TensorMesh([M]) jk=np.linspace(j1, jn, N) self._G=np.zeros((N, self.mesh.nC), dtype=float, order='C') def g(k): return ( np.exp(p*jk[k]*self.mesh.vectorCCx) * np.cos(np.pi*q*jk[k]*self.mesh.vectorCCx) ) for i in range(N): self._G[i, :] = g(i) * self.mesh.hx self._jk = jk def plot_G( self, N=20, M=100, p=-0.25, q=0.25, j1=1, jn=60, scale='log', fixed=False, ymin=-0.001, ymax=0.011 ): self.set_G( N=N, M=M, p=p, q=q, j1=j1, jn=jn, ) _, s, _ = np.linalg.svd(self.G, full_matrices=False) matplotlib.rcParams['font.size']=14 fig=plt.figure(figsize=(10, 4)) gs1 = gridspec.GridSpec(1, 4) ax1 = plt.subplot(gs1[0, :3]) ax2 = plt.subplot(gs1[0, 3:]) ax1.plot(self.mesh.vectorCCx, self.G.T) if fixed: ax1.set_ylim(ymin, ymax) ax1.set_xlabel("x") ax1.set_ylabel("g(x)") ax2.plot(np.arange(self.N)+1, s, 'ro') ax2.set_xlabel("") ax2.set_title("singular values", fontsize=12) ax2.set_xscale(scale) ax2.set_yscale(scale) ax2.xaxis.set_major_locator(plt.NullLocator()) ax2.xaxis.set_minor_locator(plt.NullLocator()) ax2.xaxis.set_major_formatter(plt.NullFormatter()) ax2.xaxis.set_minor_formatter(plt.NullFormatter()) plt.tight_layout() plt.show() def set_model( self, m_background=0., m1=1., m2=-1., m1_center=0.2, dm1=0.2, m2_center=0.5, sigma_2=1., ): m=np.zeros(self.mesh.nC) + m_background m1_inds=np.logical_and(self.mesh.vectorCCx > m1_center-dm1/2., self.mesh.vectorCCx < m1_center+dm1/2.) m[m1_inds]=m1 def gaussian(x,x0,sigma): return np.exp(-np.power((x - x0)/sigma, 2.)/2.) m += gaussian(self.mesh.vectorCCx, m2_center, sigma_2) * m2 return m def plot_model( self, m_background=0., m1=1., m1_center=0.2, dm1=0.2, m2=-1., m2_center=0.5, sigma_2=1., option="model", add_noise=True, percentage =10, floor=1e-1, ): m=self.set_model( m_background=m_background, m1=m1, m2=m2, m1_center=m1_center, dm1=dm1, m2_center=m2_center, sigma_2=sigma_2, ) np.random.seed(1) if add_noise: survey, _=self.get_problem_survey() data=survey.dpred(m) noise=abs(data)*percentage * 0.01 *np.random.randn(self.N) + np.random.randn(self.N)*floor else: survey, _=self.get_problem_survey() data=survey.dpred(m) noise=np.zeros(self.N, float) data += noise self.data=data.copy() self.m=m.copy() self.uncertainty=abs(self.data) * percentage* 0.01 + floor self.percentage = percentage self.floor = floor option_bools = [False, False, False] for item in option: if item == 'kernel': option_bools[0] = True elif item == 'model': option_bools[1] = True elif item == 'data': option_bools[2] = True fig, axes = plt.subplots(1, 3, figsize=(12*1.2, 3*1.2)) for i, ax in enumerate(axes): if option_bools[i]: if i == 0: ax.plot(self.mesh.vectorCCx, self.G.T) ax.set_title('Rows of matrix G') ax.set_xlabel("x") ax.set_ylabel("g(x)") elif i == 1: ax.plot(self.mesh.vectorCCx, m) ax.set_ylim([-2.5, 2.5]) ax.set_title('Model') ax.set_xlabel("x") ax.set_ylabel("m(x)") ax.set_ylabel("$d_j$") elif i == 2: if add_noise: # this is just for visualization of uncertainty ax.errorbar( x=self.jk, y=self.data, yerr=self.uncertainty, color='k', lw=1 ) ax.plot(self.jk, self.data, 'ko') else: ax.plot(self.jk, self.data, 'ko-') ax.set_title('Data') ax.set_xlabel("$k_j$") for i, ax in enumerate(axes): if not option_bools[i]: ax.axis('off') # ax.xaxis.set_minor_locator(plt.NullLocator()) # ax.xaxis.set_major_formatter(plt.NullFormatter()) # ax.xaxis.set_minor_formatter(plt.NullFormatter()) # ax.yaxis.set_major_locator(plt.NullLocator()) # ax.yaxis.set_minor_locator(plt.NullLocator()) # ax.yaxis.set_major_formatter(plt.NullFormatter()) # ax.yaxis.set_minor_formatter(plt.NullFormatter()) plt.tight_layout() def get_problem_survey(self): prob=Problem.LinearProblem(self.mesh, G=self.G) survey=Survey.LinearSurvey() survey.pair(prob) return survey, prob def run_inversion( self, maxIter=60, m0=0., mref=0., percentage=5, floor=0.1, chifact=1, beta0_ratio=1., coolingFactor=1, coolingRate=1, alpha_s=1., alpha_x=1., use_target=False ): survey, prob=self.get_problem_survey() survey.eps=percentage survey.std=floor survey.dobs=self.data.copy() self.uncertainty = percentage*abs(survey.dobs)*0.01 + floor m0=np.ones(self.M) * m0 mref=np.ones(self.M) * mref reg=Regularization.Tikhonov( self.mesh, alpha_s=alpha_s, alpha_x=alpha_x, mref=mref ) dmis=DataMisfit.l2_DataMisfit(survey) dmis.W=1./self.uncertainty opt=Optimization.InexactGaussNewton( maxIter=maxIter, maxIterCG=20 ) opt.remember('xc') opt.tolG=1e-10 opt.eps=1e-10 invProb=InvProblem.BaseInvProblem(dmis, reg, opt) save=Directives.SaveOutputEveryIteration() beta_schedule=Directives.BetaSchedule( coolingFactor=coolingFactor, coolingRate=coolingRate ) target=Directives.TargetMisfit(chifact=chifact) if use_target: directives=[ Directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio), beta_schedule, target, save ] else: directives=[ Directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio), beta_schedule, save ] inv=Inversion.BaseInversion(invProb, directiveList=directives) mopt=inv.run(m0) model = opt.recall('xc') model.append(mopt) pred = [] for m in model: pred.append(survey.dpred(m)) return model, pred, save def plot_inversion( self, maxIter=60, m0=0., mref=0., percentage=5, floor=0.1, chifact=1, beta0_ratio=1., coolingFactor=1, coolingRate=1, alpha_s=1., alpha_x=1., use_target=False, run=True, option ='model', i_iteration=1, ): if run: self.model, self.pred, self.save=self.run_inversion( maxIter=maxIter, m0=m0, mref=mref, percentage=percentage, floor=floor, chifact=chifact, beta0_ratio=beta0_ratio, coolingFactor=coolingFactor, coolingRate=coolingRate, alpha_s=alpha_s, alpha_x=alpha_x, use_target=use_target, ) if len(self.model) == 2: fig, axes=plt.subplots(1, 2, figsize=(14*1.2 *2/3, 3*1.2)) i_plot = -1 else: self.save.load_results() if self.save.i_target is None: i_plot = -1 else: i_plot = self.save.i_target + 1 fig, axes=plt.subplots(1, 3, figsize=(14*1.2, 3*1.2)) axes[0].plot(self.mesh.vectorCCx, self.m) if run: axes[0].plot(self.mesh.vectorCCx, self.model[i_plot]) axes[0].set_ylim([-2.5, 2.5]) axes[1].errorbar( x=self.jk, y=self.data, yerr=self.uncertainty, color='k', lw=1 ) axes[1].plot(self.jk, self.data, 'ko') if run: axes[1].plot(self.jk, self.pred[i_plot], 'bx') axes[1].legend(("Observed", "Predicted")) axes[0].legend(("True", "Pred")) axes[0].set_title('Model') axes[0].set_xlabel("x") axes[0].set_ylabel("m(x)") axes[1].set_title('Data') axes[1].set_xlabel("$k_j$") axes[1].set_ylabel("$d_j$") if len(self.model) > 2: max_iteration = len(self.model)-1 if i_iteration > max_iteration: print ((">> Warning: input iteration (%i) is greater than maximum iteration (%i)") % (i_iteration, len(self.model)-1)) i_iteration = max_iteration if option == 'misfit': if not run: axes[0].plot(self.mesh.vectorCCx, self.model[i_iteration]) axes[1].plot(self.jk, self.pred[i_iteration], 'bx') # axes[0].legend(("True", "Pred", ("%ith")%(i_iteration))) # axes[1].legend(("Observed", "Predicted", ("%ith")%(i_iteration))) axes[1].legend(("Observed", "Predicted")) if i_iteration == 0: i_iteration = 1 axes[2].plot(np.arange(len(self.save.phi_d))[i_iteration-1]+1, self.save.phi_d[i_iteration-1]*2, 'go', ms=10) ax_1 = axes[2].twinx() axes[2].semilogy(np.arange(len(self.save.phi_d))+1, self.save.phi_d*2, 'k-', lw=2) if self.save.i_target is not None: axes[2].plot(np.arange(len(self.save.phi_d))[self.save.i_target]+1, self.save.phi_d[self.save.i_target]*2, 'k*', ms=10) axes[2].plot(np.r_[axes[2].get_xlim()[0], axes[2].get_xlim()[1]], np.ones(2)*self.save.target_misfit*2, 'k:') ax_1.semilogy(np.arange(len(self.save.phi_d))+1, self.save.phi_m, 'r', lw=2) axes[2].set_xlabel("Iteration") axes[2].set_ylabel("$\phi_d$") ax_1.set_ylabel("$\phi_m$", color='r') for tl in ax_1.get_yticklabels(): tl.set_color('r') axes[2].set_title('Misfit curves') elif option == 'tikhonov': if not run: axes[0].plot(self.mesh.vectorCCx, self.model[i_iteration]) axes[1].plot(self.jk, self.pred[i_iteration], 'bx') # axes[0].legend(("True", "Pred", ("%ith")%(i_iteration))) # axes[1].legend(("Observed", "Predicted", ("%ith")%(i_iteration))) axes[0].legend(("True", "Pred")) axes[1].legend(("Observed", "Predicted")) if i_iteration == 0: i_iteration = 1 axes[2].plot(self.save.phi_m[i_iteration-1], self.save.phi_d[i_iteration-1]*2, 'go', ms=10) axes[2].plot(self.save.phi_m, self.save.phi_d*2, 'k-', lw=2) axes[2].set_xlim(np.hstack(self.save.phi_m).min(), np.hstack(self.save.phi_m).max()) axes[2].set_xlabel("$\phi_m$", fontsize=14) axes[2].set_ylabel("$\phi_d$", fontsize=14) if self.save.i_target is not None: axes[2].plot(self.save.phi_m[self.save.i_target], self.save.phi_d[self.save.i_target]*2., 'k*', ms=10) axes[2].set_title('Tikhonov curve') plt.tight_layout() def interact_plot_G(self): Q=interact( self.plot_G, N=IntSlider(min=1, max=100, step=1, value=20, continuous_update=False), M=IntSlider(min=1, max=100, step=1, value=100, continuous_update=False), p =FloatSlider(min=-1, max=0, step=0.05, value=-0.15, continuous_update=False), q=FloatSlider(min=0, max=1, step=0.05, value=0.25, continuous_update=False), j1 =FloatText(value=1.), jn=FloatText(value=19.), scale=ToggleButtons( options=["linear", "log"], value="log" ), fixed=False, ymin=FloatText(value=-0.005), ymax=FloatText(value=0.011), ) return Q def interact_plot_model(self): Q=interact( self.plot_model, m_background=FloatSlider( min=-2, max=2, step=0.05, value=0., continuous_update=False, description="m$_{background}$", ), m1=FloatSlider( min=-2, max=2, step=0.05, value=1., continuous_update=False, description="m1", ), m2=FloatSlider( min=-2, max=2, step=0.05, value=2., continuous_update=False, description="m2", ), m1_center=FloatSlider( min=-2, max=2, step=0.05, value=0.2, continuous_update=False, description="m1$_{center}$", ), dm1 =FloatSlider( min=0, max=0.5, step=0.05, value=0.2, continuous_update=False, description="m1$_{width}$", ), m2_center=FloatSlider( min=-2, max=2, step=0.05, value=0.75, continuous_update=False, description="m2$_{center}$", ), sigma_2=FloatSlider( min=0.01, max=0.1, step=0.01, value=0.07, continuous_update=False, description="m2$_{sigma}$", ), option=SelectMultiple( options=["kernel", "model", "data"], value=["model"], description='option' ), percentage=FloatText(value=5), floor=FloatText(value=0.02), ) return Q def interact_plot_inversion(self, maxIter=30): Q = interact( self.plot_inversion, maxIter=IntText(value=maxIter), m0=FloatSlider(min=-2, max=2, step=0.05, value=0., continuous_update=False), mref=FloatSlider(min=-2, max=2, step=0.05, value=0., continuous_update=False), percentage=FloatText(value=self.percentage), floor=FloatText(value=self.floor), chifact=FloatText(value=1.), beta0_ratio=FloatText(value=100), coolingFactor=FloatSlider(min=0.1, max=10, step=1, value=2, continuous_update=False), coolingRate=IntSlider(min=1, max=10, step=1, value=1, continuous_update=False), alpha_s=FloatText(value=1e-10), alpha_x=FloatText(value=0), run = True, target = False, option=ToggleButtons( options=["misfit", "tikhonov"], value="misfit" ), i_iteration=IntSlider(min=0, max=maxIter, step=1, value=0, continuous_update=False) )
geoscixyz/em_examples
em_examples/LinearInversion.py
Python
mit
17,361
[ "Gaussian" ]
858fbd86cd116b5eecd3d4750f101aa2882dbae7eec55e63e49642eddc9bdf81
import math import numpy class ElasticNet: def __init__(self, cities, param): self._cities = cities self._param = param self._num_iter = 0 self._k = self._param['init_k'] self._num_neurons = int(self._param['num_neurons_factor'] \ * self._cities.shape[0]) self._init_neurons() def iteration(self): """ Perform one iteration of the algorithm. Return True if the algorithm has finished, False otherwise. """ self._num_iter += 1 self._update_k() self._update_weights() self._update_neurons() return not self._stop_criteria() def _get_dist2(self): return self._dist2 dist2 = property(fget=_get_dist2) def _get_neurons(self): return self._neurons neurons = property(fget=_get_neurons) def _get_num_iter(self): return self._num_iter num_iter = property(fget=_get_num_iter) def _get_worst_dist(self): return self._worst_dist worst_dist = property(fget=_get_worst_dist) def _dist_force(self): """ Compute the force that minimize the distance between the cities and the neurons. """ return numpy.array( [numpy.dot(self._weights[:,i], self._delta[:,i]) for i in range(self._num_neurons)]) def _init_neurons(self): """ Initialize the neurons in a circle at the center of the cities. """ theta = numpy.linspace(0, 2 * math.pi, self._num_neurons, False) centroid = self._cities.mean(axis=0) self._neurons = numpy.vstack((numpy.cos(theta), numpy.sin(theta))) self._neurons *= self._param['radius'] self._neurons += centroid[:,numpy.newaxis] self._neurons = self._neurons.transpose() def _length_force(self): """Compute the force that minimize the length of the elastic.""" return numpy.concatenate(( [self._neurons[1] - 2 * self._neurons[0] + self._neurons[self._num_neurons - 1]], [(self._neurons[i+1] - 2 * self._neurons[i] + self._neurons[i-1]) for i in range(1, self._num_neurons - 1)], [self._neurons[0] - 2 * self._neurons[self._num_neurons - 1] + self._neurons[self._num_neurons - 2]])) def _stop_criteria(self): """Return True if the algorithm has finished, False otherwise.""" return self._worst_dist < self._param['epsilon'] \ or self._num_iter >= self._param['max_num_iter'] def _update_k(self): if (self._num_iter % self._param['k_update_period']) == 0: self._k = max(0.01, self._param['k_alpha'] * self._k) def _update_neurons(self): dist_force = self._dist_force() length_force = self._length_force() self._neurons += self._param['alpha'] * dist_force \ + self._param['beta'] * self._k * length_force def _update_weights(self): """Compute w_ij, i = 1, 2, ..., |Cities|; j = 1, 2, ...., |Neurons|""" self._delta = self._cities[:,numpy.newaxis] - self._neurons # At this point # self._delta[i,j] == (delta_x, delta_y) between city i and neuron j self._dist2 = (self._delta ** 2).sum(axis=2) # At this point # self._dist2[i,j] == square of the distance between city i and neuron j self._worst_dist = numpy.sqrt(self._dist2.min(axis=1).max()) self._weights = numpy.exp(-self._dist2 / (2 * (self._k ** 2))) # At this point # self._weights[i,j] == unnormalized weight associated to city # i and neuron j self._weights /= self._weights.sum(axis=1)[:,numpy.newaxis] # At this point # self._weights[i,j] == normalized weight associated to city i # and neuron j
larose/ena
elastic_net.py
Python
bsd-2-clause
4,067
[ "NEURON" ]
f137edffc1292d50c64d199a6a58b966798df513ac16eca8c29604a6fed8820a
from .properties import Align, Bool, DataSpec, Enum, HasProps, Size, Any, Color from .mixins import FillProps, LineProps, TextProps from .enums import Units, AngleUnits, Direction from .plot_object import Viewable from six import add_metaclass, iteritems # Size is a way to preserve a data-space-related metric all the way until # render time, when the screen dimensions are known # Align may be "center", "min", "max", or "jitter(func)" where func is name # of a random distribution to draw random samples from. Defaults to uniform # but gaussian could certainly be useful. @add_metaclass(Viewable) class BaseGlyph(HasProps): """ Base class for all glyphs/marks/geoms/whatever-you-call-'em in Bokeh. """ # use __view_model__ for type field in js dict # Common attributes for all glyphs visible = Bool margin = Size # the amount of desired space around this glyph halign = Align # when there is horizontal wiggle room (e.g. categorical) valign = Align # when there is vertical wiggle room radius_units = Enum(Units) length_units = Enum(Units) angle_units = Enum(AngleUnits) start_angle_units = Enum(AngleUnits) end_angle_units = Enum(AngleUnits) def to_glyphspec(self): """ Returns a dict mapping attributes to values, that is amenable for inclusion in a Glyph definition. """ props = self.changed_properties_with_values() props["type"] = self.__view_model__ # Iterate over all the DataSpec properties and convert them, using the # fact that DataSpecs store the dict-ified version on the object. for attr, spec in iteritems(self.dataspecs_with_refs()): props[attr] = spec.to_dict(self) return props class Marker(BaseGlyph, FillProps, LineProps): """ Base class for glyphs which are just simple markers placed at (x,y) locations. """ x = DataSpec y = DataSpec size = DataSpec(units="screen", min_value=0, default=4) class Asterisk(Marker): __view_model__ = "asterisk" class Circle(Marker): __view_model__ = "circle" radius = DataSpec(units="data", min_value=0) def to_glyphspec(self): """ Returns a dict mapping attributes to values, that is amenable for inclusion in a Glyph definition. """ d = super(Circle, self).to_glyphspec() if "size" not in self._changed_vars and "radius" not in self._changed_vars: del d["radius"] elif "size" in self._changed_vars: del d["radius"] elif "radius" in self._changed_vars: del d["size"] return d class CircleCross(Marker): __view_model__ = "circle_cross" class CircleX(Marker): __view_model__ = "circle_x" class Cross(Marker): __view_model__ = "cross" class Diamond(Marker): __view_model__ = "diamond" class DiamondCross(Marker): __view_model__ = "diamond_cross" class InvertedTriangle(Marker): __view_model__ = "inverted_triangle" class Square(Marker): __view_model__ = "square" angle = DataSpec class SquareCross(Marker): __view_model__ = "square_cross" class SquareX(Marker): __view_model__ = "square_x" class Triangle(Marker): __view_model__ = "triangle" class Xmarker(Marker): __view_model__ = "x" class AnnularWedge(BaseGlyph, FillProps, LineProps): __view_model__ = 'annular_wedge' x = DataSpec y = DataSpec inner_radius = DataSpec(min_value=0) outer_radius = DataSpec(min_value=0) start_angle = DataSpec end_angle = DataSpec direction = Enum(Direction) class Annulus(BaseGlyph, FillProps, LineProps): __view_model__ = 'annulus' x = DataSpec y = DataSpec inner_radius = DataSpec(min_value=0) outer_radius = DataSpec(min_value=0) class Arc(BaseGlyph, LineProps): __view_model__ = 'arc' x = DataSpec y = DataSpec radius = DataSpec(min_value=0) start_angle = DataSpec end_angle = DataSpec direction = Enum(Direction) class Bezier(BaseGlyph, LineProps): __view_model__ = 'bezier' x0 = DataSpec y0 = DataSpec x1 = DataSpec y1 = DataSpec cx0 = DataSpec cy0 = DataSpec cx1 = DataSpec cy1 = DataSpec class Gear(BaseGlyph, LineProps, FillProps): __view_model__ = 'gear' x = DataSpec # Float (mm, data) y = DataSpec # Float (mm, data) angle = DataSpec(default=0) # Float (rad) module = DataSpec # Float (mm, data) teeth = DataSpec # Int pressure_angle = DataSpec(default=20) # Angle (deg) shaft_size = DataSpec(default=0.3) # Percent internal = DataSpec(default=False) # Bool class Image(BaseGlyph): __view_model__ = 'image' image = DataSpec x = DataSpec y = DataSpec dw = DataSpec dh = DataSpec dilate = Bool(False) #TODO: Consider converting palette in to a first-class object, then wrap the color list and reserve values into it instead of here #Reserve represents a color/value outside of the normal range. Commonly used to setup a 'background' color for the image palette = DataSpec #TODO: Using 'False' to indicate no reserve value is not great. A flag field or sentinel is probably better, but that can be worked out when/if palette becomes its own object #The actual type of reserve_val is an instance of whatever is held in the image array, so the exact type will depend on the type of values in the dataspec of the image field. reserve_val = Any(default=False) reserve_color = DataSpec(default=0xffffff) #TODO: Why doesn't type Color work here?? (Came through as 'undefined' on the JS side) #TODO: What is the color code for transparent??? class ImageURL(BaseGlyph): __view_model__ = 'image_url' url = DataSpec x = DataSpec y = DataSpec w = DataSpec h = DataSpec angle = DataSpec dilate = Bool(False) anchor = Enum("top_left", "top_center", "top_right", "right_center", "bottom_right", "bottom_center", "bottom_left", "left_center", "center") class ImageRGBA(BaseGlyph): __view_model__ = 'image_rgba' image = DataSpec x = DataSpec y = DataSpec dw = DataSpec dh = DataSpec dilate = Bool(False) class Line(BaseGlyph, LineProps): __view_model__ = "line" x = DataSpec y = DataSpec class MultiLine(BaseGlyph, LineProps): __view_model__ = 'multi_line' xs = DataSpec ys = DataSpec class Oval(BaseGlyph, FillProps, LineProps): __view_model__ = 'oval' x = DataSpec y = DataSpec width = DataSpec height = DataSpec angle = DataSpec class Patch(BaseGlyph, FillProps, LineProps): __view_model__ = 'patch' x = DataSpec y = DataSpec class Patches(BaseGlyph, LineProps, FillProps): __view_model__ = 'patches' xs = DataSpec ys = DataSpec class Quad(BaseGlyph, FillProps, LineProps): __view_model__ = "quad" left = DataSpec right = DataSpec bottom = DataSpec top = DataSpec class Quadratic(BaseGlyph, LineProps): __view_model__ = 'quadratic' x0 = DataSpec y0 = DataSpec x1 = DataSpec y1 = DataSpec cx = DataSpec cy = DataSpec class Ray(BaseGlyph, LineProps): __view_model__ = "ray" x = DataSpec y = DataSpec angle = DataSpec length = DataSpec class Rect(BaseGlyph, FillProps, LineProps): __view_model__ = "rect" x = DataSpec y = DataSpec width = DataSpec height = DataSpec angle = DataSpec dilate = Bool(False) class Segment(BaseGlyph, LineProps): __view_model__ = 'segment' x0 = DataSpec y0 = DataSpec x1 = DataSpec y1 = DataSpec class Text(BaseGlyph, TextProps): __view_model__ = "text" x = DataSpec y = DataSpec text = DataSpec angle = DataSpec class Wedge(BaseGlyph, FillProps, LineProps): __view_model__ = 'wedge' x = DataSpec y = DataSpec radius = DataSpec(min_value=0) start_angle = DataSpec end_angle = DataSpec direction = Enum(Direction)
jakevdp/bokeh
bokeh/glyphs.py
Python
bsd-3-clause
8,148
[ "Gaussian" ]
151f658645cc43d634b523092dee664c5b557bd3c5749db1aab2a227374c50a6
#!/usr/bin/env python # This script checks and can optionally update MOOSE source files. # You should always run this script without the "-u" option # first to make sure there is a clean dry run of the files that should # be updated import os, string from optparse import OptionParser global_ignores = ['contrib', '.svn'] copyright_header = \ """/****************************************************************/ /* DO NOT MODIFY THIS HEADER */ /* MOOSE - Multiphysics Object Oriented Simulation Environment */ /* */ /* (c) 2010 Battelle Energy Alliance, LLC */ /* ALL RIGHTS RESERVED */ /* */ /* Prepared by Battelle Energy Alliance, LLC */ /* Under Contract No. DE-AC07-05ID14517 */ /* With the U. S. Department of Energy */ /* */ /* See COPYRIGHT for full restrictions */ /****************************************************************/ """ global_options = {} def fixupHeader(): for dirpath, dirnames, filenames in os.walk(os.getcwd() + "/../"): # Don't traverse into ignored directories for ignore in global_ignores: if ignore in dirnames: dirnames.remove(ignore) #print dirpath #print dirnames for file in filenames: suffix = os.path.splitext(file) if suffix[-1] == '.C' or suffix[-1] == '.h': checkAndUpdate(dirpath + '/' + file) def checkAndUpdate(filename): f = open(filename) text = f.read() f.close() # Check (exact match only) if (string.find(text, copyright_header) == -1): # print the first 10 lines or so of the file if global_options.update == False: # Report only print filename + ' does not contain an up to date header' if global_options.verbose == True: print '>'*40, '\n', '\n'.join((text.split('\n', 10))[:10]), '\n'*5 else: # Update f = open(filename + '~tmp', 'w') f.write(copyright_header) f.write(text) f.close() os.rename(filename + '~tmp', filename) if __name__ == '__main__': parser = OptionParser() parser.add_option("-u", "--update", action="store_true", dest="update", default=False) parser.add_option("-v", "--verbose", action="store_true", dest="verbose", default=False) (global_options, args) = parser.parse_args() fixupHeader()
zzyfisherman/moose
framework/scripts/fixup_headers.py
Python
lgpl-2.1
2,599
[ "MOOSE" ]
906a9402b943390b83b3a1f570b5694f2cc738bc642549a495ed666aa7ae98f3
import numpy as np import bayesianoracle as bo import bayesianoracle.plot as boplotter # Import function information from function_data import * import os os.system("function_data.py") def plot_kernels(bmao, X, x0, kernel_ranges, colors): """ Auxillary plotting function Parameters ---------- bmao : Bayesian model averaging optimization process X : The values that have been previously traversed x0 : location at which we want to evaluate the kernel kernel_ranges : (list of scalars) the desired kernel_widths to be plotted """ import matplotlib.pyplot as plt from matplotlib.collections import LineCollection from matplotlib import colors as cl from matplotlib import gridspec boplt = boplotter.Plotter1D(x_range=x_range, y_range=y_range, num_points=num_points*10) boplt.set_bma(bmao.bma) ### Plot the data and the models fig = plt.figure(figsize=(8, 6), dpi=dpi) ax = plt.gca() legend_elements = [] legend_texts = [] for i in xrange(len(kernel_ranges)): # Plot the kernels kernel_line = boplt.plot_kernel(ax, x0, kernel_ranges[i], color=colors[i], ylabel=r'$k_{\gamma}\left(x,x^\prime\right)$') # Plot data data_scat = boplt.plot_kernel_at_data(ax, x0, kernel_ranges[i], color=colors[i], bool_color_cycled=True, xlabel=r'$x$') """ # Add the information for legends legend_elements.append(kernel_line) legend_texts.append(r"$K_\gamma(x)$ for $\gamma = "+str(kernel_ranges[i])+"$") # Get the data element in the legend legend_elements.append(data_scat) legend_texts.append(r"$K_\theta(x_i)$ for $\theta = "+str(kernel_ranges[i])+"$") """ legend_elements.append((kernel_line, data_scat)) legend_texts.append(r"$\gamma = " + str(kernel_ranges[i])+'$') #boplt.plot_data_locations(ax, color='black', alpha=0.3, linestyle='--', zorder=1) # Create the x0 line boplt.draw_vertical_line(ax, x0, r'$x^\prime$', color='#FF9900') # Reverse the legend texts and elements legend_elements = reversed(legend_elements) legend_texts = reversed(legend_texts) # Create the legend legend = plt.legend(legend_elements, legend_texts, loc='center right', bbox_to_anchor=(1.05, 0.5), ncol=1, fancybox=True, shadow=False, scatterpoints=1) # Change the sizes of the scatter dots in legend """ for i in xrange(len(kernel_ranges)): legend.legendHandles[2*i+1]._sizes = [30] """ plt.setp(legend.get_texts(), fontsize=12) plt.savefig("KernelDisplay_figures/Kernels.png", dpi=dpi) plt.close(fig) bmao = bo.optimizer.QuadraticBMAOptimizer(ndim = 1, init_kernel_range=0.25, n_int=50, precision_beta = 1000.0, constraints = [constr1, constr2], bounding_box = bounding_box, bool_compact = True, kernel_type='Gaussian') # Center of the kernel x0 = np.array([-0.5]) # Initialize x_next X = None y_hist = np.array([]) for k in xrange(X_complete.shape[1]): # Get next x_next = X_complete[:,k] x = x_next if k == 0: X = np.array([x_next]) else: X = np.hstack([X, np.array([x_next])]) # Get y, grad, hess from precomputed lists f = f_complete[k] grad = grad_complete[k] Hess = Hess_complete[k] y_hist = np.append(y_hist, f) # Add the observations to the bmao bmao.add_observation(x, f, grad, Hess) kernel_ranges = [2.0, 0.25, 0.1] colors = ['#FFBBBB', '#FF7777', '#FF0000'] #colors = ['#00FF99', '#66FFCC', '#99FFCC'] #colors = ['#FFD494','#FFB870','#FF9900'] colors = ['#DDDDDD','#AAAAAA','#666666'] #colors = ['#707070','#333333','#000000'] # Plot the Kernels plot_kernels(bmao, X, x0, kernel_ranges, colors)
altaetran/bayesianoracle
tests/quadraticBayesianAveraging/paper_examples/KernelDisplay.py
Python
apache-2.0
4,106
[ "Gaussian" ]
8b9d543f1ac7c1811088fe6311ad6c6d13d67b199bd79082bc6176aea11911ea
""" :mod: ReqClient .. module: ReqClient :synopsis: implementation of client for RequestDB using DISET framework """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import six import os import time import random import json import datetime # # from DIRAC from DIRAC import gLogger, S_OK, S_ERROR from DIRAC.Core.DISET.RPCClient import RPCClient from DIRAC.Core.Utilities.List import randomize, fromChar from DIRAC.Core.Utilities.JEncode import strToIntDict from DIRAC.Core.Utilities.DEncode import ignoreEncodeWarning from DIRAC.ConfigurationSystem.Client import PathFinder from DIRAC.Core.Base.Client import Client, createClient from DIRAC.RequestManagementSystem.Client.Request import Request from DIRAC.RequestManagementSystem.private.RequestValidator import RequestValidator from DIRAC.WorkloadManagementSystem.Client import JobStatus from DIRAC.WorkloadManagementSystem.Client import JobMinorStatus from DIRAC.WorkloadManagementSystem.Client.JobMonitoringClient import JobMonitoringClient from DIRAC.WorkloadManagementSystem.Client.JobStateUpdateClient import JobStateUpdateClient @createClient('RequestManagement/ReqManager') class ReqClient(Client): """ReqClient is a class manipulating and operation on Requests. :param ~RPCClient.RPCClient requestManager: RPC client to RequestManager :param dict requestProxiesDict: RPC client to ReqestProxy :param ~DIRAC.RequestManagementSystem.private.RequestValidator.RequestValidator requestValidator: RequestValidator instance """ __requestProxiesDict = {} __requestValidator = None def __init__(self, url=None, **kwargs): """c'tor :param self: self reference :param url: url of the ReqManager :param kwargs: forwarded to the Base Client class """ super(ReqClient, self).__init__(**kwargs) self.serverURL = 'RequestManagement/ReqManager' if not url else url self.log = gLogger.getSubLogger("RequestManagement/ReqClient/pid_%s" % (os.getpid())) def requestProxies(self, timeout=120): """ get request proxies dict """ if not self.__requestProxiesDict: self.__requestProxiesDict = {} proxiesURLs = fromChar(PathFinder.getServiceURL("RequestManagement/ReqProxyURLs")) if not proxiesURLs: self.log.warn("CS option RequestManagement/ReqProxyURLs is not set!") for proxyURL in proxiesURLs: self.log.debug("creating RequestProxy for url = %s" % proxyURL) self.__requestProxiesDict[proxyURL] = RPCClient(proxyURL, timeout=timeout) return self.__requestProxiesDict def requestValidator(self): """ get request validator """ if not self.__requestValidator: self.__requestValidator = RequestValidator() return self.__requestValidator def putRequest(self, request, useFailoverProxy=True, retryMainService=0): """Put request to RequestManager :param self: self reference :param ~Request.Request request: Request instance :param bool useFailoverProxy: if False, will not attempt to forward the request to ReqProxies :param int retryMainService: Amount of time we retry on the main ReqHandler in case of failures :return: S_OK/S_ERROR """ errorsDict = {"OK": False} valid = self.requestValidator().validate(request) if not valid["OK"]: self.log.error("putRequest: request not valid", "%s" % valid["Message"]) return valid # # dump to json requestJSON = request.toJSON() if not requestJSON["OK"]: return requestJSON requestJSON = requestJSON["Value"] retryMainService += 1 while retryMainService: retryMainService -= 1 setRequestMgr = self._getRPC().putRequest(requestJSON) if setRequestMgr["OK"]: return setRequestMgr errorsDict["RequestManager"] = setRequestMgr["Message"] # sleep a bit time.sleep(random.randint(1, 5)) self.log.warn("putRequest: unable to set request '%s' at RequestManager" % request.RequestName, setRequestMgr["Message"]) proxies = self.requestProxies() if useFailoverProxy else {} for proxyURL in randomize(proxies.keys()): proxyClient = proxies[proxyURL] self.log.debug("putRequest: trying RequestProxy at %s" % proxyURL) setRequestProxy = proxyClient.putRequest(requestJSON) if setRequestProxy["OK"]: if setRequestProxy["Value"]["set"]: self.log.info("putRequest: request '%s' successfully set using RequestProxy %s" % (request.RequestName, proxyURL)) elif setRequestProxy["Value"]["saved"]: self.log.info("putRequest: request '%s' successfully forwarded to RequestProxy %s" % (request.RequestName, proxyURL)) return setRequestProxy else: self.log.warn("putRequest: unable to set request using RequestProxy %s: %s" % (proxyURL, setRequestProxy["Message"])) errorsDict["RequestProxy(%s)" % proxyURL] = setRequestProxy["Message"] # # if we're here neither requestManager nor requestProxy were successful self.log.error("putRequest: unable to set request", "'%s'" % request.RequestName) errorsDict["Message"] = "ReqClient.putRequest: unable to set request '%s'" % request.RequestName return errorsDict def getRequest(self, requestID=0): """Get request from RequestDB :param self: self reference :param int requestID: ID of the request. If 0, choice is made for you :return: S_OK( Request instance ) or S_OK() or S_ERROR """ self.log.debug("getRequest: attempting to get request.") getRequest = self._getRPC().getRequest(requestID) if not getRequest["OK"]: self.log.error("getRequest: unable to get request", "'%s' %s" % (requestID, getRequest["Message"])) return getRequest if not getRequest["Value"]: return getRequest return S_OK(Request(getRequest["Value"])) @ignoreEncodeWarning def getBulkRequests(self, numberOfRequest=10, assigned=True): """ get bulk requests from RequestDB :param self: self reference :param str numberOfRequest: size of the bulk (default 10) :return: S_OK( Successful : { requestID, RequestInstance }, Failed : message ) or S_ERROR """ self.log.debug("getRequests: attempting to get request.") getRequests = self._getRPC().getBulkRequests(numberOfRequest, assigned) if not getRequests["OK"]: self.log.error("getRequests: unable to get '%s' requests: %s" % (numberOfRequest, getRequests["Message"])) return getRequests # No Request returned if not getRequests["Value"]: return getRequests # No successful Request if not getRequests["Value"]["Successful"]: return getRequests jsonReq = getRequests["Value"]["Successful"] # Do not forget to cast back str keys to int reqInstances = {int(rId): Request(jsonReq[rId]) for rId in jsonReq} failed = strToIntDict(getRequests["Value"]["Failed"]) return S_OK({"Successful": reqInstances, "Failed": failed}) def peekRequest(self, requestID): """ peek request """ self.log.debug("peekRequest: attempting to get request.") peekRequest = self._getRPC().peekRequest(int(requestID)) if not peekRequest["OK"]: self.log.error("peekRequest: unable to peek request", "request: '%s' %s" % (requestID, peekRequest["Message"])) return peekRequest if not peekRequest["Value"]: return peekRequest return S_OK(Request(peekRequest["Value"])) def deleteRequest(self, requestID): """ delete request given it's ID :param self: self reference :param str requestID: request ID """ requestID = int(requestID) self.log.debug("deleteRequest: attempt to delete '%s' request" % requestID) deleteRequest = self._getRPC().deleteRequest(requestID) if not deleteRequest["OK"]: self.log.error("deleteRequest: unable to delete request", "'%s' request: %s" % (requestID, deleteRequest["Message"])) return deleteRequest def getRequestIDsList(self, statusList=None, limit=None, since=None, until=None, getJobID=False): """ get at most :limit: request ids with statuses in :statusList: """ statusList = statusList if statusList else list(Request.FINAL_STATES) limit = limit if limit else 100 since = since.strftime('%Y-%m-%d') if since else "" until = until.strftime('%Y-%m-%d') if until else "" return self._getRPC().getRequestIDsList(statusList, limit, since, until, getJobID) def getScheduledRequest(self, operationID): """ get scheduled request given its scheduled OperationID """ self.log.debug("getScheduledRequest: attempt to get scheduled request...") scheduled = self._getRPC().getScheduledRequest(operationID) if not scheduled["OK"]: self.log.error("getScheduledRequest failed", scheduled["Message"]) return scheduled if scheduled["Value"]: return S_OK(Request(scheduled["Value"])) return scheduled def getDBSummary(self): """ Get the summary of requests in the RequestDBs. """ self.log.debug("getDBSummary: attempting to get RequestDB summary.") dbSummary = self._getRPC().getDBSummary() if not dbSummary["OK"]: self.log.error("getDBSummary: unable to get RequestDB summary", dbSummary["Message"]) return dbSummary def getDigest(self, requestID): """ Get the request digest given a request ID. :param self: self reference :param str requestID: request id """ self.log.debug("getDigest: attempting to get digest for '%s' request." % requestID) digest = self._getRPC().getDigest(int(requestID)) if not digest["OK"]: self.log.error("getDigest: unable to get digest for request", "request: '%s' %s" % (requestID, digest["Message"])) return digest def getRequestStatus(self, requestID): """ Get the request status given a request id. :param self: self reference :param int requestID: id of the request """ if isinstance(requestID, six.string_types): requestID = int(requestID) self.log.debug("getRequestStatus: attempting to get status for '%d' request." % requestID) requestStatus = self._getRPC().getRequestStatus(requestID) if not requestStatus["OK"]: self.log.error("getRequestStatus: unable to get status for request", ": '%d' %s" % (requestID, requestStatus["Message"])) return requestStatus # def getRequestName( self, requestID ): # """ get request name for a given requestID """ # return self._getRPC().getRequestName( requestID ) def getRequestInfo(self, requestID): """ The the request info given a request id. :param self: self reference :param int requestID: request nid """ self.log.debug("getRequestInfo: attempting to get info for '%s' request." % requestID) requestInfo = self._getRPC().getRequestInfo(int(requestID)) if not requestInfo["OK"]: self.log.error("getRequestInfo: unable to get status for request", "request: '%s' %s" % (requestID, requestInfo["Message"])) return requestInfo def getRequestFileStatus(self, requestID, lfns): """ Get file status for request given a request id. :param self: self reference :param int requestID: request id :param lfns: list of LFNs :type lfns: python:list """ self.log.debug("getRequestFileStatus: attempting to get file statuses for '%s' request." % requestID) fileStatus = self._getRPC().getRequestFileStatus(int(requestID), lfns) if not fileStatus["OK"]: self.log.verbose("getRequestFileStatus: unable to get file status for request", "request: '%s' %s" % (requestID, fileStatus["Message"])) return fileStatus def finalizeRequest(self, requestID, jobID, useCertificates=True): """ check request status and perform finalization if necessary update the request status and the corresponding job parameter :param self: self reference :param str requestID: request id :param int jobID: job id """ stateServer = JobStateUpdateClient(useCertificates=useCertificates) # Checking if to update the job status - we should fail here, so it will be re-tried later # Checking the state, first res = self.getRequestStatus(requestID) if not res['OK']: self.log.error("finalizeRequest: failed to get request", "request: %s status: %s" % (requestID, res["Message"])) return res if res["Value"] != "Done": return S_ERROR("The request %s isn't 'Done' but '%s', this should never happen, why are we here?" % (requestID, res['Value'])) # The request is 'Done', let's update the job status. If we fail, we should re-try later monitorServer = JobMonitoringClient(useCertificates=useCertificates) res = monitorServer.getJobSummary(int(jobID)) if not res["OK"]: self.log.error("finalizeRequest: Failed to get job status", "JobID: %d" % jobID) return res elif not res['Value']: self.log.info("finalizeRequest: job %d does not exist (anymore): finalizing" % jobID) return S_OK() else: jobStatus = res["Value"]['Status'] jobMinorStatus = res["Value"]["MinorStatus"] jobAppStatus = '' newJobStatus = '' if jobStatus == JobStatus.STALLED: # If job is stalled, find the previous status from the logging info res = monitorServer.getJobLoggingInfo(int(jobID)) if not res['OK']: self.log.error("finalizeRequest: Failed to get job logging info", "JobID: %d" % jobID) return res # Check the last status was Stalled and get the one before if len(res['Value']) >= 2 and res['Value'][-1][0] == JobStatus.STALLED: jobStatus, jobMinorStatus, jobAppStatus = res['Value'][-2][:3] newJobStatus = jobStatus # update the job pending request digest in any case since it is modified self.log.info("finalizeRequest: Updating request digest for job %d" % jobID) digest = self.getDigest(requestID) if digest["OK"]: digest = digest["Value"] self.log.verbose(digest) res = stateServer.setJobParameter(jobID, "PendingRequest", digest) if not res["OK"]: self.log.info("finalizeRequest: Failed to set job %d parameter: %s" % (jobID, res["Message"])) return res else: self.log.error("finalizeRequest: Failed to get request digest for %s: %s" % (requestID, digest["Message"])) if jobStatus == JobStatus.COMPLETED: # What to do? Depends on what we have in the minorStatus if jobMinorStatus == JobMinorStatus.PENDING_REQUESTS: newJobStatus = JobStatus.DONE elif jobMinorStatus == JobMinorStatus.APP_ERRORS: newJobStatus = JobStatus.FAILED else: self.log.error("finalizeRequest: Unexpected jobMinorStatus", "(got %s)" % jobMinorStatus) return S_ERROR("Unexpected jobMinorStatus") if newJobStatus: self.log.info("finalizeRequest: Updating job status for %d to %s/Requests done" % (jobID, newJobStatus)) else: self.log.info( "finalizeRequest: Updating job minor status", "for %d to 'Requests done' (current status is %s)" % (jobID, jobStatus)) stateUpdate = stateServer.setJobStatus(jobID, newJobStatus, "Requests done", 'RMS') if jobAppStatus and stateUpdate['OK']: stateUpdate = stateServer.setJobApplicationStatus(jobID, jobAppStatus, 'RMS') if not stateUpdate["OK"]: self.log.error("finalizeRequest: Failed to set job status", "JobID: %d, error: %s" % (jobID, stateUpdate['Message'])) return stateUpdate return S_OK(newJobStatus) @ignoreEncodeWarning def getRequestIDsForJobs(self, jobIDs): """ get the request ids for the supplied jobIDs. :param self: self reference :param jobIDs: list of job IDs (integers) :type jobIDs: python:list :return: S_ERROR or S_OK( "Successful": { jobID1: reqID1, jobID2: requID2, ... }, "Failed" : { jobIDn: errMsg, jobIDm: errMsg, ...} ) """ self.log.verbose("getRequestIDsForJobs: attempt to get request(s) for jobs", "(n=%d)" % len(jobIDs)) res = self._getRPC().getRequestIDsForJobs(jobIDs) if not res["OK"]: self.log.error("getRequestIDsForJobs: unable to get request(s) for jobs", "%s: %s" % (jobIDs, res["Message"])) return res # Cast the JobIDs back to int successful = strToIntDict(res['Value']['Successful']) failed = strToIntDict(res['Value']['Failed']) return S_OK({'Successful': successful, 'Failed': failed}) @ignoreEncodeWarning def readRequestsForJobs(self, jobIDs): """ read requests for jobs :param jobIDs: list with jobIDs :type jobIDs: python:list :return: S_OK( { "Successful" : { jobID1 : Request, ... }, "Failed" : { jobIDn : "Fail reason" } } ) """ readReqsForJobs = self._getRPC().readRequestsForJobs(jobIDs) if not readReqsForJobs["OK"]: return readReqsForJobs ret = readReqsForJobs["Value"] # # create Requests out of JSONs for successful reads # Do not forget to cast back str keys to int successful = {int(jobID): Request(jsonReq) for jobID, jsonReq in ret['Successful'].items()} failed = strToIntDict(ret['Failed']) return S_OK({'Successful': successful, 'Failed': failed}) def resetFailedRequest(self, requestID, allR=False): """ Reset a failed request to "Waiting" status """ # # we can safely only peek the request as it is Failed and therefore not owned by an agent res = self.peekRequest(requestID) if not res['OK']: return res req = res['Value'] if allR or recoverableRequest(req): # Only reset requests that can be recovered if req.Status != 'Failed': gLogger.notice("Reset NotBefore time, was %s" % str(req.NotBefore)) else: for i, op in enumerate(req): op.Error = '' if op.Status == 'Failed': printOperation((i, op), onlyFailed=True) for fi in op: if fi.Status == 'Failed': fi.Attempt = 1 fi.Error = '' fi.Status = 'Waiting' if op.Status == 'Failed': op.Status = 'Waiting' # Reset also NotBefore req.NotBefore = datetime.datetime.utcnow().replace(microsecond=0) return self.putRequest(req) return S_OK("Not reset") # ============= Some useful functions to be shared =========== output = '' def prettyPrint(mainItem, key='', offset=0): global output if key: key += ': ' blanks = offset * ' ' if mainItem and isinstance(mainItem, dict): output += "%s%s%s\n" % (blanks, key, '{') if blanks or key else '' for key in sorted(mainItem): prettyPrint(mainItem[key], key=key, offset=offset) output += "%s%s\n" % (blanks, '}') if blanks else '' elif mainItem and isinstance(mainItem, list) or isinstance(mainItem, tuple): output += "%s%s%s\n" % (blanks, key, '[' if isinstance(mainItem, list) else '(') for item in mainItem: prettyPrint(item, offset=offset + 2) output += "%s%s\n" % (blanks, ']' if isinstance(mainItem, list) else ')') elif isinstance(mainItem, six.string_types): if '\n' in mainItem: prettyPrint(mainItem.strip('\n').split('\n'), offset=offset) else: output += "%s%s'%s'\n" % (blanks, key, mainItem) else: output += "%s%s%s\n" % (blanks, key, str(mainItem)) output = output.replace('[\n%s{' % blanks, '[{').replace('}\n%s]' % blanks, '}]') \ .replace('(\n%s{' % blanks, '({').replace('}\n%s)' % blanks, '})') \ .replace('(\n%s(' % blanks, '((').replace(')\n%s)' % blanks, '))') \ .replace('(\n%s[' % blanks, '[').replace(']\n%s)' % blanks, ']') def printFTSJobs(request): """ Prints the FTSJobs associated to a request :param request: Request object """ try: if request.RequestID: # We try first the new FTS3 system from DIRAC.DataManagementSystem.Client.FTS3Client import FTS3Client fts3Client = FTS3Client() res = fts3Client.ping() if res['OK']: associatedFTS3Jobs = [] for op in request: res = fts3Client.getOperationsFromRMSOpID(op.OperationID) if res['OK']: for fts3Op in res['Value']: associatedFTS3Jobs.extend(fts3Op.ftsJobs) if associatedFTS3Jobs: # Display the direct url and the status gLogger.always( '\n\nFTS3 jobs associated: \n%s' % '\n'.join( '%s/fts3/ftsmon/#/job/%s (%s)' % (job.ftsServer.replace(':8446', ':8449'), # Submission port is 8446, web port is 8449 job.ftsGUID, job.status) for job in associatedFTS3Jobs)) return # AttributeError can be thrown because the deserialization will not have # happened correctly on the new fts3 (CC7 typically), and the error is not # properly propagated except AttributeError as err: gLogger.debug("Could not instantiate FtsClient because of Exception", repr(err)) def printRequest(request, status=None, full=False, verbose=True, terse=False): global output if full: output = '' prettyPrint(json.loads(request.toJSON()['Value'])) gLogger.always(output) else: if not status: status = request.Status gLogger.always("Request name='%s' ID=%s Status='%s'%s%s%s" % (request.RequestName, request.RequestID if hasattr(request, 'RequestID') else '(not set yet)', request.Status, " ('%s' in DB)" % status if status != request.Status else '', (" Error='%s'" % request.Error) if request.Error and request.Error.strip() else "", (" Job=%s" % request.JobID) if request.JobID else "")) gLogger.always("Created %s, Updated %s%s" % (request.CreationTime, request.LastUpdate, (", NotBefore %s" % request.NotBefore) if request.NotBefore else "")) if request.OwnerDN: gLogger.always("Owner: '%s', Group: %s" % (request.OwnerDN, request.OwnerGroup)) for indexOperation in enumerate(request): op = indexOperation[1] if not terse or op.Status == 'Failed': printOperation(indexOperation, verbose, onlyFailed=terse) printFTSJobs(request) def printOperation(indexOperation, verbose=True, onlyFailed=False): global output i, op = indexOperation prStr = '' if op.SourceSE: prStr += 'SourceSE: %s' % op.SourceSE if op.TargetSE: prStr += (' - ' if prStr else '') + 'TargetSE: %s' % op.TargetSE if prStr: prStr += ' - ' prStr += 'Created %s, Updated %s' % (op.CreationTime, op.LastUpdate) if op.Type == 'ForwardDISET' and op.Arguments: from DIRAC.Core.Utilities import DEncode decode, _length = DEncode.decode(op.Arguments) if verbose: output = '' prettyPrint(decode, offset=10) prStr += '\n Arguments:\n' + output.strip('\n') else: prStr += '\n Service: %s' % decode[0][0] gLogger.always(" [%s] Operation Type='%s' ID=%s Order=%s Status='%s'%s%s" % (i, op.Type, op.OperationID if hasattr(op, 'OperationID') else '(not set yet)', op.Order, op.Status, (" Error='%s'" % op.Error) if op.Error and op.Error.strip() else "", (" Catalog=%s" % op.Catalog) if op.Catalog else "")) if prStr: gLogger.always(" %s" % prStr) for indexFile in enumerate(op): if not onlyFailed or indexFile[1].Status == 'Failed': printFile(indexFile) def printFile(indexFile): ind, fi = indexFile gLogger.always(" [%02d] ID=%s LFN='%s' Status='%s'%s%s%s" % (ind + 1, fi.FileID if hasattr(fi, 'FileID') else '(not set yet)', fi.LFN, fi.Status, (" Checksum='%s'" % fi.Checksum) if fi.Checksum or (fi.Error and 'checksum' in fi.Error.lower()) else "", (" Error='%s'" % fi.Error) if fi.Error and fi.Error.strip() else "", (" Attempts=%d" % fi.Attempt) if fi.Attempt > 1 else "") ) def recoverableRequest(request): excludedErrors = ('File does not exist', 'No such file or directory', 'sourceSURL equals to targetSURL', 'Max attempts limit reached', 'Max attempts reached') operationErrorsOK = ('is banned for', 'Failed to perform exists from any catalog') for op in request: if op.Status == 'Failed' and (not op.Error or not [errStr for errStr in operationErrorsOK if errStr in op.Error]): for fi in op: if fi.Status == 'Failed': if [errStr for errStr in excludedErrors if errStr in fi.Error]: return False return True return True
yujikato/DIRAC
src/DIRAC/RequestManagementSystem/Client/ReqClient.py
Python
gpl-3.0
25,398
[ "DIRAC" ]
79fd0ad9f12b3c33651cd53a5e4c28b0a972a528404ca5995a043e1f929c240b
from flask import Flask, render_template, redirect, session, request import random, datetime app = Flask(__name__) app.secret_key = "Felipe" @app.route('/') def index(): if not 'gold' in session: session['gold'] = 0 if not 'activities' in session: session['activities'] = [] return render_template('index.html', goldcount = session['gold']) @app.route('/process_money', methods=['POST']) def process(): places = { 'farm':random.randint(5,10), 'cave':random.randint(5,10), 'house':random.randint(2,5), 'casino':random.randint(-50,50) } if request.form['place'] in places: result = places[request.form['place']] session['gold']=session['gold']+ result myStr = "{} {} golds from the {} ({})".format(('lost','Earned')[result > 0], abs(result), request.form['place'], datetime.datetime.now()) session['activities'].append(myStr) return redirect('/') @app.route('/reset') def reset(): session.clear() return redirect('/') app.run(debug = True)
jiobert/python
Velez_Felipe/assignments/Ninja/ninja_gold.py
Python
mit
974
[ "CASINO" ]
0fd5fe8ba6b685a7ea6a4fabdc2b8b36a0f1351535699e601c6099eae94b8099
import mdpow.equil S = mdpow.equil.WaterSimulation(molecule="BNZ") S.topology("benzene.itp") S.solvate(struct="benzene.pdb") S.energy_minimize() S.MD_relaxed(runtime=5) # should be at least 1e3 ps for production not just 5 ps # run simulation externally or use MDrunner # (see docs for using mpi etc) import gromacs r = gromacs.run.MDrunner(dirname=S.dirs['MD_relaxed'], deffnm="md", c="md.pdb", cpi=True, append=True, v=True) r.run() # runs mdrun in the python shell S.MD(runtime=10, qscript=['local.sh']) # should be at least 10e3 ps for production, not just 10 ps # run simulation r = gromacs.run.MDrunner(dirname=S.dirs['MD_NPT'], deffnm="md", c="md.pdb", cpi=True, append=True, v=True) r.run() # runs mdrun in the python shell import mdpow.fep gwat = mdpow.fep.Ghyd(simulation=S, runtime=10) gwat.setup() # run multiple simulations on cluster O = mdpow.equil.OctanolSimulation(molecule="BNZ") O.topology("benzene.itp") O.solvate(struct="benzene.pdb") O.energy_minimize() O.MD_relaxed(runtime=0.5)
Becksteinlab/MDPOW
doc/examples/benzene/session.py
Python
gpl-3.0
1,018
[ "Gromacs" ]
63d5c9c16e38407c92fd919e443e07c9661a2425eb003c470d8f75c12014392c
"""Forest of trees-based ensemble methods Those methods include random forests and extremely randomized trees. The module structure is the following: - The ``BaseForest`` base class implements a common ``fit`` method for all the estimators in the module. The ``fit`` method of the base ``Forest`` class calls the ``fit`` method of each sub-estimator on random samples (with replacement, a.k.a. bootstrap) of the training set. The init of the sub-estimator is further delegated to the ``BaseEnsemble`` constructor. - The ``ForestClassifier`` and ``ForestRegressor`` base classes further implement the prediction logic by computing an average of the predicted outcomes of the sub-estimators. - The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived classes provide the user with concrete implementations of the forest ensemble method using classical, deterministic ``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as sub-estimator implementations. - The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived classes provide the user with concrete implementations of the forest ensemble method using the extremely randomized trees ``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as sub-estimator implementations. Single and multi-output problems are both handled. """ # Authors: Gilles Louppe <g.louppe@gmail.com> # Brian Holt <bdholt1@gmail.com> # Joly Arnaud <arnaud.v.joly@gmail.com> # Fares Hedayati <fares.hedayati@gmail.com> # # License: BSD 3 clause from __future__ import division import warnings from warnings import warn from abc import ABCMeta, abstractmethod import numpy as np from scipy.sparse import issparse from scipy.sparse import hstack as sparse_hstack from ..base import ClassifierMixin, RegressorMixin from ..externals.joblib import Parallel, delayed from ..externals import six from ..feature_selection.from_model import _LearntSelectorMixin from ..metrics import r2_score from ..preprocessing import OneHotEncoder from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor) from ..tree._tree import DTYPE, DOUBLE from ..utils import check_random_state, check_array, compute_sample_weight from ..exceptions import DataConversionWarning, NotFittedError from .base import BaseEnsemble, _partition_estimators from ..utils.fixes import bincount, parallel_helper from ..utils.multiclass import check_classification_targets __all__ = ["RandomForestClassifier", "RandomForestRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "RandomTreesEmbedding"] MAX_INT = np.iinfo(np.int32).max def _generate_sample_indices(random_state, n_samples): """Private function used to _parallel_build_trees function.""" random_instance = check_random_state(random_state) sample_indices = random_instance.randint(0, n_samples, n_samples) return sample_indices def _generate_unsampled_indices(random_state, n_samples): """Private function used to forest._set_oob_score function.""" sample_indices = _generate_sample_indices(random_state, n_samples) sample_counts = bincount(sample_indices, minlength=n_samples) unsampled_mask = sample_counts == 0 indices_range = np.arange(n_samples) unsampled_indices = indices_range[unsampled_mask] return unsampled_indices def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose=0, class_weight=None): """Private function used to fit a single tree in parallel.""" if verbose > 1: print("building tree %d of %d" % (tree_idx + 1, n_trees)) if forest.bootstrap: n_samples = X.shape[0] if sample_weight is None: curr_sample_weight = np.ones((n_samples,), dtype=np.float64) else: curr_sample_weight = sample_weight.copy() indices = _generate_sample_indices(tree.random_state, n_samples) sample_counts = bincount(indices, minlength=n_samples) curr_sample_weight *= sample_counts if class_weight == 'subsample': with warnings.catch_warnings(): warnings.simplefilter('ignore', DeprecationWarning) curr_sample_weight *= compute_sample_weight('auto', y, indices) elif class_weight == 'balanced_subsample': curr_sample_weight *= compute_sample_weight('balanced', y, indices) tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False) else: tree.fit(X, y, sample_weight=sample_weight, check_input=False) return tree class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble, _LearntSelectorMixin)): """Base class for forests of trees. Warning: This class should not be used directly. Use derived classes instead. """ @abstractmethod def __init__(self, base_estimator, n_estimators=10, estimator_params=tuple(), bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None): super(BaseForest, self).__init__( base_estimator=base_estimator, n_estimators=n_estimators, estimator_params=estimator_params) self.bootstrap = bootstrap self.oob_score = oob_score self.n_jobs = n_jobs self.random_state = random_state self.verbose = verbose self.warm_start = warm_start self.class_weight = class_weight def apply(self, X): """Apply trees in the forest to X, return leaf indices. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- X_leaves : array_like, shape = [n_samples, n_estimators] For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in. """ X = self._validate_X_predict(X) results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, backend="threading")( delayed(parallel_helper)(tree, 'apply', X, check_input=False) for tree in self.estimators_) return np.array(results).T def decision_path(self, X): """Return the decision path in the forest Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- indicator : sparse csr array, shape = [n_samples, n_nodes] Return a node indicator matrix where non zero elements indicates that the samples goes through the nodes. n_nodes_ptr : array of size (n_estimators + 1, ) The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator. """ X = self._validate_X_predict(X) indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, backend="threading")( delayed(parallel_helper)(tree, 'decision_path', X, check_input=False) for tree in self.estimators_) n_nodes = [0] n_nodes.extend([i.shape[1] for i in indicators]) n_nodes_ptr = np.array(n_nodes).cumsum() return sparse_hstack(indicators).tocsr(), n_nodes_ptr def fit(self, X, y, sample_weight=None): """Build a forest of trees from the training set (X, y). Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The training input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels in classification, real numbers in regression). sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. Returns ------- self : object Returns self. """ # Validate or convert input data X = check_array(X, accept_sparse="csc", dtype=DTYPE) y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None) if issparse(X): # Pre-sort indices to avoid that each individual tree of the # ensemble sorts the indices. X.sort_indices() # Remap output n_samples, self.n_features_ = X.shape y = np.atleast_1d(y) if y.ndim == 2 and y.shape[1] == 1: warn("A column-vector y was passed when a 1d array was" " expected. Please change the shape of y to " "(n_samples,), for example using ravel().", DataConversionWarning, stacklevel=2) if y.ndim == 1: # reshape is necessary to preserve the data contiguity against vs # [:, np.newaxis] that does not. y = np.reshape(y, (-1, 1)) self.n_outputs_ = y.shape[1] y, expanded_class_weight = self._validate_y_class_weight(y) if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous: y = np.ascontiguousarray(y, dtype=DOUBLE) if expanded_class_weight is not None: if sample_weight is not None: sample_weight = sample_weight * expanded_class_weight else: sample_weight = expanded_class_weight # Check parameters self._validate_estimator() if not self.bootstrap and self.oob_score: raise ValueError("Out of bag estimation only available" " if bootstrap=True") random_state = check_random_state(self.random_state) if not self.warm_start: # Free allocated memory, if any self.estimators_ = [] n_more_estimators = self.n_estimators - len(self.estimators_) if n_more_estimators < 0: raise ValueError('n_estimators=%d must be larger or equal to ' 'len(estimators_)=%d when warm_start==True' % (self.n_estimators, len(self.estimators_))) elif n_more_estimators == 0: warn("Warm-start fitting without increasing n_estimators does not " "fit new trees.") else: if self.warm_start and len(self.estimators_) > 0: # We draw from the random state to get the random state we # would have got if we hadn't used a warm_start. random_state.randint(MAX_INT, size=len(self.estimators_)) trees = [] for i in range(n_more_estimators): tree = self._make_estimator(append=False) tree.set_params(random_state=random_state.randint(MAX_INT)) trees.append(tree) # Parallel loop: we use the threading backend as the Cython code # for fitting the trees is internally releasing the Python GIL # making threading always more efficient than multiprocessing in # that case. trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, backend="threading")( delayed(_parallel_build_trees)( t, self, X, y, sample_weight, i, len(trees), verbose=self.verbose, class_weight=self.class_weight) for i, t in enumerate(trees)) # Collect newly grown trees self.estimators_.extend(trees) if self.oob_score: self._set_oob_score(X, y) # Decapsulate classes_ attributes if hasattr(self, "classes_") and self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] return self @abstractmethod def _set_oob_score(self, X, y): """Calculate out of bag predictions and score.""" def _validate_y_class_weight(self, y): # Default implementation return y, None def _validate_X_predict(self, X): """Validate X whenever one tries to predict, apply, predict_proba""" if self.estimators_ is None or len(self.estimators_) == 0: raise NotFittedError("Estimator not fitted, " "call `fit` before exploiting the model.") return self.estimators_[0]._validate_X_predict(X, check_input=True) @property def feature_importances_(self): """Return the feature importances (the higher, the more important the feature). Returns ------- feature_importances_ : array, shape = [n_features] """ if self.estimators_ is None or len(self.estimators_) == 0: raise NotFittedError("Estimator not fitted, " "call `fit` before `feature_importances_`.") all_importances = Parallel(n_jobs=self.n_jobs, backend="threading")( delayed(getattr)(tree, 'feature_importances_') for tree in self.estimators_) return sum(all_importances) / len(self.estimators_) class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest, ClassifierMixin)): """Base class for forest of trees-based classifiers. Warning: This class should not be used directly. Use derived classes instead. """ @abstractmethod def __init__(self, base_estimator, n_estimators=10, estimator_params=tuple(), bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None): super(ForestClassifier, self).__init__( base_estimator, n_estimators=n_estimators, estimator_params=estimator_params, bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start, class_weight=class_weight) def _set_oob_score(self, X, y): """Compute out-of-bag score""" X = check_array(X, dtype=DTYPE, accept_sparse='csr') n_classes_ = self.n_classes_ n_samples = y.shape[0] oob_decision_function = [] oob_score = 0.0 predictions = [] for k in range(self.n_outputs_): predictions.append(np.zeros((n_samples, n_classes_[k]))) for estimator in self.estimators_: unsampled_indices = _generate_unsampled_indices( estimator.random_state, n_samples) p_estimator = estimator.predict_proba(X[unsampled_indices, :], check_input=False) if self.n_outputs_ == 1: p_estimator = [p_estimator] for k in range(self.n_outputs_): predictions[k][unsampled_indices, :] += p_estimator[k] for k in range(self.n_outputs_): if (predictions[k].sum(axis=1) == 0).any(): warn("Some inputs do not have OOB scores. " "This probably means too few trees were used " "to compute any reliable oob estimates.") decision = (predictions[k] / predictions[k].sum(axis=1)[:, np.newaxis]) oob_decision_function.append(decision) oob_score += np.mean(y[:, k] == np.argmax(predictions[k], axis=1), axis=0) if self.n_outputs_ == 1: self.oob_decision_function_ = oob_decision_function[0] else: self.oob_decision_function_ = oob_decision_function self.oob_score_ = oob_score / self.n_outputs_ def _validate_y_class_weight(self, y): check_classification_targets(y) y = np.copy(y) expanded_class_weight = None if self.class_weight is not None: y_original = np.copy(y) self.classes_ = [] self.n_classes_ = [] y_store_unique_indices = np.zeros(y.shape, dtype=np.int) for k in range(self.n_outputs_): classes_k, y_store_unique_indices[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes_k) self.n_classes_.append(classes_k.shape[0]) y = y_store_unique_indices if self.class_weight is not None: valid_presets = ('auto', 'balanced', 'subsample', 'balanced_subsample') if isinstance(self.class_weight, six.string_types): if self.class_weight not in valid_presets: raise ValueError('Valid presets for class_weight include ' '"balanced" and "balanced_subsample". Given "%s".' % self.class_weight) if self.class_weight == "subsample": warn("class_weight='subsample' is deprecated in 0.17 and" "will be removed in 0.19. It was replaced by " "class_weight='balanced_subsample' using the balanced" "strategy.", DeprecationWarning) if self.warm_start: warn('class_weight presets "balanced" or "balanced_subsample" are ' 'not recommended for warm_start if the fitted data ' 'differs from the full dataset. In order to use ' '"balanced" weights, use compute_class_weight("balanced", ' 'classes, y). In place of y you can use a large ' 'enough sample of the full training set target to ' 'properly estimate the class frequency ' 'distributions. Pass the resulting weights as the ' 'class_weight parameter.') if (self.class_weight not in ['subsample', 'balanced_subsample'] or not self.bootstrap): if self.class_weight == 'subsample': class_weight = 'auto' elif self.class_weight == "balanced_subsample": class_weight = "balanced" else: class_weight = self.class_weight with warnings.catch_warnings(): if class_weight == "auto": warnings.simplefilter('ignore', DeprecationWarning) expanded_class_weight = compute_sample_weight(class_weight, y_original) return y, expanded_class_weight def predict(self, X): """Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted classes. """ proba = self.predict_proba(X) if self.n_outputs_ == 1: return self.classes_.take(np.argmax(proba, axis=1), axis=0) else: n_samples = proba[0].shape[0] predictions = np.zeros((n_samples, self.n_outputs_)) for k in range(self.n_outputs_): predictions[:, k] = self.classes_[k].take(np.argmax(proba[k], axis=1), axis=0) return predictions def predict_proba(self, X): """Predict class probabilities for X. The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- p : array of shape = [n_samples, n_classes], or a list of n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """ # Check data X = self._validate_X_predict(X) # Assign chunk of trees to jobs n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs) # Parallel loop all_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose, backend="threading")( delayed(parallel_helper)(e, 'predict_proba', X, check_input=False) for e in self.estimators_) # Reduce proba = all_proba[0] if self.n_outputs_ == 1: for j in range(1, len(all_proba)): proba += all_proba[j] proba /= len(self.estimators_) else: for j in range(1, len(all_proba)): for k in range(self.n_outputs_): proba[k] += all_proba[j][k] for k in range(self.n_outputs_): proba[k] /= self.n_estimators return proba def predict_log_proba(self, X): """Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- p : array of shape = [n_samples, n_classes], or a list of n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute `classes_`. """ proba = self.predict_proba(X) if self.n_outputs_ == 1: return np.log(proba) else: for k in range(self.n_outputs_): proba[k] = np.log(proba[k]) return proba class ForestRegressor(six.with_metaclass(ABCMeta, BaseForest, RegressorMixin)): """Base class for forest of trees-based regressors. Warning: This class should not be used directly. Use derived classes instead. """ @abstractmethod def __init__(self, base_estimator, n_estimators=10, estimator_params=tuple(), bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False): super(ForestRegressor, self).__init__( base_estimator, n_estimators=n_estimators, estimator_params=estimator_params, bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start) def predict(self, X): """Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. Returns ------- y : array of shape = [n_samples] or [n_samples, n_outputs] The predicted values. """ # Check data X = self._validate_X_predict(X) # Assign chunk of trees to jobs n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs) # Parallel loop all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose, backend="threading")( delayed(parallel_helper)(e, 'predict', X, check_input=False) for e in self.estimators_) # Reduce y_hat = sum(all_y_hat) / len(self.estimators_) return y_hat def _set_oob_score(self, X, y): """Compute out-of-bag scores""" X = check_array(X, dtype=DTYPE, accept_sparse='csr') n_samples = y.shape[0] predictions = np.zeros((n_samples, self.n_outputs_)) n_predictions = np.zeros((n_samples, self.n_outputs_)) for estimator in self.estimators_: unsampled_indices = _generate_unsampled_indices( estimator.random_state, n_samples) p_estimator = estimator.predict( X[unsampled_indices, :], check_input=False) if self.n_outputs_ == 1: p_estimator = p_estimator[:, np.newaxis] predictions[unsampled_indices, :] += p_estimator n_predictions[unsampled_indices, :] += 1 if (n_predictions == 0).any(): warn("Some inputs do not have OOB scores. " "This probably means too few trees were used " "to compute any reliable oob estimates.") n_predictions[n_predictions == 0] = 1 predictions /= n_predictions self.oob_prediction_ = predictions if self.n_outputs_ == 1: self.oob_prediction_ = \ self.oob_prediction_.reshape((n_samples, )) self.oob_score_ = 0.0 for k in range(self.n_outputs_): self.oob_score_ += r2_score(y[:, k], predictions[:, k]) self.oob_score_ /= self.n_outputs_ class RandomForestClassifier(ForestClassifier): """A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default). Read more in the :ref:`User Guide <forest>`. Parameters ---------- n_estimators : integer, optional (default=10) The number of trees in the forest. criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. Note: this parameter is tree-specific. max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto"). - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_depth : integer or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. oob_score : bool (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` The "balanced_subsample" mode is the same as "balanced" except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. increasing : list of ints, optional (default=None) Indices of features to have a monotonically increasing effect. decreasing : list of ints, optional (default=None) Indices of features to have a monotonically decreasing effect. Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators. classes_ : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). n_classes_ : int or list The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem). n_features_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_decision_function_ : array of shape = [n_samples, n_classes] Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. References ---------- .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. See also -------- DecisionTreeClassifier, ExtraTreesClassifier """ def __init__(self, n_estimators=10, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_split=1e-7, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None, increasing=None, decreasing=None): super(RandomForestClassifier, self).__init__( base_estimator=DecisionTreeClassifier(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_split", "random_state"), bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start, class_weight=class_weight) self.criterion = criterion self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.increasing = increasing self.decreasing = decreasing class RandomForestRegressor(ForestRegressor): """A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default). Read more in the :ref:`User Guide <forest>`. Parameters ---------- n_estimators : integer, optional (default=10) The number of trees in the forest. criterion : string, optional (default="mse") The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute error. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_depth : integer or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 bootstrap : boolean, optional (default=True) Whether bootstrap samples are used when building trees. oob_score : bool, optional (default=False) whether to use out-of-bag samples to estimate the R^2 on unseen data. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. increasing : list of ints, optional (default=None) Indices of features to have a monotonically increasing effect. decreasing : list of ints, optional (default=None) Indices of features to have a monotonically decreasing effect. Attributes ---------- estimators_ : list of DecisionTreeRegressor The collection of fitted sub-estimators. feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). n_features_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_prediction_ : array of shape = [n_samples] Prediction computed with out-of-bag estimate on the training set. References ---------- .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. See also -------- DecisionTreeRegressor, ExtraTreesRegressor """ def __init__(self, n_estimators=10, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_split=1e-7, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, increasing=None, decreasing=None): super(RandomForestRegressor, self).__init__( base_estimator=DecisionTreeRegressor(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_split", "random_state", "increasing", "decreasing"), bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start) self.criterion = criterion self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.increasing = increasing self.decreasing = decreasing class ExtraTreesClassifier(ForestClassifier): """An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. Read more in the :ref:`User Guide <forest>`. Parameters ---------- n_estimators : integer, optional (default=10) The number of trees in the forest. criterion : string, optional (default="gini") The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_depth : integer or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 bootstrap : boolean, optional (default=False) Whether bootstrap samples are used when building trees. oob_score : bool, optional (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` The "balanced_subsample" mode is the same as "balanced" except that weights are computed based on the bootstrap sample for every tree grown. For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. increasing : list of ints, optional (default=None) Indices of features to have a monotonically increasing effect. decreasing : list of ints, optional (default=None) Indices of features to have a monotonically decreasing effect. Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators. classes_ : array of shape = [n_classes] or a list of such arrays The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). n_classes_ : int or list The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem). feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). n_features_ : int The number of features when ``fit`` is performed. n_outputs_ : int The number of outputs when ``fit`` is performed. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_decision_function_ : array of shape = [n_samples, n_classes] Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. See also -------- sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble. RandomForestClassifier : Ensemble Classifier based on trees with optimal splits. """ def __init__(self, n_estimators=10, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_split=1e-7, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None, increasing=None, decreasing=None): super(ExtraTreesClassifier, self).__init__( base_estimator=ExtraTreeClassifier(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_split", "random_state", "increasing", "decreasing"), bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start, class_weight=class_weight) self.criterion = criterion self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.increasing = increasing self.decreasing = decreasing class ExtraTreesRegressor(ForestRegressor): """An extra-trees regressor. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. Read more in the :ref:`User Guide <forest>`. Parameters ---------- n_estimators : integer, optional (default=10) The number of trees in the forest. criterion : string, optional (default="mse") The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute error. .. versionadded:: 0.18 Mean Absolute Error (MAE) criterion. max_features : int, float, string or None, optional (default="auto") The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a percentage and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. max_depth : integer or None, optional (default=None) The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 bootstrap : boolean, optional (default=False) Whether bootstrap samples are used when building trees. oob_score : bool, optional (default=False) Whether to use out-of-bag samples to estimate the R^2 on unseen data. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. increasing : list of ints, optional (default=None) Indices of features to have a monotonically increasing effect. decreasing : list of ints, optional (default=None) Indices of features to have a monotonically decreasing effect. Attributes ---------- estimators_ : list of DecisionTreeRegressor The collection of fitted sub-estimators. feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). n_features_ : int The number of features. n_outputs_ : int The number of outputs. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. oob_prediction_ : array of shape = [n_samples] Prediction computed with out-of-bag estimate on the training set. References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. See also -------- sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble. RandomForestRegressor: Ensemble regressor using trees with optimal splits. """ def __init__(self, n_estimators=10, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_split=1e-7, bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, increasing=None, decreasing=None): super(ExtraTreesRegressor, self).__init__( base_estimator=ExtraTreeRegressor(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_split", "random_state", "increasing", "decreasing"), bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start) self.criterion = criterion self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.increasing = increasing self.decreasing = decreasing class RandomTreesEmbedding(BaseForest): """An ensemble of totally random trees. An unsupervised transformation of a dataset to a high-dimensional sparse representation. A datapoint is coded according to which leaf of each tree it is sorted into. Using a one-hot encoding of the leaves, this leads to a binary coding with as many ones as there are trees in the forest. The dimensionality of the resulting representation is ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``, the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``. Read more in the :ref:`User Guide <random_trees_embedding>`. Parameters ---------- n_estimators : integer, optional (default=10) Number of trees in the forest. max_depth : integer, optional (default=5) The maximum depth of each tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a percentage and `ceil(min_samples_split * n_samples)` is the minimum number of samples for each split. min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` is the minimum number of samples for each node. min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the input samples required to be at a leaf node. max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_split : float, optional (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. versionadded:: 0.18 sparse_output : bool, optional (default=True) Whether or not to return a sparse CSR matrix, as default behavior, or to return a dense array compatible with dense pipeline operators. n_jobs : integer, optional (default=1) The number of jobs to run in parallel for both `fit` and `predict`. If -1, then the number of jobs is set to the number of cores. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : int, optional (default=0) Controls the verbosity of the tree building process. warm_start : bool, optional (default=False) When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. increasing : list of ints, optional (default=None) Indices of features to have a monotonically increasing effect. decreasing : list of ints, optional (default=None) Indices of features to have a monotonically decreasing effect. Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators. References ---------- .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1), 3-42, 2006. .. [2] Moosmann, F. and Triggs, B. and Jurie, F. "Fast discriminative visual codebooks using randomized clustering forests" NIPS 2007 """ def __init__(self, n_estimators=10, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_leaf_nodes=None, min_impurity_split=1e-7, sparse_output=True, n_jobs=1, random_state=None, verbose=0, warm_start=False, increasing=None, decreasing=None): super(RandomTreesEmbedding, self).__init__( base_estimator=ExtraTreeRegressor(), n_estimators=n_estimators, estimator_params=("criterion", "max_depth", "min_samples_split", "min_samples_leaf", "min_weight_fraction_leaf", "max_features", "max_leaf_nodes", "min_impurity_split", "random_state", "increasing", "decreasing"), bootstrap=False, oob_score=False, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start) self.criterion = 'mse' self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = 1 self.max_leaf_nodes = max_leaf_nodes self.min_impurity_split = min_impurity_split self.sparse_output = sparse_output self.increasing = increasing self.decreasing = decreasing def _set_oob_score(self, X, y): raise NotImplementedError("OOB score not supported by tree embedding") def fit(self, X, y=None, sample_weight=None): """Fit estimator. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) The input samples. Use ``dtype=np.float32`` for maximum efficiency. Sparse matrices are also supported, use sparse ``csc_matrix`` for maximum efficiency. Returns ------- self : object Returns self. """ self.fit_transform(X, y, sample_weight=sample_weight) return self def fit_transform(self, X, y=None, sample_weight=None): """Fit estimator and transform dataset. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Input data used to build forests. Use ``dtype=np.float32`` for maximum efficiency. Returns ------- X_transformed : sparse matrix, shape=(n_samples, n_out) Transformed dataset. """ # ensure_2d=False because there are actually unit test checking we fail # for 1d. X = check_array(X, accept_sparse=['csc'], ensure_2d=False) if issparse(X): # Pre-sort indices to avoid that each individual tree of the # ensemble sorts the indices. X.sort_indices() rnd = check_random_state(self.random_state) y = rnd.uniform(size=X.shape[0]) super(RandomTreesEmbedding, self).fit(X, y, sample_weight=sample_weight) self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output) return self.one_hot_encoder_.fit_transform(self.apply(X)) def transform(self, X): """Transform dataset. Parameters ---------- X : array-like or sparse matrix, shape=(n_samples, n_features) Input data to be transformed. Use ``dtype=np.float32`` for maximum efficiency. Sparse matrices are also supported, use sparse ``csr_matrix`` for maximum efficiency. Returns ------- X_transformed : sparse matrix, shape=(n_samples, n_out) Transformed dataset. """ return self.one_hot_encoder_.transform(self.apply(X))
PatrickOReilly/scikit-learn
sklearn/ensemble/forest.py
Python
bsd-3-clause
68,627
[ "Brian" ]
0746fbc6b8650a90fef7240b8ab15b164d71534f033889c9f64af5f7f84ec244
# # Copyright (C) 2013-2018 The ESPResSo project # # This file is part of ESPResSo. # # ESPResSo is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # ESPResSo is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # import unittest as ut import espressomd import espressomd.interactions class Non_bonded_interactionsTests(ut.TestCase): system = espressomd.System(box_l=[20.0, 20.0, 20.0]) def intersMatch(self, inType, outType, inParams, outParams): """Check, if the interaction type set and gotten back as well as the bond parameters set and gotten back match. Only check keys present in ``inParams``. """ if inType != outType: print("Type mismatch:", inType, outType) return False for k in list(inParams.keys()): if k not in outParams: print(k, "missing from returned parameters") return False if outParams[k] != inParams[k]: print("Mismatch in parameter ", k, inParams[k], outParams[k]) return False return True def parameterKeys(self, interObject): """ Check :meth:`~espressomd.interactions.NonBondedInteraction.valid_keys` and :meth:`~espressomd.interactions.NonBondedInteraction.required_keys` return sets, and that :meth:`~espressomd.interactions.NonBondedInteraction.default_params` returns a dictionary with the correct keys. Parameters ---------- interObject: instance of a class derived from :class:`espressomd.interactions.NonBondedInteraction` Object of the interaction to test, e.g. :class:`~espressomd.interactions.LennardJonesInteraction` """ classname = interObject.__class__.__name__ valid_keys = interObject.valid_keys() required_keys = interObject.required_keys() default_keys = set(interObject.default_params().keys()) self.assertIsInstance(valid_keys, set, "{}.valid_keys() must return a set".format( classname)) self.assertIsInstance(required_keys, set, "{}.required_keys() must return a set".format( classname)) self.assertTrue(default_keys.issubset(valid_keys), "{}.default_params() has unknown parameters: {}".format( classname, default_keys.difference(valid_keys))) self.assertTrue(default_keys.isdisjoint(required_keys), "{}.default_params() has extra parameters: {}".format( classname, default_keys.intersection(required_keys))) self.assertSetEqual(default_keys, valid_keys - required_keys, "{}.default_params() should have keys: {}, got: {}".format( classname, valid_keys - required_keys, default_keys)) def generateTestForNon_bonded_interaction( _partType1, _partType2, _interClass, _params, _interName): """Generates test cases for checking interaction parameters set and gotten back from the espresso core actually match those in the Python classes. Only keys which are present in ``_params`` are checked. Parameters ---------- _partType1, _partType2: :obj:`int` Particle type ids to check on _interClass: class derived from :class:`espressomd.interactions.NonBondedInteraction` Class of the interaction to test, e.g. :class:`~espressomd.interactions.LennardJonesInteraction` _params: :obj:`dict` Interaction parameters, e.g. ``{"k": 1., "r_0": 0}`` _interName: :obj:`str` Name of the interaction property to set (e.g. ``"lennard_jones"``) """ partType1 = _partType1 partType2 = _partType2 interClass = _interClass params = _params interName = _interName def func(self): # This code is run at the execution of the generated function. # It will use the state of the variables in the outer function, # which was there, when the outer function was called # Set parameters getattr(self.system.non_bonded_inter[partType1, partType2], interName).set_params(**params) # Read them out again outInter = getattr( self.system.non_bonded_inter[partType1, partType2], interName) outParams = outInter.get_params() self.assertTrue( self.intersMatch( interClass, type(outInter), params, outParams), interClass(**params).type_name() + ": value set and value gotten back differ for particle types " + str(partType1) + " and " + str(partType2) + ": " + params.__str__() + " vs. " + outParams.__str__()) self.parameterKeys(outInter) return func if espressomd.has_features(["LENNARD_JONES"]): test_lj1 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.LennardJonesInteraction, {"epsilon": 1., "sigma": 2., "cutoff": 3., "shift": 4., "offset": 5., "min": 7.}, "lennard_jones") test_lj2 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.LennardJonesInteraction, {"epsilon": 1.3, "sigma": 2.2, "cutoff": 3.4, "shift": 4.1, "offset": 5.1, "min": 7.1}, "lennard_jones") test_lj3 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.LennardJonesInteraction, {"epsilon": 1.3, "sigma": 2.2, "cutoff": 3.4, "shift": 4.1, "offset": 5.1, "min": 7.1}, "lennard_jones") if espressomd.has_features(["LENNARD_JONES_GENERIC"]): test_ljgen1 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.GenericLennardJonesInteraction, {"epsilon": 1., "sigma": 2., "cutoff": 3., "shift": 4., "offset": 5., "e1": 7, "e2": 8, "b1": 9., "b2": 10.}, "generic_lennard_jones") test_ljgen2 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.GenericLennardJonesInteraction, {"epsilon": 1.1, "sigma": 2.1, "cutoff": 3.1, "shift": 4.1, "offset": 5.1, "e1": 71, "e2": 81, "b1": 9.1, "b2": 10.1}, "generic_lennard_jones") test_ljgen3 = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.GenericLennardJonesInteraction, {"epsilon": 1.2, "sigma": 2.2, "cutoff": 3.2, "shift": 4.2, "offset": 5.2, "e1": 72, "e2": 82, "b1": 9.2, "b2": 10.2}, "generic_lennard_jones") if espressomd.has_features(["GAY_BERNE"]): test_gb = generateTestForNon_bonded_interaction( 0, 0, espressomd.interactions.GayBerneInteraction, {"eps": 1.0, "sig": 1.0, "cut": 4.0, "k1": 3.0, "k2": 5.0, "mu": 2.0, "nu": 1.0}, "gay_berne") if __name__ == "__main__": ut.main()
mkuron/espresso
testsuite/python/interactions_non-bonded_interface.py
Python
gpl-3.0
7,883
[ "ESPResSo" ]
fdf3d55bdc497d7c2133a26d2f84f2c17c182d82f3a313a2084298571f001ca2
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import pytest import spack.util.spack_yaml as syaml @pytest.fixture() def minimal_configuration(): return { 'spack': { 'specs': [ 'gromacs', 'mpich', 'fftw precision=float' ], 'container': { 'format': 'docker', 'base': { 'image': 'ubuntu:18.04', 'spack': 'develop' } } } } @pytest.fixture() def config_dumper(tmpdir): """Function that dumps an environment config in a temporary folder.""" def dumper(configuration): content = syaml.dump(configuration, default_flow_style=False) config_file = tmpdir / 'spack.yaml' config_file.write(content) return str(tmpdir) return dumper @pytest.fixture() def container_config_dir(minimal_configuration, config_dumper): return config_dumper(minimal_configuration)
rspavel/spack
lib/spack/spack/test/container/conftest.py
Python
lgpl-2.1
1,160
[ "Gromacs" ]
18b12fa6fa14141476852655cecec27af8955932f960c6d2448e002f8ad049e3
import sys def set_help(input_write,dataname): """ When using the help flag. It assists in writing all your files for you""" print "\nIf you dont know parameters for these inputs, please refer to https://github.com/sipjca/cmlparser_py/blob/master/lammps_params.md\n" print "Input the whole string you intend on using for each lammps parameter, without the type" print "For example when prompted 'Enter Lammps units: ', enter real for the real style" print "For a full example refer to the above link\n" units = raw_input("Enter Lammps units: ") atom_style = raw_input("Enter Lammps atom_style: ") boundary = raw_input("Enter Lammps boundary: ") bond_style = raw_input("Enter Lammps bond_style: ") dielectric = raw_input("Enter Lammps dielectric: ") pair_style = raw_input("Enter Lammps pair_style: ") angle_style = raw_input("Enter Lammps angle_style: ") special_bonds = raw_input("Enter Lammps special_bonds: ") improper_style = raw_input("Enter Lammps improper_style: ") kspace_style = raw_input("Enter Lammps kspace_style: ") thermo_style = raw_input("Enter Lammps thermo_style: ") dump1 = raw_input("Enter Lammps dump: ") neighbor = raw_input("Enter Lammps neighbor: ") neigh_modify = raw_input("Enter Lammps neigh_modify: ") fix1 = raw_input("Enter fix 1: ") fix2 = raw_input("Enter fix 2: ") velocity = raw_input("Enter velocity: ") timestep = raw_input("Enter timestep: ") thermo = raw_input("Enter thermo: ") run = raw_input("Enter run time: ") restart1 = raw_input("Enter write_restart: ") replicate = raw_input("Enter how many times to replicate: ") fix3 = raw_input("Enter fix1 after replication: ") fix4 = raw_input("Enter fix2 after replication: ") velocity2 = raw_input("Enter velocity post replication: ") dump2 = raw_input("Enter Lammps dump for replicate: ") run2 = raw_input("Enter run time after replication: ") restart2 = raw_input("Enter restart after replication: ") #ADD IN HELP FOR HELP print "units %s " % units print "atom_style %s " % atom_style print "boundary %s " % boundary print "bond_style %s " % bond_style print "dielectric %s " % dielectric print "pair_style %s " % pair_style print "angle_style %s " % angle_style print "special_bonds %s " % special_bonds print "improper_style %s " % improper_style print "kspace_style %s " % kspace_style print "thermo_style %s " % thermo_style print "dump 1 %s " % dump1 print "neighbor %s " % neighbor print "neigh_modify %s " % neigh_modify print "fix1 %s " % fix1 print "fix2 %s " % fix2 print "velocity %s " % velocity print "timestep %s " % timestep print "thermo %s " % thermo print "run %s " % run print "write_restart %s " % restart1 print "replicate %s " % replicate print "fix1 %s " % fix3 print "fix2 %s " % fix4 print "velocity %s " % velocity2 print "dump 2 %s " % dump2 print "run %s " % run2 print "write_restart %s " % restart2 correct = raw_input("Your file will be printed out almost exactly as above. Is this correct? (y/n)") if correct == "n": set() else: #write output lammps = open(input_write,"w") sys.stdout = lammps print "# created by CMLParser\n" print "units %s " % units print "atom_style %s " % atom_style print "boundary %s " % boundary print "bond_style %s " % bond_style print "dielectric %s " % dielectric print "pair_style %s " % pair_style print "angle_style %s " % angle_style print "dihedral_style opls" print "special_bonds %s " % special_bonds print "improper_style %s " % improper_style print "kspace_style %s " % kspace_style print "read_data %s" % dataname print "thermo_style %s " % thermo_style print "dump %s " % dump1 print "neighbor %s " % neighbor print "neigh_modify %s " % neigh_modify print "fix1 %s " % fix1 print "fix2 %s " % fix2 print "velocity %s " % velocity print "timestep %s " % timestep print "thermo %s " % thermo print "run %s " % run print "unfix 1" print "unfix 2" print "write_restart %s " % restart1 print "replicate %s " % replicate print "undump 1" print "fix1 %s " % fix3 print "fix2 %s " % fix4 print "velocity %s " % velocity2 print "dump 2 %s " % dump2 print "run %s " % run2 print "write_restart %s " % restart2 print "unfix 1" print "unfix 2" lammps.close() def change_data_from_filein(file,dataname): fileread = open(file,'r') read = fileread.readlines() for i in range(len(read)): split = read[i].split() for j in range(len(split)): if split[0] == "read_data": line = i fileread.close() filewrite = open(file,'w') for i in range(len(read)): if read == line: filewrite.write('read_data %s' % dataname) else: filewrite.write(read[i]) filewrite.close()
sipjca/cmlparser_py
setparams.py
Python
apache-2.0
5,206
[ "LAMMPS" ]
bf427ff98396789e069da3d063192438a8ebc9bfc9470667073e97301d7b08c9
# import shutil # not used from collections import Counter import string, re import os, os.path, sys, subprocess, inspect import pickle import yaml #import getopt # not used from optparse import OptionParser from Bio.Blast.Applications import NcbiblastnCommandline from Bio.Blast import NCBIXML from Bio import SeqIO # from Bio.SeqFeature import SeqFeature, FeatureLocation # not used from Bio.SeqRecord import SeqRecord from Bio.Alphabet import generic_dna import Bio.Seq # import Bio.SeqIO # already imported SeqIO in line 10 # import Bio.SeqRecord # already imported SeqRecord in line 12 import fileinput from lxml import etree from time import clock, time import glob from math import * import traceback from itertools import groupby from operator import itemgetter import numpy module_folder_paths = ["modules"] for module_folder_path in module_folder_paths: module_folder = os.path.realpath(os.path.abspath(os.path.join(os.path.split(inspect.getfile( inspect.currentframe() ))[0],module_folder_path))) if module_folder not in sys.path: sys.path.insert(1, module_folder) import log_writer from utility_functions import * """ Function Main function -> Calls prepare, get_profiles, get_filesets, align_and_get_scores and write_scores functions The option of the method files[str]: The path to where the fastq file located output_directory[str]: The path to where output file located profile_file_directory[str]: The path to where reference.seq, and the EMM variant sequences (*.fas) files are located bowtie[str]: The path to Bowtie2 command samtools[str]: The path to SAMtools command ids[str]: Unique identifier number version[str]: version number """ def findST(files, output_directory, profile_file_directory, bowtie, samtools, ids, log_directory, verbose, version = ""): # start run time start = clock() #delete tmp file if not os.path.exists(output_directory + '/tmp'): #make tmp directory in output_directory os.makedirs(output_directory + '/tmp') workingDir = output_directory + '/tmp' #create EMM_log file log = open(output_directory + "/EMM_log.txt", "w") #set stderr.log and stdout.log files. #stderr_log_output from try_and_except function and logger function are appended into ids+ ".stderr.log file within output dir stderr_log_output = log_directory + "/" + 'emm_typing'+ ".stderr" stdout_log_output = log_directory + "/" + 'emm_typing'+ ".stdout" logger = log_writer.setup_logger(stdout_log_output, stderr_log_output) #Extract flanking regions of 100bp upstream and downstream of each MLST locus by blast against a reference genome try_and_except(stderr_log_output, prep_SRST,profile_file_directory, output_directory, logger) #Concatenate flanking regions extracted by prep SRST function to correspondent locus variants sequence in fasta format. #Newly concatenated sequence are then indexed by Bowtie2 lociHeader = try_and_except(stderr_log_output, prepare,output_directory + "/summary.txt", workingDir, bowtie,logger) #Calls two functions: Align and Score functions. #1. Align function: map each read set to reference sequence and creates SAM file, converts the SAM file to BAM file,Sort and index BAM file and Generate pileup #2. Score function:designate the correct allele and calculate coverage statistics for each locus top_hits = try_and_except(stderr_log_output ,align_and_get_scores,workingDir, files, bowtie, samtools,log,logger,ids, verbose) ################### SP #files with the following extension:'.fasta', '.pkl', '.sam', '.tmp', '.bt2','.out','.unmapped','.unmap','fai' are removed from tmp and output files for root, dirs, files in os.walk(output_directory): for currentFile in files: exts=('.fasta', '.pkl', '.sam', '.tmp', '.bt2','.out','.unmapped','.unmap','fai' ) if any(currentFile.lower().endswith(ext) for ext in exts): os.remove(os.path.join(root, currentFile)) elif currentFile in ['my_blast_tmp.xml', 'summary.txt', 'PHE221509-all.bam']: os.remove(os.path.join(root, currentFile)) return top_hits """ Function - Extract flanking regions of 100bp upstream and downstream of EMM by blast against a reference genome. BLAST uses the first variant sequence as a query. NB Need to make sure BLAST, EMBOSS and Biopython are in the path. - Create summary.txt file (a tab-delimited text file display the path to the variant sequences and flanking sequences) The option of the method output_directory[str]: The path to where the summary.txt file will be created profile_file_directory[str]: The path to the reference.seq and the EMM variant sequences (*.fas) files logger[str]: The path to where the stderr and stdout logged Return value return summary.txt file """ def prep_SRST(profile_file_directory, output_directory, logger): reference_fasta_file = profile_file_directory + "/reference.seq" refseq_record = SeqIO.read(reference_fasta_file, "fasta", generic_dna) locus_files = glob.glob(profile_file_directory + "/*.fas") locus_files = sorted(locus_files) summary_file_handle = open(output_directory + "/summary.txt", "w") for seq in locus_files: (seqDir,seqFileName) = os.path.split(seq) (seqBaseName,ext) = os.path.splitext(seqFileName) bait = seqBaseName + "_bait.fasta" # extract first sequence to use as bait log_writer.info_header(logger, "create bait file") process = subprocess.Popen(['seqret',seq,'-firstonly','-auto','-out',output_directory+ '/' + bait], stderr=subprocess.PIPE, stdout=subprocess.PIPE) process.wait() log_writer.log_process(logger, process, log_error_to = "info") cline = NcbiblastnCommandline(query=output_directory+ '/' + bait, db=profile_file_directory + "/reference",evalue=0.001, out=output_directory + "/my_blast_tmp.xml", outfmt=5) stdout_log_output, stderr_log_output = cline() result_handle = open(output_directory + "/my_blast_tmp.xml") blast_record = NCBIXML.read(result_handle) query_length = blast_record.query_letters for alignment in blast_record.alignments: hsp = alignment.hsps[0] # only consider top hit if hsp.align_length/float(query_length) > 0.5: if hsp.sbjct_start > hsp.sbjct_end: subject_start = hsp.sbjct_start + (hsp.query_start - 1) else: subject_start = hsp.sbjct_start - (hsp.query_start - 1) if hsp.sbjct_start > hsp.sbjct_end: subject_end = hsp.sbjct_end - (query_length - hsp.query_end) else: subject_end = hsp.sbjct_end + (query_length - hsp.query_end) revcomp = 1 # hit is in forward strand if hsp.sbjct_start > hsp.sbjct_end: revcomp = -1 left_coords = [min(subject_start,subject_end)-100,min(subject_start,subject_end)-1] right_coords = [max(subject_start,subject_end)+1,max(subject_start,subject_end)+100] left_cmd = ["seqret ",reference_fasta_file," -sbegin ",str(left_coords[0])," -send ",str(left_coords[1])," -osformat fasta -auto -out " + output_directory + "/tmp_left_flank.fasta"] os.system(''.join(left_cmd)) # extract left flank using emboss right_cmd = ["seqret ",reference_fasta_file," -sbegin ",str(right_coords[0])," -send ",str(right_coords[1])," -osformat fasta -auto -out " + output_directory + "/tmp_right_flank.fasta"] os.system(''.join(right_cmd)) # extract right flank using emboss left_record = SeqIO.read(output_directory + "/tmp_left_flank.fasta", "fasta") if revcomp < 0: left_record.id = "down" left_record.seq = left_record.seq.reverse_complement() # reverse the sequence else: left_record.id = "up" right_record = SeqIO.read(output_directory + "/tmp_right_flank.fasta", "fasta") if revcomp < 0: right_record.id = "up" right_record.seq = right_record.seq.reverse_complement() # reverse the sequence else: right_record.id = "down" right_record.description = "" left_record.description = "" out_handle = open(output_directory + "/" + seqBaseName + "_flanks.fasta", "w") out_handle.write(right_record.format("fasta")) out_handle.write(left_record.format("fasta")) out_handle.close() # generate file list for srst summary_file_handle.write('\t'.join([seqBaseName,seq,output_directory + "/" + seqBaseName + "_flanks.fasta"]) + "\n") summary_file_handle.close() """ Function 1. Concatenate flanking regions extracted by prep SRST function to EMM variant sequences in fasta format. Newly concatenated sequence are then indexed by Bowtie2 2. Then extract and store as pickled object: a. locus- variant names (loci.pkl) b. start and end position of EMM variant sequences (without the flanking sequences)(ranges.pkl) c. EMM variants sequence (refSeqs.pkl) The option of the method specFn[str]: A tab-delimited text file display the path to the flanking and EMM sequences(summary.txt) workDir[str] The path to where refSeqs.pkl, ranges.pkl and loci.pkl will be created bowtie[str]: The command used to index the reference sequence logger[str]: The path to where the stderr and stdout logged return return[list] loci name """ def prepare(specFn, workDir, bowtie,logger): (specDir,summaryFileName) = os.path.split(specFn) spc = [] # (locus name, variants fasta, flanking sequences) for l in open(specFn): spc.append(l.split()) refFn = os.path.join(workDir, "reference.fa") rf = open(refFn, "w") # file handle for reference sequence fasta file ranges = {} loci = [] # array of locus names refSeqs = {} # list of reference sequences (key = id, value = seq object) for (loc, variantsFn, flanksFn) in spc: loci.append(loc) fs = {} # flanking sequences at this locus (key = id, value = seq object) f = open(flanksFn) for r in SeqIO.parse(f, "fasta"): fs[r.id] = r.seq f = open(variantsFn) ################### SP for r in SeqIO.parse(f, "fasta"): s = Bio.Seq.MutableSeq('', generic_dna) s += fs['up'] # add upstream seq, allele seq, downstream seq s += r.seq s += fs['down'] SeqIO.write([SeqRecord(s, id=r.id)], rf, "fasta") # add to reference fasta file ranges[r.id] = (len(fs['up']), len(fs['up']) + len(r.seq)) # get range of allele sequence refSeqs[r.id] = s # store this reference sequence in list rf.close() rangesFn = os.path.join(workDir, "ranges.pkl") #start and end position of locus variant sequences (without the flanking sequences) f = open(rangesFn, 'w') pickle.dump(ranges, f) f.close() lociFn = os.path.join(workDir, "loci.pkl") f = open(lociFn, 'w') pickle.dump(loci, f) f.close() refSeqsFn = os.path.join(workDir, "refSeqs.pkl") #Locus variants sequence f = open(refSeqsFn, 'w') pickle.dump(refSeqs, f) f.close() bowtie2_index = bowtie + "-build" log_writer.info_header(logger, "bowtie_indexed") process = subprocess.Popen([bowtie2_index, refFn, refFn], stderr=subprocess.PIPE, stdout=subprocess.PIPE) # generate index of reference fasta for mapping process.wait() log_writer.log_process(logger, process, log_error_to = "info") os.system("rm -f summary.txt") return loci """ Function Calls two functions: Align and Score functions. Align function: (a) Calls Bowtie2(with very senstivie options). Bowtie2 map each read set to reference sequence and creates SAM file (b) Converts the SAM file to BAM file (c) Sort and index BAM file (d) Generate pileup file Score function: (a) From the pileup file read Depthofcoverage and calculate probability score based on Depthofcoverage (b) Designate the correct allele based on probability score and degree of variability of the read from the locus variant (by identifying present or absence of SNPs/INDELs). (c) Calculate coverage statistics for each locus (max_percenatge non consensus bases, Minimum total depth, Maximum total depth, Minimum consensus depth, Maximum consesnsus depth, mean consensus depth and stdDev of consensus depth) The option of the method fileSets[dict]: Keys are fastq filenames and value correspond to path to fastq file bowtie[str]: The path to Bowtie2 command samtools[str]: The path to SAMtools command log[str]: The path to where the EMM_log.txt will be created logger[str]: The path to where the stderr and stdout logged Return Return score[]probability and coverage statistics score value for each allele """ def align_and_get_scores(workingDir, files, bowtie, samtools, log,logger,ids, verbose): ################### SP out = sys.stdout nameSep = "-" scores = [] paired = True pair = files if os.path.exists(pair[0]) and os.path.exists(pair[1]): align(workingDir, paired, pair, sys.stderr, bowtie, samtools,logger,ids) s = score(pair, workingDir, paired, out, log, nameSep, bowtie, samtools,logger,ids, log, verbose) out.flush() log.flush() else: log_writer.info_header(logger, "the paired reads are not labelled as as sampleid.R*.fastq*") return s """ Function Prints EMM value and metrics for each variant with coverage > 90% The option for method: hits[dict] = {allele: [identity, coverage, meanDepth, minDepth, snps, indels, mixed, filteredCoverage], ...} log[str]: Location to where the EMM_log.txt file will be created """ def write_log(hits,log): # metrics[allele] = [identity, coverage, meanDepth, minDepth, snps, indels, mixed, filteredCoverage] if len(hits) > 0: print >> log print >> log, '=' * 70 print >> log, "Results Summary" print >> log, '=' * 70 print >> log print >> log, "Allele\tidentity\tcoverage\tmeanDepth\tminDepth\tsnps\tindels\tmixed\tfilteredCoverage" sorted_hits = sorted(hits.items(), key=lambda x: x[1][0], reverse=True) filtered_hits = [f for f in sorted_hits if f[1][1] >= 90] for hit in filtered_hits: print >> log, hit[0]+'\t'+str(hit[1][0])+'\t'+str(hit[1][1])+'\t'+str(hit[1][2])+'\t'+str(hit[1][3])+'\t'+str(len(hit[1][4]))+'\t'+str(hit[1][5])+'\t'+str(len(hit[1][6]))+'\t'+str(hit[1][7]) #try: # i=0 # while sorted_hits[i][1][1] >= 90: # print >> log, sorted_hits[i][0]+'\t'+str(sorted_hits[i][1][0])+'\t'+str(sorted_hits[i][1][1])+'\t'+str(sorted_hits[i][1][2])+'\t'+str(sorted_hits[i][1][3])+'\t'+str(len(sorted_hits[i][1][4]))+'\t'+str(sorted_hits[i][1][5])+'\t'+str(len(sorted_hits[i][1][6]))+'\t'+str(sorted_hits[i][1][7]) # i+=1 #except IndexError: # pass """ Function Calls bamify and pileupReads function The option for method workingDir[str]: The path to where SAM, BAM, Pileup will be created paired[bool]: true= paired end reads files[list]: The path to the fastq file location logFile[str]: The path to the EMM_log.txt file location bowtie[str]: The path to Bowtie2 command samtools[str]: The path to SAMtools command logger[str]: The path to where the stderr and stdout logged ids[str]: unique identifier number return return pileup file """ def align(workDir, paired, files, logFile, bowtie, samtools,logger,ids): # removed insertSize since it wasn't used in the function refFn = os.path.join(workDir, "reference.fa") bam = bamify(workDir, 'all', files, refFn, True, logFile, bowtie, samtools,logger,ids) pileFn = os.path.join(workDir, 'all.pileup') pileupReads(workDir, bam, refFn, open(pileFn, 'w'), logFile, samtools,logger) """ Function (a) Map each read set to each of the possible EMM variants by calling Bowtie2 (with very sensitive options) and create SAM and tmp file (b) Convert the sam to tmp file by unsetting the secondary alignment bit score (c) Convert the tmp to BAM file (d) Sort BAM The option for method: workDir[str]: The path to where the SAM, BAM and sorted BAM files will be created pref[str]: pref = "all" files[list]: The path to the fastq file location refFn[str]: The path to the reference file location expand[bool]: True or false value logFile[str]: The path to the EMM_log.txt file location bowtie[str]: The path to Bowtie2 command samtools[str]: The path to SAMtools command logger[str]: The path to where the stderr and stdout logged ids[str]: unique identifier number returns[string]: out: sorted BAM file """ def bamify(workDir, pref, files, refFn, expand, logFile, bowtie, samtools, logger, ids): # removed insertSize since it wasn't used in the function single = False out0 = os.path.join(workDir,ids + '-' + pref + '-all')# #pref = all out = os.path.join(workDir, ids + '-'+ pref + '-all.bam') tmp = os.path.join(workDir, ids + '-' + pref + '.tmp') # temporary sam output sam = os.path.join(workDir, ids + '-'+ pref + '.sam') bam = os.path.join(workDir, ids + '-'+ pref + '.bam') if expand: # expand = true log_writer.info_header(logger, "Creating tmp file") process = subprocess.Popen([bowtie, '--fr', '--no-unal', '--minins', '300', '--maxins', '1100', '-x', refFn, '-1', files[0], '-2', files[1], '-S', tmp, '-k', '99999', '-D', '20', '-R', '3', '-N', '0', '-L', '20', '-i', 'S,1,0.50'], stderr=subprocess.PIPE, stdout=subprocess.PIPE) #refFn = refrence sequence, tmp = temorary sam output, -k = report up to 99999 good alignments per read, -D 20 -R 3 -N 0 -L 20 -i S,1,0.50 --very-sensitive option process.wait() #print ' '.join([bowtie, '--fr', '--minins', '300', '--maxins', '1100', '-x', refFn, '-1', files[0], '-2', files[1],'-S', tmp, '-k', '99999', '-D', '20', '-R', '3', '-N', '0', '-L', '20', '-i', 'S,1,0.50', '--un', un_reads , '--un-conc',un_conc_reads]) log_writer.log_process(logger, process, log_error_to = "info") log_writer.info_header(logger, "remove_secondary_mapping_bit") i = open(tmp) o = open(sam, 'w') remove_secondary_mapping_bit(tmp, sam) i.close() o.close() else:# expand = false, command is called within getNovelAllele function log_writer.info_header(logger, "Creating sam file") process= subprocess.Popen([bowtie, '--fr', '--no-unal', '--minins', '300', '--maxins', '1100', '-x', refFn, '-1', files[0], '-2', files[1],'-S', sam, '-k', '99999', '-D', '20', '-R', '3', '-N', '0', '-L', '20', '-i', 'S,1,0.50'], stderr=subprocess.PIPE, stdout=subprocess.PIPE) process.wait() log_writer.log_process(logger, process, log_error_to = "info") log_writer.info_header(logger, "Converting sam to bam") process = subprocess.Popen([samtools, 'view', '-bhS', '-o', bam, sam], stderr=subprocess.PIPE, stdout=subprocess.PIPE)# convert to bam process.wait() log_writer.log_process(logger, process, log_error_to = "info") log_writer.info_header(logger, "Sorting bam") process = subprocess.Popen([samtools, 'sort', bam, out0], stderr=subprocess.PIPE, stdout=subprocess.PIPE) # sort bam process.wait() log_writer.log_process(logger, process, log_error_to = "info") return out """ Function: Takes a SAM file and deducts 256 from the second column(FLAG) that unset the secondary alignment bit score NB: reads with bit(250) set are not reported when using Samtools pileup The option for method: sam[string]: SAM file sam_parsed[string]: parsed SAM file Return returns[string]: Parsed SAM file """ def remove_secondary_mapping_bit(sam,sam_parsed): lines = iter(fileinput.input([sam])) sam_parsed_file = open(sam_parsed, "w") headers = [] body = [] for line in lines: if line.startswith('@'): sam_parsed_file.write(line) else: # chomp line line = line.rstrip('\n') details = line.split("\t") flag = int(details[1]) if flag > 256: details[1] = str(flag - 256) print >> sam_parsed_file, '\t'.join(details) sam_parsed_file.close() """ Function Generate pileup file by using SAMtools mpileup command. NB: use -B -A -f option to optimises coverage --A flag count anomalous read The option for method: workDir[str]: The path to where pileup file will be created bam[str]: The path to the BAM file location refFn[str]: The path to the reference file location pileupFile[str]: The path to pileup file location logFile[str]: The path to the EMM_log.txt file location samtools[str]: The path to SAMtools command logger[str]: The path to where the stderr and stdout logged return returns: pileup file """ def pileupReads(workDir, bam, refFn, pileupFile, logFile, samtools,logger): log_writer.info_header(logger, "index bam file") process = subprocess.Popen([samtools, 'index', bam], stderr=subprocess.PIPE, stdout=subprocess.PIPE) process.wait() log_writer.log_process(logger, process, log_error_to = "info") log_writer.info_header(logger, "Generate pileup file") process = subprocess.Popen([samtools, 'mpileup', '-B', '-A', '-f', refFn, bam], stderr=subprocess.PIPE, stdout=subprocess.PIPE)# -A -count anomalous read pairs, -B - disable BAQ computation and -f FILE - indexed reference sequence file for l in process.stdout: pileupFile.write(l) process.wait() log_writer.log_process(logger, process, log_error_to = "info") pileupFile.close() """ Function Parse through the pileup file and count the number of observed bases The option for method pileupFile[str]: The path to pileup file location refSeq[dict]: reference sequence for each allele ranges[dict]: start and end position of EMM variant sequences (without the flanking sequences) Return hash_alignment[dict]: hash_alignment[allele]: [pos, ref, orig_depth, filtered_match, filtered_mismatch, filtered_depth, total_indels, alt_bps, insertions_to_report] for each position """ def read_pileup(pileupFile, refSeq, ranges): # read pileup file with open(pileupFile) as pileup: hash_alignment = {} # Split all lines in the pileup by whitespace pileup_split = ( x.split() for x in pileup ) # Group the split lines based on the first field (allele) for allele, lines in groupby(pileup_split, itemgetter(0)): hash_alignment[allele] = [] for fields in lines: alt_bps = {} locus = fields[0] nuc_num = int(fields[1])# Actual position in ref allele nuc_ref = fields[2] orig_depth = int(fields[3]) if nuc_num <= ranges[locus][0] or nuc_num > ranges[locus][1]: continue elif nuc_num == ranges[locus][0]+1: # replaced +1 with + 30 ref_pos = nuc_num else: try: ref_pos += 1 except UnboundLocalError: ref_pos = nuc_num if nuc_num != ref_pos: for i in range(ref_pos, nuc_num-1): # if bps not covered hash_alignment[allele].append([i+1, refSeq[allele][i], 0, 0, 0, 0, 'None', alt_bps, []]) ref_pos = nuc_num # filter reads based on Phred scores - cutoff Q20 and calculate matches and mismatches if orig_depth != 0: orig_match, orig_mismatch, nuc_match,nuc_mismatch, total_indels, alt_bps, report_insertions= pileup_extract_information(nuc_ref, fields[4], fields[5]) if nuc_num == ranges[allele][1] and report_insertions != []: report_insertions = total_indels = [] elif report_insertions != []: #i = 0 #while report_insertions: # ins = report_insertions[i] # ins_length = int(re.search('[0-9]+', ins).group()) # ins_seq = re.search('[A-Z]+', ins).group() # if (ins_seq + str(refSeq[allele][nuc_num:ranges[allele][1]]))[:len(str(refSeq[allele][nuc_num:ranges[allele][1]]))] == str(refSeq[allele][nuc_num:ranges[allele][1]]): # report_insertions = report_insertions.remove(ins) # else: # i += 1 for ins in report_insertions: ins_length = int(re.search('[0-9]+', ins).group()) ins_seq = re.search('[A-Z]+', ins).group() if (ins_seq + str(refSeq[allele][nuc_num:ranges[allele][1]]))[:len(str(refSeq[allele][nuc_num:ranges[allele][1]]))] == str(refSeq[allele][nuc_num:ranges[allele][1]]): try: report_insertions = report_insertions.remove(ins) if len(report_insertions)>1 else [] except TypeError: pass if report_insertions == None: report_insertions = [] nuc_depth = nuc_match + nuc_mismatch if orig_match+orig_mismatch != orig_depth or nuc_depth > orig_depth: print "Attention required!" print "Line: {0}".format(fields) elif nuc_num > ref_pos: for i in range(ref_pos, nuc_num): hash_alignment[allele].append([i+1, refSeq[allele][i], 0, 0, 0, 0, 'None', alt_bps, []]) hash_alignment[allele].append([nuc_num, nuc_ref, orig_depth, nuc_match, nuc_mismatch, nuc_depth, total_indels, alt_bps, report_insertions]) ref_pos = nuc_num else: # Hash for later processing in R hash_alignment[allele].append([nuc_num, nuc_ref, orig_depth, nuc_match, nuc_mismatch, nuc_depth, total_indels, alt_bps, report_insertions]) #ref_pos += 1 else: hash_alignment[allele].append([nuc_num, nuc_ref, 0, 0, 0, 0, 'None', alt_bps, []]) ref_pos += 1 return hash_alignment """ Function Parse through a single line of the pileup file and count the number of observed bases The option for method ref_bp[str]: Reference base: 2nd column within the pileup file align_bps[str]: reads bases: 4th column within the pileup file qualities[str]: base qualities: 5th column within the pileup file Return filtered_match[int]: number of reads that match the reference with base quality > 20 filtered_mismatch[int]: number of reads that do not match the reference with base quality > 20 filtered_depth[int]: number of reads with base quality > 20 total_indels[list]: list of tuples (indel1, number of bases with indel1) alt_bps[dict]: number of observed bases in this position report_insertions[list]: insertion that occur in positions with depth > 4 and in more than half the reads """ def pileup_extract_information(ref_bp, align_bps, qualities): match = 0 mismatch = 0 probabilities = {} # remove all indels of format -1a and +1a since they do not have corresponding qualities and they refer to the following bp. # for example deletion cat in positions 242-4 first appears in pos 241 with the actual deletions appearing as * in # the respective positions #ndh 241 C 79 ,$,,,,,,,,,,,,,,,,,,,,,-3catG,-3cat,,-3cat,,,,,,,,,,,,..,.,....,.........,.,.......,,...,,..... ;FFFFHGHIIHIJIIJJHII!)!*G)!!I!GGCD!!CCDDDDDDDDDCDBDDDDDDDDDCDDDJGJEDDJJJD@HFFFF #ndh 242 C 79 ,,,,,,,,,,,,,,,,,,,,-2at*A*,*,-2at,-2at,,-2at,,,,,-2at,-2at,,..,.,....,.........,.,.......,,...,,.....^~. BCCFFFHIHBGIGGIIGHG!!!!J!!!H!GECC!!C>DDCDCBDDDDDDDDDBDDDDDCDDDJJJBCCJGJCDHFFFFC #ndh 243 A 79 ,,,,,,,,,,,,,,,,,,,**C*,***,*,,,,**,,..,.,....,.........,.,.......,,...,,...... @CCDDFHHGFGHGEHIFAH!!!!G!!!G!ACCC!!AADDDDCDDDDCDDDDDBDBDC?ABDDIIJD@CJIIACHHHGFC #ndh 244 T 79 ,$,$,$,,,,,,,,,,,,,,,,**C*,***,*,,,,**,,..,.,....,.........,.,.......,,...,,...... @B@DDFDEBDFEEFEI?CB!!!!H!!!B!@C>C!!:>DDDC@CDDDCDDDDDDDCCCC>DDDIGJF:>JJJ>CJHHHFC # The script below demonstrates that # >>> st1 = ',$,$.$,...,...,,.,,.,.,,,,.,,...,,.,,.,,.....,.,,,..,,.,.....,,......,,...,,,,.,.,,,,.,.,,.,,.,,,....,.,,.,.,,...+2AG,,,,,,,.,' # >>> st2 = '>>>CDB>HD<B@FBFCBFDJHDJBJJDDDJIDJI@H<DDDDDDDDHBDDDJDDDDDDDDDBDDDDDDDDDDDDDBDDDDDD>GDIDDJBDJDB<JIJJDIB@JDJBBJH8DBB<BDDCD' # >>> re.findall(r'[^,\.]', st1) # ['$', '$', '$', '+', '2', 'A', 'G'] # >>> len(st1) - len(re.findall(r'[^,\.]', st1)) # 119 # >>> len(st2) # 119 indels = re.findall(r'[\+\-]\d{1,2}[acgtnACGTN]*', align_bps) # it rectifies cases where the deletion is followed by a mismatch base - see above at position 241 (-3catG) Indels = list(set(indels)) for index, i in enumerate(Indels): if len(re.search(r'[a-zA-Z]+', i).group()) != int(re.search(r'\d+', i).group()): dif = len(re.search(r'[a-zA-Z]+', i).group()) - int(re.search(r'\d+', i).group()) indels = [i[:-dif] if x == i else x for x in indels] for e in list(set(indels)): align_bps = (align_bps.replace(e,'')) indels = [x.upper() for x in indels] indels_freq = Counter(indels).most_common() if indels != [] else 'None' # find all matches of format ^~. or .$ and remove extra symbols leaving only . and , match1 = re.findall(r'\^[0-9a-zA-Z\!\ "#\$%&\'()\*\+,\.\-\/:;<>\?@\[\]\\\^_`\{\}\|~]{1}[,\.]{1}', align_bps) for e in list(set(match1)): align_bps = align_bps.replace(e, e[-1:]) match2 = re.findall(r'[,\.]{1}\$', align_bps) for e in list(set(match2)): align_bps = align_bps.replace(e, e[:-1]) # look for possible mismatches and remove extra symbols if set(','.join(align_bps)) != set([",","."]): mm1= re.findall(r'\^[0-9a-zA-Z\!\ "#\$%&\'()\*\+,\.\-\/:;<>\?@\[\]\\\^_`\{\}\|~]{1}[acgtnACGTN]{1}', align_bps) for e in list(set(mm1)): align_bps = align_bps.replace(e, e[-1:]) mm2 = re.findall(r'[acgtnACGTN]{1}\$', align_bps) for e in list(set(mm2)): align_bps = align_bps.replace(e, e[:-1]) # calculate matches and mismatches match = align_bps.count('.') + align_bps.count(',') mismatch = sum([align_bps.upper().count(x) for x in ('*', 'A', 'C', 'G', 'T', 'N')]) # filter positions based on Q score - cutoff 20 alt_bps = {} filtered_mismatch = 0 filtered_match = 0 match_filtered = [] # calculate filtered match and mismatch positions if mismatch > 0: mismatch_pos = {} for x in (r'\*{1}', r'A{1}', r'C{1}', 'G{1}', 'T{1}', 'N{1}'): pt = re.compile(x) alt = re.search(r'[\*\+ACGTN]{1}', x).group()[0] try: pt.search(align_bps.upper()).group() except AttributeError: continue else: mismatch_pos[alt] = [m.start() for m in pt.finditer(align_bps.upper())] mismatch_filtered = {} for bp in mismatch_pos.keys(): if bp != 'N': mismatch_filtered[bp] = [] for m in mismatch_pos[bp]: try: Q=ord(qualities[m])-33 except IndexError: pass if Q > 20: mismatch_filtered[bp].append(m) match_pos = [m.start() for m in re.finditer(r'[,\.]{1}', align_bps)] for m in match_pos: try: Q= ord(qualities[m])-33 except IndexError: pass if Q > 20: match_filtered.append(m) for bp in ('A', 'C', 'G', 'T', 'N', '*'): if bp != ref_bp: alt_bps[bp] = len(mismatch_filtered[bp]) if mismatch > 0 and bp in mismatch_filtered.keys() else 0 filtered_mismatch += alt_bps[bp] else: alt_bps[bp] = len(match_filtered) filtered_match = alt_bps[bp] insertions = [x for x in filter(lambda x:x[0]=="+", indels)] report_insertions = [] for ins in list(set(insertions)): if filtered_match+filtered_mismatch > 4 and insertions.count(ins) >= int(len(qualities)*0.40): # accepts a read if it occurs in more than 40% of the reads report_insertions.append(ins) if report_insertions == [] and len(insertions) > len(qualities)*0.80: report_insertions = list(set(insertions)) return match, mismatch, filtered_match, filtered_mismatch, indels_freq, alt_bps, report_insertions """ Function Score function: (a) Parse through the pileup file to capture DepthofCoverage (b) Calculate probability score for all the EMM variants based on DepthofCoverage (c) Calculate coverage statistics for all EMM variants (max_percentage non consensus bases, Minimum total depth, Maximum total depth, Minimum consensus depth, Maximum consesnsus depth, mean consensus depth and stdDev of consensus depth) (d)For each locus display: locus names, variant number(allele number), number of snps different between the readset and locus, minimum probability score , locus variant name, probability score value for each locus variant position (the probability score for each of the three bases other than the majority consensus base), list of snps (SNP position, Reference base) and coverage statistics for each locus variants (e) Then filter the correct allele based on probability score and degree of variability of the read from the locus variant (by identifying present or absence of SNPs/INDELs). - If number of snps different between the readset and locus variant is zero and probability score greater than -10, the locus variant is assigned (f) Calculate percentage coverage (check if the reads are mapped to all locus variant position) The option for method: files[dict]: Keys are fastq filenames and value correspond to path to fastq file workDir[str]: output_directory -> The path to where logfile files will be created sig[int]: -10 CUTOFF paired[bool]: assigned to False insertSize[Nonetype]: insertSize assigned as None outFile[str]: print result output logFile[str]: The path to where the EMM_log.txt will be created nameSep[str]: nameSep assigned as "-" verboseFiles[bool] False bowtie[str]: The path to Bowtie2 command samtools[str]: The path to SAMtools command logger[str]: The path to where the stderr and stdout logged ids[str]: unique identifier number return log file """ def score(files, workDir, paired, outFile, logFile, nameSep, bowtie, samtools,logger,ids, log, verbose): # removed insertSize since it wasn't used in the function ################### SP pileFn = os.path.join(workDir, 'all.pileup') rangesFn = os.path.join(workDir, "ranges.pkl")# start and end position of locus variant sequences (without the flanking sequences)(ranges.pkl) refSeqsFn = os.path.join(workDir, "refSeqs.pkl")#Locus variants sequence ( refSeqs.pkl) ranges = pickle.load(open(rangesFn)) refSeqs = pickle.load(open(refSeqsFn)) # PILEUP FILE SIZE = ZERO: no reads mapped to emm references if os.path.getsize(pileFn) == 0 : ################### SP print >> logFile, "No reads mapped to any of the EMM reference sequences. Suggestion: check sequencing yield" ################### SP print >> logFile ################### SP return("Failed","No mapping to EMM references") validatedTypes = ['emm'+str(f) for f in range(1, 125)] hash_alignment = read_pileup(pileFn, refSeqs, ranges) top_hits = {} metrics = {} for allele in hash_alignment.keys(): if hash_alignment[allele] == []: continue flanking = ranges[allele][0] + (len(refSeqs[allele])- ranges[allele][1])# added + 29 matched_bps = len([f for f in hash_alignment[allele] if f[5]>4 and f[3]/float(f[5]) >= 0.8 and f[8] == []]) unmatched_bps = [f for f in hash_alignment[allele] if f[5]<5 or f[3]/float(f[5]) < 0.8 or f[8] != [] or (f[6]!='None' and [m for m in f[6] if m[0].startswith('-') and m[1]/float(f[2]) > 0.5])] try: coverage = round(len(hash_alignment[allele])/float(len(refSeqs[allele])-flanking)*100, 4) identity = round(matched_bps/float(len(hash_alignment[allele]))*100, 4) filteredCoverage = round(len([f for f in hash_alignment[allele] if f[5]>4])/float(len(refSeqs[allele])-flanking)*100, 4) except ZeroDivisionError: identity = 0 # select snps if mismatches more than 80% of the filtered depth (Q>20) snps = [(f[0],f[1], f[7]) for f in hash_alignment[allele] if f[5] > 4 and f[4]/float(f[5]) >= 0.8] try: indels = [(f[0], [m for m in f[6] if (m[0] in f[8]) or (m[1]/float(f[2]) > 0.5 and f[0]!=ranges[allele][1])]) for f in unmatched_bps if f[8] != [] or f[6]!= 'None'] # an insertion at the very last bp of the allele would be a result of a mismatch in the flanking region and not a mutation in the allele except TypeError: pass indels = [f for f in indels if f[1] != []] mixed = [(f[0],f[1], f[7]) for f in hash_alignment[allele] if f[5] > 4 and f[3]/float(f[5]) < 0.8 and f[4]/float(f[5]) < 0.8] posDelEvents = [] for f in indels: if f[1][0][0].startswith('-'): posDelEvents += range(f[0]+1, f[0]+1+int(re.search(r'\d+', f[1][0][0]).group())) # separate snps and deletions deletions = [f for f in snps if Counter(f[2]).most_common()[0][0] == '*' and f[0] not in posDelEvents] snps = [f for f in snps if Counter(f[2]).most_common()[0][0] != '*'] depths = [f[5] for f in hash_alignment[allele]] meanDepth = round(numpy.mean(depths), 2) if depths != [] else 0 minDepth = min(depths) if depths != [] else 0 if indels != [] and [f for f in indels if f[1][0][0].startswith('+')]: # only do this for insertions lenIndel = int(re.search('\d+', indels[0][1][0][0]).group()) matched_bps -= lenIndel - 1 identity = round(matched_bps/float(len(hash_alignment[allele])+lenIndel)*100, 4) metrics[allele] = [identity, coverage, meanDepth, minDepth, snps, indels, mixed, filteredCoverage] write_log(metrics, log) filtCovId = filter(lambda x: x[1][0]>=90.0 and x[1][-1]==100.0, metrics.items()) if verbose: with open(workDir+'/summary.yml', 'w') as out_fp: out_fp.write(yaml.dump(metrics, default_flow_style=True)) if filtCovId == []: validatedMetrics = [(f, metrics[f]) for f in metrics.keys() if f.split('.')[0] in validatedTypes] nonValidatedMetrics = [(f, metrics[f]) for f in metrics.keys() if f.split('.')[0] not in validatedTypes] top_hit_failedValidated = sorted(validatedMetrics, key=lambda x:x[1][0], reverse=True) top_hits['validated'] = ('Failed:'+top_hit_failedValidated[0][0], top_hit_failedValidated[0][1]) if validatedMetrics != [] else (None, ['n/a', 'n/a', 'n/a', 'n/a', [], [], [], 'n/a']) top_hit_failedNonValidated = sorted(nonValidatedMetrics, key=lambda x:x[1][0], reverse=True) top_hits['nonValidated'] = ('Failed:'+top_hit_failedNonValidated[0][0], top_hit_failedNonValidated[0][1]) if nonValidatedMetrics != [] else (None, ['n/a', 'n/a', 'n/a', 'n/a', [], [], [], 'n/a']) else: validated = [f for f in filtCovId if f[0].split('.')[0] in validatedTypes] nonValidated = [f for f in filtCovId if f[0].split('.')[0] not in validatedTypes] sortedValidated = sorted(validated, key=lambda x:x[1][0], reverse=True) sortedNonValidated = sorted(nonValidated, key=lambda x:x[1][0], reverse=True) if sortedValidated == []: validatedMetrics = [(f, metrics[f]) for f in metrics.keys() if f.split('.')[0] in validatedTypes] sortedMetrics = sorted(validatedMetrics, key=lambda x:x[1][-1], reverse=True) if sortedMetrics and sortedMetrics[0][1][0] > 95 and sortedMetrics[0][1][3] < 5: top_hits['validated'] = (sortedMetrics[0][0]+'**',sortedMetrics[0][1]) else: top_hits['validated'] = (None, ['n/a', 'n/a', 'n/a', 'n/a', [], [], [], 'n/a']) elif len(set([f[1][0] for f in sortedValidated[:2]])) == 1: hits = [f for f in sortedValidated if f[1][0] == sortedValidated[0][1][0]] top_hits['validated'] = sortedValidated[0] if len(hits) == 1 else hits else: top_hits['validated'] = sortedValidated[0] if sortedNonValidated == []: top_hits['nonValidated'] = (None, ['n/a', 'n/a', 'n/a', 'n/a', [], [], [], 'n/a']) elif len(set([f[1][0] for f in sortedNonValidated[:2]])) == 1: hits = [f for f in sortedNonValidated if f[1][0] == sortedNonValidated[0][1][0]] top_hits['nonValidated'] = sortedNonValidated[0] if len(hits) == 1 else hits else: top_hits['nonValidated'] = sortedNonValidated[0] return top_hits """ Extracts the following values from scores data structure and writes data to results.xml in the format below: <ngs_sample id="PHE221920"> <script value="emm typing tool" version="1-0"/> <results> <result type="EMM_validated" value="89.0"> <result_data type="percentage_identity" value="100.00"/? <result_data type="percentage_coverage" value="100.00"/> <result_data type="mean_consensus_depth" value="109.09"/> <result_data type="minimum_consensus_depth" value="26"/> </result> <result type="EMM_Nonvalidated" value="232.0"> <result_data type="percentage_identity" value="100.00"/? <result_data type="percentage_coverage" value="100.00"/> <result_data type="mean_consensus_depth" value="109.09"/> <result_data type="minimum_consensus_depth" value="26"/> </result> </results> </ngs_sample> - Id : NGS sample id (sample identifier) - Version : software version number - EMM validated : assigned validated variant (emm1-124) - ENN_Nonvalidated: assigned non validate variant (emm125+) - mean consensus depth - the minimum average consensus depth - number_of_reads_mapped: the number of reads mapped across allele length - percentage_coverage: percentage coverage across allele length - percentage_identity: percentage identity across allele length - minimum consensus depth- the minimum consensus depth values The option for method: output_directory[str]: The path to the result.xml file xml_values: EMM value, emm score, QC mean consensus depth, QC max percentage non consensus base value, QC percentage coverage and QC minimum consensus depth ids : NGS sample id (sample identifier) Workflow_name: streptococcus-pyogenes-typing Version : version number return print results to "id".results.xml file """ def create_xml_file(top_hits,output_directory,ids,version): ################### SP #print scores xml_log_file = open(output_directory + "/" + ids + ".results.xml", "w") # open a file and write xml result root = etree.Element("ngs_sample", id = ids) script = etree.SubElement(root, "script", value="emm typing tool", version = version) results = etree.SubElement(root, 'results') if type(top_hits) == tuple: result = etree.SubElement(results, "result", type='Final_EMM_type', value = ':'.join(top_hits)) # Failed:No mapping to EMM references else: if type(top_hits['validated']) == tuple and top_hits['validated'][1][0] == 100: finalMtype = top_hits['validated'][0] elif type(top_hits['validated']) != tuple and top_hits['validated'][0][1][0] == 100: if len(set([f[0].split('.')[0] for f in top_hits['validated']])) == 1: finalMtype = top_hits['validated'][0][0].split('.')[0] + ": mixed subtypes" else: finalMtype = "Mixed sample: " + '/'.join([f[0] for f in top_hits['validated']]) elif top_hits['validated'][0] == None and ((type(top_hits['nonValidated']) == tuple and top_hits['nonValidated'][1][0] == 100) or (type(top_hits['nonValidated']) != tuple and top_hits['nonValidated'][0][1][0] == 100)): finalMtype = top_hits['nonValidated'][0] if type(top_hits['nonValidated']) == tuple else '/'.join([f[0] for f in top_hits['nonValidated']]) elif (type(top_hits['validated']) != tuple and top_hits['validated'][0][1][0] < 100) or (type(top_hits['validated']) == tuple and top_hits['validated'][1][0]<100 and top_hits['validated'][1][-1] == 100): if (type(top_hits['validated']) != tuple and len(set([f[0].split('.')[0] for f in top_hits['validated']])) == 1) or (type(top_hits['validated']) == tuple): top_hit = top_hits['validated'][0] if type(top_hits['validated']) != tuple else top_hits['validated'] mutPos = 0 # investigate region 30-120 to determine whether the type can be reported or the sample should be ivestigated further before new type is reported for i,f in enumerate(top_hit[1][4:-1]): for m in f: if m[0] in range(131, 222): if i == 1: # if ind for indel in m[1]: mutPos += int(re.search('\d+', indel[0]).group()) else: mutPos+=1 newID = (90-mutPos)/float(90) * 100 if newID >= 92: finalMtype = top_hit[0].split('.')[0] else: if type(top_hits['nonValidated']) == tuple and (type(top_hits['nonValidated']) == tuple and top_hits['nonValidated'][1][0] == 100) or (type(top_hits['nonValidated']) != tuple and top_hits['nonValidated'][0][1][0] == 100): finalMtype = top_hits['nonValidated'][0] else: finalMtype = 'Investigate new type' elif (type(top_hits['validated']) != tuple and len(set([f[0].split('.')[0] for f in top_hits['validated']])) != 1): finalMtype = 'Not determined' elif [f for f in top_hits.values() if f[0].startswith('Failed')]: finalMtype = 'Failed' else: finalMtype =[] for top_hit in top_hits['validated']: mutPos = 0 # investigate region 30-120 to determine whether the type can be reported or the sample should be investigated further before new type is reported for i,f in enumerate(top_hit[1][4:-1]): for m in f: if m[0] in range(131, 222): if i == 1: # if indels for indel in m[1]: mutPos += int(re.search('\d+', indel[0]).group()) else: mutPos+=1 newID = (90-mutPos)/float(90) * 100 if newID >= 92: if top_hit[0].split('.')[0] not in finalMtype: finalMtype.append(top_hit[0].split('.')[0]) finalMtype='/'.join(finalMtype) if finalMtype==[]: if type(top_hits['nonValidated']) == tuple and (type(top_hits['nonValidated']) == tuple and top_hits['nonValidated'][1][0] == 100) or (type(top_hits['nonValidated']) != tuple and top_hits['nonValidated'][0][1][0] == 100): finalMtype = top_hits['nonValidated'][0] else: finalMtype = 'Investigate new type' elif top_hits['validated'][0] == None and (type(top_hits['nonValidated']) == tuple and top_hits['nonValidated'][1][0] < 100) or (type(top_hits['nonValidated']) != tuple and top_hits['nonValidated'][0][1][0] < 100): if type(top_hits['nonValidated']) != tuple and len(set([f[0].split('.')[0] for f in top_hits['nonValidated']])) != 1: finalMtype = 'Not determined' # if two different types are reported with the same coverage and covereage < 100 --> de novo assembly to resolve else: top_hit = top_hits['nonValidated'] if type(top_hits['nonValidated']) == tuple else top_hits['nonValidated'][0] mutPos = 0 # investigate region 30-120 to determine whether the type can be reported or the sample should be ivestigated further before new type is reported for i,f in enumerate(top_hit[1][4:-1]): for m in f: if m[0] in range(131, 232): if i == 1: # if indels for indel in m[1]: mutPos += int(re.search('\d+', indel[0]).group()) else: mutPos+=1 newID = (90-mutPos)/float(90) * 100 if newID >= 92: finalMtype = top_hit[0].split('.')[0] else: finalMtype = 'Investigate new type' elif top_hits["validated"] == tuple and top_hits['validated'][0].find('**') != -1: finalMtype = 'Investigate mapping issues' else: finalMtype = 'Not determined' comment = etree.Comment('(START) EMM Typing Results (START)') results.append(comment) result = etree.SubElement(results, "result", type='Final_EMM_type', value = finalMtype) for key in top_hits.keys(): emm_type = 'EMM_'+key EMM = str(top_hits[key][0]) if type(top_hits[key]) == tuple else '/'.join([f[0] for f in top_hits[key]]) result = etree.SubElement(results, "result", type=emm_type, value = EMM) pctID = top_hits[key][1][0] if type(top_hits[key]) == tuple else top_hits[key][0][1][0] pct_coverage = top_hits[key][1][7] if type(top_hits[key]) == tuple else top_hits[key][0][1][7] mean_depth = str(top_hits[key][1][2]) if type(top_hits[key]) == tuple else '/'.join([str(f[1][2]) for f in top_hits[key]]) min_depth = str(top_hits[key][1][3]) if type(top_hits[key]) == tuple else '/'.join([str(f[1][3]) for f in top_hits[key]]) snps = str(len([f for f in top_hits[key][1][4] if Counter(f[2]).most_common()[0][0] != '*'])) if type(top_hits[key]) == tuple else '/'.join([str(len([f for f in hit[1][4] if Counter(f[2]).most_common()[0][0] != '*'])) for hit in top_hits[key]]) if type(top_hits[key]) == tuple: indels = re.search('\d+', top_hits[key][1][5][0][1][0][0]).group() if top_hits[key][1][5] != [] else '0' else: indels = [] for hit in top_hits[key]: if hit[1][5] != []: indels.append(re.search('\d+', hit[1][5][0][1][0][0]).group()) else: indels.append('0') indels = '/'.join(indels) mixed = str(len(top_hits[key][1][6])) if type(top_hits[key]) == tuple else '/'.join([str(len(hit[1][6])) for hit in top_hits[key]]) etree.SubElement(result, "result_data", type="percentage_identity", value = str(pctID)) etree.SubElement(result, "result_data", type="percentage_coverage", value=str(pct_coverage)) etree.SubElement(result, "result_data", type="mean_consensus_depth", value=mean_depth) etree.SubElement(result, "result_data", type="minimum_consensus_depth", value=min_depth) etree.SubElement(result, "result_data", type="snps", value=snps) etree.SubElement(result, "result_data", type="indels", value = indels) etree.SubElement(result, "result_data", type="mixed", value = mixed) print ids, emm_type, EMM, "indels:", indels, "SNPS:", snps, "mixed:", mixed print >> xml_log_file, etree.tostring(root, pretty_print=True)
phe-bioinformatics/emm-typing-tool
modules/EMM_determiner_functions.py
Python
gpl-3.0
54,076
[ "BLAST", "Biopython", "Bowtie" ]
1bb5132479b50fee43c776bf7c72d15a7e9a6ec01afb8cbbd567a05aa6b00bac
""" Tests for models.py. """ import json import os from django.conf import settings from django.test import TestCase from utils.import_util import import_reference_genome_from_local_file from utils.import_util import copy_dataset_to_entity_data_dir from main.models import AlignmentGroup from main.models import Chromosome from main.models import Dataset from main.models import ExperimentSample from main.models import ExperimentSampleToAlignment from main.models import Project from main.models import ReferenceGenome from main.models import User from main.models import Variant from main.models import VariantCallerCommonData from main.model_utils import clean_filesystem_location from main.model_utils import get_dataset_with_type from main.testing_util import create_common_entities from utils import uppercase_underscore import subprocess TEST_USERNAME = 'testuser' TEST_PASSWORD = 'password' TEST_EMAIL = 'test@example.com' TEST_PROJECT_NAME = 'testModels_project' TEST_REF_GENOME_NAME = 'mg1655_partial' TEST_REF_GENOME_PATH = os.path.join(settings.PWD, 'test_data/full_vcf_test_set/mg1655_tolC_through_zupT.gb') class TestModels(TestCase): def setUp(self): """Override. """ common_entities = create_common_entities() self.user = common_entities['user'] def test_delete(self): """Test deleting models and their associated data. This test was written in response to an error being thrown when deleting data: https://github.com/churchlab/genome-designer-v2/issues/219 """ # TODO: Add more models until we started reproducing issue #219 # when we try to delete. self.user.delete() class TestAlignmentGroup(TestCase): def test_get_samples(self): user = User.objects.create_user(TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) self.test_project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) self.test_ref_genome = ReferenceGenome.objects.create( project=self.test_project, label='blah') alignment_group = AlignmentGroup.objects.create( label='Alignment 1', reference_genome=self.test_ref_genome, aligner=AlignmentGroup.ALIGNER.BWA) # Create a bunch of samples and relate them. for sample_idx in range(10): sample = ExperimentSample.objects.create( uid=str(sample_idx), project=self.test_project, label='some label' ) ExperimentSampleToAlignment.objects.create( alignment_group=alignment_group, experiment_sample=sample) # Test the method. samples = alignment_group.get_samples() sample_uid_set = set([sample.uid for sample in samples]) self.assertEqual(sample_uid_set, set([str(x) for x in range(10)])) class TestDataset(TestCase): def test_get_related_model_set(self): user = User.objects.create_user(TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) self.test_project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) self.test_ref_genome = ReferenceGenome.objects.create( project=self.test_project, label='blah') alignment_group = AlignmentGroup.objects.create( label='Alignment 1', reference_genome=self.test_ref_genome, aligner=AlignmentGroup.ALIGNER.BWA) dataset = Dataset.objects.create( label='the label', type=Dataset.TYPE.VCF_FREEBAYES) alignment_group.dataset_set.add(dataset) alignment_group_set = dataset.get_related_model_set() self.assertTrue(alignment_group in alignment_group_set.all()) def test_dataset_compression_piping(self): """ Make sure data set compression behaves correctly. """ dataset = Dataset.objects.create( label='test_dataset', type=Dataset.TYPE.FASTQ1) GZIPPED_FASTQ_FILEPATH = os.path.join(settings.PWD, 'test_data', 'compressed_fastq', 'sample0.simLibrary.1.fq.gz') dataset.filesystem_location = clean_filesystem_location( GZIPPED_FASTQ_FILEPATH) assert dataset.is_compressed() process = subprocess.Popen( ('head '+dataset.wrap_if_compressed()+' | wc -l'), shell=True, executable=settings.BASH_PATH, stdout=subprocess.PIPE, stderr=subprocess.PIPE) wc_output, errmsg = process.communicate() rc = process.returncode assert rc == 0, ( "Compression process returned non-zero exit status: %s" % ( errmsg)) assert int(wc_output) == 10, ( "Compression failed: %s" % (errmsg)) def test_compress_dataset(self): """ Make sure that compressing a dataset and putting a new dataset entry into the db works correctly. """ user = User.objects.create_user(TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) self.test_project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) self.test_ref_genome = import_reference_genome_from_local_file( self.test_project, TEST_REF_GENOME_NAME, TEST_REF_GENOME_PATH, 'genbank') dataset = get_dataset_with_type(self.test_ref_genome, type= Dataset.TYPE.REFERENCE_GENOME_GENBANK) # All the magic happens here compressed_dataset = dataset.make_compressed('.gz') # Grab the new compressed dataset through the ref genome to # make sure that it got added compressed_dataset_through_ref_genome = get_dataset_with_type( entity= self.test_ref_genome, type= Dataset.TYPE.REFERENCE_GENOME_GENBANK, compressed= True) assert compressed_dataset == compressed_dataset_through_ref_genome def test_dataset_strings(self): user = User.objects.create_user(TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) self.test_project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) self.test_ref_genome = import_reference_genome_from_local_file( self.test_project, TEST_REF_GENOME_NAME, TEST_REF_GENOME_PATH, 'genbank') dataset = get_dataset_with_type(self.test_ref_genome, type= Dataset.TYPE.REFERENCE_GENOME_GENBANK) self.assertEquals( dataset.internal_string(self.test_ref_genome), (str(self.test_ref_genome.uid) + '_' + uppercase_underscore(Dataset.TYPE.REFERENCE_GENOME_GENBANK))) class TestModelsStatic(TestCase): """Tests for static methods. """ def test_clean_filesystem_location(self): FAKE_ABS_ROOT = '/root/of/all/evil' EXPECTED_CLEAN_URL = 'projects/blah' dirty_full_url = os.path.join(FAKE_ABS_ROOT, settings.MEDIA_ROOT, EXPECTED_CLEAN_URL) clean_location = clean_filesystem_location(dirty_full_url) self.assertEqual(EXPECTED_CLEAN_URL, clean_location) class TestVariantCallerCommonData(TestCase): def test_json_data_field(self): """Tests the data field which uses the Postgresql 9.3 json type. """ user = User.objects.create_user(TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) test_project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) reference_genome = ReferenceGenome.objects.create( project=test_project, label='ref1') chromosome = Chromosome.objects.create( reference_genome=reference_genome, label='Chromosome', num_bases=9001) variant = Variant.objects.create( reference_genome=reference_genome, type='UNKNOWN', chromosome=chromosome, position=100, ref_value='A' ) alignment_group = AlignmentGroup.objects.create( label='Alignment 1', reference_genome=reference_genome, aligner=AlignmentGroup.ALIGNER.BWA) raw_data_dict = { 'key1': 'val1', 'key2': 'val2', } # Test storing as dictionary. vccd = VariantCallerCommonData.objects.create( variant=variant, source_dataset_id=1, alignment_group=alignment_group, data=raw_data_dict ) vccd_lookup = VariantCallerCommonData.objects.get( id=vccd.id) self.assertEquals(raw_data_dict, vccd_lookup.data) # Test storing as string. vccd = VariantCallerCommonData.objects.create( variant=variant, source_dataset_id=1, alignment_group=alignment_group, data=json.dumps(raw_data_dict) ) vccd_lookup = VariantCallerCommonData.objects.get( id=vccd.id) self.assertEquals(raw_data_dict, vccd_lookup.data) # Test blank value. vccd = VariantCallerCommonData.objects.create( variant=variant, source_dataset_id=1, alignment_group=alignment_group, ) self.assertEquals(0, len(vccd.data)) # Test assigning after initial create. vccd = VariantCallerCommonData.objects.create( variant=variant, source_dataset_id=1, alignment_group=alignment_group, ) vccd.data=json.dumps(raw_data_dict) vccd.save() vccd_lookup = VariantCallerCommonData.objects.get( id=vccd.id) self.assertEquals(raw_data_dict, vccd_lookup.data) class TestExperimentSample(TestCase): def setUp(self): """Override. """ self.common_entities = create_common_entities() self.ref_genome = self.common_entities['reference_genome'] def test_data_dir_create_and_delete(self): """Make sure data directory gets deleted. """ es = ExperimentSample.objects.create( project=self.ref_genome.project, label='test_es') es_data_dir = es.get_model_data_dir() self.assertTrue(os.path.exists(es_data_dir)) es.delete() self.assertFalse(os.path.exists(es_data_dir)) def test_add_child(self): """ Make sure parent/child relationships work. """ assert len(self.common_entities['sample_1'].get_children()) == 0 assert len(self.common_entities['sample_1'].get_parents()) == 0 self.common_entities['sample_1'].add_child( self.common_entities['sample_2']) assert len(self.common_entities['sample_1'].get_children()) == 1 assert len(self.common_entities['sample_2'].get_parents()) == 1 assert(self.common_entities['sample_1'].get_children()[0].uid == ( self.common_entities['sample_2'].uid)) class TestChromosome(TestCase): def test_multiple_chromosome_dataset_import(self): user = User.objects.create_user( TEST_USERNAME, password=TEST_PASSWORD, email=TEST_EMAIL) project = Project.objects.create( title=TEST_PROJECT_NAME, owner=user.get_profile()) test_yeast_genome = ReferenceGenome.objects.create( project=project, label='superbrewer2000') test_dataset_path = os.path.join(settings.PWD, 'test_data/yeast_chrom_jkl.fasta') dataset_path = copy_dataset_to_entity_data_dir(test_yeast_genome, test_dataset_path) test_chroms_dataset = Dataset.objects.create( label='jkl_chroms', type=Dataset.TYPE.REFERENCE_GENOME_FASTA, filesystem_location=clean_filesystem_location(dataset_path)) test_yeast_genome.dataset_set.add(test_chroms_dataset) # Assert correct number of chromosomes assert(test_yeast_genome.num_chromosomes == 3) # Assert correct number of bases assert(test_yeast_genome.num_bases == sum([chrom.num_bases for chrom in Chromosome.objects.filter(reference_genome=test_yeast_genome)])) # Assert correct chromosome labels expected_chrom_names = [ 'gi|448092123|ref|NC_020215.1|', 'gi|448096713|ref|NC_020216.1|', 'gi|448100869|ref|NC_020217.1|'] assert([chrom.label for chrom in Chromosome.objects.filter(reference_genome=test_yeast_genome)] == expected_chrom_names)
woodymit/millstone_accidental_source
genome_designer/main/tests/test_models.py
Python
mit
12,943
[ "BWA" ]
753e1221b2c2036d776b25dc41a468d2baafbd42d24c9b756321f8957f6de039
""" Copyright (C) 2017 Roberto Bruttomesso <roberto.bruttomesso@gmail.com> This file is distributed under the terms of the 3-clause BSD License. A copy of the license can be found in the root directory or at https://opensource.org/licenses/BSD-3-Clause. Author: Roberto Bruttomesso <roberto.bruttomesso@gmail.com> Date: 27/03/2017 This module implements a simple visitable decorator """ class Visitable(object): """ Implementation of a base class that adds the capability of being visited by a visitor """ def accept(self, visitor): """ Function to be called to trigger a visitor """ return visitor.visit(self)
formalmethods/intrepyd
intrepyd/visitable.py
Python
bsd-3-clause
669
[ "VisIt" ]
8720f113fb62b7a8a5024f2b5f8803a20cc3d4ff96aa68006e6752f40d6e3745
import dataclasses from dataclasses import dataclass from typing import Any import typing import sys import numpy as np import h5py import Pyro5.api # metadata support from .mupifobject import MupifObject from . import units, pyroutil, dumpable from . import dataid from .heavydata import HeavyDataBase, HeavyDataBase_ModeChoice from .pyrofile import PyroFile import types import json import tempfile import logging import os import pydantic import subprocess import shutil log = logging.getLogger(__name__) ''' The *heavydata* module defines classes for sematic access to potentially large (that is, larger than RAM) structured data, both locally and over the network. The data structure is defined using *schemas* in JSON, where each schema defines table-like structure with rows of data, each row possibly further referencing another table with a different schema. The data is internally stored in a HDF5 file, which includes the schemas, making the file self-describing. JSON schema is thus only required when the data is being created, not for opening an already existing data. JSON schema specification -------------------------- The schema is defined as dictionary (nesting is possible). The top-level dictionary of each schema #. **must** include ``_schema`` (which **must** define ``name`` and ``version``), #. **must** include ``_datasetName`` entries, #. **may** include other, regular entries, as described below; names **must not** be reserved names. Reserved names are those starting with ``_`` (underscore) plus ``dtype``, ``lookup`` and ``path``. Regular entries """""""""""""""" Regular entries are one of the following. #. Computed attribute (identified via the ``lookup`` keyword); computed attribute **must** define ``lookup``, which is a lookup table (key-value dictionary), **dtype** (datatype being returned from lookup) and **key** (descriptor of data attribute used for lookup); it **may** define ``unit``. #. Data attribute (identified via the ``dtype`` key, but not having ``lookup``); it **must** define ``dtype`` and **may** define ``unit``, ``shape``. #. subschema reference (identified by the ``path`` keyword); it **must** define ``path`` and ``schema``. ``path`` must contain the substring ``{ROW}`` (is replaced by row number to which the nested data structure belongs) and end with ``/`` (forward slash). #. dictionary possibly including other regular entries (directory, creating hierarchy). Data types ^^^^^^^^^^^^ ``dtype`` specifies datatype for the entry value using the ``numpy.dtype`` notation (see `Data type objects <https://numpy.org/doc/stable/reference/arrays.dtypes.html>`__), for example ``f8`` for 8-byte (64-bit) floating-point number. Strings are stored as utf-8 encoded byte arrays (thus their storage length might be larger than number of characters). use ``"dtype":"a"`` for variable-length strings (``a`` implies ``"shape":"variable"``), and ``"dtype":"a10"`` for string of maximum 10 bytes. ``shape`` is a tuple specifying fixed shape: e.g. ``"shape":(3)`` is rank-1 3-vector, ``"shape":(3,3)`` is rank-2 3×3 matrix and so on. The special value of ``"shape":"variable"`` denotes dynamic 1d array of given ``dtype``. .. note:: Variable-length data (both strings and numerical arrays) are handled in a special way by the HDF5 storage; each (non-empty) entry has about 30b overhead, plus necessitates allocationes. If your data can always fit into a fixed-size array (such as string of maximum 20 bytes, ``a20``), prefer that for both performance ans storage reasons. All data are initialized to the default when constructed, which is: * ``NaN`` (not-a-number) for floating-point types (scalars and arrays), * ``0`` (zero) for integer types (scalars and arrays), * empty array for dynamic arrays, * empty string for both static-sized (``"dtype":"a10"``) and dynamic-sized (``"dtype":"a"``) strings. Assignments of incompatible data (which cannot be converted to the underlying storage type), including mismatched shape of arrays, will raise exception. Units ^^^^^^ Entries specifying ``unit`` (which is any string `astropy.units.Unit <https://docs.astropy.org/en/stable/api/astropy.units.Unit.html>`__ can grok) **must** be assigned with quantities including compatible units; the value will be converted to the schema unit before being set. The field will be returned as a Quantity (including the unit) when read back. Subschema """"""""""" Subschema entries associate full (nested hierarchical) data stratucture with each table line. The entry **must** specify ``schema`` name (which must be present in the *schemaRegistry* argument of :obj:`HeavyStruct.openData`) and ``path``. Path defines where the nested data is stored within the HDF5 file and **must** contain ``{ROW}`` (as string, including the curly braces) and end with ``/``. Accessing data ---------------- Data are accesse using *Contexts*, special classes abstracting away the underlying storage. They define getters (and setters) for each data level (the are called simply ``get``/``set`` followed by capitalized entry name). Rows are selected using the usual indexing operator ``[index]``, though a whole column can be returned when index is not specified. Top contexts (on the level of the schema) define a few special methods: * ``resize`` which will change the number of rows; new rows will be always set to the default values. When passing the argument ``reset=True`` to ``resize``, all rows will be default-initialized. * ``inject`` will replace the current context's data with data from another context (recursively); the routine will take care to resize structures as necessary. Schema names must be matching, and differences in schema versions will be reported as warning (it will be possible to user-define transformation for converting between different schema versions). The data exchange happens using serialized format which can be obtained and consumed using ``to_dump()`` and ``from_dump(…)`` methods. ''' sampleSchemas_json = ''' [ { "_schema": { "name": "org.mupif.sample.atom", "version": "1.0" }, "_datasetName": "atoms", "identity": { "element": { "dtype": "a2" }, "atomicNumber": { "dtype": "l", "key": "identity.element", "lookup": { "H": 1, "C": 6, "N": 7, "Na": 11, "Cl": 17, "Fe": 26 } }, "atomicMass": { "dtype": "f", "key": "identity.element", "unit": "Dalton", "lookup": { "H": 1.0079, "C": 12.0107, "N": 14.0067, "Na": 22.9897, "Cl": 35.453, "Fe": 55.845 } } }, "properties": { "physical": { "partialCharge": { "neutral": { "dtype": "d", "unit": "e" }, "anion": { "dtype": "d", "unit": "e" }, "cation": { "dtype": "d", "unit": "e" } }, "polarizability": { "neutral": { "dtype": "d", "unit": "AA^2 s^4 kg^-1" }, "anion": { "dtype": "d", "unit": "AA^2 s^4 kg^-1" }, "cation": { "dtype": "d", "unit": "AA^2 s^4 kg^-1" } } }, "topology": { "parent": { "dtype": "l" }, "type": { "dtype": "a", "shape": "variable" }, "name": { "dtype": "a", "shape": "variable" }, "position": { "dtype": "d", "shape": [ 3 ], "unit": "AA" }, "velocity": { "dtype": "d", "shape": [ 3 ], "unit": "AA/ps" }, "structure": { "dtype": "l", "shape": "variable" } } } }, { "_schema": { "name": "org.mupif.sample.molecule", "version": "1.0" }, "_datasetName": "molecules", "identity": { "chemicalName": { "dtype": "a", "shape": "variable" }, "molecularWeight": { "dtype": "d", "unit": "Dalton" } }, "properties": { "electrical": { "HOMO": { "dtype": "d", "unit": "eV" }, "LUMO": { "dtype": "d", "unit": "eV" }, "siteEnergy": { "orbital": { "dtype": "d", "unit": "eV" }, "electrostatic": { "dtype": "d", "unit": "eV" }, "polarization": { "dtype": "d", "unit": "eV" } }, "transferIntegrals": { "dtype": "d", "shape": "variable" }, "reorganizationEnergyInternal": { "anion": { "dtype": "d", "unit": "eV" }, "cation": { "dtype": "d", "unit": "eV" } } }, "physical": { "polarizability": { "neutral": { "dtype": "d", "shape": [ 3, 3 ], "unit": "AA^2 s^4 kg^-1" }, "anion": { "dtype": "d", "shape": [ 3, 3 ], "unit": "AA^2 s^4 kg^-1" }, "cation": { "dtype": "d", "shape": [ 3, 3 ], "unit": "AA^2 s^4 kg^-1" } } }, "chemical": {} }, "topology": { "parent": { "dtype": "l", "unit": "none" }, "centerOfMass": { "dtype": "d", "shape": [ 3 ], "unit": "AA" }, "symmetryAxis": { "dtype": "d", "shape": [ 3 ], "unit": "AA" }, "structureNeighbors": { "dtype": "l", "shape": "variable" } }, "implementation": { "forceFieldType": { "dtype": "a", "shape": "variable" } }, "atoms": { "path": "molecule/{ROW}/", "schema": "org.mupif.sample.atom" } }, { "_schema": { "name": "org.mupif.sample.grain", "version": "1.0" }, "_datasetName": "grains", "identity": { "material": { "dtype": "a", "shape": "variable" } }, "properties": { "eletrical": { "freeElectrons": { "dtype": "l", "unit": "none" }, "freeHoles": { "dtype": "l", "unit": "none" } }, "physical": { "reorganizationEnergyExternal": { "dtype": "d", "unit": "eV" } }, "chemical": {}, "symmetry": { "choice":["none","axial","periodic","translational","rotational"] } }, "topology": { "parent": { "dtype": "l" }, "cellSize": { "dtype": "d", "shape": [ 3 ], "unit": "m" } }, "implementation": { "boundaryCondition": { "dtype": "a" } }, "molecules": { "path": "grain/{ROW}/", "schema": "org.mupif.sample.molecule" } } ] ''' def _cookSchema(desc, prefix='', schemaName='', fakeModule='', datasetName=''): __doc0__ = ''' Transform dictionary-structured data schema into context access types. The access types are created using the "type" builtin and only stored in closures of the functions returning them. The top-level context is returned from this function to the user. get/set methods (and others) are not created on the fly but are instead put into those context types. This is substantially more efficient than hijacking __getattr__ and __setattr__. Closures in Python are somewhat unintuitive, since e.g. loop does not create a new scope (thus variable reference would later have the value in the last iteration step). Therefore local variables are captured via local function defaults, which makes some of the code less readable. ''' @dataclass class CookedSchemaFragment: 'Internal data used when cookSchema is called recursively' dtypes: list # accumulates numpy dtypes for compound datatype defaults: dict # default values, nan for floats and 0 for integers subpaths: dict # accumulates nested paths (for deletion when resizing), as (path,schema) tuple, keyed by FQ units: dict # accumulates units for normal values types (for dict export), keyed by FQ T: Any = None # nested context type doc: typing.List[str] = dataclasses.field(default_factory=list) # accumulates documentation (as markdown nested list) def append(self, other): self.dtypes += other.dtypes self.defaults.update(other.defaults) self.doc += other.doc self.subpaths.update(other.subpaths) self.units.update(other.units) def dtypeUnitDefaultDoc(v): 'Parse dictionary *v* (part of the schema) and return (dtype,unit,default,doc) tuple' shape = v['shape'] if 'shape' in v else () if isinstance(shape, list): shape = tuple(shape) ddoc = {} if shape: ddoc['shape'] = f'[{"×".join([str(s) for s in shape])}]' unit = units.Unit(v['unit']) if 'unit' in v else None dtype = v['dtype'] default = None if dtype == 'a': dtype = h5py.string_dtype(encoding='utf-8') shape = None ddoc['dtype'] = 'string (utf-8 encoded)' ddoc['shape'] = 'dynamic' elif shape == 'variable': ddoc['dtype'] = f'`[{np.dtype(dtype).name},…]`' dtype = h5py.vlen_dtype(np.dtype(dtype)) shape = None ddoc['shape'] = 'dynamic' else: dtype = np.dtype((dtype, shape)) # log.warning(f'{fq}: defaults for non-scalar quantities (dtype.subdtype) not yet supported.') basedtype = (dtype if (not hasattr(dtype, 'subdtype') or dtype.subdtype is None) else dtype.subdtype[0]) # basedtype=dtype # workaround if basedtype.kind == 'f': default = np.nan elif basedtype.kind in 'iu': default = 0 ddoc['dtype'] = f'`{basedtype.name}`' if unit: ddoc['unit'] = f"`{str(unit)}`" if 'lookup' in v: ddoc['read-only'] = f'table look-up by `{v["key"]}`' default = None if default is not None: ddoc['default'] = f"`{str(default)}`" return dtype, unit, default, ', '.join(f'{k}: {v}' for k, v in ddoc.items()) def capitalize(k): 'Turn the first letter into uppercase' return k[0].upper()+k[1:] ret = CookedSchemaFragment(dtypes=[], defaults={}, subpaths={}, units={}) meth = {} # accumulate attribute access methods docLevel = (0 if not schemaName else prefix.count('.')+1) # top-level only if not schemaName: schemaName = desc['_schema']['name'] schemaVersion = desc['_schema']['version'] datasetName = desc['_datasetName'] assert len(prefix) == 0 T_name = 'Context_'+schemaName.replace('.', '_') import hashlib h = hashlib.blake2b(digest_size=6) h.update(json.dumps(desc).encode('utf-8')) fakeModule = types.ModuleType('_mupif_heavydata_'+h.hexdigest(), 'Synthetically generated module for mupif.HeavyStruct schemas') # this somehow breaks imports, so better to avoid it until understood # if fakeModule.__name__ in sys.modules: return getattr(sys.modules[fakeModule.__name__],T_name) # sys.modules[fakeModule.__name__]=fakeModule ret.doc += [f'**schema {schemaName}**', ''] else: T_name = 'Context_'+schemaName+'_'+prefix.replace('.', '_') for key, val in desc.items(): # fully-qualified name: for messages and compound field name in h5py fq = (f"{prefix}.{key}" if prefix else key) docHead = docLevel*3*' '+f'* `{key}`' # special keys start with underscore, so far only _schema is used if key.startswith('_'): if key == '_schema': continue elif key == '_datasetName': continue else: raise ValueError(f"Unrecognized special key '{key}' in prefix '{prefix}'.") if not isinstance(val, dict): raise TypeError("{fq}: value is not a dictionary.") # attribute defined via lookup, not stored if 'lookup' in val: dtype, unit, default, doc = dtypeUnitDefaultDoc(val) ret.doc += [docHead+f': `get{capitalize(key)}()`: '+doc] lKey, lDict = val['key'], val['lookup'] if isinstance(lKey, bytes): lKey = lKey.decode('utf8') # bind local values via default args (closure) def inherentGetter(self, *, fq=fq, dtype=dtype, unit=unit, lKey=lKey, lDict=lDict): _T_assertDataset(self, f"when looking up '{fq}' based on '{lKey}'.") def _lookup(row): k=self.ctx.dataset[lKey, row] if isinstance(k, bytes): k = k.decode('utf8') try: val = np.array(lDict[k], dtype=dtype)[()] # [()] unpacks rank-0 scalar except KeyError: raise KeyError(f"{fq}: key '{k}' ({lKey}) not found in the lookup table with keys {list(lDict.keys())}") from None return val # fake broadcasting if self.row is None: val = np.array([_lookup(r) for r in range(self.ctx.dataset.shape[0])]) else: val = _lookup(self.row) if unit: return units.Quantity(value=val, unit=unit) else: return val meth['get'+capitalize(key)] = inherentGetter elif 'choice' in val: choices=val['choice'] num=0 str2num,num2str={},{} if not isinstance(choices,(list,tuple)): raise TypeError(f"'{fq}': choice must be a sequence") for ilabel,label in enumerate(choices): if not isinstance(label,str): raise ValueError(f"'{fq}': choice items must be strings (item {ilabel} is a {type(label).__name__})") if label in str2num: raise ValueError(f"'{fq}': duplicate choice label '{label}'") num2str[num],str2num[label]=label,num num+=1 mn,mx=min(num2str.keys()),max(num2str.keys()) # min_scalar_type: signed when mn<0, otherwise unsigned; min_scalar_type should ensure appropriate range dtype=(np.min_scalar_type(mx) if mn>=0 else mn.min_scalar_type(-max(abs(mn),abs(mx)))) ret.dtypes+=[(fq,dtype)] ret.doc+=[docHead+f': `get{capitalize(key)}()`, `set{capitalize(key)}(…)`: named enumeration stored as {dtype.name}: '+', '.join([f'{k} (v)' for k,v in str2num.items()])] ret.defaults[fq]=list(num2str.keys())[0] # first key is the default ret.units[fq]=None def getter(self,*,fq=fq): if self.row is not None: return num2str[self.ctx.dataset[fq,self.row]] else: return [num2str[self.ctx.dataset[fq,r]] for r in range(self.ctx.dataset.shape[0])] def setter(self,val,*,fq=fq): if self.row is not None: self.ctx.dataset[fq,self.row]=str2num[val] else: self.ctx.dataset[fq]=np.full(self.ctx.dataset.shape[0],str2num[val]) meth['get'+capitalize(key)]=getter meth['set'+capitalize(key)]=setter # normal data attribute elif 'dtype' in val: dtype,unit,default,doc=dtypeUnitDefaultDoc(val) basedtype=(b[0] if (b:=getattr(dtype,'subdtype',None)) else dtype) ret.dtypes+=[(fq,dtype)] # add to the compound type ret.doc+=[docHead+f': `get{capitalize(key)}()`, `set{capitalize(key)}(…)`: '+doc] ret.units[fq]=unit if default is not None: ret.defaults[fq]=default # add to the defaults def getter(self,*,fq=fq,unit=unit): _T_assertDataset(self,f"when getting the value of '{fq}'") if self.row is not None: value=self.ctx.dataset[fq,self.row] else: value=self.ctx.dataset[fq] if isinstance(value,bytes): value=value.decode('utf-8') if unit is None: return value return units.Quantity(value=value,unit=unit) def _cookValue(val,*,unit,dtype,basedtype): 'Unit conversion, type conversion before assignment' if unit: val=(units.Quantity(val).to(unit)).value if isinstance(val,str): val=val.encode('utf-8') #sys.stderr.write(f"{fq}: {basedtype}\n") ret=np.array(val).astype(basedtype,casting='safe',copy=False) # for object (variable-length) types, convertibility was checked but the result is discarded if basedtype.kind=='O': return val #sys.stderr.write(f"{fq}: cook {val} → {ret}\n") return ret def setter_direct(self,val,*,fq=fq,unit=unit,dtype=dtype,basedtype=basedtype): _T_assertDataset(self,f"when setting the value of '{fq}'") #_T_assertWritable(self,f"when setting the value of '{fq}'") val=_cookValue(val,unit=unit,dtype=dtype,basedtype=basedtype) # sys.stderr.write(f'{fq}: direct setting {val}\n') if self.row is None: self.ctx.dataset[fq]=val else: self.ctx.dataset[self.row,fq]=val def setter_wholeRow(self,val,*,fq=fq,unit=unit,dtype=dtype,basedtype=basedtype): _T_assertDataset(self,f"when setting the value of '{fq}'") #_T_assertWritable(self,f"when setting the value of '{fq}'") val=_cookValue(val,unit=unit,dtype=dtype,basedtype=basedtype) #sys.stderr.write(f'{fq}: wholeRow setting {repr(val)}\n') # workaround for bugs in h5py: for variable-length fields, and dim>1 subarrays: # direct assignment does not work; must read the whole row, modify, write it back # see https://stackoverflow.com/q/67192725/761090 and https://stackoverflow.com/q/67451714/761090 # kind=='O' covers h5py.vlen_dtype and strings (h5py.string_dtype) with variable length if self.row is None: raise NotImplementedError('Broadcasting to variable-length fields or multidimensional subarrays not yet implemented.') rowdata=self.ctx.dataset[self.row] rowdata[self.ctx.dataset.dtype.names.index(fq)]=val self.ctx.dataset[self.row]=rowdata meth['get'+capitalize(key)]=getter meth['set'+capitalize(key)]=(setter_wholeRow if (dtype.kind=='O' or dtype.ndim>1) else setter_direct) elif 'schema' in val: schema,path=val['schema'],val.get('path','{NAME}/{ROW}/') path=path.replace('{NAME}',key) if '{ROW}' not in path: raise ValueError(f"'{fq}': schema ref path '{path}' does not contain '{{ROW}}'.") if not path.endswith('/'): raise ValueError(f"'{fq}': schema ref path '{path}' does not end with '/'.") ret.subpaths[fq]=(path,schema) # path=path[:-1] # remove trailing slash def subschemaGetter(self,row=None,*,fq=fq,path=path,schema=schema): rr=[self.row is None,row is None] if sum(rr)==2: raise AttributeError(f"'{fq}': row index not set (or given as arg), unable to follow schema ref.") if sum(rr)==0: raise AttributeError(f"'{fq}': row given both as index ({self.row}) and arg ({row}).") if row is None: row=self.row #_T_assertDataset(self,f"when accessing subschema '{path}'.") #self.ctx.dataset[self.row] # catch invalid row index, data unused #print(f"{fq}: getting {path}") path=path.replace('{ROW}',str(row)) subgrp=self.ctx.h5group.require_group(path) SchemaT=self.ctx.schemaRegistry[schema] ret=SchemaT(top=HeavyStruct.TopContext(h5group=subgrp,schemaRegistry=self.ctx.schemaRegistry,pyroIds=self.ctx.pyroIds),row=None) # print(f"{fq}: schema is {SchemaT}, returning: {ret}.") return _registeredWithDaemon(self,ret) ret.doc+=[docHead+f': `get{capitalize(key)}()`: nested data at `{path}`, schema `{schema}`.'] meth['get'+capitalize(key)]=subschemaGetter else: # recurse ret.doc+=[docHead+f': `get{capitalize(key)}()`',''] # empty line for nesting in restructured text cooked=_cookSchema(val,prefix=fq,schemaName=schemaName,fakeModule=fakeModule,datasetName=datasetName) ret.append(cooked) def nestedGetter(self,*,T=cooked.T): #print('nestedGetter',T) ret=T(other=self) return _registeredWithDaemon(self,ret) meth['get'+capitalize(key)]=nestedGetter # lambda self, T=cooked.T: T(self) def _registeredWithDaemon(context,obj): if not hasattr(context,'_pyroDaemon'): return obj context._pyroDaemon.register(obj) context.ctx.pyroIds.append(obj._pyroId) return obj def T_init(self,*,top=None,other=None,row=None): ''' The constructor is a bit hairy, as the new context either: (1) nests inside TopContext (think of dataset); (2) nests inside an already nested context (think of sub-dataset); (3) adds row information, not changing location (row in (sub)dataset) (4) nests & adds row, such as in getMolecules(0) which is a shorthand for getMolecules()[0] ''' if top is not None: assert isinstance(top,HeavyStruct.TopContext) self.ctx,self.row=top,row elif other is not None: assert not isinstance(other,HeavyStruct.TopContext) # print(f'other.row={other.row}, row={row}') if (other.row is not None) and (row is not None): raise IndexError(f'Context already indexed, with row={row}.') self.ctx,self.row=other.ctx,(other.row if row is None else row) # print(f"[LEAF] {self}, other={other}") else: raise ValueError('One of *top* or *other* must be given.') def T_str(self): 'Context string representation' return F"<{self.__class__.__name__}, row={self.row}, ctx={self.ctx}{', _pyroId='+self._pyroId if hasattr(self,'_pyroDaemon') else ''}>" def T_getitem(self,row): 'Indexing access; checks index validity and returns new context with the row set' _T_assertDataset(self,msg=f'when trying to index row {row}') if(row<0 or row>=self.ctx.dataset.shape[0]): raise IndexError(f"{fq}: row index {row} out of range 0…{self.ctx.dataset.shape[0]}.") # self.ctx.dataset[row] # this would raise ValueError but iteration protocol needs IndexError # print(f'Item #{row}: returning {self.__class__(self,row=row)}') ret=self.__class__(other=self,row=row) return _registeredWithDaemon(self,ret) return ret def T_len(self): 'Return sequence length' if not _T_hasDataset(self): return 0 _T_assertDataset(self,msg=f'querying dataset length') if self.row is not None: return IndexError('Row index already set, not behaving as sequence.') return self.ctx.dataset.shape[0] def _T_hasDataset(self): return self.ctx.dataset or (self.__class__.datasetName in self.ctx.h5group) def _T_assertDataset(self,msg=''): 'checks that the backing dataset it present/open. Raises exception otherwise.' if self.ctx.dataset is None: if self.__class__.datasetName in self.ctx.h5group: self.ctx.dataset=self.ctx.h5group[self.__class__.datasetName] else: raise RuntimeError(f'Dataset not yet initialized, use resize first{" ("+msg+")" if msg else ""}: {self.ctx.h5group.name}/{self.__class__.datasetName}.') def _T_assertWritable(self,msg): if self.ctx.h5group.file.mode!='r+': raise RuntimeError(f'Underlying HDF5 file was not open for writing ({msg}).') def T_resize(self,size,reset=False,*,ret=ret): 'Resizes the backing dataset; this will, as necessary, create a new dataset, or grow/shrink size of an existing dataset. New records are always default-initialized.' def _initrows(ds,rowmin,rowmax): 'default-initialize contiguous range of rows rmin…rmax (inclusive), create groups for subpaths' defrow=ds[rowmin] # use first row as storage, assign all defaults into it, then copy over all other rows for fq,val in ret.defaults.items(): defrow[fq]=val ds[rowmin+1:rowmax+1]=defrow assert size>=0 _T_assertWritable(self,msg=f'when resizing to {size}.') if reset: self.resize(size=0) if self.ctx.dataset is None: dsname=self.__class__.datasetName if dsname not in self.ctx.h5group: # create new dataset, initialize, return if size==0: return # request to reset but nothing is here self.ctx.dataset=self.ctx.h5group.create_dataset(dsname,shape=(size,),maxshape=(None,),dtype=ret.dtypes,compression='gzip') _initrows(ds=self.ctx.dataset,rowmin=0,rowmax=size-1) return else: # open existing dataset self.ctx.dataset=self.ctx.h5group[dsname] size0=self.ctx.dataset.shape[0] if size==size0: return self.ctx.dataset.resize((size,)) # this changes size of the underlying HDF5 data # default-initialize added rows if size0<size: _initrows(ds=self.ctx.dataset,rowmin=size0,rowmax=size-1) else: # remove stale subpaths # sys.stderr.write(f'Removing stale subpaths {str(ret.subpaths)}, {size0} → {size}…\n') for fq,(subpath,schema) in ret.subpaths.items(): for r in range(size,size0): p=subpath.replace('{ROW}',str(r)) # sys.stderr.write(f'Resizing {self.ctx.dataset}, {prevSize} → {size}: deleting {p}\n') if p in self.ctx.h5group: del self.ctx.h5group[p] else: pass # sys.stderr.write(f'{self.ctx.h5group}: does not contain {p}, not deleted') def T_inject(self,other): self.from_dump(other.to_dump()) def T_to_dump(self,*,ret=ret): _T_assertDataset(self,msg=f'when dumping') def _onerow(row): d={'_schema':{"name":schemaName,"version":schemaVersion}} for fq,unit in ret.units.items(): # d[fq]=(self.ctx.dataset[row,fq],unit) for fq,(subpath,schema) in ret.subpaths.items(): SchemaT=self.ctx.schemaRegistry[schema] subpath=subpath.replace('{ROW}',str(row)) if subpath not in self.ctx.h5group: continue subgrp=self.ctx.h5group[subpath] subcontext=SchemaT(top=HeavyStruct.TopContext(h5group=subgrp,schemaRegistry=self.ctx.schemaRegistry,pyroIds=[]),row=None) d[fq]=subcontext.to_dump() return d if self.row is not None: return _onerow(self.row) else: return [_onerow(r) for r in range(self.ctx.dataset.shape[0])] def T_from_dump(self,dump,*,ret=ret): _T_assertWritable(self,msg=f'when applying dump') def _onerow(row,di): rowdata=self.ctx.dataset[row] s2n,s2v=di['_schema']['name'],di['_schema']['version'] if s2n!=self.schemaName: raise ValueError(f'Schema mismatch: source {s2n}, target {self.schemaName}') if s2v!=self.schemaVersion: log.warning('Schema {s2n} version mismatch: source {s2v}, target {self.schemaVersion}') for fq,valUnit in di.items(): if fq=='_schema': continue if fq in ret.units: # value field rowdata[fq]=valUnit[0] if (valUnit[1] is None) else units.Quantity(value=valUnit[0],unit=valUnit[1]).to(ret.units[fq]).value elif fq in ret.subpaths: # subpath assert isinstance(valUnit,list) subpath,schema=ret.subpaths[fq] SchemaT=self.ctx.schemaRegistry[schema] subpath=subpath.replace('{ROW}',str(row)) subgrp=self.ctx.h5group.require_group(subpath) subcontext=SchemaT(top=HeavyStruct.TopContext(h5group=subgrp,schemaRegistry=self.ctx.schemaRegistry,pyroIds=[]),row=None) subcontext.from_dump(valUnit) else: raise ValueError(f'Key {fq} not in target schema {self.schemaName}, in {self.ctx.h5group}.') # key not in target schema self.ctx.dataset[row]=rowdata if self.row is not None: assert isinstance(dump,dict) _T_assertDataset(self,msg=f'when applying dump with row={self.row}') _onerow(self.row,dump) else: assert isinstance(dump,list) self.resize(len(dump),reset=True) _T_assertDataset(self,msg=f'when applying dump') for row,di in enumerate(dump): _onerow(row,di) def T_iter(self): _T_assertDataset(self,msg=f'when iterating') for row in range(self.ctx.dataset.shape[0]): yield self[row] meth['__init__']=T_init meth['__str__']=meth['__repr__']=T_str meth['__getitem__']=T_getitem meth['__len__']=T_len meth['row']=None meth['ctx']=None # __del__ note: it would be nice to use context destructor to unregister contexts from Pyro # (those which registered automatically). Since the daemon is holding one reference, however, # the dtor will never be called, unfortunately # those are defined only for the "root" context if not prefix: meth['resize']=T_resize meth['to_dump']=T_to_dump meth['from_dump']=T_from_dump meth['inject']=T_inject ret.dtypes=np.dtype(ret.dtypes) T_bases=() else: T_bases=() # only top-level has metadata T=type(T_name,T_bases,meth) T.__module__=fakeModule.__name__ ## make the (T.__module__,T.__name__) tuple used in serialization unique T.datasetName=datasetName T=Pyro5.api.expose(T) setattr(fakeModule,T_name,T) if not prefix: T.schemaName=schemaName # schema knows its own name, for convenience of creating schema registry T.schemaVersion=schemaVersion T.__doc__='\n'.join(ret.doc)+'\n' return T else: ret.T=T return ret def makeSchemaRegistry(dd): ''' Compile schema registry from dictionary representation; use ``json.loads`` to convert JSON schema to its dictionary representation. ''' return dict([((T:=_cookSchema(d)).schemaName,T) for d in dd]) def _make_grains(h5name): import time, random from mupif.units import U as u t0=time.time() atomCounter=0 # precompiled schemas schemaRegistry=makeSchemaRegistry(json.loads(sampleSchemas_json)) with h5py.File(h5name,'w') as h5: grp=h5.require_group('test') schemaT=schemaRegistry['org.mupif.sample.grain'] grp.attrs['schemas']=sampleSchemas_json grp.attrs['schema']=schemaT.schemaName grains=schemaT(top=HeavyStruct.TopContext(h5group=grp,schemaRegistry=schemaRegistry,pyroIds=[])) log.debug(f"{grains}") grains.resize(size=2) log.info(f"There is {len(grains)} grains.") for ig,g in enumerate(grains): #g=grains[ig] log.debug(f'grain {ig} {g}') g.getMolecules().resize(size=random.randint(5,20)) log.debug(f"Grain #{ig} has {len(g.getMolecules())} molecules") for m in g.getMolecules(): #for im in range(len(g.getMolecules())): #m=g.getMolecules()[im] # print('molecule: ',m) m.getIdentity().setMolecularWeight(random.randint(1,10)*u.yg) m.getAtoms().resize(size=random.randint(30,60)) for a in m.getAtoms(): #for ia in range(len(m.getAtoms())): #a=m.getAtoms()[ia] a.getIdentity().setElement(random.choice(['H','N','Cl','Na','Fe'])) a.getProperties().getTopology().setPosition((1,2,3)*u.nm) a.getProperties().getTopology().setVelocity((24,5,77)*u.m/u.s) # not yet, see https://stackoverflow.com/q/67192725/761090 struct=np.array([random.randint(1,20) for i in range(random.randint(5,20))],dtype='l') a.getProperties().getTopology().setStructure(struct) atomCounter+=1 t1=time.time() log.info(f'{atomCounter} atoms created in {t1-t0:g} sec ({atomCounter/(t1-t0):g}/sec).') def _read_grains(h5name): import time # note how this does NOT need any schemas defined, they are all pulled from the HDF5 t0 = time.time() atomCounter = 0 with h5py.File(h5name, 'r') as h5: grp = h5['test'] schemaRegistry = makeSchemaRegistry(json.loads(grp.attrs['schemas'])) grains = schemaRegistry[grp.attrs['schema']](top=HeavyStruct.TopContext(h5group=grp, schemaRegistry=schemaRegistry, pyroIds=[])) for g in grains: # print(g) log.info(f'Grain #{g.row} has {len(g.getMolecules())} molecules.') for m in g.getMolecules(): m.getIdentity().getMolecularWeight() for a in m.getAtoms(): a.getIdentity().getElement() a.getProperties().getTopology().getPosition() a.getProperties().getTopology().getVelocity() a.getProperties().getTopology().getStructure() atomCounter += 1 t1 = time.time() log.info(f'{atomCounter} atoms read in {t1-t0:g} sec ({atomCounter/(t1-t0):g}/sec).') def HeavyDataHandle(*args, **kwargs): import warnings warnings.warn("HeavyDataHandle class was renamed to HeavyStruct, update your code.",DeprecationWarning) return HeavyStruct(*args, **kwargs) @Pyro5.api.expose class HeavyStruct(HeavyDataBase): h5group: str='/' schemaName: typing.Optional[str] = None schemasJson: typing.Optional[str] = None id: dataid.DataID = dataid.DataID.ID_None # __doc__ is a computed property which will add documentation for the sample JSON schemas __doc0__ = ''' *mode* specifies how the underlying HDF5 file (:obj:`h5path`) is to be opened: * ``readonly`` only allows reading; * ``readwrite`` alows reading and writing; * ``create`` creates new HDF5 file, raising an exception if the file exists already; if :obj:`h5path` is empty, a temporary file will be created automatically; * ``overwrite`` create new HDF5 file, allowing overwriting an existing file; * ``create-memory`` create HDF5 file in RAM only; if :obj:`h5path` is non-empty, it will be written out when data is closed via :obj:`closeData` (and discarded otherwise); *schemaName* and *schemasJson* must be provided when creating new data (``overwrite``, ``create``, ``create-memory``) and are ignored otherwise. This class can be used as context manager, in which case the :obj:`openData` and :obj:`closeData` will be called automatically. ''' # from https://stackoverflow.com/a/3203659/761090 class _classproperty(object): def __init__(self, getter): self.getter = getter def __get__(self, instance, owner): return self.getter(owner) @_classproperty def __doc__(cls): ret = cls.__doc0__ reg = makeSchemaRegistry(json.loads(sampleSchemas_json)) for key, val in reg.items(): ret += '\n\n'+val.__doc__.replace('`', '``') return ret # this is not useful over Pyro (the Proxy defines its own context manager) but handy for local testing def __enter__(self): return self.openData(mode=self.mode) def __exit__(self, exc_type, exc_value, traceback): self.closeData() @dataclass @Pyro5.api.expose class TopContext: 'This class is for internal use only. It is the return type of :obj:`HeavyStruct.openData` and others.' h5group: Any pyroIds: list schemaRegistry: dict dataset: Any = None def __str__(self): return f'{self.__module__}.{self.__class__.__name__}(h5group={str(self.h5group)},dataset={str(self.dataset)},schemaRegistry=<<{",".join(self.schemaRegistry.keys())}>>)' def __init__(self, **kw): super().__init__(**kw) @pydantic.validate_arguments def openData(self,mode=typing.Optional[HeavyDataBase_ModeChoice]): ''' Return top context for the underlying HDF5 data. The context is automatically published through Pyro5 daemon, if the :obj:`HeavyStruct` instance is also published (this is true recursively, for all subcontexts). The contexts are unregistered when :obj:`HeavyStruct.closeData` is called (directly or via context manager). ''' self.openStorage(mode=mode) extant=(self.h5group in self._h5obj and 'schema' in self._h5obj[self.h5group].attrs) if extant: # for modes readonly, readwrite grp = self._h5obj[self.h5group] schemaRegistry = makeSchemaRegistry(json.loads(grp.attrs['schemas'])) top=schemaRegistry[grp.attrs['schema']](top=HeavyStruct.TopContext(h5group=grp, schemaRegistry=schemaRegistry, pyroIds=self.pyroIds)) self.updateMetadata(json.loads(grp.attrs['metadata'])) return self._returnProxy(top) else: if not self.schemaName or not self.schemasJson: raise ValueError(f'Both *schema* and *schemaJson* must be given (opening {self.h5path} in mode {self.mode})') # modes: overwrite, create, create-memory grp = self._h5obj.require_group(self.h5group) grp.attrs['schemas'] = self.schemasJson grp.attrs['schema'] = self.schemaName grp.attrs['metadata'] = json.dumps(self.getAllMetadata()) schemaRegistry = makeSchemaRegistry(json.loads(self.schemasJson)) top = schemaRegistry[grp.attrs['schema']](top=HeavyStruct.TopContext(h5group=grp, schemaRegistry=schemaRegistry, pyroIds=self.pyroIds)) return self._returnProxy(top) ''' future ideas: * Create all context classes as Ctx_<md5 of the JSON schema> so that the name is unique.\ * Register classes to Pyro when the schema is read * Register classes to remote Pyro when the heavy file is transferred? ''' # uses relative imports, therefore run stand-alone as as: # # PYTHONPATH=.. python3 -m mupif.heavydata # if __name__ == '__main__': import json import pprint print(HeavyStruct.__doc__) # print(json.dumps(json.loads(sampleSchemas_json),indent=2)) _make_grains('/tmp/grains.h5') _read_grains('/tmp/grains.h5') # this won't work through Pyro yet pp = HeavyStruct(h5path='/tmp/grains.h5', h5group='test') for key, val in pp.getSchemaRegistry(compile=True).items(): print(val.__doc__.replace('`', '``')) grains = pp.openData('readonly') print(pp.openData(mode='readonly')[0].getMolecules()) print(grains.getMolecules(0).getAtoms(5).getIdentity().getElement()) print(grains[0].getMolecules()[5].getAtoms().getIdentity().getElement()) import pprint mol5dump = grains[0].getMolecules()[5].to_dump() pp.closeData() grains = pp.openData('readwrite') grains[0].getMolecules()[4].from_dump(mol5dump) mol4dump = grains[0].getMolecules()[4].to_dump() # pprint.pprint(mol4dump) # pprint.pprint(mol4dump,stream=open('/tmp/m4.txt','w')) # pprint.pprint(mol5dump,stream=open('/tmp/m5.txt','w')) print(str(mol4dump) == str(mol5dump)) pp.closeData()
mupif/mupif
mupif/heavystruct.py
Python
lgpl-3.0
46,818
[ "Dalton" ]
36b54db37fc75bd91818e76413980b42e889a24f4e992990cab81c7c95195aa1
import logging import random import numpy as np from ray.rllib.agents import with_common_config from ray.rllib.agents.dreamer.dreamer_torch_policy import DreamerTorchPolicy from ray.rllib.agents.trainer_template import build_trainer from ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, \ LEARNER_INFO, _get_shared_metrics from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch from ray.rllib.evaluation.metrics import collect_metrics from ray.rllib.agents.dreamer.dreamer_model import DreamerModel from ray.rllib.execution.rollout_ops import ParallelRollouts from ray.rllib.utils.typing import SampleBatchType logger = logging.getLogger(__name__) # yapf: disable # __sphinx_doc_begin__ DEFAULT_CONFIG = with_common_config({ # PlaNET Model LR "td_model_lr": 6e-4, # Actor LR "actor_lr": 8e-5, # Critic LR "critic_lr": 8e-5, # Grad Clipping "grad_clip": 100.0, # Discount "discount": 0.99, # Lambda "lambda": 0.95, # Clipping is done inherently via policy tanh. "clip_actions": False, # Training iterations per data collection from real env "dreamer_train_iters": 100, # Horizon for Enviornment (1000 for Mujoco/DMC) "horizon": 1000, # Number of episodes to sample for Loss Calculation "batch_size": 50, # Length of each episode to sample for Loss Calculation "batch_length": 50, # Imagination Horizon for Training Actor and Critic "imagine_horizon": 15, # Free Nats "free_nats": 3.0, # KL Coeff for the Model Loss "kl_coeff": 1.0, # Distributed Dreamer not implemented yet "num_workers": 0, # Prefill Timesteps "prefill_timesteps": 5000, # This should be kept at 1 to preserve sample efficiency "num_envs_per_worker": 1, # Exploration Gaussian "explore_noise": 0.3, # Batch mode "batch_mode": "complete_episodes", # Custom Model "dreamer_model": { "custom_model": DreamerModel, # RSSM/PlaNET parameters "deter_size": 200, "stoch_size": 30, # CNN Decoder Encoder "depth_size": 32, # General Network Parameters "hidden_size": 400, # Action STD "action_init_std": 5.0, }, "env_config": { # Repeats action send by policy for frame_skip times in env "frame_skip": 2, } }) # __sphinx_doc_end__ # yapf: enable class EpisodicBuffer(object): def __init__(self, max_length: int = 1000, length: int = 50): """Data structure that stores episodes and samples chunks of size length from episodes Args: max_length: Maximum episodes it can store length: Episode chunking lengh in sample() """ # Stores all episodes into a list: List[SampleBatchType] self.episodes = [] self.max_length = max_length self.timesteps = 0 self.length = length def add(self, batch: SampleBatchType): """Splits a SampleBatch into episodes and adds episodes to the episode buffer Args: batch: SampleBatch to be added """ self.timesteps += batch.count episodes = batch.split_by_episode() for i, e in enumerate(episodes): episodes[i] = self.preprocess_episode(e) self.episodes.extend(episodes) if len(self.episodes) > self.max_length: delta = len(self.episodes) - self.max_length # Drop oldest episodes self.episodes = self.episodes[delta:] def preprocess_episode(self, episode: SampleBatchType): """Batch format should be in the form of (s_t, a_(t-1), r_(t-1)) When t=0, the resetted obs is paired with action and reward of 0. Args: episode: SampleBatch representing an episode """ obs = episode["obs"] new_obs = episode["new_obs"] action = episode["actions"] reward = episode["rewards"] act_shape = action.shape act_reset = np.array([0.0] * act_shape[-1])[None] rew_reset = np.array(0.0)[None] obs_end = np.array(new_obs[act_shape[0] - 1])[None] batch_obs = np.concatenate([obs, obs_end], axis=0) batch_action = np.concatenate([act_reset, action], axis=0) batch_rew = np.concatenate([rew_reset, reward], axis=0) new_batch = { "obs": batch_obs, "rewards": batch_rew, "actions": batch_action } return SampleBatch(new_batch) def sample(self, batch_size: int): """Samples [batch_size, length] from the list of episodes Args: batch_size: batch_size to be sampled """ episodes_buffer = [] while len(episodes_buffer) < batch_size: rand_index = random.randint(0, len(self.episodes) - 1) episode = self.episodes[rand_index] if episode.count < self.length: continue available = episode.count - self.length index = int(random.randint(0, available)) episodes_buffer.append(episode.slice(index, index + self.length)) batch = {} for k in episodes_buffer[0].keys(): batch[k] = np.stack([e[k] for e in episodes_buffer], axis=0) return SampleBatch(batch) def total_sampled_timesteps(worker): return worker.policy_map[DEFAULT_POLICY_ID].global_timestep class DreamerIteration: def __init__(self, worker, episode_buffer, dreamer_train_iters, batch_size, act_repeat): self.worker = worker self.episode_buffer = episode_buffer self.dreamer_train_iters = dreamer_train_iters self.repeat = act_repeat self.batch_size = batch_size def __call__(self, samples): # Dreamer Training Loop for n in range(self.dreamer_train_iters): print(n) batch = self.episode_buffer.sample(self.batch_size) if n == self.dreamer_train_iters - 1: batch["log_gif"] = True fetches = self.worker.learn_on_batch(batch) # Custom Logging policy_fetches = self.policy_stats(fetches) if "log_gif" in policy_fetches: gif = policy_fetches["log_gif"] policy_fetches["log_gif"] = self.postprocess_gif(gif) # Metrics Calculation metrics = _get_shared_metrics() metrics.info[LEARNER_INFO] = fetches metrics.counters[STEPS_SAMPLED_COUNTER] = self.episode_buffer.timesteps metrics.counters[STEPS_SAMPLED_COUNTER] *= self.repeat res = collect_metrics(local_worker=self.worker) res["info"] = metrics.info res["info"].update(metrics.counters) res["timesteps_total"] = metrics.counters[STEPS_SAMPLED_COUNTER] self.episode_buffer.add(samples) return res def postprocess_gif(self, gif: np.ndarray): gif = np.clip(255 * gif, 0, 255).astype(np.uint8) B, T, C, H, W = gif.shape frames = gif.transpose((1, 2, 3, 0, 4)).reshape((1, T, C, H, B * W)) return frames def policy_stats(self, fetches): return fetches[DEFAULT_POLICY_ID]["learner_stats"] def execution_plan(workers, config): # Special Replay Buffer for Dreamer agent episode_buffer = EpisodicBuffer(length=config["batch_length"]) local_worker = workers.local_worker() # Prefill episode buffer with initial exploration (uniform sampling) while total_sampled_timesteps(local_worker) < config["prefill_timesteps"]: samples = local_worker.sample() episode_buffer.add(samples) batch_size = config["batch_size"] dreamer_train_iters = config["dreamer_train_iters"] act_repeat = config["action_repeat"] rollouts = ParallelRollouts(workers) rollouts = rollouts.for_each( DreamerIteration(local_worker, episode_buffer, dreamer_train_iters, batch_size, act_repeat)) return rollouts def get_policy_class(config): return DreamerTorchPolicy def validate_config(config): config["action_repeat"] = config["env_config"]["frame_skip"] if config["num_gpus"] > 1: raise ValueError("`num_gpus` > 1 not yet supported for Dreamer!") if config["framework"] != "torch": raise ValueError("Dreamer not supported in Tensorflow yet!") if config["batch_mode"] != "complete_episodes": raise ValueError("truncate_episodes not supported") if config["num_workers"] != 0: raise ValueError("Distributed Dreamer not supported yet!") if config["clip_actions"]: raise ValueError("Clipping is done inherently via policy tanh!") if config["action_repeat"] > 1: config["horizon"] = config["horizon"] / config["action_repeat"] DREAMERTrainer = build_trainer( name="Dreamer", default_config=DEFAULT_CONFIG, default_policy=DreamerTorchPolicy, get_policy_class=get_policy_class, execution_plan=execution_plan, validate_config=validate_config)
pcmoritz/ray-1
rllib/agents/dreamer/dreamer.py
Python
apache-2.0
9,028
[ "Gaussian" ]
044ccba1331ef60590669098c1308a624e9a0413206f831f4804c187ccef5c8e
#!/usr/bin/env python # ============================================================================= # Initialization # ============================================================================= from active_worker.task import task from task_types import TaskTypes as tt import numpy as np import h5py num_surrs = 10 @task def crosscorrelogram_task(inputdata, number_of_jobs, job_id): ''' Task Manifest Version: 1 Full Name: crosscorrelogram_task Caption: cross-correlogram Author: Elephant-Developers Description: | This task calculates all pair-wise cross-correlograms between all combinations of spike trains in the input file. Significance of the correlation is evaluated based on spike-dither surrogates. Categories: - FDAT Compatible_queues: ['cscs_viz'] Accepts: inputdata: application/unknown number_of_jobs: long job_id: long Returns: res: application/unknown ''' import quantities as pq import neo import elephant if job_id > number_of_jobs: print "Input data is invalid, exiting" return # ========================================================================= # Load data # ========================================================================= # stage the input file original_path = crosscorrelogram_task.task.uri.get_file(inputdata) session = neo.NeoHdf5IO(filename=original_path) block = session.read_block() # select spike trains sts = block.filter(use_st=True) # print("Number of spike trains: " + str(len(sts))) # ========================================================================= # Cross-correlograms # ========================================================================= max_lag_bins = 200 lag_res = 1 * pq.ms max_lag = max_lag_bins * lag_res smoothing = 10 * pq.ms num_neurons = len(sts) cc = {} cc['unit_i'] = {} cc['unit_j'] = {} cc['times_ms'] = {} cc['original'] = {} cc['surr'] = {} cc['original_measure'] = {} cc['surr_measure'] = {} cc['pvalue'] = {} # create all combinations of tasks num_total_pairs = 0 all_combos_unit_i = [] all_combos_unit_j = [] for ni in range(num_neurons): for nj in range(ni, num_neurons): all_combos_unit_i.append(ni) all_combos_unit_j.append(nj) num_total_pairs += 1 # calculate indices in cc['unit_i'] list which to calculate for each task idx = np.linspace(0, num_total_pairs, number_of_jobs + 1, dtype=int) task_starts_idx = idx[:-1] task_stop_idx = idx[1:] # Loop over all pairs of neurons for calc_i in range(task_starts_idx[job_id], task_stop_idx[job_id]): # save neuron i,j index ni = all_combos_unit_i[calc_i] nj = all_combos_unit_j[calc_i] cc['unit_i'][calc_i] = ni cc['unit_j'][calc_i] = nj print("Cross-correlating %i and %i" % (ni, nj)) # original CCH cco = elephant.spikecorr.cch( sts[ni], sts[nj], w=lag_res, lag=max_lag, smooth=smoothing) cc['original'][calc_i] = cco.magnitude cc['times_ms'][calc_i] = cco.times.rescale(pq.ms).magnitude # extract measure ind = np.argmin(np.abs(cco.times)) ccom = cch_measure(cco, ind) cc['original_measure'][calc_i] = ccom surr_i = elephant.surrogates.spike_dithering( sts[ni], dither=50. * pq.ms, n=num_surrs) surr_j = elephant.surrogates.spike_dithering( sts[nj], dither=50. * pq.ms, n=num_surrs) ccs = [] ccsm = [] # cross-correlogram of each surrogate pair for surrogate in range(num_surrs): scc = elephant.spikecorr.cch( surr_i[surrogate], surr_j[surrogate], w=lag_res, lag=max_lag, smooth=smoothing) ccs.append(scc.magnitude) ccsm.append(cch_measure(scc, ind)) cc['surr'][calc_i] = np.array(ccs) cc['surr_measure'][calc_i] = np.sort(ccsm) cc['pvalue'][calc_i] = np.count_nonzero(np.array(ccsm) >= ccom) # save result to hdf5 outputname = 'cc_result'+str(number_of_jobs)+'_'+str(job_id)+'.h5' export_hdf5(cc, outputname) return crosscorrelogram_task.task.uri.save_file(mime_type='\ application/unknown', src_path=outputname, dst_path=outputname) # write parameters to disk # import h5py_wrapper.wrapper # h5py_wrapper.wrapper.add_to_h5( # 'correlation_output_' + filename + '_' + str(job_id) + '.h5', # cc, write_mode='w', overwrite_dataset=True) def cch_measure(cch, ind): return np.sum(cch[ind - 5:ind + 5].magnitude) def export_hdf5(cc, outputname): # cc has type dict with 8-keys file = h5py.File(outputname, 'w') get_hdf5_surr_measure(cc, file) get_hdf5_original_measure(cc, file) get_hdf5_pvalue(cc, file) get_hdf5_unit_i(cc, file) get_hdf5_unit_j(cc, file) get_hdf5_times_ms(cc, file) get_hdf5_surr(cc, file) get_hdf5_original(cc, file) file.close() return file def get_hdf5_surr_measure(cc, file): # --------------------- cc['surr_measure'] # numsurr = 10 -> create_dataset(...(l_surr_measure, 10), ...) l_surr_measure = len(cc['surr_measure']) dataset_surr_measure = file.create_dataset("/cc_group/surr_measure", (l_surr_measure, num_surrs), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_surr_measure, num_surrs)) for i in range(l_surr_measure): for j in range(len(cc['surr_measure'][i])): data[i][j] = cc['surr_measure'][i][j] dataset_surr_measure[...] = data def get_hdf5_original_measure(cc, file): # --------------------- cc['original_measure'] dict l_orig_measure = len(cc['original_measure']) dataset_orig_measure = file.create_dataset("/cc_group/original_measure", (l_orig_measure, 1), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_orig_measure, 1)) for i in range(l_orig_measure): data[i] = cc['original_measure'].items()[i][1] dataset_orig_measure[...] = data def get_hdf5_pvalue(cc, file): # --------------------- cc['pvalue'] dict l_pvalue = len(cc['pvalue']) dataset_pvalue = file.create_dataset("/cc_group/pvalue", (l_pvalue, 1), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_pvalue, 1)) for i in range(l_pvalue): data[i] = cc['pvalue'].items()[i][1] dataset_pvalue[...] = data def get_hdf5_unit_i(cc, file): # --------------------- cc['unit_i'] dict l_unit_i = len(cc['unit_i']) dataset_unit_i = file.create_dataset("/cc_group/unit_i", (l_unit_i, 1), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_unit_i, 1)) for i in range(l_unit_i): data[i] = cc['unit_i'].items()[i][1] dataset_unit_i[...] = data def get_hdf5_unit_j(cc, file): # --------------------- cc['unit_j'] dict l_unit_j = len(cc['unit_j']) dataset_unit_j = file.create_dataset("/cc_group/unit_j", (l_unit_j, 1), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_unit_j, 1)) for i in range(l_unit_j): data[i] = cc['unit_j'].items()[i][1] dataset_unit_j[...] = data def get_hdf5_times_ms(cc, file): # --------------------- cc['times_ms'] dict array l_times_ms = len(cc['times_ms']) dataset_times_ms = file.create_dataset("/cc_group/times_ms", (l_times_ms, cc['times_ms'][0].size), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_times_ms, cc['times_ms'][0].size)) for i in range(l_times_ms): for j in range(cc['times_ms'][i].size): data[i][j] = cc['times_ms'][i].item(j) dataset_times_ms[...] = data def get_hdf5_surr(cc, file): # --------------------- cc['surr'] dict numpy.ndarray l_surr = len(cc['surr']) dataset_surr = file.create_dataset("/cc_group/surr", (l_surr, cc['surr'][0].size), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_surr, cc['surr'][0].size)) for i in range(l_surr): for j in range(cc['surr'][i].size): data[i][j] = cc['surr'][i].item(j) dataset_surr[...] = data def get_hdf5_original(cc, file): # --------------------- cc['original'] l_original = len(cc['original']) dataset_orig = file.create_dataset("/cc_group/original", (l_original, cc['original'][0].size), dtype=h5py.h5t.NATIVE_FLOAT) data = np.zeros((l_original, cc['original'][0].size)) for i in range(l_original): for j in range(cc['original'][i].size): data[i][j] = cc['original'][i].item(j) dataset_orig[...] = data if __name__ == '__main__': # this number relates to the "-t" parameter: # -t 0-X => number_of_jobs=X+1 # INPUT-second parameter # number_of_jobs is (0, 200] number_of_jobs = 1 # INPUT-third parameter # job parameter: a number between 0 and number_of_jobs-1 import os PBS_value = os.getenv('PBS_ARRAYID') if PBS_value is not None: job_id = int(PBS_value) else: job_id = 0 # INPUT-first parameter inputdata = tt.URI('application/unknown', 'data/experiment.h5') crosscorrelogram_task(inputdata, number_of_jobs, job_id)
jakobj/UP-Tasks
Elephant/crosscorrelogram_task/crosscorrelogram_task.py
Python
gpl-2.0
10,090
[ "NEURON" ]
487cce2d8f47c92705c1f7c1de2e98ef5965835709d5601703694edb58b06b50
#!/usr/bin/env python # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*- # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 fileencoding=utf-8 # # MDAnalysis --- http://www.mdanalysis.org # Copyright (c) 2006-2016 The MDAnalysis Development Team and contributors # (see the file AUTHORS for the full list of names) # # Released under the GNU Public Licence, v2 or any higher version # # Please cite your use of MDAnalysis in published work: # # R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, # D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. # MDAnalysis: A Python package for the rapid analysis of molecular dynamics # simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th # Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy. # # N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. # MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787 # """Setuptools-based setup script for MDAnalysis. A working installation of NumPy <http://numpy.scipy.org> is required. For a basic installation just type the command:: python setup.py install For more in-depth instructions, see the installation section at the MDAnalysis Wiki: https://github.com/MDAnalysis/mdanalysis/wiki/INSTALL Also free to ask on the MDAnalysis mailing list for help: http://groups.google.com/group/mdnalysis-discussion (Note that the group really is called `mdnalysis-discussion' because Google groups forbids any name that contains the string `anal'.) """ from __future__ import print_function from setuptools import setup, Extension, find_packages from distutils.ccompiler import new_compiler import codecs import os import sys import shutil import tempfile import warnings # Make sure I have the right Python version. if sys.version_info[:2] < (2, 7): print('MDAnalysis requires Python 2.7 or better. Python {0:d}.{1:d} detected'.format(* sys.version_info[:2])) print('Please upgrade your version of Python.') sys.exit(-1) if sys.version_info[0] < 3: import ConfigParser as configparser open_kwargs = {} else: import configparser open_kwargs = {'encoding': 'utf-8'} # Handle cython modules try: from Cython.Distutils import build_ext cython_found = True cmdclass = {'build_ext': build_ext} except ImportError: cython_found = False cmdclass = {} # NOTE: keep in sync with MDAnalysis.__version__ in version.py RELEASE = "0.16.1-dev0" is_release = 'dev' not in RELEASE if cython_found: # cython has to be >=0.16 to support cython.parallel import Cython from Cython.Build import cythonize from distutils.version import LooseVersion required_version = "0.16" if not LooseVersion(Cython.__version__) >= LooseVersion(required_version): # We don't necessarily die here. Maybe we already have # the cythonized '.c' files. print("Cython version {0} was found but won't be used: version {1} " "or greater is required because it offers a handy " "parallelization module".format( Cython.__version__, required_version)) cython_found = False del Cython del LooseVersion else: if not is_release: print("*** package: Cython not found ***") print("MDAnalysis requires cython for development builds") sys.exit(1) class Config(object): """Config wrapper class to get build options This class looks for options in the environment variables and the 'setup.cfg' file. The order how we look for an option is. 1. Environment Variable 2. set in 'setup.cfg' 3. given default Environment variables should start with 'MDA_' and be all uppercase. Values passed to environment variables are checked (case-insensitively) for specific strings with boolean meaning: 'True' or '1' will cause `True` to be returned. '0' or 'False' cause `False` to be returned. """ def __init__(self, fname='setup.cfg'): if os.path.exists(fname): self.config = configparser.SafeConfigParser() self.config.read(fname) def get(self, option_name, default=None): environ_name = 'MDA_' + option_name.upper() if environ_name in os.environ: val = os.environ[environ_name] if val.upper() in ('1', 'TRUE'): return True elif val.upper() in ('0', 'FALSE'): return False return val try: option = self.config.get('options', option_name) return option except configparser.NoOptionError: return default class MDAExtension(Extension, object): """Derived class to cleanly handle setup-time (numpy) dependencies. """ # The only setup-time numpy dependency comes when setting up its # include dir. # The actual numpy import and call can be delayed until after pip # has figured it must install numpy. # This is accomplished by passing the get_numpy_include function # as one of the include_dirs. This derived Extension class takes # care of calling it when needed. def __init__(self, *args, **kwargs): self._mda_include_dirs = [] super(MDAExtension, self).__init__(*args, **kwargs) @property def include_dirs(self): if not self._mda_include_dirs: for item in self._mda_include_dir_args: try: self._mda_include_dirs.append(item()) #The numpy callable except TypeError: self._mda_include_dirs.append(item) return self._mda_include_dirs @include_dirs.setter def include_dirs(self, val): self._mda_include_dir_args = val def get_numpy_include(): # Obtain the numpy include directory. This logic works across numpy # versions. # setuptools forgets to unset numpy's setup flag and we get a crippled # version of it unless we do it ourselves. try: # Python 3 renamed the ``__builin__`` module into ``builtins``. # Here we import the python 2 or the python 3 version of the module # with the python 3 name. This could be done with ``six`` but that # module may not be installed at that point. import __builtin__ as builtins except ImportError: import builtins builtins.__NUMPY_SETUP__ = False try: import numpy as np except ImportError: print('*** package "numpy" not found ***') print('MDAnalysis requires a version of NumPy (>=1.10.4), even for setup.') print('Please get it from http://numpy.scipy.org/ or install it through ' 'your package manager.') sys.exit(-1) return np.get_include() def hasfunction(cc, funcname, include=None, extra_postargs=None): # From http://stackoverflow.com/questions/ # 7018879/disabling-output-when-compiling-with-distutils tmpdir = tempfile.mkdtemp(prefix='hasfunction-') devnull = oldstderr = None try: try: fname = os.path.join(tmpdir, 'funcname.c') with open(fname, 'w') as f: if include is not None: f.write('#include {0!s}\n'.format(include)) f.write('int main(void) {\n') f.write(' {0!s};\n'.format(funcname)) f.write('}\n') # Redirect stderr to /dev/null to hide any error messages # from the compiler. # This will have to be changed if we ever have to check # for a function on Windows. devnull = open('/dev/null', 'w') oldstderr = os.dup(sys.stderr.fileno()) os.dup2(devnull.fileno(), sys.stderr.fileno()) objects = cc.compile([fname], output_dir=tmpdir, extra_postargs=extra_postargs) cc.link_executable(objects, os.path.join(tmpdir, "a.out")) except Exception: return False return True finally: if oldstderr is not None: os.dup2(oldstderr, sys.stderr.fileno()) if devnull is not None: devnull.close() shutil.rmtree(tmpdir) def detect_openmp(): """Does this compiler support OpenMP parallelization?""" print("Attempting to autodetect OpenMP support... ", end="") compiler = new_compiler() compiler.add_library('gomp') include = '<omp.h>' extra_postargs = ['-fopenmp'] hasopenmp = hasfunction(compiler, 'omp_get_num_threads()', include=include, extra_postargs=extra_postargs) if hasopenmp: print("Compiler supports OpenMP") else: print("Did not detect OpenMP support.") return hasopenmp def extensions(config): # dev installs must build their own cythonized files. use_cython = config.get('use_cython', default=not is_release) use_openmp = config.get('use_openmp', default=True) if config.get('debug_cflags', default=False): extra_compile_args = '\ -std=c99 -pedantic -Wall -Wcast-align -Wcast-qual -Wpointer-arith \ -Wchar-subscripts -Winline -Wnested-externs -Wbad-function-cast \ -Wunreachable-code -Werror' define_macros = [('DEBUG', '1')] else: extra_compile_args = '' define_macros = [] # Needed for large-file seeking under 32bit systems (for xtc/trr indexing # and access). largefile_macros = [ ('_LARGEFILE_SOURCE', None), ('_LARGEFILE64_SOURCE', None), ('_FILE_OFFSET_BITS', '64') ] has_openmp = detect_openmp() if use_openmp and not has_openmp: print('No openmp compatible compiler found default to serial build.') parallel_args = ['-fopenmp'] if has_openmp and use_openmp else [] parallel_libraries = ['gomp'] if has_openmp and use_openmp else [] parallel_macros = [('PARALLEL', None)] if has_openmp and use_openmp else [] if use_cython: print('Will attempt to use Cython.') if not cython_found: print("Couldn't find a Cython installation. " "Not recompiling cython extensions.") use_cython = False else: print('Will not attempt to use Cython.') source_suffix = '.pyx' if use_cython else '.c' # The callable is passed so that it is only evaluated at install time. include_dirs = [get_numpy_include] dcd = MDAExtension('coordinates._dcdmodule', ['MDAnalysis/coordinates/src/dcd.c'], include_dirs=include_dirs + ['MDAnalysis/coordinates/include'], define_macros=define_macros, extra_compile_args=extra_compile_args) dcd_time = MDAExtension('coordinates.dcdtimeseries', ['MDAnalysis/coordinates/dcdtimeseries' + source_suffix], include_dirs=include_dirs + ['MDAnalysis/coordinates/include'], define_macros=define_macros, extra_compile_args=extra_compile_args) distances = MDAExtension('lib.c_distances', ['MDAnalysis/lib/c_distances' + source_suffix], include_dirs=include_dirs + ['MDAnalysis/lib/include'], libraries=['m'], define_macros=define_macros, extra_compile_args=extra_compile_args) distances_omp = MDAExtension('lib.c_distances_openmp', ['MDAnalysis/lib/c_distances_openmp' + source_suffix], include_dirs=include_dirs + ['MDAnalysis/lib/include'], libraries=['m'] + parallel_libraries, define_macros=define_macros + parallel_macros, extra_compile_args=parallel_args, extra_link_args=parallel_args) qcprot = MDAExtension('lib.qcprot', ['MDAnalysis/lib/qcprot' + source_suffix], include_dirs=include_dirs, extra_compile_args=["-O3", "-ffast-math"]) transformation = MDAExtension('lib._transformations', ['MDAnalysis/lib/src/transformations/transformations.c'], libraries=['m'], define_macros=define_macros, include_dirs=include_dirs, extra_compile_args=extra_compile_args) libmdaxdr = MDAExtension('lib.formats.libmdaxdr', sources=['MDAnalysis/lib/formats/libmdaxdr' + source_suffix, 'MDAnalysis/lib/formats/src/xdrfile.c', 'MDAnalysis/lib/formats/src/xdrfile_xtc.c', 'MDAnalysis/lib/formats/src/xdrfile_trr.c', 'MDAnalysis/lib/formats/src/trr_seek.c', 'MDAnalysis/lib/formats/src/xtc_seek.c', ], include_dirs=include_dirs + ['MDAnalysis/lib/formats/include', 'MDAnalysis/lib/formats'], define_macros=largefile_macros) util = MDAExtension('lib.formats.cython_util', sources=['MDAnalysis/lib/formats/cython_util' + source_suffix], include_dirs=include_dirs) encore_utils = MDAExtension('analysis.encore.cutils', sources = ['MDAnalysis/analysis/encore/cutils' + source_suffix], include_dirs = include_dirs, extra_compile_args = ["-O3", "-ffast-math"]) ap_clustering = MDAExtension('analysis.encore.clustering.affinityprop', sources = ['MDAnalysis/analysis/encore/clustering/affinityprop' + source_suffix, 'MDAnalysis/analysis/encore/clustering/src/ap.c'], include_dirs = include_dirs+['MDAnalysis/analysis/encore/clustering/include'], libraries=["m"], extra_compile_args=["-O3", "-ffast-math","-std=c99"]) spe_dimred = MDAExtension('analysis.encore.dimensionality_reduction.stochasticproxembed', sources = ['MDAnalysis/analysis/encore/dimensionality_reduction/stochasticproxembed' + source_suffix, 'MDAnalysis/analysis/encore/dimensionality_reduction/src/spe.c'], include_dirs = include_dirs+['MDAnalysis/analysis/encore/dimensionality_reduction/include'], libraries=["m"], extra_compile_args=["-O3", "-ffast-math","-std=c99"]) pre_exts = [dcd, dcd_time, distances, distances_omp, qcprot, transformation, libmdaxdr, util, encore_utils, ap_clustering, spe_dimred] cython_generated = [] if use_cython: extensions = cythonize(pre_exts) for pre_ext, post_ext in zip(pre_exts, extensions): for source in post_ext.sources: if source not in pre_ext.sources: cython_generated.append(source) else: #Let's check early for missing .c files extensions = pre_exts for ext in extensions: for source in ext.sources: if not (os.path.isfile(source) and os.access(source, os.R_OK)): raise IOError("Source file '{}' not found. This might be " "caused by a missing Cython install, or a " "failed/disabled Cython build.".format(source)) return extensions, cython_generated def dynamic_author_list(): """Generate __authors__ from AUTHORS This function generates authors.py that contains the list of the authors from the AUTHORS file. This avoids having that list maintained in several places. Note that AUTHORS is sorted chronologically while we want __authors__ in authors.py to be sorted alphabetically. The authors are written in AUTHORS as bullet points under the "Chronological list of authors" title. """ authors = [] with codecs.open('AUTHORS', encoding='utf-8') as infile: # An author is a bullet point under the title "Chronological list of # authors". We first want move the cursor down to the title of # interest. for line_no, line in enumerate(infile, start=1): if line[:-1] == "Chronological list of authors": break else: # If we did not break, it means we did not find the authors. raise IOError('EOF before the list of authors') # Skip the next line as it is the title underlining line = next(infile) line_no += 1 if line[:4] != '----': raise IOError('Unexpected content on line {0}, ' 'should be a string of "-".'.format(line_no)) # Add each bullet point as an author until the next title underlining for line in infile: if line[:4] in ('----', '====', '~~~~'): # The previous line was a title, hopefully it did not start as # a bullet point so it got ignored. Since we hit a title, we # are done reading the list of authors. break elif line.strip()[:2] == '- ': # This is a bullet point, so it should be an author name. name = line.strip()[2:].strip() authors.append(name) # So far, the list of authors is sorted chronologically. We want it # sorted alphabetically of the last name. authors.sort(key=lambda name: name.split()[-1]) # Move Naveen and Elizabeth first, and Oliver last. authors.remove('Naveen Michaud-Agrawal') authors.remove('Elizabeth J. Denning') authors.remove('Oliver Beckstein') authors = (['Naveen Michaud-Agrawal', 'Elizabeth J. Denning'] + authors + ['Oliver Beckstein']) # Write the authors.py file. out_path = 'MDAnalysis/authors.py' with codecs.open(out_path, 'w', encoding='utf-8') as outfile: # Write the header header = '''\ #-*- coding:utf-8 -*- # This file is generated from the AUTHORS file during the installation process. # Do not edit it as your changes will be overwritten. ''' print(header, file=outfile) # Write the list of authors as a python list template = u'__authors__ = [\n{}\n]' author_string = u',\n'.join(u' u"{}"'.format(name) for name in authors) print(template.format(author_string), file=outfile) if __name__ == '__main__': try: dynamic_author_list() except (OSError, IOError): warnings.warn('Cannot write the list of authors.') with open("SUMMARY.txt") as summary: LONG_DESCRIPTION = summary.read() CLASSIFIERS = [ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License (GPL)', 'Operating System :: POSIX', 'Operating System :: MacOS :: MacOS X', 'Programming Language :: Python', 'Programming Language :: C', 'Topic :: Scientific/Engineering :: Bio-Informatics', 'Topic :: Scientific/Engineering :: Chemistry', 'Topic :: Software Development :: Libraries :: Python Modules', ] config = Config() exts, cythonfiles = extensions(config) setup(name='MDAnalysis', version=RELEASE, description='An object-oriented toolkit to analyze molecular dynamics ' 'trajectories generated by CHARMM, Gromacs, NAMD, LAMMPS, or Amber.', long_description=LONG_DESCRIPTION, author='Naveen Michaud-Agrawal', author_email='naveen.michaudagrawal@gmail.com', maintainer='Richard Gowers', maintainer_email='mdnalysis-discussion@googlegroups.com', url='http://www.mdanalysis.org', download_url='https://github.com/MDAnalysis/mdanalysis/releases', provides=['MDAnalysis'], license='GPL 2', packages=find_packages(), package_dir={'MDAnalysis': 'MDAnalysis'}, ext_package='MDAnalysis', ext_modules=exts, classifiers=CLASSIFIERS, cmdclass=cmdclass, requires=['numpy (>=1.10.4)', 'biopython', 'mmtf (>=1.0.0)', 'networkx (>=1.0)', 'GridDataFormats (>=0.3.2)', 'joblib'], # all standard requirements are available through PyPi and # typically can be installed without difficulties through setuptools setup_requires=[ 'numpy>=1.9.3', ], install_requires=[ 'numpy>=1.10.4', 'biopython>=1.59', 'networkx>=1.0', 'GridDataFormats>=0.3.2', 'six>=1.4.0', 'mmtf-python>=1.0.0', 'joblib', ], # extras can be difficult to install through setuptools and/or # you might prefer to use the version available through your # packaging system extras_require={ 'AMBER': ['netCDF4>=1.0'], # for AMBER netcdf, also needs HDF5 # and netcdf-4 'analysis': [ 'matplotlib>=1.5.1', 'scipy', 'seaborn', # for annotated heat map and nearest neighbor # plotting in PSA 'sklearn', # For clustering and dimensionality reduction # functionality in encore ], }, test_suite="MDAnalysisTests", tests_require=[ 'nose>=1.3.7', 'MDAnalysisTests=={0}'.format(RELEASE), # same as this release! ], zip_safe=False, # as a zipped egg the *.so files are not found (at # least in Ubuntu/Linux) ) # Releases keep their cythonized stuff for shipping. if not config.get('keep_cythonized', default=is_release): for cythonized in cythonfiles: try: os.unlink(cythonized) except OSError as err: print("Warning: failed to delete cythonized file {0}: {1}. " "Moving on.".format(cythonized, err.strerror))
kain88-de/mdanalysis
package/setup.py
Python
gpl-2.0
22,850
[ "Amber", "Biopython", "CHARMM", "Gromacs", "LAMMPS", "MDAnalysis", "NAMD", "NetCDF" ]
cd129645f77b4a8ef5ae58eef59bfe9164f3e45ec59de671c6f227be47021d8f
from functools import wraps import os import random import re import pytest from flask.testing import FlaskClient from flask import session os.environ['PYDRILL_CONFIG'] = os.path.join(os.path.dirname(__file__), 'pydrill.cfg') from pydrill import app, db, redis_store from pydrill import models from pydrill.jinja_env import get_score_text from pydrill.utils import User, TEAM_APPLE, TEAM_HN, TEAM_LINUX EASY_Q = 'average' MEDIUM_Q = 'static-decorator' HARD_Q = 'mro' REFERER = 'HTTP_REFERER' USER_AGENT = 'HTTP_USER_AGENT' MAC_USER_AGENT = ('Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 ' '(KHTML, like Gecko) Chrome/41.0.2227.1 Safari/537.36') LINUX_USER_AGENT = ('Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 ' '(KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36') # TODO: switch to app.test_client when Flask 1.0 is ready def new_test_client(environ_base, *args, **kwargs): """Copy-pasted from Flask.test_client because we need to pass environ_base in .get() and .post() which test_client can do only from version 1.0 which is not production ready yet. """ return Client(environ_base, app, app.response_class, *args, **kwargs) class Client(FlaskClient): def __init__(self, environ_base, *args, **kwargs): self.__environ_base = environ_base super(Client, self).__init__(*args, **kwargs) def get(self, *args, **kwargs): return super(Client, self).get(*args, environ_base=self.__environ_base, **kwargs) def checked_get(self, path): rv = self.get(path) assert rv.status_code == 200 return rv def post(self, *args, **kwargs): return super(Client, self).post(*args, environ_base=self.__environ_base, **kwargs) def checked_post(self, path): rv = self.post(path) assert rv.status_code == 302 return rv def ask_me(self, question_id): path = make_path('ask', question_id) return self.checked_get(path) def ask_me_without_seed(self, question_id): path = make_path_without_seed('ask', question_id) return self.get(path) def explain_to_me(self, question_id): question = models.Question.query.get(question_id) path = make_path('explain', question_id, get_any_answer(question).id) return self.checked_get(path) def answer(self, question_id, is_correct=None): if is_correct is None: is_correct = random_boolean() self.ask_me(question_id) question = models.Question.query.get(question_id) path = make_path('answer', question_id, get_answer(question, is_correct).id) return self.checked_post(path) def answer_correct(self, question_id): return self.answer(question_id, is_correct=True) def answer_wrong(self, question_id): return self.answer(question_id, is_correct=False) def score(self): return self.checked_get('/score/') def make_path(*path_parts): with_seed = path_parts + (random.randint(1, 100),) return make_path_without_seed(*with_seed) def make_path_without_seed(*path_parts): return '/'.join([''] + map(str, path_parts) + ['']) @pytest.fixture(autouse=True) def flush_redis_db(): redis_store.flushdb() # this fixture runs once @pytest.fixture(autouse=True, scope='session') def create_sql_db(): db.drop_all() db.create_all() questions_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'questions') for question in [EASY_Q, MEDIUM_Q, HARD_Q]: models.read_question(os.path.join(questions_dir, question + '.yml')) db.session.commit() @pytest.fixture(autouse=True) def run_app_in_testing_mode(): app.config['TESTING'] = True def client_fixture(fn): """ :param fn: callable returning test client """ @pytest.yield_fixture @wraps(fn) def yielding_fn(): with fn() as client: yield client return yielding_fn @client_fixture def steve(): return new_test_client({USER_AGENT: MAC_USER_AGENT, REFERER: 'parse this'}) @client_fixture def paul(): return new_test_client({USER_AGENT: LINUX_USER_AGENT, REFERER: 'https://news.ycombinator.com/item?id=test'}) @client_fixture def tim(): return new_test_client({USER_AGENT: MAC_USER_AGENT}) def get_user(): return User(**session['user']) def test_user_id(paul): paul.ask_me(EASY_Q) user = get_user() assert len(user.id) == 36 # length of str(uuid4) is 36 paul.ask_me(EASY_Q) # id doesn't change after the first visit assert get_user().id == user.id def test_new_user_score(paul): paul.ask_me(EASY_Q) assert get_user().score == 0 def test_user_teams(paul): paul.ask_me(EASY_Q) assert_same_items(get_user().teams, [TEAM_LINUX, TEAM_HN]) def test_questions(): assert models.Question.query.count() == 3 def test_only_first_answer_can_increase_score(steve): steve.answer_wrong(EASY_Q) steve.answer_correct(EASY_Q) assert_team_score(TEAM_APPLE, score_sum=0) def test_cant_increase_score_twice(steve): steve.answer_correct(EASY_Q) assert_team_score(TEAM_APPLE, score_sum=1) steve.answer_correct(EASY_Q) assert_team_score(TEAM_APPLE, score_sum=1) def matches_any_ask_path(*question_ids): parts = [r'/ask/{}/(\d+)/$'.format(q) for q in question_ids] return '|'.join(parts) def test_answer_redirects(steve): rv = steve.answer(EASY_Q) assert redirects_to_question(rv, MEDIUM_Q) or redirects_to_question(rv, HARD_Q) rv = steve.answer(MEDIUM_Q) assert redirects_to_question(rv, HARD_Q) def redirects_to_question(rv, question_id): regex = matches_any_ask_path(question_id) return re.search(regex, rv.location) def random_boolean(): return random.choice([True, False]) def assert_same_items(xs, ys): assert sorted(xs) == sorted(ys) def get_answer(question, is_correct): return question.answers.filter_by(is_correct=is_correct).first() def get_any_answer(question): answers = list(question.answers) return random.choice(answers) def get_correct_answer(question): return get_answer(question, is_correct=True) def test_ask_without_seed(paul): rv = paul.ask_me_without_seed(EASY_Q) assert rv.status_code == 302 assert redirects_to_question(rv, EASY_Q) def test_team_scores(steve, paul, tim): steve.answer_correct(EASY_Q) assert_team_score(TEAM_APPLE, num_users=1, score_sum=1) tim.answer_wrong(EASY_Q) assert_team_score(TEAM_APPLE, num_users=2, score_sum=1) paul.answer_correct(EASY_Q) assert_team_score(TEAM_APPLE, num_users=2, score_sum=1) # paul is not in Apple team assert_team_score(TEAM_LINUX, num_users=1, score_sum=1) assert_team_score(TEAM_HN, num_users=1, score_sum=1) steve.answer_correct(MEDIUM_Q) assert_team_score(TEAM_APPLE, num_users=2, score_sum=3) def assert_team_score(team, **expected): team_score = redis_store.hgetall('team:{}'.format(team)) score = {k: int(v) for k, v in team_score.viewitems()} for key, value in expected.viewitems(): assert score[key] == value def test_never_ask_the_same_question_twice_in_a_row(steve): # we need to answer every question, because otherwise # steve.answer(EASY_Q) will always redirect to the unanswered question. # We want to test that even if every question is answered, # then we don't ask the same question twice in row anyway. for question in models.Question.query.all(): steve.answer(question.id) rv = steve.answer(EASY_Q) assert not redirects_to_question(rv, EASY_Q) @pytest.mark.parametrize('rank, num_users, expected_text', [ (1, 1, 'top 1%'), (1, 2, 'top 1%'), (2, 2, 'bottom 50%'), (1, 3, 'top 1%'), (2, 3, 'top 50%'), (3, 3, 'bottom 33%'), ]) def test_get_score_text(rank, num_users, expected_text): assert get_score_text(rank, num_users) == expected_text def test_ask_question_rendering(steve): rv = steve.ask_me(EASY_Q) # checking that '... / 2' is highlighted assert '<span class="o">/</span> <span class="mi">2</span>' in rv.data def test_explain_question_rendering(steve): rv = steve.explain_to_me(EASY_Q) assert '__future__' in rv.data # 'from __future__ import division' part def test_score_rendering(steve): steve.answer_correct(EASY_Q) rv = steve.score() assert_has_score(rv, 1) assert '{} is your team'.format(TEAM_APPLE) in rv.data def test_score_top_text(steve, paul): steve.answer_correct(EASY_Q) assert "You're in the top 1%" in steve.score().data paul.answer_correct(MEDIUM_Q) assert "You're in the top 1%" in paul.score().data assert "You're in the bottom 50%" in steve.score().data def test_score_during_ask(steve): rv = steve.ask_me(EASY_Q) assert_has_score(rv, 0) # TODO: don't test markup with string comparisons, use css selectors def assert_has_score(rv, expected_score): assert 'score: <strong>{:d}</strong>'.format(expected_score) in rv.data.lower()
alexandershov/pydrill
tests/test_pydrill.py
Python
mit
9,111
[ "VisIt" ]
ccc4d23540f0c085a11f987b90e8afdb83f98b009dcdb64d2e6c53fb6cdb1ec1
# Please do not change the configuration file quisk_conf_defaults.py. # Instead copy one of the other quisk_conf_*.py files to your own # .quisk_conf.py and make changes there. For a normal sound card # configuration, copy quisk_conf_model.py to your .quisk_conf.py. # # PLEASE DO **NOT** COPY THIS FILE AND USE IT AS A START FOR YOUR CONFIGURATION FILE! # YOUR CONFIGURATION FILE SHOULD ONLY HAVE LINES THAT DIFFER FROM THIS FILE. QUISK # IMPORTS THIS FILE FIRST, AND THEN YOUR CONFIG FILE OVERWRITES A FEW ITEMS SUCH AS # SOUND CARD NAMES. # # Quisk imports quisk_conf_defaults to set its configuration. # If you have a configuration file, it then overwrites the defaults # with your parameters. Your configuration file must be named # ~/.quisk_conf.py, where "~" means your home directory. Or # you may specify a different name with the -c or --config command # line option. Try --help. Check the config screen to make sure that # the correct configuration file is in use. # # The Quisk receiver can use a high quality sound card for capture and playback, # or it can use the SDR-IQ by RfSpace for capture and a lower quality # sound card for playback. Quisk can also be used as a panadapter. # Quisk can control some rigs. See quisk_hardware_*.py. If you have a rig # to control, copy one of the quisk_hardware_*.py files to your own file named # quisk_hardware.py, and edit that file. If there is no quisk_hardware.py, then # quisk_hardware_model.py is used instead. import sys import wx # Import the default Hardware module. You can import a different module in # your .quisk_conf.py. import quisk_hardware_model as quisk_hardware # Module for additional widgets (advanced usage). See n2adr/quisk_widgets.py for an example. # import n2adr.quisk_widgets as quisk_widgets quisk_widgets = None # Select the default screen when Quisk starts: default_screen = 'Graph' #default_screen = 'WFall' #default_screen = 'Config' # The width of the graph data as a fraction of the total screen size. This # will be adjusted by Quisk to accommodate preferred FFT sizes. It can # not be changed once Quisk starts. It can not be made too small because # of the space needed for all the buttons. graph_width = 0.8 # The graph_width parameter controls the width of Quisk unless a larger width is forced. # If the Quisk screen is too wide or the buttons are too crowded, perhaps due to a low screen # resolution, you can reduce the font sizes. Thanks to Christof, DJ4CM. button_font_size = 10 # button_font_size = 9 # button_font_size = 8 default_font_size = 12 status_font_size = 14 config_font_size = 14 graph_font_size = 10 favorites_font_size = 14 # This controls the speed of the graph peak hold. Lower numbers give a longer time constant. graph_peak_hold_1 = 0.25 graph_peak_hold_2 = 0.10 # Select the default mode when Quisk starts (overruled by persistent_state): # default_mode = 'FM' default_mode = 'USB' # Select the way the waterfall screen scrolls: # waterfall_scroll_mode = 0 # scroll at a constant rate. waterfall_scroll_mode = 1 # scroll faster at the top so that a new signal appears sooner. # Select the initial size in pixels (minimum 1) of the graph at the top of the waterfall. waterfall_graph_size = 80 # These are the initial values for the Y-scale and Y-zero sliders for each screen. # The sliders go from zero to 160. graph_y_scale = 100 graph_y_zero = 0 waterfall_y_scale = 80 # Initial value; new values are saved for each band waterfall_y_zero = 40 # Initial value; new values are saved for each band waterfall_graph_y_scale = 100 waterfall_graph_y_zero = 60 scope_y_scale = 80 scope_y_zero = 0 # Currently doesn't do anything filter_y_scale = 90 filter_y_zero = 0 # Quisk can save its current state in a file on exit, and restore it when you restart. # State includes band, frequency and mode, but not every item of state (not screen). # The file is .quisk_init.pkl in the same directory as your config file. If this file # becomes corrupted, just delete it and it will be reconstructed. #persistent_state = False persistent_state = True # The quisk config screen has a "favorites" tab where you can enter the frequencies and modes of # stations. The data is stored in this file; default quisk_favorites.txt in the directory # where your config file is located. favorites_file_path = '' # These control the typeface used in fonts. The objective is to choose an available font that # offers good support for the Unicode characters used on buttons and windows. if sys.platform == "win32": quisk_typeface = 'Lucida Sans Unicode' # Typeface for quisk buttons and text #quisk_typeface = 'Arial Unicode MS' else: quisk_typeface = '' # Use a default typeface # This controls whether the "U" symbols or the "T" symbols are used on buttons and windows. # You can change the "U" and "T" symbols to anything you want; either Unicode or text. use_unicode_symbols = True # Use the symbols starting with "U" #use_unicode_symbols = False # Use the symbols starting with "T" # These are the Unicode symbols used in the station window. Thanks to Christof, DJ4CM. Usym_stat_fav = unichr(0x2605) # Symbol for favorites, a star Usym_stat_mem = unichr(0x24C2) # Symbol for memory stations, an "M" in a circle #Usym_stat_dx = unichr(0x2691) # Symbol for DX Cluster stations, a flag Usym_stat_dx = unichr(0x25B2) # Symbol for DX Cluster stations, a Delta # These are the text symbols used in the station window. Tsym_stat_fav = 'F' Tsym_stat_mem = 'M' Tsym_stat_dx = 'Dx' # These are the Unicode symbols to display on buttons. Thanks to Christof, DJ4CM. Ubtn_text_range_dn = unichr(0x2190) # Down band, left arrow Ubtn_text_range_up = unichr(0x2192) # Up band, right arrow Ubtn_text_play = unichr(0x25BA) # Play button Ubtn_text_rec = unichr(0x25CF) # Record button, a filled dot Ubtn_text_file_rec = "File " + unichr(0x25CF) # Record to file Ubtn_text_fav_add = unichr(0x2605) + unichr(0x2191) # Add to favorites Ubtn_text_fav_recall = unichr(0x2605) + unichr(0x2193) # Jump to favorites screen Ubtn_text_mem_add = unichr(0x24C2) + unichr(0x2191) # Add to memory Ubtn_text_mem_next = unichr(0x24C2) + unichr(0x27B2) # Next memory Ubtn_text_mem_del = unichr(0x24C2) + unichr(0x2613) # Delete from memory # These are the text symbols to display on buttons. Tbtn_text_range_dn = "Dn" Tbtn_text_range_up = "Up" Tbtn_text_play = "Play" Tbtn_text_rec = "Rec" Tbtn_text_file_rec = "File Rec" Tbtn_text_fav_add = ">Fav" Tbtn_text_fav_recall = "Fav" Tbtn_text_mem_add = "Save" Tbtn_text_mem_next = "Next" Tbtn_text_mem_del = "Del" # Station info display configuration, thanks to DJ4CM. This displays a window of station names # below the graph frequency (X axis). station_display_lines = 1 # number of station info display lines below the graph X axis #station_display_lines = 0 #station_display_lines = 3 # DX cluster telent login data, thanks to DJ4CM. Must have station_display_lines > 0. dxClHost = '' # host name for telnet server, null string to disable #dxClHost = 'example.host.net' dxClPort = 7373 # port number for telnet user_call_sign = 'CALL' # your radio station call sign; probably needed for telnet login dxClPassword = None # telnet password dxClExpireTime = 20 # Time in minutes until DX Cluster entries will be removed # This converts from dB to S-units for the S-meter (it is in S-units). correct_smeter = 15.5 # This is the fraction of spectrum to display from zero to one. It is needed if # the passband edges are not valid. Use 0.85 for the SDR-IQ. display_fraction = 1.00 # Quisk has record and playback buttons to save radio sound. If there is no more room for # sound, the old sound is discarded and the most recent sound is retained. This controls # the maximum time of sound storage in minutes for this recorded audio, and also the record # time for the Tx Audio test screen. # # If you want to transmit recorded sound, then mic_sample_rate must equal playback_rate and both must be 48000. max_record_minutes = 1.00 # Quisk can save recorded sound and samples to files. There is a button on the Config/Config screen # to set the file names. You can set the initial names with these variables: file_name_audio = "" #file_name_audio = "/home/jim/tmp/qaudio.wav" file_name_samples = "" #file_name_samples = "C:/tmp/qsamples.wav" # Thanks to Steve Murphy, KB8RWQ for the patch adding additional color control. # Thanks to Christof, DJ4CM for the patch adding additional color control. # Define colors used by all widgets in wxPython colour format: color_bg = 'light steel blue' # Lower screen background color_bg_txt = 'black' # Lower screen text color color_graph = 'lemonchiffon1' # Graph background color_config2 = 'lemonchiffon3' # color in tab row of config screen color_gl = 'grey' # Lines on the graph color_graphticks = 'black' # Graph ticks color_graphline = '#005500' # graph data line color color_graphlabels = '#555555' # graph label color color_btn = 'steelblue2' # button color color_check_btn = 'yellow2' # color of a check button when it is checked color_cycle_btn = 'goldenrod3' # color of a cycle button when it is checked color_adjust_btn = 'orange3' # color of an adjustable button when it is checked color_test = 'hot pink' # color of a button used for test (turn off for tx) color_freq = 'lightcyan1' # background color of frequency and s-meter color_freq_txt = 'black' # text color of frequency display color_entry = color_freq # frequency entry box color_entry_txt = 'black' # text color of entry box color_enable = 'black' # text color for an enabled button color_disable = 'white' # text color for a disabled button color_bandwidth = 'lemonchiffon2' # color for bandwidth display; thanks to WB4JFI #color_bandwidth = 'lemonchiffon3' color_txline = 'red' # vertical line color for tx in graph color_rxline = 'green' # vertical line color for rx in graph color_notebook_txt = 'black' # text of notebook labels # This is a dark color scheme designed by Steve Murphy, KB8RWQ. #color_bg = '#111111' #color_bg_txt = 'white' #color_graph = '#111111' #color_config2 = color_bg #color_gl = '#555555' #color_graphticks = '#DDDDDD' #color_graphline = '#00AA00' #color_graphlabels = '#FFFFFF' #color_btn = '#666666' #color_check_btn = '#996699' #color_cycle_btn = '#666699' #color_adjust_btn = '#669999' #color_test = 'hot pink' #color_freq = '#333333' #color_freq_txt = 'white' #color_entry = color_freq #color_entry_txt = color_freq_txt #color_enable = 'white' #color_disable = 'black' #color_bandwidth = '#333333' #color_txline = 'red' #color_rxline = 'green' #color_notebook_txt = 'white' filter_display = 1 # Display the filter bandwidth on the graph screen; 0 or 1; thanks to WB4JFI # Quisk can operate in Split mode and can receive both the Tx and Rx frequency signals. This option # controls where the sound goes. You may need to try 1 or 2 depending on your wiring. #split_rxtx = 1 # Play both signals in stereo with the higher frequency on the real channel. split_rxtx = 2 # Play both signals in stereo with the lower frequency on the real channel. #split_rxtx = 3 # Play the receive signal on both channels. #split_rxtx = 4 # Play the transmit signal on both channels. # These are the palettes for the waterfall. The one used is named waterfallPallette, # so to use a different one, overwrite this name in your .quisk_conf.py. waterfallPalette = ( ( 0, 0, 0, 0), ( 36, 85, 0, 255), ( 73, 153, 0, 255), (109, 255, 0, 128), (146, 255, 119, 0), (182, 85, 255, 100), (219, 255, 255, 0), (255, 255, 255, 255) ) digipanWaterfallPalette = ( ( 0, 0, 0, 0), ( 32, 0, 0, 62), ( 64, 0, 0, 126), ( 96, 145, 142, 96), (128, 181, 184, 48), (160, 223, 226, 105), (192, 254, 254, 4), (255, 255, 58, 0) ) # On Linux, Quisk can access your sound card through ALSA, PortAudio or PulseAudio. # On Windows, Quisk uses DirectX for sound card access. # In PortAudio, soundcards have an index number 0, 1, 2, ... and a name. # The name can be something like "HDA NVidia: AD198x Analog (hw:0,0)" or # "surround41". In Quisk, all PortAudio device names start with "portaudio". # A device name like "portaudio#6" directly specifies the index. A name like # "portaudio:text" means to search for "text" in all available devices. And # there is a default device "portaudiodefault". So these portaudio names are useful: #name_of_sound_capt = "portaudio:(hw:0,0)" # First sound card #name_of_sound_capt = "portaudio:(hw:1,0)" # Second sound card, etc. #name_of_sound_capt = "portaudio#1" # Directly specified index #name_of_sound_capt = "portaudiodefault" # May give poor performance on capture # In ALSA, soundcards have these names. The "hw" devices are the raw # hardware devices, and should be used for soundcard capture. #name_of_sound_capt = "hw:0" # First sound card #name_of_sound_capt = "hw:1" # Second sound card, etc. #name_of_sound_capt = "plughw" #name_of_sound_capt = "plughw:1" #name_of_sound_capt = "default" # It is usually best to use ALSA names because they provide minimum latency. But # you may need to use PulseAudio to connect to other programs such as wsjt-x. # Pulseaudio support was added by Philip G. Lee. Many thanks! # For PulseAudio devices, use the name "pulse:name" and connect the streams # to your hardware devices using a PulseAudio control program. The name "pulse" # alone refers to the "default" device. The PulseAudio names are quite long; # for example "alsa_output.pci-0000_00_1b.0.analog-stereo". Look on the screen # Config/Sound to see the device names. There is a description, a PulseAudio name, # and for ALSA devices, the ALSA name. An example is: # # CM106 Like Sound Device Analog Stereo # alsa_output.usb-0d8c_USB_Sound_Device-00-Device.analog-stereo # USB Sound Device USB Audio (hw:1,0) # # Instead of the long PulseAudio name, you can enter a substring of any of # these three strings. # # Use the default pulse device for radio sound: #name_of_sound_play = "pulse" # Use a PulseAudio name for radio sound: #name_of_sound_play = "pulse:alsa_output.usb-0d8c_USB_Sound_Device-00-Device.analog-stereo" # Abbreviate the PulseAudio name: #name_of_sound_play = "pulse:alsa_output.usb" # Another abbreviation: #name_of_sound_play = "pulse:CM106" # This controls whether the PulseAudio devices are shown in the device list. show_pulse_audio_devices = True # Normally you would capture and play on the same soundcard to avoid problems with the # two clocks running at slightly different rates. But you can define name_of_sound_play # to play back on a different device. Define this as the empty string "" to turn off # play (for a panadapter). # # For the SDR-IQ the soundcard is not used for capture; it only plays back audio. # Quisk has a custom decimation scheme for each sample rate. The allowable sample rates # are the four SDR-IQ rates plus 24, 48, 96, 192, 240, 384, 480, and 960 ksps. Other rates # can be added. # Configuration for soundcard capture and playback use_sdriq = 0 # Get ADC samples from SDR-IQ is not used use_rx_udp = 0 # Get ADC samples from UDP is not used sample_rate = 48000 # ADC hardware sample rate in Hertz if sys.platform == "win32": name_of_sound_capt = "Primary" else: name_of_sound_capt = "hw:0" # Name of soundcard capture hardware device. name_of_sound_play = name_of_sound_capt # Use the same device for play back #name_of_sound_play = "" # Panadapter: Do not play channel_i = 0 # Soundcard index of in-phase channel: 0, 1, 2, ... channel_q = 1 # Soundcard index of quadrature channel: 0, 1, 2, ... # Thanks to Franco Spinelli for this fix: # The H101 hardware using the PCM2904 chip has a one-sample delay between # channels, which must be fixed in software. If you have this problem, # change channel_delay to either channel_i or channel_q. Use -1 for no delay. channel_delay = -1 # This is for mic playback (SoftRock transmit): tx_channel_delay = -1 # If you use a soundcard with Ethernet control of the VFO, set these parameters: rx_ip = "" # Receiver IP address for VFO control # If you use an SDR-IQ for capture, see the sample config file quisk_conf_sdriq.py. # For the N2ADR 2010 transceiver described in QEX, and for the improved version HiQSDR, # see the sample config file in the hiqsdr package directory, and set these: # tx_level sets the transmit level 0 to 255 for each band. The None band is the default. # The config screen has a slider 0 to 100% so you can reduce the transmit power. The sliders # only appear if your hardware defines the method SetTxLevel(). The hardware only supports a # power adjustment range of 20 dB, so zero is still a small amount of power. tx_level = {None:120, '60':110} # Adjust your power for each band # Digital modes reduce power by the percentage on the config screen. # The maximum value of the slider is digital_tx_level. digital_tx_level = 20 # The slider on the config screen is 20% maximum. # # If you use the HiQSDR hardware, set these: # The HiQSDR_BandDict sets the preselect (4 bits) on the X1 connector. HiQSDR_BandDict = {'160':1, '80':2, '40':3, '30':4, '20':5, '15':6, '17':7, '12':8, '10':9, '6':10, '500k':11, '137k':12 } # For the original N2ADR hardware set this: # use_rx_udp = 1 # For the newer HiQSDR hardware set this: # use_rx_udp = 2 # For FPGA firmware version 1.4 and newer, the hardware is set to the rx_udp_ip you enter here. # For older firmware, the IP address is programmed into the FPGA, and you must enter that address as rx_udp_ip. rx_udp_ip = "192.168.2.196" # Sample source IP address rx_udp_ip_netmask = '255.255.255.0' # The netmask for the network of rx_udp_ip rx_udp_port = 0xBC77 # Sample source UDP port rx_udp_clock = 122880000 # ADC sample rate in Hertz sndp_active = True # Enable setting the hardware IP to rx_udp_ip # Vendor and product ID's for the SoftRock usb_vendor_id = 0x16c0 usb_product_id = 0x05dc # I2C-address of the SI570 in the softrock; Thanks to Joachim Schneider, DB6QS si570_i2c_address = 0x55 #si570_i2c_address = 0x70 # Thanks to Ethan Blanton, KB8OJH, for this patch for the Si570 (many SoftRock's): # If you are using a DG8SAQ interface to set a Si570 clock directly, set # this to True. Complex controllers which have their own internal # crystal calibration do not require this. si570_direct_control = False # This is the Si570 startup frequency in Hz. 114.285MHz is the typical # value from the data sheet; you can use 'usbsoftrock calibrate' to find # the value for your device. si570_xtal_freq = 114285000 # This is the received radio sound playback rate. The default will # be 48 kHz for the SDR-IQ and UDP port samples, and sample_rate for sound # card capture. Set it yourself for other rates or hardware. # The playback_rate must be 24000, 48000, 96000 or 192000. # The preferred rate is 48000 for use with digital modes and transmit of recorded audio. # playback_rate = 48000 # If you use quisk_hardware_fixed.py, this is the fixed VFO frequency in Hertz fixed_vfo_freq = 7056000 # Softrock hardware must be polled to get the key up/down state when using CW mode. # This is the time between polls in milliseconds. Use zero to turn off the poll if you # only use SSB, or if your SoftRock does not have a key jack and USB control. key_poll_msec = 0 #key_poll_msec = 5 # Softrock transmit hardware uses semi break-in for CW operation. This is the time in # seconds before changing back to receive. key_hang_time = 0.7 # This determines what happens when you tune by dragging the mouse. The correct # choice depends on how your hardware performs tuning. You may want to use a # custom hardware file with a custom ChangeFrequency() method too. mouse_tune_method = 0 # The Quisk tune frequency changes and the VFO frequency is unchanged. #mouse_tune_method = 1 # The Quisk tune frequency is unchanged and the VFO changes. # configurable mouse wheel thanks to DG7MGY mouse_wheelmod = 50 # Round frequency when using mouse wheel (50 Hz) # If freq_spacing is not zero, frequencies are rounded to the freq_base plus the # freq_spacing; frequency = freq_base + N * freq_spacing. This is useful at # VHF and higher when Quisk is used with a transverter. freq_spacing = 0 freq_base = 0 # This is the CW tone frequency in Hertz cwTone = 600 # These options are used by the digital modes that send audio to an external # program, and receive audio to transmit. Set Fldigi to USB, XML-RPC control. digital_xmlrpc_url = "http://localhost:7362" # URL for control by XML-RPC #digital_xmlrpc_url = "" # Do not poll socket for XML-RPC control # Input audio from an external program for use with digital modes. The input must be # stereo at 48000 sps, and you must set mic_sample_rate to 48000 also. digital_input_name = "" # device name for transmit audio # digital_input_name = 'hw:Loopback,0' # Output audio to an external program for use with digital modes. The output is # stereo at the same sample rate as the radio sound playback. digital_output_name = "" # device name for received audio # digital_output_name = digital_input_name digital_output_level = 0.7 # This is the volume control 0.0 to 1.0 for digital playback to fldigi, etc. # This option sends the raw I/Q samples to another program using a loopback device (Linux) or # a Virtual Audio Cable (Windows). The sample rate is the same as the hardware sample rate. # Read the samples from the loopback device with another program. sample_playback_name = "" #sample_playback_name = "hw:Loopback,0" # You can control Quisk from Hamlib. Set the Hamlib rig to 2 and the device for rig 2 to # localhost:4575, or other hamlib_port as used by Quisk. hamlib_port = 4575 # Standard port for Quisk control. Set the port in Hamlib to 4575 too. #hamlib_port = 4532 # Default port for rig 2. Use this if you can not set the Hamlib port. #hamlib_port = 0 # Turn off Hamlib control. # If you use the microphone feature, the mic_channel_I and Q are the two capture # microphone channels. Quisk uses a monophonic mic, so audio is taken from the I # channel, and the Q channel is (currently) ignored. It is OK to set the same # channel number for both, and this is necessary for a USB mono mic. The mic sample rate # should be 48000 to enable digital modes and the sound recorder to work, but 8000 can be used. # Mic samples can be sent to an Ethernet device (use tx_ip and name_of_mic_play = "") # or to a sound card (use name_of_mic_play="hw:1" or other device). # # If mic samples are sent to a sound card for Tx, the samples are tuned to the audio # transmit frequency, and are set to zero unless the key is down. You must set both # microphone_name and name_of_mic_play even for CW. For softrock hardware, you usually # capture radio samples and play Tx audio on one soundcard; and capture the mic and play radio # sound on the other sound card at 48000 sps. For example: # name_of_sound_capt = "hw:0" # high quality sound card at 48, 96, or 192 ksps # name_of_sound_play = "hw:1" # lower quality sound card at 48 ksps # microphone_name = name_of_sound_play # name_of_mic_play = name_of_sound_capt # Microphone capture: microphone_name = "" # Name of microphone capture device (or "hw:1") mic_sample_rate = 48000 # Microphone capture sample rate in Hertz, should be 48000, can be 8000 mic_channel_I = 0 # Soundcard index of mic capture audio channel mic_channel_Q = 0 # Soundcard index of ignored capture channel # Microphone samples sent to soundcard: name_of_mic_play = "" # Name of play device if mic I/Q is sent to a sound card mic_playback_rate = 48000 # Playback rate must be a multiple 1, 2, ... of mic_sample_rate mic_play_chan_I = 0 # Soundcard index of mic I play channel mic_play_chan_Q = 1 # Soundcard index of mic Q play channel mic_out_volume = 0.7 # Microphone output volume (after all processing) as a fraction 0.0 to 0.7 # Microphone samples sent to UDP: tx_ip = "" # Transmit IP address for mic sent to UDP (or "192.168.2.195") tx_audio_port = 0 # UDP port for mic samples (or 0x553B) # These microphone parameters are obsolete, and have no effect. Use the Config/Tx Audio screen instead. # mic_clip, mic_preemphasis, mic_avg_gain, mic_max_gain # If your mixing scheme inverts the RF spectrum, set this option to un-invert it invertSpectrum = 0 # This is a list of mixer settings. It only works for Linux; it has no effect in Windows. # Use "amixer -c 1 contents" to get a list of mixer controls and their numid's for # card 1 (or "-c 0" for card 0). Then make a list of (device_name, numid, value) # for each control you need to set. For a decimal fraction, use a Python float; for example, # use "1.0", not the integer "1". #mixer_settings = [ # ("hw:1", 2, 0.80), # numid of microphone volume control, volume 0.0 to 1.0; # ("hw:1", 1, 1) # numid of capture on/off control, turn on with 1; # ] # If you want Quisk to add a button to generate a 2-tone IMD test signal, # set this to 1. This feature requires the microphone to work. add_imd_button = 0 # If you want Quisk to add a full duplex button (transmit and receive at the # same time), set this to 1. add_fdx_button = 0 # If you want to write your own I/Q filter and demodulation module, set # this to the name of the button to add, and change extdemod.c. # add_extern_demod = "WFM" add_extern_demod = "" # These are the suppressed carrier frequencies for 60 meters freq60 = (5330500, 5346500, 5357000, 5371500, 5403500) # These are the filter bandwidths for each mode. Quisk has built-in optimized filters # for these values, but you can change them if you want. FilterBwCW = (200, 400, 600, 1000, 1500, 3000) FilterBwSSB = (2000, 2200, 2500, 2800, 3000, 3300) FilterBwAM = (4000, 5000, 6000, 8000, 10000, 9000) FilterBwFM = (8000, 10000, 12000, 15000, 17000, 20000) FilterBwIMD = FilterBwSSB FilterBwDGT= (1600, 3200, 4800, 10000, 20000, 20000) FilterBwEXT = (8000, 10000, 12000, 15000, 17000, 20000) # This is the data used to draw colored lines on the frequency X axis to # indicate CW and Phone sub-bands. You can make it anything you want. # These are the colors used for sub-bands: CW = '#FF4444' # General class CW eCW = '#FF8888' # Extra class CW Phone = '#4444FF' # General class phone ePhone = '#8888FF' # Extra class phone # ARRL band plan special frequencies Data = '#FF9900' DxData = '#CC6600' RTTY = '#FF9900' SSTV = '#FFFF00' AM = '#00FF00' Packet = '#00FFFF' Beacons = '#66FF66' Satellite = '#22AA88' Repeater = '#AA00FF' # Repeater outputs RepInput = '#AA88FF' # Repeater inputs Simplex = '#00FF44' Special = 'hot pink' Other = '#888888' # Colors start at the indicated frequency and continue until the # next frequency. The special color "None" turns off color. # # To change BandPlan in your config file, first remove any frequencies in the range # you want to change; then add your frequencies; and then sort the list. Or you could just # replace the whole list. BandPlan = [ # Test display of colors #[ 0, CW], [ 50000, eCW], [ 100000, Phone], [ 150000, ePhone], [ 200000, Data], [ 250000, DxData], [ 300000, RTTY], [ 350000, SSTV], #[ 400000, AM], [ 450000, Packet], [ 500000, Beacons], [ 550000, Satellite], [ 600000, Repeater], [ 650000, RepInput], [ 700000, Simplex], #[ 750000, Other], [ 800000, Special], [ 850000, None], # 160 meters [ 1800000, Data], [ 1809000, Other], [ 1811000, CW], [ 1843000, Phone], [ 1908000, Other], [ 1912000, Phone], [ 1995000, Other], [ 2000000, None], # 80 meters [ 3500000, eCW], [ 3525000, CW], [ 3570000, Data], [ 3589000, DxData], [ 3591000, Data], [ 3600000, ePhone], [ 3790000, Other], [ 3800000, Phone], [ 3844000, SSTV], [ 3846000, Phone], [ 3880000, AM], [ 3890000, Phone], [ 4000000, None], # 60 meters [ freq60[0], Phone], [ freq60[0] + 2800, None], [ freq60[1], Phone], [ freq60[1] + 2800, None], [ freq60[2], Phone], [ freq60[2] + 2800, None], [ freq60[3], Phone], [ freq60[3] + 2800, None], [ freq60[4], Phone], [ freq60[4] + 2800, None], # 40 meters [ 7000000, eCW], [ 7025000, CW], [ 7039000, DxData], [ 7041000, CW], [ 7080000, Data], [ 7125000, ePhone], [ 7170000, SSTV], [ 7172000, ePhone], [ 7175000, Phone], [ 7285000, AM], [ 7295000, Phone], [ 7300000, None], # 30 meters [10100000, CW], [10130000, RTTY], [10140000, Packet], [10150000, None], # 20 meters [14000000, eCW], [14025000, CW], [14070000, RTTY], [14095000, Packet], [14099500, Other], [14100500, Packet], [14112000, CW], [14150000, ePhone], [14225000, Phone], [14229000, SSTV], [14231000, Phone], [14281000, AM], [14291000, Phone], [14350000, None], # 17 meters [18068000, CW], [18100000, RTTY], [18105000, Packet], [18110000, Phone], [18168000, None], # 15 meters [21000000, eCW], [21025000, CW], [21070000, RTTY], [21110000, CW], [21200000, ePhone], [21275000, Phone], [21339000, SSTV], [21341000, Phone], [21450000, None], # 12 meters [24890000, CW], [24920000, RTTY], [24925000, Packet], [24930000, Phone], [24990000, None], # 10 meters [28000000, CW], [28070000, RTTY], [28150000, CW], [28200000, Beacons], [28300000, Phone], [28679000, SSTV], [28681000, Phone], [29000000, AM], [29200000, Phone], [29300000, Satellite], [29520000, Repeater], [29590000, Simplex], [29610000, Repeater], [29700000, None], # 6 meters [50000000, Beacons], [50100000, Phone], [54000000, None], # 2 meters [144000000, CW], [144200000, Phone], [144275000, Beacons], [144300000, Satellite], [144380000, Special], [144400000, Satellite], [144500000, RepInput], [144900000, Other], [145100000, Repeater], [145500000, Other], [145800000, Satellite], [146010000, RepInput], [146400000, Simplex], [146510000, Special], # Simplex calling frequency [146530000, Simplex], [146610000, Repeater], [147420000, Simplex], [147600000, RepInput], [148000000, None], # 1.25 meters [222000000, Phone], [222250000, RepInput], [223400000, Simplex], [223520000, Data], [223640000, Repeater], [225000000, None], #70 centimeters [420000000, SSTV], [432000000, Satellite], [432070000, Phone], [432300000, Beacons], [432400000, Phone], [433000000, Repeater], [435000000, Satellite], [438000000, Repeater], [445900000, Simplex], [445990000, Special], # Simplex calling frequency [446010000, Simplex], [446100000, Repeater], [450000000, None], # 33 centimeters [902000000, Other], [928000000, None], # 23 centimeters [1240000000, Other], [1300000000, None], ] # For each band, this dictionary gives the lower and upper band edges. Frequencies # outside these limits will not be remembered as the last frequency in the band. BandEdge = { '160':( 1800000, 2000000), '80' :( 3500000, 4000000), '60' :( 5300000, 5430000), '40' :( 7000000, 7300000), '30' :(10100000, 10150000), '20' :(14000000, 14350000), '17' :(18068000, 18168000), '15' :(21000000, 21450000), '12' :(24890000, 24990000), '10' :(28000000, 29700000), '6' :( 50000000, 54000000), '2' :( 144000000, 148000000), '1.25' :( 222000000, 225000000), '70cm' :( 420000000, 450000000), '33cm' :( 902000000, 928000000), '23cm' :(1240000000, 1300000000), } # For each band, this dictionary gives the initial center frequency, tuning # frequency as an offset from the center frequency, and the mode. This is # no longer too useful because the persistent_state feature saves and then # overwrites these values anyway. bandState = {'Audio':(0, 0, 'LSB'), '160':( 1890000, -10000, 'LSB'), '80' :( 3660000, -10000, 'LSB'), '60' :( 5370000, 1500, 'USB'), '40' :( 7180000, -5000, 'LSB'), '30':(10120000, -10000, 'CWL'), 'Time':( 5000000, 0, 'AM')} for band, (f1, f2) in BandEdge.items(): if f1 > 13500000: f = (f1 + f2) / 2 f = (f + 5000) / 10000 f *= 10000 bandState[band] = (f, 10000, 'USB') # For the Time band, this is the center frequency, tuning frequency and mode: bandTime = [ ( 2500000-10000, 10000, 'AM'), ( 3330000-10000, 10000, 'AM'), ( 5000000-10000, 10000, 'AM'), ( 7335000-10000, 10000, 'AM'), (10000000-10000, 10000, 'AM'), (14670000-10000, 10000, 'AM'), (15000000-10000, 10000, 'AM'), (20000000-10000, 10000, 'AM'), ] # This is the list of band buttons that Quisk displays, and it should have # a length of 14 or less. Empty buttons can have a null string "" label. # Note that the 60 meter band and the Time band have buttons that support # multiple presses. bandLabels = ['Audio', '160', '80', ('60',) * 5, '40', '30', '20', '17', '15', '12', '10', ('Time',) * len(bandTime)] # If you use a transverter, you need to tune your hardware to a frequency lower than # the frequency displayed by Quisk. For example, if you have a 2 meter transverter, # you may need to tune your hardware from 28 to 30 MHz to receive 144 to 146 MHz. # Enter the transverter offset in Hertz in this dictionary. For this to work, your # hardware must support it. Currently, the HiQSDR, SDR-IQ and SoftRock are supported. bandTransverterOffset = { # '2': 144000000 - 28000000 } # If you get your I/Q samples from a sound card, you will need to correct the # amplitude and phase for inaccuracies in the analog hardware. The data is # entered using the controls from the "Rx Phase" button on the config screen. # The corrections are saved by the persistent_state feature. # # The available range of the amplitude and phase controls for receive: rx_max_amplitude_correct = 0.2 # Correction relative to 1.000000 (ideally 0.0000) rx_max_phase_correct = 10.0 # Correction in degrees of phase (ideally 0.0000) # # The bandAmplPhase dictionary gives the amplitude and phase corrections for # sound card data. The format is a dictionary with key "band", giving a dictionary # with key "rx" or "tx", giving a list of tuples (VFO, tune, amplitude, phase). # # If you use Quisk as a panadapter, the corrections will not depend on the band. # In that case create a band "panadapter" in your config file, and all corrections # will be read/written to that band. bandAmplPhase = {} # Empty dictionary to start #bandAmplPhase = {'panadapter':{}} # Create "panadapter" band for all corrections # The program polls the soundcard or SDR-IQ for data every data_poll_usec microseconds. # A lower time reduces latency; a higher time is less taxing on the hardware. if sys.platform == "win32": data_poll_usec = 20000 # poll time in microseconds else: data_poll_usec = 5000 # poll time in microseconds # The fft_size is the width of the data on the screen (about 800 to # 1200 pixels) times the fft_size_multiplier. Multiple FFTs are averaged # together to achieve your graph refresh rate. If fft_size_multiplier is # too small you will get many fft errors. You can specify fft_size_multiplier, # or enter a large number (use 9999) to maximize it, or enter zero to let # quisk calculate it for you. Look for fft_size_multiplier in quisk.py. # Your fft_size_multiplier should have many small factors. Avoid 7 and 13, and # use 8 or 12 instead. # # If your hardware can change the decimation, there are further compilcations. # The FFT size is fixed, and only the average count can change to adjust the # refresh rate. fft_size_multiplier = 0 # The graph_refresh is the frequency at which the graph is updated, # and should be about 5 to 10 Hertz. Higher rates require more processor power. graph_refresh = 7 # update the graph at this rate in Hertz # latency_millisecs determines how many samples are in the soundcard play buffer. # A larger number makes it less likely that you will run out of samples to play, # but increases latency. It is OK to suffer a certain number of play buffer # underruns in order to get lower latency. latency_millisecs = 150 # latency time in milliseconds # Select the method to test the state of the key; see is_key_down.c key_method = "" # No keying, or internal method # key_method = "/dev/parport0" # Use the named parallel port # key_method = "/dev/ttyS0" # Use the named serial port # key_method = "192.168.1.44" # Use UDP from this address # If your hardware file defines the method OnButtonPTT(self, event), then Quisk will # display a PTT button you can press. The method must switch your hardware to # transmit somehow, for example, by setting a serial port pin to high. # # You can define two hot keys that when pressed simultaneously, will push the PTT button. # If you want only one hot key, set hot_key_ptt2 to None. Use any of the wx.WXK_* key codes, # or the ord() of the letter. Do not choose hot keys that interfere with other features # on your system; for example, system menus or the frequency entry box. hot_key_ptt1 = None hot_key_ptt2 = None # hot_key_ptt1 = wx.WXK_CONTROL # hot_key_ptt1 = wx.WXK_SHIFT # hot_key_ptt1 = wx.WXK_ALT # hot_key_ptt1 = wx.WXK_F5 # hot_key_ptt2 = ord(' ') # hot_key_ptt2 = ord('A') # If you want Quisk to generate a sidetone, include self.use_sidetone = 1 # in the __init__ method of your hardware file. # # If you are using keying, key-down throws away the current capture buffer # and starts a sidetone with a rise time of 5 milliseconds. For # key-up, the sidetone is ended with a fall time of 5 milliseconds, then # a silent period starts, then normal audio starts with a rise time of # 5 milliseconds. The length of the silent period is given by keyupDelay, # but will be at least the time necessary to collect enough samples to # refill the filters. A larger keyupDelay may be needed to accomodate # antenna switching or other requirement of your hardware. keyupDelay = 23 # extra milliseconds silence on key up # For FM transmit, this is the modulation index. modulation_index = 1.67 # These are the tuning parameters for the AGC. There is a button to turn AGC on or off, # but AGC still limits the peak amplitude to avoid clipping even if it is off. # Right click the AGC button to show the adjustment slider. If the slider is at maximum, # all signals will have the same (maximum) amplitude. For lower values, weak signals # will be somewhat less loud than strong signals; that is, some variation in signal # amplitude remains. # The AGC parameters are not used for FM, and the AGC button becomes a squelch button # with a squelch slider control. # This controls the maximum AGC gain and thus the scale of the AGC slider control. If # it is too high, all signals reach the same amplitude at much less than 100% slider. # If it is too low, then all signals fail to have the same amplitude even at 100%. But # the value is not critical, because you can adjust the slider a bit more. agc_max_gain = 15000.0 # agc_off_gain is obsolete and has no effect. Turn off AGC and adjust the slider instead. # This is the AGC release time in seconds. It must be greater than zero. It is the time # constant for gain recovery after a strong signal disappears. agc_release_time = 1.0
shenki/quisk
quisk_conf_defaults.py
Python
gpl-2.0
39,603
[ "CRYSTAL" ]
3eb5dba55135fd65e5efd66f3db66e2684aa347774a412d80f5c31a8bdc62021
# -*- coding: utf-8 -*- import numpy as np from numpy.linalg import matrix_rank from joblib import Parallel, delayed from scipy.stats import multivariate_normal as _N from scipy.optimize import minimize from fitr.stats import lme from fitr.stats import bic from fitr.inference import OptimizationResult def l_bfgs_b(f, i, data, nparams, jac, minstarts=2, maxstarts=10, maxstarts_without_improvement=3, init_sd=2): """ Minimizes the negative log-probability of data with respect to some parameters under function `f` using the L-BFGS-B algorithm. This function is specified for use with parallel CPU resources. Arguments: f: (Negative!) Log likelihood function i: `int`. Subject being optimized (slices first dimension of `data`) data: Object subscriptable along first dimension to indicate subject being optimized nparams: `int`. Number of parameters in the model jac: `bool`. Set to `True` if `f` returns a Jacobian as the second element of the returned values minstarts: `int`. Minimum number of restarts with new initial values maxstarts: `int`. Maximum number of restarts with new initial values maxstarts_without_improvement: `int`. Maximum number of restarts without improvement in objective function value init_sd: Standard deviation for Gaussian initial values Returns: i: `int`. Subject being optimized (slices first dimension of `data`) xmin: `ndarray((nparams,))`. Parameter values at optimum fmin: Scalar objective function value at optimum fevals: `int`. Number of function evaluations niters: `int`. Number of iterations lme_: Scalar log-model evidence at optimum bic_: Scalar Bayesian Information Criterion at optimum hess_inv: `ndarray((nparams, nparams))`. Inv at optimum """ nlog_prob = lambda x: f(x, data[i]) fmin = np.inf fevals = 0 niters = 0 nstarts = 0 nstarts_without_improvement = 0 done = False succeeded = False while not done: xinit = np.random.normal(0, init_sd, size=nparams) res = minimize(nlog_prob, xinit, jac=jac, method='L-BFGS-B') nstarts += 1 fevals += res.nfev niters += res.nit # Convergence test if nstarts < maxstarts: if res.success is True and res.fun < fmin: fmin = -res.fun xmin = res.x hess_inv = res.hess_inv.todense() lme_ = lme(fmin, nparams, hess_inv) bic_ = bic(fmin, nparams, data[i].shape[1]) succeeded = True if res.fun >= fmin: nstarts_without_improvement += 1 if nstarts_without_improvement >= maxstarts_without_improvement: done = True print('Subject %s Fit | %s Starts | Fevals %s | lp_= %s' %(i, nstarts, fevals, fmin)) else: done = True print('Subject %s Fit | %s Starts | Fevals %s | lp_= %s' %(i, nstarts, fevals, fmin)) if succeeded is False: print('Subject %s failed to converge after %s iterations (%s fx evals)' %(i, niters, fevals)) fmin = np.nan xmin = np.array([np.nan]*xinit.size) hess_inv = np.array([[np.nan]*xinit.size]*xinit.size) lme_ = np.nan bic_ = np.nan succeeded = False return i, xmin, fmin, fevals, niters, lme_, bic_, hess_inv def second_order_optimizer(f, i, data, nparams, jac, hess, minstarts=2, maxstarts=10, maxstarts_without_improvement=3, init_sd=2, method='trust-exact'): """ Minimizes the negative log-probability of data with respect to some parameters under function `f` using the exact . This function is specified for use with parallel CPU resources. Arguments: f: (Negative!) Log likelihood function. i: `int`. Subject being optimized (slices first dimension of `data`) data: Object subscriptable along first dimension to indicate subject being optimized nparams: `int`. Number of parameters in the model jac: `bool`. Set to `True` if `f` returns a Jacobian as the second element of the returned values hess: `bool`. Set to `True` if third output value of `f` is the Hessian matrix minstarts: `int`. Minimum number of restarts with new initial values maxstarts: `int`. Maximum number of restarts with new initial values maxstarts_without_improvement: `int`. Maximum number of restarts without improvement in objective function value init_sd: Standard deviation for Gaussian initial values Returns: i: `int`. Subject being optimized (slices first dimension of `data`) xmin: `ndarray((nparams,))`. Parameter values at optimum fmin: Scalar objective function value at optimum fevals: `int`. Number of function evaluations niters: `int`. Number of iterations lme_: Scalar log-model evidence at optimum bic_: Scalar Bayesian Information Criterion at optimum hess: `ndarray((nparams, nparams))`. Inv at optimum """ nlog_prob = lambda x: f(x, data[i])[:-1] hessian = lambda x: f(x, data[i])[-1] fmin = np.inf fevals = 0 niters = 0 nstarts = 0 nstarts_without_improvement = 0 done = False succeeded = False while not done: xinit = None lbest = np.inf for _ in range(15): xtest = np.random.normal(0, init_sd, size=nparams) ll, _ = nlog_prob(xtest) if ll <= lbest: lbest = ll xinit = xtest res = minimize(nlog_prob, xinit, jac=jac, hess=hessian, method=method) nstarts += 1 fevals += res.nfev niters += res.nit # Convergence test if nstarts < maxstarts: if res.success is True and res.fun < fmin: fmin = -res.fun xmin = res.x hess_inv = np.linalg.pinv(res.hess) lme_ = lme(fmin, nparams, hess_inv) bic_ = bic(fmin, nparams, data[i].shape[1]) succeeded = True if res.fun >= fmin: nstarts_without_improvement += 1 if nstarts_without_improvement >= maxstarts_without_improvement: done = True print('Subject %s Fit | %s Starts | Fevals %s | lp_= %s' %(i, nstarts, fevals, fmin)) else: done = True print('Subject %s Fit | %s Starts | Fevals %s | lp_= %s' %(i, nstarts, fevals, fmin)) if succeeded is False: print('Subject %s failed to converge after %s iterations (%s fx evals)' %(i, niters, fevals)) fmin = np.nan xmin = np.array([np.nan]*xinit.size) hess_inv = np.array([[np.nan]*xinit.size]*xinit.size) lme_ = np.nan bic_ = np.nan succeeded = False return i, xmin, fmin, fevals, niters, lme_, bic_, hess_inv def mlepar(f, data, nparams, minstarts=2, maxstarts=10, maxstarts_without_improvement=3, init_sd=2, njobs=-1, jac=None, hess=None, method='L-BFGS-B'): """ Computes maximum likelihood estimates using parallel CPU resources. Wraps over the `fitr.optimization.mle_parallel.mle` function. Arguments: f: Likelihood function data: A subscriptable object whose first dimension indexes subjects optimizer: Optimization function (currently only `l_bfgs_b` supported) nparams: `int` number of parameters to be estimated minstarts: `int`. Minimum number of restarts with new initial values maxstarts: `int`. Maximum number of restarts with new initial values maxstarts_without_improvement: `int`. Maximum number of restarts without improvement in objective function value init_sd: Standard deviation for Gaussian initial values jac: `bool`. Set to `True` if `f` returns a Jacobian as the second element of the returned values hess: `bool`. Set to `True` if third output value of `f` is the Hessian matrix method: `str`. One of the `scipy.optimize` methods. Returns: `fitr.inference.OptimizationResult` Todo: - [ ] Raise errors when user selects inappropriate optimization function given values for `jac` and `hess` """ nsubjects = len(data) if method == 'L-BFGS-B': plist = [[f, i, data, nparams, jac, minstarts, maxstarts, maxstarts_without_improvement, init_sd] for i in range(nsubjects)] y = Parallel(n_jobs=njobs)(delayed(l_bfgs_b)(z[0],z[1],z[2],z[3],z[4],z[5],z[6],z[7], z[8]) for z in plist) elif method in ['trust-exact', 'trust-ncg', 'trust-krylov', 'dogleg']: plist = [[f, i, data, nparams, jac, hess, minstarts, maxstarts, maxstarts_without_improvement, init_sd, method] for i in range(nsubjects)] y = Parallel(n_jobs=njobs)(delayed(second_order_optimizer)(z[0],z[1],z[2],z[3],z[4],z[5],z[6],z[7],z[8],z[9],z[10]) for z in plist) res = OptimizationResult(nsubjects, nparams) for i, item in enumerate(y): sid = item[0] res.subject_id[sid] = sid res.xmin[sid,:]= item[1] res.fmin[sid]=item[2] res.fevals[sid] = item[3] res.niters[sid] = item[4] res.lme[sid]=item[5] res.bic[sid] = item[6] res.err[sid,:]=np.sqrt(np.diag(item[7])) res.hess_inv[sid,:,:]=item[7] return res
ComputationalPsychiatry/fitr
fitr/inference/mle_parallel.py
Python
gpl-3.0
10,027
[ "Gaussian" ]
5bfcc9601f60a22cabe857c555b20faf90651c191e102d68e2581c9546b4ae27
# coding: utf-8 # # Pollen # This is the time of year when even thinking about flowers makes my eyes water. # While it doesn't completely stop me from enjoying the outdoors, I find forecasts useful for planning and setting expectations. # # There are pollen tracking services that I find useful, but have shortcomings in their coverage in time or granularity. Some don't measure pollen on the weekends, when I'm most likely to be out, and some just give an aggregated count, ignoring the more relevant tree pollen count I'm interested in. This gap left me wondering how far I could get predicting pollen levels from the weather on my own, and looked like an opportunity to apply my recent interest in deep learning models, and RNNs in particular. # In[ ]: from imports import * # %mkdir cache import joblib; mem = joblib.Memory(cachedir='cache') get_ipython().magic('matplotlib inline') # In[ ]: from util.pollen_utils import pscale import util.utils; reload(util.utils); from util.utils import ( check_one2one, yrmths, flatten_multindex, ends_with, BatchArray, ravel, repackage_hidden, mse, replace_with_dummies, filter_dtypes, log_, join_pollen_weather, read ) date = lambda xs: dt.datetime(*xs) # ## The training data # For the forecasting model to be useful for me, it needs to be trained on data that will be available when I need to make prediction. # # If I want to know tomorrow's pollen levels and I have a summary of today's weather available, then it will be enough to train the model on pollen counts using weather data from the previous day. The problem is simpler if I simply want an estimate of today's count based on today's weather (say it's the weekend, when the regular source is unavailable), in which case the input and output of the training data are aligned to the same day. Another variation would be to train on historical _forecasts_ of the data, since this would give longer range estimates and would allow for more flexibility in use cases. # # For this first iteration of the model I decided to train on daily weather summaries from Dark Sky as the inputs, and same-day pollen counts as the output, due to the ease in accessing the data. This is suboptimal for many use cases, but can at least be a start for a ballpark estimate of expected model performance. (The code for pulling this data is in the data-fetcher.ipynb notebook) # # In addition to the weather data, I used different date fields (for example, month number and day of the year) and the previous day's pollen count, which turns out to be super correlated with today's pollen count (almost 90%). # In[ ]: dailydf = feather.read_dataframe('cache/dark_day.fth') dailydf = ( dailydf.sort_values('Time', ascending=True).reset_index(drop=1) .assign(Dt=lambda x: pd.to_datetime(x.Time, unit='s')) .assign( Day=lambda x: x.Dt.dt.day, Doy=lambda x: x.Dt.dt.dayofyear, M=lambda x: x.Dt.dt.month, Y=lambda x: x.Dt.dt.year, Day_int=lambda x: (x['Dt'] - x['Dt'].min()).dt.days, ) .drop('Ozone', axis=1) # This is a new field, I guess ) # Here's a sample of the daily weather data: # In[ ]: dailydf[:3] # # Nulls # I've yet to find perfectly clean dataset that's ready to use out of the box, and this is no exception. While better than some weather data sources I tried, it still has some columns with nulls that we'll have to deal with. Some are straightforward, like `Precip_type` and `Precip_accumulation`: # In[ ]: dailydf.loc[dailydf.eval('Precip_type != Precip_type'), 'Precip_type'] = 'none' dailydf['Precip_accumulation'] = dailydf.Precip_accumulation.fillna(0) # For the field that records the time of day with the maximum precipitation, I just filled in the missing values (for days when it didn't rain) with the minimum time of the day: # # In[ ]: def fill_pimt_null(s, timecol): """This column is null when there is no precipitation. Not sure of anything better to do, so I'm just setting it to the minimum time of the day in question """ s2 = s.copy() null_ptime = s.isnull() s2.loc[null_ptime] = timecol[null_ptime] return s2.astype(int) dailydf['Min_time'] = dailydf.Dt.map(lambda t: int(t.replace(hour=0).strftime('%s'))) dailydf.Precip_intensity_max_time = fill_pimt_null(dailydf.Precip_intensity_max_time, dailydf.Min_time) # The nulls in the cloud cover were a bit trickier to tease out. Throwing it through a decision tree didn't reveal any obvious rules for when nulls would occur, so I just built a quick random forest model to use the other features to determine what should go in the missing rows for `Cloud_cover`. The predictions from this imputation model on held out data shows a pretty good correlation with the actual values: # In[ ]: from IPython.display import Image Image('plots/cloud_cover_model_perf.png', height=400, width=400) # so I went ahead and stuck with it. The nulls don't appear to be randomly distributed, however (they seemed to be correlated with snow and visibility), so it may be worthwhile to look for a better way to deal with them some other time. # In[ ]: from sklearn.ensemble import RandomForestRegressor def fill_cloud_cover_null(cc, X): """Solution wasn't obvious, so I just imputed the nulls with a random forest using the other columns. """ null = cc != cc if not null.any(): return cc rf = RandomForestRegressor(n_estimators=30, oob_score=True) rf.fit(X[~null], cc[~null]) cc2 = cc.copy() cc2.loc[null] = rf.predict(X[null]) return cc2 _feats = [k for k, d in dailydf.dtypes.items() if (d == float or d == int) and (k != 'Cloud_cover') ] dailydf['Cloud_cover'] = fill_cloud_cover_null(dailydf.Cloud_cover, dailydf[_feats]) # ### Check times # In addition to replacing the text categorical variables with dummy values, I also adjusted the time attributes (sunrise, sunset, maximum temperature &c) so that they reflected time of the day rather than UNIX time. # In[ ]: ddf = replace_with_dummies(dailydf, 'Icon Precip_type'.split()) assert (ddf == ddf).all().all(), "Don't want nulls here" # In[ ]: # Check that within a day the difference between maximum # and minimum times are not greater than the # number of seconds in a day times = lfilter(lambda x: x.endswith('ime'), ddf) minmax = DataFrame({ 'Min': ddf[times].min(axis=1), 'Max': ddf[times].max(axis=1), }).assign(Diff=lambda x: x.Max.sub(x.Min).div(60 * 60 * 24)) assert 0 <= minmax.Diff.max() <= 1, "All times within a day should be no more than 24 hrs apart" minmax.Diff.max() # should be no more than 1 # In[ ]: assert (ddf[times].min(axis=1) == ddf.Min_time).all(), 'By definition' # In[ ]: unix_time_to_day_hrs = lambda s, min_time: (s - min_time) / 3600 for t in set(times) - {'Min_time'}: c = t + 's' ddf[c] = unix_time_to_day_hrs(ddf[t], ddf.Min_time) # Some cols now have a single value. Drop them. # In[ ]: slen = lambda x: len(set(x)) nunique = ddf.apply(slen) ddf = ddf[nunique[nunique > 1].index].copy() # ### Pollen # From personal experience, the symptoms have a nonlinear response rate to the pollen count that is mirrored in the [NAB](http://www.aaaai.org/global/nab-pollen-counts/reading-the-charts) classification. Here's the scale: # In[ ]: pscale # Since the symptoms increase with each order of magnitude change in the count, rather than by a constant, this tells me the target would be well modeled by the log of the pollen count. The standard MSE would penalize a prediction that's off by 50 the same, regardless of whether the actual count is in the low or high range, but modeling the log of the count would improve the metric. # # The plot below shows what I mean. The raw plot on the left shows a more drastically varying scale, which would be trickier to learn with a simple MSE loss, compared to the more evenly scaled log plot on the right. # In[ ]: _, [ax1, ax2] = plt.subplots(1, 2, figsize=(10, 4)) cutoffs = np.array([1, 15, 90, 1500]) ax1.set_title('Linear scale') ax1.plot(cutoffs) ax2.set_title('Log scale') ax2.plot(np.log10(cutoffs)); # ### Weather & pollen become one # This is where weather in pollen tables are joined, the target variable is logified, features are standardized, and data is wrapped in torch variables. You can see `utils.join_pollen_weather` if such things do not bore you. # # I'm pretty sure that not every variable that is helpful and many convey redundant information, but since I haven't seen the straightforward way to do feature selection with deep networks, I'll have to save that part for another day. # In[ ]: poldf = feather.read_dataframe('cache/pollen.fth') xdf, xt, yt, rx, rxdf, ry = join_pollen_weather( poldf, ddf, time_cols=times, ycol='Logcnt' ) # In[ ]: print('|X|:', xt.size()) print('|y|:', yt.size()) print("Pollen count's 1st lag auto-correlation: {:.2%}" .format(xdf.Logcnt.corr(xdf.Logcnt.shift()))) # Sanity check that it's ordered ascending my date and not null assert xdf.Dt.is_monotonic_increasing assert xdf.Time.is_monotonic_increasing assert (xdf.Doy > xdf.Doy.shift(1)).mean() > .98, ( "Day of year int should increase once a year") assert not xdf.isnull().any().any() # Sanity check that I didn't accidentally include a transformation of target variable in the predictors: # In[ ]: corrs = (rxdf.corrwith(ry).to_frame().rename(columns={0: 'Corr'}) .assign(Abs=lambda x: x.Corr.abs()) .sort_values('Abs', ascending=0).Corr) assert corrs.abs().max() < .9 corrs[:5] # ## RNN # While a standard model that works on tabular data could work (think linear regression, KNN, GBMs) their standard usage doesn't take into account the sequential structure of the data, and ignores useful information. There are more classical models that take the sequential structure into account, like HMMs and Kalman filters, # but since I'm going through a phase of aiming deep learning solutions at problems, a recurrent neural network is the favored choice. While different in many ways from an HMM, it does share the element of hidden variables that track state over time, even if they don't convey the kind of useful probabilistic information that an HMM's hidden states would. # # There are roughly a gajillion deep learning frameworks around these days, but I went with [Pytorch](http://pytorch.org/) because it looked fun. # In[ ]: import torch as T from torch.autograd import Variable from torch import optim from torch import nn tofloat = lambda x: x.data[0] unvar = lambda x: x.data.numpy().ravel() # And here is the main model. It basically uses an RNN with GRUs (a simplified version of the standard LSTM cell, named after a data science twitter celebrity), with the output units leading to a dense layer after a dropout layer # In[ ]: class Rnn(nn.Module): def __init__(self, P=3, nhidden=21, num_layers=1, dropout=0): super().__init__() self.P, self.nhidden, self.num_layers, self.dropout = ( P, nhidden, num_layers, dropout ) self.rnn = nn.GRU(P, nhidden, num_layers, batch_first=True, dropout=dropout) self.Dropout = nn.Dropout(p=dropout) self.decoder = nn.Linear(nhidden, 1) self.init_weights() self.zero_grad() def __dir__(self): return super().__dir__() + list(self._modules) def forward(self, input, hidden=None, outputh=False): if hidden is None: hidden = self.hidden out1, hout = self.rnn(input, hidden) out1d = self.Dropout(out1) out2 = self.decoder(ravel(out1d)) self.hidden = repackage_hidden(hout) # don't waste time tracking the grad if outputh: return out2, hout return out2 def init_weights(self): initrange = 0.1 for p in self.rnn.parameters(): xavier_init(p.data) self.decoder.bias.data.fill_(0) xavier_init(self.decoder.weight.data) def init_hidden(self, bsz): "For lstm I'll need to return 2" weight = next(self.rnn.parameters()).data mkvar = lambda: Variable(weight.new(self.num_layers, bsz, self.nhidden).zero_()) return mkvar() def set_hidden(self, bsz): h = self.init_hidden(bsz) self.hidden = h def xavier_init(t): "This seems to be the recommended distribution for weight initialization" n = max(t.size()) return t.normal_(std=n ** -.5) criterion = nn.MSELoss() # The training routine is pretty standard and self explanatory: # In[ ]: def train_epoch(barray, model=None, hidden=None, optimizer=None, eval=False, batch_size=None): batch_size = batch_size or barray.batch_size assert batch_size or hidden hidden = model.init_hidden(batch_size) if hidden is None else hidden res = [] ss, n = 0, 0 for bix in barray.batch_ix_iter(batch_size=batch_size): x, y = barray[bix] optimizer.zero_grad() output = model(x, hidden) res.append(output.data.squeeze()) if eval: continue loss = criterion(output, y.view(-1, 1)) loss.backward() T.nn.utils.clip_grad_norm(model.parameters(), 3) optimizer.step() ss += tofloat(loss) * len(output) # keep track of ss n += len(output) res = T.stack(res).view(-1).numpy() if eval: return res tot_loss = ss / n return tot_loss, res # When I want to make a prediction on the validation set, I first run the model over a few preceding examples to update the hidden weights, since the validation set is relatively small (that is, with `warmup=True` in `val_pred`): # In[ ]: def val_pred(model, warmup=True): if warmup: model.set_hidden(1) ix = int(not warmup) Dt = baval.Dt[ix] xs, ysv = baval[[ix]] ys = Series(unvar(ysv), index=Dt) yspred = model(xs) yspred_s = Series(unvar(yspred), index=Dt) return yspred_s, ys # And here are the functions to run the training routines and log the progress # In[ ]: # %mkdir /tmp/res/ VALFN = '/tmp/res/val.txt' TRNFN = '/tmp/res/trn.txt' def report_hook(model, res, vals=None): print() val_pred(model, warmup=True) yspred, ys = val_pred(model, warmup=False) val_acc = mse(yspred, ys) vals.append(val_acc) trn_acc = mse(ba.train_samples_y, res) with open(VALFN, 'a') as f: f.write('{:}\n'.format(val_acc)) with open(TRNFN, 'a') as f: f.write('{:}\n'.format(trn_acc)) print('{:,.3f}; val: {:,.4f}'.format(trn_acc, val_acc), end='; ') def train_epochs(model, optimizer=None, rng=(500, ), print_every=10, report_hook=None, report_kw={}): with open(VALFN, 'w') as f: pass with open(TRNFN, 'w') as f: pass vals = [] for i in range(*rng): _, res = train_epoch(ba, model=model, hidden=None, optimizer=optimizer) print('.', end='') if i % print_every == 0: if report_hook: report_hook(model, res, vals=vals) return res, min(vals) # ## Training and Model parameters # # A lot of standard tips for RNN settings didn't seem to apply to this problem, since approximately 100% of the use cases I see are for NLP tasks. For one, I have a lot less data, so the batch size remainders matter more. Using a batch size of 32 and sequence length of 25, I use all the full batches for the training set (4000 samples), and the remaining samples (~400) for the validation set. # # I also found that fewer hidden units worked pretty well. For some settings, 128 units was overkill, and completely overfit the data, though using just 8 served as a decent form of regularization. I settled on a higher number of units for reasons explained later, but dropout helped prevent overfitting. # # I used [skopt's](https://scikit-optimize.github.io/) Gaussian process optimizer to find a good set of hyperparameters. # In[ ]: # training batches seq_len = 25 bcz = 32 ba = BatchArray(x=xt, y=yt, seq_len=seq_len, batch_size=bcz) # validation batches l = ba.num_leftover_rows baval = BatchArray(x=xt[-2 * l:], y=yt[-2 * l:], seq_len=l, batch_size=1) assert (xdf.index == rxdf.index).all(), 'Dropped some nulls?' baval.Dt = [xdf.Dt.iloc[-2*l:-l], xdf.Dt.iloc[-l:]] print('Training size: {}\nValidation size: {}'.format(ba.num_truncated_rows, l)) # In[ ]: nhidden = 128 num_layers = 2 model = Rnn(P=rx.shape[-1], nhidden=nhidden, num_layers=num_layers, dropout=.05) model.set_hidden(bcz) optimizer = optim.Adam(model.parameters(), lr = 0.001) model # With these settings, the loss plot below shows that the validation score doesn't improve much after about 20 epochs # In[ ]: Image('plots/valid.png') # In[ ]: st = time.perf_counter() res, mvals = train_epochs(model=model, optimizer=optimizer, rng=(25, ), print_every=10, report_hook=report_hook) tt = time.perf_counter() - st print('\n\nTime: {:.2f}'.format(tt)) print('Acc: {:.2f}; Val: {:.3f}'.format(mse(res, ba.train_samples_y), mvals)) # ## Results...with uncertainty # # Although they use probabilistic activations within the cells, RNNs aren't usually chosen for quantifying the uncertainty of their predictions. Being a Bayesian, this makes me want to wash my hands, but it also makes RNNs a lot less useful for forecasting than other methods when you have no idea what the model's confidence is in its predictions. # # A [fascinating blog post by Yarin Gal](http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html), however, draws a connection between deep networks using dropout and Gaussian processes, with a simple formula to provide probabilistic uncertainty estimates for fully connected layers with dropout. While I'm not sure (in fact, quite doubtful) that the exact equations transfer to RNNs, I was curious about the results using dropout to simulate variation in the prediction. # In[ ]: (x_warm, y_warm) = baval[0] (x_val, y_val) = baval[1] y_val = y_val.data.numpy().ravel() x_warm = x_warm.unsqueeze(0) x_val = x_val.unsqueeze(0) # In[ ]: def eval_val(model, x_val): model(x_warm) val_pred = model(x_val).data #.numpy().ravel() return val_pred model.set_hidden(1) # Here I get simulated results on the held out data # In[ ]: get_ipython().run_cell_magic('time', '', '# ressv = np.array([eval_val(model, x_val) for _ in range(100)])\nressv = T.cat([eval_val(model, x_val) for _ in range(100)], 1).numpy()') # and calculate the mean and variance # In[ ]: mu = ressv.mean(axis=1) var = ressv.var(axis=1) lparam = 50 tau = lparam**2 * (1 - model.dropout) / (2 * l * .9) var += tau**-1 # In[ ]: plt.figure(figsize=(16, 10)) dates = xdf.Dt[-l:].values datify = lambda x: Series(x, index=dates) datify(y_val).plot() datify(mu).plot() plt.legend(['Y', 'Pred']) lo = datify(mu - var) hi = datify(mu + var) plt.fill_between(dates, lo, hi, alpha=.35, edgecolor='none') # ### Errors # # To my eye, it looks like the predictions track the held out values pretty nicely, though they look like they have something of a smoothing effect when the actual values jump around a lot. Looking at the residuals show that the error is higher when the pollen count jumps a lot from day to day: # In[ ]: resid = y_val - mu diffy = y_val[1:] - y_val[:-1] # diffpred = mu[1:] - mu[:-1] plt.scatter(diffy, resid[1:], alpha=.3) plt.xlabel('Daily difference') plt.ylabel('Residual') plt.text(-1, 1, 'Corr coef: {:.1%}'.format(np.corrcoef(diffy, resid[1:])[0][1])); # Since the pollen counts are so highly correlated with the count of the previous day, I thought that on weekends or other days with missing counts could be associated with the big jumps, that tend to have the highest error. I even included the number of skips days in the model as `Day_diff`, but this seems to have almost relation with the errors (see the left plot). # # My other thought was that the amount of uncertainty in the simulated results should also increase around these big changes, and therefore around the residuals. Sadly, the model didn't learn this, and the variance looks completely uncorrelated with the residuals (on the right). # In[ ]: _, [ax1, ax2] = plt.subplots(1, 2, figsize=(16, 6)) sns.swarmplot(data=rxdf[-l:].assign(Resid=resid), x='Day_diff', y='Resid', ax=ax1) # uncert_diff = (m9 - m10)[1:] uncert_diff = var[1:] ax2.scatter(uncert_diff, resid[1:], alpha=.3) plt.xlabel('Daily difference') plt.ylabel('Residual') plt.text(.35, 1, 'Corr coef: {:.1%}'.format(np.corrcoef(uncert_diff, resid[1:])[0][1])); # Overall I'm pretty happy in the RNN's ability to capture the dynamics of the weather and make pollen count predictions. Some improvements I would be interested in making, would be to give the model a better idea of when to be less confident. Because of the suspected feature information redundancy, it would also be worthwhile to look into applying feature selection to the model. But all in all this looks like a good start to a making a useful personalized pollen forecasting system.
d10genes/pollen
pollen2.py
Python
mit
21,073
[ "Gaussian" ]
181be1499b46ca92cf7b9ba7c9af39a46627dfa7a01d938b580528df0f636bb2
from datetime import datetime, date from django.contrib.auth.models import User from django.forms import ( BooleanField, CharField, CheckboxInput, ChoiceField, EmailField, FileField, Form, ModelForm, Select, SelectMultiple, Textarea, ValidationError, ) from datetimewidget.widgets import DateWidget from crispy_forms.helper import FormHelper from crispy_forms.layout import Layout, Fieldset, ButtonHolder, Submit, HTML from crispy_forms.bootstrap import PrependedText from .models import * TODAY_YEAR = datetime.now().year SELECT_DATE_WIDGE_YEARS = [TODAY_YEAR + delta for delta in range(-3, 4)] class GarlicForm(ModelForm): not_send_email_field = BooleanField( widget=CheckboxInput, required=False, initial=False, label="Suppress email notification for this update to claimant?" ) not_copy_email_field = BooleanField( widget=CheckboxInput, required=False, initial=True, label="Suppress copy of email to staff?" ) def __init__(self, *args, **kwargs): # Add staff option to not send email notification self.is_staff = kwargs.pop("is_staff", False) # Set up Garlic attribute to persistent data super(GarlicForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.attrs = { 'data_persist': "garlic", } class ClaimantForm(GarlicForm): class Meta: model = Claimant fields = [ 'forenames', 'surname', 'email', 'phone', 'gender', 'home_country', 'home_city', 'career_stage_when_apply', 'affiliation', 'work_description', 'institutional_website', 'website', 'website_feed', 'orcid', 'google_scholar', 'github', 'gitlab', 'bitbucket', 'twitter', 'linkedin', 'facebook', ] required_css_class = 'form-field-required' def __init__(self, *args, **kwargs): super(ClaimantForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', HTML('<h2>Personal details</h2>'), 'forenames', 'surname', 'email', 'phone', 'gender', 'home_country', 'home_city', HTML('<h2>Professional details</h2>'), 'career_stage_when_apply', 'affiliation', 'work_description', HTML('<h2>Social Networks</h2>'), 'institutional_website', 'website', 'website_feed', PrependedText( 'orcid', 'https://orcid.org/' ), PrependedText( 'google_scholar', 'https://scholar.google.co.uk/citations?user=' ), PrependedText( 'github', 'https://gihub.com/' ), PrependedText( 'gitlab', 'https://gitlab.com/' ), PrependedText( 'bitbucket', 'https://bitbucket.org/' ), PrependedText( 'linkedin', 'https://www.linkedin.com/in/' ), PrependedText( 'twitter', 'https://twitter.com/' ), PrependedText( 'facebook', 'https://facebook.com/' ), ButtonHolder( Submit('submit', 'Add') ) ) ) class FellowForm(GarlicForm): class Meta: model = Claimant fields = [ 'forenames', 'surname', 'email', 'phone', 'gender', 'home_country', 'home_city', 'photo', 'photo_work_description', 'career_stage_when_apply', 'job_title_when_apply', 'research_area', 'research_area_code', 'affiliation', 'department', 'group', 'funding', 'funding_notes', 'interests', 'work_description', 'institutional_website', 'website', 'website_feed', 'orcid', 'google_scholar', 'github', 'gitlab', 'bitbucket', 'twitter', 'linkedin', 'facebook', ] labels = { 'home_country': "Country", 'home_city': "City", 'photo': "Photo (Thumbnail)", 'photo_work_description': "Photo (Main)", 'career_stage_when_apply': "Career Stage", 'job_title_when_apply': "Job Title", 'research_area_code': "Research Classification", 'affiliation': "Home institution", 'department': "Department", 'group': "Group within Department", 'funding': "Primary funding body/charity/organisation", 'funding_notes': "Any additional funders", 'work_description': "Short Biography", } required_css_class = 'form-field-required' def __init__(self, *args, **kwargs): super(FellowForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', HTML('<h2>Personal details</h2>'), 'forenames', 'surname', 'email', 'phone', 'gender', 'home_country', 'home_city', 'photo_work_description', 'photo', HTML('<h2>Professional details</h2>'), 'career_stage_when_apply', 'job_title_when_apply', 'research_area', 'research_area_code', 'affiliation', 'department', 'group', 'funding', 'funding_notes', HTML('<h2>Information for the website</h2>'), 'interests', 'work_description', HTML('<h2>Social Networks</h2>'), 'institutional_website', 'website', 'website_feed', PrependedText( 'orcid', 'https://orcid.org/' ), PrependedText( 'google_scholar', 'https://scholar.google.co.uk/citations?user=' ), PrependedText( 'github', 'https://gihub.com/' ), PrependedText( 'gitlab', 'https://gitlab.com/' ), PrependedText( 'bitbucket', 'https://bitbucket.org/' ), PrependedText( 'linkedin', 'https://www.linkedin.com/in/' ), PrependedText( 'twitter', 'https://twitter.com/' ), PrependedText( 'facebook', 'https://facebook.com/' ), ButtonHolder( Submit('submit', 'Add') ) ) ) class FundForm(GarlicForm): class Meta: model = Fund exclude = [ # pylint: disable=modelform-uses-exclude "success_reported", "status", "ad_status", "budget_approved", "required_blog_posts", "grant_heading", "grant", "notes_from_admin", "added", "approved", "updated", "approval_chain", ] labels = { 'claimant': 'Requester name', 'mandatory': 'Is this related with Fellows face to face selection meeting, Fellows inaugural meeting or Collaborations Workshop?', 'title': 'Event title', 'url': 'Event webpage link', 'country': 'Country in which event is taking place', 'city': 'City in which the event is taking place', 'start_date': 'Start date of event', 'end_date': 'End date of event', 'budget_request_travel': "Travel costs (e.g. airfare or ground transportation)", 'budget_request_attendance_fees': "Attendance fees (e.g. workshop / event registration costs)", 'budget_request_subsistence_cost': "Subsistence costs (e.g. accommodation and meals)", 'budget_request_venue_hire': "Venue hire", 'budget_request_catering': "Catering", 'budget_request_others': "Other costs", 'success_targeted': "Successful outputs and outcomes", 'can_be_included_in_calendar': "Can we include your participation in this event into the private Fellows calendar?", 'can_be_advertise_before': "Can we public promote your involvement in this event before it takes place?", 'can_be_advertise_after': "Can we public promote your involvement in this event after it takes place?" } widgets = { 'claimant': Select(attrs={"class": "select-single-item"}), 'category': Select(attrs={"class": "select-single-item"}), 'focus': Select(attrs={"class": "select-single-item"}), 'country': Select(attrs={"class": "select-single-item"}), 'start_date': DateWidget( usel10n=True, bootstrap_version=3 ), 'end_date': DateWidget( usel10n=True, bootstrap_version=3 ), } required_css_class = 'form-field-required' total_budget = CharField(required=False) def clean_start_date(self): if 'start_date' in self.cleaned_data: date_from_today = self.cleaned_data['start_date'] - date.today() if date_from_today.days <= 0: raise ValidationError('"Start date of event" must be in the future.') return self.cleaned_data['start_date'] def clean_end_date(self): if 'end_date' in self.cleaned_data: date_from_today = self.cleaned_data['end_date'] - date.today() if date_from_today.days <= 0: raise ValidationError('"End date of event" must be in the future.') if 'start_date' in self.cleaned_data and 'end_date' in self.cleaned_data: duration = self.cleaned_data['end_date'] - self.cleaned_data['start_date'] if duration.days < 0: raise ValidationError('"End date of event" must be after "Start date of event".') return self.cleaned_data['end_date'] def __init__(self, *args, **kwargs): super(FundForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', HTML('<p>To apply for expenses for eligible events, please fill in this form at least one month before the start date of the event you wish to attend or organise.</p><h2>Requester details</h2>'), 'claimant', HTML('<h2>Funding request details</h2>'), 'category', 'focus', 'mandatory', 'title', 'url', 'country', 'city', 'start_date', 'end_date', HTML('<h2>Costs</h2><p>Please provide an estimate of your costs below. All values should be entered in GBP. See the terms and conditions for details (<a href="{{ terms_and_conditions_url }}">{{ terms_and_conditions_url }}</a>)</p><p>Please fill in all cost sections that are relevant to your event type.</p>'), PrependedText( 'budget_request_travel', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_attendance_fees', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_subsistence_cost', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_venue_hire', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_catering', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_others', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'total_budget', '£', disabled=True, value=0.00 ), HTML('<h2>Justification for attending or organising the event</h2><p>When filling in the questions below please consider the following points:</p><ul><li>For attending conferences/workshops: will the conference focus on a significant field, will you meet significant researchers, will there be a focus on research software?</li><li>For organising workshops: how will the event help your domain, how will the event help the Institute, how will the event help you.</li><li>For policy related work: how might participation or organisation help the policy goals of the Institute, such as improving software and improved research (this can include people and tools perspectives).</li><li>For other: please state reasons - note it maybe good to discuss matter with the Institute Community Lead before filling the form to make sure the rationale is aligned to the Institute and to your own objectives.</li></ul>'), 'justification', HTML('<p>Please specify what outputs (what maybe be produced) and outcomes (what change it could lead to) are likely to be produced from your participation in this event. These can include learning goals being met, collaborations, reports etc.</p>'), 'success_targeted', 'additional_info', HTML('<h2>Details of people being sponsored from your Fellowship funds</h2><p>If you are sponsoring others to take part in this event from your Fellowship funds please give their names and email addresses below, if you do not know their names at this stage please state whether there is sponsorship of others needed in this request. In either case please provide some justification.</p>'), 'extra_sponsored', HTML('<h2>Publicity</h2>'), 'can_be_included_in_calendar', 'can_be_advertise_before', 'can_be_advertise_after', 'not_send_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', '{{ title }}') ) ) ) # Force user to select one category self.fields['category'].widget.choices.insert(0, ('', '---------')) self.fields['category'].initial = '' # Force user to select one focus self.fields['focus'].widget.choices.insert(0, ('', '---------')) self.fields['focus'].initial = '' class FundPublicForm(GarlicForm): forenames = CharField( max_length=MAX_CHAR_LENGTH, required=True ) surname = CharField( max_length=MAX_CHAR_LENGTH, required=True ) email = EmailField( required=True ) phone = CharField( max_length=MAX_CHAR_LENGTH, required=True, help_text="The number that we can contact you." ) #gender = CharField( # choices=GENDERS, # max_length=1, # default="R" #) #home_country = CountryField( # required=True, # default='GB' # Default for United Kingdom #) home_city = CharField( required=True, max_length=MAX_CHAR_LENGTH ) affiliation = CharField( # Home institution max_length=MAX_CHAR_LENGTH, required=True, ) department = CharField( # Department within home institution max_length=MAX_CHAR_LENGTH, required=True ) class Meta: model = Fund exclude = [ # pylint: disable=modelform-uses-exclude 'claimant', 'mandatory', 'additional_info', 'extra_sponsored', 'can_be_included_in_calendar', 'can_be_advertise_before', 'can_be_advertise_after', "status", "ad_status", "budget_approved", "required_blog_posts", "grant_heading", "grant", "notes_from_admin", "added", "approved", "updated", "approval_chain", ] labels = { 'mandatory': 'Is this related with Fellows face to face selection meeting, Fellows inaugural meeting or Collaborations Workshop?', 'title': 'Event title', 'url': 'Event webpage link', 'country': 'Country in which event is taking place', 'city': 'City in which the event is taking place', 'start_date': 'Start date of event', 'end_date': 'End date of event', 'budget_request_travel': "Travel costs (e.g. airfare or ground transportation)", 'budget_request_attendance_fees': "Attendance fees (e.g. workshop / event registration costs)", 'budget_request_subsistence_cost': "Subsistence costs (e.g. accommodation and meals)", 'budget_request_venue_hire': "Venue hire", 'budget_request_catering': "Catering", 'budget_request_others': "Other costs", 'success_targeted': "Successful outputs and outcomes", 'can_be_included_in_calendar': "Can we include your participation in this event into the private Fellows calendar?", 'can_be_advertise_before': "Can we public promote your involvement in this event before it takes place?", 'can_be_advertise_after': "Can we public promote your involvement in this event after it takes place?" } widgets = { 'claimant': Select(attrs={"class": "select-single-item"}), 'category': Select(attrs={"class": "select-single-item"}), 'focus': Select(attrs={"class": "select-single-item"}), 'country': Select(attrs={"class": "select-single-item"}), 'start_date': DateWidget( usel10n=True, bootstrap_version=3 ), 'end_date': DateWidget( usel10n=True, bootstrap_version=3 ), } required_css_class = 'form-field-required' total_budget = CharField(required=False) def clean_start_date(self): if 'start_date' in self.cleaned_data: date_from_today = self.cleaned_data['start_date'] - date.today() if date_from_today.days <= 0: raise ValidationError('"Start date of event" must be in the future.') return self.cleaned_data['start_date'] def clean_end_date(self): if 'end_date' in self.cleaned_data: date_from_today = self.cleaned_data['end_date'] - date.today() if date_from_today.days <= 0: raise ValidationError('"End date of event" must be in the future.') if 'start_date' in self.cleaned_data and 'end_date' in self.cleaned_data: duration = self.cleaned_data['end_date'] - self.cleaned_data['start_date'] if duration.days < 0: raise ValidationError('"End date of event" must be after "Start date of event".') return self.cleaned_data['end_date'] def __init__(self, *args, **kwargs): super(FundPublicForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', HTML('<h2>Your details</h2>'), 'forenames', 'surname', #'gender', 'email', 'phone', 'home_city', #'home_country', 'affiliation', 'department', HTML('<h2>Funding request details</h2>'), 'category', 'focus', 'title', 'url', 'country', 'city', 'start_date', 'end_date', HTML('<h2>Costs</h2><p>Please provide an estimate of your costs below. All values should be entered in GBP. See the terms and conditions for details (<a href="{{ terms_and_conditions_url }}">{{ terms_and_conditions_url }}</a>)</p><p>Please fill in all cost sections that are relevant to your event type.</p>'), PrependedText( 'budget_request_travel', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_attendance_fees', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_subsistence_cost', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_venue_hire', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_catering', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'budget_request_others', '£', onblur="update_budget()", min=0.00, step=0.01 ), PrependedText( 'total_budget', '£', disabled=True, value=0.00 ), HTML('<h2>Justification for attending or organising the event</h2><p>When filling in the questions below please consider the following points:</p><ul><li>For attending conferences/workshops: will the conference focus on a significant field, will you meet significant researchers, will there be a focus on research software?</li><li>For organising workshops: how will the event help your domain, how will the event help the Institute, how will the event help you.</li><li>For policy related work: how might participation or organisation help the policy goals of the Institute, such as improving software and improved research (this can include people and tools perspectives).</li><li>For other: please state reasons - note it maybe good to discuss matter with the Institute Community Lead before filling the form to make sure the rationale is aligned to the Institute and to your own objectives.</li></ul>'), 'justification', 'success_targeted', 'not_send_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', '{{ title }}') ) ) ) # Force user to select one category self.fields['category'].widget.choices.insert(0, ('', '---------')) self.fields['category'].initial = '' # Force user to select one focus self.fields['focus'].widget.choices.insert(0, ('', '---------')) self.fields['focus'].initial = '' class FundGDPRForm(GarlicForm): class Meta: model = Fund fields = [ 'can_be_included_in_calendar', 'can_be_advertise_before', 'can_be_advertise_after', ] labels = { 'can_be_included_in_calendar': "Can we include your participation in this event into the Fellows calendar?", 'can_be_advertise_before': "Can we public promote your involvement in this event before it takes place?", 'can_be_advertise_after': "Can we public promote your involvement in this event after it takes place?" } required_css_class = 'form-field-required' def __init__(self, *args, **kwargs): super(FundGDPRForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', HTML('<h2>GDPR</h2>'), HTML('<h3>Publicity</h3>'), 'can_be_advertise_before', 'can_be_advertise_after', 'can_be_included_in_calendar', ButtonHolder( Submit('submit', '{{ title }}') ) ) ) class FundReviewForm(GarlicForm): class Meta: model = Fund fields = [ "status", #"ad_status", # TODO uncomment in the future "category", "focus", "mandatory", "grant_heading", "grant", "activity", "required_blog_posts", "budget_approved", "notes_from_admin", ] labels = { "mandatory": "Is this a mandatory event?", "grant_heading": "Default Grant Heading", "grant": "Default Grant", "activity": "Activities tag", 'budget_approved': 'Total budget approved', } required_css_class = 'form-field-required' email = CharField(widget=Textarea, required=False) def __init__(self, *args, **kwargs): super(FundReviewForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', "status", "category", "focus", "mandatory", "grant", "grant_heading", "activity", HTML("""Visit <a href="/pages/guide/activities-tag/">Activities Tag Taxonomy</a> for a description about the tags."""), "required_blog_posts", PrependedText( "budget_approved", '£', min=0.00, step=0.01, onblur="this.value = parseFloat(this.value).toFixed(2);" ), "notes_from_admin", "email", 'not_send_email_field' if self.is_staff else None, 'not_copy_email_field' if self.is_staff else None, ) ) self.helper.add_input(Submit('submit', 'Submit')) class FundImportForm(Form): required_css_class = 'form-field-required' csv = FileField() def __init__(self, *args, **kwargs): super(FundImportForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.attrs = { 'data_persist': "garlic", } self.helper.layout = Layout( Fieldset( '', HTML("""Your CSV <strong>must</strong> have the following columns: <ul> <li>Forename(s)</li> <li>Surname</li> <li>Event type</li> <li>Event title</li> <li>Event website</li> <li>Event Country</li> <li>Event City</li> <li>Start date</li> <li>End date</li> <li>Travel costs</li> <li>Conference/Workshop attendance fees</li> <li>Subsistence costs</li> <li>Venue hire</li> <li>Catering</li> <li>Travel and subsistence cost for those being paid to attend your organised event</li> <li>Other costs</li> <li>How is the event relevant to the work of the Software Sustainability Institute?</li> <li>Any other information relevant to this application?</li> <li>Estimate</li> <li>Submitted</li> <li>Revised estimate</li> <li>Approved</li> </ul> <p class="text-danger">You will not have access to debug information!</p>"""), 'csv', ButtonHolder( Submit('submit', '{{ title }}') ) ) ) class ExpenseForm(GarlicForm): class Meta: model = Expense fields = [ 'fund', 'claim', 'amount_claimed', 'justification_for_extra', 'invoice', 'final', 'advance_booking', 'recipient_fullname', 'recipient_email', 'recipient_affiliation', 'recipient_group', 'recipient_connection', ] labels = { 'fund': 'Choose approved funding request', 'claim': 'PDF copy of claim and receipt(s)', 'justification_for_extra': "If the claim is greater by 20% than the amount requested please provide justification", 'invoice': "Do you need to claim this expense via an invoice from your institution or company?", 'final': "Is this the final expense claim associated with this funding request?", 'recipient_fullname': "Full name", 'recipient_email': "E-mail", 'recipient_affiliation': "Affiliation", 'recipient_group': "Group", 'recipient_connection': "Reason for submit the recipient claim", } widgets = { 'fund': Select(attrs={"class": "select-single-item"}), } required_css_class = 'form-field-required' def __init__(self, *args, **kwargs): super(ExpenseForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', 'fund', HTML("</p>If your funding request isn't on the drop down menu above please email <a href='mailto:{{ config.FELLOWS_MANAGEMENT_EMAIL }}'>us</a>."), HTML("</p><a href='{{ terms_and_conditions_url }}'>Fellowship Programme's terms and conditions</a> applies to your request. Please follow the guidelines at <a href='{{ terms_and_conditions_url }}#how-to-apply-for-and-claim-expenses'>How to apply for, and claim, expenses</a> section of <a href='{{ terms_and_conditions_url }}'>Fellowship Programme's terms and conditions.</a></p>"), 'claim', PrependedText( 'amount_claimed', '£', min=0.00, step=0.01, onblur="this.value = parseFloat(this.value).toFixed(2);" ), HTML("{% if fund %}<p class='text-warning'>Note that you only have <strong>£{{ fund.expenses_claimed_left }}</strong> left.</p>{% endif %}"), 'justification_for_extra', 'invoice', 'final', 'advance_booking' if self.is_staff else None, HTML("<h2>Recipient</h2><p>Only fill this part if you are claiming this expense on behalf of someone.</p>"), 'recipient_fullname', 'recipient_email', 'recipient_affiliation', 'recipient_group', 'recipient_connection', 'not_send_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', '{{ title }}') ) ) ) if "initial" in kwargs and "fund" in kwargs["initial"]: self.fields['fund'].queryset = Fund.objects.filter(id=kwargs["initial"]["fund"].id) else: self.fields['fund'].queryset = Fund.objects.filter(status__in=FUND_STATUS_APPROVED_SET) class ExpenseShortlistedForm(GarlicForm): class Meta: model = Expense fields = [ 'fund', 'claim', 'amount_claimed', 'justification_for_extra', ] labels = { 'fund': 'Choose approved funding request', 'claim': 'PDF copy of claim and receipt(s)', 'justification_for_extra': "If the claim is greater by 20% than the amount requested please provide justification", } widgets = { 'fund': Select(attrs={"class": "select-single-item"}), } required_css_class = 'form-field-required' def __init__(self, *args, **kwargs): super(ExpenseShortlistedForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', 'fund', HTML("</p>If your funding request isn't on the drop down menu above please email <a href='mailto:{{ config.FELLOWS_MANAGEMENT_EMAIL }}'>us</a>."), 'claim', PrependedText( 'amount_claimed', '£', min=0.00, step=0.01, onblur="this.value = parseFloat(this.value).toFixed(2);" ), 'justification_for_extra', 'not_send_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', '{{ title }}') ) ) ) self.fields['fund'].queryset = Fund.objects.filter(status__in=FUND_STATUS_APPROVED_SET) class ExpenseReviewForm(GarlicForm): class Meta: model = Expense fields = [ 'status', 'final', 'asked_for_authorization_date', 'send_to_finance_date', 'amount_authorized_for_payment', 'grant_heading', 'grant', 'notes_from_admin', ] widgets = { 'asked_for_authorization_date': DateWidget( usel10n=True, bootstrap_version=3 ), 'send_to_finance_date': DateWidget( usel10n=True, bootstrap_version=3 ), } required_css_class = 'form-field-required' email = CharField(widget=Textarea, required=False) def __init__(self, *args, **kwargs): super(ExpenseReviewForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', 'status', 'final', 'asked_for_authorization_date', 'send_to_finance_date', PrependedText( 'amount_authorized_for_payment', '£', min=0.00, step=0.01, onblur="this.value = parseFloat(this.value).toFixed(2);" ), 'grant', 'grant_heading', 'notes_from_admin', 'email', 'not_send_email_field' if self.is_staff else None, 'not_copy_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', 'Update') ) ) ) class BlogForm(GarlicForm): success_reported = CharField( widget=Textarea, required=False, initial="", label="What outputs were produced and which outcomes were achieved by your participation in the event." ) class Meta: model = Blog fields = [ 'fund', 'coauthor', 'draft_url', 'final', 'notes_from_author', ] labels = { 'fund': 'Open approved funding request', 'coauthor': 'Co-author', 'draft_url': 'URL of blog post draft', 'final': "Is this the final blog post draft associated with this funding request?", 'notes_from_author': "Notes" } widgets = { 'fund': Select(attrs={"class": "select-single-item"}), 'coauthor': SelectMultiple(attrs={"class": "select-many-item"}), } required_css_class = 'form-field-required' # workaround for "no such table: lowfat_claimant" try: author_choices = [(this_claimant.id, this_claimant) for this_claimant in Claimant.objects.all()] except: # pylint: disable=bare-except author_choices = [] author = ChoiceField( widget=Select(attrs={"class": "select-single-item"}), required=False, choices=author_choices, label='Main author of draft' ) def __init__(self, *args, user=None, **kwargs): super(BlogForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', 'fund', 'final', 'author' if self.is_staff else None, 'coauthor', HTML("<p>We prefer to receive links to <a href='https://www.google.co.uk/docs/about/'>Google Docs</a> (tips <a href='/pages/guide/google-docs/'>here</a>), <a href='https://products.office.com/en-gb/office-365-home'>Microsoft Office 365 document</a> or any other online live collaborative document platform you like to use. Posts published somewhere already, e.g. your personal blog, are welcome as well.</p>"), 'draft_url', 'success_reported', 'notes_from_author', 'not_send_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', '{{ title }}') ) ) ) if "initial" in kwargs and "fund" in kwargs["initial"]: self.fields['fund'].queryset = Fund.objects.filter(id=kwargs["initial"]["fund"].id) else: self.fields['fund'].queryset = Fund.objects.filter(status__in=FUND_STATUS_APPROVED_SET) if user: self.fields['fund'].queryset = Fund.objects.filter(status__in=FUND_STATUS_APPROVED_SET) if self.is_staff: # Force staff to select one author self.fields['author'].widget.choices.insert(0, ('', '---------')) self.fields['author'].initial = '' class BlogReviewForm(GarlicForm): class Meta: model = Blog exclude = [ # pylint: disable=modelform-uses-exclude "fund", "author", "coauthor", "notes_from_author", "added", "updated", ] required_css_class = 'form-field-required' email = CharField(widget=Textarea, required=False) def __init__(self, *args, **kwargs): super(BlogReviewForm, self).__init__(*args, **kwargs) self.helper.layout = Layout( Fieldset( '', 'draft_url', 'final', 'status', 'reviewer', 'notes_from_admin', 'published_url', 'title', 'tweet_url', 'email', 'not_send_email_field' if self.is_staff else None, 'not_copy_email_field' if self.is_staff else None, ButtonHolder( Submit('submit', 'Update') ) ) ) self.fields['reviewer'].queryset = User.objects.filter(is_staff=True)
softwaresaved/fat
lowfat/forms.py
Python
bsd-3-clause
40,592
[ "VisIt" ]
6ad8f285381852d21f1966492b1c28ad941e8f81651fb43f3f0ce072bb51ceb2
######################################################################## # File: Operation.py # Date: 2012/07/24 12:12:05 ######################################################################## """ :mod: Operation .. module: Operation :synopsis: Operation implementation Operation implementation """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # Disable invalid names warning # pylint: disable=invalid-name __RCSID__ = "$Id$" import datetime import json import six # # from DIRAC from DIRAC import S_OK, S_ERROR from DIRAC.RequestManagementSystem.Client.File import File from DIRAC.RequestManagementSystem.private.JSONUtils import RMSEncoder ######################################################################## class Operation(object): """ :param int OperationID: OperationID as read from DB backend :param int RequestID: parent RequestID :param str Status: execution status :param str Type: operation to perform :param str Arguments: additional arguments :param str SourceSE: source SE name :param str TargetSE: target SE names as comma separated list :param str Catalog: catalog to use as comma separated list :param str Error: error string if any :param Request.Request parent: parent Request instance It is managed by SQLAlchemy, so the RequestID, OperationID should never be set by hand (except when constructed from JSON of course...) In principle, the _parent attribute could be totally managed by SQLAlchemy. However, it is set only when inserted into the DB, this is why I manually set it in the Request _notify """ # # max files in a single operation MAX_FILES = 10000 # # all states ALL_STATES = ("Queued", "Waiting", "Scheduled", "Assigned", "Failed", "Done", "Canceled") # # final states FINAL_STATES = ("Failed", "Done", "Canceled") # # valid attributes ATTRIBUTE_NAMES = ['OperationID', 'RequestID', "Type", "Status", "Arguments", "Order", "SourceSE", "TargetSE", "Catalog", "Error", "CreationTime", "SubmitTime", "LastUpdate"] _datetimeFormat = '%Y-%m-%d %H:%M:%S' def __init__(self, fromDict=None): """ c'tor :param self: self reference :param dict fromDict: attributes dictionary """ self._parent = None now = datetime.datetime.utcnow().replace(microsecond=0) self._SubmitTime = now self._LastUpdate = now self._CreationTime = now self._Status = "Queued" self._Order = 0 self.__files__ = [] self.TargetSE = None self.SourceSE = None self._Arguments = None self.Error = None self.Type = None self._Catalog = None if isinstance(fromDict, six.string_types): fromDict = json.loads(fromDict) elif not isinstance(fromDict, dict): fromDict = {} if "Files" in fromDict: for fileDict in fromDict.get("Files", []): self.addFile(File(fileDict)) del fromDict["Files"] for key, value in fromDict.items(): # The JSON module forces the use of UTF-8, which is not properly # taken into account in DIRAC. # One would need to replace all the '== str' with 'in six.string_types' # This is converting `unicode` to `str` and doesn't make sense in Python 3 if six.PY2 and isinstance(value, six.string_types): value = value.encode() if value: setattr(self, key, value) # # protected methods for parent only def _notify(self): """ notify self about file status change """ fStatus = set(self.fileStatusList()) if fStatus == set(['Failed']): # All files Failed -> Failed newStatus = 'Failed' elif 'Scheduled' in fStatus: newStatus = 'Scheduled' elif "Waiting" in fStatus: newStatus = 'Queued' elif 'Failed' in fStatus: newStatus = 'Failed' else: self.Error = '' newStatus = 'Done' # If the status moved to Failed or Done, update the lastUpdate time if newStatus in ('Failed', 'Done', 'Scheduled'): if self._Status != newStatus: self._LastUpdate = datetime.datetime.utcnow().replace(microsecond=0) self._Status = newStatus if self._parent: self._parent._notify() def _setQueued(self, caller): """ don't touch """ if caller == self._parent: self._Status = "Queued" def _setWaiting(self, caller): """ don't touch as well """ if caller == self._parent: self._Status = "Waiting" # # Files arithmetics def __contains__(self, opFile): """ in operator """ return opFile in self.__files__ def __iadd__(self, opFile): """ += operator """ if len(self) >= Operation.MAX_FILES: raise RuntimeError("too many Files in a single Operation") self.addFile(opFile) return self def addFile(self, opFile): """ add :opFile: to operation .. warning:: You cannot add a File object that has already been added to another operation. They must be different objects """ if len(self) >= Operation.MAX_FILES: raise RuntimeError("too many Files in a single Operation") if opFile not in self: self.__files__.append(opFile) opFile._parent = self self._notify() # # helpers for looping def __iter__(self): """ files iterator """ return self.__files__.__iter__() def __getitem__(self, i): """ [] op for opFiles """ return self.__files__.__getitem__(i) def __delitem__(self, i): """ remove file from op, only if OperationID is NOT set """ self.__files__.__delitem__(i) self._notify() def __setitem__(self, i, opFile): """ overwrite opFile """ self.__files__.__setitem__(i, opFile) opFile._parent = self self._notify() def fileStatusList(self): """ get list of files statuses """ return [subFile.Status for subFile in self] def __bool__(self): """ for comparisons """ return True # For Python 2 compatibility __nonzero__ = __bool__ def __len__(self): """ nb of subFiles """ return len(self.__files__) @property def sourceSEList(self): """ helper property returning source SEs as a list""" return self.SourceSE.split(",") if self.SourceSE else [''] @property def targetSEList(self): """ helper property returning target SEs as a list""" return self.TargetSE.split(",") if self.TargetSE else [''] @property def Arguments(self): return self._Arguments @Arguments.setter def Arguments(self, value): if isinstance(value, six.text_type): value = value.encode() if not isinstance(value, bytes): raise TypeError("Arguments should be bytes!") self._Arguments = value @property def Catalog(self): """ catalog prop """ return self._Catalog @Catalog.setter def Catalog(self, value): """ catalog setter """ if not isinstance(value, six.string_types + (list,)): raise TypeError("wrong type for value") if isinstance(value, six.string_types): value = value.split(',') value = ",".join(list(set([str(item).strip() for item in value if str(item).strip()]))) if len(value) > 255: raise ValueError("Catalog list too long") self._Catalog = value if value else "" @property def catalogList(self): """ helper property returning catalogs as list """ return self._Catalog.split(",") if self._Catalog else [] @property def Status(self): """ Status prop """ return self._Status @Status.setter def Status(self, value): """ Status setter """ if value not in Operation.ALL_STATES: raise ValueError("unknown Status '%s'" % str(value)) if self.__files__: self._notify() else: # If the status moved to Failed or Done, update the lastUpdate time if value in ('Failed', 'Done'): if self._Status != value: self._LastUpdate = datetime.datetime.utcnow().replace(microsecond=0) self._Status = value if self._parent: self._parent._notify() if self._Status == 'Done': self.Error = '' @property def Order(self): """ order prop """ if self._parent: self._Order = self._parent.indexOf(self) if self._parent else -1 return self._Order @Order.setter def Order(self, value): """ order prop """ self._Order = value @property def CreationTime(self): """ operation creation time prop """ return self._CreationTime @CreationTime.setter def CreationTime(self, value=None): """ creation time setter """ if not isinstance(value, (datetime.datetime,) + six.string_types): raise TypeError("CreationTime should be a datetime.datetime!") if isinstance(value, six.string_types): value = datetime.datetime.strptime(value.split(".")[0], self._datetimeFormat) self._CreationTime = value @property def SubmitTime(self): """ subrequest's submit time prop """ return self._SubmitTime @SubmitTime.setter def SubmitTime(self, value=None): """ submit time setter """ if not isinstance(value, (datetime.datetime,) + six.string_types): raise TypeError("SubmitTime should be a datetime.datetime!") if isinstance(value, six.string_types): value = datetime.datetime.strptime(value.split(".")[0], self._datetimeFormat) self._SubmitTime = value @property def LastUpdate(self): """ last update prop """ return self._LastUpdate @LastUpdate.setter def LastUpdate(self, value=None): """ last update setter """ if not isinstance(value, (datetime.datetime,) + six.string_types): raise TypeError("LastUpdate should be a datetime.datetime!") if isinstance(value, six.string_types): value = datetime.datetime.strptime(value.split(".")[0], self._datetimeFormat) self._LastUpdate = value if self._parent: self._parent.LastUpdate = value def __str__(self): """ str operator """ return self.toJSON()['Value'] def toJSON(self): """ Returns the JSON description string of the Operation """ try: jsonStr = json.dumps(self, cls=RMSEncoder) return S_OK(jsonStr) except Exception as e: return S_ERROR(str(e)) def _getJSONData(self): """ Returns the data that have to be serialized by JSON """ jsonData = {} for attrName in Operation.ATTRIBUTE_NAMES: # RequestID and OperationID might not be set since they are managed by SQLAlchemy if not hasattr(self, attrName): continue value = getattr(self, attrName) if isinstance(value, datetime.datetime): # We convert date time to a string jsonData[attrName] = value.strftime(self._datetimeFormat) # pylint: disable=no-member else: jsonData[attrName] = value jsonData['Files'] = self.__files__ return jsonData
yujikato/DIRAC
src/DIRAC/RequestManagementSystem/Client/Operation.py
Python
gpl-3.0
10,783
[ "DIRAC" ]
9f34204433c62d5696027f0b122590aea904425cdccee9157018a00f2ca11e7d
from math import sqrt import numpy as np from ase.atoms import Atoms from ase.units import Bohr, Hartree from ase.dft.stm import STM from ase.io.cube import write_cube from ase.io.plt import write_plt import gpaw.mpi as mpi from gpaw.mpi import MASTER from gpaw.io.plt import read_plt from gpaw.grid_descriptor import GridDescriptor class SimpleStm: """Simple STM object to simulate STM pictures. The simulation uses either a single pseudo-wavefunction (PWF) or the PWFs inside the given bias range.""" def __init__(self, atoms): self.file = None self.is_wf = False self.bias = None self.ldos = None self.heights = None if isinstance(atoms, str): self.read_3D(atoms) self.calc = None else: if isinstance(atoms, Atoms): self.calc = atoms.get_calculator() else: self.calc = atoms self.calc.converge_wave_functions() self.gd = self.calc.wfs.gd self.offset_c = [int(not a) for a in self.gd.pbc_c] def calculate_ldos(self, bias): """bias is the n, k, s list/tuple.""" if self.calc is None: return self.bias = bias self.is_wf = True self.ldos = self.gd.zeros() if hasattr(bias, '__len__') and len(bias) == 3: n, k, s = bias # only a single wf requested u = self.calc.get_myu(k, s) if u is not None: self.add_wf_to_ldos(n, u, weight=1) else: # energy bias try: if self.calc.occupations.fixmagmom is True: efermi_s = self.calc.get_fermi_levels() else: efermi_s = np.array([self.calc.get_fermi_level()] * 2) except: efermi_s = np.array([self.calc.get_homo_lumo().mean()] * 2) if isinstance(bias, (int, long, float)): # bias given if bias > 0: # positive bias = negative tip # -> probe unoccupied states emin_s = efermi_s emax_s = efermi_s + bias occupied = False else: # negative bias = positive tip # -> probe occupied states emin_s = efermi_s + bias emax_s = efermi_s occupied = True else: # emin and emax given emin, emax = bias if abs(emin) > abs(emax): occupied = True else: occupied = False emin_s = np.array([emin + efermi] * 2) emax_s = np.array([emax + efermi] * 2) emin_s /= Hartree emax_s /= Hartree for u in range(len(self.calc.wfs.kpt_u)): kpt = self.calc.wfs.kpt_u[u] emin = emin_s[kpt.s] emax = emax_s[kpt.s] for n, eps in enumerate(kpt.eps_n): if eps > emin and eps < emax: if occupied: weight = kpt.f_n[n] else: weight = kpt.weight - kpt.f_n[n] self.add_wf_to_ldos(n, u, weight) def add_wf_to_ldos(self, n, u, weight=None): """Add the wf with given kpoint and spin to the ldos""" kpt = self.calc.wfs.kpt_u[u] psi = kpt.psit_nG[n] w = weight if w is None: w = kpt.weight ## print "w=", w, kpt.weight self.ldos += w * (psi * np.conj(psi)).real def write_3D(self, bias, file, filetype=None): """Write the density as a 3D file. Units: [e/A^3]""" self.calculate_ldos(bias) self.calc.wfs.kpt_comm.sum(self.ldos) ldos = self.gd.collect(self.ldos) ## print "write: integrated =", self.gd.integrate(self.ldos) if mpi.rank != MASTER: return if filetype is None: # estimate file type from name ending filetype = file.split('.')[-1] filetype.lower() if filetype == 'cube': write_cube(file, self.calc.get_atoms(), ldos / Bohr**3) elif filetype == 'plt': write_plt(file, self.calc.get_atoms(), ldos / Bohr**3) else: raise NotImplementedError('unknown file type "' + filetype + '"') def read_3D(self, file, filetype=None): """Read the density from a 3D file""" if filetype is None: # estimate file type from name ending filetype = file.split('.')[-1] filetype.lower() if filetype == 'plt': cell, grid = read_plt(file)[:2] pbc_c = [True, True, True] N_c = np.array(grid.shape) for c in range(3): if N_c[c] % 2 == 1: pbc_c[c] = False N_c[c] += 1 self.gd = GridDescriptor(N_c, cell.diagonal() / Bohr, pbc_c) self.offset_c = [int(not a) for a in self.gd.pbc_c] else: raise NotImplementedError('unknown file type "' + filetype + '"') self.file = file self.ldos = np.array(grid * Bohr**3, np.float) ## print "read: integrated =", self.gd.integrate(self.ldos) def current_to_density(self, current): """The connection between density n and current I n [e/Angstrom^3] = 0.0002 sqrt(I [nA]) as given in Hofer et al., RevModPhys 75 (2003) 1287 """ return 0.0002 * sqrt(current) def density_to_current(self, density): return 5000. * density**2 def scan_const_current(self, current, bias=None, interpolate=False, hmax=None): """Get the height image for constant current I [nA]. hmax is the maximal height to consider """ return self.scan_const_density(self.current_to_density(current), bias, interpolate, hmax) def scan_const_density(self, density, bias, interpolate=False, hmax=None): """Get the height image for constant density [e/Angstrom^3]. """ self.calculate_ldos(bias) self.density = density gd = self.gd h_c = [np.linalg.norm(gd.h_cv[c]) for c in range(3)] nx, ny = (gd.N_c - self.offset_c)[:2] # each cpu will have the full array, but works on its # own part only heights = np.zeros((nx, ny)) - 1 if hmax is None: hmax = h_c[2] * self.ldos.shape[2] + h_c[2] / 2. else: hmax /= Bohr ihmax = min(gd.end_c[2]-1, int(hmax / h_c[2])) for i in range(gd.beg_c[0], gd.end_c[0]): ii = i - gd.beg_c[0] for j in range(gd.beg_c[1], gd.end_c[1]): jj = j - gd.beg_c[1] zline = self.ldos[ii, jj] # check from above until you find the required density for k in range(ihmax, gd.beg_c[2]-1, -1): kk = k - gd.beg_c[2] if zline[kk] > density: heights[i - self.offset_c[0], j - self.offset_c[1]] = k break # collect the results gd.comm.max(heights) if interpolate: # collect the full grid to enable interpolation fullgrid = gd.collect(self.ldos) kmax = self.ldos.shape[2] - 1 for i in range(gd.beg_c[0], gd.end_c[0]): ii = i - gd.beg_c[0] i -= self.offset_c[0] for j in range(gd.beg_c[1], gd.end_c[1]): jj = j - gd.beg_c[1] j -= self.offset_c[1] if heights[i, j] > 0: if heights[i, j] < kmax: c1 = fullgrid[i, j, int(heights[i, j])] c2 = fullgrid[i, j, int(heights[i, j])+1] k = heights[i, j] + (density - c1) / (c2 - c1) else: k = kmax self.heights = np.where(heights > 0, (heights + self.offset_c[2]) * h_c[2], -1) return heights def write(self, file=None): """Write STM data to a file in gnuplot readable tyle.""" if mpi.rank != MASTER: return xvals, yvals, heights = self.pylab_contour() nx, ny = heights.shape[:2] if file is None: n, k, s = bias fname = 'stm_n%dk%ds%d.dat' % (n, k, s) else: fname = file f = open(fname, 'w') try: import datetime print >> f, '#', datetime.datetime.now().ctime() except: pass print >> f, '# Simulated STM picture' if hasattr(self, 'file'): print >> f, '# density read from', self.file else: if self.is_wf: print >> f, '# pseudo-wf n=%d k=%d s=%d' % tuple(self.bias) else: print >> f, '# bias=', self.bias, '[eV]' print >> f, '#' print >> f, '# density=', self.density, '[e/Angstrom^3]', print >> f, '(current=', self.density_to_current(self.density), '[nA])' print >> f, '# x[Angs.] y[Angs.] h[Angs.] (-1 is not found)' for i in range(nx): for j in range(ny): if heights[i, j] == -1: height = -1 else: height = heights[i, j] * Bohr print >> f, '%10g %10g %12g' % (yvals[j], xvals[i], height) print >> f f.close() def pylab_contour(self): """Return the countour to be plotted using pylab.""" nx, ny = self.heights.shape[:2] h_c = np.array([np.linalg.norm(self.gd.h_cv[c]) for c in range(3)]) * Bohr # the lowest point is not stored for non-periodic BCs xvals = [(i + self.offset_c[0]) * h_c[0] for i in range(nx)] yvals = [(i + self.offset_c[1]) * h_c[1] for i in range(ny)] heights = self.heights * Bohr # pylab interprets heights[y_i][x_i] return np.array(xvals), np.array(yvals), heights.swapaxes(0,1)
robwarm/gpaw-symm
gpaw/analyse/simple_stm.py
Python
gpl-3.0
10,576
[ "ASE", "GPAW" ]
7f1d5458fcead3c00ad153ee90efc235c82e9acec66756c4727eea5cbbca79d6
# -*- coding: utf-8 -*- """ Acceptance tests for Video. """ from nose.plugins.attrib import attr from unittest import skipIf, skip from ..helpers import UniqueCourseTest, is_youtube_available, YouTubeStubConfig from ...pages.lms.video.video import VideoPage from ...pages.lms.tab_nav import TabNavPage from ...pages.lms.course_nav import CourseNavPage from ...pages.lms.auto_auth import AutoAuthPage from ...pages.lms.course_info import CourseInfoPage from ...fixtures.course import CourseFixture, XBlockFixtureDesc from ..helpers import skip_if_browser from flaky import flaky VIDEO_SOURCE_PORT = 8777 HTML5_SOURCES = [ 'http://localhost:{0}/gizmo.mp4'.format(VIDEO_SOURCE_PORT), 'http://localhost:{0}/gizmo.webm'.format(VIDEO_SOURCE_PORT), 'http://localhost:{0}/gizmo.ogv'.format(VIDEO_SOURCE_PORT), ] HTML5_SOURCES_INCORRECT = [ 'http://localhost:{0}/gizmo.mp99'.format(VIDEO_SOURCE_PORT), ] @attr('shard_4') @skipIf(is_youtube_available() is False, 'YouTube is not available!') class VideoBaseTest(UniqueCourseTest): """ Base class for tests of the Video Player Sets up the course and provides helper functions for the Video tests. """ def setUp(self): """ Initialization of pages and course fixture for video tests """ super(VideoBaseTest, self).setUp() self.video = VideoPage(self.browser) self.tab_nav = TabNavPage(self.browser) self.course_nav = CourseNavPage(self.browser) self.course_info_page = CourseInfoPage(self.browser, self.course_id) self.auth_page = AutoAuthPage(self.browser, course_id=self.course_id) self.course_fixture = CourseFixture( self.course_info['org'], self.course_info['number'], self.course_info['run'], self.course_info['display_name'] ) self.metadata = None self.assets = [] self.verticals = None self.youtube_configuration = {} self.user_info = {} # reset youtube stub server self.addCleanup(YouTubeStubConfig.reset) def navigate_to_video(self): """ Prepare the course and get to the video and render it """ self._install_course_fixture() self._navigate_to_courseware_video_and_render() def navigate_to_video_no_render(self): """ Prepare the course and get to the video unit however do not wait for it to render, because the has been an error. """ self._install_course_fixture() self._navigate_to_courseware_video_no_render() def _install_course_fixture(self): """ Install the course fixture that has been defined """ if self.assets: self.course_fixture.add_asset(self.assets) chapter_sequential = XBlockFixtureDesc('sequential', 'Test Section') chapter_sequential.add_children(*self._add_course_verticals()) chapter = XBlockFixtureDesc('chapter', 'Test Chapter').add_children(chapter_sequential) self.course_fixture.add_children(chapter) self.course_fixture.install() if len(self.youtube_configuration) > 0: YouTubeStubConfig.configure(self.youtube_configuration) def _add_course_verticals(self): """ Create XBlockFixtureDesc verticals :return: a list of XBlockFixtureDesc """ xblock_verticals = [] _verticals = self.verticals # Video tests require at least one vertical with a single video. if not _verticals: _verticals = [[{'display_name': 'Video', 'metadata': self.metadata}]] for vertical_index, vertical in enumerate(_verticals): xblock_verticals.append(self._create_single_vertical(vertical, vertical_index)) return xblock_verticals def _create_single_vertical(self, vertical, vertical_index): """ Create a single course vertical of type XBlockFixtureDesc with category `vertical`. A single course vertical can contain single or multiple video modules. :param vertical: vertical data list :param vertical_index: index for the vertical display name :return: XBlockFixtureDesc """ xblock_course_vertical = XBlockFixtureDesc('vertical', 'Test Vertical-{0}'.format(vertical_index)) for video in vertical: xblock_course_vertical.add_children( XBlockFixtureDesc('video', video['display_name'], metadata=video.get('metadata'))) return xblock_course_vertical def _navigate_to_courseware_video(self): """ Register for the course and navigate to the video unit """ self.auth_page.visit() self.user_info = self.auth_page.user_info self.course_info_page.visit() self.tab_nav.go_to_tab('Courseware') def _navigate_to_courseware_video_and_render(self): """ Wait for the video player to render """ self._navigate_to_courseware_video() self.video.wait_for_video_player_render() def _navigate_to_courseware_video_no_render(self): """ Wait for the video Xmodule but not for rendering """ self._navigate_to_courseware_video() self.video.wait_for_video_class() def metadata_for_mode(self, player_mode, additional_data=None): """ Create a dictionary for video player configuration according to `player_mode` :param player_mode (str): Video player mode :param additional_data (dict): Optional additional metadata. :return: dict """ metadata = {} if player_mode == 'html5': metadata.update({ 'youtube_id_1_0': '', 'youtube_id_0_75': '', 'youtube_id_1_25': '', 'youtube_id_1_5': '', 'html5_sources': HTML5_SOURCES }) if player_mode == 'youtube_html5': metadata.update({ 'html5_sources': HTML5_SOURCES, }) if player_mode == 'youtube_html5_unsupported_video': metadata.update({ 'html5_sources': HTML5_SOURCES_INCORRECT }) if player_mode == 'html5_unsupported_video': metadata.update({ 'youtube_id_1_0': '', 'youtube_id_0_75': '', 'youtube_id_1_25': '', 'youtube_id_1_5': '', 'html5_sources': HTML5_SOURCES_INCORRECT }) if additional_data: metadata.update(additional_data) return metadata def go_to_sequential_position(self, position): """ Navigate to sequential specified by `video_display_name` """ self.course_nav.go_to_sequential_position(position) self.video.wait_for_video_player_render() class YouTubeVideoTest(VideoBaseTest): """ Test YouTube Video Player """ def setUp(self): super(YouTubeVideoTest, self).setUp() def test_youtube_video_rendering_wo_html5_sources(self): """ Scenario: Video component is rendered in the LMS in Youtube mode without HTML5 sources Given the course has a Video component in "Youtube" mode Then the video has rendered in "Youtube" mode """ self.navigate_to_video() # Verify that video has rendered in "Youtube" mode self.assertTrue(self.video.is_video_rendered('youtube')) def test_cc_button_wo_english_transcript(self): """ Scenario: CC button works correctly w/o english transcript in Youtube mode Given the course has a Video component in "Youtube" mode And I have defined a non-english transcript for the video And I have uploaded a non-english transcript file to assets Then I see the correct text in the captions """ data = {'transcripts': {'zh': 'chinese_transcripts.srt'}} self.metadata = self.metadata_for_mode('youtube', data) self.assets.append('chinese_transcripts.srt') self.navigate_to_video() self.video.show_captions() # Verify that we see "好 各位同学" text in the captions unicode_text = "好 各位同学".decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) def test_cc_button_transcripts_and_sub_fields_empty(self): """ Scenario: CC button works correctly if transcripts and sub fields are empty, but transcript file exists in assets (Youtube mode of Video component) Given the course has a Video component in "Youtube" mode And I have uploaded a .srt.sjson file to assets Then I see the correct english text in the captions """ self._install_course_fixture() self.course_fixture.add_asset(['subs_3_yD_cEKoCk.srt.sjson']) self.course_fixture._upload_assets() self._navigate_to_courseware_video_and_render() self.video.show_captions() # Verify that we see "Welcome to edX." text in the captions self.assertIn('Welcome to edX.', self.video.captions_text) def test_cc_button_hidden_no_translations(self): """ Scenario: CC button is hidden if no translations Given the course has a Video component in "Youtube" mode Then the "CC" button is hidden """ self.navigate_to_video() self.assertFalse(self.video.is_button_shown('CC')) def test_fullscreen_video_alignment_with_transcript_hidden(self): """ Scenario: Video is aligned with transcript hidden in fullscreen mode Given the course has a Video component in "Youtube" mode When I view the video at fullscreen Then the video with the transcript hidden is aligned correctly """ self.navigate_to_video() # click video button "fullscreen" self.video.click_player_button('fullscreen') # check if video aligned correctly without enabled transcript self.assertTrue(self.video.is_aligned(False)) def test_download_button_wo_english_transcript(self): """ Scenario: Download button works correctly w/o english transcript in YouTube mode Given the course has a Video component in "Youtube" mode And I have defined a downloadable non-english transcript for the video And I have uploaded a non-english transcript file to assets Then I can download the transcript in "srt" format """ data = {'download_track': True, 'transcripts': {'zh': 'chinese_transcripts.srt'}} self.metadata = self.metadata_for_mode('youtube', additional_data=data) self.assets.append('chinese_transcripts.srt') # go to video self.navigate_to_video() # check if we can download transcript in "srt" format that has text "好 各位同学" unicode_text = "好 各位同学".decode('utf-8') self.assertTrue(self.video.downloaded_transcript_contains_text('srt', unicode_text)) def test_download_button_two_transcript_languages(self): """ Scenario: Download button works correctly for multiple transcript languages Given the course has a Video component in "Youtube" mode And I have defined a downloadable non-english transcript for the video And I have defined english subtitles for the video Then I see the correct english text in the captions And the english transcript downloads correctly And I see the correct non-english text in the captions And the non-english transcript downloads correctly """ self.assets.extend(['chinese_transcripts.srt', 'subs_3_yD_cEKoCk.srt.sjson']) data = {'download_track': True, 'transcripts': {'zh': 'chinese_transcripts.srt'}, 'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('youtube', additional_data=data) # go to video self.navigate_to_video() # check if "Welcome to edX." text in the captions self.assertIn('Welcome to edX.', self.video.captions_text) # check if we can download transcript in "srt" format that has text "Welcome to edX." self.assertTrue(self.video.downloaded_transcript_contains_text('srt', 'Welcome to edX.')) # select language with code "zh" self.assertTrue(self.video.select_language('zh')) # check if we see "好 各位同学" text in the captions unicode_text = "好 各位同学".decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) # check if we can download transcript in "srt" format that has text "好 各位同学" unicode_text = "好 各位同学".decode('utf-8') self.assertTrue(self.video.downloaded_transcript_contains_text('srt', unicode_text)) def test_fullscreen_video_alignment_on_transcript_toggle(self): """ Scenario: Video is aligned correctly on transcript toggle in fullscreen mode Given the course has a Video component in "Youtube" mode And I have uploaded a .srt.sjson file to assets And I have defined subtitles for the video When I view the video at fullscreen Then the video with the transcript enabled is aligned correctly And the video with the transcript hidden is aligned correctly """ self.assets.append('subs_3_yD_cEKoCk.srt.sjson') data = {'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('youtube', additional_data=data) # go to video self.navigate_to_video() # make sure captions are opened self.video.show_captions() # click video button "fullscreen" self.video.click_player_button('fullscreen') # check if video aligned correctly with enabled transcript self.assertTrue(self.video.is_aligned(True)) # click video button "CC" self.video.click_player_button('CC') # check if video aligned correctly without enabled transcript self.assertTrue(self.video.is_aligned(False)) def test_video_rendering_with_default_response_time(self): """ Scenario: Video is rendered in Youtube mode when the YouTube Server responds quickly Given the YouTube server response time less than 1.5 seconds And the course has a Video component in "Youtube_HTML5" mode Then the video has rendered in "Youtube" mode """ # configure youtube server self.youtube_configuration['time_to_response'] = 0.4 self.metadata = self.metadata_for_mode('youtube_html5') self.navigate_to_video() self.assertTrue(self.video.is_video_rendered('youtube')) def test_video_rendering_wo_default_response_time(self): """ Scenario: Video is rendered in HTML5 when the YouTube Server responds slowly Given the YouTube server response time is greater than 1.5 seconds And the course has a Video component in "Youtube_HTML5" mode Then the video has rendered in "HTML5" mode """ # configure youtube server self.youtube_configuration['time_to_response'] = 2.0 self.metadata = self.metadata_for_mode('youtube_html5') self.navigate_to_video() self.assertTrue(self.video.is_video_rendered('html5')) def test_video_with_youtube_blocked(self): """ Scenario: Video is rendered in HTML5 mode when the YouTube API is blocked Given the YouTube server response time is greater than 1.5 seconds And the YouTube API is blocked And the course has a Video component in "Youtube_HTML5" mode Then the video has rendered in "HTML5" mode """ # configure youtube server self.youtube_configuration.update({ 'time_to_response': 2.0, 'youtube_api_blocked': True, }) self.metadata = self.metadata_for_mode('youtube_html5') self.navigate_to_video() self.assertTrue(self.video.is_video_rendered('html5')) def test_download_transcript_button_works_correctly(self): """ Scenario: Download Transcript button works correctly Given the course has Video components A and B in "Youtube" mode And Video component C in "HTML5" mode And I have defined downloadable transcripts for the videos Then I can download a transcript for Video A in "srt" format And I can download a transcript for Video A in "txt" format And I can download a transcript for Video B in "txt" format And the Download Transcript menu does not exist for Video C """ data_a = {'sub': '3_yD_cEKoCk', 'download_track': True} youtube_a_metadata = self.metadata_for_mode('youtube', additional_data=data_a) self.assets.append('subs_3_yD_cEKoCk.srt.sjson') data_b = {'youtube_id_1_0': 'b7xgknqkQk8', 'sub': 'b7xgknqkQk8', 'download_track': True} youtube_b_metadata = self.metadata_for_mode('youtube', additional_data=data_b) self.assets.append('subs_b7xgknqkQk8.srt.sjson') data_c = {'track': 'http://example.org/', 'download_track': True} html5_c_metadata = self.metadata_for_mode('html5', additional_data=data_c) self.verticals = [ [{'display_name': 'A', 'metadata': youtube_a_metadata}], [{'display_name': 'B', 'metadata': youtube_b_metadata}], [{'display_name': 'C', 'metadata': html5_c_metadata}] ] # open the section with videos (open video "A") self.navigate_to_video() # check if we can download transcript in "srt" format that has text "00:00:00,260" self.assertTrue(self.video.downloaded_transcript_contains_text('srt', '00:00:00,260')) # select the transcript format "txt" self.assertTrue(self.video.select_transcript_format('txt')) # check if we can download transcript in "txt" format that has text "Welcome to edX." self.assertTrue(self.video.downloaded_transcript_contains_text('txt', 'Welcome to edX.')) # open video "B" self.course_nav.go_to_sequential('B') # check if we can download transcript in "txt" format that has text "Equal transcripts" self.assertTrue(self.video.downloaded_transcript_contains_text('txt', 'Equal transcripts')) # open video "C" self.course_nav.go_to_sequential('C') # menu "download_transcript" doesn't exist self.assertFalse(self.video.is_menu_present('download_transcript')) def _verify_caption_text(self, text): self.video._wait_for( lambda: (text in self.video.captions_text), u'Captions contain "{}" text'.format(text), timeout=5 ) def test_video_language_menu_working(self): """ Scenario: Language menu works correctly in Video component Given the course has a Video component in "Youtube" mode And I have defined multiple language transcripts for the videos And I make sure captions are closed And I see video menu "language" with correct items And I select language with code "zh" Then I see "好 各位同学" text in the captions And I select language with code "en" Then I see "Welcome to edX." text in the captions """ self.assets.extend(['chinese_transcripts.srt', 'subs_3_yD_cEKoCk.srt.sjson']) data = {'transcripts': {"zh": "chinese_transcripts.srt"}, 'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('youtube', additional_data=data) # go to video self.navigate_to_video() self.video.hide_captions() correct_languages = {'en': 'English', 'zh': 'Chinese'} self.assertEqual(self.video.caption_languages, correct_languages) self.video.select_language('zh') unicode_text = "好 各位同学".decode('utf-8') self._verify_caption_text(unicode_text) self.video.select_language('en') self._verify_caption_text('Welcome to edX.') def test_multiple_videos_in_sequentials_load_and_work(self): """ Scenario: Multiple videos in sequentials all load and work, switching between sequentials Given it has videos "A,B" in "Youtube" mode in position "1" of sequential And videos "E,F" in "Youtube" mode in position "2" of sequential """ self.verticals = [ [{'display_name': 'A'}, {'display_name': 'B'}], [{'display_name': 'C'}, {'display_name': 'D'}] ] tab1_video_names = ['A', 'B'] tab2_video_names = ['C', 'D'] def execute_video_steps(video_names): """ Execute video steps """ for video_name in video_names: self.video.use_video(video_name) self.video.click_player_button('play') self.assertIn(self.video.state, ['playing', 'buffering']) self.video.click_player_button('pause') # go to video self.navigate_to_video() execute_video_steps(tab1_video_names) # go to second sequential position self.go_to_sequential_position(2) execute_video_steps(tab2_video_names) # go back to first sequential position # we are again playing tab 1 videos to ensure that switching didn't broke some video functionality. self.go_to_sequential_position(1) execute_video_steps(tab1_video_names) def test_video_component_stores_speed_correctly_for_multiple_videos(self): """ Scenario: Video component stores speed correctly when each video is in separate sequential Given I have a video "A" in "Youtube" mode in position "1" of sequential And a video "B" in "Youtube" mode in position "2" of sequential And a video "C" in "HTML5" mode in position "3" of sequential """ self.verticals = [ [{'display_name': 'A'}], [{'display_name': 'B'}], [{'display_name': 'C', 'metadata': self.metadata_for_mode('html5')}] ] self.navigate_to_video() # select the "2.0" speed on video "A" self.course_nav.go_to_sequential('A') self.video.speed = '2.0' # select the "0.50" speed on video "B" self.course_nav.go_to_sequential('B') self.video.speed = '0.50' # open video "C" self.course_nav.go_to_sequential('C') # check if video "C" should start playing at speed "0.75" self.assertEqual(self.video.speed, '0.75x') # open video "A" self.course_nav.go_to_sequential('A') # check if video "A" should start playing at speed "2.0" self.assertEqual(self.video.speed, '2.0x') # reload the page self.video.reload_page() # open video "A" self.course_nav.go_to_sequential('A') # check if video "A" should start playing at speed "2.0" self.assertEqual(self.video.speed, '2.0x') # select the "1.0" speed on video "A" self.video.speed = '1.0' # open video "B" self.course_nav.go_to_sequential('B') # check if video "B" should start playing at speed "0.50" self.assertEqual(self.video.speed, '0.50x') # open video "C" self.course_nav.go_to_sequential('C') # check if video "C" should start playing at speed "1.0" self.assertEqual(self.video.speed, '1.0x') def test_video_has_correct_transcript(self): """ Scenario: Youtube video has correct transcript if fields for other speeds are filled Given it has a video in "Youtube" mode And I have uploaded multiple transcripts And I make sure captions are opened Then I see "Welcome to edX." text in the captions And I select the "1.50" speed And I reload the page with video Then I see "Welcome to edX." text in the captions And I see duration "1:56" """ self.assets.extend(['subs_3_yD_cEKoCk.srt.sjson', 'subs_b7xgknqkQk8.srt.sjson']) data = {'sub': '3_yD_cEKoCk', 'youtube_id_1_5': 'b7xgknqkQk8'} self.metadata = self.metadata_for_mode('youtube', additional_data=data) # go to video self.navigate_to_video() self.video.show_captions() self.assertIn('Welcome to edX.', self.video.captions_text) self.video.speed = '1.50' self.video.reload_page() self.assertIn('Welcome to edX.', self.video.captions_text) self.assertTrue(self.video.duration, '1.56') def test_video_position_stored_correctly_wo_seek(self): """ Scenario: Video component stores position correctly when page is reloaded Given the course has a Video component in "Youtube" mode Then the video has rendered in "Youtube" mode And I click video button "play"" Then I wait until video reaches at position "0.05" And I click video button "pause" And I reload the page with video And I click video button "play"" And I click video button "pause" Then video slider should be Equal or Greater than "0:05" """ self.navigate_to_video() self.video.click_player_button('play') self.video.wait_for_position('0:05') self.video.click_player_button('pause') self.video.reload_page() self.video.click_player_button('play') self.video.click_player_button('pause') self.assertGreaterEqual(self.video.seconds, 5) @skip("Intermittently fails 03 June 2014") def test_video_position_stored_correctly_with_seek(self): """ Scenario: Video component stores position correctly when page is reloaded Given the course has a Video component in "Youtube" mode Then the video has rendered in "Youtube" mode And I click video button "play"" And I click video button "pause" Then I seek video to "0:10" position And I click video button "play"" And I click video button "pause" And I reload the page with video Then video slider should be Equal or Greater than "0:10" """ self.navigate_to_video() self.video.click_player_button('play') self.video.seek('0:10') self.video.click_player_button('pause') self.video.reload_page() self.video.click_player_button('play') self.video.click_player_button('pause') self.assertGreaterEqual(self.video.seconds, 10) def test_simplified_and_traditional_chinese_transcripts(self): """ Scenario: Simplified and Traditional Chinese transcripts work as expected in Youtube mode Given the course has a Video component in "Youtube" mode And I have defined a Simplified Chinese transcript for the video And I have defined a Traditional Chinese transcript for the video Then I see the correct subtitle language options in cc menu Then I see the correct text in the captions for Simplified and Traditional Chinese transcripts And I can download the transcripts for Simplified and Traditional Chinese And video subtitle menu has 'zh_HANS', 'zh_HANT' translations for 'Simplified Chinese' and 'Traditional Chinese' respectively """ data = { 'download_track': True, 'transcripts': {'zh_HANS': 'simplified_chinese.srt', 'zh_HANT': 'traditional_chinese.srt'} } self.metadata = self.metadata_for_mode('youtube', data) self.assets.extend(['simplified_chinese.srt', 'traditional_chinese.srt']) self.navigate_to_video() langs = {'zh_HANS': '在线学习是革', 'zh_HANT': '在線學習是革'} for lang_code, text in langs.items(): self.assertTrue(self.video.select_language(lang_code)) unicode_text = text.decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) self.assertTrue(self.video.downloaded_transcript_contains_text('srt', unicode_text)) self.assertEqual(self.video.caption_languages, {'zh_HANS': 'Simplified Chinese', 'zh_HANT': 'Traditional Chinese'}) def test_video_bumper_render(self): """ Scenario: Multiple videos with bumper in sequentials all load and work, switching between sequentials Given it has videos "A,B" in "Youtube" and "HTML5" modes in position "1" of sequential And video "C" in "Youtube" mode in position "2" of sequential When I open sequential position "1" Then I see video "B" has a poster When I click on it Then I see video bumper is playing When I skip the bumper Then I see the main video When I click on video "A" Then the main video starts playing When I open sequential position "2" And click on the poster Then the main video starts playing Then I see that the main video starts playing once I go back to position "2" of sequential When I reload the page Then I see that the main video starts playing when I click on the poster """ additional_data = { u'video_bumper': { u'value': { "transcripts": {}, "video_id": "video_001" } } } self.verticals = [ [{'display_name': 'A'}, {'display_name': 'B', 'metadata': self.metadata_for_mode('html5')}], [{'display_name': 'C'}] ] tab1_video_names = ['A', 'B'] tab2_video_names = ['C'] def execute_video_steps(video_names): """ Execute video steps """ for video_name in video_names: self.video.use_video(video_name) self.assertTrue(self.video.is_poster_shown) self.video.click_on_poster() self.video.wait_for_video_player_render(autoplay=True) self.assertIn(self.video.state, ['playing', 'buffering', 'finished']) self.course_fixture.add_advanced_settings(additional_data) self.navigate_to_video_no_render() self.video.use_video('B') self.assertTrue(self.video.is_poster_shown) self.video.click_on_poster() self.video.wait_for_video_bumper_render() self.assertIn(self.video.state, ['playing', 'buffering', 'finished']) self.video.click_player_button('skip_bumper') # no autoplay here, maybe video is too small, so pause is not switched self.video.wait_for_video_player_render() self.assertIn(self.video.state, ['playing', 'buffering', 'finished']) self.video.use_video('A') execute_video_steps(['A']) # go to second sequential position self.course_nav.go_to_sequential_position(2) execute_video_steps(tab2_video_names) # go back to first sequential position # we are again playing tab 1 videos to ensure that switching didn't broke some video functionality. self.course_nav.go_to_sequential_position(1) execute_video_steps(tab1_video_names) self.video.browser.refresh() execute_video_steps(tab1_video_names) class YouTubeHtml5VideoTest(VideoBaseTest): """ Test YouTube HTML5 Video Player """ def setUp(self): super(YouTubeHtml5VideoTest, self).setUp() @flaky # TODO fix this, see TNL-1642 def test_youtube_video_rendering_with_unsupported_sources(self): """ Scenario: Video component is rendered in the LMS in Youtube mode with HTML5 sources that doesn't supported by browser Given the course has a Video component in "Youtube_HTML5_Unsupported_Video" mode Then the video has rendered in "Youtube" mode """ self.metadata = self.metadata_for_mode('youtube_html5_unsupported_video') self.navigate_to_video() # Verify that the video has rendered in "Youtube" mode self.assertTrue(self.video.is_video_rendered('youtube')) class Html5VideoTest(VideoBaseTest): """ Test HTML5 Video Player """ def setUp(self): super(Html5VideoTest, self).setUp() def test_autoplay_disabled_for_video_component(self): """ Scenario: Autoplay is disabled by default for a Video component Given the course has a Video component in "HTML5" mode When I view the Video component Then it does not have autoplay enabled """ self.metadata = self.metadata_for_mode('html5') self.navigate_to_video() # Verify that the video has autoplay mode disabled self.assertFalse(self.video.is_autoplay_enabled) def test_html5_video_rendering_with_unsupported_sources(self): """ Scenario: LMS displays an error message for HTML5 sources that are not supported by browser Given the course has a Video component in "HTML5_Unsupported_Video" mode When I view the Video component Then and error message is shown And the error message has the correct text """ self.metadata = self.metadata_for_mode('html5_unsupported_video') self.navigate_to_video_no_render() # Verify that error message is shown self.assertTrue(self.video.is_error_message_shown) # Verify that error message has correct text correct_error_message_text = 'No playable video sources found.' self.assertIn(correct_error_message_text, self.video.error_message_text) # Verify that spinner is not shown self.assertFalse(self.video.is_spinner_shown) def test_download_button_wo_english_transcript(self): """ Scenario: Download button works correctly w/o english transcript in HTML5 mode Given the course has a Video component in "HTML5" mode And I have defined a downloadable non-english transcript for the video And I have uploaded a non-english transcript file to assets Then I see the correct non-english text in the captions And the non-english transcript downloads correctly """ data = {'download_track': True, 'transcripts': {'zh': 'chinese_transcripts.srt'}} self.metadata = self.metadata_for_mode('html5', additional_data=data) self.assets.append('chinese_transcripts.srt') # go to video self.navigate_to_video() # check if we see "好 各位同学" text in the captions unicode_text = "好 各位同学".decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) # check if we can download transcript in "srt" format that has text "好 各位同学" unicode_text = "好 各位同学".decode('utf-8') self.assertTrue(self.video.downloaded_transcript_contains_text('srt', unicode_text)) def test_download_button_two_transcript_languages(self): """ Scenario: Download button works correctly for multiple transcript languages in HTML5 mode Given the course has a Video component in "HTML5" mode And I have defined a downloadable non-english transcript for the video And I have defined english subtitles for the video Then I see the correct english text in the captions And the english transcript downloads correctly And I see the correct non-english text in the captions And the non-english transcript downloads correctly """ self.assets.extend(['chinese_transcripts.srt', 'subs_3_yD_cEKoCk.srt.sjson']) data = {'download_track': True, 'transcripts': {'zh': 'chinese_transcripts.srt'}, 'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('html5', additional_data=data) # go to video self.navigate_to_video() # check if "Welcome to edX." text in the captions self.assertIn('Welcome to edX.', self.video.captions_text) # check if we can download transcript in "srt" format that has text "Welcome to edX." self.assertTrue(self.video.downloaded_transcript_contains_text('srt', 'Welcome to edX.')) # select language with code "zh" self.assertTrue(self.video.select_language('zh')) # check if we see "好 各位同学" text in the captions unicode_text = "好 各位同学".decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) # Then I can download transcript in "srt" format that has text "好 各位同学" unicode_text = "好 各位同学".decode('utf-8') self.assertTrue(self.video.downloaded_transcript_contains_text('srt', unicode_text)) def test_full_screen_video_alignment_with_transcript_visible(self): """ Scenario: Video is aligned correctly with transcript enabled in fullscreen mode Given the course has a Video component in "HTML5" mode And I have uploaded a .srt.sjson file to assets And I have defined subtitles for the video When I show the captions And I view the video at fullscreen Then the video with the transcript enabled is aligned correctly """ self.assets.append('subs_3_yD_cEKoCk.srt.sjson') data = {'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('html5', additional_data=data) # go to video self.navigate_to_video() # make sure captions are opened self.video.show_captions() # click video button "fullscreen" self.video.click_player_button('fullscreen') # check if video aligned correctly with enabled transcript self.assertTrue(self.video.is_aligned(True)) def test_cc_button_with_english_transcript(self): """ Scenario: CC button works correctly with only english transcript in HTML5 mode Given the course has a Video component in "HTML5" mode And I have defined english subtitles for the video And I have uploaded an english transcript file to assets Then I see the correct text in the captions """ self.assets.append('subs_3_yD_cEKoCk.srt.sjson') data = {'sub': '3_yD_cEKoCk'} self.metadata = self.metadata_for_mode('html5', additional_data=data) # go to video self.navigate_to_video() # make sure captions are opened self.video.show_captions() # check if we see "Welcome to edX." text in the captions self.assertIn("Welcome to edX.", self.video.captions_text) def test_cc_button_wo_english_transcript(self): """ Scenario: CC button works correctly w/o english transcript in HTML5 mode Given the course has a Video component in "HTML5" mode And I have defined a non-english transcript for the video And I have uploaded a non-english transcript file to assets Then I see the correct text in the captions """ self.assets.append('chinese_transcripts.srt') data = {'transcripts': {'zh': 'chinese_transcripts.srt'}} self.metadata = self.metadata_for_mode('html5', additional_data=data) # go to video self.navigate_to_video() # make sure captions are opened self.video.show_captions() # check if we see "好 各位同学" text in the captions unicode_text = "好 各位同学".decode('utf-8') self.assertIn(unicode_text, self.video.captions_text) def test_video_rendering(self): """ Scenario: Video component is fully rendered in the LMS in HTML5 mode Given the course has a Video component in "HTML5" mode Then the video has rendered in "HTML5" mode And video sources are correct """ self.metadata = self.metadata_for_mode('html5') self.navigate_to_video() self.assertTrue(self.video.is_video_rendered('html5')) self.assertTrue(all([source in HTML5_SOURCES for source in self.video.sources])) class YouTubeQualityTest(VideoBaseTest): """ Test YouTube Video Quality Button """ def setUp(self): super(YouTubeQualityTest, self).setUp() @skip_if_browser('firefox') def test_quality_button_visibility(self): """ Scenario: Quality button appears on play. Given the course has a Video component in "Youtube" mode Then I see video button "quality" is hidden And I click video button "play" Then I see video button "quality" is visible """ self.navigate_to_video() self.assertFalse(self.video.is_quality_button_visible) self.video.click_player_button('play') self.assertTrue(self.video.is_quality_button_visible) @skip_if_browser('firefox') def test_quality_button_works_correctly(self): """ Scenario: Quality button works correctly. Given the course has a Video component in "Youtube" mode And I click video button "play" And I see video button "quality" is inactive And I click video button "quality" Then I see video button "quality" is active """ self.navigate_to_video() self.video.click_player_button('play') self.assertFalse(self.video.is_quality_button_active) self.video.click_player_button('quality') self.assertTrue(self.video.is_quality_button_active)
zerobatu/edx-platform
common/test/acceptance/tests/video/test_video_module.py
Python
agpl-3.0
41,492
[ "VisIt" ]
76740d0cf1572d39ae951d5ebcca3f57d3a4d630faf577bc7f184ccc0b7eef52
"""Test that compute_delta_cchalf returns required values""" from __future__ import annotations import json from unittest import mock from dxtbx.model import Crystal, Experiment, Scan from dxtbx.model.experiment_list import ExperimentList from libtbx import phil from dials.algorithms.scaling.model.model import KBScalingModel from dials.algorithms.scaling.scale_and_filter import AnalysisResults, log_cycle_results from dials.algorithms.statistics.cc_half_algorithm import CCHalfFromDials, DeltaCCHalf from dials.array_family import flex from dials.util.options import ArgumentParser def generate_test_reflections(n=2): """Generate data for testing.""" reflections = flex.reflection_table() for id_ in range(0, n): r = flex.reflection_table() r["id"] = flex.int(10, id_) r["xyzobs.px.value"] = flex.vec3_double([(0, 0, i + 0.5) for i in range(0, 10)]) r.experiment_identifiers()[id_] = str(id_) r.set_flags(flex.bool(10, True), r.flags.integrated) r.set_flags(flex.bool(10, True), r.flags.scaled) reflections.extend(r) return reflections def generated_params(): """Generate a param phil scope.""" phil_scope = phil.parse( """ include scope dials.algorithms.scaling.scaling_options.phil_scope include scope dials.algorithms.scaling.model.model.model_phil_scope include scope dials.algorithms.scaling.scaling_refiner.scaling_refinery_phil_scope """, process_includes=True, ) parser = ArgumentParser(phil=phil_scope, check_format=False) parameters, _ = parser.parse_args(args=[], quick_parse=True, show_diff_phil=False) parameters.model = "KB" return parameters def get_scaling_model(): """Make a KB Scaling model instance""" return KBScalingModel.from_data(generated_params(), [], []) def generate_test_experiments(n=2): """Make a test experiment list""" experiments = ExperimentList() exp_dict = { "__id__": "crystal", "real_space_a": [1.0, 0.0, 0.0], "real_space_b": [0.0, 1.0, 0.0], "real_space_c": [0.0, 0.0, 2.0], "space_group_hall_symbol": " C 2y", } for i in range(n): experiments.append( Experiment( crystal=Crystal.from_dict(exp_dict), scan=Scan(image_range=[1, 10], oscillation=[0.0, 1.0]), scaling_model=get_scaling_model(), identifier=str(i), ) ) return experiments def test_scale_and_filter_results_logging(): """Test ScaleAndFilter.log_cycle_results method.""" results = AnalysisResults() scaling_script = mock.Mock() scaling_script.merging_statistics_result = "stats_results" scaling_script.scaled_miller_array.size.return_value = 1000 filter_script = mock.Mock() filter_script.results_summary = { "dataset_removal": { "mode": "image_group", "image_ranges_removed": [[(6, 10), 0]], "experiments_fully_removed": [], "experiment_ids_fully_removed": [], "n_reflections_removed": 50, }, "mean_cc_half": 80.0, "per_dataset_delta_cc_half_values": { "delta_cc_half_values": [-0.1, 0.1, -0.2, 0.2] }, } def _parse_side_effect(*args): return args[0] with mock.patch.object( results, "_parse_merging_stats", side_effect=_parse_side_effect ): res = log_cycle_results(results, scaling_script, filter_script) # test things have been logged correctly cycle_results = res.get_cycle_results() assert len(cycle_results) == 1 assert cycle_results[0]["cumul_percent_removed"] == 100 * 50.0 / 1000.0 assert cycle_results[0]["n_removed"] == 50 assert cycle_results[0]["image_ranges_removed"] == [[(6, 10), 0]] assert cycle_results[0]["removed_datasets"] == [] assert cycle_results[0]["delta_cc_half_values"] == [-0.1, 0.1, -0.2, 0.2] assert res.get_merging_stats()[0] == "stats_results" assert res.initial_n_reflections == 1000 # add another cycle of results with mock.patch.object( results, "_parse_merging_stats", side_effect=_parse_side_effect ): res = log_cycle_results(res, scaling_script, filter_script) cycle_results = res.get_cycle_results() assert len(cycle_results) == 2 assert cycle_results[1]["cumul_percent_removed"] == 100 * 2 * 50.0 / 1000.0 assert cycle_results[1]["n_removed"] == 50 assert cycle_results[1]["image_ranges_removed"] == [[(6, 10), 0]] assert cycle_results[1]["removed_datasets"] == [] assert cycle_results[0]["delta_cc_half_values"] == [-0.1, 0.1, -0.2, 0.2] assert res.get_merging_stats()[1] == "stats_results" assert res.initial_n_reflections == 1000 def test_compute_delta_cchalf_returned_results(): """Test that delta cchalf return necessary values for scale_and_filter.""" # Check for correct recording of # results_summary['per_dataset_delta_cc_half_values']['delta_cc_half_values'] summary = {} delta_cc = {0: -4, 1: 2, 2: -3, 3: -5, 4: 1} sorted_data, sorted_ccs = DeltaCCHalf.sort_deltacchalf_values(delta_cc, summary) expected_data_order = [3, 0, 2, 4, 1] expected_cc_order = [-5, -4, -3, 1, 2] assert list(sorted_data) == expected_data_order assert list(sorted_ccs) == expected_cc_order assert ( summary["per_dataset_delta_cc_half_values"]["delta_cc_half_values"] == expected_cc_order ) # Check for correct recording for dataset mode exp = generate_test_experiments(2) refls = generate_test_reflections(2) ids_to_remove = [0] results_summary = {"dataset_removal": {}} _ = CCHalfFromDials.remove_datasets_below_cutoff( exp, refls, ids_to_remove, results_summary ) assert "experiments_fully_removed" in results_summary["dataset_removal"] assert "n_reflections_removed" in results_summary["dataset_removal"] assert results_summary["dataset_removal"]["experiments_fully_removed"] == ["0"] assert results_summary["dataset_removal"]["n_reflections_removed"] == 10 # Check for correct recording for image group mode. exp = generate_test_experiments(2) refls = generate_test_reflections(2) ids_to_remove = [0, 1] image_group_to_expid_and_range = { 0: ("0", (1, 5)), 1: ("0", (6, 10)), 2: ("1", (1, 5)), 3: ("1", (6, 10)), } expids_to_image_groups = {"0": [0, 1], "1": [2, 3]} results_summary = {"dataset_removal": {}} _ = CCHalfFromDials.remove_image_ranges_below_cutoff( exp, refls, ids_to_remove, image_group_to_expid_and_range, expids_to_image_groups, results_summary, ) assert "experiments_fully_removed" in results_summary["dataset_removal"] assert "n_reflections_removed" in results_summary["dataset_removal"] assert "image_ranges_removed" in results_summary["dataset_removal"] assert results_summary["dataset_removal"]["experiments_fully_removed"] == ["0"] assert results_summary["dataset_removal"]["n_reflections_removed"] == 10 assert [(6, 10), 0] in results_summary["dataset_removal"]["image_ranges_removed"] assert [(1, 5), 0] in results_summary["dataset_removal"]["image_ranges_removed"] assert len(results_summary["dataset_removal"]["image_ranges_removed"]) == 2 def test_analysis_results_to_from_dict(): d = { "termination_reason": "made up", "initial_expids_and_image_ranges": [["foo", [1, 42]], ["bar", [1, 10]]], "expids_and_image_ranges": [["foo", [1, 42]]], "cycle_results": {"1": {"some stat": -424242}}, "initial_n_reflections": 424242, "final_stats": "some final stats", } results = AnalysisResults.from_dict(d) # The cycle_results dict output by AnalysisResults has integer keys but after # conversion to json has str keys. AnalysisResults.from_dict expects str keys, # hence do the comparison after converting to/from json assert json.loads(json.dumps(results.to_dict())) == d
dials/dials
tests/algorithms/scaling/test_scale_and_filter.py
Python
bsd-3-clause
8,168
[ "CRYSTAL" ]
1edaf40c11ec874ea8bebf395a6522ebfc1f891d7c6e2b300937eb761ea15b5d
#!/usr/bin/python import os import itertools experiments = [ [1,"gaussian",1.5], [0,"mixture",1,0.005], [0.7,"mixture",1,0.1], [0.7,"mixture",1,0.075], [0.7,"mixture",2,0.05], [0.7,"mixture",2,0.075] ] os.chdir("../dboost") for e in experiments: if e[1] == "gaussian": f = "sensors_dirty_stat{}_{}{}.out".format(*e) cmd = "python dboost-stdin.py --minimal -F ' ' ../datasets/real/intel/sensors-1000-dirty.txt --statistical {} --{} {} -d fracpart -d unix2date_float > ../results/{}".format(*(e+[f])) elif e[1] == "mixture": f = "sensors_dirty_stat{}_{}{}_{}.out".format(*e) cmd = "python dboost-stdin.py --minimal -F ' ' ../datasets/real/intel/sensors-1000-dirty.txt --statistical {} --{} {} {} -d fracpart -d unix2date_float > ../results/{}".format(*(e+[f])) else: assert(False) print(cmd) os.system(cmd)
cpitclaudel/dBoost
scripts/run_sensor_experiments.py
Python
gpl-3.0
888
[ "Gaussian" ]
39c881e62185bc2751f31fe0642299e1a484d1bec9fbcb82044130fdc97d71e2
import numpy as N from image import * import mylogger from copy import deepcopy as cp from . import has_pl if has_pl: import matplotlib.pyplot as pl import scipy import scipy.signal as S import _cbdsm import functions as func import _pytesselate as _pytess import shapelets as sh from scipy.optimize import leastsq import nat from math import * import statusbar from const import fwsig import multi_proc as mp import itertools class Op_psf_vary(Op): """Computes variation of psf across the image """ def __call__(self, img): if img.opts.psf_vary_do: mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Psf_Vary") mylogger.userinfo(mylog, '\nEstimating PSF variations') opts = img.opts dir = img.basedir + '/misc/' plot = False # debug figures image = img.ch0_arr try: from astropy.io import fits as pyfits old_pyfits = False except ImportError, err: from distutils.version import StrictVersion import pyfits if StrictVersion(pyfits.__version__) < StrictVersion('2.2'): old_pyfits = True else: old_pyfits = False if old_pyfits: mylog.warning('PyFITS version is too old: psf_vary module skipped') return if opts.psf_fwhm is not None: # User has specified a constant PSF to use, so skip PSF fitting/etc. psf_maj = opts.psf_fwhm[0] # FWHM in deg psf_min = opts.psf_fwhm[1] # FWHM in deg psf_pa = opts.psf_fwhm[2] # PA in deg mylogger.userinfo(mylog, 'Using constant PSF (major, minor, pos angle)', '(%.5e, %.5e, %s) degrees' % (psf_maj, psf_maj, round(psf_pa, 1))) else: # Use did not specify a constant PSF to use, so estimate it over = 2 generators = opts.psf_generators; nsig = opts.psf_nsig; kappa2 = opts.psf_kappa2 snrtop = opts.psf_snrtop; snrbot = opts.psf_snrbot; snrcutstack = opts.psf_snrcutstack gencode = opts.psf_gencode; primarygen = opts.psf_primarygen; itess_method = opts.psf_itess_method tess_sc = opts.psf_tess_sc; tess_fuzzy= opts.psf_tess_fuzzy bright_snr_cut = opts.psf_high_snr s_only = opts.psf_stype_only if opts.psf_snrcut < 5.0: mylogger.userinfo(mylog, "Value of psf_snrcut too low; increasing to 5") snrcut = 5.0 else: snrcut = opts.psf_snrcut img.psf_snrcut = snrcut if opts.psf_high_snr is not None: if opts.psf_high_snr < 10.0: mylogger.userinfo(mylog, "Value of psf_high_snr too low; increasing to 10") high_snrcut = 10.0 else: high_snrcut = opts.psf_high_snr else: high_snrcut = opts.psf_high_snr img.psf_high_snr = high_snrcut wtfns=['unity', 'roundness', 'log10', 'sqrtlog10'] if 0 <= itess_method < 4: tess_method=wtfns[itess_method] else: tess_method='unity' ### now put all relevant gaussian parameters into a list ngaus = img.ngaus nsrc = img.nsrc num = N.zeros(nsrc, dtype=N.int32) peak = N.zeros(nsrc) xc = N.zeros(nsrc) yc = N.zeros(nsrc) bmaj = N.zeros(nsrc) bmin = N.zeros(nsrc) bpa = N.zeros(nsrc) code = N.array(['']*nsrc); rms = N.zeros(nsrc) src_id_list = [] for i, src in enumerate(img.sources): src_max = 0.0 for gmax in src.gaussians: # Take only brightest Gaussian per source if gmax.peak_flux > src_max: src_max = gmax.peak_flux g = gmax num[i] = i peak[i] = g.peak_flux xc[i] = g.centre_pix[0] yc[i] = g.centre_pix[1] bmaj[i] = g.size_pix[0] bmin[i] = g.size_pix[1] bpa[i] = g.size_pix[2] code[i] = img.sources[g.source_id].code rms[i] = img.islands[g.island_id].rms gauls = (num, peak, xc, yc, bmaj, bmin, bpa, code, rms) tr_gauls = self.trans_gaul(gauls) # takes gaussians with code=S and snr > snrcut. if s_only: tr = [n for n in tr_gauls if n[1]/n[8]>snrcut and n[7] == 'S'] else: tr = [n for n in tr_gauls if n[1]/n[8]>snrcut] g_gauls = self.trans_gaul(tr) # computes statistics of fitted sizes. Same as psfvary_fullstat.f in fBDSM. bmaj_a, bmaj_r, bmaj_ca, bmaj_cr, ni = _cbdsm.bstat(bmaj, None, nsig) bmin_a, bmin_r, bmin_ca, bmin_cr, ni = _cbdsm.bstat(bmin, None, nsig) bpa_a, bpa_r, bpa_ca, bpa_cr, ni = _cbdsm.bstat(bpa, None, nsig) # get subset of sources deemed to be unresolved. Same as size_ksclip_wenss.f in fBDSM. flag_unresolved = self.get_unresolved(g_gauls, img.beam, nsig, kappa2, over, img.psf_high_snr, plot) if len(flag_unresolved) == 0: mylog.warning('Insufficient number of sources to determine PSF variation.\nTry changing the PSF options or specify a (constant) PSF with the "psf_fwhm" option') return # see how much the SNR-weighted sizes of unresolved sources differ from the synthesized beam. wtsize_beam_snr = self.av_psf(g_gauls, img.beam, flag_unresolved) # filter out resolved sources tr_gaul = self.trans_gaul(g_gauls) tr = [n for i, n in enumerate(tr_gaul) if flag_unresolved[i]] g_gauls = self.trans_gaul(tr) mylogger.userinfo(mylog, 'Number of unresolved sources', str(len(g_gauls[0]))) # get a list of voronoi generators. vorogenS has values (and not None) if generators='field'. vorogenP, vorogenS = self.get_voronoi_generators(g_gauls, generators, gencode, snrcut, snrtop, snrbot, snrcutstack) mylogger.userinfo(mylog, 'Number of generators for PSF variation', str(len(vorogenP[0]))) if len(vorogenP[0]) < 3: mylog.warning('Insufficient number of generators') return mylogger.userinfo(mylog, 'Tesselating image') # group generators into tiles tile_prop = self.edit_vorogenlist(vorogenP, frac=0.9) # tesselate the image volrank, vorowts = self.tesselate(vorogenP, vorogenS, tile_prop, tess_method, tess_sc, tess_fuzzy, \ generators, gencode, image.shape) if opts.output_all: func.write_image_to_file(img.use_io, img.imagename + '.volrank.fits', volrank, img, dir) tile_list, tile_coord, tile_snr = tile_prop ntile = len(tile_list) bar = statusbar.StatusBar('Determining PSF variation ............... : ', 0, ntile) mylogger.userinfo(mylog, 'Number of tiles for PSF variation', str(ntile)) # For each tile, calculate the weighted averaged psf image. Also for all the sources in the image. cdelt = list(img.wcs_obj.acdelt[0:2]) factor=3. psfimages, psfcoords, totpsfimage, psfratio, psfratio_aper = self.psf_in_tile(image, img.beam, g_gauls, \ cdelt, factor, snrcutstack, volrank, tile_prop, plot, img) npsf = len(psfimages) if opts.psf_use_shap: if opts.psf_fwhm is None: # use totpsfimage to get beta, centre and nmax for shapelet decomposition. Use nmax=5 or 6 mask=N.zeros(totpsfimage.shape, dtype=bool) (m1, m2, m3)=func.moment(totpsfimage, mask) betainit=sqrt(m3[0]*m3[1])*2.0 * 1.4 tshape = totpsfimage.shape cen = N.array(N.unravel_index(N.argmax(totpsfimage), tshape))+[1,1] cen = tuple(cen) nmax = 12 basis = 'cartesian' betarange = [0.5,sqrt(betainit*max(tshape))] beta, error = sh.shape_varybeta(totpsfimage, mask, basis, betainit, cen, nmax, betarange, plot) if error == 1: print ' Unable to find minimum in beta' # decompose all the psf images using the beta from above nmax=12; psf_cf=[] for i in range(npsf): psfim = psfimages[i] cf = sh.decompose_shapelets(psfim, mask, basis, beta, cen, nmax, mode='') psf_cf.append(cf) if img.opts.quiet == False: bar.increment() bar.stop() # transpose the psf image list xt, yt = N.transpose(tile_coord) tr_psf_cf = N.transpose(N.array(psf_cf)) # interpolate the coefficients across the image. Ok, interpolate in scipy for # irregular grids is crap. doesnt even pass through some of the points. # for now, fit polynomial. compress = 100.0 x, y = N.transpose(psfcoords) if len(x) < 3: mylog.warning('Insufficient number of tiles to do interpolation of PSF variation') return psf_coeff_interp, xgrid, ygrid = self.interp_shapcoefs(nmax, tr_psf_cf, psfcoords, image.shape, \ compress, plot) psfshape = psfimages[0].shape skip = 5 aa = self.create_psf_grid(psf_coeff_interp, image.shape, xgrid, ygrid, skip, nmax, psfshape, \ basis, beta, cen, totpsfimage, plot) img.psf_images = aa else: if opts.psf_fwhm is None: if ntile < 4: mylog.warning('Insufficient number of tiles to do interpolation of PSF variation') return else: # Fit stacked PSFs with Gaussians and measure aperture fluxes bm_pix = N.array([img.pixel_beam()[0]*fwsig, img.pixel_beam()[1]*fwsig, img.pixel_beam()[2]]) psf_maj = N.zeros(npsf) psf_min = N.zeros(npsf) psf_pa = N.zeros(npsf) if img.opts.quiet == False: bar.start() for i in range(ntile): psfim = psfimages[i] mask = N.zeros(psfim.shape, dtype=bool) x_ax, y_ax = N.indices(psfim.shape) maxv = N.max(psfim) p_ini = [maxv, (psfim.shape[0]-1)/2.0*1.1, (psfim.shape[1]-1)/2.0*1.1, bm_pix[0]/fwsig*1.3, bm_pix[1]/fwsig*1.1, bm_pix[2]*2] para, ierr = func.fit_gaus2d(psfim, p_ini, x_ax, y_ax, mask) ### first extent is major if para[3] < para[4]: para[3:5] = para[4:2:-1] para[5] += 90 ### clip position angle para[5] = divmod(para[5], 180)[1] psf_maj[i] = para[3] psf_min[i] = para[4] posang = para[5] while posang >= 180.0: posang -= 180.0 psf_pa[i] = posang if img.opts.quiet == False: bar.increment() bar.stop() # Interpolate Gaussian parameters if img.aperture is None: psf_maps = [psf_maj, psf_min, psf_pa, psfratio] else: psf_maps = [psf_maj, psf_min, psf_pa, psfratio, psfratio_aper] nimgs = len(psf_maps) bar = statusbar.StatusBar('Interpolating PSF images ................ : ', 0, nimgs) if img.opts.quiet == False: bar.start() map_list = mp.parallel_map(func.eval_func_tuple, itertools.izip(itertools.repeat(self.interp_prop), psf_maps, itertools.repeat(psfcoords), itertools.repeat(image.shape)), numcores=opts.ncores, bar=bar) if img.aperture is None: psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list else: psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list # Smooth if desired if img.opts.psf_smooth is not None: sm_scale = img.opts.psf_smooth / img.pix2beam([1.0, 1.0, 0.0])[0] / 3600.0 # pixels if img.opts.aperture is None: psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int] else: psf_maps = [psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int] nimgs = len(psf_maps) bar = statusbar.StatusBar('Smoothing PSF images .................... : ', 0, nimgs) if img.opts.quiet == False: bar.start() map_list = mp.parallel_map(func.eval_func_tuple, itertools.izip(itertools.repeat(self.blur_image), psf_maps, itertools.repeat(sm_scale)), numcores=opts.ncores, bar=bar) if img.aperture is None: psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int = map_list else: psf_maj_int, psf_min_int, psf_pa_int, psf_ratio_int, psf_ratio_aper_int = map_list # Make sure all smoothed, interpolated images are ndarrays psf_maj_int = N.array(psf_maj_int) psf_min_int = N.array(psf_min_int) psf_pa_int = N.array(psf_pa_int) psf_ratio_int = N.array(psf_ratio_int) if img.aperture is None: psf_ratio_aper_int = N.zeros(psf_maj_int.shape, dtype=N.float32) else: psf_ratio_aper_int = N.array(psf_ratio_aper_int, dtype=N.float32) # Blank with NaNs if needed mask = img.mask_arr if isinstance(mask, N.ndarray): pix_masked = N.where(mask == True) psf_maj_int[pix_masked] = N.nan psf_min_int[pix_masked] = N.nan psf_pa_int[pix_masked] = N.nan psf_ratio_int[pix_masked] = N.nan psf_ratio_aper_int[pix_masked] = N.nan # Store interpolated images. The major and minor axis images are # the sigma in units of arcsec, the PA image in units of degrees east of # north, the ratio images in units of 1/beam. img.psf_vary_maj_arr = psf_maj_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec img.psf_vary_min_arr = psf_min_int * img.pix2beam([1.0, 1.0, 0.0])[0] * 3600.0 # sigma in arcsec img.psf_vary_pa_arr = psf_pa_int img.psf_vary_ratio_arr = psf_ratio_int # in 1/beam img.psf_vary_ratio_aper_arr = psf_ratio_aper_int # in 1/beam if opts.output_all: func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_maj.fits', img.psf_vary_maj_arr*fwsig, img, dir) func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_min.fits', img.psf_vary_min_arr*fwsig, img, dir) func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_pa.fits', img.psf_vary_pa_arr, img, dir) func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio.fits', img.psf_vary_ratio_arr, img, dir) func.write_image_to_file(img.use_io, img.imagename + '.psf_vary_ratio_aper.fits', img.psf_vary_ratio_aper_arr, img, dir) # Loop through source and Gaussian lists and deconvolve the sizes using appropriate beam bar2 = statusbar.StatusBar('Correcting deconvolved source sizes ..... : ', 0, img.nsrc) if img.opts.quiet == False: bar2.start() for src in img.sources: src_pos = img.sky2pix(src.posn_sky_centroid) src_pos_int = (int(src_pos[0]), int(src_pos[1])) gaus_c = img.gaus2pix(src.size_sky, src.posn_sky_centroid) if opts.psf_fwhm is None: gaus_bm = [psf_maj_int[src_pos_int]*fwsig, psf_min_int[src_pos_int]*fwsig, psf_pa_int[src_pos_int]] else: # Use user-specified constant PSF instead gaus_bm = img.beam2pix(opts.psf_fwhm) gaus_dc, err = func.deconv2(gaus_bm, gaus_c) src.deconv_size_sky = img.pix2gaus(gaus_dc, src_pos) src.deconv_size_skyE = [0.0, 0.0, 0.0] for g in src.gaussians: gaus_c = img.gaus2pix(g.size_sky, src.posn_sky_centroid) gaus_dc, err = func.deconv2(gaus_bm, gaus_c) g.deconv_size_sky = img.pix2gaus(gaus_dc, g.centre_pix) g.deconv_size_skyE = [0.0, 0.0, 0.0] if img.opts.quiet == False: bar2.spin() if img.opts.quiet == False: bar2.increment() bar2.stop() img.completed_Ops.append('psf_vary') ################################################################################################## def trans_gaul(self, q): " transposes a tuple of .gaul values " y=[] for i in range(len(q[0])): elem=[] for j in range(len(q)): elem.append(q[j][i]) y.append(elem) return y ################################################################################################## def bindata(self, over, num): #ptpbin,nbin,ptplastbin, same as get_bins in fBDSM. if num <= 100: ptpbin=num/5 if num > 100: ptpbin=num/10 if num > 1000: ptpbin=num/20 if ptpbin % 2 == 1: ptpbin=ptpbin+1 if num < 10: ptpbin=num ptpbin = float(ptpbin) # cast to float to avoid integer division errors nbin=int((num-ptpbin)/(ptpbin/over)+1) ptplastbin=int((num-1)-(nbin-1)*ptpbin/over) nbin=nbin+1 return ptpbin, nbin, ptplastbin ################################################################################################## def bin_and_stats_ny(self, x,y,over,ptpbin,nbin,ptplastbin,nsig): import math n1=N.array(range(nbin))+1 # bin number n2=N.array([ptpbin]*nbin); n2[nbin-2]=ptplastbin; n2[nbin-1]=ptpbin/over n3=N.array([ptpbin]*nbin, dtype=float); n3[nbin-1]=float(over)*(len(x)-ptpbin/2)/(nbin-1) xval=N.zeros(nbin) meany=N.zeros(nbin); stdy=N.zeros(nbin); mediany=N.zeros(nbin) for i in range(nbin): lb=round(1+(n1[i]-1)*n3[i]/over+(1-1))-1 # -1 for python indexing ub=round(1+(n1[i]-1)*n3[i]/over+(n2[i]-1))-1 # -1 for python indexing x1=x[lb:ub+1]; y1=y[lb:ub+1] # do calcmedianclip2vec.f for code=YYN if len(x1) > 0 and len(y1) > 0: nout=100; niter=0 while nout>0 and niter<6: med1=N.median(y1[:]) med2=10.**(N.median(N.log10(x1[:]))) medstd=0 # calcmedianstd.f for j in y1: medstd += (j-med1)*(j-med1) medstd=math.sqrt(medstd/len(y1)) # av1=N.mean(y1); std1=func.std(y1) av2=N.mean(x1); std2=func.std(x1) # get_medianclip_vec2 z=N.transpose([x1, y1]) z1=N.transpose([n for n in z if abs(n[1]-med1)<=nsig*medstd]) nout=len(x1)-len(z1[0]) x1=z1[0]; y1=z1[1]; niter+=1 xval[i]=med2; meany[i]=av1; stdy[i]=std1; mediany[i]=med1 if stdy[nbin-1]/mediany[nbin-1] > stdy[nbin-2]/mediany[nbin-2]: stdy[nbin-1]=stdy[nbin-2]/mediany[nbin-2]*mediany[nbin-1] return xval, meany, stdy, mediany ################################################################################################## def LM_fit(self, x, y, err, funct, order=0): if funct == func.poly: p0=N.array([y[N.argmax(x)]] + [0]*order) if funct == func.wenss_fit: p0=N.array([y[N.argmax(x)]] + [1.]) res=lambda p, x, y, err: (y-funct(p, x))/err (p, flag)=leastsq(res, p0, args=(x, y, err)) return p ################################################################################################## def fit_bins_func(self, x,y,over,ptpbin,nbin,ptplastbin,nsig): # sub_size_ksclip import math (xval,meany,stdy,medy)=self.bin_and_stats_ny(x,y,over,ptpbin,nbin,ptplastbin,nsig) yfit=stdy/medy err=N.array([1.]*nbin) if ptplastbin > 0: err[nbin-2]=err[0]*math.sqrt(1.0*ptpbin/ptplastbin) err[nbin-1]=err[0]*math.sqrt(1.0*ptpbin*over/ptplastbin) i=0 while i<nbin-4 and (N.all(N.sort(yfit[i:i+4])[::-1] == yfit[i:i+4]) == False): i+=1 if i==nbin-4: sind=0 else: sind=i-1 if sind < 1: sind = 0 if sind > 0.25*nbin: sind=int(round(0.25*nbin))-1 s_c=self.LM_fit(xval[sind:],yfit[sind:],err[sind:], func.wenss_fit) err[:]=1. s_cm=self.LM_fit(N.log10(xval),medy,err,func.poly, order=1) if len(xval) >= 3: s_dm=self.LM_fit(N.log10(xval),medy,err,func.poly, order=2) else: s_dm = (N.array([s_cm[0], s_cm[1], 0.0]), 0) if ptpbin<75: s_dm=N.append(s_cm[:], [0.]) return s_c, s_dm ################################################################################################## def get_unresolved(self, g_gauls, beam, nsig, kappa2, over, bright_snr_cut=20.0, plot=False): """"Gets subset of unresolved sources Also flags as unresolved all sources with SNRs above bright_cut_snr, since fitting below is unreliable for bright sources. """ num=len(g_gauls[0]) if num < 10: # Too few sources to do fitting return [] b1=N.asarray(g_gauls[4])/(beam[0]*3600.) b2=N.asarray(g_gauls[5])/(beam[1]*3600.) s1=N.asarray(g_gauls[1])/N.array(g_gauls[8]) snr=N.array(s1) index=snr.argsort() snr=snr[index] nmaj=N.array(b1)[index] nmin=N.array(b2)[index] # if plot: pl.figure() f_sclip=N.zeros((2,num), dtype=bool) for idx, nbeam in enumerate([nmaj, nmin]): xarr=N.copy(snr) yarr=N.copy(nbeam) niter=0; nout=num; noutold=nout*2 while niter<10 and nout >0.75*num: (ptpbin, nbin, ptplastbin)=self.bindata(over,nout) # get_bins in fBDSM (s_c,s_dm) = self.fit_bins_func(xarr,yarr,over,ptpbin,nbin,ptplastbin,nsig) # size_ksclip_wenss in fBDSM noutold = len(xarr) z = N.transpose([xarr, yarr, s_dm[0]+s_dm[1]*N.log10(xarr)+s_dm[2]*(N.log10(xarr)**2.), \ N.sqrt(s_c[0]*s_c[0]+s_c[1]*s_c[1]/(xarr*xarr)) ]) z1 = N.transpose([n for n in z if abs(n[1]-n[2])/(n[2]*n[3])<kappa2]) # sub_size_wenss_getnum in fBDSM if len(z1) == 0: break nout = len(z1[0]) niter += 1 xarr = z1[0]; yarr = z1[1]; # end of sub_size_wenss_getnum if noutold == nout: break # flag in the 'unresolved' sources. returns flag array, True ==> unresolved logsnr=N.log10(snr) dumr = N.sqrt(s_c[0]*s_c[0]+s_c[1]*s_c[1]/(snr*snr)) med = s_dm[0]+s_dm[1]*logsnr+s_dm[2]*(logsnr*logsnr) f_sclip[idx] = N.abs((nbeam-med)/(med*dumr)) < N.array([kappa2]*num) f_s = f_sclip[0]*f_sclip[1] # Add bright sources if bright_snr_cut is not None: if bright_snr_cut < 20.0: bright_snr_cut = 20.0 bright_srcs = N.where(snr >= bright_snr_cut) if len(bright_srcs[0]) > 0: f_s[bright_srcs] = True # now make plots # if plot: # bb=[b1, b2] # pl.subplot(211+idx) # pl.semilogx(s1, bb[idx], 'og') # f0=f_sclip[idx][index.argsort()] # sf=[n for i, n in enumerate(s1) if f0[i]] # b1f=[n for i, n in enumerate(bb[idx]) if f0[i]] # pl.semilogx(sf, b1f, 'or') # pl.semilogx(snr,med,'-') # pl.semilogx(snr,med+med*dumr*(N.array([kappa2]*num)),'-') # pl.semilogx(snr,med-med*dumr*(N.array([kappa2]*num)),'-') # pl.title(' axis ' + str(idx)) # return f_s[index.argsort()] ################################################################################################## def av_psf(self, g_gauls, beam, flag): """ calculate how much the SNR-weighted sizes of unresolved sources differs from the synthesized beam. Same as av_psf.f in fBDSM.""" from math import sqrt bmaj = N.asarray(g_gauls[4]) bmin = N.asarray(g_gauls[5]) bpa = N.asarray(g_gauls[6]) wt = N.asarray(g_gauls[1])/N.asarray(g_gauls[8]) flagwt = wt*flag sumwt = N.sum(flagwt) w1 = N.sum(flagwt*flagwt) wtavbm = N.array([N.sum(bmaj*flagwt), N.sum(bmin*flagwt), N.sum(bpa*flagwt)])/sumwt dumrar = N.array([N.sum(bmaj*bmaj*flagwt), N.sum(bmin*bmin*flagwt), N.sum(bpa*bpa*flagwt)]) dd = sumwt*sumwt-w1 wtstdbm = N.sqrt((dumrar - wtavbm*wtavbm*sumwt)*sumwt/dd) avpa = N.sum(bpa*flagwt-180.0*flagwt*N.array(bpa >= 90))/sumwt stdpa = N.sum(bpa*flagwt+(180.0*180.0-360.0*bpa)*flagwt*N.array(bpa >= 90)) stdpa = sqrt(abs((stdpa-avpa*avpa*sumwt)*sumwt/dd)) if stdpa < wtstdbm[2]: wtstdbm[2] = stdpa wtavbm[2] = avpa return (wtavbm - N.array([beam[0]*3600.0, beam[1]*3600.0, beam[2]]))/wtstdbm ################################################################################################## def get_voronoi_generators(self, g_gauls, generators, gencode, snrcut, snrtop, snrbot, snrcutstack): """This gets the list of all voronoi generators. It is either the centres of the brightest sources, or is imported from metadata (in future).""" from math import sqrt num=len(g_gauls[0]) snr=N.asarray(g_gauls[1])/N.asarray(g_gauls[8]) index=snr.argsort() snr_incr = snr[index] snr = snr_incr[::-1] x = N.asarray(g_gauls[2])[index] y = N.asarray(g_gauls[3])[index] cutoff = 0 if generators == 'calibrators' or generators == 'field': if gencode != 'file': gencode = 'list' if gencode == 'list': cutoff = int(round(num*(snrtop))) if cutoff > len(snr): cutoff = len(snr) # Make sure we don't fall below snrcutstack (SNR cut for stacking of PSFs), since # it makes no sense to make tiles with generators that fall below this cut. if snr[cutoff-1] < snrcutstack: cutoff = num - snr_incr.searchsorted(snrcutstack) if generators == 'calibrators': if gencode == 'file': raise NotImplementedError, "gencode=file not yet implemented." x1 = x.tolist() y1 = y.tolist() x1.reverse() y1.reverse() snr1 = snr.tolist() vorogenP = N.asarray([x1[0:cutoff], y1[0:cutoff], snr1[0:cutoff]]) vorogenS = None return vorogenP, vorogenS ################################################################################################## def edit_vorogenlist(self, vorogenP, frac): """ Edit primary voronoi generator list. Each tile has a tile centre and can have more than one generator to be averaged. tile_list is a list of arrays, indexed by the tile number and each array is an array of numbers in the ngen list which are the generators in that tile. xtile, ytile and snrtile are arrays of length number_of_tiles and have x,y,snr of each tile. Group together generators if closer than a fraction of dist to third closest.""" xgen, ygen, snrgen = vorogenP flag = N.zeros(len(xgen)) coord=N.array([xgen,ygen]).transpose() tile_list = [] tile_coord = []; tile_snr = [] for i in range(len(xgen)): dist = N.array(map(lambda t: func.dist_2pt(coord[i], t), coord)) indi = N.argsort(dist) sortdist = dist[indi] if sortdist[1] < frac * sortdist[2]: # first is the element itself if flag[indi[1]] + flag[i] == 0: # not already deleted from other pair tile_list.append([i, indi[1]]) tile_coord.append((coord[i]*snrgen[i]+coord[indi[1]]*snrgen[indi[1]])/(snrgen[i]+snrgen[indi[1]])) tile_snr.append(snrgen[i]+snrgen[indi[1]]) flag[i] = 1 flag[indi[1]] = 1 else: if len(dist) > 3: if sortdist[1]+sortdist[2] < 2.0*frac*sortdist[3]: # for 3 close-by sources in1=indi[1] in2=indi[2] if flag[in1]+flag[in2]+flag[i] == 0: # not already deleted from others tile_list.append([i, in1, in2]) tile_coord.append((coord[i]*snrgen[i]+coord[in1]*snrgen[in1]+coord[in2]*snrgen[in2]) \ /(snrgen[i]+snrgen[in1]+snrgen[in2])) tile_snr.append(snrgen[i]+snrgen[in1]+snrgen[in2]) flag[i] = 1 flag[in1] = 1 flag[in2] = 1 else: tile_list.append([i]) tile_coord.append(coord[i]) tile_snr.append(snrgen[i]) # Assign any leftover generators for i in range(len(xgen)): if flag[i] == 0: tile_list.append([i]) tile_coord.append(coord[i]) tile_snr.append(snrgen[i]) return tile_list, tile_coord, tile_snr ################################################################################################## def tess_simple(self, vorogenP, wts, tess_sc, tess_fuzzy, shape): """ Simple tesselation """ xgen, ygen, snrgen = vorogenP volrank = _pytess.pytess_simple(shape[0], shape[1], xgen, ygen, snrgen, \ wts, tess_fuzzy, tess_sc) return volrank ################################################################################################## def tess_roundness(self, vorogenP, tess_sc, tess_fuzzy, shape): """ Tesselation, modified to make the tiles more round. """ xgen, ygen, snrgen = vorogenP volrank = _pytess.pytess_roundness(shape[0], shape[1], xgen, ygen, snrgen, \ tess_fuzzy, tess_sc) return volrank ################################################################################################## def pixintile(self, tilecoord, pixel, tess_method, wts, tess_sc, tess_fuzzy): """ This has routines to find out which tile a given pixel belongs to. """ if tess_method == 'roundness': #tilenum = pytess_roundness(tilecoord, pixel, wts, tess_sc, tess_fuzzy) print " Not yet implemented !!!! " return 0 else: xgen, ygen = tilecoord xgen = N.asarray(xgen) ygen = N.asarray(ygen) ngen = len(xgen) i,j = pixel dist = N.sqrt((i-xgen)*(i-xgen)+(j-ygen)*(j-ygen))/wts minind = dist.argmin() if tess_sc == 's': tilenum=minind else: print " Not yet implemented !!!! " return tilenum ################################################################################################## def tesselate(self, vorogenP, vorogenS, tile_prop, tess_method, tess_sc, tess_fuzzy, generators, gencode, shape): """ Various ways of tesselating. If generators='calibrator', no need to tesselate, just get modified list based on very nearby sources. If generators='field' then tesselate. The image is tesselated based on tile_prop. """ wtfn={'unity' : lambda x : N.ones(len(x)), \ 'log10' : N.log10, \ 'sqrtlog10' : lambda x : N.sqrt(N.log10(x)), \ 'roundness' : N.array} tile_list, tile_coord, tile_snr = tile_prop xt = self.trans_gaul(tile_coord)[0] yt = self.trans_gaul(tile_coord)[1] vorogenT = xt, yt, tile_snr wt_fn = wtfn[tess_method] wts = wt_fn(tile_snr) if tess_method == 'roundness': volrank = self.tess_roundness(vorogenT, tess_sc, tess_fuzzy, shape) else: volrank = self.tess_simple(vorogenT, wts, tess_sc, tess_fuzzy, shape) return volrank, wts ################################################################################################## def edit_tile(self, ltnum, g_gauls, flag_unresolved, snrcutstack, volrank, tile_prop, tess_sc, \ tess_fuzzy, wts, tess_method, plot): """ Looks at tiles with no (or one) unresolved source inside it and deletes it and recomputes the tiling. For now, does not recompute since we wont use the rank for those pixels anyway.""" if ltnum > 1: raise NotImplementedError, "NOT YET IMPLEMENTED FOR LTNUM>1" tile_list, tile_coord, tile_snr = tile_prop tr_gaul = self.trans_gaul(g_gauls) tr=[n for i, n in enumerate(tr_gaul) if flag_unresolved[i] and n[1]/n[8] >= snrcutstack] ntile = len(tile_list) ngenpertile=N.zeros(ntile) for itile in range(ntile): tile_gauls = [n for n in tr if volrank[int(round(n[2])),int(round(n[3]))]-1 \ == itile] ngenpertile[itile]=len(tile_gauls) new_n = N.sum(ngenpertile >= ltnum) # prepare list of good tiles to pass to pixintile goodtiles = N.array(N.where(ngenpertile >= ltnum)[0]) new_n = len(goodtiles) tile_coord_n = [n for i,n in enumerate(tile_coord) if i in goodtiles] wts_n = [n for i,n in enumerate(wts) if i in goodtiles] r2t = N.zeros(ntile, dtype=int) entry = -1 for itile in range(ntile): if ngenpertile[itile] >= ltnum: r2t[itile] = itile else: pixel = tile_coord[itile] tilenum = self.pixintile(self.trans_gaul(tile_coord_n), pixel, tess_method, wts_n, tess_sc, tess_fuzzy) r2t[itile] = tilenum for itile in range(new_n): num = N.sum(r2t == itile) if num == 0: minarr = -999 while minarr != itile: arr = N.where(r2t > itile)[0] minarr = r2t[arr].min()-1 for i in arr: r2t[i]=r2t[i]-1 n_tile_list = []; n_tile_coord = []; n_tile_snr = [] for itile in range(new_n): ind = N.where(r2t == itile)[0]; ind1 = [] for i in ind: ind1 = ind1 + tile_list[i] n_tile_list.append(ind1) snrs = N.array([tile_snr[i] for i in ind]) coords = N.array([tile_coord[i] for i in ind]) n_tile_snr.append(N.sum(snrs)) n_tile_coord.append(N.sum([snrs[i]*coords[i] for i in range(len(snrs))], 0)/N.sum(snrs)) ngenpertile=N.zeros(new_n) for itile in range(new_n): tile_gauls = [n for n in tr if r2t[volrank[int(round(n[2])),int(round(n[3]))]-1] \ == itile] ngenpertile[itile]=len(tile_gauls) tile_prop = n_tile_list, n_tile_coord, n_tile_snr return ngenpertile, tile_prop, r2t ################################################################################################## def stackpsf(self, image, beam, g_gauls, wts, cdelt, factor): """ Stacks all the images of sources in the gaussian list gauls from image, out to a factor times the beam size. Currently the mask is for the whole image but need to modify it for masks for each gaussian. These gaussians are supposed to be relatively isolated unresolved sources. Cut out an image a big bigger than facXbeam and imageshift to nearest half pixel and then add. Does not handle masks etc well at all. Masks for image for blanks, masks for \ islands, etc.""" gxcens_pix = g_gauls[2] gycens_pix = g_gauls[3] peak = g_gauls[1] psfimsize = int(round(max(beam[0], beam[1])/max(cdelt[0], cdelt[1]) * factor)) # fac X fwhm; fac ~ 2 psfimage = N.zeros((psfimsize, psfimsize), dtype=N.float32) cs2=cutoutsize2 = int(round(psfimsize*(1. + 2./factor)/2.)) # size/2. factor => to avoid edge effects etc cc = cutoutcen_ind=[cs2, cs2] cpsf=cen_psf_ind = N.array([int(round(psfimsize))/2]*2) wt=0. num=len(gxcens_pix) for isrc in range(num): # MASK !!!!!!!!!!! wt += wts[isrc] gcp=N.array([gxcens_pix[isrc], gycens_pix[isrc]]) gcen_ind=gcp-1 rc=rcen_ind = N.asarray(N.round(gcen_ind), dtype=int) shift=cc-(gcen_ind-(rc-cs2)) cutimage = image[rc[0]-cs2:rc[0]+cs2,rc[1]-cs2:rc[1]+cs2] if len(cutimage.shape) == 3: cutimage=cutimage[:,:,0] if 0 not in cutimage.shape: if sum(sum(N.isnan(cutimage))) == 0: im_shift = func.imageshift(cutimage, shift) im_shift = im_shift/peak[isrc]*wts[isrc] subim_shift = im_shift[cc[0]-cpsf[0]:cc[0]-cpsf[0]+psfimsize,cc[1]-cpsf[1]:cc[1]-cpsf[1]+psfimsize] if subim_shift.shape == psfimage.shape: # Check shapes, as they can differ if source is near edge of image. # If they do differ, don't use that source (may be distorted). psfimage += subim_shift psfimage = psfimage/wt return psfimage ################################################################################################## def psf_in_tile(self, image, beam, g_gauls, cdelt, factor, snrcutstack, volrank, \ tile_prop, plot, img): """ For each tile given by tile_prop, make a list of all gaussians in the constituent tesselations and pass it to stackpsf with a weight for each gaussian, to calculate the average psf per tile. Should define weights inside a tile to include closure errors """ mylog = mylogger.logging.getLogger("PyBDSM."+img.log+"Psf_Vary") tile_list, tile_coord, tile_snr = tile_prop tr_gaul = self.trans_gaul(g_gauls) tr=[n for i, n in enumerate(tr_gaul)]# if n[1]/n[8] >= snrcutstack] ntile = len(tile_list) psfimages = [] psfcoords = [] psfratio = [] # ratio of peak flux to total flux psfratio_aper = [] # ratio of peak flux to aperture flux srcpertile = N.zeros(ntile) snrpertile = N.zeros(ntile) xt, yt = N.transpose(tile_coord) if plot: pl.figure(None) colours=['b','g','r','c','m','y','k']*(len(xt)/7+1) pl.axis([0.0, image.shape[0], 0.0, image.shape[1]]) pl.title('Tesselated image with tile centres and unresolved sources') for i in range(ntile): pl.plot([xt[i]], [yt[i]], 'D'+colours[i]) pl.text(xt[i], yt[i], str(i)) for itile in range(ntile): tile_gauls = [n for n in tr if volrank[int(round(n[2])),int(round(n[3]))]-1 \ == itile] t_gauls = self.trans_gaul(tile_gauls) srcpertile[itile] = len(tile_gauls) if plot: pl.plot(t_gauls[2], t_gauls[3], 'x'+'k', mew=1.3)#colours[itile]) for i, ig in enumerate(t_gauls[2]): xx=[xt[itile], ig] yy=[yt[itile], t_gauls[3][i]] pl.plot(xx,yy,'-'+colours[itile]) wts = N.asarray(t_gauls[1])/N.asarray(t_gauls[8]) # wt is SNR snrpertile[itile] = sum(wts) mylog.info('PSF tile #%i (center = %i, %i): %i unresolved sources, SNR = %.1f' % (itile, xt[itile], yt[itile], srcpertile[itile], snrpertile[itile])) a = self.stackpsf(image, beam, t_gauls, wts, cdelt, factor) psfimages.append(a) psfcoords.append([sum(N.asarray(t_gauls[2])*wts)/sum(wts), sum(N.asarray(t_gauls[3])*wts)/sum(wts)]) # Find peak/total flux ratio for sources in tile. If an aperture is given, # use the aperture flux as well. # t_gauls[0] is source_id src_ratio = [] src_wts = [] src_ratio_aper = [] src_wts_aper = [] for gt in tile_gauls: src = img.sources[gt[0]] if img.aperture is not None: src_ratio_aper.append(src.peak_flux_max / src.aperture_flux) src_wts_aper.append(src.total_flux / src.aperture_fluxE) src_ratio.append(src.peak_flux_max / src.total_flux) src_wts.append(src.total_flux / src.total_fluxE) if img.aperture is not None: psfratio_aper.append(sum(N.asarray(src_ratio_aper)*src_wts_aper)/sum(src_wts_aper)) else: psfratio_aper.append(0.0) psfratio.append(sum(N.asarray(src_ratio)*src_wts)/sum(src_wts)) totpsfimage = psfimages[0]*snrpertile[0] for itile in range(1,ntile): totpsfimage += psfimages[itile]*snrpertile[itile] totpsfimage = totpsfimage/sum(snrpertile) if plot: pl.imshow(N.transpose(volrank), origin='lower', interpolation='nearest'); pl.colorbar() if plot: pl.figure(None) pl.clf() ax = pl.subplot(1,1,1) pax = ax.get_position() start = N.array((pax.xmin, pax.ymin)) stop = N.array((pax.xmax, pax.ymax)) plaxis = pl.axis([0, image.shape[0], 0, image.shape[1]]) pl.title('Stacked psf for each tile') for itile in range(ntile): im=psfimages[itile] sz=0.07 spt = int(round(snrpertile[itile]*10))/10. titl='n='+str(int(round(srcpertile[itile])))+'; SNR='+str(spt) posn=[psfcoords[itile][0], psfcoords[itile][1]] normposn=N.array(stop-start, dtype=float)/N.array(image.shape[0:2])*posn+start a=pl.axes([normposn[0]-sz/2., normposn[1]-sz/2., sz, sz]) pl.contour(im,15) pl.title(titl, fontsize='small') pl.setp(a, xticks=[], yticks=[]) pl.show() return psfimages, psfcoords, totpsfimage, psfratio, psfratio_aper ################################################################################################## def interp_shapcoefs(self, nmax, tr_psf_cf, psfcoords, imshape, compress, plot): """Interpolate using natgrid. Check to see if variation is significant. """ x, y = N.transpose(psfcoords) index = [(i,j) for i in range(nmax+1) for j in range(nmax+1-i)] xi=x yi=y xo=N.arange(0.0,round(imshape[0]), round(compress)) yo=N.arange(0.0,round(imshape[1]), round(compress)) rgrid=nat.Natgrid(xi,yi,xo,yo) p={} for coord in index: z = N.array(tr_psf_cf[coord]) # else natgrid cant deal with noncontiguous memory p[coord] = rgrid.rgrd(z) # if plot: # for i,coord in enumerate(index): # if i % 36 == 0: # pl.figure(None) # pl.clf() # title = 'Interpolated shapelet coefficients' # if i>0: title = title+' (cont)' # pl.suptitle(title) # pl.subplot(6,6,(i%36)+1) # pl.title(str(coord)) # pl.plot(xi/compress, yi/compress, 'xk') # pl.imshow(p[coord], interpolation='nearest') # pl.colorbar() return p, xo, yo ################################################################################################## def interp_prop(self, prop, psfcoords, imshape, compress=1): """Interpolate using natgrid. Should check to see if variation is significant. """ x, y = N.transpose(psfcoords) xi=x yi=y xo=N.arange(0.0,round(imshape[0]), round(compress)) yo=N.arange(0.0,round(imshape[1]), round(compress)) rgrid=nat.Natgrid(xi,yi,xo,yo) prop_int = rgrid.rgrd(prop) return prop_int ################################################################################################## def create_psf_grid(self, psf_coeff_interp, imshape, xgrid, ygrid, skip, nmax, psfshape, basis, beta, cen, totpsfimage, plot): """ Creates a image with the gridded interpolated psfs. xgrid and ygrid are 1d numpy arrays with the x and y coordinates of the grids. """ # if plot: # plnum=N.zeros(2) # for i in range(2): # dum=pl.figure(None) # plnum[i]=dum.number # pl.clf() # if i == 0: pl.suptitle('Gridded psfs') # if i == 1: pl.suptitle('Gridded residual psfs') # ax = pl.subplot(1,1,1) # plaxis = pl.axis([0, imshape[0], 0, imshape[1]]) # pax = ax.get_position() # start = N.array((pax.xmin, pax.ymin)) # stop = N.array((pax.xmax, pax.ymax)) # sz=0.07 mask=N.zeros(psfshape, dtype=bool) # right now doesnt matter xg=xgrid[::skip+1] yg=ygrid[::skip+1] index = [(i,j) for i in range(0,len(xgrid),skip+1) for j in range(0,len(ygrid),skip+1)] xy = [(i,j) for i in xgrid[::skip+1] for j in ygrid[::skip+1]] blah=[] for i, coord in enumerate(index): maxpsfshape = [0, 0] for k in psf_coeff_interp: if k[0]+1 > maxpsfshape[0]: maxpsfshape[0] = k[0]+1 if k[1]+1 > maxpsfshape[1]: maxpsfshape[1] = k[1]+1 cf = N.zeros(maxpsfshape) for k in psf_coeff_interp: cf[k]=psf_coeff_interp[k][coord] cf = N.transpose(cf) psfgridim = sh.reconstruct_shapelets(psfshape, mask, basis, beta, cen, nmax, cf) blah.append(psfgridim) # if plot: # for j in range(2): # pl.figure(plnum[j]) # posn = [xy[i][0], xy[i][1]] # normposn =N.array(stop-start, dtype=float)/N.array(imshape[0:2])*posn+start # a=pl.axes([normposn[0]-sz/2., normposn[1]-sz/2., sz, sz]) # if j == 0: pl.contour(psfgridim,15) # if j == 1: pl.contour(psfgridim-totpsfimage,15) # pl.setp(a, xticks=[], yticks=[]) # pl.colorbar() # if plot: # pl.figure(plnum[0]) # pl.figure(plnum[1]) # return blah ################################################################################################## def blur_image(self, im, n, ny=None) : """ blurs the image by convolving with a gaussian kernel of typical size n. The optional keyword argument ny allows for a different size in the y direction. """ from scipy.ndimage import gaussian_filter sx = n if ny is not None: sy = ny else: sy = n improc = gaussian_filter(im, [sy, sx]) return improc
jjdmol/LOFAR
CEP/PyBDSM/src/python/psf_vary.py
Python
gpl-3.0
48,910
[ "Gaussian" ]
4abe0d40eb88ff081f22b182bea7e249f2315a4762eaf67196556ce244b9ff84
# # Gramps - a GTK+/GNOME based genealogy program # # Copyright (C) 2002-2007 Donald N. Allingham # Copyright (C) 2007-2008 Brian G. Matherly # Copyright (C) 2008 Jerome Rapinat # Copyright (C) 2008 Benny Malengier # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # $Id$ #------------------------------------------------------------------------- # # Standard Python modules # #------------------------------------------------------------------------- from ....const import GRAMPS_LOCALE as glocale _ = glocale.get_translation().gettext #------------------------------------------------------------------------- # # GRAMPS modules # #------------------------------------------------------------------------- from .._hasnotebase import HasNoteBase #------------------------------------------------------------------------- # "Events having notes" #------------------------------------------------------------------------- class HasNote(HasNoteBase): """Events having notes""" name = _('Events having <count> notes') description = _("Matches events having a certain number of notes")
Forage/Gramps
gramps/gen/filters/rules/event/_hasnote.py
Python
gpl-2.0
1,764
[ "Brian" ]
377319d86fb6f725efc69658e3a4b795f7771b8960b38051b0070590b626082a
""" ============================================== Visualizing the enegy-sensor-weather structure ============================================== This example employs several unsupervised learning techniques to extract the energy data structure from variations in Building Automation System (BAS) and historial weather data. The fundermental timelet for analysis are 15 min, referred to as Q. ** currently use H (Hour) as a fundermental timelet, need to change later ** Learning a graph structure -------------------------- We use sparse inverse covariance estimation to find which quotes are correlated conditionally on the others. Specifically, sparse inverse covariance gives us a graph, that is a list of connection. For each symbol, the symbols that it is connected too are those useful to explain its fluctuations. Clustering ---------- We use clustering to group together quotes that behave similarly. Here, amongst the :ref:`various clustering techniques <clustering>` available in the scikit-learn, we use :ref:`affinity_propagation` as it does not enforce equal-size clusters, and it can choose automatically the number of clusters from the data. Note that this gives us a different indication than the graph, as the graph reflects conditional relations between variables, while the clustering reflects marginal properties: variables clustered together can be considered as having a similar impact at the level of the full stock market. Embedding in 2D space --------------------- For visualization purposes, we need to lay out the different symbols on a 2D canvas. For this we use :ref:`manifold` techniques to retrieve 2D embedding. Visualization ------------- The output of the 3 models are combined in a 2D graph where nodes represents the stocks and edges the: - cluster labels are used to define the color of the nodes - the sparse covariance model is used to display the strength of the edges - the 2D embedding is used to position the nodes in the plan This example has a fair amount of visualization-related code, as visualization is crucial here to display the graph. One of the challenge is to position the labels minimizing overlap. For this we use an heuristic based on the direction of the nearest neighbor along each axis. """ #print(__doc__) # Author: Deokwooo Jung deokwoo.jung@gmail.compile from __future__ import division # To forace float point division import os import sys import numpy as np import pylab as pl from scipy import stats import matplotlib.pyplot as plt #from datetime import datetime import datetime as dt from dateutil import tz import shlex, subprocess import mytool as mt import time import retrieve_weather as rw import itertools import mpl_toolkits.mplot3d.axes3d as p3 import calendar from sklearn import cluster, covariance, manifold # Machine Learning Packeage ############################################################################### # Constant global variables ############################################################################### # in seconds MIN=60; HOUR=60*MIN; DAY=HOUR*24; MONTH=DAY*31 # Hour, Weekday, Day, Month MIN_IDX=0;HR_IDX=1; WD_IDX=2; MD_IDX=3 ;MN_IDX=4 # Define the period for analysis - year, month, day,hour # Note: The sample data in the currently downloaded files are from 1 Apr 2013 to # 30 Nov 2013. ANS_START_T=dt.datetime(2013,7,1,0) ANS_END_T=dt.datetime(2013,7,5,0) #ANS_END_T=dt.datetime(2013,8,30,0) # Interval of timelet, currently set to 1 Hour TIMELET_INV=dt.timedelta(hours=1) # UTC time of weather data from_zone = tz.gettz('UTC') # VTT local time to_zone = tz.gettz('Europe/Helsinki') # Multi-dimensional lists of hash tables time_slots=[] start=ANS_START_T while start < ANS_END_T: #print start time_slots.append(start) start = start + TIMELET_INV # Data dictionary # All sensor and weather data is processed and structred into # a consistent single data format -- Dictionary data_dict={} # This is the list of non-digit symbolic weather data # The symbolic weather data is such as Conditions (e.g Cloudy or Clear) # and Events (e.g. Rain or Fog ...) # Those symblic data is replaced with integer state representation whose # pairs are stored in a hash table using Dictionary. # If no data is given, key value is set to 0. Conditions_dict={};Conditions_val=[];key_val_c=0 Events_dict={};Events_val=[]; key_val_e=0 Is_CSV=bool(0) Start_t=time.time() argv_len=len(sys.argv) print 'arg length:',argv_len ############################################################################### # Function ############################################################################### def daterange(start, stop, step=dt.timedelta(days=1), inclusive=False): # inclusive=False to behave like range by default if step.days > 0: while start < stop: yield start start = start + step # not +=! don't modify object passed in if it's mutable # since this function is not restricted to # only types from datetime module elif step.days < 0: while start > stop: yield start start = start + step if inclusive and start == stop: yield start ############################################################################### # Retrive weather data from internet for the specified periods # prefix_order=TS (default) [Time][Sensor] # prefix_order=ST [Sensor][Time] ############################################################################### """ def get_weather(t_start, t_end, perfix_order='TS'): print 'getting weater data ' print 'start time:', t_start, ' ~ end time:',t_end data_days=[] for date in daterange(t_start, t_end, inclusive=True): #print date.strftime("%Y-%m-%d") temp=date.strftime("%Y,%m,%d").rsplit(',') data_day=rw.retrieve_data('VTT', int(temp[0]), int(temp[1]), int(temp[2]), view='d') data_day=data_day.split('\n') if perfix_order=='TS': # order by [Sensor][Time] # Paring the strings of daily weather data day_sample_parse=[] for hour_sample in data_day: #print hour_sample day_sample_parse.append(hour_sample.split(',')) data_days.append(day_sample_parse) else: # order by [Time][Sensor] # Paring the strings of daily weather data #f=open('weather_data.txt','w') day_sample_parse=[] for h_idx,hour_sample in enumerate(data_day): #print hour_sample if h_idx==0: sensor_name_list=hour_sample.split(',') # f.write(str(sensor_name_list)+'\n') else: hour_samples=hour_sample.split(',') #print hour_samples #f.write(str(hour_samples)+'\n') for sample_idx,each_sample in enumerate(hour_samples): sensor_name=sensor_name_list[sample_idx] if sensor_name in data_dict: data_dict[sensor_name].append(each_sample) else: data_dict.update({sensor_name:[each_sample]}) if perfix_order=='TS': return data_days else: return sensor_name_list #f.close() """ ############################################################################### # Plotting tool ############################################################################### def plotting_data(plot_list,opt='val'): # times is seconds, but it might not correct for months with 30 days. #times_in_secs=(time_val[:,[HR_IDX,MD_IDX,MN_IDX]]*[HOUR,DAY,MONTH]).sum(axis=1) # Minute,Hour, Weekday, Day, Month - total 5 time fields time_mat=np.zeros([len(time_slots),5]) for i, time_sample in enumerate(time_slots): time_mat[i,HR_IDX]=time_sample.hour time_mat[i,WD_IDX]=time_sample.weekday() time_mat[i,MD_IDX]=time_sample.day time_mat[i,MN_IDX]=time_sample.month monthDict={1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'} weekDict={0:'Mon', 1:'Tue', 2:'Wed', 3:'Thur', 4:'Fri', 5:'Sat', 6:'Sun'} # Month indicator time_mn_diff=np.diff(time_mat[:,MN_IDX]) m_label_idx=time_mn_diff.nonzero()[0] m_label_str=[] for m_num in time_mat[m_label_idx,MN_IDX]: m_label_str.append(monthDict[m_num]) time_wk_diff=np.diff(time_mat[:,WD_IDX]) w_label_idx=time_wk_diff.nonzero()[0] w_label_str=[] for w_num in time_mat[w_label_idx,WD_IDX]: w_label_str.append(weekDict[int(w_num)]) for k,sensor in enumerate(plot_list): #print k, sensor num_samples=[] mean_samples=[] for i,(t,samples) in enumerate(zip(time_slots,data_dict[sensor])): #print i,str(t),len(samples) num_samples.append(len(samples)) # Mean value with masking mean_samples.append(np.mean(samples)) #mean_samples.append(np.mean(np.ma.masked_invalid(samples)) #sensor_samples.append(num_samples) plt.figure(1) plt.subplot(len(plot_list),1,k+1) plt.plot(time_slots,num_samples) plt.title(sensor,fontsize=8) plt.xticks(fontsize=8) plt.yticks(fontsize=8) plt.ylabel('# Samples/Hour',fontsize=8) if k<len(plot_list)-1: frame1 = plt.gca() frame1.axes.get_xaxis().set_visible(False) #frame1.axes.get_yaxis().set_visible(False) plt.figure(2) plt.subplot(len(plot_list),1,k+1) plt.plot(time_slots,mean_samples) plt.title(sensor,fontsize=8) plt.xticks(fontsize=8) plt.yticks(fontsize=8) plt.ylabel('Avg Val/Hour',fontsize=8) if k<len(plot_list)-1: frame1 = plt.gca() frame1.axes.get_xaxis().set_visible(False) #frame1.axes.get_yaxis().set_visible(False) #plt.xticks(w_label_idx.tolist(),w_label_str,fontsize=8) #plt.text(m_label_idx, np.max(num_samples)*0.8, m_label_str, fontsize=12) print ' End of Plotting' return time_mat ############################################################################### # Parsing sensor data ############################################################################### def get_val(filename): if Is_CSV==True: openfile=open(filename,"r") sensor_val=[] time_val=[]; for line in openfile: tmp=line.rstrip().rsplit(",") sensor_val.append(float(tmp[1])) temp=dt.datetime.strptime(tmp[0],"%Y-%m-%d %H:%M:%S") temp=temp.timetuple() # Hour, Weekday, Day, Month time_val.append([temp[3],temp[6],temp[2],temp[1]]) openfile.close() #print 'list of input csv files: ' else: data = mt.loadObjectBinary(filename) sensor_val = data["value"] time_val = data["ts"] #print 'list of input bin files: ' return sensor_val,time_val def get_val_timelet(filename,t_slots): print ' get_val_timelet' if Is_CSV==True: openfile=open(filename,"r") sensor_val=[] time_val=[]; for line in openfile: tmp=line.rstrip().rsplit(",") sensor_val.append(float(tmp[1])) temp=dt.datetime.strptime(tmp[0],"%Y-%m-%d %H:%M:%S") temp=temp.timetuple() # Hour, Weekday, Day, Month time_val.append([temp[3],temp[6],temp[2],temp[1]]) openfile.close() #print 'list of input csv files: ' else: data = mt.loadObjectBinary(filename) sensor_val = data["value"] time_val = data["ts"] # Creat the list of lists sensor_read=[[] for i in range(len(t_slots))] for t_sample, v_sample in zip(time_val,sensor_val): #import pdb; pdb.set_trace() # If data in 2013 is only available after Aprile, Otherwise it is 2014 data if t_sample[MN_IDX]>3: temp_dt=dt.datetime(2013,t_sample[MN_IDX],t_sample[MD_IDX],t_sample[HR_IDX]) else: temp_dt=dt.datetime(2014,t_sample[MN_IDX],t_sample[MD_IDX],t_sample[HR_IDX]) #print temp_dt try: idx=t_slots.index(temp_dt) sensor_read[idx].append(v_sample) except ValueError: idx=-1 return sensor_read, time_val ############################################################################### # Parsing sensor data # Data samples are regularized for specified times with timelet ############################################################################### def symbol_to_state(symbol_list): #list(itertools.chain(*list_of_lists)) symbol_dict={};symbol_val=[];key_val=1 print 'start' for i,key_set in enumerate(symbol_list): symbol_val_let=[] for key in key_set: if key not in symbol_dict: if len(key)==0: symbol_dict.update({key:0}) symbol_val_let.append(0) else: symbol_dict.update({key:key_val}) symbol_val_let.append(key_val) key_val=key_val+1 else: symbol_val_let.append(symbol_dict[key]) symbol_val.append(symbol_val_let) return symbol_val,symbol_dict def get_weather(t_start, t_end, perfix_order='TS'): print 'getting weater data new ' print 'start time:', t_start, ' ~ end time:',t_end data_days=[] # Date iteration given start time and end-time for date in daterange(t_start, t_end, inclusive=True): print date.strftime("%Y-%m-%d") temp=date.strftime("%Y,%m,%d").rsplit(',') data_day=rw.retrieve_data('VTT', int(temp[0]), int(temp[1]), int(temp[2]), view='d') data_day=data_day.split('\n') if perfix_order=='TS': # order by [Sensor][Time] # Paring the strings of daily weather data day_sample_parse=[] for hour_sample in data_day: #print hour_sample day_sample_parse.append(hour_sample.split(',')) data_days.append(day_sample_parse) else: # order by [Time][Sensor] # Paring the strings of daily weather data #f=open('weather_data.txt','w') day_sample_parse=[] for h_idx,hour_sample in enumerate(data_day): #print hour_sample if h_idx==0: sensor_name_list=hour_sample.split(',') # f.write(str(sensor_name_list)+'\n') else: hour_samples=hour_sample.split(',') #print hour_samples #f.write(str(hour_samples)+'\n') for sample_idx,each_sample in enumerate(hour_samples): sensor_name=sensor_name_list[sample_idx] if sensor_name in data_dict: data_dict[sensor_name].append(each_sample) else: data_dict.update({sensor_name:[each_sample]}) if perfix_order=='TS': return data_days else: return sensor_name_list def get_weather_timelet(t_slots): print 'getting weater data new ' t_start=t_slots[0] t_end=t_slots[-1] print 'start time:', t_start, ' ~ end time:',t_end # Date iteration given start time and end-time # Iterate for each day for all weather data types for date_idx,date in enumerate(daterange(t_start, t_end, inclusive=True)): print date.strftime("%Y-%m-%d") temp=date.strftime("%Y,%m,%d").rsplit(',') data_day=rw.retrieve_data('VTT', int(temp[0]), int(temp[1]), int(temp[2]), view='d') # split the data into t data_day=data_day.split('\n') # Iterate for each time index(h_idx) of a day for all weather data types for h_idx,hour_sample in enumerate(data_day): hour_samples=hour_sample.split(',') # Initialize weather data lists of dictionary # The first row is always the list of weather data types if (h_idx==0) and (date_idx==0): sensor_name_list=hour_sample.split(',') for sample_idx,each_sample in enumerate(hour_samples): sensor_name=sensor_name_list[sample_idx] sensor_read=[[] for i in range(len(t_slots))] data_dict.update({sensor_name:sensor_read}) elif h_idx>0: # 'DateUTC' is the one sample_DateUTC=hour_samples[sensor_name_list.index('DateUTC')] # convert to UTC time to VTT local time. utc_dt=dt.datetime.strptime(sample_DateUTC, "%Y-%m-%d %H:%M:%S") vtt_dt_aware = utc_dt.replace(tzinfo=from_zone).astimezone(to_zone) # convert to offset-naive from offset-aware datetimes vtt_dt=dt.datetime(*(vtt_dt_aware.timetuple()[:4])) # time slot index a given weather sample time try: vtt_dt_idx=t_slots.index(vtt_dt) for sample_idx,each_sample in enumerate(hour_samples): # convert string type to float time if possible try: each_sample=float(each_sample) except ValueError: each_sample=each_sample sensor_name=sensor_name_list[sample_idx] #import pdb; pdb.set_trace() if sensor_name in data_dict: if each_sample!='N/A' and each_sample!=[]: data_dict[sensor_name][vtt_dt_idx].append(each_sample) else: raise NameError('Inconsistency in the list of weather data') except ValueError: vtt_dt_idx=-1 else: # hour_sample is list of weather filed name, discard hour_sample=[] return sensor_name_list def data_dict_purge(purge_list): for key in purge_list: print 'purge', key if key in data_dict.keys(): data_dict.pop(key,None) #data_dict_purge(weather_list) ############################################################################### # Reading sensor data from CSV or BIN files - use linux commands ############################################################################### input_csvs=[] num_csvs=[] if argv_len==1: if Is_CSV==True: temp = subprocess.check_output("ls *.csv |grep _ACTIVE_POWER_", shell=True) else: temp = subprocess.check_output("ls *.bin |grep _ACTIVE_POWER_", shell=True) input_csvs =shlex.split(temp) plt.ion() print 'argv 1' elif argv_len>1: input_csvs=sys.argv[1:] print 'getting args' else: input_csvs=[] print '...' num_csvs=len(input_csvs) num_col_subplot=np.ceil(np.sqrt(num_csvs)) ############################################################################### # Analysis script starts here .... # List of sensors from BMS print 'mapping sensor list into hasing table using dictionary' sensor_list=input_csvs # List of sensors from Weather data # getting weather files # Weather parameter list #['TimeEEST', 'TemperatureC', 'Dew PointC', 'Humidity', # 'Sea Level PressurehPa', 'VisibilityKm', 'Wind Direction', # 'Wind SpeedKm/h', 'Gust SpeedKm/h', 'Precipitationmm', # 'Events', 'Conditions', 'WindDirDegrees', 'DateUTC'] # Note: We select 'TemperatureC', 'Dew PointC', 'Humidity', # 'Events', 'Conditions' for the main weather parameter #weather_list=get_weather(ANS_START_T, ANS_END_T,'ST') # Checking length of weather sample data print "lenth of dictionary" for key in data_dict.keys(): print 'len of ', key, len(data_dict[key]) # data dictionary that map all types of sensor readings into a single hash table ############################################################################### # Read out all sensor files in the file list time_set_temp=[] for i,argv in enumerate(sensor_list): print 'index ',i+1,': ', argv # sensor value is read by time start__dictproc_t=time.time() dict_sensor_val, dict_time_val=get_val_timelet(argv,time_slots) data_dict.update({argv:dict_sensor_val}) end__dictproc_t=time.time() print argv,'- dict.proc time is ', end__dictproc_t-start__dictproc_t print 'Check sample density over time slots' time_mat=plotting_data(sensor_list[0:2]) """ weather_list -that is pretty much fixed from database (*) is the data to be used for our analysis 0 TimeEEST 1 TemperatureC (*) 2 Dew PointC (*) 3 Humidity (*) 4 Sea Level PressurehPa 5 VisibilityKm 6 Wind Direction 7 Wind SpeedKm/h 8 Gust SpeedKm/h 9 Precipitationmm 10 Events (*) 11 Conditions (*) 12 WindDirDegrees 13 DateUTC """ weather_list=get_weather_timelet(time_slots) # Convert symbols to Integer representaion data_dict['Conditions'],Conditions_dict=symbol_to_state(data_dict['Conditions']) data_dict['Events'],Events_dict=symbol_to_state(data_dict['Events']) # Weather data to be used weather_list_used = [weather_list[i] for i in [1,2,3,10,11]] # All (sensor + weather) data to be used data_used=weather_list_used + sensor_list def verify_data_format(key_list): # Verify there is no [] or N/A in the list print 'Checking any inconsisent data format.....' print '---------------------------------' list_of_wrong_data_format=[] for key in key_list: print 'checking ', key, '...' for i,samples in enumerate(data_dict[key]): for j,each_sample in enumerate(samples): if each_sample==[]: list_of_wrong_data_format.append([key,i,j]) print each_sample, 'at', time_slots[j], 'in', key elif (isinstance(each_sample,int)==False and isinstance(each_sample,float)==False): list_of_wrong_data_format.append([key,i,j]) print each_sample, 'at', time_slots[j], 'in', key print '---------------------------------' if len(list_of_wrong_data_format)==0: print ' no inconsistent data format' return list_of_wrong_data_format # Verify there is no [] or N/A in the list list_of_wrong_data_format=verify_data_format(data_used) if len(list_of_wrong_data_format)!=0: raise NameError('Inconsistent data format in the list of data_used') # Weighted averge to impute missing value # Imputing missing data -using weighted mean value hr_set=time_mat[:,HR_IDX].astype(int) wd_set=time_mat[:,WD_IDX].astype(int) day_set=time_mat[:,MD_IDX].astype(int) mn_set=time_mat[:,MN_IDX].astype(int) cumnum_days_mn=np.r_[0,np.array([calendar.monthrange(2013, i)[1] for i in np.r_[1:12]]).cumsum()] daycount_set=[ int(day+cumnum_days_mn[mn-1]) for i,(day,mn) in enumerate(zip(day_set,mn_set))] # X.shape (1258, 7) # type(X) <type 'numpy.ndarray'> # type(X) <type 'numpy.ndarray'> num_of_data=len(data_used) num_of_samples=len(time_slots) X=np.zeros([num_of_samples,num_of_data]) INT_type_cols=[] FLOAT_type_cols=[] for j,key in enumerate(data_used): for i,sample in enumerate(data_dict[key]): if len(sample)==0: X[i,j]=np.infty elif isinstance(sample[0],int): X[i,j]=int(stats.mode(sample)[0]) if i==0: INT_type_cols.append(j) elif isinstance(sample[0],float): X[i,j]=np.mean(sample) if i==0: FLOAT_type_cols.append(j) else: raise NameError('Sample type must either INT or FLOAT type') # If no data availalbe, then imputes the data by weighted mean print 'Before imputation' for i,key in enumerate(data_used): print key print [k for k in np.nonzero(X[:,i]==np.infty)[0]] # If no data availalbe, then imputes the data by weighted mean for i,key in enumerate(data_used): for inf_idx in np.nonzero(X[:,i]==np.infty)[0]: whgt_bottom_sum=0;whgt_top_sum=0 for h_idx in np.nonzero(hr_set==hr_set[inf_idx])[0]: #import pdb; pdb.set_trace() sample_temp=X[h_idx,i] if (sample_temp<np.infty and h_idx!=inf_idx): wght=1/np.abs(daycount_set[h_idx]-daycount_set[inf_idx]) whgt_bottom_sum=whgt_bottom_sum+wght whgt_top_sum=whgt_top_sum+wght*sample_temp new_sample=whgt_top_sum/whgt_bottom_sum X[inf_idx,i]=new_sample # If no data availalbe, then imputes the data by weighted mean print 'After imputation' for i,key in enumerate(data_used): print key print [k for k in np.nonzero(X[:,i]==np.infty)[0]] # If no data availalbe, then imputes the data by weighted mean X_INT=X[:,INT_type_cols] X_FLOAT=X[:,FLOAT_type_cols] ############################################################################### # Learn a graphical structure from the correlations edge_model = covariance.GraphLassoCV() # standardize the time series: using correlations rather than covariance # is more efficient for structure recovery edge_model.fit(X_FLOAT) # Using mode if interger type, using mean if real type """ vak1_power_sys_sum=[] vak1_power_p1_sum=[] vak1_power_p2_sum=[] vak1_power_p3_sum=[] for i,(psys,p1,p2,p3) in enumerate(zip(vak1_power_sys,vak1_power_p1,vak1_power_p2,vak1_power_p3)): vak1_power_sys_sum.append(sum(psys)) vak1_power_p1_sum.append(sum(p1)) vak1_power_p2_sum.append(sum(p2)) vak1_power_p3_sum.append(sum(p3)) plt.subplot(2,1,1) plt.plot(vak1_power_sys_sum) plt.plot(np.array(vak1_power_p1_sum)+np.array(vak1_power_p2_sum)+np.array(vak1_power_p3_sum),'-s') plt.subplot(2,1,2) plt.plot(vak1_power_p1_sum,'-*') plt.plot(vak1_power_p2_sum,'-s') plt.plot(vak1_power_p3_sum,'-o') """ # Using the following weather data for variables # # Regularized the weather data into a single time referece # For symbolic data, use mode, and for real number data, use average # Gaussian Process (GP) model and interploation for power consumption data #Conditions_dict,Events_dict """ 3D plotting fig=pl.figure() ax = p3.Axes3D(fig) ax.scatter(gw2_power_p1_sum.T, gw2_power_p2_sum, gw2_power_p3_sum, c=colors) ax.set_xlabel('P1') ax.set_ylabel('P2') ax.set_zlabel('P3') fig.add_axes(ax) """ if argv_len>1: print 'end of program' plt.show()
TinyOS-Camp/DDEA-DEV
Development/plot_csv.py
Python
gpl-2.0
27,192
[ "Gaussian" ]
26728dcf6988faf4196c34ef18d3eb40375a6efc93ec9d3495115c34ddba919a
""" """ from abc import ABCMeta, abstractmethod PULSAR_UNKNOWN_RETURN_CODE = '__unknown__' class ManagerInterface(object): """ Defines the interface to various job managers. """ __metaclass__ = ABCMeta @abstractmethod def setup_job(self, input_job_id, tool_id, tool_version): """ Setup a job directory for specified input (galaxy) job id, tool id, and tool version. """ @abstractmethod def clean(self, job_id): """ Delete job directory and clean up resources associated with job with id `job_id`. """ @abstractmethod def launch(self, job_id, command_line, submit_params={}, dependencies_description=None, env=[]): """ Called to indicate that the client is ready for this job with specified job id and command line to be executed (i.e. run or queue this job depending on implementation). """ @abstractmethod def get_status(self, job_id): """ Return status of job as string, currently supported statuses include 'cancelled', 'running', 'queued', and 'complete'. """ @abstractmethod def return_code(self, job_id): """ Return integer indicating return code of specified execution or PULSAR_UNKNOWN_RETURN_CODE. """ @abstractmethod def stdout_contents(self, job_id): """ After completion, return contents of stdout associated with specified job. """ @abstractmethod def stderr_contents(self, job_id): """ After completion, return contents of stderr associated with specified job. """ @abstractmethod def kill(self, job_id): """ End or cancel execution of the specified job. """ @abstractmethod def job_directory(self, job_id): """ Return a JobDirectory abstraction describing the state of the job working directory. """ class ManagerProxy(object): """ Subclass to build override proxy a manager and override specific functionality. """ def __init__(self, manager): self._proxied_manager = manager def setup_job(self, *args, **kwargs): return self._proxied_manager.setup_job(*args, **kwargs) def clean(self, *args, **kwargs): return self._proxied_manager.clean(*args, **kwargs) def launch(self, *args, **kwargs): return self._proxied_manager.launch(*args, **kwargs) def get_status(self, *args, **kwargs): return self._proxied_manager.get_status(*args, **kwargs) def return_code(self, *args, **kwargs): return self._proxied_manager.return_code(*args, **kwargs) def stdout_contents(self, *args, **kwargs): return self._proxied_manager.stdout_contents(*args, **kwargs) def stderr_contents(self, *args, **kwargs): return self._proxied_manager.stderr_contents(*args, **kwargs) def kill(self, *args, **kwargs): return self._proxied_manager.kill(*args, **kwargs) def shutdown(self, timeout=None): """ Optional. """ try: shutdown_method = self._proxied_manager.shutdown except AttributeError: return shutdown_method(timeout) def job_directory(self, *args, **kwargs): return self._proxied_manager.job_directory(*args, **kwargs) def system_properties(self): return self._proxied_manager.system_properties()
jmchilton/pulsar
pulsar/managers/__init__.py
Python
apache-2.0
3,493
[ "Galaxy" ]
50cbe84f70f54fa08a8b16655b47d894ca9c09f39a8d1e4efffc4043882f2f70
# This python script contains functions and classes used in the # two synthetic galaxy model notebooks in this directory. import astropy.io.ascii as asciitable from scipy import interpolate import numpy as np def LoadData(galaxy_datafile, HIrad, ddensdR): # Read the galaxy description file which contains rotation velocity and # density information in a comma-delimited file where each row contains # radius (in kpc), a rotational velocity (in km/s), and a density # (in atoms/cm^3). # raw_data = asciitable.read(galaxy_datafile) # Restructure the data into a 3xN array containing radius, rot. vel., # and gas density in "columns" galaxy_data = np.hstack((raw_data['radius'].reshape(-1, 1), raw_data['rot_vel'].reshape(-1, 1), raw_data['density'].reshape(-1, 1))) # # Interpolate any extension to the rotation curve as flat, # but with dropping density out to HI radius limit. # extrapol_step = (25 - galaxy_data[-1][0])/4 while (galaxy_data[-1][0] < HIrad): new_rad = galaxy_data[-1][0]+extrapol_step new_vel = galaxy_data[-1][1] if (galaxy_data[-1][2] > ddensdR*extrapol_step): new_dens = galaxy_data[-1][2] - ddensdR*extrapol_step else: new_dens = 0.0 new_row = np.array([new_rad, new_vel, new_dens]) galaxy_data = np.vstack((galaxy_data, new_row)) # Save raw values rad_raw = np.copy(galaxy_data[:, 0]) rotvel_raw = np.copy(galaxy_data[:, 1]) density_raw = np.copy(galaxy_data[:, 2]) return (rad_raw, rotvel_raw, density_raw) def spline_curves(rad, vel, dens, dr): # Do a modified spline fit to smooth the rotation curve and gas density # data to smooth out data gaps and make sure density and rotational # velocity don't go negative # Find the spline representation of rotation curve (default is cubic # spline) rotvel_fit = interpolate.splrep(rad, vel, s=0) density_fit = interpolate.splrep(rad, dens, s=0) # Fit spline along evenly spaced points (in radius) and restrict rotational # velocity and density to be positive (since spline fit is bit wiggly here # and caused very small scale 'negative' values at origin for velocity and # at high radii for density). rad_sp = np.linspace(0, rad[-1], int(rad[-1]/dr)) rotvel_sp = np.absolute(interpolate.splev(rad_sp, rotvel_fit, der=0).round(1)) density_sp = np.absolute(interpolate.splev(rad_sp, density_fit, der=0).round(3)) return(rad_sp, rotvel_sp, density_sp) def RotCoord(x, y): # Converts (x, y) to (r,theta) # Can work on entire arrays of x and y return (np.sqrt(x*x+y*y), np.arctan2(y, x)) class nf(float): # This class allows floating point numbers to be printed as integers. # Based on # http://matplotlib.sourceforge.net/examples/pylab_examples/contour_label_demo.html def __repr__(self): str = '%.1f' % (self.__float__(),) if str[-1] == '0': return '%.0f' % self.__float__() else: return '%.1f' % self.__float__()
JuanCab/synthetic_HI_models
galaxyparam.py
Python
gpl-3.0
3,144
[ "Galaxy" ]
8fa8acec8aa55b652378d520eb8c2c09c0fddeab760f63e541cbcaf98d3c80bd
import os import sys import json from datamanagerpkg import ProtonCommunication_data_manager from datamanagerpkg import GalaxyCommunication_data_manager from sequencer.models import Experiments, GalaxyUsers from sequencer.models import GalaxyJobs, ExperimentRawData ########################## #URL SEQUENCER ########################## from GlobalVariables import sequencer_base_url from GlobalVariables import sequencer_user from GlobalVariables import sequencer_password from GlobalVariables import sequencer_severName from GlobalVariables import sequencer_ExperimentLimit from GlobalVariables import toolsInformation ########################## #URL GALAXY ########################## from GlobalVariables import galaxy_base_url from GlobalVariables import apiKey ########################## #NAs DIr folder ########################## from GlobalVariables import nasInput from GlobalVariables import CNVfolderName from GlobalVariables import plasmaFolderName from GlobalVariables import nasResults from GlobalVariables import workflowPath ########################## #SMTP folder ########################## from GlobalVariables import smtpServerAphp from GlobalVariables import smtpPortServer from GlobalVariables import fromAddrOfficial from sequencer.views import getDataPath from datamanagerpkg import ProtonCommunication_data_manager from datamanagerpkg import GalaxyCommunication_data_manager from pprint import pprint #open a workflow with open('/nas_Dir/workflow/Galaxy-Workflow-Plasma_mutation.ga') as data_file: data = json.load(data_file) pprint(data) #now I have the key in order stepkey=data['steps'].keys() stepkey = [int(x) for x in stepkey] stepkey.sort() #create a workflow object #~ u'annotation': u'plasma workflow to generates all the data', #~ u'name': u'Plasma_mutation', from sequencer.models import Workflows,WorkflowsTools,Supportedfiles tryexp = None try: tryexp = Workflows.objects.get(name=str(data['name'])) except Workflows.DoesNotExist: tryexp = None if (tryexp == None): workflow_local=Workflows(name=str(data['name']),description=str(data['name'])) workflow_local.save() workflow_local = Workflows.objects.get(name=str(data['name'])) for step in stepkey: if data['steps'][str(step)]['tool_id']!=None: #create a tool print("grou1") try: tryexp = WorkflowsTools.objects.get(primary_name=str(data['steps'][str(step)]['tool_id']+"_"+data['steps'][str(step)]['tool_version']+".json")) except WorkflowsTools.DoesNotExist: tryexp = None #~ if tryexp == None: print("grou2") newtool=WorkflowsTools(primary_name=str(data['steps'][str(step)]['tool_id']+"_"+data['steps'][str(step)]['tool_version']+".json"), name=str(data['steps'][str(step)]['tool_id']), version=str(data['steps'][str(step)]['tool_version'])) newtool.save() print("grou3") workflow_local.tools_list.add(newtool) print("grou4") workflow_local.save() print( data['steps'][str(step)]['tool_id']+"_"+data['steps'][str(step)]['tool_version']+".json") #create a tool with open(toolsInformation+data['steps'][str(step)]['tool_id']+"_"+data['steps'][str(step)]['tool_version']+".json") as data_file_tool: tool = json.load(data_file_tool) #~ print(tool['function'][0]) print("#######################input") #~ print(tool['function'][0]['input']) for dataInput in tool['function'][0]['input'] : try: tryexp = Supportedfiles.objects.get(dataDescription=str(dataInput['dataDescription'])) except Supportedfiles.DoesNotExist: tryexp = None newfile=Supportedfiles(dataHandle=str(dataInput['dataHandle']), dataDescription=str(dataInput['dataDescription']), dataFormatEdamOntology=str(dataInput['dataFormat'][0]['uri'])) newfile.save() newtool.inputlist.add(newfile) newtool.save() #~ print("#######################dataInpty") print("#######################output") for dataInput in tool['function'][0]['input'] : try: tryexp = Supportedfiles.objects.get(dataDescription=str(dataInput['dataDescription'])) except Supportedfiles.DoesNotExist: tryexp = None #~ if tryexp == None: newfile=Supportedfiles(dataHandle=str(dataInput['dataHandle']), dataDescription=str(dataInput['dataDescription']), dataFormatEdamOntology=str(dataInput['dataFormat'][0]['uri']) ) newfile.save() newtool.outputlist.add(newfile) newtool.save()
CARPEM/GalaxyDocker
data-manager-hegp/analysisManager/analysismanager/addPlasmaMutation_Information.py
Python
mit
4,872
[ "Galaxy" ]
6a3f07bcc7b2b508e7311eb63c95a313e4ba71175535b37c8d9ba23c32803da3
""" A simple VTK widget for PyQt v4, the Qt v4 bindings for Python. See http://www.trolltech.com for Qt documentation, and http://www.riverbankcomputing.co.uk for PyQt. This class is based on the vtkGenericRenderWindowInteractor and is therefore fairly powerful. It should also play nicely with the vtk3DWidget code. Created by Prabhu Ramachandran, May 2002 Based on David Gobbi's QVTKRenderWidget.py Changes by Gerard Vermeulen Feb. 2003 Win32 support. Changes by Gerard Vermeulen, May 2003 Bug fixes and better integration with the Qt framework. Changes by Phil Thompson, Nov. 2006 Ported to PyQt v4. Added support for wheel events. Changes by Phil Thompson, Oct. 2007 Bug fixes. Changes by Phil Thompson, Mar. 2008 Added cursor support. Changes by Rodrigo Mologni, Sep. 2013 (Credit to Daniele Esposti) Bug fix to PySide: Converts PyCObject to void pointer. """ try: from PyQt4 import QtCore, QtGui except ImportError: try: from PySide import QtCore, QtGui except ImportError: raise ImportError("Cannot load either PyQt or PySide") import vtk class QVTKRenderWindowInteractor(QtGui.QWidget): """ A QVTKRenderWindowInteractor for Python and Qt. Uses a vtkGenericRenderWindowInteractor to handle the interactions. Use GetRenderWindow() to get the vtkRenderWindow. Create with the keyword stereo=1 in order to generate a stereo-capable window. The user interface is summarized in vtkInteractorStyle.h: - Keypress j / Keypress t: toggle between joystick (position sensitive) and trackball (motion sensitive) styles. In joystick style, motion occurs continuously as long as a mouse button is pressed. In trackball style, motion occurs when the mouse button is pressed and the mouse pointer moves. - Keypress c / Keypress o: toggle between camera and object (actor) modes. In camera mode, mouse events affect the camera position and focal point. In object mode, mouse events affect the actor that is under the mouse pointer. - Button 1: rotate the camera around its focal point (if camera mode) or rotate the actor around its origin (if actor mode). The rotation is in the direction defined from the center of the renderer's viewport towards the mouse position. In joystick mode, the magnitude of the rotation is determined by the distance the mouse is from the center of the render window. - Button 2: pan the camera (if camera mode) or translate the actor (if object mode). In joystick mode, the direction of pan or translation is from the center of the viewport towards the mouse position. In trackball mode, the direction of motion is the direction the mouse moves. (Note: with 2-button mice, pan is defined as <Shift>-Button 1.) - Button 3: zoom the camera (if camera mode) or scale the actor (if object mode). Zoom in/increase scale if the mouse position is in the top half of the viewport; zoom out/decrease scale if the mouse position is in the bottom half. In joystick mode, the amount of zoom is controlled by the distance of the mouse pointer from the horizontal centerline of the window. - Keypress 3: toggle the render window into and out of stereo mode. By default, red-blue stereo pairs are created. Some systems support Crystal Eyes LCD stereo glasses; you have to invoke SetStereoTypeToCrystalEyes() on the rendering window. Note: to use stereo you also need to pass a stereo=1 keyword argument to the constructor. - Keypress e: exit the application. - Keypress f: fly to the picked point - Keypress p: perform a pick operation. The render window interactor has an internal instance of vtkCellPicker that it uses to pick. - Keypress r: reset the camera view along the current view direction. Centers the actors and moves the camera so that all actors are visible. - Keypress s: modify the representation of all actors so that they are surfaces. - Keypress u: invoke the user-defined function. Typically, this keypress will bring up an interactor that you can type commands in. - Keypress w: modify the representation of all actors so that they are wireframe. """ # Map between VTK and Qt cursors. _CURSOR_MAP = { 0: QtCore.Qt.ArrowCursor, # VTK_CURSOR_DEFAULT 1: QtCore.Qt.ArrowCursor, # VTK_CURSOR_ARROW 2: QtCore.Qt.SizeBDiagCursor, # VTK_CURSOR_SIZENE 3: QtCore.Qt.SizeFDiagCursor, # VTK_CURSOR_SIZENWSE 4: QtCore.Qt.SizeBDiagCursor, # VTK_CURSOR_SIZESW 5: QtCore.Qt.SizeFDiagCursor, # VTK_CURSOR_SIZESE 6: QtCore.Qt.SizeVerCursor, # VTK_CURSOR_SIZENS 7: QtCore.Qt.SizeHorCursor, # VTK_CURSOR_SIZEWE 8: QtCore.Qt.SizeAllCursor, # VTK_CURSOR_SIZEALL 9: QtCore.Qt.PointingHandCursor, # VTK_CURSOR_HAND 10: QtCore.Qt.CrossCursor, # VTK_CURSOR_CROSSHAIR } def __init__(self, parent=None, wflags=QtCore.Qt.WindowFlags(), **kw): # the current button self._ActiveButton = QtCore.Qt.NoButton # private attributes self.__saveX = 0 self.__saveY = 0 self.__saveModifiers = QtCore.Qt.NoModifier self.__saveButtons = QtCore.Qt.NoButton # do special handling of some keywords: # stereo, rw stereo = 0 if kw.has_key('stereo'): if kw['stereo']: stereo = 1 rw = None if kw.has_key('rw'): rw = kw['rw'] # create qt-level widget QtGui.QWidget.__init__(self, parent, wflags|QtCore.Qt.MSWindowsOwnDC) if rw: # user-supplied render window self._RenderWindow = rw else: self._RenderWindow = vtk.vtkRenderWindow() WId = self.winId() if type(WId).__name__ == 'PyCObject': from ctypes import pythonapi, c_void_p, py_object pythonapi.PyCObject_AsVoidPtr.restype = c_void_p pythonapi.PyCObject_AsVoidPtr.argtypes = [py_object] WId = pythonapi.PyCObject_AsVoidPtr(WId) self._RenderWindow.SetWindowInfo(str(int(WId))) if stereo: # stereo mode self._RenderWindow.StereoCapableWindowOn() self._RenderWindow.SetStereoTypeToCrystalEyes() if kw.has_key('iren'): self._Iren = kw['iren'] else: self._Iren = vtk.vtkGenericRenderWindowInteractor() self._Iren.SetRenderWindow(self._RenderWindow) # do all the necessary qt setup self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent) self.setAttribute(QtCore.Qt.WA_PaintOnScreen) self.setMouseTracking(True) # get all mouse events self.setFocusPolicy(QtCore.Qt.WheelFocus) self.setSizePolicy(QtGui.QSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)) self._Timer = QtCore.QTimer(self) self.connect(self._Timer, QtCore.SIGNAL('timeout()'), self.TimerEvent) self._Iren.AddObserver('CreateTimerEvent', self.CreateTimer) self._Iren.AddObserver('DestroyTimerEvent', self.DestroyTimer) self._Iren.GetRenderWindow().AddObserver('CursorChangedEvent', self.CursorChangedEvent) #Create a hidden child widget and connect its destroyed signal to its #parent ``Finalize`` slot. The hidden children will be destroyed before #its parent thus allowing cleanup of VTK elements. self._hidden = QtGui.QWidget(self) self._hidden.hide() self.connect(self._hidden, QtCore.SIGNAL('destroyed()'), self.Finalize) def __getattr__(self, attr): """Makes the object behave like a vtkGenericRenderWindowInteractor""" if attr == '__vtk__': return lambda t=self._Iren: t elif hasattr(self._Iren, attr): return getattr(self._Iren, attr) else: raise AttributeError, self.__class__.__name__ + \ " has no attribute named " + attr def Finalize(self): ''' Call internal cleanup method on VTK objects ''' self._RenderWindow.Finalize() def CreateTimer(self, obj, evt): self._Timer.start(10) def DestroyTimer(self, obj, evt): self._Timer.stop() return 1 def TimerEvent(self): self._Iren.TimerEvent() def CursorChangedEvent(self, obj, evt): """Called when the CursorChangedEvent fires on the render window.""" # This indirection is needed since when the event fires, the current # cursor is not yet set so we defer this by which time the current # cursor should have been set. QtCore.QTimer.singleShot(0, self.ShowCursor) def HideCursor(self): """Hides the cursor.""" self.setCursor(QtCore.Qt.BlankCursor) def ShowCursor(self): """Shows the cursor.""" vtk_cursor = self._Iren.GetRenderWindow().GetCurrentCursor() qt_cursor = self._CURSOR_MAP.get(vtk_cursor, QtCore.Qt.ArrowCursor) self.setCursor(qt_cursor) def closeEvent(self, evt): self.Finalize() def sizeHint(self): return QtCore.QSize(400, 400) def paintEngine(self): return None def paintEvent(self, ev): self._Iren.Render() def resizeEvent(self, ev): w = self.width() h = self.height() vtk.vtkRenderWindow.SetSize(self._RenderWindow, w, h) self._Iren.SetSize(w, h) self._Iren.ConfigureEvent() self.update() def _GetCtrlShift(self, ev): ctrl = shift = False if hasattr(ev, 'modifiers'): if ev.modifiers() & QtCore.Qt.ShiftModifier: shift = True if ev.modifiers() & QtCore.Qt.ControlModifier: ctrl = True else: if self.__saveModifiers & QtCore.Qt.ShiftModifier: shift = True if self.__saveModifiers & QtCore.Qt.ControlModifier: ctrl = True return ctrl, shift def enterEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY, ctrl, shift, chr(0), 0, None) self._Iren.EnterEvent() def leaveEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY, ctrl, shift, chr(0), 0, None) self._Iren.LeaveEvent() def mousePressEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) repeat = 0 if ev.type() == QtCore.QEvent.MouseButtonDblClick: repeat = 1 self._Iren.SetEventInformationFlipY(ev.x(), ev.y(), ctrl, shift, chr(0), repeat, None) self._ActiveButton = ev.button() if self._ActiveButton == QtCore.Qt.LeftButton: self._Iren.LeftButtonPressEvent() elif self._ActiveButton == QtCore.Qt.RightButton: self._Iren.RightButtonPressEvent() elif self._ActiveButton == QtCore.Qt.MidButton: self._Iren.MiddleButtonPressEvent() def mouseReleaseEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) self._Iren.SetEventInformationFlipY(ev.x(), ev.y(), ctrl, shift, chr(0), 0, None) if self._ActiveButton == QtCore.Qt.LeftButton: self._Iren.LeftButtonReleaseEvent() elif self._ActiveButton == QtCore.Qt.RightButton: self._Iren.RightButtonReleaseEvent() elif self._ActiveButton == QtCore.Qt.MidButton: self._Iren.MiddleButtonReleaseEvent() def mouseMoveEvent(self, ev): self.__saveModifiers = ev.modifiers() self.__saveButtons = ev.buttons() self.__saveX = ev.x() self.__saveY = ev.y() ctrl, shift = self._GetCtrlShift(ev) self._Iren.SetEventInformationFlipY(ev.x(), ev.y(), ctrl, shift, chr(0), 0, None) self._Iren.MouseMoveEvent() def keyPressEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) if ev.key() < 256: key = str(ev.text()) else: key = chr(0) self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY, ctrl, shift, key, 0, None) self._Iren.KeyPressEvent() self._Iren.CharEvent() def keyReleaseEvent(self, ev): ctrl, shift = self._GetCtrlShift(ev) if ev.key() < 256: key = chr(ev.key()) else: key = chr(0) self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY, ctrl, shift, key, 0, None) self._Iren.KeyReleaseEvent() def wheelEvent(self, ev): if ev.delta() >= 0: self._Iren.MouseWheelForwardEvent() else: self._Iren.MouseWheelBackwardEvent() def GetRenderWindow(self): return self._RenderWindow def Render(self): self.update() def QVTKRenderWidgetConeExample(): """A simple example that uses the QVTKRenderWindowInteractor class.""" # every QT app needs an app app = QtGui.QApplication(['QVTKRenderWindowInteractor']) # create the widget widget = QVTKRenderWindowInteractor() widget.Initialize() widget.Start() # if you dont want the 'q' key to exit comment this. widget.AddObserver("ExitEvent", lambda o, e, a=app: a.quit()) ren = vtk.vtkRenderer() widget.GetRenderWindow().AddRenderer(ren) cone = vtk.vtkConeSource() cone.SetResolution(8) coneMapper = vtk.vtkPolyDataMapper() coneMapper.SetInputConnection(cone.GetOutputPort()) coneActor = vtk.vtkActor() coneActor.SetMapper(coneMapper) ren.AddActor(coneActor) # show the widget widget.show() # start event processing app.exec_() if __name__ == "__main__": QVTKRenderWidgetConeExample()
timkrentz/SunTracker
IMU/VTK-6.2.0/Wrapping/Python/vtk/qt4/QVTKRenderWindowInteractor.py
Python
mit
14,630
[ "CRYSTAL", "VTK" ]
57cee05725deb35e7fd3e9780074a9916d112e211549ba0a090c5014a6ac5dfc
"""Generators for geometric graphs. """ from bisect import bisect_left from itertools import accumulate, combinations, product from math import sqrt import math try: from scipy.spatial import cKDTree as KDTree except ImportError: _is_scipy_available = False else: _is_scipy_available = True import networkx as nx from networkx.utils import nodes_or_number, py_random_state __all__ = [ "geographical_threshold_graph", "waxman_graph", "navigable_small_world_graph", "random_geometric_graph", "soft_random_geometric_graph", "thresholded_random_geometric_graph", ] def euclidean(x, y): """Returns the Euclidean distance between the vectors ``x`` and ``y``. Each of ``x`` and ``y`` can be any iterable of numbers. The iterables must be of the same length. """ return sqrt(sum((a - b) ** 2 for a, b in zip(x, y))) def _fast_edges(G, radius, p): """Returns edge list of node pairs within `radius` of each other using scipy KDTree and Minkowski distance metric `p` Requires scipy to be installed. """ pos = nx.get_node_attributes(G, "pos") nodes, coords = list(zip(*pos.items())) kdtree = KDTree(coords) # Cannot provide generator. edge_indexes = kdtree.query_pairs(radius, p) edges = ((nodes[u], nodes[v]) for u, v in edge_indexes) return edges def _slow_edges(G, radius, p): """Returns edge list of node pairs within `radius` of each other using Minkowski distance metric `p` Works without scipy, but in `O(n^2)` time. """ # TODO This can be parallelized. edges = [] for (u, pu), (v, pv) in combinations(G.nodes(data="pos"), 2): if sum(abs(a - b) ** p for a, b in zip(pu, pv)) <= radius ** p: edges.append((u, v)) return edges @py_random_state(5) @nodes_or_number(0) def random_geometric_graph(n, radius, dim=2, pos=None, p=2, seed=None): """Returns a random geometric graph in the unit cube of dimensions `dim`. The random geometric graph model places `n` nodes uniformly at random in the unit cube. Two nodes are joined by an edge if the distance between the nodes is at most `radius`. Edges are determined using a KDTree when SciPy is available. This reduces the time complexity from $O(n^2)$ to $O(n)$. Parameters ---------- n : int or iterable Number of nodes or iterable of nodes radius: float Distance threshold value dim : int, optional Dimension of graph pos : dict, optional A dictionary keyed by node with node positions as values. p : float, optional Which Minkowski distance metric to use. `p` has to meet the condition ``1 <= p <= infinity``. If this argument is not specified, the :math:`L^2` metric (the Euclidean distance metric), p = 2 is used. This should not be confused with the `p` of an Erdős-Rényi random graph, which represents probability. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- Graph A random geometric graph, undirected and without self-loops. Each node has a node attribute ``'pos'`` that stores the position of that node in Euclidean space as provided by the ``pos`` keyword argument or, if ``pos`` was not provided, as generated by this function. Examples -------- Create a random geometric graph on twenty nodes where nodes are joined by an edge if their distance is at most 0.1:: >>> G = nx.random_geometric_graph(20, 0.1) Notes ----- This uses a *k*-d tree to build the graph. The `pos` keyword argument can be used to specify node positions so you can create an arbitrary distribution and domain for positions. For example, to use a 2D Gaussian distribution of node positions with mean (0, 0) and standard deviation 2:: >>> import random >>> n = 20 >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)} >>> G = nx.random_geometric_graph(n, 0.2, pos=pos) References ---------- .. [1] Penrose, Mathew, *Random Geometric Graphs*, Oxford Studies in Probability, 5, 2003. """ # TODO Is this function just a special case of the geographical # threshold graph? # # n_name, nodes = n # half_radius = {v: radius / 2 for v in nodes} # return geographical_threshold_graph(nodes, theta=1, alpha=1, # weight=half_radius) # n_name, nodes = n G = nx.Graph() G.add_nodes_from(nodes) # If no positions are provided, choose uniformly random vectors in # Euclidean space of the specified dimension. if pos is None: pos = {v: [seed.random() for i in range(dim)] for v in nodes} nx.set_node_attributes(G, pos, "pos") if _is_scipy_available: edges = _fast_edges(G, radius, p) else: edges = _slow_edges(G, radius, p) G.add_edges_from(edges) return G @py_random_state(6) @nodes_or_number(0) def soft_random_geometric_graph( n, radius, dim=2, pos=None, p=2, p_dist=None, seed=None ): r"""Returns a soft random geometric graph in the unit cube. The soft random geometric graph [1] model places `n` nodes uniformly at random in the unit cube in dimension `dim`. Two nodes of distance, `dist`, computed by the `p`-Minkowski distance metric are joined by an edge with probability `p_dist` if the computed distance metric value of the nodes is at most `radius`, otherwise they are not joined. Edges within `radius` of each other are determined using a KDTree when SciPy is available. This reduces the time complexity from :math:`O(n^2)` to :math:`O(n)`. Parameters ---------- n : int or iterable Number of nodes or iterable of nodes radius: float Distance threshold value dim : int, optional Dimension of graph pos : dict, optional A dictionary keyed by node with node positions as values. p : float, optional Which Minkowski distance metric to use. `p` has to meet the condition ``1 <= p <= infinity``. If this argument is not specified, the :math:`L^2` metric (the Euclidean distance metric), p = 2 is used. This should not be confused with the `p` of an Erdős-Rényi random graph, which represents probability. p_dist : function, optional A probability density function computing the probability of connecting two nodes that are of distance, dist, computed by the Minkowski distance metric. The probability density function, `p_dist`, must be any function that takes the metric value as input and outputs a single probability value between 0-1. The scipy.stats package has many probability distribution functions implemented and tools for custom probability distribution definitions [2], and passing the .pdf method of scipy.stats distributions can be used here. If the probability function, `p_dist`, is not supplied, the default function is an exponential distribution with rate parameter :math:`\lambda=1`. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- Graph A soft random geometric graph, undirected and without self-loops. Each node has a node attribute ``'pos'`` that stores the position of that node in Euclidean space as provided by the ``pos`` keyword argument or, if ``pos`` was not provided, as generated by this function. Examples -------- Default Graph: G = nx.soft_random_geometric_graph(50, 0.2) Custom Graph: Create a soft random geometric graph on 100 uniformly distributed nodes where nodes are joined by an edge with probability computed from an exponential distribution with rate parameter :math:`\lambda=1` if their Euclidean distance is at most 0.2. Notes ----- This uses a *k*-d tree to build the graph. The `pos` keyword argument can be used to specify node positions so you can create an arbitrary distribution and domain for positions. For example, to use a 2D Gaussian distribution of node positions with mean (0, 0) and standard deviation 2 The scipy.stats package can be used to define the probability distribution with the .pdf method used as `p_dist`. :: >>> import random >>> import math >>> n = 100 >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)} >>> p_dist = lambda dist : math.exp(-dist) >>> G = nx.soft_random_geometric_graph(n, 0.2, pos=pos, p_dist=p_dist) References ---------- .. [1] Penrose, Mathew D. "Connectivity of soft random geometric graphs." The Annals of Applied Probability 26.2 (2016): 986-1028. [2] scipy.stats - https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html """ n_name, nodes = n G = nx.Graph() G.name = f"soft_random_geometric_graph({n}, {radius}, {dim})" G.add_nodes_from(nodes) # If no positions are provided, choose uniformly random vectors in # Euclidean space of the specified dimension. if pos is None: pos = {v: [seed.random() for i in range(dim)] for v in nodes} nx.set_node_attributes(G, pos, "pos") # if p_dist function not supplied the default function is an exponential # distribution with rate parameter :math:`\lambda=1`. if p_dist is None: def p_dist(dist): return math.exp(-dist) def should_join(pair): u, v = pair u_pos, v_pos = pos[u], pos[v] dist = (sum(abs(a - b) ** p for a, b in zip(u_pos, v_pos))) ** (1 / p) # Check if dist <= radius parameter. This check is redundant if scipy # is available and _fast_edges routine is used, but provides the # check in case scipy is not available and all edge combinations # need to be checked if dist <= radius: return seed.random() < p_dist(dist) else: return False if _is_scipy_available: edges = _fast_edges(G, radius, p) G.add_edges_from(filter(should_join, edges)) else: G.add_edges_from(filter(should_join, combinations(G, 2))) return G @py_random_state(7) @nodes_or_number(0) def geographical_threshold_graph( n, theta, dim=2, pos=None, weight=None, metric=None, p_dist=None, seed=None ): r"""Returns a geographical threshold graph. The geographical threshold graph model places $n$ nodes uniformly at random in a rectangular domain. Each node $u$ is assigned a weight $w_u$. Two nodes $u$ and $v$ are joined by an edge if .. math:: (w_u + w_v)h(r) \ge \theta where `r` is the distance between `u` and `v`, h(r) is a probability of connection as a function of `r`, and :math:`\theta` as the threshold parameter. h(r) corresponds to the p_dist parameter. Parameters ---------- n : int or iterable Number of nodes or iterable of nodes theta: float Threshold value dim : int, optional Dimension of graph pos : dict Node positions as a dictionary of tuples keyed by node. weight : dict Node weights as a dictionary of numbers keyed by node. metric : function A metric on vectors of numbers (represented as lists or tuples). This must be a function that accepts two lists (or tuples) as input and yields a number as output. The function must also satisfy the four requirements of a `metric`_. Specifically, if $d$ is the function and $x$, $y$, and $z$ are vectors in the graph, then $d$ must satisfy 1. $d(x, y) \ge 0$, 2. $d(x, y) = 0$ if and only if $x = y$, 3. $d(x, y) = d(y, x)$, 4. $d(x, z) \le d(x, y) + d(y, z)$. If this argument is not specified, the Euclidean distance metric is used. .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29 p_dist : function, optional A probability density function computing the probability of connecting two nodes that are of distance, r, computed by metric. The probability density function, `p_dist`, must be any function that takes the metric value as input and outputs a single probability value between 0-1. The scipy.stats package has many probability distribution functions implemented and tools for custom probability distribution definitions [2], and passing the .pdf method of scipy.stats distributions can be used here. If the probability function, `p_dist`, is not supplied, the default exponential function :math: `r^{-2}` is used. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- Graph A random geographic threshold graph, undirected and without self-loops. Each node has a node attribute ``pos`` that stores the position of that node in Euclidean space as provided by the ``pos`` keyword argument or, if ``pos`` was not provided, as generated by this function. Similarly, each node has a node attribute ``weight`` that stores the weight of that node as provided or as generated. Examples -------- Specify an alternate distance metric using the ``metric`` keyword argument. For example, to use the `taxicab metric`_ instead of the default `Euclidean metric`_:: >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y)) >>> G = nx.geographical_threshold_graph(10, 0.1, metric=dist) .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance Notes ----- If weights are not specified they are assigned to nodes by drawing randomly from the exponential distribution with rate parameter $\lambda=1$. To specify weights from a different distribution, use the `weight` keyword argument:: >>> import random >>> n = 20 >>> w = {i: random.expovariate(5.0) for i in range(n)} >>> G = nx.geographical_threshold_graph(20, 50, weight=w) If node positions are not specified they are randomly assigned from the uniform distribution. References ---------- .. [1] Masuda, N., Miwa, H., Konno, N.: Geographical threshold graphs with small-world and scale-free properties. Physical Review E 71, 036108 (2005) .. [2] Milan Bradonjić, Aric Hagberg and Allon G. Percus, Giant component and connectivity in geographical threshold graphs, in Algorithms and Models for the Web-Graph (WAW 2007), Antony Bonato and Fan Chung (Eds), pp. 209--216, 2007 """ n_name, nodes = n G = nx.Graph() G.add_nodes_from(nodes) # If no weights are provided, choose them from an exponential # distribution. if weight is None: weight = {v: seed.expovariate(1) for v in G} # If no positions are provided, choose uniformly random vectors in # Euclidean space of the specified dimension. if pos is None: pos = {v: [seed.random() for i in range(dim)] for v in nodes} # If no distance metric is provided, use Euclidean distance. if metric is None: metric = euclidean nx.set_node_attributes(G, weight, "weight") nx.set_node_attributes(G, pos, "pos") # if p_dist is not supplied, use default r^-2 if p_dist is None: def p_dist(r): return r ** -2 # Returns ``True`` if and only if the nodes whose attributes are # ``du`` and ``dv`` should be joined, according to the threshold # condition. def should_join(pair): u, v = pair u_pos, v_pos = pos[u], pos[v] u_weight, v_weight = weight[u], weight[v] return (u_weight + v_weight) * p_dist(metric(u_pos, v_pos)) >= theta G.add_edges_from(filter(should_join, combinations(G, 2))) return G @py_random_state(6) @nodes_or_number(0) def waxman_graph( n, beta=0.4, alpha=0.1, L=None, domain=(0, 0, 1, 1), metric=None, seed=None ): r"""Returns a Waxman random graph. The Waxman random graph model places `n` nodes uniformly at random in a rectangular domain. Each pair of nodes at distance `d` is joined by an edge with probability .. math:: p = \beta \exp(-d / \alpha L). This function implements both Waxman models, using the `L` keyword argument. * Waxman-1: if `L` is not specified, it is set to be the maximum distance between any pair of nodes. * Waxman-2: if `L` is specified, the distance between a pair of nodes is chosen uniformly at random from the interval `[0, L]`. Parameters ---------- n : int or iterable Number of nodes or iterable of nodes beta: float Model parameter alpha: float Model parameter L : float, optional Maximum distance between nodes. If not specified, the actual distance is calculated. domain : four-tuple of numbers, optional Domain size, given as a tuple of the form `(x_min, y_min, x_max, y_max)`. metric : function A metric on vectors of numbers (represented as lists or tuples). This must be a function that accepts two lists (or tuples) as input and yields a number as output. The function must also satisfy the four requirements of a `metric`_. Specifically, if $d$ is the function and $x$, $y$, and $z$ are vectors in the graph, then $d$ must satisfy 1. $d(x, y) \ge 0$, 2. $d(x, y) = 0$ if and only if $x = y$, 3. $d(x, y) = d(y, x)$, 4. $d(x, z) \le d(x, y) + d(y, z)$. If this argument is not specified, the Euclidean distance metric is used. .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29 seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- Graph A random Waxman graph, undirected and without self-loops. Each node has a node attribute ``'pos'`` that stores the position of that node in Euclidean space as generated by this function. Examples -------- Specify an alternate distance metric using the ``metric`` keyword argument. For example, to use the "`taxicab metric`_" instead of the default `Euclidean metric`_:: >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y)) >>> G = nx.waxman_graph(10, 0.5, 0.1, metric=dist) .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance Notes ----- Starting in NetworkX 2.0 the parameters alpha and beta align with their usual roles in the probability distribution. In earlier versions their positions in the expression were reversed. Their position in the calling sequence reversed as well to minimize backward incompatibility. References ---------- .. [1] B. M. Waxman, *Routing of multipoint connections*. IEEE J. Select. Areas Commun. 6(9),(1988) 1617--1622. """ n_name, nodes = n G = nx.Graph() G.add_nodes_from(nodes) (xmin, ymin, xmax, ymax) = domain # Each node gets a uniformly random position in the given rectangle. pos = {v: (seed.uniform(xmin, xmax), seed.uniform(ymin, ymax)) for v in G} nx.set_node_attributes(G, pos, "pos") # If no distance metric is provided, use Euclidean distance. if metric is None: metric = euclidean # If the maximum distance L is not specified (that is, we are in the # Waxman-1 model), then find the maximum distance between any pair # of nodes. # # In the Waxman-1 model, join nodes randomly based on distance. In # the Waxman-2 model, join randomly based on random l. if L is None: L = max(metric(x, y) for x, y in combinations(pos.values(), 2)) def dist(u, v): return metric(pos[u], pos[v]) else: def dist(u, v): return seed.random() * L # `pair` is the pair of nodes to decide whether to join. def should_join(pair): return seed.random() < beta * math.exp(-dist(*pair) / (alpha * L)) G.add_edges_from(filter(should_join, combinations(G, 2))) return G @py_random_state(5) def navigable_small_world_graph(n, p=1, q=1, r=2, dim=2, seed=None): r"""Returns a navigable small-world graph. A navigable small-world graph is a directed grid with additional long-range connections that are chosen randomly. [...] we begin with a set of nodes [...] that are identified with the set of lattice points in an $n \times n$ square, $\{(i, j): i \in \{1, 2, \ldots, n\}, j \in \{1, 2, \ldots, n\}\}$, and we define the *lattice distance* between two nodes $(i, j)$ and $(k, l)$ to be the number of "lattice steps" separating them: $d((i, j), (k, l)) = |k - i| + |l - j|$. For a universal constant $p >= 1$, the node $u$ has a directed edge to every other node within lattice distance $p$---these are its *local contacts*. For universal constants $q >= 0$ and $r >= 0$ we also construct directed edges from $u$ to $q$ other nodes (the *long-range contacts*) using independent random trials; the $i$th directed edge from $u$ has endpoint $v$ with probability proportional to $[d(u,v)]^{-r}$. -- [1]_ Parameters ---------- n : int The length of one side of the lattice; the number of nodes in the graph is therefore $n^2$. p : int The diameter of short range connections. Each node is joined with every other node within this lattice distance. q : int The number of long-range connections for each node. r : float Exponent for decaying probability of connections. The probability of connecting to a node at lattice distance $d$ is $1/d^r$. dim : int Dimension of grid seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. References ---------- .. [1] J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. """ if p < 1: raise nx.NetworkXException("p must be >= 1") if q < 0: raise nx.NetworkXException("q must be >= 0") if r < 0: raise nx.NetworkXException("r must be >= 1") G = nx.DiGraph() nodes = list(product(range(n), repeat=dim)) for p1 in nodes: probs = [0] for p2 in nodes: if p1 == p2: continue d = sum((abs(b - a) for a, b in zip(p1, p2))) if d <= p: G.add_edge(p1, p2) probs.append(d ** -r) cdf = list(accumulate(probs)) for _ in range(q): target = nodes[bisect_left(cdf, seed.uniform(0, cdf[-1]))] G.add_edge(p1, target) return G @py_random_state(7) @nodes_or_number(0) def thresholded_random_geometric_graph( n, radius, theta, dim=2, pos=None, weight=None, p=2, seed=None ): r"""Returns a thresholded random geometric graph in the unit cube. The thresholded random geometric graph [1] model places `n` nodes uniformly at random in the unit cube of dimensions `dim`. Each node `u` is assigned a weight :math:`w_u`. Two nodes `u` and `v` are joined by an edge if they are within the maximum connection distance, `radius` computed by the `p`-Minkowski distance and the summation of weights :math:`w_u` + :math:`w_v` is greater than or equal to the threshold parameter `theta`. Edges within `radius` of each other are determined using a KDTree when SciPy is available. This reduces the time complexity from :math:`O(n^2)` to :math:`O(n)`. Parameters ---------- n : int or iterable Number of nodes or iterable of nodes radius: float Distance threshold value theta: float Threshold value dim : int, optional Dimension of graph pos : dict, optional A dictionary keyed by node with node positions as values. weight : dict, optional Node weights as a dictionary of numbers keyed by node. p : float, optional Which Minkowski distance metric to use. `p` has to meet the condition ``1 <= p <= infinity``. If this argument is not specified, the :math:`L^2` metric (the Euclidean distance metric), p = 2 is used. This should not be confused with the `p` of an Erdős-Rényi random graph, which represents probability. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness<randomness>`. Returns ------- Graph A thresholded random geographic graph, undirected and without self-loops. Each node has a node attribute ``'pos'`` that stores the position of that node in Euclidean space as provided by the ``pos`` keyword argument or, if ``pos`` was not provided, as generated by this function. Similarly, each node has a nodethre attribute ``'weight'`` that stores the weight of that node as provided or as generated. Examples -------- Default Graph: G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1) Custom Graph: Create a thresholded random geometric graph on 50 uniformly distributed nodes where nodes are joined by an edge if their sum weights drawn from a exponential distribution with rate = 5 are >= theta = 0.1 and their Euclidean distance is at most 0.2. Notes ----- This uses a *k*-d tree to build the graph. The `pos` keyword argument can be used to specify node positions so you can create an arbitrary distribution and domain for positions. For example, to use a 2D Gaussian distribution of node positions with mean (0, 0) and standard deviation 2 If weights are not specified they are assigned to nodes by drawing randomly from the exponential distribution with rate parameter :math:`\lambda=1`. To specify weights from a different distribution, use the `weight` keyword argument:: :: >>> import random >>> import math >>> n = 50 >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)} >>> w = {i: random.expovariate(5.0) for i in range(n)} >>> G = nx.thresholded_random_geometric_graph(n, 0.2, 0.1, 2, pos, w) References ---------- .. [1] http://cole-maclean.github.io/blog/files/thesis.pdf """ n_name, nodes = n G = nx.Graph() G.name = f"thresholded_random_geometric_graph({n}, {radius}, {theta}, {dim})" G.add_nodes_from(nodes) # If no weights are provided, choose them from an exponential # distribution. if weight is None: weight = {v: seed.expovariate(1) for v in G} # If no positions are provided, choose uniformly random vectors in # Euclidean space of the specified dimension. if pos is None: pos = {v: [seed.random() for i in range(dim)] for v in nodes} # If no distance metric is provided, use Euclidean distance. nx.set_node_attributes(G, weight, "weight") nx.set_node_attributes(G, pos, "pos") # Returns ``True`` if and only if the nodes whose attributes are # ``du`` and ``dv`` should be joined, according to the threshold # condition and node pairs are within the maximum connection # distance, ``radius``. def should_join(pair): u, v = pair u_weight, v_weight = weight[u], weight[v] u_pos, v_pos = pos[u], pos[v] dist = (sum(abs(a - b) ** p for a, b in zip(u_pos, v_pos))) ** (1 / p) # Check if dist is <= radius parameter. This check is redundant if # scipy is available and _fast_edges routine is used, but provides # the check in case scipy is not available and all edge combinations # need to be checked if dist <= radius: return theta <= u_weight + v_weight else: return False if _is_scipy_available: edges = _fast_edges(G, radius, p) G.add_edges_from(filter(should_join, edges)) else: G.add_edges_from(filter(should_join, combinations(G, 2))) return G
SpaceGroupUCL/qgisSpaceSyntaxToolkit
esstoolkit/external/networkx/generators/geometric.py
Python
gpl-3.0
28,960
[ "Gaussian" ]
a517d8a2d0d3867c5876eff37e568d44c852589933bb5637b4c9449ccd85b1a2
#!/usr/bin/env python import sys, os from argparse import ArgumentParser, FileType def get_data(small = False): data_root = "http://www.ccb.jhu.edu/software/hisat2/downloads/evaluation" # Download the reference human genome, SNPs, and gene annotations if not os.path.exists("data"): os.mkdir("data") os.chdir("data") genome_files = ["genome.fa", "genome.fa.fai", "genome.gtf", "snpCommon.txt", "genome.snp", "genome.ss", "genome.exon"] small_genome_files = ["22.fa", "22.fa.fai", "22.gtf", "22.snp", "22.ss", "22.exon", \ "22_20-21M.fa", "22_20-21M.fa.fai", "22_20-21M.gtf", "22_20-21M.snp", "22_20-21M.ss", "22_20-21M.exon"] files = [] if not small: files += genome_files files += small_genome_files for file in files: if os.path.exists(file): continue wget_cmd = "wget %s/data/%s" % (data_root, file) print >> sys.stderr, wget_cmd os.system(wget_cmd) os.chdir("..") # Download indexes if not os.path.exists("indexes"): os.mkdir("indexes") os.chdir("indexes") aligners = ["HISAT2", "HISAT", "Bowtie", "STAR", "GSNAP"] for genome in ["genome", "22", "22_20-21M"]: if small and genome == "genome": continue for aligner in aligners: if genome == "genome": aligner_dir = aligner else: aligner_dir = aligner + "_" + genome if os.path.exists(aligner_dir): continue cmd = "wget %s/indexes/%s.tar.gz; tar xvzf %s.tar.gz; rm %s.tar.gz" % \ (data_root, aligner_dir, aligner_dir, aligner_dir) print >> sys.stderr, cmd os.system(cmd) os.chdir("..") # Download simulated and real reads if not os.path.exists("reads"): os.mkdir("reads") os.chdir("reads") for type in ["simulation", "real"]: if small and type == "real": continue if not os.path.exists(type): os.mkdir(type) os.chdir(type) if type == "simulation": files = ["1M_DNA_reads_22", "1M_DNA_mismatch_reads_22", "1M_DNA_snp_reads_22", "1M_DNA_mismatch_snp_reads_22", "1M_RNA_reads_22", "1M_RNA_constant_reads_22", "1M_RNA_mismatch_reads_22", "1M_RNA_snp_reads_22", "1M_RNA_mismatch_snp_reads_22", "1M_RNA_reads_22_20-21M", "20M_DNA_reads_genome", "20M_DNA_snp_reads_genome", "20M_RNA_reads_genome", "20M_RNA_snp_reads_genome"] else: files = ["108M_RNA_wgEncodeCshlLongRnaSeq", "62M_RNA_SRR353653", "80M_DNA_SRR345300", "5M_DNA_NA12878D"] for file in files: if small and file.find("20M") != -1: continue if os.path.exists(file): continue cmd = "wget %s/reads/%s/%s.tar.gz; tar xvzf %s.tar.gz; rm %s.tar.gz" % \ (data_root, type, file, file, file) print >> sys.stderr, cmd os.system(cmd) os.chdir("..") os.chdir("..") if __name__ == "__main__": parser = ArgumentParser( description='Get reference genome, annotations, and indexes') parser.add_argument('-s', '--small', dest='small', action='store_true', default=False, help='small testset') args = parser.parse_args() get_data(args.small)
infphilo/hisat2
evaluation/get_data.py
Python
gpl-3.0
3,781
[ "Bowtie" ]
1e0e8df3017445271da4de1e4e63a9abca55765d227daf4fca0b8ec39afaf627
#!/usr/bin/env python # # AST.py # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License version 2 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA import logging import PyV8 import traceback log = logging.getLogger("Thug") class AST(object): (AssignBreakPoint, LoopBreakPoint) = range(0, 2) AssignOps = [PyV8.AST.Op.ASSIGN, PyV8.AST.Op.ASSIGN_ADD, PyV8.AST.Op.ASSIGN_BIT_AND, PyV8.AST.Op.ASSIGN_BIT_OR, PyV8.AST.Op.ASSIGN_BIT_XOR, PyV8.AST.Op.ASSIGN_DIV, PyV8.AST.Op.ASSIGN_MOD, PyV8.AST.Op.ASSIGN_MUL, PyV8.AST.Op.ASSIGN_SAR, PyV8.AST.Op.ASSIGN_SHL, PyV8.AST.Op.ASSIGN_SHR, PyV8.AST.Op.ASSIGN_SUB, PyV8.AST.Op.INIT_VAR] def __init__(self, window, script): self.names = set() self.inLoop = False self.inBlock = True self.exitingLoop = 0 self.assignStatement = False self.breakpoints = set() self.window = window self.walk(script) self.debug(self.breakpoints) self.debug(self.names) def debug(self, msg): if log.ThugOpts.ast_debug: log.debug(msg) def checkExitingLoop(self, pos): if self.exitingLoop > 0: self.debug("\tExiting Loop: %d" % (self.exitingLoop, )) self.exitingLoop -= 1 self.breakpoints.add((self.LoopBreakPoint, pos)) def walk(self, script): self.block_no = 1 try: PyV8.JSEngine().compile(script).visit(self) except UnicodeDecodeError: enc = log.Encoding.detect(script, safe = True) if enc is None: return PyV8.JSEngine().compile(script.decode(enc['encoding'])).visit(self) except: #pylint:disable=bare-except pass def onProgram(self, prog): self.json = prog.toJSON() self.ast = prog.toAST() self.debug(self.json) self.debug("[*] Program") self.debug("\tProgram startPos: %d" % (prog.startPos, )) self.debug("\tProgram endPos: %d" % (prog.endPos, )) for decl in prog.scope.declarations: decl.visit(self) for stmt in prog.body: stmt.visit(self) def _enterBlock(self): self.inBlock = True def _exitBlock(self): self.inBlock = False def onBlock(self, block): self.debug("[*] Entering Block #%d" % (self.block_no, )) self._enterBlock() for stmt in block.statements: stmt.visit(self) self._exitBlock() self.debug("[*] Exiting Block #%d" % (self.block_no, )) self.block_no += 1 def onExpressionStatement(self, stmt): self.debug("[*] Expression Statement") self.debug("\tStatement: %s" % (stmt, )) self.debug("\tStatement type: %s" % (stmt.type, )) self.debug("\tStatement position: %s" % (stmt.expression.pos, )) self.checkExitingLoop(stmt.expression.pos) stmt.expression.visit(self) if self.assignStatement: if self.inBlock: # FIXME # AstCallRuntime has no 'pos' attribute try: pos = stmt.expression.pos except: #pylint:disable=bare-except traceback.print_exc() return else: pos = stmt.expression.pos self.breakpoints.add((self.AssignBreakPoint, pos)) self.assignStatement = False def onVariableDeclaration(self, decl): var = decl.proxy self.debug("[*] Variable Declaration Statement") self.debug("\tVariable name: %s" % (var.name, )) if decl.scope.isGlobal: getattr(self.window, var.name, None) if decl.mode == PyV8.AST.VarMode.var: self.names.add(var.name) def onFunctionDeclaration(self, decl): f = decl.proxy self.debug("[*] Function Declaration Statement") self.debug("\tFunction name: %s" % (f.name, )) if decl.scope.isGlobal: getattr(self.window, f.name, None) for d in decl.scope.declarations: if not getattr(d, 'function', None): continue d.function.visit(self) #for stmt in d.function.body: # stmt.visit(self) def onAssignment(self, expr): self.debug("[*] Assignment Statement") self.debug("\tAssignment op: %s" % (expr.op, )) self.debug("\tAssignment pos: %s" % (expr.pos, )) self.debug("\tAssignment target: %s" % (expr.target, )) self.debug("\tAssignment value: %s" % (expr.value, )) if not self.inLoop: if expr.op in self.AssignOps: self.assignStatement = True self.names.add(str(expr.target)) expr.target.visit(self) expr.value.visit(self) def onIfStatement(self, stmt): self.debug("[*] If Statement") self.debug("\tIf condition: %s" % (stmt.condition, )) self.debug("\tIf position: %s" % (stmt.pos, )) stmt.condition.visit(self) if stmt.hasThenStatement: stmt.thenStatement.visit(self) if stmt.hasElseStatement: stmt.elseStatement.visit(self) def enterLoop(self): self.inLoop = True def exitLoop(self): self.inLoop = False self.exitingLoop += 1 def onForStatement(self, stmt): self.debug("[*] For Statement") self.debug("\tInit condition: %s" % (stmt.init, )) self.debug("\tNext condition: %s" % (stmt.nextStmt, )) self.debug("\tEnd condition: %s" % (stmt.condition, )) self.debug("\tFor position: %s" % (stmt.pos)) self.checkExitingLoop(stmt.pos) self.enterLoop() if stmt.init: stmt.init.visit(self) if stmt.nextStmt: stmt.nextStmt.visit(self) if stmt.condition: stmt.condition.visit(self) if stmt.body: stmt.body.visit(self) self.exitLoop() def onWhileStatement(self, stmt): self.debug("[*] While Statement") self.debug("\tWhile position: %s" % (stmt.pos,)) self.checkExitingLoop(stmt.pos) self.enterLoop() stmt.condition.visit(self) stmt.body.visit(self) self.exitLoop() def onDoWhileStatement(self, stmt): self.debug("[*] Do-While Statement") self.debug("\tDo-While position: %s" % (stmt.pos,)) self.checkExitingLoop(stmt.pos) self.enterLoop() stmt.condition.visit(self) stmt.body.visit(self) self.exitLoop() def onForInStatement(self, stmt): self.debug("[*] For-In Statement") self.debug("\tFor-In position: %s" % (stmt.pos,)) self.checkExitingLoop(stmt.pos) self.enterLoop() stmt.enumerable.visit(self) stmt.body.visit(self) self.exitLoop() def handle_eval(self, args): for arg in args: if len(str(arg)) > 64: log.warning("[AST]: Eval argument length > 64") def onCall(self, expr): self.debug("[*] Call") self.debug("\tCall position: %s" % (expr.pos, )) self.debug("\tCall expr: %s" % (expr.expression, )) self.debug("\tCall arguments") for arg in expr.args: arg.visit(self) handle = getattr(self, "handle_%s" % (expr.expression, ), None) if handle: handle(expr.args) expr.expression.visit(self) def onCallNew(self, expr): self.debug("[*] CallNew") self.debug("\tCall position: %s" % (expr.pos, )) self.debug("\tCall expr: %s" % (expr.expression, )) handle = getattr(self, "handle_%s" % (expr.expression, ), None) if handle: handle(expr.args) for arg in expr.args: arg.visit(self) def onCallRuntime(self, expr): self.debug("[*] CallRuntime") self.debug("\tCall name: %s" % (expr.name, )) for arg in expr.args: arg.visit(self) def onFunctionLiteral(self, litr): self.debug("\tFunction Literal: %s" % (litr.name, )) for decl in litr.scope.declarations: decl.visit(self) for e in litr.body: e.visit(self) def onLiteral(self, litr): if len(str(litr)) > 256: log.ThugLogging.shellcodes.add(str(litr).lstrip('"').rstrip('"')) self.debug("\tLiteral: %s" % (litr, )) def onReturnStatement(self, stmt): self.debug("[*] Return Statement") self.debug("\tReturn position: %s" % (stmt.pos, )) stmt.expression.visit(self) def onCompareOperation(self, stmt): self.debug("[*] Compare Operation") self.debug("\tCompare Left: %s" % (stmt.left, )) self.debug("\tCompare Operation: %s" % (stmt.op, )) self.debug("\tCompare Right: %s" % (stmt.right, )) stmt.left.visit(self) stmt.right.visit(self) def onCountOperation(self, stmt): self.debug("[*] Count Operation: %s" % (stmt.op, )) stmt.expression.visit(self) def onVariableProxy(self, expr): self.debug("\tVariable: %s" % (expr, ))
qistoph/thug
src/AST/AST.py
Python
gpl-2.0
10,198
[ "VisIt" ]
aded855bd0880bc47cd3fbccd664ef7636498a13473d80924f92eecb0c348786
#!/usr/bin/python import os, subprocess import textwrap, argparse if __name__ == '__main__': argument_parser = argparse.ArgumentParser( prog='stability_multiple_files.py', formatter_class=argparse.RawDescriptionHelpFormatter, description=textwrap.dedent('''\ create a graphical functions from input data, multiple files -------------------------------- example of use $python3 %(prog)s ''')) argument_parser.add_argument('--inputdir', dest='inputdir', required=True, type=str , help='input directory (required)') argument_parser.add_argument('--language', dest='lng', action='append', required=False, type=str , help='language (default all, optional)') args = argument_parser.parse_args() inputdir = args.inputdir lng = args.lng list_directories = [] lang_files = [] for d1 in os.listdir(inputdir): list_directories.append(d1) for d2 in os.listdir(inputdir+"/"+d1): lng_file = d2.replace('.tab','') if lng_file not in lang_files: lang_files.append(lng_file) print(lang_files) # if no introduce language parameter, all language. otherwise check that introduced language is in MCR if lng is None: languages = lang_files else: for l in lng: if not l in lang_files: print("Language must be present in files, options:"+str(lang_files)+"\n") exit(1) languages = lng # build header ####################################################################################################################### out_file_2 = open(inputdir+'/../stability_senses_2.csv', "w") visit = [] for elem_1 in list_directories: for elem_2 in list_directories: if elem_1 != elem_2 and elem_1 not in visit and elem_2 not in visit: out_file_2.write(elem_1 + " vs " + elem_2 + " / " + elem_1 + " , ") out_file_2.write(elem_1 + " vs " + elem_2 + " / " + elem_2 + " , ") if elem_1 not in visit: visit.append(elem_1) out_file_2.write("\n") # build body ######################################################################################################################### for lang in languages: visit = [] out_file = open(inputdir+'/../stability_senses_'+lang+'.tab', "w") for elem_1 in list_directories: for elem_2 in list_directories: if elem_1 != elem_2 and elem_1 not in visit and elem_2 not in visit: num_lines_senses_1 = sum(1 for line in open(inputdir+"/"+elem_1+"/"+lang+".tab")) num_lines_senses_2 = sum(1 for line in open(inputdir+"/"+elem_2+"/"+lang+".tab")) subprocess.check_output("sort "+inputdir+"/"+elem_1+"/"+lang+".tab > tmp1.txt", shell=True) subprocess.check_output("sort "+inputdir+"/"+elem_2+"/"+lang+".tab > tmp2.txt", shell=True) equals = subprocess.check_output("comm -1 -2 tmp1.txt tmp2.txt | wc | gawk '{print $1}'", shell=True).strip() out_file.write(str(float(equals)/num_lines_senses_1)+" ") out_file.write(elem_1 + "_vs_" + elem_2 + "\n") out_file_2.write(str(float(equals)/num_lines_senses_1)+" , ") out_file_2.write(str(float(equals)/num_lines_senses_2)+" , ") if elem_1 not in visit: visit.append(elem_1) out_file.close() out_file_2.write("\n") out_file_2.close() #subprocess.check_output("sort -r "+inputdir+"/../stability_senses.tab > "+inputdir+"/../stability_senses_ord.tab", shell=True)
daniparera/MCR
Senses/analisys/others/stability_multiple_files.py
Python
gpl-2.0
3,732
[ "VisIt" ]
20509b749f594c268086890d73c32126f7487e3a89add5aefed3692a37aa5066
#!/usr/bin/env python # # E-Mail post-processing script for NZBGet # # Copyright (C) 2013-2017 Andrey Prygunkov <hugbug@users.sourceforge.net> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ############################################################################## ### NZBGET POST-PROCESSING SCRIPT ### # Send E-Mail notification. # # This script sends E-Mail notification when the job is done. # # NOTE: This script requires Python to be installed on your system. ############################################################################## ### OPTIONS ### # When to send the message (Always, OnFailure). #SendMail=Always # Email address you want this email to be sent from. #From="NZBGet" <myaccount@gmail.com> # Email address you want this email to be sent to. # # Multiple addresses can be separated with comma. #To=myaccount@gmail.com # SMTP server host. #Server=smtp.gmail.com # SMTP server port (1-65535). #Port=25 # Secure communication using TLS/SSL (yes, no, force). # no - plain text communication (insecure); # yes - switch to secure session using StartTLS command; # force - start secure session on encrypted socket. #Encryption=yes # SMTP server user name, if required. #Username=myaccount # SMTP server password, if required. #Password=mypass # To check connection parameters click the button. #ConnectionTest@Send Test E-Mail # Append statistics to the message (yes, no). #Statistics=yes # Append list of files to the message (yes, no). # # Add the list of downloaded files (the content of destination directory). #FileList=yes # Append broken-log to the message (yes, no). # # Add the content of file _brokenlog.txt. This file contains the list of damaged # files and the result of par-check/repair. For successful downloads the broken-log # is usually deleted by cleanup-script and therefore is not sent. #BrokenLog=yes # Append nzb log to the message (Always, Never, OnFailure). # # Add the download and post-processing log of active job. #NzbLog=OnFailure ### NZBGET POST-PROCESSING SCRIPT ### ############################################################################## import os import sys import datetime import smtplib from email.mime.text import MIMEText from email.header import Header try: # python 2 from urllib2 import quote from xmlrpclib import ServerProxy except ImportError: # python 3 from urllib.parse import quote from xmlrpc.client import ServerProxy # Exit codes used by NZBGet POSTPROCESS_SUCCESS=93 POSTPROCESS_ERROR=94 POSTPROCESS_NONE=95 # Check if the script is called from nzbget 15.0 or later if not 'NZBOP_NZBLOG' in os.environ: print('*** NZBGet post-processing script ***') print('This script is supposed to be called from nzbget (15.0 or later).') sys.exit(POSTPROCESS_ERROR) print('[DETAIL] Script successfully started') sys.stdout.flush() required_options = ('NZBPO_FROM', 'NZBPO_TO', 'NZBPO_SERVER', 'NZBPO_PORT', 'NZBPO_ENCRYPTION', 'NZBPO_USERNAME', 'NZBPO_PASSWORD') for optname in required_options: if (not optname in os.environ): print('[ERROR] Option %s is missing in configuration file. Please check script settings' % optname[6:]) sys.exit(POSTPROCESS_ERROR) # Check if the script is executed from settings page with a custom command command = os.environ.get('NZBCP_COMMAND') test_mode = command == 'ConnectionTest' if command != None and not test_mode: print('[ERROR] Invalid command ' + command) sys.exit(POSTPROCESS_ERROR) status = os.environ.get('NZBPP_STATUS') if not test_mode else 'SUCCESS/ALL' total_status = os.environ.get('NZBPP_TOTALSTATUS') if not test_mode else 'SUCCESS' # If any script fails the status of the item in the history is "WARNING/SCRIPT". # This status however is not passed to pp-scripts in the env var "NZBPP_STATUS" # because most scripts are independent of each other and should work even # if a previous script has failed. But not in the case of E-Mail script, # which should take the status of the previous scripts into account as well. if total_status == 'SUCCESS' and os.environ.get('NZBPP_SCRIPTSTATUS') == 'FAILURE': total_status = 'WARNING' status = 'WARNING/SCRIPT' success = total_status == 'SUCCESS' if success and os.environ.get('NZBPO_SENDMAIL') == 'OnFailure' and not test_mode: print('[INFO] Skipping sending of message for successful download') sys.exit(POSTPROCESS_NONE) if success: subject = 'Success for "%s"' % (os.environ.get('NZBPP_NZBNAME', 'Test download')) text = 'Download of "%s" has successfully completed.' % (os.environ.get('NZBPP_NZBNAME', 'Test download')) else: subject = 'Failure for "%s"' % (os.environ['NZBPP_NZBNAME']) text = 'Download of "%s" has failed.' % (os.environ['NZBPP_NZBNAME']) text += '\nStatus: %s' % status if (os.environ.get('NZBPO_STATISTICS') == 'yes' or \ os.environ.get('NZBPO_NZBLOG') == 'Always' or \ (os.environ.get('NZBPO_NZBLOG') == 'OnFailure' and not success)) and \ not test_mode: # To get statistics or the post-processing log we connect to NZBGet via XML-RPC. # For more info visit http://nzbget.net/api # First we need to know connection info: host, port and password of NZBGet server. # NZBGet passes all configuration options to post-processing script as # environment variables. host = os.environ['NZBOP_CONTROLIP']; port = os.environ['NZBOP_CONTROLPORT']; username = os.environ['NZBOP_CONTROLUSERNAME']; password = os.environ['NZBOP_CONTROLPASSWORD']; if host == '0.0.0.0': host = '127.0.0.1' # Build a URL for XML-RPC requests rpcUrl = 'http://%s:%s@%s:%s/xmlrpc' % (quote(username), quote(password), host, port); # Create remote server object server = ServerProxy(rpcUrl) if os.environ.get('NZBPO_STATISTICS') == 'yes' and not test_mode: # Find correct nzb in method listgroups groups = server.listgroups(0) nzbID = int(os.environ['NZBPP_NZBID']) for nzbGroup in groups: if nzbGroup['NZBID'] == nzbID: break text += '\n\nStatistics:'; # add download size DownloadedSize = float(nzbGroup['DownloadedSizeMB']) unit = ' MB' if DownloadedSize > 1024: DownloadedSize = DownloadedSize / 1024 # GB unit = ' GB' text += '\nDownloaded size: %.2f' % (DownloadedSize) + unit # add average download speed DownloadedSizeMB = float(nzbGroup['DownloadedSizeMB']) DownloadTimeSec = float(nzbGroup['DownloadTimeSec']) if DownloadTimeSec > 0: # check x/0 errors avespeed = (DownloadedSizeMB/DownloadTimeSec) # MB/s unit = ' MB/s' if avespeed < 1: avespeed = avespeed * 1024 # KB/s unit = ' KB/s' text += '\nAverage download speed: %.2f' % (avespeed) + unit def format_time_sec(sec): Hour = sec/3600 Min = (sec - (sec/3600)*3600)/60 Sec = (sec - (sec/3600)*3600)%60 return '%d:%02d:%02d' % (Hour,Min,Sec) # add times text += '\nTotal time: ' + format_time_sec(int(nzbGroup['DownloadTimeSec']) + int(nzbGroup['PostTotalTimeSec'])) text += '\nDownload time: ' + format_time_sec(int(nzbGroup['DownloadTimeSec'])) text += '\nVerification time: ' + format_time_sec(int(nzbGroup['ParTimeSec']) - int(nzbGroup['RepairTimeSec'])) text += '\nRepair time: ' + format_time_sec(int(nzbGroup['RepairTimeSec'])) text += '\nUnpack time: ' + format_time_sec(int(nzbGroup['UnpackTimeSec'])) # add list of downloaded files files = False if os.environ.get('NZBPO_FILELIST') == 'yes' and not test_mode: text += '\n\nFiles:' for dirname, dirnames, filenames in os.walk(os.environ['NZBPP_DIRECTORY']): for filename in filenames: text += '\n' + os.path.join(dirname, filename)[len(os.environ['NZBPP_DIRECTORY']) + 1:] files = True if not files: text += '\n<no files found in the destination directory (moved by a script?)>' # add _brokenlog.txt (if exists) if os.environ.get('NZBPO_BROKENLOG') == 'yes' and not test_mode: brokenlog = '%s/_brokenlog.txt' % os.environ['NZBPP_DIRECTORY'] if os.path.exists(brokenlog): text += '\n\nBrokenlog:\n' + open(brokenlog, 'r').read().strip() # add post-processing log if (os.environ.get('NZBPO_NZBLOG') == 'Always' or \ (os.environ.get('NZBPO_NZBLOG') == 'OnFailure' and not success)) and \ not test_mode: # To get the item log we connect to NZBGet via XML-RPC and call # method "loadlog", which returns the log for a given nzb item. # For more info visit http://nzbget.net/api # Call remote method 'loadlog' nzbid = int(os.environ['NZBPP_NZBID']) log = server.loadlog(nzbid, 0, 10000) # Now iterate through entries and save them to message text if len(log) > 0: text += '\n\nNzb-log:'; for entry in log: text += '\n%s\t%s\t%s' % (entry['Kind'], datetime.datetime.fromtimestamp(int(entry['Time'])), entry['Text']) # Create message print('[DETAIL] Creating Email') msg = MIMEText(text.encode('utf-8'), 'plain', 'utf-8') msg['Subject'] = Header(subject, 'utf-8') msg['From'] = os.environ['NZBPO_FROM'] msg['To'] = os.environ['NZBPO_TO'] msg['Date'] = datetime.datetime.utcnow().strftime("%a, %d %b %Y %H:%M:%S +0000") msg['X-Application'] = 'NZBGet' # Send message print('[DETAIL] Sending E-Mail') sys.stdout.flush() try: if os.environ['NZBPO_ENCRYPTION'] == 'force': smtp = smtplib.SMTP_SSL(os.environ['NZBPO_SERVER'], os.environ['NZBPO_PORT']) else: smtp = smtplib.SMTP(os.environ['NZBPO_SERVER'], os.environ['NZBPO_PORT']) if os.environ['NZBPO_ENCRYPTION'] == 'yes': smtp.starttls() if os.environ['NZBPO_USERNAME'] != '' and os.environ['NZBPO_PASSWORD'] != '': smtp.login(os.environ['NZBPO_USERNAME'], os.environ['NZBPO_PASSWORD']) smtp.sendmail(os.environ['NZBPO_FROM'], os.environ['NZBPO_TO'].split(','), msg.as_string()) smtp.quit() except Exception as err: print('[ERROR] %s' % err) sys.exit(POSTPROCESS_ERROR) # All OK, returning exit status 'POSTPROCESS_SUCCESS' (int <93>) to let NZBGet know # that our script has successfully completed. sys.exit(POSTPROCESS_SUCCESS)
nzbget/nzbget
scripts/EMail.py
Python
gpl-2.0
10,475
[ "VisIt" ]
14b9192cfb4b13d715866c3ae31cbc55052da2ea0272ebd6aeb8d741ca58ca5b
# class generated by DeVIDE::createDeVIDEModuleFromVTKObject from module_kits.vtk_kit.mixins import SimpleVTKClassModuleBase import vtk class vtkMedicalImageReader2(SimpleVTKClassModuleBase): def __init__(self, module_manager): SimpleVTKClassModuleBase.__init__( self, module_manager, vtk.vtkMedicalImageReader2(), 'Processing.', (), ('vtkImageData',), replaceDoc=True, inputFunctions=None, outputFunctions=None)
nagyistoce/devide
modules/vtk_basic/vtkMedicalImageReader2.py
Python
bsd-3-clause
486
[ "VTK" ]
d88b6762995d9a702587597572829c04c588a824268cf0514d3676a5d1698396
#!/usr/bin/env python # # Copyright (c) 2016 Matt Davis, <mdavis@ansible.com> # Chris Houseknecht, <house@redhat.com> # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # ''' Azure External Inventory Script =============================== Generates dynamic inventory by making API requests to the Azure Resource Manager using the AAzure Python SDK. For instruction on installing the Azure Python SDK see http://azure-sdk-for-python.readthedocs.org/ Authentication -------------- The order of precedence is command line arguments, environment variables, and finally the [default] profile found in ~/.azure/credentials. If using a credentials file, it should be an ini formatted file with one or more sections, which we refer to as profiles. The script looks for a [default] section, if a profile is not specified either on the command line or with an environment variable. The keys in a profile will match the list of command line arguments below. For command line arguments and environment variables specify a profile found in your ~/.azure/credentials file, or a service principal or Active Directory user. Command line arguments: - profile - client_id - secret - subscription_id - tenant - ad_user - password Environment variables: - AZURE_PROFILE - AZURE_CLIENT_ID - AZURE_SECRET - AZURE_SUBSCRIPTION_ID - AZURE_TENANT - AZURE_AD_USER - AZURE_PASSWORD Run for Specific Host ----------------------- When run for a specific host using the --host option, a resource group is required. For a specific host, this script returns the following variables: { "ansible_host": "XXX.XXX.XXX.XXX", "computer_name": "computer_name2", "fqdn": null, "id": "/subscriptions/subscription-id/resourceGroups/galaxy-production/providers/Microsoft.Compute/virtualMachines/object-name", "image": { "offer": "CentOS", "publisher": "OpenLogic", "sku": "7.1", "version": "latest" }, "location": "westus", "mac_address": "00-00-5E-00-53-FE", "name": "object-name", "network_interface": "interface-name", "network_interface_id": "/subscriptions/subscription-id/resourceGroups/galaxy-production/providers/Microsoft.Network/networkInterfaces/object-name1", "network_security_group": null, "network_security_group_id": null, "os_disk": { "name": "object-name", "operating_system_type": "Linux" }, "plan": null, "powerstate": "running", "private_ip": "172.26.3.6", "private_ip_alloc_method": "Static", "provisioning_state": "Succeeded", "public_ip": "XXX.XXX.XXX.XXX", "public_ip_alloc_method": "Static", "public_ip_id": "/subscriptions/subscription-id/resourceGroups/galaxy-production/providers/Microsoft.Network/publicIPAddresses/object-name", "public_ip_name": "object-name", "resource_group": "galaxy-production", "security_group": "object-name", "security_group_id": "/subscriptions/subscription-id/resourceGroups/galaxy-production/providers/Microsoft.Network/networkSecurityGroups/object-name", "tags": { "db": "database" }, "type": "Microsoft.Compute/virtualMachines", "virtual_machine_size": "Standard_DS4" } Groups ------ When run in --list mode, instances are grouped by the following categories: - azure - location - resource_group - security_group - tag key - tag key_value Control groups using azure_rm.ini or set environment variables: AZURE_GROUP_BY_RESOURCE_GROUP=yes AZURE_GROUP_BY_LOCATION=yes AZURE_GROUP_BY_SECURITY_GROUP=yes AZURE_GROUP_BY_TAG=yes Select hosts within specific resource groups by assigning a comma separated list to: AZURE_RESOURCE_GROUPS=resource_group_a,resource_group_b Select hosts for specific tag key by assigning a comma separated list of tag keys to: AZURE_TAGS=key1,key2,key3 Select hosts for specific locations: AZURE_LOCATIONS=eastus,westus,eastus2 Or, select hosts for specific tag key:value pairs by assigning a comma separated list key:value pairs to: AZURE_TAGS=key1:value1,key2:value2 If you don't need the powerstate, you can improve performance by turning off powerstate fetching: AZURE_INCLUDE_POWERSTATE=no azure_rm.ini ------------ As mentioned above, you can control execution using environment variables or a .ini file. A sample azure_rm.ini is included. The name of the .ini file is the basename of the inventory script (in this case 'azure_rm') with a .ini extension. It also assumes the .ini file is alongside the script. To specify a different path for the .ini file, define the AZURE_INI_PATH environment variable: export AZURE_INI_PATH=/path/to/custom.ini Powerstate: ----------- The powerstate attribute indicates whether or not a host is running. If the value is 'running', the machine is up. If the value is anything other than 'running', the machine is down, and will be unreachable. Examples: --------- Execute /bin/uname on all instances in the galaxy-qa resource group $ ansible -i azure_rm.py galaxy-qa -m shell -a "/bin/uname -a" Use the inventory script to print instance specific information $ contrib/inventory/azure_rm.py --host my_instance_host_name --pretty Use with a playbook $ ansible-playbook -i contrib/inventory/azure_rm.py my_playbook.yml --limit galaxy-qa Insecure Platform Warning ------------------------- If you receive InsecurePlatformWarning from urllib3, install the requests security packages: pip install requests[security] author: - Chris Houseknecht (@chouseknecht) - Matt Davis (@nitzmahone) Company: Ansible by Red Hat Version: 1.0.0 ''' import argparse import ConfigParser import json import os import re import sys from distutils.version import LooseVersion from os.path import expanduser HAS_AZURE = True HAS_AZURE_EXC = None try: from msrestazure.azure_exceptions import CloudError from azure.mgmt.compute import __version__ as azure_compute_version from azure.common import AzureMissingResourceHttpError, AzureHttpError from azure.common.credentials import ServicePrincipalCredentials, UserPassCredentials from azure.mgmt.network.network_management_client import NetworkManagementClient from azure.mgmt.resource.resources.resource_management_client import ResourceManagementClient from azure.mgmt.compute.compute_management_client import ComputeManagementClient except ImportError as exc: HAS_AZURE_EXC = exc HAS_AZURE = False AZURE_CREDENTIAL_ENV_MAPPING = dict( profile='AZURE_PROFILE', subscription_id='AZURE_SUBSCRIPTION_ID', client_id='AZURE_CLIENT_ID', secret='AZURE_SECRET', tenant='AZURE_TENANT', ad_user='AZURE_AD_USER', password='AZURE_PASSWORD' ) AZURE_CONFIG_SETTINGS = dict( resource_groups='AZURE_RESOURCE_GROUPS', tags='AZURE_TAGS', locations='AZURE_LOCATIONS', include_powerstate='AZURE_INCLUDE_POWERSTATE', group_by_resource_group='AZURE_GROUP_BY_RESOURCE_GROUP', group_by_location='AZURE_GROUP_BY_LOCATION', group_by_security_group='AZURE_GROUP_BY_SECURITY_GROUP', group_by_tag='AZURE_GROUP_BY_TAG' ) AZURE_MIN_VERSION = "0.30.0rc5" def azure_id_to_dict(id): pieces = re.sub(r'^\/', '', id).split('/') result = {} index = 0 while index < len(pieces) - 1: result[pieces[index]] = pieces[index + 1] index += 1 return result class AzureRM(object): def __init__(self, args): self._args = args self._compute_client = None self._resource_client = None self._network_client = None self.debug = False if args.debug: self.debug = True self.credentials = self._get_credentials(args) if not self.credentials: self.fail("Failed to get credentials. Either pass as parameters, set environment variables, " "or define a profile in ~/.azure/credentials.") if self.credentials.get('subscription_id', None) is None: self.fail("Credentials did not include a subscription_id value.") self.log("setting subscription_id") self.subscription_id = self.credentials['subscription_id'] if self.credentials.get('client_id') is not None and \ self.credentials.get('secret') is not None and \ self.credentials.get('tenant') is not None: self.azure_credentials = ServicePrincipalCredentials(client_id=self.credentials['client_id'], secret=self.credentials['secret'], tenant=self.credentials['tenant']) elif self.credentials.get('ad_user') is not None and self.credentials.get('password') is not None: self.azure_credentials = UserPassCredentials(self.credentials['ad_user'], self.credentials['password']) else: self.fail("Failed to authenticate with provided credentials. Some attributes were missing. " "Credentials must include client_id, secret and tenant or ad_user and password.") def log(self, msg): if self.debug: print (msg + u'\n') def fail(self, msg): raise Exception(msg) def _get_profile(self, profile="default"): path = expanduser("~") path += "/.azure/credentials" try: config = ConfigParser.ConfigParser() config.read(path) except Exception as exc: self.fail("Failed to access {0}. Check that the file exists and you have read " "access. {1}".format(path, str(exc))) credentials = dict() for key in AZURE_CREDENTIAL_ENV_MAPPING: try: credentials[key] = config.get(profile, key, raw=True) except: pass if credentials.get('client_id') is not None or credentials.get('ad_user') is not None: return credentials return None def _get_env_credentials(self): env_credentials = dict() for attribute, env_variable in AZURE_CREDENTIAL_ENV_MAPPING.items(): env_credentials[attribute] = os.environ.get(env_variable, None) if env_credentials['profile'] is not None: credentials = self._get_profile(env_credentials['profile']) return credentials if env_credentials['client_id'] is not None or env_credentials['ad_user'] is not None: return env_credentials return None def _get_credentials(self, params): # Get authentication credentials. # Precedence: cmd line parameters-> environment variables-> default profile in ~/.azure/credentials. self.log('Getting credentials') arg_credentials = dict() for attribute, env_variable in AZURE_CREDENTIAL_ENV_MAPPING.items(): arg_credentials[attribute] = getattr(params, attribute) # try module params if arg_credentials['profile'] is not None: self.log('Retrieving credentials with profile parameter.') credentials = self._get_profile(arg_credentials['profile']) return credentials if arg_credentials['client_id'] is not None: self.log('Received credentials from parameters.') return arg_credentials # try environment env_credentials = self._get_env_credentials() if env_credentials: self.log('Received credentials from env.') return env_credentials # try default profile from ~./azure/credentials default_credentials = self._get_profile() if default_credentials: self.log('Retrieved default profile credentials from ~/.azure/credentials.') return default_credentials return None def _register(self, key): try: # We have to perform the one-time registration here. Otherwise, we receive an error the first # time we attempt to use the requested client. resource_client = self.rm_client resource_client.providers.register(key) except Exception as exc: self.fail("One-time registration of {0} failed - {1}".format(key, str(exc))) @property def network_client(self): self.log('Getting network client') if not self._network_client: self._network_client = NetworkManagementClient(self.azure_credentials, self.subscription_id) self._register('Microsoft.Network') return self._network_client @property def rm_client(self): self.log('Getting resource manager client') if not self._resource_client: self._resource_client = ResourceManagementClient(self.azure_credentials, self.subscription_id) return self._resource_client @property def compute_client(self): self.log('Getting compute client') if not self._compute_client: self._compute_client = ComputeManagementClient(self.azure_credentials, self.subscription_id) self._register('Microsoft.Compute') return self._compute_client class AzureInventory(object): def __init__(self): self._args = self._parse_cli_args() try: rm = AzureRM(self._args) except Exception as e: sys.exit("{0}".format(str(e))) self._compute_client = rm.compute_client self._network_client = rm.network_client self._resource_client = rm.rm_client self._security_groups = None self.resource_groups = [] self.tags = None self.locations = None self.replace_dash_in_groups = False self.group_by_resource_group = True self.group_by_location = True self.group_by_security_group = True self.group_by_tag = True self.include_powerstate = True self._inventory = dict( _meta=dict( hostvars=dict() ), azure=[] ) self._get_settings() if self._args.resource_groups: self.resource_groups = self._args.resource_groups.split(',') if self._args.tags: self.tags = self._args.tags.split(',') if self._args.locations: self.locations = self._args.locations.split(',') if self._args.no_powerstate: self.include_powerstate = False self.get_inventory() print (self._json_format_dict(pretty=self._args.pretty)) sys.exit(0) def _parse_cli_args(self): # Parse command line arguments parser = argparse.ArgumentParser( description='Produce an Ansible Inventory file for an Azure subscription') parser.add_argument('--list', action='store_true', default=True, help='List instances (default: True)') parser.add_argument('--debug', action='store_true', default=False, help='Send debug messages to STDOUT') parser.add_argument('--host', action='store', help='Get all information about an instance') parser.add_argument('--pretty', action='store_true', default=False, help='Pretty print JSON output(default: False)') parser.add_argument('--profile', action='store', help='Azure profile contained in ~/.azure/credentials') parser.add_argument('--subscription_id', action='store', help='Azure Subscription Id') parser.add_argument('--client_id', action='store', help='Azure Client Id ') parser.add_argument('--secret', action='store', help='Azure Client Secret') parser.add_argument('--tenant', action='store', help='Azure Tenant Id') parser.add_argument('--ad-user', action='store', help='Active Directory User') parser.add_argument('--password', action='store', help='password') parser.add_argument('--resource-groups', action='store', help='Return inventory for comma separated list of resource group names') parser.add_argument('--tags', action='store', help='Return inventory for comma separated list of tag key:value pairs') parser.add_argument('--locations', action='store', help='Return inventory for comma separated list of locations') parser.add_argument('--no-powerstate', action='store_true', default=False, help='Do not include the power state of each virtual host') return parser.parse_args() def get_inventory(self): if len(self.resource_groups) > 0: # get VMs for requested resource groups for resource_group in self.resource_groups: try: virtual_machines = self._compute_client.virtual_machines.list(resource_group) except Exception as exc: sys.exit("Error: fetching virtual machines for resource group {0} - {1}".format(resource_group, str(exc))) if self._args.host or self.tags: selected_machines = self._selected_machines(virtual_machines) self._load_machines(selected_machines) else: self._load_machines(virtual_machines) else: # get all VMs within the subscription try: virtual_machines = self._compute_client.virtual_machines.list_all() except Exception as exc: sys.exit("Error: fetching virtual machines - {0}".format(str(exc))) if self._args.host or self.tags or self.locations: selected_machines = self._selected_machines(virtual_machines) self._load_machines(selected_machines) else: self._load_machines(virtual_machines) def _load_machines(self, machines): for machine in machines: id_dict = azure_id_to_dict(machine.id) #TODO - The API is returning an ID value containing resource group name in ALL CAPS. If/when it gets # fixed, we should remove the .lower(). Opened Issue # #574: https://github.com/Azure/azure-sdk-for-python/issues/574 resource_group = id_dict['resourceGroups'].lower() if self.group_by_security_group: self._get_security_groups(resource_group) host_vars = dict( ansible_host=None, private_ip=None, private_ip_alloc_method=None, public_ip=None, public_ip_name=None, public_ip_id=None, public_ip_alloc_method=None, fqdn=None, location=machine.location, name=machine.name, type=machine.type, id=machine.id, tags=machine.tags, network_interface_id=None, network_interface=None, resource_group=resource_group, mac_address=None, plan=(machine.plan.name if machine.plan else None), virtual_machine_size=machine.hardware_profile.vm_size, computer_name=machine.os_profile.computer_name, provisioning_state=machine.provisioning_state, ) host_vars['os_disk'] = dict( name=machine.storage_profile.os_disk.name, operating_system_type=machine.storage_profile.os_disk.os_type.value ) if self.include_powerstate: host_vars['powerstate'] = self._get_powerstate(resource_group, machine.name) if machine.storage_profile.image_reference: host_vars['image'] = dict( offer=machine.storage_profile.image_reference.offer, publisher=machine.storage_profile.image_reference.publisher, sku=machine.storage_profile.image_reference.sku, version=machine.storage_profile.image_reference.version ) # Add windows details if machine.os_profile.windows_configuration is not None: host_vars['windows_auto_updates_enabled'] = \ machine.os_profile.windows_configuration.enable_automatic_updates host_vars['windows_timezone'] = machine.os_profile.windows_configuration.time_zone host_vars['windows_rm'] = None if machine.os_profile.windows_configuration.win_rm is not None: host_vars['windows_rm'] = dict(listeners=None) if machine.os_profile.windows_configuration.win_rm.listeners is not None: host_vars['windows_rm']['listeners'] = [] for listener in machine.os_profile.windows_configuration.win_rm.listeners: host_vars['windows_rm']['listeners'].append(dict(protocol=listener.protocol, certificate_url=listener.certificate_url)) for interface in machine.network_profile.network_interfaces: interface_reference = self._parse_ref_id(interface.id) network_interface = self._network_client.network_interfaces.get( interface_reference['resourceGroups'], interface_reference['networkInterfaces']) if network_interface.primary: if self.group_by_security_group and \ self._security_groups[resource_group].get(network_interface.id, None): host_vars['security_group'] = \ self._security_groups[resource_group][network_interface.id]['name'] host_vars['security_group_id'] = \ self._security_groups[resource_group][network_interface.id]['id'] host_vars['network_interface'] = network_interface.name host_vars['network_interface_id'] = network_interface.id host_vars['mac_address'] = network_interface.mac_address for ip_config in network_interface.ip_configurations: host_vars['private_ip'] = ip_config.private_ip_address host_vars['private_ip_alloc_method'] = ip_config.private_ip_allocation_method if ip_config.public_ip_address: public_ip_reference = self._parse_ref_id(ip_config.public_ip_address.id) public_ip_address = self._network_client.public_ip_addresses.get( public_ip_reference['resourceGroups'], public_ip_reference['publicIPAddresses']) host_vars['ansible_host'] = public_ip_address.ip_address host_vars['public_ip'] = public_ip_address.ip_address host_vars['public_ip_name'] = public_ip_address.name host_vars['public_ip_alloc_method'] = public_ip_address.public_ip_allocation_method host_vars['public_ip_id'] = public_ip_address.id if public_ip_address.dns_settings: host_vars['fqdn'] = public_ip_address.dns_settings.fqdn self._add_host(host_vars) def _selected_machines(self, virtual_machines): selected_machines = [] for machine in virtual_machines: if self._args.host and self._args.host == machine.name: selected_machines.append(machine) if self.tags and self._tags_match(machine.tags, self.tags): selected_machines.append(machine) if self.locations and machine.location in self.locations: selected_machines.append(machine) return selected_machines def _get_security_groups(self, resource_group): ''' For a given resource_group build a mapping of network_interface.id to security_group name ''' if not self._security_groups: self._security_groups = dict() if not self._security_groups.get(resource_group): self._security_groups[resource_group] = dict() for group in self._network_client.network_security_groups.list(resource_group): if group.network_interfaces: for interface in group.network_interfaces: self._security_groups[resource_group][interface.id] = dict( name=group.name, id=group.id ) def _get_powerstate(self, resource_group, name): try: vm = self._compute_client.virtual_machines.get(resource_group, name, expand='instanceview') except Exception as exc: sys.exit("Error: fetching instanceview for host {0} - {1}".format(name, str(exc))) return next((s.code.replace('PowerState/', '') for s in vm.instance_view.statuses if s.code.startswith('PowerState')), None) def _add_host(self, vars): host_name = self._to_safe(vars['name']) resource_group = self._to_safe(vars['resource_group']) security_group = None if vars.get('security_group'): security_group = self._to_safe(vars['security_group']) if self.group_by_resource_group: if not self._inventory.get(resource_group): self._inventory[resource_group] = [] self._inventory[resource_group].append(host_name) if self.group_by_location: if not self._inventory.get(vars['location']): self._inventory[vars['location']] = [] self._inventory[vars['location']].append(host_name) if self.group_by_security_group and security_group: if not self._inventory.get(security_group): self._inventory[security_group] = [] self._inventory[security_group].append(host_name) self._inventory['_meta']['hostvars'][host_name] = vars self._inventory['azure'].append(host_name) if self.group_by_tag and vars.get('tags'): for key, value in vars['tags'].items(): safe_key = self._to_safe(key) safe_value = safe_key + '_' + self._to_safe(value) if not self._inventory.get(safe_key): self._inventory[safe_key] = [] if not self._inventory.get(safe_value): self._inventory[safe_value] = [] self._inventory[safe_key].append(host_name) self._inventory[safe_value].append(host_name) def _json_format_dict(self, pretty=False): # convert inventory to json if pretty: return json.dumps(self._inventory, sort_keys=True, indent=2) else: return json.dumps(self._inventory) def _get_settings(self): # Load settings from the .ini, if it exists. Otherwise, # look for environment values. file_settings = self._load_settings() if file_settings: for key in AZURE_CONFIG_SETTINGS: if key in ('resource_groups', 'tags', 'locations') and file_settings.get(key): values = file_settings.get(key).split(',') if len(values) > 0: setattr(self, key, values) elif file_settings.get(key): val = self._to_boolean(file_settings[key]) setattr(self, key, val) else: env_settings = self._get_env_settings() for key in AZURE_CONFIG_SETTINGS: if key in('resource_groups', 'tags', 'locations') and env_settings.get(key): values = env_settings.get(key).split(',') if len(values) > 0: setattr(self, key, values) elif env_settings.get(key, None) is not None: val = self._to_boolean(env_settings[key]) setattr(self, key, val) def _parse_ref_id(self, reference): response = {} keys = reference.strip('/').split('/') for index in range(len(keys)): if index < len(keys) - 1 and index % 2 == 0: response[keys[index]] = keys[index + 1] return response def _to_boolean(self, value): if value in ['Yes', 'yes', 1, 'True', 'true', True]: result = True elif value in ['No', 'no', 0, 'False', 'false', False]: result = False else: result = True return result def _get_env_settings(self): env_settings = dict() for attribute, env_variable in AZURE_CONFIG_SETTINGS.items(): env_settings[attribute] = os.environ.get(env_variable, None) return env_settings def _load_settings(self): basename = os.path.splitext(os.path.basename(__file__))[0] default_path = os.path.join(os.path.dirname(__file__), (basename + '.ini')) path = os.path.expanduser(os.path.expandvars(os.environ.get('AZURE_INI_PATH', default_path))) config = None settings = None try: config = ConfigParser.ConfigParser() config.read(path) except: pass if config is not None: settings = dict() for key in AZURE_CONFIG_SETTINGS: try: settings[key] = config.get('azure', key, raw=True) except: pass return settings def _tags_match(self, tag_obj, tag_args): ''' Return True if the tags object from a VM contains the requested tag values. :param tag_obj: Dictionary of string:string pairs :param tag_args: List of strings in the form key=value :return: boolean ''' if not tag_obj: return False matches = 0 for arg in tag_args: arg_key = arg arg_value = None if re.search(r':', arg): arg_key, arg_value = arg.split(':') if arg_value and tag_obj.get(arg_key, None) == arg_value: matches += 1 elif not arg_value and tag_obj.get(arg_key, None) is not None: matches += 1 if matches == len(tag_args): return True return False def _to_safe(self, word): ''' Converts 'bad' characters in a string to underscores so they can be used as Ansible groups ''' regex = "[^A-Za-z0-9\_" if not self.replace_dash_in_groups: regex += "\-" return re.sub(regex + "]", "_", word) def main(): if not HAS_AZURE: sys.exit("The Azure python sdk is not installed (try 'pip install azure>=2.0.0rc5') - {0}".format(HAS_AZURE_EXC)) if LooseVersion(azure_compute_version) < LooseVersion(AZURE_MIN_VERSION): sys.exit("Expecting azure.mgmt.compute.__version__ to be {0}. Found version {1} " "Do you have Azure >= 2.0.0rc5 installed?".format(AZURE_MIN_VERSION, azure_compute_version)) AzureInventory() if __name__ == '__main__': main()
crafty78/ansible
contrib/inventory/azure_rm.py
Python
gpl-3.0
32,305
[ "Galaxy" ]
81fddfcf0d7a3fd538f5467d702d6e091bfdfa396df3043b1a3e2a8260c77183
import time import argparse import numpy as np import tensorflow as tf import reader import model import pickle import os def parsing_args(): parser = argparse.ArgumentParser() parser.add_argument('--mode', type=str, default='train', help='train or test') parser.add_argument('--init_from', type=str, default=None, help='init model path') parser.add_argument('--init_method', type=str, default=None, help='lstm/att init from lstm or full model') parser.add_argument('--word_vector_path', type=str, default=None, help='pretrain word2vector model') parser.add_argument('--data_dir', type=str, default=None, help='data directory containing train valid test data') parser.add_argument('--save', type=str, default=None, help='directory to store checkpointed models') parser.add_argument('--model_result', type=str, default=None, help='save model result') parser.add_argument('--att_file', type=str, default=None, help='file storing attention weights for analysis') parser.add_argument('--rnn_size', type=int, default=300, help='size of LSTM internal state') parser.add_argument('--emb_size', type=int, default=300, help='word embedding size') parser.add_argument('--num_layers', type=int, default=1, help='number of layers in the RNN') parser.add_argument('--window_size', type=int, default=5, help='history window size') parser.add_argument('--batch_size', type=int, default=20, help='minibatch size') parser.add_argument('--max_seq_length', type=int, default=60, help='max number of timesteps to unroll during BPTT') parser.add_argument('--min_seq_length', type=int, default=0, help='min number of timesteps to unroll during BPTT') parser.add_argument('--max_epochs', type=int, default=50, help='number of full passes through the training data') parser.add_argument('--dropout', type=float, default=1, help='dropout for regularization, neuron keep probabitity. 1 = no dropout') parser.add_argument('--max_grad_norm', type=float, default=5., help='clip gradients at this value') parser.add_argument('--entropy_reg', type=float, default=0.1, help='entropy regulizar') parser.add_argument('--learning_rate', type=float, default=1.0, help='learning rate') parser.add_argument('--init_scale', type=float, default=0.1, help='initialization scale') parser.add_argument('--decay_rate', type=float, default=0.5, help='decay rate') parser.add_argument('--learning_rate_decay_after', type=int, default=10, help='in number of epochs, when to start decaying the learning rate') parser.add_argument('--gpu_id', type=float, default=0, help='% of gpu memory to be allocated to this process. Default is 66.6%') parser.add_argument('--print_every', type=int, default=200, help='how many steps/minibatches between printing out the loss') args = parser.parse_args() return args def run_epoch_training(sess, all_op, data, lr, dropout, print_every): start_time = time.time() nbatch = data.get_batch_number() total_words_num = 0 total_cost = 0 fetches = {} fetches['train'] = all_op['train'] fetches['total_label_loss'] = all_op['total_label_loss'] for idx in range(nbatch): x, y = data.get_data(idx) feed_dict = { all_op['input_data']:x, all_op['labels']:y, all_op['learning_rate']:lr, all_op['dropout']:dropout } result = sess.run(fetches,feed_dict=feed_dict) total_cost += result['total_label_loss'] total_words_num += x.size if (idx+1)%print_every == 0: print (idx+1), '/', nbatch, ': ', 'perplexity: ', np.exp(result['total_label_loss']/x.size) total_perplexity = np.exp(total_cost/total_words_num) print 'training perplexity in this epoch: ' , total_perplexity print 'epoch training time: ', (time.time() - start_time) return total_perplexity def evaluating(sess, all_op, data): nbatch = data.get_batch_number() total_words_num = 0 total_cost = 0 fetches = {} fetches['total_label_loss'] = all_op['total_label_loss'] for idx in range(nbatch): x, y = data.get_data(idx) feed_dict = { all_op['input_data']:x, all_op['labels']:y, all_op['dropout']:1 } result = sess.run(fetches,feed_dict=feed_dict) total_cost += result['total_label_loss'] total_words_num += x.size total_perplexity = np.exp(total_cost/total_words_num) return total_perplexity def train(args): #read data train_data = reader.data(data_dir=args.data_dir, batch_size=args.batch_size, min_seq_length=args.min_seq_length, max_seq_length=args.max_seq_length, min_count=0) train_data.load('train') valid_data = reader.data(data_dir=args.data_dir, batch_size=args.batch_size, min_seq_length=args.min_seq_length, max_seq_length=args.max_seq_length, min_count=0) valid_data.load('valid') test_data = reader.data(data_dir=args.data_dir, batch_size=args.batch_size, min_seq_length=args.min_seq_length, max_seq_length=args.max_seq_length, min_count=0) test_data.load('test') #load model if args.init_from: if not os.path.isfile(args.init_from): print 'init file not found' os.exit() #the placeholder need for training input_data_ph = tf.placeholder(tf.int32, [None, None]) labels_ph = tf.placeholder(tf.int32, [None, None]) learning_rate_ph = tf.placeholder(tf.float32, []) dropout_ph = tf.placeholder(tf.float32, []) #build model vocab_size=train_data.vocab_size default_initializer = tf.random_uniform_initializer(-args.init_scale, args.init_scale) with tf.variable_scope('model',initializer=default_initializer): logits, pretrain_list, output_linear_list = model.inference( input_x=input_data_ph, embedding_dim=args.emb_size, lstm_hidden_dim_1=args.rnn_size, vocab_size=vocab_size, dropout=dropout_ph, window_size=args.window_size) total_label_loss, loss = model.loss(logits=logits, labels=labels_ph) train_op = model.training(loss, learning_rate_ph, args.max_grad_norm) all_op = {'input_data':input_data_ph, 'labels':labels_ph, 'learning_rate':learning_rate_ph, 'dropout':dropout_ph, 'total_label_loss':total_label_loss, 'train':train_op} #pretrain if args.init_from: if args.init_method == 'lstm': with tf.variable_scope('model'): with tf.variable_scope('output_lstm1_linear'): lstm_linear_W = tf.get_variable('W', [args.rnn_size, vocab_size]) lstm_linear_b = tf.get_variable('b', [vocab_size], initializer=tf.constant_initializer(0.0)) pretrain_list += [lstm_linear_W,lstm_linear_b] init_att_W = output_linear_list[0].assign(tf.concat(0,[lstm_linear_W,lstm_linear_W])) init_att_b = output_linear_list[1].assign(lstm_linear_b) saver_restore = tf.train.Saver(pretrain_list) else: saver_restore = tf.train.Saver() #pretrain word embedding if args.word_vector_path: emb_matrix = pretrain_list[0] pretrain_emb = emb_matrix.assign(train_data.generate_word_embedding_matrix(args.word_vector_path)) global_step = tf.Variable(0,name='global_step',trainable=False) init = tf.initialize_all_variables() saver_save = tf.train.Saver() training_process_perplexity = {'train':[],'valid':[],'test':[],'best_val_test':[]} file_name = 'rnn_size' + str(args.rnn_size) with tf.Session() as sess: sess.run(init) #pretrain word embedding if args.word_vector_path: sess.run(pretrain_emb) if args.init_from: if args.init_method == 'lstm': saver_restore.restore(sess, args.init_from) sess.run(init_att_W) sess.run(init_att_b) else: saver_restore.restore(sess, args.init_from) #training best_val_perplexity = np.inf best_val_test_perplexity = np.inf for i in range(args.max_epochs): lr_decay = args.decay_rate ** max(i + 1 - args.learning_rate_decay_after, 0.0) learning_rate = args.learning_rate * lr_decay print("Epoch: %d Learning rate: %.3f" % (i + 1, learning_rate)) #training training_perplexity = run_epoch_training(sess, all_op, train_data, learning_rate, args.dropout, args.print_every) print("Epoch: %d Train Perplexity: %.3f" % (i + 1, training_perplexity)) test_training_perplexity = evaluating(sess, all_op, train_data) print("Epoch: %d test training Perplexity: %.3f" % (i + 1, test_training_perplexity)) #validation val_perplexity = evaluating(sess, all_op, valid_data) print("Epoch: %d validation Perplexity: %.3f" % (i + 1, val_perplexity)) #peeking testing test_perplexity = evaluating(sess, all_op, test_data) print("Epoch: %d peeking testing Perplexity: %.3f" % (i + 1, test_perplexity)) if val_perplexity < best_val_perplexity : best_val_perplexity = val_perplexity best_val_test_perplexity = test_perplexity #save saver_save.save(sess, os.path.join(args.save,file_name), global_step=global_step) print("So far best val testing Perplexity: %.3f" % (best_val_test_perplexity)) training_process_perplexity['train'].append(test_training_perplexity) training_process_perplexity['valid'].append(val_perplexity) training_process_perplexity['test'].append(test_perplexity) training_process_perplexity['best_val_test'].append(best_val_test_perplexity) with open(os.path.join(args.model_result,file_name),'wb') as f: pickle.dump(training_process_perplexity, f) def test(args): test_data = reader.data(data_dir=args.data_dir, batch_size=args.batch_size, min_seq_length=args.min_seq_length, max_seq_length=args.max_seq_length, min_count=args.min_count) test_data.load('test') #load model if args.init_from: if not os.path.isfile(args.init_from): print 'init file not found' os.exit() #the placeholder need for training input_data_ph = tf.placeholder(tf.int32, [None, None]) labels_ph = tf.placeholder(tf.int32, [None, None]) learning_rate_ph = tf.placeholder(tf.float32, []) dropout_ph = tf.placeholder(tf.float32, []) #build model vocab_size=test_data.vocab_size logits, pretrain_list, output_linear_list = model.inference(input_x=input_data_ph, embedding_dim=args.emb_size, lstm_hidden_dim_1=args.rnn_size, vocab_size=vocab_size, dropout=dropout_ph) total_label_loss, loss = model.loss(logits=logits, labels=labels_ph) all_op = {'input_data':input_data_ph, 'labels':labels_ph, 'learning_rate':learning_rate_ph, 'dropout':dropout_ph, 'total_label_loss':total_label_loss} #pretrain if args.init_from: saver_restore = tf.train.Saver() #load model init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) saver_restore.restore(sess, args.init_from) test_perplexity = evaluating(sess, all_op, test_data) print ("Testing Perplexity: %.3f" % (test_perplexity)) if __name__ == "__main__": args = parsing_args() if args.mode == 'train': train(args) else : test(args)
darongliu/Lstm_Turing_LM_tf
exp_merge_method/LSTM+att_fix+matrix_concat/run.py
Python
mit
13,336
[ "NEURON" ]
ffdd850a030e80eb43edd58a55b923f8d518a9030d1565d97033233be4703d12
# -*- coding: utf-8 -*- # # hl_api_models.py # # This file is part of NEST. # # Copyright (C) 2004 The NEST Initiative # # NEST is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # NEST is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with NEST. If not, see <http://www.gnu.org/licenses/>. """ Functions for model handling """ from ..ll_api import * from .hl_api_helper import * __all__ = [ 'ConnectionRules', 'CopyModel', 'GetDefaults', 'Models', 'SetDefaults', ] @check_stack def Models(mtype="all", sel=None): """Return a tuple of model names, sorted by name. All available models are neurons, devices and synapses. Parameters ---------- mtype : str, optional Use ``'mtype='nodes'`` to only see neuron and device models, or ``'type='synapses'`` to only see synapse models. sel : str, optional String used to filter the result list and only return models containing it. Returns ------- tuple Available model names Raises ------ ValueError Description Notes ----- - Synapse model names ending with ``'_hpc'`` provide minimal memory requirements by using thread-local target neuron IDs and fixing the ``'rport'`` to 0. - Synapse model names ending with ``'_lbl'`` allow to assign an individual integer label (``'synapse_label'``) to created synapses at the cost of increased memory requirements. """ if mtype not in ("all", "nodes", "synapses"): raise ValueError("type has to be one of 'all', 'nodes' or 'synapses'") models = [] if mtype in ("all", "nodes"): sr("modeldict") models += spp().keys() if mtype in ("all", "synapses"): sr("synapsedict") models += spp().keys() if sel is not None: models = [x for x in models if x.find(sel) >= 0] models.sort() return tuple(models) @check_stack def ConnectionRules(): """Return a typle of all available connection rules, sorted by name. Returns ------- tuple Available connection rules """ sr('connruledict') return tuple(sorted(spp().keys())) @check_stack def SetDefaults(model, params, val=None): """Set the default parameter values of the given model. New default values are used for all subsequently created instances of the model. Parameters ---------- model : str Name of the model params : str or dict Dictionary of new default parameter values val : str, optional If given, `params` has to be the name of a model property. """ if val is not None: if is_literal(params): params = {params: val} sps(params) sr('/{0} exch SetDefaults'.format(model)) @check_stack def GetDefaults(model, keys=None, output=''): """Return default parameters of the given model, specified by a string. Parameters ---------- model : str Name of the model keys : str or list, optional String or a list of strings naming model properties. `GetDefaults` then returns a single value or a list of values belonging to the keys given. output : str, optional Whether the returned data should be in a format (``output='json'``). Default is ''. Returns ------- dict A dictionary of default parameters. type If keys is a string, the corrsponding default parameter is returned. list If keys is a list of strings, a list of corrsponding default parameters is returned. str : If `output` is ``json``, returns parameters in JSON format. Raises ------ TypeError """ if keys is None: cmd = "/{0} GetDefaults".format(model) elif is_literal(keys): cmd = '/{0} GetDefaults /{1} get'.format(model, keys) elif is_iterable(keys): keys_str = " ".join("/{0}".format(x) for x in keys) cmd = "/{0} GetDefaults [ {1} ] {{ 1 index exch get }}"\ .format(model, keys_str) + " Map exch pop" else: raise TypeError("keys should be either a string or an iterable") sr(cmd) result = spp() if output == 'json': result = to_json(result) return result @check_stack def CopyModel(existing, new, params=None): """Create a new model by copying an existing one. Parameters ---------- existing : str Name of existing model new : str Name of the copied model params : dict, optional Default parameters assigned to the copy. Not provided parameters are taken from the existing model. """ model_deprecation_warning(existing) if params is not None: sps(params) sr("/%s /%s 3 2 roll CopyModel" % (existing, new)) else: sr("/%s /%s CopyModel" % (existing, new))
hakonsbm/nest-simulator
pynest/nest/lib/hl_api_models.py
Python
gpl-2.0
5,333
[ "NEURON" ]
a72f930125c36ed1e4e7ddc1d206fa0481742dddf46e7ed0f03f99f8cda71554
from django.conf import settings from django.db import models from reviews.models import Review, ReviewSegment """ These models extend the basic review models with new fields. You do not need to do this, but you can do it. Take a look at the forms.py, too. Also visit the __init__.py. This all works only if the REVIEW_APP value in settings.py is set to this app. """ class TestReview(Review): price = models.IntegerField() class TestReviewSegment(ReviewSegment): title = models.CharField(max_length=200) """ These are test models. They are used to show how reviews are assigned to models. Please look at the "entry.html" to see how this works. Like the comments you need to combine your models and the review system in the templates. The models itself are not important, we have two to show that the system can do this. """ class Car(models.Model): name = models.CharField(max_length = 100) def __unicode__(self): return self.name @models.permalink def get_absolute_url(self): return ('car_detail', [str(self.id)]) class Restaurant(models.Model): name = models.CharField(max_length = 100) def __unicode__(self): return self.name @models.permalink def get_absolute_url(self): return ('restaurant_detail', [str(self.id)])
shockflash/reviews
testapp/testdata/models.py
Python
bsd-3-clause
1,308
[ "VisIt" ]
958937a55a0870d954622b9dd9b6293f6a20cbe73db5d878605fd3a17ca75e73
# Tests writing amber format with amber parameters # Special isopeptide bond between two residues import os from vmd import atomsel, molecule dir = os.path.dirname(__file__) #============================================================================== def test_amber_custom_residues(tmpdir): from Dabble.param import AmberWriter # Generate the file p = str(tmpdir.mkdir("3nob_custom")) molid = molecule.load("mae", os.path.join(dir, "prepped.mae")) w = AmberWriter(molid, tmp_dir=p, forcefield="amber", hmr=False, extra_topos=[os.path.join(dir, "glx.off"), os.path.join(dir, "lyx.off")], extra_params=[os.path.join(dir, "join.frcmod"), os.path.join(dir, "analogies.frcmod")], override_defaults=False) w.write(os.path.join(p, "test")) # Load the output file and start checking it m2 = molecule.load("parm7", os.path.join(p, "test.prmtop"), "rst7", os.path.join(p, "test.inpcrd")) molecule.set_top(m2) # Check the two custom residues are present assert(len(atomsel("resname GLX")) == 7) assert(len(atomsel("resname LYX")) == 20) # Check the custom residues have gaff2 atom types assert("n" in atomsel("resname LYX").get("type")) assert("n2" in atomsel("resname GLX").get("type")) # Check the normal residues have ff14SB atom types assert("N" in atomsel("resname LYS").get("type")) assert("N" in atomsel("resname GLY").get("type")) # Check that the isopeptide bond is there lybonds = [] for x in atomsel("resname LYX").bonds: lybonds.extend(x) assert(any(x in lybonds for x in atomsel("resname GLX").get("index"))) #==============================================================================
drorlab/dabble
test/3NOB/test_3nob.py
Python
gpl-2.0
1,849
[ "Amber", "VMD" ]
005fe33e2022f55cacd3041aba3cd42e067ae4b6f8a72899bc4bc4f29be0ef7a
# Authors: # Martin Kosek <mkosek@redhat.com> # Pavel Zuna <pzuna@redhat.com> # # Copyright (C) 2010 Red Hat # see file 'COPYING' for use and warranty information # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. from __future__ import absolute_import import netaddr import time import re import binascii import dns.name import dns.exception import dns.rdatatype import dns.resolver import encodings.idna from ipalib.request import context from ipalib import api, errors, output from ipalib import Command from ipalib.capabilities import VERSION_WITHOUT_CAPABILITIES from ipalib.parameters import (Flag, Bool, Int, Decimal, Str, StrEnum, Any, DeprecatedParam, DNSNameParam) from ipalib.plugable import Registry from ipalib.plugins.baseldap import * from ipalib import _, ngettext from ipalib import messages from ipalib.util import (normalize_zonemgr, get_dns_forward_zone_update_policy, get_dns_reverse_zone_update_policy, get_reverse_zone_default, REVERSE_DNS_ZONES, normalize_zone, validate_dnssec_global_forwarder, DNSSECSignatureMissingError, UnresolvableRecordError, EDNS0UnsupportedError, DNSSECValidationError, validate_dnssec_zone_forwarder_step1, validate_dnssec_zone_forwarder_step2) from ipapython.ipautil import CheckedIPAddress, is_host_resolvable from ipapython.dnsutil import DNSName __doc__ = _(""" Domain Name System (DNS) """) + _(""" Manage DNS zone and resource records. """) + _(""" SUPPORTED ZONE TYPES * Master zone (dnszone-*), contains authoritative data. * Forward zone (dnsforwardzone-*), forwards queries to configured forwarders (a set of DNS servers). """) + _(""" USING STRUCTURED PER-TYPE OPTIONS """) + _(""" There are many structured DNS RR types where DNS data stored in LDAP server is not just a scalar value, for example an IP address or a domain name, but a data structure which may be often complex. A good example is a LOC record [RFC1876] which consists of many mandatory and optional parts (degrees, minutes, seconds of latitude and longitude, altitude or precision). """) + _(""" It may be difficult to manipulate such DNS records without making a mistake and entering an invalid value. DNS module provides an abstraction over these raw records and allows to manipulate each RR type with specific options. For each supported RR type, DNS module provides a standard option to manipulate a raw records with format --<rrtype>-rec, e.g. --mx-rec, and special options for every part of the RR structure with format --<rrtype>-<partname>, e.g. --mx-preference and --mx-exchanger. """) + _(""" When adding a record, either RR specific options or standard option for a raw value can be used, they just should not be combined in one add operation. When modifying an existing entry, new RR specific options can be used to change one part of a DNS record, where the standard option for raw value is used to specify the modified value. The following example demonstrates a modification of MX record preference from 0 to 1 in a record without modifying the exchanger: ipa dnsrecord-mod --mx-rec="0 mx.example.com." --mx-preference=1 """) + _(""" EXAMPLES: """) + _(""" Add new zone: ipa dnszone-add example.com --admin-email=admin@example.com """) + _(""" Add system permission that can be used for per-zone privilege delegation: ipa dnszone-add-permission example.com """) + _(""" Modify the zone to allow dynamic updates for hosts own records in realm EXAMPLE.COM: ipa dnszone-mod example.com --dynamic-update=TRUE """) + _(""" This is the equivalent of: ipa dnszone-mod example.com --dynamic-update=TRUE \\ --update-policy="grant EXAMPLE.COM krb5-self * A; grant EXAMPLE.COM krb5-self * AAAA; grant EXAMPLE.COM krb5-self * SSHFP;" """) + _(""" Modify the zone to allow zone transfers for local network only: ipa dnszone-mod example.com --allow-transfer=192.0.2.0/24 """) + _(""" Add new reverse zone specified by network IP address: ipa dnszone-add --name-from-ip=192.0.2.0/24 """) + _(""" Add second nameserver for example.com: ipa dnsrecord-add example.com @ --ns-rec=nameserver2.example.com """) + _(""" Add a mail server for example.com: ipa dnsrecord-add example.com @ --mx-rec="10 mail1" """) + _(""" Add another record using MX record specific options: ipa dnsrecord-add example.com @ --mx-preference=20 --mx-exchanger=mail2 """) + _(""" Add another record using interactive mode (started when dnsrecord-add, dnsrecord-mod, or dnsrecord-del are executed with no options): ipa dnsrecord-add example.com @ Please choose a type of DNS resource record to be added The most common types for this type of zone are: NS, MX, LOC DNS resource record type: MX MX Preference: 30 MX Exchanger: mail3 Record name: example.com MX record: 10 mail1, 20 mail2, 30 mail3 NS record: nameserver.example.com., nameserver2.example.com. """) + _(""" Delete previously added nameserver from example.com: ipa dnsrecord-del example.com @ --ns-rec=nameserver2.example.com. """) + _(""" Add LOC record for example.com: ipa dnsrecord-add example.com @ --loc-rec="49 11 42.4 N 16 36 29.6 E 227.64m" """) + _(""" Add new A record for www.example.com. Create a reverse record in appropriate reverse zone as well. In this case a PTR record "2" pointing to www.example.com will be created in zone 2.0.192.in-addr.arpa. ipa dnsrecord-add example.com www --a-rec=192.0.2.2 --a-create-reverse """) + _(""" Add new PTR record for www.example.com ipa dnsrecord-add 2.0.192.in-addr.arpa. 2 --ptr-rec=www.example.com. """) + _(""" Add new SRV records for LDAP servers. Three quarters of the requests should go to fast.example.com, one quarter to slow.example.com. If neither is available, switch to backup.example.com. ipa dnsrecord-add example.com _ldap._tcp --srv-rec="0 3 389 fast.example.com" ipa dnsrecord-add example.com _ldap._tcp --srv-rec="0 1 389 slow.example.com" ipa dnsrecord-add example.com _ldap._tcp --srv-rec="1 1 389 backup.example.com" """) + _(""" The interactive mode can be used for easy modification: ipa dnsrecord-mod example.com _ldap._tcp No option to modify specific record provided. Current DNS record contents: SRV record: 0 3 389 fast.example.com, 0 1 389 slow.example.com, 1 1 389 backup.example.com Modify SRV record '0 3 389 fast.example.com'? Yes/No (default No): Modify SRV record '0 1 389 slow.example.com'? Yes/No (default No): y SRV Priority [0]: (keep the default value) SRV Weight [1]: 2 (modified value) SRV Port [389]: (keep the default value) SRV Target [slow.example.com]: (keep the default value) 1 SRV record skipped. Only one value per DNS record type can be modified at one time. Record name: _ldap._tcp SRV record: 0 3 389 fast.example.com, 1 1 389 backup.example.com, 0 2 389 slow.example.com """) + _(""" After this modification, three fifths of the requests should go to fast.example.com and two fifths to slow.example.com. """) + _(""" An example of the interactive mode for dnsrecord-del command: ipa dnsrecord-del example.com www No option to delete specific record provided. Delete all? Yes/No (default No): (do not delete all records) Current DNS record contents: A record: 192.0.2.2, 192.0.2.3 Delete A record '192.0.2.2'? Yes/No (default No): Delete A record '192.0.2.3'? Yes/No (default No): y Record name: www A record: 192.0.2.2 (A record 192.0.2.3 has been deleted) """) + _(""" Show zone example.com: ipa dnszone-show example.com """) + _(""" Find zone with "example" in its domain name: ipa dnszone-find example """) + _(""" Find records for resources with "www" in their name in zone example.com: ipa dnsrecord-find example.com www """) + _(""" Find A records with value 192.0.2.2 in zone example.com ipa dnsrecord-find example.com --a-rec=192.0.2.2 """) + _(""" Show records for resource www in zone example.com ipa dnsrecord-show example.com www """) + _(""" Delegate zone sub.example to another nameserver: ipa dnsrecord-add example.com ns.sub --a-rec=203.0.113.1 ipa dnsrecord-add example.com sub --ns-rec=ns.sub.example.com. """) + _(""" Delete zone example.com with all resource records: ipa dnszone-del example.com """) + _(""" If a global forwarder is configured, all queries for which this server is not authoritative (e.g. sub.example.com) will be routed to the global forwarder. Global forwarding configuration can be overridden per-zone. """) + _(""" Semantics of forwarding in IPA matches BIND semantics and depends on the type of zone: * Master zone: local BIND replies authoritatively to queries for data in the given zone (including authoritative NXDOMAIN answers) and forwarding affects only queries for names below zone cuts (NS records) of locally served zones. * Forward zone: forward zone contains no authoritative data. BIND forwards queries, which cannot be answered from its local cache, to configured forwarders. """) + _(""" Semantics of the --forwarder-policy option: * none - disable forwarding for the given zone. * first - forward all queries to configured forwarders. If they fail, do resolution using DNS root servers. * only - forward all queries to configured forwarders and if they fail, return failure. """) + _(""" Disable global forwarding for given sub-tree: ipa dnszone-mod example.com --forward-policy=none """) + _(""" This configuration forwards all queries for names outside the example.com sub-tree to global forwarders. Normal recursive resolution process is used for names inside the example.com sub-tree (i.e. NS records are followed etc.). """) + _(""" Forward all requests for the zone external.example.com to another forwarder using a "first" policy (it will send the queries to the selected forwarder and if not answered it will use global root servers): ipa dnsforwardzone-add external.example.com --forward-policy=first \\ --forwarder=203.0.113.1 """) + _(""" Change forward-policy for external.example.com: ipa dnsforwardzone-mod external.example.com --forward-policy=only """) + _(""" Show forward zone external.example.com: ipa dnsforwardzone-show external.example.com """) + _(""" List all forward zones: ipa dnsforwardzone-find """) + _(""" Delete forward zone external.example.com: ipa dnsforwardzone-del external.example.com """) + _(""" Resolve a host name to see if it exists (will add default IPA domain if one is not included): ipa dns-resolve www.example.com ipa dns-resolve www """) + _(""" GLOBAL DNS CONFIGURATION """) + _(""" DNS configuration passed to command line install script is stored in a local configuration file on each IPA server where DNS service is configured. These local settings can be overridden with a common configuration stored in LDAP server: """) + _(""" Show global DNS configuration: ipa dnsconfig-show """) + _(""" Modify global DNS configuration and set a list of global forwarders: ipa dnsconfig-mod --forwarder=203.0.113.113 """) register = Registry() # supported resource record types _record_types = ( u'A', u'AAAA', u'A6', u'AFSDB', u'APL', u'CERT', u'CNAME', u'DHCID', u'DLV', u'DNAME', u'DS', u'HIP', u'HINFO', u'IPSECKEY', u'KEY', u'KX', u'LOC', u'MD', u'MINFO', u'MX', u'NAPTR', u'NS', u'NSEC', u'NXT', u'PTR', u'RRSIG', u'RP', u'SIG', u'SPF', u'SRV', u'SSHFP', u'TLSA', u'TXT', ) # DNS zone record identificator _dns_zone_record = DNSName.empty # most used record types, always ask for those in interactive prompt _top_record_types = ('A', 'AAAA', ) _rev_top_record_types = ('PTR', ) _zone_top_record_types = ('NS', 'MX', 'LOC', ) # attributes derived from record types _record_attributes = [str('%srecord' % t.lower()) for t in _record_types] # Deprecated # supported DNS classes, IN = internet, rest is almost never used _record_classes = (u'IN', u'CS', u'CH', u'HS') # IN record class _IN = dns.rdataclass.IN # NS record type _NS = dns.rdatatype.from_text('NS') _output_permissions = ( output.summary, output.Output('result', bool, _('True means the operation was successful')), output.Output('value', unicode, _('Permission value')), ) def _rname_validator(ugettext, zonemgr): try: DNSName(zonemgr) # test only if it is valid domain name except (ValueError, dns.exception.SyntaxError) as e: return unicode(e) return None def _create_zone_serial(): """ Generate serial number for zones. bind-dyndb-ldap expects unix time in to be used for SOA serial. SOA serial in a date format would also work, but it may be set to far future when many DNS updates are done per day (more than 100). Unix timestamp is more resilient to this issue. """ return int(time.time()) def _reverse_zone_name(netstr): try: netaddr.IPAddress(str(netstr)) except (netaddr.AddrFormatError, ValueError): pass else: # use more sensible default prefix than netaddr default return unicode(get_reverse_zone_default(netstr)) net = netaddr.IPNetwork(netstr) items = net.ip.reverse_dns.split('.') if net.version == 4: return u'.'.join(items[4 - net.prefixlen / 8:]) elif net.version == 6: return u'.'.join(items[32 - net.prefixlen / 4:]) else: return None def _validate_ipaddr(ugettext, ipaddr, ip_version=None): try: ip = netaddr.IPAddress(str(ipaddr), flags=netaddr.INET_PTON) if ip_version is not None: if ip.version != ip_version: return _('invalid IP address version (is %(value)d, must be %(required_value)d)!') \ % dict(value=ip.version, required_value=ip_version) except (netaddr.AddrFormatError, ValueError): return _('invalid IP address format') return None def _validate_ip4addr(ugettext, ipaddr): return _validate_ipaddr(ugettext, ipaddr, 4) def _validate_ip6addr(ugettext, ipaddr): return _validate_ipaddr(ugettext, ipaddr, 6) def _validate_ipnet(ugettext, ipnet): try: net = netaddr.IPNetwork(ipnet) except (netaddr.AddrFormatError, ValueError, UnboundLocalError): return _('invalid IP network format') return None def _validate_bind_aci(ugettext, bind_acis): if not bind_acis: return bind_acis = bind_acis.split(';') if bind_acis[-1]: return _('each ACL element must be terminated with a semicolon') else: bind_acis.pop(-1) for bind_aci in bind_acis: if bind_aci in ("any", "none", "localhost", "localnets"): continue if bind_aci.startswith('!'): bind_aci = bind_aci[1:] try: ip = CheckedIPAddress(bind_aci, parse_netmask=True, allow_network=True, allow_loopback=True) except (netaddr.AddrFormatError, ValueError) as e: return unicode(e) except UnboundLocalError: return _(u"invalid address format") def _normalize_bind_aci(bind_acis): if not bind_acis: return bind_acis = bind_acis.split(';') normalized = [] for bind_aci in bind_acis: if not bind_aci: continue if bind_aci in ("any", "none", "localhost", "localnets"): normalized.append(bind_aci) continue prefix = "" if bind_aci.startswith('!'): bind_aci = bind_aci[1:] prefix = "!" try: ip = CheckedIPAddress(bind_aci, parse_netmask=True, allow_network=True, allow_loopback=True) if '/' in bind_aci: # addr with netmask netmask = "/%s" % ip.prefixlen else: netmask = "" normalized.append(u"%s%s%s" % (prefix, str(ip), netmask)) continue except: normalized.append(bind_aci) continue acis = u';'.join(normalized) acis += u';' return acis def _validate_bind_forwarder(ugettext, forwarder): ip_address, sep, port = forwarder.partition(u' port ') ip_address_validation = _validate_ipaddr(ugettext, ip_address) if ip_address_validation is not None: return ip_address_validation if sep: try: port = int(port) if port < 0 or port > 65535: raise ValueError() except ValueError: return _('%(port)s is not a valid port' % dict(port=port)) return None def _validate_nsec3param_record(ugettext, value): _nsec3param_pattern = (r'^(?P<alg>\d+) (?P<flags>\d+) (?P<iter>\d+) ' r'(?P<salt>([0-9a-fA-F]{2})+|-)$') rec = re.compile(_nsec3param_pattern, flags=re.U) result = rec.match(value) if result is None: return _(u'expected format: <0-255> <0-255> <0-65535> ' 'even-length_hexadecimal_digits_or_hyphen') alg = int(result.group('alg')) flags = int(result.group('flags')) iterations = int(result.group('iter')) salt = result.group('salt') if alg > 255: return _('algorithm value: allowed interval 0-255') if flags > 255: return _('flags value: allowed interval 0-255') if iterations > 65535: return _('iterations value: allowed interval 0-65535') if salt == u'-': return None try: binascii.a2b_hex(salt) except TypeError as e: return _('salt value: %(err)s') % {'err': e} return None def _hostname_validator(ugettext, value): assert isinstance(value, DNSName) if len(value.make_absolute().labels) < 3: return _('invalid domain-name: not fully qualified') return None def _no_wildcard_validator(ugettext, value): """Disallow usage of wildcards as RFC 4592 section 4 recommends """ assert isinstance(value, DNSName) if value.is_wild(): return _('should not be a wildcard domain name (RFC 4592 section 4)') return None def is_forward_record(zone, str_address): addr = netaddr.IPAddress(str_address) if addr.version == 4: result = api.Command['dnsrecord_find'](zone, arecord=str_address) elif addr.version == 6: result = api.Command['dnsrecord_find'](zone, aaaarecord=str_address) else: raise ValueError('Invalid address family') return result['count'] > 0 def add_forward_record(zone, name, str_address): addr = netaddr.IPAddress(str_address) try: if addr.version == 4: api.Command['dnsrecord_add'](zone, name, arecord=str_address) elif addr.version == 6: api.Command['dnsrecord_add'](zone, name, aaaarecord=str_address) else: raise ValueError('Invalid address family') except errors.EmptyModlist: pass # the entry already exists and matches def get_reverse_zone(ipaddr, prefixlen=None): ip = netaddr.IPAddress(str(ipaddr)) revdns = DNSName(unicode(ip.reverse_dns)) if prefixlen is None: revzone = None result = api.Command['dnszone_find']()['result'] for zone in result: zonename = zone['idnsname'][0] if (revdns.is_subdomain(zonename.make_absolute()) and (revzone is None or zonename.is_subdomain(revzone))): revzone = zonename else: if ip.version == 4: pos = 4 - prefixlen / 8 elif ip.version == 6: pos = 32 - prefixlen / 4 items = ip.reverse_dns.split('.') revzone = DNSName(items[pos:]) try: api.Command['dnszone_show'](revzone) except errors.NotFound: revzone = None if revzone is None: raise errors.NotFound( reason=_('DNS reverse zone for IP address %(addr)s not found') % dict(addr=ipaddr) ) revname = revdns.relativize(revzone) return revzone, revname def add_records_for_host_validation(option_name, host, domain, ip_addresses, check_forward=True, check_reverse=True): assert isinstance(host, DNSName) assert isinstance(domain, DNSName) try: api.Command['dnszone_show'](domain)['result'] except errors.NotFound: raise errors.NotFound( reason=_('DNS zone %(zone)s not found') % dict(zone=domain) ) if not isinstance(ip_addresses, (tuple, list)): ip_addresses = [ip_addresses] for ip_address in ip_addresses: try: ip = CheckedIPAddress(ip_address, match_local=False) except Exception as e: raise errors.ValidationError(name=option_name, error=unicode(e)) if check_forward: if is_forward_record(domain, unicode(ip)): raise errors.DuplicateEntry( message=_(u'IP address %(ip)s is already assigned in domain %(domain)s.')\ % dict(ip=str(ip), domain=domain)) if check_reverse: try: prefixlen = None if not ip.defaultnet: prefixlen = ip.prefixlen # we prefer lookup of the IP through the reverse zone revzone, revname = get_reverse_zone(ip, prefixlen) reverse = api.Command['dnsrecord_find'](revzone, idnsname=revname) if reverse['count'] > 0: raise errors.DuplicateEntry( message=_(u'Reverse record for IP address %(ip)s already exists in reverse zone %(zone)s.')\ % dict(ip=str(ip), zone=revzone)) except errors.NotFound: pass def add_records_for_host(host, domain, ip_addresses, add_forward=True, add_reverse=True): assert isinstance(host, DNSName) assert isinstance(domain, DNSName) if not isinstance(ip_addresses, (tuple, list)): ip_addresses = [ip_addresses] for ip_address in ip_addresses: ip = CheckedIPAddress(ip_address, match_local=False) if add_forward: add_forward_record(domain, host, unicode(ip)) if add_reverse: try: prefixlen = None if not ip.defaultnet: prefixlen = ip.prefixlen revzone, revname = get_reverse_zone(ip, prefixlen) addkw = {'ptrrecord': host.derelativize(domain).ToASCII()} api.Command['dnsrecord_add'](revzone, revname, **addkw) except errors.EmptyModlist: # the entry already exists and matches pass def _dns_name_to_string(value, raw=False): if isinstance(value, unicode): try: value = DNSName(value) except Exception: return value assert isinstance(value, DNSName) if raw: return value.ToASCII() else: return unicode(value) def _check_entry_objectclass(entry, objectclasses): """ Check if entry contains all objectclasses """ if not isinstance(objectclasses, (list, tuple)): objectclasses = [objectclasses, ] if not entry.get('objectclass'): return False entry_objectclasses = [o.lower() for o in entry['objectclass']] for o in objectclasses: if o not in entry_objectclasses: return False return True def _check_DN_objectclass(ldap, dn, objectclasses): try: entry = ldap.get_entry(dn, [u'objectclass', ]) except Exception: return False else: return _check_entry_objectclass(entry, objectclasses) class DNSRecord(Str): # a list of parts that create the actual raw DNS record parts = None # an optional list of parameters used in record-specific operations extra = None supported = True # supported RR types: https://fedorahosted.org/bind-dyndb-ldap/browser/doc/schema label_format = _("%s record") part_label_format = "%s %s" doc_format = _('Raw %s records') option_group_format = _('%s Record') see_rfc_msg = _("(see RFC %s for details)") part_name_format = "%s_part_%s" extra_name_format = "%s_extra_%s" cli_name_format = "%s_%s" format_error_msg = None kwargs = Str.kwargs + ( ('validatedns', bool, True), ('normalizedns', bool, True), ) # should be replaced in subclasses rrtype = None rfc = None def __init__(self, name=None, *rules, **kw): if self.rrtype not in _record_types: raise ValueError("Unknown RR type: %s. Must be one of %s" % \ (str(self.rrtype), ", ".join(_record_types))) if not name: name = "%srecord*" % self.rrtype.lower() kw.setdefault('cli_name', '%s_rec' % self.rrtype.lower()) kw.setdefault('label', self.label_format % self.rrtype) kw.setdefault('doc', self.doc_format % self.rrtype) kw.setdefault('option_group', self.option_group_format % self.rrtype) kw['csv'] = True if not self.supported: kw['flags'] = ('no_option',) super(DNSRecord, self).__init__(name, *rules, **kw) def _get_part_values(self, value): values = value.split() if len(values) != len(self.parts): return None return tuple(values) def _part_values_to_string(self, values, index, idna=True): self._validate_parts(values) parts = [] for v in values: if v is None: continue elif isinstance(v, DNSName) and idna: v = v.ToASCII() elif not isinstance(v, unicode): v = unicode(v) parts.append(v) return u" ".join(parts) def get_parts_from_kw(self, kw, raise_on_none=True): part_names = tuple(self.part_name_format % (self.rrtype.lower(), part.name) \ for part in self.parts) vals = tuple(kw.get(part_name) for part_name in part_names) if all(val is None for val in vals): return if raise_on_none: for val_id,val in enumerate(vals): if val is None and self.parts[val_id].required: cli_name = self.cli_name_format % (self.rrtype.lower(), self.parts[val_id].name) raise errors.ConversionError(name=self.name, error=_("'%s' is a required part of DNS record") % cli_name) return vals def _validate_parts(self, parts): if len(parts) != len(self.parts): raise errors.ValidationError(name=self.name, error=_("Invalid number of parts!")) def _convert_scalar(self, value, index=None): if isinstance(value, (tuple, list)): return self._part_values_to_string(value, index) return super(DNSRecord, self)._convert_scalar(value, index) def normalize(self, value): if self.normalizedns: if isinstance(value, (tuple, list)): value = tuple( self._normalize_parts(v) for v in value \ if v is not None ) elif value is not None: value = (self._normalize_parts(value),) return super(DNSRecord, self).normalize(value) def _normalize_parts(self, value): """ Normalize a DNS record value using normalizers for its parts. """ if self.parts is None: return value try: values = self._get_part_values(value) if not values: return value converted_values = [ part._convert_scalar(values[part_id]) \ if values[part_id] is not None else None for part_id, part in enumerate(self.parts) ] new_values = [ part.normalize(converted_values[part_id]) \ for part_id, part in enumerate(self.parts) ] value = self._convert_scalar(new_values) except Exception: # cannot normalize, rather return original value than fail pass return value def _rule_validatedns(self, _, value): if not self.validatedns: return if value is None: return if value is None: return if not self.supported: return _('DNS RR type "%s" is not supported by bind-dyndb-ldap plugin') \ % self.rrtype if self.parts is None: return # validate record format values = self._get_part_values(value) if not values: if not self.format_error_msg: part_names = [part.name.upper() for part in self.parts] if self.rfc: see_rfc_msg = " " + self.see_rfc_msg % self.rfc else: see_rfc_msg = "" return _('format must be specified as "%(format)s" %(rfcs)s') \ % dict(format=" ".join(part_names), rfcs=see_rfc_msg) else: return self.format_error_msg # validate every part for part_id, part in enumerate(self.parts): val = part.normalize(values[part_id]) val = part.convert(val) part.validate(val) return None def _convert_dnsrecord_part(self, part): """ All parts of DNSRecord need to be processed and modified before they can be added to global DNS API. For example a prefix need to be added before part name so that the name is unique in the global namespace. """ name = self.part_name_format % (self.rrtype.lower(), part.name) cli_name = self.cli_name_format % (self.rrtype.lower(), part.name) label = self.part_label_format % (self.rrtype, unicode(part.label)) option_group = self.option_group_format % self.rrtype flags = list(part.flags) + ['dnsrecord_part', 'virtual_attribute',] if not part.required: flags.append('dnsrecord_optional') if not self.supported: flags.append("no_option") return part.clone_rename(name, cli_name=cli_name, label=label, required=False, option_group=option_group, flags=flags, hint=self.name,) # name of parent RR param def _convert_dnsrecord_extra(self, extra): """ Parameters for special per-type behavior need to be processed in the same way as record parts in _convert_dnsrecord_part(). """ name = self.extra_name_format % (self.rrtype.lower(), extra.name) cli_name = self.cli_name_format % (self.rrtype.lower(), extra.name) label = self.part_label_format % (self.rrtype, unicode(extra.label)) option_group = self.option_group_format % self.rrtype flags = list(extra.flags) + ['dnsrecord_extra', 'virtual_attribute',] return extra.clone_rename(name, cli_name=cli_name, label=label, required=False, option_group=option_group, flags=flags, hint=self.name,) # name of parent RR param def get_parts(self): if self.parts is None: return tuple() return tuple(self._convert_dnsrecord_part(part) for part in self.parts) def get_extra(self): if self.extra is None: return tuple() return tuple(self._convert_dnsrecord_extra(extra) for extra in self.extra) def __get_part_param(self, cmd, part, output_kw, default=None): name = self.part_name_format % (self.rrtype.lower(), part.name) label = self.part_label_format % (self.rrtype, unicode(part.label)) optional = not part.required output_kw[name] = cmd.prompt_param(part, optional=optional, label=label) def prompt_parts(self, cmd, mod_dnsvalue=None): mod_parts = None if mod_dnsvalue is not None: mod_parts = self._get_part_values(mod_dnsvalue) user_options = {} if self.parts is None: return user_options for part_id, part in enumerate(self.parts): if mod_parts: default = mod_parts[part_id] else: default = None self.__get_part_param(cmd, part, user_options, default) return user_options def prompt_missing_parts(self, cmd, kw, prompt_optional=False): user_options = {} if self.parts is None: return user_options for part in self.parts: name = self.part_name_format % (self.rrtype.lower(), part.name) if name in kw: continue optional = not part.required if optional and not prompt_optional: continue default = part.get_default(**kw) self.__get_part_param(cmd, part, user_options, default) return user_options # callbacks for per-type special record behavior def dnsrecord_add_pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) def dnsrecord_add_post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) class ForwardRecord(DNSRecord): extra = ( Flag('create_reverse?', label=_('Create reverse'), doc=_('Create reverse record for this IP Address'), flags=['no_update'] ), ) def dnsrecord_add_pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) reverse_option = self._convert_dnsrecord_extra(self.extra[0]) if options.get(reverse_option.name): records = entry_attrs.get(self.name, []) if not records: # --<rrtype>-create-reverse is set, but there are not records raise errors.RequirementError(name=self.name) for record in records: add_records_for_host_validation(self.name, keys[-1], keys[-2], record, check_forward=False, check_reverse=True) setattr(context, '%s_reverse' % self.name, entry_attrs.get(self.name)) def dnsrecord_add_post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) rev_records = getattr(context, '%s_reverse' % self.name, []) if rev_records: # make sure we don't run this post callback action again in nested # commands, line adding PTR record in add_records_for_host delattr(context, '%s_reverse' % self.name) for record in rev_records: try: add_records_for_host(keys[-1], keys[-2], record, add_forward=False, add_reverse=True) except Exception as e: raise errors.NonFatalError( reason=_('Cannot create reverse record for "%(value)s": %(exc)s') \ % dict(value=record, exc=unicode(e))) class UnsupportedDNSRecord(DNSRecord): """ Records which are not supported by IPA CLI, but we allow to show them if LDAP contains these records. """ supported = False def _get_part_values(self, value): return tuple() class ARecord(ForwardRecord): rrtype = 'A' rfc = 1035 parts = ( Str('ip_address', _validate_ip4addr, label=_('IP Address'), ), ) class A6Record(DNSRecord): rrtype = 'A6' rfc = 3226 parts = ( Str('data', label=_('Record data'), ), ) def _get_part_values(self, value): # A6 RR type is obsolete and only a raw interface is provided return (value,) class AAAARecord(ForwardRecord): rrtype = 'AAAA' rfc = 3596 parts = ( Str('ip_address', _validate_ip6addr, label=_('IP Address'), ), ) class AFSDBRecord(DNSRecord): rrtype = 'AFSDB' rfc = 1183 parts = ( Int('subtype?', label=_('Subtype'), minvalue=0, maxvalue=65535, ), DNSNameParam('hostname', label=_('Hostname'), ), ) class APLRecord(UnsupportedDNSRecord): rrtype = 'APL' rfc = 3123 class CERTRecord(DNSRecord): rrtype = 'CERT' rfc = 4398 parts = ( Int('type', label=_('Certificate Type'), minvalue=0, maxvalue=65535, ), Int('key_tag', label=_('Key Tag'), minvalue=0, maxvalue=65535, ), Int('algorithm', label=_('Algorithm'), minvalue=0, maxvalue=255, ), Str('certificate_or_crl', label=_('Certificate/CRL'), ), ) class CNAMERecord(DNSRecord): rrtype = 'CNAME' rfc = 1035 parts = ( DNSNameParam('hostname', label=_('Hostname'), doc=_('A hostname which this alias hostname points to'), ), ) class DHCIDRecord(UnsupportedDNSRecord): rrtype = 'DHCID' rfc = 4701 class DNAMERecord(DNSRecord): rrtype = 'DNAME' rfc = 2672 parts = ( DNSNameParam('target', label=_('Target'), ), ) class DSRecord(DNSRecord): rrtype = 'DS' rfc = 4034 parts = ( Int('key_tag', label=_('Key Tag'), minvalue=0, maxvalue=65535, ), Int('algorithm', label=_('Algorithm'), minvalue=0, maxvalue=255, ), Int('digest_type', label=_('Digest Type'), minvalue=0, maxvalue=255, ), Str('digest', label=_('Digest'), pattern=r'^[0-9a-fA-F]+$', pattern_errmsg=u'only hexadecimal digits are allowed' ), ) class DLVRecord(DSRecord): # must use same attributes as DSRecord rrtype = 'DLV' rfc = 4431 class HINFORecord(UnsupportedDNSRecord): rrtype = 'HINFO' rfc = 1035 class HIPRecord(UnsupportedDNSRecord): rrtype = 'HIP' rfc = 5205 class KEYRecord(UnsupportedDNSRecord): # managed by BIND itself rrtype = 'KEY' rfc = 2535 class IPSECKEYRecord(UnsupportedDNSRecord): rrtype = 'IPSECKEY' rfc = 4025 class KXRecord(DNSRecord): rrtype = 'KX' rfc = 2230 parts = ( Int('preference', label=_('Preference'), doc=_('Preference given to this exchanger. Lower values are more preferred'), minvalue=0, maxvalue=65535, ), DNSNameParam('exchanger', label=_('Exchanger'), doc=_('A host willing to act as a key exchanger'), ), ) class LOCRecord(DNSRecord): rrtype = 'LOC' rfc = 1876 parts = ( Int('lat_deg', label=_('Degrees Latitude'), minvalue=0, maxvalue=90, ), Int('lat_min?', label=_('Minutes Latitude'), minvalue=0, maxvalue=59, ), Decimal('lat_sec?', label=_('Seconds Latitude'), minvalue='0.0', maxvalue='59.999', precision=3, ), StrEnum('lat_dir', label=_('Direction Latitude'), values=(u'N', u'S',), ), Int('lon_deg', label=_('Degrees Longitude'), minvalue=0, maxvalue=180, ), Int('lon_min?', label=_('Minutes Longitude'), minvalue=0, maxvalue=59, ), Decimal('lon_sec?', label=_('Seconds Longitude'), minvalue='0.0', maxvalue='59.999', precision=3, ), StrEnum('lon_dir', label=_('Direction Longitude'), values=(u'E', u'W',), ), Decimal('altitude', label=_('Altitude'), minvalue='-100000.00', maxvalue='42849672.95', precision=2, ), Decimal('size?', label=_('Size'), minvalue='0.0', maxvalue='90000000.00', precision=2, ), Decimal('h_precision?', label=_('Horizontal Precision'), minvalue='0.0', maxvalue='90000000.00', precision=2, ), Decimal('v_precision?', label=_('Vertical Precision'), minvalue='0.0', maxvalue='90000000.00', precision=2, ), ) format_error_msg = _("""format must be specified as "d1 [m1 [s1]] {"N"|"S"} d2 [m2 [s2]] {"E"|"W"} alt["m"] [siz["m"] [hp["m"] [vp["m"]]]]" where: d1: [0 .. 90] (degrees latitude) d2: [0 .. 180] (degrees longitude) m1, m2: [0 .. 59] (minutes latitude/longitude) s1, s2: [0 .. 59.999] (seconds latitude/longitude) alt: [-100000.00 .. 42849672.95] BY .01 (altitude in meters) siz, hp, vp: [0 .. 90000000.00] (size/precision in meters) See RFC 1876 for details""") def _get_part_values(self, value): regex = re.compile( r'(?P<d1>\d{1,2}\s+)' r'(?:(?P<m1>\d{1,2}\s+)' r'(?P<s1>\d{1,2}(?:\.\d{1,3})?\s+)?)?' r'(?P<dir1>[NS])\s+' r'(?P<d2>\d{1,3}\s+)' r'(?:(?P<m2>\d{1,2}\s+)' r'(?P<s2>\d{1,2}(?:\.\d{1,3})?\s+)?)?' r'(?P<dir2>[WE])\s+' r'(?P<alt>-?\d{1,8}(?:\.\d{1,2})?)m?' r'(?:\s+(?P<siz>\d{1,8}(?:\.\d{1,2})?)m?' r'(?:\s+(?P<hp>\d{1,8}(?:\.\d{1,2})?)m?' r'(?:\s+(?P<vp>\d{1,8}(?:\.\d{1,2})?)m?\s*)?)?)?$') m = regex.match(value) if m is None: return None return tuple(x.strip() if x is not None else x for x in m.groups()) def _validate_parts(self, parts): super(LOCRecord, self)._validate_parts(parts) # create part_name -> part_id map first part_name_map = dict((part.name, part_id) \ for part_id,part in enumerate(self.parts)) requirements = ( ('lat_sec', 'lat_min'), ('lon_sec', 'lon_min'), ('h_precision', 'size'), ('v_precision', 'h_precision', 'size') ) for req in requirements: target_part = req[0] if parts[part_name_map[target_part]] is not None: required_parts = req[1:] if any(parts[part_name_map[part]] is None for part in required_parts): target_cli_name = self.cli_name_format % (self.rrtype.lower(), req[0]) required_cli_names = [ self.cli_name_format % (self.rrtype.lower(), part) for part in req[1:] ] error = _("'%(required)s' must not be empty when '%(name)s' is set") % \ dict(required=', '.join(required_cli_names), name=target_cli_name) raise errors.ValidationError(name=self.name, error=error) class MDRecord(UnsupportedDNSRecord): # obsoleted, use MX instead rrtype = 'MD' rfc = 1035 class MINFORecord(UnsupportedDNSRecord): rrtype = 'MINFO' rfc = 1035 class MXRecord(DNSRecord): rrtype = 'MX' rfc = 1035 parts = ( Int('preference', label=_('Preference'), doc=_('Preference given to this exchanger. Lower values are more preferred'), minvalue=0, maxvalue=65535, ), DNSNameParam('exchanger', label=_('Exchanger'), doc=_('A host willing to act as a mail exchanger'), ), ) class NSRecord(DNSRecord): rrtype = 'NS' rfc = 1035 parts = ( DNSNameParam('hostname', label=_('Hostname'), ), ) class NSECRecord(UnsupportedDNSRecord): # managed by BIND itself rrtype = 'NSEC' rfc = 4034 def _validate_naptr_flags(ugettext, flags): allowed_flags = u'SAUP' flags = flags.replace('"','').replace('\'','') for flag in flags: if flag not in allowed_flags: return _('flags must be one of "S", "A", "U", or "P"') class NAPTRRecord(DNSRecord): rrtype = 'NAPTR' rfc = 2915 parts = ( Int('order', label=_('Order'), minvalue=0, maxvalue=65535, ), Int('preference', label=_('Preference'), minvalue=0, maxvalue=65535, ), Str('flags', _validate_naptr_flags, label=_('Flags'), normalizer=lambda x:x.upper() ), Str('service', label=_('Service'), ), Str('regexp', label=_('Regular Expression'), ), Str('replacement', label=_('Replacement'), ), ) class NXTRecord(UnsupportedDNSRecord): rrtype = 'NXT' rfc = 2535 class PTRRecord(DNSRecord): rrtype = 'PTR' rfc = 1035 parts = ( DNSNameParam('hostname', #RFC 2317 section 5.2 -- can be relative label=_('Hostname'), doc=_('The hostname this reverse record points to'), ), ) class RPRecord(UnsupportedDNSRecord): rrtype = 'RP' rfc = 1183 class SRVRecord(DNSRecord): rrtype = 'SRV' rfc = 2782 parts = ( Int('priority', label=_('Priority'), minvalue=0, maxvalue=65535, ), Int('weight', label=_('Weight'), minvalue=0, maxvalue=65535, ), Int('port', label=_('Port'), minvalue=0, maxvalue=65535, ), DNSNameParam('target', label=_('Target'), doc=_('The domain name of the target host or \'.\' if the service is decidedly not available at this domain'), ), ) def _sig_time_validator(ugettext, value): time_format = "%Y%m%d%H%M%S" try: time.strptime(value, time_format) except ValueError: return _('the value does not follow "YYYYMMDDHHMMSS" time format') class SIGRecord(UnsupportedDNSRecord): # managed by BIND itself rrtype = 'SIG' rfc = 2535 class SPFRecord(UnsupportedDNSRecord): rrtype = 'SPF' rfc = 4408 class RRSIGRecord(UnsupportedDNSRecord): # managed by BIND itself rrtype = 'RRSIG' rfc = 4034 class SSHFPRecord(DNSRecord): rrtype = 'SSHFP' rfc = 4255 parts = ( Int('algorithm', label=_('Algorithm'), minvalue=0, maxvalue=255, ), Int('fp_type', label=_('Fingerprint Type'), minvalue=0, maxvalue=255, ), Str('fingerprint', label=_('Fingerprint'), ), ) def _get_part_values(self, value): # fingerprint part can contain space in LDAP, return it as one part values = value.split(None, 2) if len(values) != len(self.parts): return None return tuple(values) class TLSARecord(DNSRecord): rrtype = 'TLSA' rfc = 6698 parts = ( Int('cert_usage', label=_('Certificate Usage'), minvalue=0, maxvalue=255, ), Int('selector', label=_('Selector'), minvalue=0, maxvalue=255, ), Int('matching_type', label=_('Matching Type'), minvalue=0, maxvalue=255, ), Str('cert_association_data', label=_('Certificate Association Data'), ), ) class TXTRecord(DNSRecord): rrtype = 'TXT' rfc = 1035 parts = ( Str('data', label=_('Text Data'), ), ) def _get_part_values(self, value): # ignore any space in TXT record return (value,) _dns_records = ( ARecord(), AAAARecord(), A6Record(), AFSDBRecord(), APLRecord(), CERTRecord(), CNAMERecord(), DHCIDRecord(), DLVRecord(), DNAMERecord(), DSRecord(), HIPRecord(), IPSECKEYRecord(), KEYRecord(), KXRecord(), LOCRecord(), MXRecord(), NAPTRRecord(), NSRecord(), NSECRecord(), PTRRecord(), RRSIGRecord(), RPRecord(), SIGRecord(), SPFRecord(), SRVRecord(), SSHFPRecord(), TLSARecord(), TXTRecord(), ) def __dns_record_options_iter(): for opt in (Any('dnsrecords?', label=_('Records'), flags=['no_create', 'no_search', 'no_update'],), Str('dnstype?', label=_('Record type'), flags=['no_create', 'no_search', 'no_update'],), Str('dnsdata?', label=_('Record data'), flags=['no_create', 'no_search', 'no_update'],)): # These 3 options are used in --structured format. They are defined # rather in takes_params than has_output_params because of their # order - they should be printed to CLI before any DNS part param yield opt for option in _dns_records: yield option for part in option.get_parts(): yield part for extra in option.get_extra(): yield extra _dns_record_options = tuple(__dns_record_options_iter()) _dns_supported_record_types = tuple(record.rrtype for record in _dns_records \ if record.supported) def check_ns_rec_resolvable(zone, name): assert isinstance(zone, DNSName) assert isinstance(name, DNSName) if name.is_empty(): name = zone.make_absolute() elif not name.is_absolute(): # this is a DNS name relative to the zone name = name.derelativize(zone.make_absolute()) try: return api.Command['dns_resolve'](unicode(name)) except errors.NotFound: raise errors.NotFound( reason=_('Nameserver \'%(host)s\' does not have a corresponding ' 'A/AAAA record') % {'host': name} ) def dns_container_exists(ldap): try: ldap.get_entry(DN(api.env.container_dns, api.env.basedn), []) except errors.NotFound: return False return True def default_zone_update_policy(zone): if zone.is_reverse(): return get_dns_reverse_zone_update_policy(api.env.realm, zone.ToASCII()) else: return get_dns_forward_zone_update_policy(api.env.realm) dnszone_output_params = ( Str('managedby', label=_('Managedby permission'), ), ) def _convert_to_idna(value): """ Function converts a unicode value to idna, without extra validation. If conversion fails, None is returned """ assert isinstance(value, unicode) try: idna_val = value start_dot = u'' end_dot = u'' if idna_val.startswith(u'.'): idna_val = idna_val[1:] start_dot = u'.' if idna_val.endswith(u'.'): idna_val = idna_val[:-1] end_dot = u'.' idna_val = encodings.idna.nameprep(idna_val) idna_val = re.split(r'(?<!\\)\.', idna_val) idna_val = u'%s%s%s' % (start_dot, u'.'.join(encodings.idna.ToASCII(x) for x in idna_val), end_dot) return idna_val except Exception: pass return None def _create_idn_filter(cmd, ldap, *args, **options): term = args[-1] if term: #include idna values to search term_idna = _convert_to_idna(term) if term_idna and term != term_idna: term = (term, term_idna) search_kw = {} attr_extra_filters = [] for attr, value in cmd.args_options_2_entry(**options).iteritems(): if not isinstance(value, list): value = [value] for i, v in enumerate(value): if isinstance(v, DNSName): value[i] = v.ToASCII() elif attr in map_names_to_records: record = map_names_to_records[attr] parts = record._get_part_values(v) if parts is None: value[i] = v continue try: value[i] = record._part_values_to_string(parts, None) except errors.ValidationError: value[i] = v #create MATCH_ANY filter for multivalue if len(value) > 1: f = ldap.make_filter({attr: value}, rules=ldap.MATCH_ANY) attr_extra_filters.append(f) else: search_kw[attr] = value if cmd.obj.search_attributes: search_attrs = cmd.obj.search_attributes else: search_attrs = cmd.obj.default_attributes if cmd.obj.search_attributes_config: config = ldap.get_ipa_config() config_attrs = config.get(cmd.obj.search_attributes_config, []) if len(config_attrs) == 1 and (isinstance(config_attrs[0], basestring)): search_attrs = config_attrs[0].split(',') search_kw['objectclass'] = cmd.obj.object_class attr_filter = ldap.make_filter(search_kw, rules=ldap.MATCH_ALL) if attr_extra_filters: #combine filter if there is any idna value attr_extra_filters.append(attr_filter) attr_filter = ldap.combine_filters(attr_extra_filters, rules=ldap.MATCH_ALL) search_kw = {} for a in search_attrs: search_kw[a] = term term_filter = ldap.make_filter(search_kw, exact=False) member_filter = cmd.get_member_filter(ldap, **options) filter = ldap.combine_filters( (term_filter, attr_filter, member_filter), rules=ldap.MATCH_ALL ) return filter map_names_to_records = {"%srecord" % record.rrtype.lower(): record for record in _dns_records if record.supported} def _records_idn_postprocess(record, **options): for attr in record.keys(): attr = attr.lower() try: param = map_names_to_records[attr] except KeyError: continue if not isinstance(param, DNSRecord): continue part_params = param.get_parts() rrs = [] for dnsvalue in record[attr]: parts = param._get_part_values(dnsvalue) if parts is None: continue parts = list(parts) try: for (i, p) in enumerate(parts): if isinstance(part_params[i], DNSNameParam): parts[i] = DNSName(p) rrs.append(param._part_values_to_string(parts, None, idna=options.get('raw', False))) except (errors.ValidationError, errors.ConversionError): rrs.append(dnsvalue) record[attr] = rrs def _normalize_zone(zone): if isinstance(zone, unicode): # normalize only non-IDNA zones try: return unicode(zone.encode('ascii')).lower() except UnicodeError: pass return zone def _get_auth_zone_ldap(name): """ Find authoritative zone in LDAP for name. Only active zones are considered. :param name: :return: (zone, truncated) zone: authoritative zone, or None if authoritative zone is not in LDAP """ assert isinstance(name, DNSName) ldap = api.Backend.ldap2 # Create all possible parent zone names search_name = name.make_absolute() zone_names = [] for i in xrange(len(search_name)): zone_name_abs = DNSName(search_name[i:]).ToASCII() zone_names.append(zone_name_abs) # compatibility with IPA < 4.0, zone name can be relative zone_names.append(zone_name_abs[:-1]) # Create filters objectclass_filter = ldap.make_filter({'objectclass':'idnszone'}) zonenames_filter = ldap.make_filter({'idnsname': zone_names}) zoneactive_filter = ldap.make_filter({'idnsZoneActive': 'true'}) complete_filter = ldap.combine_filters( [objectclass_filter, zonenames_filter, zoneactive_filter], rules=ldap.MATCH_ALL ) try: entries, truncated = ldap.find_entries( filter=complete_filter, attrs_list=['idnsname'], base_dn=DN(api.env.container_dns, api.env.basedn), scope=ldap.SCOPE_ONELEVEL ) except errors.NotFound: return None, False # always use absolute zones matched_auth_zones = [entry.single_value['idnsname'].make_absolute() for entry in entries] # return longest match return max(matched_auth_zones, key=len), truncated def _get_longest_match_ns_delegation_ldap(zone, name): """ Searches for deepest delegation for name in LDAP zone. NOTE: NS record in zone apex is not considered as delegation. It returns None if there is no delegation outside of zone apex. Example: zone: example.com. name: ns.sub.example.com. records: extra.ns.sub.example.com. sub.example.com. example.com result: sub.example.com. :param zone: zone name :param name: :return: (match, truncated); match: delegation name if success, or None if no delegation record exists """ assert isinstance(zone, DNSName) assert isinstance(name, DNSName) ldap = api.Backend.ldap2 # get zone DN zone_dn = api.Object.dnszone.get_dn(zone) if name.is_absolute(): relative_record_name = name.relativize(zone.make_absolute()) else: relative_record_name = name # Name is zone apex if relative_record_name.is_empty(): return None, False # create list of possible record names possible_record_names = [DNSName(relative_record_name[i:]).ToASCII() for i in xrange(len(relative_record_name))] # search filters name_filter = ldap.make_filter({'idnsname': [possible_record_names]}) objectclass_filter = ldap.make_filter({'objectclass': 'idnsrecord'}) complete_filter = ldap.combine_filters( [name_filter, objectclass_filter], rules=ldap.MATCH_ALL ) try: entries, truncated = ldap.find_entries( filter=complete_filter, attrs_list=['idnsname', 'nsrecord'], base_dn=zone_dn, scope=ldap.SCOPE_ONELEVEL ) except errors.NotFound: return None, False matched_records = [] # test if entry contains NS records for entry in entries: if entry.get('nsrecord'): matched_records.append(entry.single_value['idnsname']) if not matched_records: return None, truncated # return longest match return max(matched_records, key=len), truncated def _find_subtree_forward_zones_ldap(name, child_zones_only=False): """ Search for forwardzone <name> and all child forwardzones Filter: (|(*.<name>.)(<name>.)) :param name: :param child_zones_only: search only for child zones :return: (list of zonenames, truncated), list is empty if no zone found """ assert isinstance(name, DNSName) ldap = api.Backend.ldap2 # prepare for filter "*.<name>." search_name = u".%s" % name.make_absolute().ToASCII() # we need to search zone with and without last dot, due compatibility # with IPA < 4.0 search_names = [search_name, search_name[:-1]] # Create filters objectclass_filter = ldap.make_filter({'objectclass':'idnsforwardzone'}) zonenames_filter = ldap.make_filter({'idnsname': search_names}, exact=False, trailing_wildcard=False) if not child_zones_only: # find also zone with exact name exact_name = name.make_absolute().ToASCII() # we need to search zone with and without last dot, due compatibility # with IPA < 4.0 exact_names = [exact_name, exact_name[-1]] exact_name_filter = ldap.make_filter({'idnsname': exact_names}) zonenames_filter = ldap.combine_filters([zonenames_filter, exact_name_filter]) zoneactive_filter = ldap.make_filter({'idnsZoneActive': 'true'}) complete_filter = ldap.combine_filters( [objectclass_filter, zonenames_filter, zoneactive_filter], rules=ldap.MATCH_ALL ) try: entries, truncated = ldap.find_entries( filter=complete_filter, attrs_list=['idnsname'], base_dn=DN(api.env.container_dns, api.env.basedn), scope=ldap.SCOPE_ONELEVEL ) except errors.NotFound: return [], False result = [entry.single_value['idnsname'].make_absolute() for entry in entries] return result, truncated def _get_zone_which_makes_fw_zone_ineffective(fwzonename): """ Check if forward zone is effective. If parent zone exists as authoritative zone, the forward zone will not forward queries by default. It is necessary to delegate authority to forward zone with a NS record. Example: Forward zone: sub.example.com Zone: example.com Forwarding will not work, because the server thinks it is authoritative for zone and will return NXDOMAIN Adding record: sub.example.com NS ns.sub.example.com. will delegate authority, and IPA DNS server will forward DNS queries. :param fwzonename: forwardzone :return: (zone, truncated) zone: None if effective, name of authoritative zone otherwise """ assert isinstance(fwzonename, DNSName) auth_zone, truncated_zone = _get_auth_zone_ldap(fwzonename) if not auth_zone: return None, truncated_zone delegation_record_name, truncated_ns =\ _get_longest_match_ns_delegation_ldap(auth_zone, fwzonename) truncated = truncated_ns or truncated_zone if delegation_record_name: return None, truncated return auth_zone, truncated def _add_warning_fw_zone_is_not_effective(result, fwzone, version): """ Adds warning message to result, if required """ authoritative_zone, truncated = \ _get_zone_which_makes_fw_zone_ineffective(fwzone) if authoritative_zone: # forward zone is not effective and forwarding will not work messages.add_message( version, result, messages.ForwardzoneIsNotEffectiveWarning( fwzone=fwzone, authzone=authoritative_zone, ns_rec=fwzone.relativize(authoritative_zone) ) ) class DNSZoneBase(LDAPObject): """ Base class for DNS Zone """ container_dn = api.env.container_dns object_class = ['top'] possible_objectclasses = ['ipadnszone'] default_attributes = [ 'idnsname', 'idnszoneactive', 'idnsforwarders', 'idnsforwardpolicy' ] takes_params = ( DNSNameParam('idnsname', _no_wildcard_validator, # RFC 4592 section 4 only_absolute=True, cli_name='name', label=_('Zone name'), doc=_('Zone name (FQDN)'), default_from=lambda name_from_ip: _reverse_zone_name(name_from_ip), normalizer=_normalize_zone, primary_key=True, ), Str('name_from_ip?', _validate_ipnet, label=_('Reverse zone IP network'), doc=_('IP network to create reverse zone name from'), flags=('virtual_attribute',), ), Bool('idnszoneactive?', cli_name='zone_active', label=_('Active zone'), doc=_('Is zone active?'), flags=['no_create', 'no_update'], attribute=True, ), Str('idnsforwarders*', _validate_bind_forwarder, cli_name='forwarder', label=_('Zone forwarders'), doc=_('Per-zone forwarders. A custom port can be specified ' 'for each forwarder using a standard format "IP_ADDRESS port PORT"'), csv=True, ), StrEnum('idnsforwardpolicy?', cli_name='forward_policy', label=_('Forward policy'), doc=_('Per-zone conditional forwarding policy. Set to "none" to ' 'disable forwarding to global forwarder for this zone. In ' 'that case, conditional zone forwarders are disregarded.'), values=(u'only', u'first', u'none'), ), ) def get_dn(self, *keys, **options): if not dns_container_exists(self.api.Backend.ldap2): raise errors.NotFound(reason=_('DNS is not configured')) zone = keys[-1] assert isinstance(zone, DNSName) assert zone.is_absolute() zone_a = zone.ToASCII() # special case when zone is the root zone ('.') if zone == DNSName.root: return super(DNSZoneBase, self).get_dn(zone_a, **options) # try first relative name, a new zone has to be added as absolute # otherwise ObjectViolation is raised zone_a = zone_a[:-1] dn = super(DNSZoneBase, self).get_dn(zone_a, **options) try: self.backend.get_entry(dn, ['']) except errors.NotFound: zone_a = u"%s." % zone_a dn = super(DNSZoneBase, self).get_dn(zone_a, **options) return dn def permission_name(self, zone): assert isinstance(zone, DNSName) return u"Manage DNS zone %s" % zone.ToASCII() def get_name_in_zone(self, zone, hostname): """ Get name of a record that is to be added to a new zone. I.e. when we want to add record "ipa.lab.example.com" in a zone "example.com", this function should return "ipa.lab". Returns None when record cannot be added to a zone. Returns '@' when the hostname is the zone record. """ assert isinstance(zone, DNSName) assert zone.is_absolute() assert isinstance(hostname, DNSName) if not hostname.is_absolute(): return hostname if hostname.is_subdomain(zone): return hostname.relativize(zone) return None def _remove_permission(self, zone): permission_name = self.permission_name(zone) try: api.Command['permission_del'](permission_name, force=True) except errors.NotFound as e: if zone == DNSName.root: # special case root zone raise # compatibility, older IPA versions which allows to create zone # without absolute zone name permission_name_rel = self.permission_name( zone.relativize(DNSName.root) ) try: api.Command['permission_del'](permission_name_rel, force=True) except errors.NotFound: raise e # re-raise original exception def _make_zonename_absolute(self, entry_attrs, **options): """ Zone names can be relative in IPA < 4.0, make sure we always return absolute zone name from ldap """ if options.get('raw'): return if "idnsname" in entry_attrs: entry_attrs.single_value['idnsname'] = ( entry_attrs.single_value['idnsname'].make_absolute()) class DNSZoneBase_add(LDAPCreate): has_output_params = LDAPCreate.has_output_params + dnszone_output_params def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) try: entry = ldap.get_entry(dn) except errors.NotFound: pass else: if _check_entry_objectclass(entry, self.obj.object_class): self.obj.handle_duplicate_entry(*keys) else: raise errors.DuplicateEntry( message=_(u'Only one zone type is allowed per zone name') ) entry_attrs['idnszoneactive'] = 'TRUE' return dn class DNSZoneBase_del(LDAPDelete): def pre_callback(self, ldap, dn, *nkeys, **options): assert isinstance(dn, DN) if not _check_DN_objectclass(ldap, dn, self.obj.object_class): self.obj.handle_not_found(*nkeys) return dn def post_callback(self, ldap, dn, *keys, **options): try: self.obj._remove_permission(keys[-1]) except errors.NotFound: pass return True class DNSZoneBase_mod(LDAPUpdate): has_output_params = LDAPUpdate.has_output_params + dnszone_output_params def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) self.obj._make_zonename_absolute(entry_attrs, **options) return dn class DNSZoneBase_find(LDAPSearch): __doc__ = _('Search for DNS zones (SOA records).') has_output_params = LDAPSearch.has_output_params + dnszone_output_params def args_options_2_params(self, *args, **options): # FIXME: Check that name_from_ip is valid. This is necessary because # custom validation rules, including _validate_ipnet, are not # used when doing a search. Once we have a parameter type for # IP network objects, this will no longer be necessary, as the # parameter type will handle the validation itself (see # <https://fedorahosted.org/freeipa/ticket/2266>). if 'name_from_ip' in options: self.obj.params['name_from_ip'](unicode(options['name_from_ip'])) return super(DNSZoneBase_find, self).args_options_2_params(*args, **options) def args_options_2_entry(self, *args, **options): if 'name_from_ip' in options: if 'idnsname' not in options: options['idnsname'] = self.obj.params['idnsname'].get_default(**options) del options['name_from_ip'] search_kw = super(DNSZoneBase_find, self).args_options_2_entry(*args, **options) name = search_kw.get('idnsname') if name: search_kw['idnsname'] = [name, name.relativize(DNSName.root)] return search_kw def pre_callback(self, ldap, filter, attrs_list, base_dn, scope, *args, **options): assert isinstance(base_dn, DN) # Check if DNS container exists must be here for find methods if not dns_container_exists(self.api.Backend.ldap2): raise errors.NotFound(reason=_('DNS is not configured')) filter = _create_idn_filter(self, ldap, *args, **options) return (filter, base_dn, scope) def post_callback(self, ldap, entries, truncated, *args, **options): for entry_attrs in entries: self.obj._make_zonename_absolute(entry_attrs, **options) return truncated class DNSZoneBase_show(LDAPRetrieve): has_output_params = LDAPRetrieve.has_output_params + dnszone_output_params def pre_callback(self, ldap, dn, attrs_list, *keys, **options): assert isinstance(dn, DN) if not _check_DN_objectclass(ldap, dn, self.obj.object_class): self.obj.handle_not_found(*keys) return dn def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) self.obj._make_zonename_absolute(entry_attrs, **options) return dn class DNSZoneBase_disable(LDAPQuery): has_output = output.standard_value def execute(self, *keys, **options): ldap = self.obj.backend dn = self.obj.get_dn(*keys, **options) entry = ldap.get_entry(dn, ['idnszoneactive', 'objectclass']) if not _check_entry_objectclass(entry, self.obj.object_class): self.obj.handle_not_found(*keys) entry['idnszoneactive'] = ['FALSE'] try: ldap.update_entry(entry) except errors.EmptyModlist: pass return dict(result=True, value=pkey_to_value(keys[-1], options)) class DNSZoneBase_enable(LDAPQuery): has_output = output.standard_value def execute(self, *keys, **options): ldap = self.obj.backend dn = self.obj.get_dn(*keys, **options) entry = ldap.get_entry(dn, ['idnszoneactive', 'objectclass']) if not _check_entry_objectclass(entry, self.obj.object_class): self.obj.handle_not_found(*keys) entry['idnszoneactive'] = ['TRUE'] try: ldap.update_entry(entry) except errors.EmptyModlist: pass return dict(result=True, value=pkey_to_value(keys[-1], options)) class DNSZoneBase_add_permission(LDAPQuery): has_output = _output_permissions msg_summary = _('Added system permission "%(value)s"') def execute(self, *keys, **options): ldap = self.obj.backend dn = self.obj.get_dn(*keys, **options) try: entry_attrs = ldap.get_entry(dn, ['objectclass']) except errors.NotFound: self.obj.handle_not_found(*keys) else: if not _check_entry_objectclass(entry_attrs, self.obj.object_class): self.obj.handle_not_found(*keys) permission_name = self.obj.permission_name(keys[-1]) # compatibility with older IPA versions which allows relative zonenames if keys[-1] != DNSName.root: # special case root zone permission_name_rel = self.obj.permission_name( keys[-1].relativize(DNSName.root) ) try: api.Object['permission'].get_dn_if_exists(permission_name_rel) except errors.NotFound: pass else: # permission exists without absolute domain name raise errors.DuplicateEntry( message=_('permission "%(value)s" already exists') % { 'value': permission_name } ) permission = api.Command['permission_add_noaci'](permission_name, ipapermissiontype=u'SYSTEM' )['result'] dnszone_ocs = entry_attrs.get('objectclass') if dnszone_ocs: for oc in dnszone_ocs: if oc.lower() == 'ipadnszone': break else: dnszone_ocs.append('ipadnszone') entry_attrs['managedby'] = [permission['dn']] ldap.update_entry(entry_attrs) return dict( result=True, value=pkey_to_value(permission_name, options), ) class DNSZoneBase_remove_permission(LDAPQuery): has_output = _output_permissions msg_summary = _('Removed system permission "%(value)s"') def execute(self, *keys, **options): ldap = self.obj.backend dn = self.obj.get_dn(*keys, **options) try: entry = ldap.get_entry(dn, ['managedby', 'objectclass']) except errors.NotFound: self.obj.handle_not_found(*keys) else: if not _check_entry_objectclass(entry, self.obj.object_class): self.obj.handle_not_found(*keys) entry['managedby'] = None try: ldap.update_entry(entry) except errors.EmptyModlist: # managedBy attribute is clean, lets make sure there is also no # dangling DNS zone permission pass permission_name = self.obj.permission_name(keys[-1]) self.obj._remove_permission(keys[-1]) return dict( result=True, value=pkey_to_value(permission_name, options), ) @register() class dnszone(DNSZoneBase): """ DNS Zone, container for resource records. """ object_name = _('DNS zone') object_name_plural = _('DNS zones') object_class = DNSZoneBase.object_class + ['idnsrecord', 'idnszone'] default_attributes = DNSZoneBase.default_attributes + [ 'idnssoamname', 'idnssoarname', 'idnssoaserial', 'idnssoarefresh', 'idnssoaretry', 'idnssoaexpire', 'idnssoaminimum', 'idnsallowquery', 'idnsallowtransfer', 'idnssecinlinesigning', ] + _record_attributes label = _('DNS Zones') label_singular = _('DNS Zone') takes_params = DNSZoneBase.takes_params + ( DNSNameParam('idnssoamname?', cli_name='name_server', label=_('Authoritative nameserver'), doc=_('Authoritative nameserver domain name'), default=None, # value will be added in precallback from ldap ), DNSNameParam('idnssoarname', _rname_validator, cli_name='admin_email', label=_('Administrator e-mail address'), doc=_('Administrator e-mail address'), default=DNSName(u'hostmaster'), normalizer=normalize_zonemgr, autofill=True, ), Int('idnssoaserial', cli_name='serial', label=_('SOA serial'), doc=_('SOA record serial number'), minvalue=1, maxvalue=4294967295, default_from=_create_zone_serial, autofill=True, ), Int('idnssoarefresh', cli_name='refresh', label=_('SOA refresh'), doc=_('SOA record refresh time'), minvalue=0, maxvalue=2147483647, default=3600, autofill=True, ), Int('idnssoaretry', cli_name='retry', label=_('SOA retry'), doc=_('SOA record retry time'), minvalue=0, maxvalue=2147483647, default=900, autofill=True, ), Int('idnssoaexpire', cli_name='expire', label=_('SOA expire'), doc=_('SOA record expire time'), default=1209600, minvalue=0, maxvalue=2147483647, autofill=True, ), Int('idnssoaminimum', cli_name='minimum', label=_('SOA minimum'), doc=_('How long should negative responses be cached'), default=3600, minvalue=0, maxvalue=2147483647, autofill=True, ), Int('dnsttl?', cli_name='ttl', label=_('Time to live'), doc=_('Time to live for records at zone apex'), minvalue=0, maxvalue=2147483647, # see RFC 2181 ), StrEnum('dnsclass?', # Deprecated cli_name='class', flags=['no_option'], values=_record_classes, ), Str('idnsupdatepolicy?', cli_name='update_policy', label=_('BIND update policy'), doc=_('BIND update policy'), default_from=lambda idnsname: default_zone_update_policy(idnsname), autofill=True ), Bool('idnsallowdynupdate?', cli_name='dynamic_update', label=_('Dynamic update'), doc=_('Allow dynamic updates.'), attribute=True, default=False, autofill=True ), Str('idnsallowquery?', _validate_bind_aci, normalizer=_normalize_bind_aci, cli_name='allow_query', label=_('Allow query'), doc=_('Semicolon separated list of IP addresses or networks which are allowed to issue queries'), default=u'any;', # anyone can issue queries by default autofill=True, ), Str('idnsallowtransfer?', _validate_bind_aci, normalizer=_normalize_bind_aci, cli_name='allow_transfer', label=_('Allow transfer'), doc=_('Semicolon separated list of IP addresses or networks which are allowed to transfer the zone'), default=u'none;', # no one can issue queries by default autofill=True, ), Bool('idnsallowsyncptr?', cli_name='allow_sync_ptr', label=_('Allow PTR sync'), doc=_('Allow synchronization of forward (A, AAAA) and reverse (PTR) records in the zone'), ), Bool('idnssecinlinesigning?', cli_name='dnssec', default=False, label=_('Allow in-line DNSSEC signing'), doc=_('Allow inline DNSSEC signing of records in the zone'), ), Str('nsec3paramrecord?', _validate_nsec3param_record, cli_name='nsec3param_rec', label=_('NSEC3PARAM record'), doc=_('NSEC3PARAM record for zone in format: hash_algorithm flags iterations salt'), pattern=r'^\d+ \d+ \d+ (([0-9a-fA-F]{2})+|-)$', pattern_errmsg=(u'expected format: <0-255> <0-255> <0-65535> ' 'even-length_hexadecimal_digits_or_hyphen'), ), ) # Permissions will be apllied for forwardzones too # Store permissions into api.env.basedn, dns container could not exists managed_permissions = { 'System: Add DNS Entries': { 'non_object': True, 'ipapermright': {'add'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('idnsname=*', 'cn=dns', api.env.basedn), 'replaces': [ '(target = "ldap:///idnsname=*,cn=dns,$SUFFIX")(version 3.0;acl "permission:add dns entries";allow (add) groupdn = "ldap:///cn=add dns entries,cn=permissions,cn=pbac,$SUFFIX";)', ], 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, 'System: Read DNS Entries': { 'non_object': True, 'ipapermright': {'read', 'search', 'compare'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('idnsname=*', 'cn=dns', api.env.basedn), 'ipapermdefaultattr': { 'objectclass', 'a6record', 'aaaarecord', 'afsdbrecord', 'aplrecord', 'arecord', 'certrecord', 'cn', 'cnamerecord', 'dhcidrecord', 'dlvrecord', 'dnamerecord', 'dnsclass', 'dnsttl', 'dsrecord', 'hinforecord', 'hiprecord', 'idnsallowdynupdate', 'idnsallowquery', 'idnsallowsyncptr', 'idnsallowtransfer', 'idnsforwarders', 'idnsforwardpolicy', 'idnsname', 'idnssecinlinesigning', 'idnssoaexpire', 'idnssoaminimum', 'idnssoamname', 'idnssoarefresh', 'idnssoaretry', 'idnssoarname', 'idnssoaserial', 'idnsupdatepolicy', 'idnszoneactive', 'ipseckeyrecord','keyrecord', 'kxrecord', 'locrecord', 'managedby', 'mdrecord', 'minforecord', 'mxrecord', 'naptrrecord', 'nsecrecord', 'nsec3paramrecord', 'nsrecord', 'nxtrecord', 'ptrrecord', 'rprecord', 'rrsigrecord', 'sigrecord', 'spfrecord', 'srvrecord', 'sshfprecord', 'tlsarecord', 'txtrecord', 'unknownrecord', }, 'replaces_system': ['Read DNS Entries'], 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, 'System: Remove DNS Entries': { 'non_object': True, 'ipapermright': {'delete'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('idnsname=*', 'cn=dns', api.env.basedn), 'replaces': [ '(target = "ldap:///idnsname=*,cn=dns,$SUFFIX")(version 3.0;acl "permission:remove dns entries";allow (delete) groupdn = "ldap:///cn=remove dns entries,cn=permissions,cn=pbac,$SUFFIX";)', ], 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, 'System: Update DNS Entries': { 'non_object': True, 'ipapermright': {'write'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('idnsname=*', 'cn=dns', api.env.basedn), 'ipapermdefaultattr': { 'a6record', 'aaaarecord', 'afsdbrecord', 'aplrecord', 'arecord', 'certrecord', 'cn', 'cnamerecord', 'dhcidrecord', 'dlvrecord', 'dnamerecord', 'dnsclass', 'dnsttl', 'dsrecord', 'hinforecord', 'hiprecord', 'idnsallowdynupdate', 'idnsallowquery', 'idnsallowsyncptr', 'idnsallowtransfer', 'idnsforwarders', 'idnsforwardpolicy', 'idnsname', 'idnssecinlinesigning', 'idnssoaexpire', 'idnssoaminimum', 'idnssoamname', 'idnssoarefresh', 'idnssoaretry', 'idnssoarname', 'idnssoaserial', 'idnsupdatepolicy', 'idnszoneactive', 'ipseckeyrecord','keyrecord', 'kxrecord', 'locrecord', 'managedby', 'mdrecord', 'minforecord', 'mxrecord', 'naptrrecord', 'nsecrecord', 'nsec3paramrecord', 'nsrecord', 'nxtrecord', 'ptrrecord', 'rprecord', 'rrsigrecord', 'sigrecord', 'spfrecord', 'srvrecord', 'sshfprecord', 'tlsarecord', 'txtrecord', 'unknownrecord', }, 'replaces': [ '(targetattr = "idnsname || cn || idnsallowdynupdate || dnsttl || dnsclass || arecord || aaaarecord || a6record || nsrecord || cnamerecord || ptrrecord || srvrecord || txtrecord || mxrecord || mdrecord || hinforecord || minforecord || afsdbrecord || sigrecord || keyrecord || locrecord || nxtrecord || naptrrecord || kxrecord || certrecord || dnamerecord || dsrecord || sshfprecord || rrsigrecord || nsecrecord || idnsname || idnszoneactive || idnssoamname || idnssoarname || idnssoaserial || idnssoarefresh || idnssoaretry || idnssoaexpire || idnssoaminimum || idnsupdatepolicy")(target = "ldap:///idnsname=*,cn=dns,$SUFFIX")(version 3.0;acl "permission:update dns entries";allow (write) groupdn = "ldap:///cn=update dns entries,cn=permissions,cn=pbac,$SUFFIX";)', '(targetattr = "idnsname || cn || idnsallowdynupdate || dnsttl || dnsclass || arecord || aaaarecord || a6record || nsrecord || cnamerecord || ptrrecord || srvrecord || txtrecord || mxrecord || mdrecord || hinforecord || minforecord || afsdbrecord || sigrecord || keyrecord || locrecord || nxtrecord || naptrrecord || kxrecord || certrecord || dnamerecord || dsrecord || sshfprecord || rrsigrecord || nsecrecord || idnsname || idnszoneactive || idnssoamname || idnssoarname || idnssoaserial || idnssoarefresh || idnssoaretry || idnssoaexpire || idnssoaminimum || idnsupdatepolicy || idnsallowquery || idnsallowtransfer || idnsallowsyncptr || idnsforwardpolicy || idnsforwarders")(target = "ldap:///idnsname=*,cn=dns,$SUFFIX")(version 3.0;acl "permission:update dns entries";allow (write) groupdn = "ldap:///cn=update dns entries,cn=permissions,cn=pbac,$SUFFIX";)', '(targetattr = "idnsname || cn || idnsallowdynupdate || dnsttl || dnsclass || arecord || aaaarecord || a6record || nsrecord || cnamerecord || ptrrecord || srvrecord || txtrecord || mxrecord || mdrecord || hinforecord || minforecord || afsdbrecord || sigrecord || keyrecord || locrecord || nxtrecord || naptrrecord || kxrecord || certrecord || dnamerecord || dsrecord || sshfprecord || rrsigrecord || nsecrecord || idnsname || idnszoneactive || idnssoamname || idnssoarname || idnssoaserial || idnssoarefresh || idnssoaretry || idnssoaexpire || idnssoaminimum || idnsupdatepolicy || idnsallowquery || idnsallowtransfer || idnsallowsyncptr || idnsforwardpolicy || idnsforwarders || managedby")(target = "ldap:///idnsname=*,cn=dns,$SUFFIX")(version 3.0;acl "permission:update dns entries";allow (write) groupdn = "ldap:///cn=update dns entries,cn=permissions,cn=pbac,$SUFFIX";)', ], 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, 'System: Read DNSSEC metadata': { 'non_object': True, 'ipapermright': {'read', 'search', 'compare'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('cn=dns', api.env.basedn), 'ipapermtargetfilter': ['(objectclass=idnsSecKey)'], 'ipapermdefaultattr': { 'idnsSecAlgorithm', 'idnsSecKeyCreated', 'idnsSecKeyPublish', 'idnsSecKeyActivate', 'idnsSecKeyInactive', 'idnsSecKeyDelete', 'idnsSecKeyZone', 'idnsSecKeyRevoke', 'idnsSecKeySep', 'idnsSecKeyRef', 'cn', 'objectclass', }, 'default_privileges': {'DNS Administrators'}, }, 'System: Manage DNSSEC metadata': { 'non_object': True, 'ipapermright': {'all'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('cn=dns', api.env.basedn), 'ipapermtargetfilter': ['(objectclass=idnsSecKey)'], 'ipapermdefaultattr': { 'idnsSecAlgorithm', 'idnsSecKeyCreated', 'idnsSecKeyPublish', 'idnsSecKeyActivate', 'idnsSecKeyInactive', 'idnsSecKeyDelete', 'idnsSecKeyZone', 'idnsSecKeyRevoke', 'idnsSecKeySep', 'idnsSecKeyRef', 'cn', 'objectclass', }, 'default_privileges': {'DNS Servers'}, }, 'System: Manage DNSSEC keys': { 'non_object': True, 'ipapermright': {'all'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('cn=keys', 'cn=sec', 'cn=dns', api.env.basedn), 'ipapermdefaultattr': { 'ipaPublicKey', 'ipaPrivateKey', 'ipaSecretKey', 'ipaWrappingMech','ipaWrappingKey', 'ipaSecretKeyRef', 'ipk11Private', 'ipk11Modifiable', 'ipk11Label', 'ipk11Copyable', 'ipk11Destroyable', 'ipk11Trusted', 'ipk11CheckValue', 'ipk11StartDate', 'ipk11EndDate', 'ipk11UniqueId', 'ipk11PublicKeyInfo', 'ipk11Distrusted', 'ipk11Subject', 'ipk11Id', 'ipk11Local', 'ipk11KeyType', 'ipk11Derive', 'ipk11KeyGenMechanism', 'ipk11AllowedMechanisms', 'ipk11Encrypt', 'ipk11Verify', 'ipk11VerifyRecover', 'ipk11Wrap', 'ipk11WrapTemplate', 'ipk11Sensitive', 'ipk11Decrypt', 'ipk11Sign', 'ipk11SignRecover', 'ipk11Unwrap', 'ipk11Extractable', 'ipk11AlwaysSensitive', 'ipk11NeverExtractable', 'ipk11WrapWithTrusted', 'ipk11UnwrapTemplate', 'ipk11AlwaysAuthenticate', 'objectclass', }, 'default_privileges': {'DNS Servers'}, }, } def _rr_zone_postprocess(self, record, **options): #Decode IDN ACE form to Unicode, raw records are passed directly from LDAP if options.get('raw', False): return _records_idn_postprocess(record, **options) def _warning_forwarding(self, result, **options): if ('idnsforwarders' in result['result']): messages.add_message(options.get('version', VERSION_WITHOUT_CAPABILITIES), result, messages.ForwardersWarning()) def _warning_dnssec_experimental(self, result, *keys, **options): # add warning when user use option --dnssec if 'idnssecinlinesigning' in options: if options['idnssecinlinesigning'] is True: messages.add_message(options['version'], result, messages.DNSSECWarning( additional_info=_("Visit 'http://www.freeipa.org/page/Releases/4.1.0#DNSSEC_Support'.") )) else: messages.add_message(options['version'], result, messages.DNSSECWarning( additional_info=_("If you encounter any problems please " "report them and restart 'named' service on affected IPA " "server.") )) def _warning_name_server_option(self, result, context, **options): if getattr(context, 'show_warning_nameserver_option', False): messages.add_message( options['version'], result, messages.OptionSemanticChangedWarning( label=_(u"setting Authoritative nameserver"), current_behavior=_(u"It is used only for setting the " u"SOA MNAME attribute."), hint=_(u"NS record(s) can be edited in zone apex - '@'. ") ) ) def _warning_fw_zone_is_not_effective(self, result, *keys, **options): """ Warning if any operation with zone causes, a child forward zone is not effective """ zone = keys[-1] affected_fw_zones, truncated = _find_subtree_forward_zones_ldap( zone, child_zones_only=True) if not affected_fw_zones: return for fwzone in affected_fw_zones: _add_warning_fw_zone_is_not_effective(result, fwzone, options['version']) @register() class dnszone_add(DNSZoneBase_add): __doc__ = _('Create new DNS zone (SOA record).') takes_options = DNSZoneBase_add.takes_options + ( Flag('force', label=_('Force'), doc=_('Force DNS zone creation even if nameserver is not resolvable.'), ), # Deprecated # ip-address option is not used anymore, we have to keep it # due to compability with clients older than 4.1 Str('ip_address?', flags=['no_option', ] ), ) def _warning_deprecated_option(self, result, **options): if 'ip_address' in options: messages.add_message( options['version'], result, messages.OptionDeprecatedWarning( option='ip-address', additional_info=u"Value will be ignored.") ) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) dn = super(dnszone_add, self).pre_callback( ldap, dn, entry_attrs, attrs_list, *keys, **options) nameservers = [normalize_zone(x) for x in api.Object.dnsrecord.get_dns_masters()] server = normalize_zone(api.env.host) zone = keys[-1] if entry_attrs.get('idnssoamname'): if zone.is_reverse() and not entry_attrs['idnssoamname'].is_absolute(): raise errors.ValidationError( name='name-server', error=_("Nameserver for reverse zone cannot be a relative DNS name")) # verify if user specified server is resolvable if not options['force']: check_ns_rec_resolvable(keys[0], entry_attrs['idnssoamname']) # show warning about --name-server option context.show_warning_nameserver_option = True else: # user didn't specify SOA mname if server in nameservers: # current ipa server is authoritative nameserver in SOA record entry_attrs['idnssoamname'] = [server] else: # a first DNS capable server is authoritative nameserver in SOA record entry_attrs['idnssoamname'] = [nameservers[0]] # all ipa DNS servers should be in NS zone record (as absolute domain name) entry_attrs['nsrecord'] = nameservers return dn def execute(self, *keys, **options): result = super(dnszone_add, self).execute(*keys, **options) self._warning_deprecated_option(result, **options) self.obj._warning_forwarding(result, **options) self.obj._warning_dnssec_experimental(result, *keys, **options) self.obj._warning_name_server_option(result, context, **options) self.obj._warning_fw_zone_is_not_effective(result, *keys, **options) return result def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) # Add entry to realmdomains # except for our own domain, forward zones, reverse zones and root zone zone = keys[0] if (zone != DNSName(api.env.domain).make_absolute() and not options.get('idnsforwarders') and not zone.is_reverse() and zone != DNSName.root): try: api.Command['realmdomains_mod'](add_domain=unicode(zone), force=True) except (errors.EmptyModlist, errors.ValidationError): pass self.obj._rr_zone_postprocess(entry_attrs, **options) return dn @register() class dnszone_del(DNSZoneBase_del): __doc__ = _('Delete DNS zone (SOA record).') msg_summary = _('Deleted DNS zone "%(value)s"') def execute(self, *keys, **options): result = super(dnszone_del, self).execute(*keys, **options) nkeys = keys[-1] # we can delete more zones for key in nkeys: self.obj._warning_fw_zone_is_not_effective(result, key, **options) return result def post_callback(self, ldap, dn, *keys, **options): super(dnszone_del, self).post_callback(ldap, dn, *keys, **options) # Delete entry from realmdomains # except for our own domain, reverse zone, and root zone zone = keys[0].make_absolute() if (zone != DNSName(api.env.domain).make_absolute() and not zone.is_reverse() and zone != DNSName.root ): try: api.Command['realmdomains_mod'](del_domain=unicode(zone), force=True) except (errors.AttrValueNotFound, errors.ValidationError): pass return True @register() class dnszone_mod(DNSZoneBase_mod): __doc__ = _('Modify DNS zone (SOA record).') takes_options = DNSZoneBase_mod.takes_options + ( Flag('force', label=_('Force'), doc=_('Force nameserver change even if nameserver not in DNS'), ), ) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): if not _check_DN_objectclass(ldap, dn, self.obj.object_class): self.obj.handle_not_found(*keys) if 'idnssoamname' in entry_attrs: nameserver = entry_attrs['idnssoamname'] if nameserver: if not nameserver.is_empty() and not options['force']: check_ns_rec_resolvable(keys[0], nameserver) context.show_warning_nameserver_option = True else: # empty value, this option is required by ldap raise errors.ValidationError( name='name_server', error=_(u"is required")) return dn def execute(self, *keys, **options): result = super(dnszone_mod, self).execute(*keys, **options) self.obj._warning_forwarding(result, **options) self.obj._warning_dnssec_experimental(result, *keys, **options) self.obj._warning_name_server_option(result, context, **options) return result def post_callback(self, ldap, dn, entry_attrs, *keys, **options): dn = super(dnszone_mod, self).post_callback(ldap, dn, entry_attrs, *keys, **options) self.obj._rr_zone_postprocess(entry_attrs, **options) return dn @register() class dnszone_find(DNSZoneBase_find): __doc__ = _('Search for DNS zones (SOA records).') takes_options = DNSZoneBase_find.takes_options + ( Flag('forward_only', label=_('Forward zones only'), cli_name='forward_only', doc=_('Search for forward zones only'), ), ) def pre_callback(self, ldap, filter, attrs_list, base_dn, scope, *args, **options): assert isinstance(base_dn, DN) filter, base, dn = super(dnszone_find, self).pre_callback(ldap, filter, attrs_list, base_dn, scope, *args, **options) if options.get('forward_only', False): search_kw = {} search_kw['idnsname'] = [revzone.ToASCII() for revzone in REVERSE_DNS_ZONES.keys()] rev_zone_filter = ldap.make_filter(search_kw, rules=ldap.MATCH_NONE, exact=False, trailing_wildcard=False) filter = ldap.combine_filters((rev_zone_filter, filter), rules=ldap.MATCH_ALL) return (filter, base_dn, scope) def post_callback(self, ldap, entries, truncated, *args, **options): truncated = super(dnszone_find, self).post_callback(ldap, entries, truncated, *args, **options) for entry_attrs in entries: self.obj._rr_zone_postprocess(entry_attrs, **options) return truncated @register() class dnszone_show(DNSZoneBase_show): __doc__ = _('Display information about a DNS zone (SOA record).') def execute(self, *keys, **options): result = super(dnszone_show, self).execute(*keys, **options) self.obj._warning_forwarding(result, **options) return result def post_callback(self, ldap, dn, entry_attrs, *keys, **options): dn = super(dnszone_show, self).post_callback(ldap, dn, entry_attrs, *keys, **options) self.obj._rr_zone_postprocess(entry_attrs, **options) return dn @register() class dnszone_disable(DNSZoneBase_disable): __doc__ = _('Disable DNS Zone.') msg_summary = _('Disabled DNS zone "%(value)s"') def execute(self, *keys, **options): result = super(dnszone_disable, self).execute(*keys, **options) self.obj._warning_fw_zone_is_not_effective(result, *keys, **options) return result @register() class dnszone_enable(DNSZoneBase_enable): __doc__ = _('Enable DNS Zone.') msg_summary = _('Enabled DNS zone "%(value)s"') def execute(self, *keys, **options): result = super(dnszone_enable, self).execute(*keys, **options) self.obj._warning_fw_zone_is_not_effective(result, *keys, **options) return result @register() class dnszone_add_permission(DNSZoneBase_add_permission): __doc__ = _('Add a permission for per-zone access delegation.') @register() class dnszone_remove_permission(DNSZoneBase_remove_permission): __doc__ = _('Remove a permission for per-zone access delegation.') @register() class dnsrecord(LDAPObject): """ DNS record. """ parent_object = 'dnszone' container_dn = api.env.container_dns object_name = _('DNS resource record') object_name_plural = _('DNS resource records') object_class = ['top', 'idnsrecord'] permission_filter_objectclasses = ['idnsrecord'] default_attributes = ['idnsname'] + _record_attributes rdn_is_primary_key = True label = _('DNS Resource Records') label_singular = _('DNS Resource Record') takes_params = ( DNSNameParam('idnsname', cli_name='name', label=_('Record name'), doc=_('Record name'), primary_key=True, ), Int('dnsttl?', cli_name='ttl', label=_('Time to live'), doc=_('Time to live'), ), StrEnum('dnsclass?', # Deprecated cli_name='class', flags=['no_option'], values=_record_classes, ), ) + _dns_record_options structured_flag = Flag('structured', label=_('Structured'), doc=_('Parse all raw DNS records and return them in a structured way'), ) def _dsrecord_pre_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) dsrecords = entry_attrs.get('dsrecord') if dsrecords and self.is_pkey_zone_record(*keys): raise errors.ValidationError( name='dsrecord', error=unicode(_('DS record must not be in zone apex (RFC 4035 section 2.4)'))) def _nsrecord_pre_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) nsrecords = entry_attrs.get('nsrecord') if options.get('force', False) or nsrecords is None: return for nsrecord in nsrecords: check_ns_rec_resolvable(keys[0], DNSName(nsrecord)) def _idnsname_pre_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) if keys[-1].is_absolute(): if keys[-1].is_subdomain(keys[-2]): entry_attrs['idnsname'] = [keys[-1].relativize(keys[-2])] elif not self.is_pkey_zone_record(*keys): raise errors.ValidationError(name='idnsname', error=unicode(_('out-of-zone data: record name must ' 'be a subdomain of the zone or a ' 'relative name'))) # dissallowed wildcard (RFC 4592 section 4) no_wildcard_rtypes = ['DNAME', 'DS', 'NS'] if (keys[-1].is_wild() and any(entry_attrs.get('%srecord' % r.lower()) for r in no_wildcard_rtypes) ): raise errors.ValidationError( name='idnsname', error=(_('owner of %(types)s records ' 'should not be a wildcard domain name (RFC 4592 section 4)') % {'types': ', '.join(no_wildcard_rtypes)} ) ) def _ptrrecord_pre_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) ptrrecords = entry_attrs.get('ptrrecord') if ptrrecords is None: return zone = keys[-2] if self.is_pkey_zone_record(*keys): addr = _dns_zone_record else: addr = keys[-1] zone_len = 0 for valid_zone in REVERSE_DNS_ZONES: if zone.is_subdomain(valid_zone): zone = zone.relativize(valid_zone) zone_name = valid_zone zone_len = REVERSE_DNS_ZONES[valid_zone] if not zone_len: allowed_zones = ', '.join([unicode(revzone) for revzone in REVERSE_DNS_ZONES.keys()]) raise errors.ValidationError(name='ptrrecord', error=unicode(_('Reverse zone for PTR record should be a sub-zone of one the following fully qualified domains: %s') % allowed_zones)) addr_len = len(addr.labels) # Classless zones (0/25.0.0.10.in-addr.arpa.) -> skip check # zone has to be checked without reverse domain suffix (in-addr.arpa.) for sign in ('/', '-'): for name in (zone, addr): for label in name.labels: if sign in label: return ip_addr_comp_count = addr_len + len(zone.labels) if ip_addr_comp_count != zone_len: raise errors.ValidationError(name='ptrrecord', error=unicode(_('Reverse zone %(name)s requires exactly ' '%(count)d IP address components, ' '%(user_count)d given') % dict(name=zone_name, count=zone_len, user_count=ip_addr_comp_count))) def run_precallback_validators(self, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) ldap = self.api.Backend.ldap2 for rtype in entry_attrs.keys(): rtype_cb = getattr(self, '_%s_pre_callback' % rtype, None) if rtype_cb: rtype_cb(ldap, dn, entry_attrs, *keys, **options) def is_pkey_zone_record(self, *keys): assert isinstance(keys[-1], DNSName) assert isinstance(keys[-2], DNSName) idnsname = keys[-1] zonename = keys[-2] if idnsname.is_empty() or idnsname == zonename: return True return False def check_zone(self, zone, **options): """ Check if zone exists and if is master zone """ parent_object = self.api.Object[self.parent_object] dn = parent_object.get_dn(zone, **options) ldap = self.api.Backend.ldap2 try: entry = ldap.get_entry(dn, ['objectclass']) except errors.NotFound: parent_object.handle_not_found(zone) else: # only master zones can contain records if 'idnszone' not in [x.lower() for x in entry.get('objectclass', [])]: raise errors.ValidationError( name='dnszoneidnsname', error=_(u'only master zones can contain records') ) return dn def get_dn(self, *keys, **options): if not dns_container_exists(self.api.Backend.ldap2): raise errors.NotFound(reason=_('DNS is not configured')) dn = self.check_zone(keys[-2], **options) if self.is_pkey_zone_record(*keys): return dn #Make RR name relative if possible relative_name = keys[-1].relativize(keys[-2]).ToASCII() keys = keys[:-1] + (relative_name,) return super(dnsrecord, self).get_dn(*keys, **options) def attr_to_cli(self, attr): try: cliname = attr[:-len('record')].upper() except IndexError: cliname = attr return cliname def get_dns_masters(self): ldap = self.api.Backend.ldap2 base_dn = DN(('cn', 'masters'), ('cn', 'ipa'), ('cn', 'etc'), self.api.env.basedn) ldap_filter = '(&(objectClass=ipaConfigObject)(cn=DNS))' dns_masters = [] try: entries = ldap.find_entries(filter=ldap_filter, base_dn=base_dn)[0] for entry in entries: try: master = entry.dn[1]['cn'] dns_masters.append(master) except (IndexError, KeyError): pass except errors.NotFound: return [] return dns_masters def has_cli_options(self, options, no_option_msg, allow_empty_attrs=False): if any(k in options for k in ('setattr', 'addattr', 'delattr', 'rename')): return has_options = False for attr in options.keys(): if attr in self.params and not self.params[attr].primary_key: if options[attr] or allow_empty_attrs: has_options = True break if not has_options: raise errors.OptionError(no_option_msg) def get_record_entry_attrs(self, entry_attrs): entry_attrs = entry_attrs.copy() for attr in entry_attrs.keys(): if attr not in self.params or self.params[attr].primary_key: del entry_attrs[attr] return entry_attrs def postprocess_record(self, record, **options): if options.get('structured', False): for attr in record.keys(): # attributes in LDAPEntry may not be normalized attr = attr.lower() try: param = self.params[attr] except KeyError: continue if not isinstance(param, DNSRecord): continue parts_params = param.get_parts() for dnsvalue in record[attr]: dnsentry = { u'dnstype' : unicode(param.rrtype), u'dnsdata' : dnsvalue } values = param._get_part_values(dnsvalue) if values is None: continue for val_id, val in enumerate(values): if val is not None: #decode IDN if isinstance(parts_params[val_id], DNSNameParam): dnsentry[parts_params[val_id].name] = \ _dns_name_to_string(val, options.get('raw', False)) else: dnsentry[parts_params[val_id].name] = val record.setdefault('dnsrecords', []).append(dnsentry) del record[attr] elif not options.get('raw', False): #Decode IDN ACE form to Unicode, raw records are passed directly from LDAP _records_idn_postprocess(record, **options) def get_rrparam_from_part(self, part_name): """ Get an instance of DNSRecord parameter that has part_name as its part. If such parameter is not found, None is returned :param part_name Part parameter name """ try: param = self.params[part_name] if not any(flag in param.flags for flag in \ ('dnsrecord_part', 'dnsrecord_extra')): return None # All DNS record part or extra parameters contain a name of its # parent RR parameter in its hint attribute rrparam = self.params[param.hint] except (KeyError, AttributeError): return None return rrparam def iterate_rrparams_by_parts(self, kw, skip_extra=False): """ Iterates through all DNSRecord instances that has at least one of its parts or extra options in given dictionary. It returns the DNSRecord instance only for the first occurence of part/extra option. :param kw Dictionary with DNS record parts or extra options :param skip_extra Skip DNS record extra options, yield only DNS records with a real record part """ processed = [] for opt in kw: rrparam = self.get_rrparam_from_part(opt) if rrparam is None: continue if skip_extra and 'dnsrecord_extra' in self.params[opt].flags: continue if rrparam.name not in processed: processed.append(rrparam.name) yield rrparam def updated_rrattrs(self, old_entry, entry_attrs): """Returns updated RR attributes """ rrattrs = {} if old_entry is not None: old_rrattrs = dict((key, value) for key, value in old_entry.iteritems() if key in self.params and isinstance(self.params[key], DNSRecord)) rrattrs.update(old_rrattrs) new_rrattrs = dict((key, value) for key, value in entry_attrs.iteritems() if key in self.params and isinstance(self.params[key], DNSRecord)) rrattrs.update(new_rrattrs) return rrattrs def check_record_type_collisions(self, keys, rrattrs): # Test that only allowed combination of record types was created # CNAME record validation cnames = rrattrs.get('cnamerecord') if cnames is not None: if len(cnames) > 1: raise errors.ValidationError(name='cnamerecord', error=_('only one CNAME record is allowed per name ' '(RFC 2136, section 1.1.5)')) if any(rrvalue is not None and rrattr != 'cnamerecord' for rrattr, rrvalue in rrattrs.iteritems()): raise errors.ValidationError(name='cnamerecord', error=_('CNAME record is not allowed to coexist ' 'with any other record (RFC 1034, section 3.6.2)')) # DNAME record validation dnames = rrattrs.get('dnamerecord') if dnames is not None: if len(dnames) > 1: raise errors.ValidationError(name='dnamerecord', error=_('only one DNAME record is allowed per name ' '(RFC 6672, section 2.4)')) # DNAME must not coexist with CNAME, but this is already checked earlier # NS record validation # NS record can coexist only with A, AAAA, DS, and other NS records (except zone apex) # RFC 2181 section 6.1, allowed_records = ['AAAA', 'A', 'DS', 'NS'] nsrecords = rrattrs.get('nsrecord') if nsrecords and not self.is_pkey_zone_record(*keys): for r_type in _record_types: if (r_type not in allowed_records and rrattrs.get('%srecord' % r_type.lower()) ): raise errors.ValidationError( name='nsrecord', error=_('NS record is not allowed to coexist with an ' '%(type)s record except when located in a ' 'zone root record (RFC 2181, section 6.1)') % {'type': r_type}) def check_record_type_dependencies(self, keys, rrattrs): # Test that all record type dependencies are satisfied # DS record validation # DS record requires to coexists with NS record dsrecords = rrattrs.get('dsrecord') nsrecords = rrattrs.get('nsrecord') # DS record cannot be in zone apex, checked in pre-callback validators if dsrecords and not nsrecords: raise errors.ValidationError( name='dsrecord', error=_('DS record requires to coexist with an ' 'NS record (RFC 4592 section 4.6, RFC 4035 section 2.4)')) def _entry2rrsets(self, entry_attrs, dns_name, dns_domain): '''Convert entry_attrs to a dictionary {rdtype: rrset}. :returns: None if entry_attrs is None {rdtype: None} if RRset of given type is empty {rdtype: RRset} if RRset of given type is non-empty ''' record_attr_suf = 'record' ldap_rrsets = {} if not entry_attrs: # all records were deleted => name should not exist in DNS return None for attr, value in entry_attrs.iteritems(): if not attr.endswith(record_attr_suf): continue rdtype = dns.rdatatype.from_text(attr[0:-len(record_attr_suf)]) if not value: ldap_rrsets[rdtype] = None # RRset is empty continue try: # TTL here can be arbitrary value because it is ignored # during comparison ldap_rrset = dns.rrset.from_text( dns_name, 86400, dns.rdataclass.IN, rdtype, *map(str, value)) # make sure that all names are absolute so RRset # comparison will work for ldap_rr in ldap_rrset: ldap_rr.choose_relativity(origin=dns_domain, relativize=False) ldap_rrsets[rdtype] = ldap_rrset except dns.exception.SyntaxError as e: self.log.error('DNS syntax error: %s %s %s: %s', dns_name, dns.rdatatype.to_text(rdtype), value, e) raise return ldap_rrsets def wait_for_modified_attr(self, ldap_rrset, rdtype, dns_name): '''Wait until DNS resolver returns up-to-date answer for given RRset or until the maximum number of attempts is reached. Number of attempts is controlled by self.api.env['wait_for_dns']. :param ldap_rrset: None if given rdtype should not exist or dns.rrset.RRset to match against data in DNS. :param dns_name: FQDN to query :type dns_name: dns.name.Name :return: None if data in DNS and LDAP match :raises errors.DNSDataMismatch: if data in DNS and LDAP doesn't match :raises dns.exception.DNSException: if DNS resolution failed ''' resolver = dns.resolver.Resolver() resolver.set_flags(0) # disable recursion (for NS RR checks) max_attempts = int(self.api.env['wait_for_dns']) warn_attempts = max_attempts / 2 period = 1 # second attempt = 0 log_fn = self.log.debug log_fn('querying DNS server: expecting answer {%s}', ldap_rrset) wait_template = 'waiting for DNS answer {%s}: got {%s} (attempt %s); '\ 'waiting %s seconds before next try' while attempt < max_attempts: if attempt >= warn_attempts: log_fn = self.log.warn attempt += 1 try: dns_answer = resolver.query(dns_name, rdtype, dns.rdataclass.IN, raise_on_no_answer=False) dns_rrset = None if rdtype == _NS: # NS records can be in Authority section (sometimes) dns_rrset = dns_answer.response.get_rrset( dns_answer.response.authority, dns_name, _IN, rdtype) if not dns_rrset: # Look for NS and other data in Answer section dns_rrset = dns_answer.rrset if dns_rrset == ldap_rrset: log_fn('DNS answer matches expectations (attempt %s)', attempt) return log_msg = wait_template % (ldap_rrset, dns_answer.response, attempt, period) except (dns.resolver.NXDOMAIN, dns.resolver.YXDOMAIN, dns.resolver.NoNameservers, dns.resolver.Timeout) as e: if attempt >= max_attempts: raise else: log_msg = wait_template % (ldap_rrset, type(e), attempt, period) log_fn(log_msg) time.sleep(period) # Maximum number of attempts was reached else: raise errors.DNSDataMismatch(expected=ldap_rrset, got=dns_rrset) def wait_for_modified_attrs(self, entry_attrs, dns_name, dns_domain): '''Wait until DNS resolver returns up-to-date answer for given entry or until the maximum number of attempts is reached. :param entry_attrs: None if the entry was deleted from LDAP or LDAPEntry instance containing at least all modified attributes. :param dns_name: FQDN :type dns_name: dns.name.Name :raises errors.DNSDataMismatch: if data in DNS and LDAP doesn't match ''' # represent data in LDAP as dictionary rdtype => rrset ldap_rrsets = self._entry2rrsets(entry_attrs, dns_name, dns_domain) nxdomain = ldap_rrsets is None if nxdomain: # name should not exist => ask for A record and check result ldap_rrsets = {dns.rdatatype.from_text('A'): None} for rdtype, ldap_rrset in ldap_rrsets.iteritems(): try: self.wait_for_modified_attr(ldap_rrset, rdtype, dns_name) except dns.resolver.NXDOMAIN as e: if nxdomain: continue else: e = errors.DNSDataMismatch(expected=ldap_rrset, got="NXDOMAIN") self.log.error(e) raise e except dns.resolver.NoNameservers as e: # Do not raise exception if we have got SERVFAILs. # Maybe the user has created an invalid zone intentionally. self.log.warn('waiting for DNS answer {%s}: got {%s}; ' 'ignoring', ldap_rrset, type(e)) continue except dns.exception.DNSException as e: err_desc = str(type(e)) err_str = str(e) if err_str: err_desc += ": %s" % err_str e = errors.DNSDataMismatch(expected=ldap_rrset, got=err_desc) self.log.error(e) raise e def wait_for_modified_entries(self, entries): '''Call wait_for_modified_attrs for all entries in given dict. :param entries: Dict {(dns_domain, dns_name): entry_for_wait_for_modified_attrs} ''' for entry_name, entry in entries.iteritems(): dns_domain = entry_name[0] dns_name = entry_name[1].derelativize(dns_domain) self.wait_for_modified_attrs(entry, dns_name, dns_domain) def warning_if_ns_change_cause_fwzone_ineffective(self, result, *keys, **options): """Detect if NS record change can make forward zones ineffective due missing delegation. Run after parent's execute method. """ record_name_absolute = keys[-1] zone = keys[-2] if not record_name_absolute.is_absolute(): record_name_absolute = record_name_absolute.derelativize(zone) affected_fw_zones, truncated = _find_subtree_forward_zones_ldap( record_name_absolute) if not affected_fw_zones: return for fwzone in affected_fw_zones: _add_warning_fw_zone_is_not_effective(result, fwzone, options['version']) @register() class dnsrecord_add(LDAPCreate): __doc__ = _('Add new DNS resource record.') no_option_msg = 'No options to add a specific record provided.\n' \ "Command help may be consulted for all supported record types." takes_options = LDAPCreate.takes_options + ( Flag('force', label=_('Force'), flags=['no_option', 'no_output'], doc=_('force NS record creation even if its hostname is not in DNS'), ), dnsrecord.structured_flag, ) def args_options_2_entry(self, *keys, **options): self.obj.has_cli_options(options, self.no_option_msg) return super(dnsrecord_add, self).args_options_2_entry(*keys, **options) def interactive_prompt_callback(self, kw): try: self.obj.has_cli_options(kw, self.no_option_msg) # Some DNS records were entered, do not use full interactive help # We should still ask user for required parts of DNS parts he is # trying to add in the same way we do for standard LDAP parameters # # Do not ask for required parts when any "extra" option is used, # it can be used to fill all required params by itself new_kw = {} for rrparam in self.obj.iterate_rrparams_by_parts(kw, skip_extra=True): user_options = rrparam.prompt_missing_parts(self, kw, prompt_optional=False) new_kw.update(user_options) kw.update(new_kw) return except errors.OptionError: pass try: idnsname = DNSName(kw['idnsname']) except Exception as e: raise errors.ValidationError(name='idnsname', error=unicode(e)) try: zonename = DNSName(kw['dnszoneidnsname']) except Exception as e: raise errors.ValidationError(name='dnszoneidnsname', error=unicode(e)) # check zone type if idnsname.is_empty(): common_types = u', '.join(_zone_top_record_types) elif zonename.is_reverse(): common_types = u', '.join(_rev_top_record_types) else: common_types = u', '.join(_top_record_types) self.Backend.textui.print_plain(_(u'Please choose a type of DNS resource record to be added')) self.Backend.textui.print_plain(_(u'The most common types for this type of zone are: %s\n') %\ common_types) ok = False while not ok: rrtype = self.Backend.textui.prompt(_(u'DNS resource record type')) if rrtype is None: return try: name = '%srecord' % rrtype.lower() param = self.params[name] if not isinstance(param, DNSRecord): raise ValueError() if not param.supported: raise ValueError() except (KeyError, ValueError): all_types = u', '.join(_dns_supported_record_types) self.Backend.textui.print_plain(_(u'Invalid or unsupported type. Allowed values are: %s') % all_types) continue ok = True user_options = param.prompt_parts(self) kw.update(user_options) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) precallback_attrs = [] processed_attrs = [] for option in options: try: param = self.params[option] except KeyError: continue rrparam = self.obj.get_rrparam_from_part(option) if rrparam is None: continue if 'dnsrecord_part' in param.flags: if rrparam.name in processed_attrs: # this record was already entered continue if rrparam.name in entry_attrs: # this record is entered both via parts and raw records raise errors.ValidationError(name=param.cli_name or param.name, error=_('Raw value of a DNS record was already set by "%(name)s" option') \ % dict(name=rrparam.cli_name or rrparam.name)) parts = rrparam.get_parts_from_kw(options) dnsvalue = [rrparam._convert_scalar(parts)] entry_attrs[rrparam.name] = dnsvalue processed_attrs.append(rrparam.name) continue if 'dnsrecord_extra' in param.flags: # do not run precallback for unset flags if isinstance(param, Flag) and not options[option]: continue # extra option is passed, run per-type pre_callback for given RR type precallback_attrs.append(rrparam.name) # Run pre_callback validators self.obj.run_precallback_validators(dn, entry_attrs, *keys, **options) # run precallback also for all new RR type attributes in entry_attrs for attr in entry_attrs.keys(): try: param = self.params[attr] except KeyError: continue if not isinstance(param, DNSRecord): continue precallback_attrs.append(attr) precallback_attrs = list(set(precallback_attrs)) for attr in precallback_attrs: # run per-type try: param = self.params[attr] except KeyError: continue param.dnsrecord_add_pre_callback(ldap, dn, entry_attrs, attrs_list, *keys, **options) # Store all new attrs so that DNSRecord post callback is called for # new attributes only and not for all attributes in the LDAP entry setattr(context, 'dnsrecord_precallback_attrs', precallback_attrs) # We always want to retrieve all DNS record attributes to test for # record type collisions (#2601) try: old_entry = ldap.get_entry(dn, _record_attributes) except errors.NotFound: old_entry = None else: for attr in entry_attrs.keys(): if attr not in _record_attributes: continue if entry_attrs[attr] is None: entry_attrs[attr] = [] if not isinstance(entry_attrs[attr], (tuple, list)): vals = [entry_attrs[attr]] else: vals = list(entry_attrs[attr]) entry_attrs[attr] = list(set(old_entry.get(attr, []) + vals)) rrattrs = self.obj.updated_rrattrs(old_entry, entry_attrs) self.obj.check_record_type_dependencies(keys, rrattrs) self.obj.check_record_type_collisions(keys, rrattrs) context.dnsrecord_entry_mods = getattr(context, 'dnsrecord_entry_mods', {}) context.dnsrecord_entry_mods[(keys[0], keys[1])] = entry_attrs.copy() return dn def exc_callback(self, keys, options, exc, call_func, *call_args, **call_kwargs): if call_func.__name__ == 'add_entry': if isinstance(exc, errors.DuplicateEntry): # A new record is being added to existing LDAP DNS object # Update can be safely run as old record values has been # already merged in pre_callback ldap = self.obj.backend entry_attrs = self.obj.get_record_entry_attrs(call_args[0]) update = ldap.get_entry(entry_attrs.dn, entry_attrs.keys()) update.update(entry_attrs) ldap.update_entry(update, **call_kwargs) return raise exc def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) for attr in getattr(context, 'dnsrecord_precallback_attrs', []): param = self.params[attr] param.dnsrecord_add_post_callback(ldap, dn, entry_attrs, *keys, **options) if self.obj.is_pkey_zone_record(*keys): entry_attrs[self.obj.primary_key.name] = [_dns_zone_record] self.obj.postprocess_record(entry_attrs, **options) if self.api.env['wait_for_dns']: self.obj.wait_for_modified_entries(context.dnsrecord_entry_mods) return dn @register() class dnsrecord_mod(LDAPUpdate): __doc__ = _('Modify a DNS resource record.') no_option_msg = 'No options to modify a specific record provided.' takes_options = LDAPUpdate.takes_options + ( dnsrecord.structured_flag, ) def args_options_2_entry(self, *keys, **options): self.obj.has_cli_options(options, self.no_option_msg, True) return super(dnsrecord_mod, self).args_options_2_entry(*keys, **options) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) if options.get('rename') and self.obj.is_pkey_zone_record(*keys): # zone rename is not allowed raise errors.ValidationError(name='rename', error=_('DNS zone root record cannot be renamed')) # check if any attr should be updated using structured instead of replaced # format is recordname : (old_value, new_parts) updated_attrs = {} for param in self.obj.iterate_rrparams_by_parts(options, skip_extra=True): parts = param.get_parts_from_kw(options, raise_on_none=False) if parts is None: # old-style modification continue old_value = entry_attrs.get(param.name) if not old_value: raise errors.RequirementError(name=param.name) if isinstance(old_value, (tuple, list)): if len(old_value) > 1: raise errors.ValidationError(name=param.name, error=_('DNS records can be only updated one at a time')) old_value = old_value[0] updated_attrs[param.name] = (old_value, parts) # Run pre_callback validators self.obj.run_precallback_validators(dn, entry_attrs, *keys, **options) # current entry is needed in case of per-dns-record-part updates and # for record type collision check try: old_entry = ldap.get_entry(dn, _record_attributes) except errors.NotFound: self.obj.handle_not_found(*keys) if updated_attrs: for attr in updated_attrs: param = self.params[attr] old_dnsvalue, new_parts = updated_attrs[attr] if old_dnsvalue not in old_entry.get(attr, []): attr_name = unicode(param.label or param.name) raise errors.AttrValueNotFound(attr=attr_name, value=old_dnsvalue) old_entry[attr].remove(old_dnsvalue) old_parts = param._get_part_values(old_dnsvalue) modified_parts = tuple(part if part is not None else old_parts[part_id] \ for part_id,part in enumerate(new_parts)) new_dnsvalue = [param._convert_scalar(modified_parts)] entry_attrs[attr] = list(set(old_entry[attr] + new_dnsvalue)) rrattrs = self.obj.updated_rrattrs(old_entry, entry_attrs) self.obj.check_record_type_dependencies(keys, rrattrs) self.obj.check_record_type_collisions(keys, rrattrs) context.dnsrecord_entry_mods = getattr(context, 'dnsrecord_entry_mods', {}) context.dnsrecord_entry_mods[(keys[0], keys[1])] = entry_attrs.copy() return dn def execute(self, *keys, **options): result = super(dnsrecord_mod, self).execute(*keys, **options) # remove if empty if not self.obj.is_pkey_zone_record(*keys): rename = options.get('rename') if rename is not None: keys = keys[:-1] + (rename,) dn = self.obj.get_dn(*keys, **options) ldap = self.obj.backend old_entry = ldap.get_entry(dn, _record_attributes) del_all = True for attr in old_entry.keys(): if old_entry[attr]: del_all = False break if del_all: result = self.obj.methods.delentry(*keys, version=options['version']) # we need to modify delete result to match mod output type # only one value is expected, not a list if client_has_capability(options['version'], 'primary_key_types'): assert len(result['value']) == 1 result['value'] = result['value'][0] # indicate that entry was deleted context.dnsrecord_entry_mods[(keys[0], keys[1])] = None if self.api.env['wait_for_dns']: self.obj.wait_for_modified_entries(context.dnsrecord_entry_mods) if 'nsrecord' in options: self.obj.warning_if_ns_change_cause_fwzone_ineffective(result, *keys, **options) return result def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) if self.obj.is_pkey_zone_record(*keys): entry_attrs[self.obj.primary_key.name] = [_dns_zone_record] self.obj.postprocess_record(entry_attrs, **options) return dn def interactive_prompt_callback(self, kw): try: self.obj.has_cli_options(kw, self.no_option_msg, True) except errors.OptionError: pass else: # some record type entered, skip this helper return # get DNS record first so that the NotFound exception is raised # before the helper would start dns_record = api.Command['dnsrecord_show'](kw['dnszoneidnsname'], kw['idnsname'])['result'] rec_types = [rec_type for rec_type in dns_record if rec_type in _record_attributes] self.Backend.textui.print_plain(_("No option to modify specific record provided.")) # ask user for records to be removed self.Backend.textui.print_plain(_(u'Current DNS record contents:\n')) record_params = [] for attr in dns_record: try: param = self.params[attr] except KeyError: continue if not isinstance(param, DNSRecord): continue record_params.append(param) rec_type_content = u', '.join(dns_record[param.name]) self.Backend.textui.print_plain(u'%s: %s' % (param.label, rec_type_content)) self.Backend.textui.print_plain(u'') # ask what records to remove for param in record_params: rec_values = list(dns_record[param.name]) for rec_value in dns_record[param.name]: rec_values.remove(rec_value) mod_value = self.Backend.textui.prompt_yesno( _("Modify %(name)s '%(value)s'?") % dict(name=param.label, value=rec_value), default=False) if mod_value is True: user_options = param.prompt_parts(self, mod_dnsvalue=rec_value) kw[param.name] = [rec_value] kw.update(user_options) if rec_values: self.Backend.textui.print_plain(ngettext( u'%(count)d %(type)s record skipped. Only one value per DNS record type can be modified at one time.', u'%(count)d %(type)s records skipped. Only one value per DNS record type can be modified at one time.', 0) % dict(count=len(rec_values), type=param.rrtype)) break @register() class dnsrecord_delentry(LDAPDelete): """ Delete DNS record entry. """ msg_summary = _('Deleted record "%(value)s"') NO_CLI = True @register() class dnsrecord_del(LDAPUpdate): __doc__ = _('Delete DNS resource record.') has_output = output.standard_multi_delete no_option_msg = _('Neither --del-all nor options to delete a specific record provided.\n'\ "Command help may be consulted for all supported record types.") takes_options = ( Flag('del_all', default=False, label=_('Delete all associated records'), ), dnsrecord.structured_flag, ) def get_options(self): for option in super(dnsrecord_del, self).get_options(): if any(flag in option.flags for flag in \ ('dnsrecord_part', 'dnsrecord_extra',)): continue elif option.name in ('rename', ): # options only valid for dnsrecord-mod continue elif isinstance(option, DNSRecord): yield option.clone(option_group=None) continue yield option def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) try: old_entry = ldap.get_entry(dn, _record_attributes) except errors.NotFound: self.obj.handle_not_found(*keys) for attr in entry_attrs.keys(): if attr not in _record_attributes: continue if not isinstance(entry_attrs[attr], (tuple, list)): vals = [entry_attrs[attr]] else: vals = entry_attrs[attr] for val in vals: try: old_entry[attr].remove(val) except (KeyError, ValueError): try: param = self.params[attr] attr_name = unicode(param.label or param.name) except: attr_name = attr raise errors.AttrValueNotFound(attr=attr_name, value=val) entry_attrs[attr] = list(set(old_entry[attr])) rrattrs = self.obj.updated_rrattrs(old_entry, entry_attrs) self.obj.check_record_type_dependencies(keys, rrattrs) del_all = False if not self.obj.is_pkey_zone_record(*keys): record_found = False for attr in old_entry.keys(): if old_entry[attr]: record_found = True break del_all = not record_found # set del_all flag in context # when the flag is enabled, the entire DNS record object is deleted # in a post callback context.del_all = del_all context.dnsrecord_entry_mods = getattr(context, 'dnsrecord_entry_mods', {}) context.dnsrecord_entry_mods[(keys[0], keys[1])] = entry_attrs.copy() return dn def execute(self, *keys, **options): if options.get('del_all', False): if self.obj.is_pkey_zone_record(*keys): raise errors.ValidationError( name='del_all', error=_('Zone record \'%s\' cannot be deleted') \ % _dns_zone_record ) result = self.obj.methods.delentry(*keys, version=options['version']) if self.api.env['wait_for_dns']: entries = {(keys[0], keys[1]): None} self.obj.wait_for_modified_entries(entries) else: result = super(dnsrecord_del, self).execute(*keys, **options) result['value'] = pkey_to_value([keys[-1]], options) if getattr(context, 'del_all', False) and not \ self.obj.is_pkey_zone_record(*keys): result = self.obj.methods.delentry(*keys, version=options['version']) context.dnsrecord_entry_mods[(keys[0], keys[1])] = None if self.api.env['wait_for_dns']: self.obj.wait_for_modified_entries(context.dnsrecord_entry_mods) if 'nsrecord' in options or options.get('del_all', False): self.obj.warning_if_ns_change_cause_fwzone_ineffective(result, *keys, **options) return result def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) if self.obj.is_pkey_zone_record(*keys): entry_attrs[self.obj.primary_key.name] = [_dns_zone_record] self.obj.postprocess_record(entry_attrs, **options) return dn def args_options_2_entry(self, *keys, **options): self.obj.has_cli_options(options, self.no_option_msg) return super(dnsrecord_del, self).args_options_2_entry(*keys, **options) def interactive_prompt_callback(self, kw): if kw.get('del_all', False): return try: self.obj.has_cli_options(kw, self.no_option_msg) except errors.OptionError: pass else: # some record type entered, skip this helper return # get DNS record first so that the NotFound exception is raised # before the helper would start dns_record = api.Command['dnsrecord_show'](kw['dnszoneidnsname'], kw['idnsname'])['result'] rec_types = [rec_type for rec_type in dns_record if rec_type in _record_attributes] self.Backend.textui.print_plain(_("No option to delete specific record provided.")) user_del_all = self.Backend.textui.prompt_yesno(_("Delete all?"), default=False) if user_del_all is True: kw['del_all'] = True return # ask user for records to be removed self.Backend.textui.print_plain(_(u'Current DNS record contents:\n')) present_params = [] for attr in dns_record: try: param = self.params[attr] except KeyError: continue if not isinstance(param, DNSRecord): continue present_params.append(param) rec_type_content = u', '.join(dns_record[param.name]) self.Backend.textui.print_plain(u'%s: %s' % (param.label, rec_type_content)) self.Backend.textui.print_plain(u'') # ask what records to remove for param in present_params: deleted_values = [] for rec_value in dns_record[param.name]: user_del_value = self.Backend.textui.prompt_yesno( _("Delete %(name)s '%(value)s'?") % dict(name=param.label, value=rec_value), default=False) if user_del_value is True: deleted_values.append(rec_value) if deleted_values: kw[param.name] = tuple(deleted_values) @register() class dnsrecord_show(LDAPRetrieve): __doc__ = _('Display DNS resource.') takes_options = LDAPRetrieve.takes_options + ( dnsrecord.structured_flag, ) def post_callback(self, ldap, dn, entry_attrs, *keys, **options): assert isinstance(dn, DN) if self.obj.is_pkey_zone_record(*keys): entry_attrs[self.obj.primary_key.name] = [_dns_zone_record] self.obj.postprocess_record(entry_attrs, **options) return dn @register() class dnsrecord_find(LDAPSearch): __doc__ = _('Search for DNS resources.') takes_options = LDAPSearch.takes_options + ( dnsrecord.structured_flag, ) def get_options(self): for option in super(dnsrecord_find, self).get_options(): if any(flag in option.flags for flag in \ ('dnsrecord_part', 'dnsrecord_extra',)): continue elif isinstance(option, DNSRecord): yield option.clone(option_group=None) continue yield option def pre_callback(self, ldap, filter, attrs_list, base_dn, scope, *args, **options): assert isinstance(base_dn, DN) # validate if zone is master zone self.obj.check_zone(args[-2], **options) filter = _create_idn_filter(self, ldap, *args, **options) return (filter, base_dn, ldap.SCOPE_SUBTREE) def post_callback(self, ldap, entries, truncated, *args, **options): if entries: zone_obj = self.api.Object[self.obj.parent_object] zone_dn = zone_obj.get_dn(args[0]) if entries[0].dn == zone_dn: entries[0][zone_obj.primary_key.name] = [_dns_zone_record] for entry in entries: self.obj.postprocess_record(entry, **options) return truncated @register() class dns_resolve(Command): __doc__ = _('Resolve a host name in DNS.') has_output = output.standard_value msg_summary = _('Found \'%(value)s\'') takes_args = ( Str('hostname', label=_('Hostname'), ), ) def execute(self, *args, **options): query=args[0] if query.find(api.env.domain) == -1 and query.find('.') == -1: query = '%s.%s.' % (query, api.env.domain) if query[-1] != '.': query = query + '.' if not is_host_resolvable(query): raise errors.NotFound( reason=_('Host \'%(host)s\' not found') % {'host': query} ) return dict(result=True, value=query) @register() class dns_is_enabled(Command): """ Checks if any of the servers has the DNS service enabled. """ NO_CLI = True has_output = output.standard_value base_dn = DN(('cn', 'masters'), ('cn', 'ipa'), ('cn', 'etc'), api.env.basedn) filter = '(&(objectClass=ipaConfigObject)(cn=DNS))' def execute(self, *args, **options): ldap = self.api.Backend.ldap2 dns_enabled = False try: ent = ldap.find_entries(filter=self.filter, base_dn=self.base_dn) if len(ent): dns_enabled = True except Exception as e: pass return dict(result=dns_enabled, value=pkey_to_value(None, options)) @register() class dnsconfig(LDAPObject): """ DNS global configuration object """ object_name = _('DNS configuration options') default_attributes = [ 'idnsforwardpolicy', 'idnsforwarders', 'idnsallowsyncptr' ] label = _('DNS Global Configuration') label_singular = _('DNS Global Configuration') takes_params = ( Str('idnsforwarders*', _validate_bind_forwarder, cli_name='forwarder', label=_('Global forwarders'), doc=_('Global forwarders. A custom port can be specified for each ' 'forwarder using a standard format "IP_ADDRESS port PORT"'), csv=True, ), StrEnum('idnsforwardpolicy?', cli_name='forward_policy', label=_('Forward policy'), doc=_('Global forwarding policy. Set to "none" to disable ' 'any configured global forwarders.'), values=(u'only', u'first', u'none'), ), Bool('idnsallowsyncptr?', cli_name='allow_sync_ptr', label=_('Allow PTR sync'), doc=_('Allow synchronization of forward (A, AAAA) and reverse (PTR) records'), ), DeprecatedParam('idnszonerefresh?', cli_name='zone_refresh', label=_('Zone refresh interval'), ), ) managed_permissions = { 'System: Write DNS Configuration': { 'non_object': True, 'ipapermright': {'write'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('cn=dns', api.env.basedn), 'ipapermtargetfilter': ['(objectclass=idnsConfigObject)'], 'ipapermdefaultattr': { 'idnsallowsyncptr', 'idnsforwarders', 'idnsforwardpolicy', 'idnspersistentsearch', 'idnszonerefresh' }, 'replaces': [ '(targetattr = "idnsforwardpolicy || idnsforwarders || idnsallowsyncptr || idnszonerefresh || idnspersistentsearch")(target = "ldap:///cn=dns,$SUFFIX")(version 3.0;acl "permission:Write DNS Configuration";allow (write) groupdn = "ldap:///cn=Write DNS Configuration,cn=permissions,cn=pbac,$SUFFIX";)', ], 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, 'System: Read DNS Configuration': { 'non_object': True, 'ipapermright': {'read'}, 'ipapermlocation': api.env.basedn, 'ipapermtarget': DN('cn=dns', api.env.basedn), 'ipapermtargetfilter': ['(objectclass=idnsConfigObject)'], 'ipapermdefaultattr': { 'objectclass', 'idnsallowsyncptr', 'idnsforwarders', 'idnsforwardpolicy', 'idnspersistentsearch', 'idnszonerefresh' }, 'default_privileges': {'DNS Administrators', 'DNS Servers'}, }, } def get_dn(self, *keys, **kwargs): if not dns_container_exists(self.api.Backend.ldap2): raise errors.NotFound(reason=_('DNS is not configured')) return DN(api.env.container_dns, api.env.basedn) def get_dnsconfig(self, ldap): entry = ldap.get_entry(self.get_dn(), None) return entry def postprocess_result(self, result): if not any(param in result['result'] for param in self.params): result['summary'] = unicode(_('Global DNS configuration is empty')) @register() class dnsconfig_mod(LDAPUpdate): __doc__ = _('Modify global DNS configuration.') def interactive_prompt_callback(self, kw): # show informative message on client side # server cannot send messages asynchronous if kw.get('idnsforwarders', False): self.Backend.textui.print_plain( _("Server will check DNS forwarder(s).")) self.Backend.textui.print_plain( _("This may take some time, please wait ...")) def execute(self, *keys, **options): # test dnssec forwarders forwarders = options.get('idnsforwarders') result = super(dnsconfig_mod, self).execute(*keys, **options) self.obj.postprocess_result(result) if forwarders: for forwarder in forwarders: try: validate_dnssec_global_forwarder(forwarder, log=self.log) except DNSSECSignatureMissingError as e: messages.add_message( options['version'], result, messages.DNSServerDoesNotSupportDNSSECWarning( server=forwarder, error=e, ) ) except EDNS0UnsupportedError as e: messages.add_message( options['version'], result, messages.DNSServerDoesNotSupportEDNS0Warning( server=forwarder, error=e, ) ) except UnresolvableRecordError as e: messages.add_message( options['version'], result, messages.DNSServerValidationWarning( server=forwarder, error=e ) ) return result @register() class dnsconfig_show(LDAPRetrieve): __doc__ = _('Show the current global DNS configuration.') def execute(self, *keys, **options): result = super(dnsconfig_show, self).execute(*keys, **options) self.obj.postprocess_result(result) return result @register() class dnsforwardzone(DNSZoneBase): """ DNS Forward zone, container for resource records. """ object_name = _('DNS forward zone') object_name_plural = _('DNS forward zones') object_class = DNSZoneBase.object_class + ['idnsforwardzone'] label = _('DNS Forward Zones') label_singular = _('DNS Forward Zone') default_forward_policy = u'first' # managed_permissions: permissions was apllied in dnszone class, do NOT # add them here, they should not be applied twice. def _warning_fw_zone_is_not_effective(self, result, *keys, **options): fwzone = keys[-1] _add_warning_fw_zone_is_not_effective(result, fwzone, options['version']) def _warning_if_forwarders_do_not_work(self, result, new_zone, *keys, **options): fwzone = keys[-1] forwarders = options.get('idnsforwarders', []) any_forwarder_work = False for forwarder in forwarders: try: validate_dnssec_zone_forwarder_step1(forwarder, fwzone, log=self.log) except UnresolvableRecordError as e: messages.add_message( options['version'], result, messages.DNSServerValidationWarning( server=forwarder, error=e ) ) except EDNS0UnsupportedError as e: messages.add_message( options['version'], result, messages.DNSServerDoesNotSupportEDNS0Warning( server=forwarder, error=e ) ) else: any_forwarder_work = True if not any_forwarder_work: # do not test DNSSEC validation if there is no valid forwarder return # resolve IP address of any DNS replica # FIXME: https://fedorahosted.org/bind-dyndb-ldap/ticket/143 # we currenly should to test all IPA DNS replica, because DNSSEC # validation is configured just in named.conf per replica ipa_dns_masters = [normalize_zone(x) for x in api.Object.dnsrecord.get_dns_masters()] if not ipa_dns_masters: # something very bad happened, DNS is installed, but no IPA DNS # servers available self.log.error("No IPA DNS server can be found, but integrated DNS " "is installed") return ipa_dns_ip = None for rdtype in (dns.rdatatype.A, dns.rdatatype.AAAA): try: ans = dns.resolver.query(ipa_dns_masters[0], rdtype) except dns.exception.DNSException: continue else: ipa_dns_ip = str(ans.rrset.items[0]) break if not ipa_dns_ip: self.log.error("Cannot resolve %s hostname", ipa_dns_masters[0]) return # sleep a bit, adding new zone to BIND from LDAP may take a while if new_zone: time.sleep(5) # Test if IPA is able to receive replies from forwarders try: validate_dnssec_zone_forwarder_step2(ipa_dns_ip, fwzone, log=self.log) except DNSSECValidationError as e: messages.add_message( options['version'], result, messages.DNSSECValidationFailingWarning(error=e) ) except UnresolvableRecordError as e: messages.add_message( options['version'], result, messages.DNSServerValidationWarning( server=ipa_dns_ip, error=e ) ) @register() class dnsforwardzone_add(DNSZoneBase_add): __doc__ = _('Create new DNS forward zone.') def interactive_prompt_callback(self, kw): # show informative message on client side # server cannot send messages asynchronous if kw.get('idnsforwarders', False): self.Backend.textui.print_plain( _("Server will check DNS forwarder(s).")) self.Backend.textui.print_plain( _("This may take some time, please wait ...")) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): assert isinstance(dn, DN) dn = super(dnsforwardzone_add, self).pre_callback(ldap, dn, entry_attrs, attrs_list, *keys, **options) if 'idnsforwardpolicy' not in entry_attrs: entry_attrs['idnsforwardpolicy'] = self.obj.default_forward_policy if (not entry_attrs.get('idnsforwarders') and entry_attrs['idnsforwardpolicy'] != u'none'): raise errors.ValidationError(name=u'idnsforwarders', error=_('Please specify forwarders.')) return dn def execute(self, *keys, **options): result = super(dnsforwardzone_add, self).execute(*keys, **options) self.obj._warning_fw_zone_is_not_effective(result, *keys, **options) if options.get('idnsforwarders'): print result, keys, options self.obj._warning_if_forwarders_do_not_work( result, True, *keys, **options) return result @register() class dnsforwardzone_del(DNSZoneBase_del): __doc__ = _('Delete DNS forward zone.') msg_summary = _('Deleted DNS forward zone "%(value)s"') @register() class dnsforwardzone_mod(DNSZoneBase_mod): __doc__ = _('Modify DNS forward zone.') def interactive_prompt_callback(self, kw): # show informative message on client side # server cannot send messages asynchronous if kw.get('idnsforwarders', False): self.Backend.textui.print_plain( _("Server will check DNS forwarder(s).")) self.Backend.textui.print_plain( _("This may take some time, please wait ...")) def pre_callback(self, ldap, dn, entry_attrs, attrs_list, *keys, **options): try: entry = ldap.get_entry(dn) except errors.NotFound: self.obj.handle_not_found(*keys) if not _check_entry_objectclass(entry, self.obj.object_class): self.obj.handle_not_found(*keys) policy = self.obj.default_forward_policy forwarders = [] if 'idnsforwarders' in entry_attrs: forwarders = entry_attrs['idnsforwarders'] elif 'idnsforwarders' in entry: forwarders = entry['idnsforwarders'] if 'idnsforwardpolicy' in entry_attrs: policy = entry_attrs['idnsforwardpolicy'] elif 'idnsforwardpolicy' in entry: policy = entry['idnsforwardpolicy'] if not forwarders and policy != u'none': raise errors.ValidationError(name=u'idnsforwarders', error=_('Please specify forwarders.')) return dn def execute(self, *keys, **options): result = super(dnsforwardzone_mod, self).execute(*keys, **options) if options.get('idnsforwarders'): self.obj._warning_if_forwarders_do_not_work(result, False, *keys, **options) return result @register() class dnsforwardzone_find(DNSZoneBase_find): __doc__ = _('Search for DNS forward zones.') @register() class dnsforwardzone_show(DNSZoneBase_show): __doc__ = _('Display information about a DNS forward zone.') has_output_params = LDAPRetrieve.has_output_params + dnszone_output_params @register() class dnsforwardzone_disable(DNSZoneBase_disable): __doc__ = _('Disable DNS Forward Zone.') msg_summary = _('Disabled DNS forward zone "%(value)s"') @register() class dnsforwardzone_enable(DNSZoneBase_enable): __doc__ = _('Enable DNS Forward Zone.') msg_summary = _('Enabled DNS forward zone "%(value)s"') def execute(self, *keys, **options): result = super(dnsforwardzone_enable, self).execute(*keys, **options) self.obj._warning_fw_zone_is_not_effective(result, *keys, **options) return result @register() class dnsforwardzone_add_permission(DNSZoneBase_add_permission): __doc__ = _('Add a permission for per-forward zone access delegation.') @register() class dnsforwardzone_remove_permission(DNSZoneBase_remove_permission): __doc__ = _('Remove a permission for per-forward zone access delegation.')
pspacek/freeipa
ipalib/plugins/dns.py
Python
gpl-3.0
168,418
[ "VisIt" ]
a3b50959ecd9756cfee5063d953b81e4155e4196cdc20079dd0d2c8f342f009c
#!/usr/bin/env python # Copyright 2014-2018 The PySCF Developers. 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 unittest from functools import reduce import numpy from pyscf import gto from pyscf import scf from pyscf.lo import orth mol = gto.Mole() mol.verbose = 0 mol.output = None mol.atom = ''' O 0. 0. 0 1 0. -0.757 0.587 1 0. 0.757 0.587''' mol.basis = 'cc-pvdz' mol.build() mf = scf.RHF(mol) class KnowValues(unittest.TestCase): def test_orth(self): numpy.random.seed(10) n = 100 a = numpy.random.random((n,n)) s = numpy.dot(a.T, a) c = orth.lowdin(s) self.assertTrue(numpy.allclose(reduce(numpy.dot, (c.T, s, c)), numpy.eye(n))) x1 = numpy.dot(a, c) x2 = orth.vec_lowdin(a) d = numpy.dot(x1.T,x2) d[numpy.diag_indices(n)] = 0 self.assertAlmostEqual(numpy.linalg.norm(d), 0, 9) self.assertAlmostEqual(numpy.linalg.norm(c), 36.56738258719514, 9) self.assertAlmostEqual(abs(c).sum(), 2655.5580057303964, 7) def test_schmidt(self): numpy.random.seed(10) n = 100 a = numpy.random.random((n,n)) s = numpy.dot(a.T, a) c = orth.schmidt(s) self.assertTrue(numpy.allclose(reduce(numpy.dot, (c.T, s, c)), numpy.eye(n))) x1 = numpy.dot(a, c) x2 = orth.vec_schmidt(a) d = numpy.dot(x1.T,x2) d[numpy.diag_indices(n)] = 0 self.assertAlmostEqual(numpy.linalg.norm(d), 0, 9) self.assertAlmostEqual(numpy.linalg.norm(c), 36.56738258719514, 9) self.assertAlmostEqual(abs(c).sum(), 1123.2089785000373, 7) def test_weight_orth(self): numpy.random.seed(10) n = 100 a = numpy.random.random((n,n)) s = numpy.dot(a.T, a) weight = numpy.random.random(n) c = orth.weight_orth(s, weight) self.assertTrue(numpy.allclose(reduce(numpy.dot, (c.T, s, c)), numpy.eye(n))) self.assertAlmostEqual(numpy.linalg.norm(c), 36.56738258719514, 8) self.assertAlmostEqual(abs(c).sum(), 1908.8535852660757, 6) def test_orth_ao(self): c0 = orth.pre_orth_ao(mol, method='scf') self.assertAlmostEqual(abs(c0).sum(), 33.48215772351, 8) c = orth.orth_ao(mol, 'lowdin', c0) self.assertAlmostEqual(abs(c).sum(), 94.21571091299639, 8) c = orth.orth_ao(mol, 'meta_lowdin', c0) self.assertAlmostEqual(abs(c).sum(), 92.15697348744733, 8) c = orth.orth_ao(mol, 'meta_lowdin', 'sto-3g') self.assertAlmostEqual(abs(c).sum(), 90.12324660084619, 8) c = orth.orth_ao(mol, 'meta_lowdin', None) self.assertAlmostEqual(abs(c).sum(), 83.71349158130113, 8) def test_ghost_atm_meta_lowdin(self): mol = gto.Mole() mol.atom = [["O" , (0. , 0. , 0.)], ['ghost' , (0. , -0.757, 0.587)], [1 , (0. , 0.757 , 0.587)] ] mol.spin = 1 mol.basis = {'O':'ccpvdz', 'H':'ccpvdz', 'GHOST': gto.basis.load('631g','H')} mol.build() c = orth.orth_ao(mol, method='meta_lowdin') self.assertAlmostEqual(numpy.linalg.norm(c), 7.9067188905237256, 9) def test_pre_orth_ao_with_ecp(self): mol = gto.M(atom='Cu 0. 0. 0.; H 0. 0. -1.56; H 0. 0. 1.56', basis={'Cu':'lanl2dz', 'H':'ccpvdz'}, ecp = {'cu':'lanl2dz'}, charge=-1, verbose=0) c0 = orth.pre_orth_ao(mol, method='ano') self.assertAlmostEqual(numpy.linalg.norm(c0), 5.9621174285790959, 9) if __name__ == "__main__": print("Test orth") unittest.main()
sunqm/pyscf
pyscf/lo/test/test_orth.py
Python
apache-2.0
4,348
[ "PySCF" ]
c23c5a7399fd1d1859cd661db19b267682a520a255dc863dcefcff5c22762137
#!/usr/bin/env python # # Appcelerator Titanium Module Packager # # import os, sys, glob, string import zipfile from datetime import date try: import json except: import simplejson as json cwd = os.path.abspath(os.path.dirname(sys._getframe(0).f_code.co_filename)) os.chdir(cwd) required_module_keys = ['name','version','moduleid','description','copyright','license','copyright','platform','minsdk'] module_defaults = { 'description':'My module', 'author': 'Your Name', 'license' : 'Specify your license', 'copyright' : 'Copyright (c) %s by Your Company' % str(date.today().year), } module_license_default = "TODO: place your license here and we'll include it in the module distribution" def find_sdk(config): sdk = config['TITANIUM_SDK'] return os.path.expandvars(os.path.expanduser(sdk)) def replace_vars(config,token): idx = token.find('$(') while idx != -1: idx2 = token.find(')',idx+2) if idx2 == -1: break key = token[idx+2:idx2] if not config.has_key(key): break token = token.replace('$(%s)' % key, config[key]) idx = token.find('$(') return token def read_ti_xcconfig(): contents = open(os.path.join(cwd,'titanium.xcconfig')).read() config = {} for line in contents.splitlines(False): line = line.strip() if line[0:2]=='//': continue idx = line.find('=') if idx > 0: key = line[0:idx].strip() value = line[idx+1:].strip() config[key] = replace_vars(config,value) return config def generate_doc(config): docdir = os.path.join(cwd,'documentation') if not os.path.exists(docdir): print "Couldn't find documentation file at: %s" % docdir return None sdk = find_sdk(config) support_dir = os.path.join(sdk,'module','support') sys.path.append(support_dir) try: import markdown2 as markdown except ImportError: import markdown documentation = [] for file in os.listdir(docdir): if file in ignoreFiles or os.path.isdir(os.path.join(docdir, file)): continue md = open(os.path.join(docdir,file)).read() html = markdown.markdown(md) documentation.append({file:html}); return documentation def compile_js(manifest,config): js_file = os.path.join(cwd,'assets','com.meeech.mixpanel.js') if not os.path.exists(js_file): return sdk = find_sdk(config) iphone_dir = os.path.join(sdk,'iphone') sys.path.insert(0,iphone_dir) from compiler import Compiler path = os.path.basename(js_file) compiler = Compiler(cwd, manifest['moduleid'], manifest['name'], 'commonjs') metadata = compiler.make_function_from_file(path,js_file) exports = open('metadata.json','w') json.dump({'exports':compiler.exports }, exports) exports.close() method = metadata['method'] eq = path.replace('.','_') method = ' return %s;' % method f = os.path.join(cwd,'Classes','ComMeeechMixpanelModuleAssets.m') c = open(f).read() idx = c.find('return ') before = c[0:idx] after = """ } @end """ newc = before + method + after if newc!=c: x = open(f,'w') x.write(newc) x.close() def die(msg): print msg sys.exit(1) def warn(msg): print "[WARN] %s" % msg def validate_license(): c = open(os.path.join(cwd,'LICENSE')).read() if c.find(module_license_default)!=-1: warn('please update the LICENSE file with your license text before distributing') def validate_manifest(): path = os.path.join(cwd,'manifest') f = open(path) if not os.path.exists(path): die("missing %s" % path) manifest = {} for line in f.readlines(): line = line.strip() if line[0:1]=='#': continue if line.find(':') < 0: continue key,value = line.split(':') manifest[key.strip()]=value.strip() for key in required_module_keys: if not manifest.has_key(key): die("missing required manifest key '%s'" % key) if module_defaults.has_key(key): defvalue = module_defaults[key] curvalue = manifest[key] if curvalue==defvalue: warn("please update the manifest key: '%s' to a non-default value" % key) return manifest,path ignoreFiles = ['.DS_Store','.gitignore','libTitanium.a','titanium.jar','README','com.meeech.mixpanel.js'] ignoreDirs = ['.DS_Store','.svn','.git','CVSROOT'] def zip_dir(zf,dir,basepath,ignore=[]): for root, dirs, files in os.walk(dir): for name in ignoreDirs: if name in dirs: dirs.remove(name) # don't visit ignored directories for file in files: if file in ignoreFiles: continue e = os.path.splitext(file) if len(e)==2 and e[1]=='.pyc':continue from_ = os.path.join(root, file) to_ = from_.replace(dir, basepath, 1) zf.write(from_, to_) def glob_libfiles(): files = [] for libfile in glob.glob('build/**/*.a'): if libfile.find('Release-')!=-1: files.append(libfile) return files def build_module(manifest,config): rc = os.system("xcodebuild -sdk iphoneos -configuration Release") if rc != 0: die("xcodebuild failed") rc = os.system("xcodebuild -sdk iphonesimulator -configuration Release") if rc != 0: die("xcodebuild failed") # build the merged library using lipo moduleid = manifest['moduleid'] libpaths = '' for libfile in glob_libfiles(): libpaths+='%s ' % libfile os.system("lipo %s -create -output build/lib%s.a" %(libpaths,moduleid)) def package_module(manifest,mf,config): name = manifest['name'].lower() moduleid = manifest['moduleid'].lower() version = manifest['version'] modulezip = '%s-iphone-%s.zip' % (moduleid,version) if os.path.exists(modulezip): os.remove(modulezip) zf = zipfile.ZipFile(modulezip, 'w', zipfile.ZIP_DEFLATED) modulepath = 'modules/iphone/%s/%s' % (moduleid,version) zf.write(mf,'%s/manifest' % modulepath) libname = 'lib%s.a' % moduleid zf.write('build/%s' % libname, '%s/%s' % (modulepath,libname)) docs = generate_doc(config) if docs!=None: for doc in docs: for file, html in doc.iteritems(): filename = string.replace(file,'.md','.html') zf.writestr('%s/documentation/%s'%(modulepath,filename),html) for dn in ('assets','example','platform'): if os.path.exists(dn): zip_dir(zf,dn,'%s/%s' % (modulepath,dn),['README']) zf.write('LICENSE','%s/LICENSE' % modulepath) zf.write('module.xcconfig','%s/module.xcconfig' % modulepath) exports_file = 'metadata.json' if os.path.exists(exports_file): zf.write(exports_file, '%s/%s' % (modulepath, exports_file)) zf.close() if __name__ == '__main__': manifest,mf = validate_manifest() validate_license() config = read_ti_xcconfig() compile_js(manifest,config) build_module(manifest,config) package_module(manifest,mf,config) sys.exit(0)
meeech/Titanium-Mixpanel-Module
build.py
Python
mit
6,445
[ "VisIt" ]
1834be6bff81678da1ac9a4fce147478e64fc7cb8c3d03f7c2c9ff0da4604ceb
# -*- mode: python; -*- ## ## Utilities for working with paired-end reads and ## fragment distributions ## import os import sys import glob import time import pysam from scipy import * from numpy import * import misopy from misopy.parse_csv import * import misopy.Gene as gene_utils import misopy.sam_utils as sam_utils import misopy.exon_utils as exon_utils from collections import defaultdict def get_insert_dist_array(interval_to_paired_dists, delim='\t'): """ Read insert length distribution as array of numbers. """ insert_dist = [] for interval, paired_dists in interval_to_paired_dists.iteritems(): insert_dist.extend(paired_dists) return array(insert_dist) def parse_insert_len_params(insert_len_header): params = {} insert_len_header = insert_len_header.strip() if insert_len_header.startswith("#"): insert_len_header = insert_len_header[1:] for param in insert_len_header.split(","): p, v = param.split("=") params[p] = v return params def filter_insert_len(interval_to_dists, sd_max): # Get vector of insert lengths insert_dist = get_insert_dist_array(interval_to_dists) mu, sdev, dispersion, num_pairs = \ compute_insert_len_stats(insert_dist) filtered_interval_to_dists = defaultdict(list) min_cutoff = mu - (sd_max * sdev) max_cutoff = mu + (sd_max * sdev) print "Excluding values < %.2f or > %.2f" \ %(min_cutoff, max_cutoff) for interval, dists in interval_to_dists.iteritems(): dists = array(dists) filtered_dists = delete(dists, nonzero(dists < min_cutoff)[0]) filtered_dists = delete(filtered_dists, nonzero(dists > max_cutoff)[0]) filtered_interval_to_dists[interval] = filtered_dists return filtered_interval_to_dists def load_insert_len(insert_dist_filename, delim='\t'): print "Loading insert length from: %s" %(insert_dist_filename) insert_dist_file = open(insert_dist_filename, "r") insert_lens = [] params_header = insert_dist_file.readline().strip() # Get parameters of distribution from header params = parse_insert_len_params(params_header) for line in insert_dist_file: # Skip header if line.startswith("#"): continue insert_fields = line.strip().split(delim) if len(insert_fields) != 2: continue lens_list = insert_fields[1].split(",") curr_lens = [int(l) for l in lens_list] insert_lens.extend(curr_lens) insert_dist = array(insert_lens) insert_dist_file.close() return insert_dist, params def bedtools_map_bam_to_bed(bam_filename, gff_intervals_filename): """ Map BAM file to GFF intervals and return the result as a BED file. Returns a stream to a BED file with the results """ bedtools_cmd = "intersectBed -abam %s -b %s -wa -wb -bed -f 1" \ %(bam_filename, gff_intervals_filename) print "Executing: %s" %(bedtools_cmd) if (not os.path.isfile(bam_filename)) or \ (not os.path.isfile(gff_intervals_filename)): raise Exception, "Error: %s or %s do not exist." \ %(bam_filename, gff_intervals_filename) bed_stream = os.popen(bedtools_cmd) return bed_stream def parse_tagBam_intervals(bam_read, gff_coords=True): """ Return a list of intervals that are present in the current BAM line returned by tagBam. These intervals are encoded in the YB option of the BAM flag. - If convert_coords is True, we add 1 to the BAM coordinate to make it 1-based """ gff_aligned_regions = bam_read.opt("YB") parsed_regions = gff_aligned_regions.split("gff:")[1:] gff_intervals = [] for region in parsed_regions: strand = region.split(",")[3] chrom, coord_field = region.split(",")[0].split(":") region_start, region_end = coord_field.split("-") region_start, region_end = int(region_start), \ int(region_end) if gff_coords: region_start += 1 curr_interval_str = "%s:%d-%d:%s" \ %(chrom, region_start, region_end, strand) gff_intervals.append(curr_interval_str) return gff_intervals def compute_inserts_from_paired_mates(paired_reads): """ Get insert lengths from paired-up paired ends reads aligned to a set of constitutive exon intervals. Return mapping from intervals to distances of read pairs that land in them. """ # Mapping from interval to interval_to_paired_dists = defaultdict(list) num_skipped = 0 num_kept = 0 for read_id, read_pair in paired_reads.iteritems(): to_skip = False # Get the intervals that each read pair lands in # Consider here only the mate pairs that map to # the same interval, and to exactly one interval, and # not in a junction left_mate, right_mate = read_pair left_mate_intervals = parse_tagBam_intervals(left_mate) right_mate_intervals = parse_tagBam_intervals(right_mate) # If either of the mates lands in more than one set of intervals, # discard it. if (len(left_mate_intervals) != 1 or \ len(right_mate_intervals) != 1): to_skip = True elif left_mate_intervals[0] != right_mate_intervals[0]: # If each maps to one interval, but it's not the same, # also discard it. to_skip = True elif (len(left_mate.cigar) != 1 or \ len(right_mate.cigar) != 1): # One of the read mates was in a junction to_skip = True elif (left_mate.cigar[0][0] != 0 or \ right_mate.cigar[0][0] != 0): # Both CIGAR operations must be M (matches) to_skip = True if to_skip: # One of the conditions was violated, so skip read pair num_skipped += 1 continue # We have a match, so compute insert length distance, # defined as the distance between the start position # of the left and the end position of the right mate left_start = left_mate.pos left_end = sam_utils.cigar_to_end_coord(left_start, left_mate.cigar) right_start = right_mate.pos right_end = sam_utils.cigar_to_end_coord(right_start, right_mate.cigar) # Get the current GFF interval string curr_gff_interval = left_mate_intervals[0] # Insert length is right.end - left.start + 1 insert_len = right_end - left_start + 1 if insert_len <= 0: print "WARNING: 0 or negative insert length detected " \ "in region %s." %(curr_gff_interval) continue interval_to_paired_dists[curr_gff_interval].append(insert_len) num_kept += 1 print "Used %d paired mates, threw out %d" \ %(num_kept, num_skipped) return interval_to_paired_dists def compute_insert_len(bams_to_process, const_exons_gff_filename, output_dir, min_exon_size, no_bam_filter=False, sd_max=2): """ Compute insert length distribution and output it to the given directory. Arguments: - bams_to_process: a list of BAM files to process - const_gff_filename: GFF with constitutive exons """ bams_str = "\n ".join(bams_to_process) num_bams = len(bams_to_process) print "Computing insert length distribution of %d files:\n %s" \ %(num_bams, bams_str) print " - Using const. exons from: %s" %(const_exons_gff_filename) print " - Outputting to: %s" %(output_dir) print " - Minimum exon size used: %d" %(min_exon_size) if not os.path.isdir(output_dir): print "Making directory: %s" %(output_dir) os.makedirs(output_dir) all_constitutive = True const_exons, f = \ exon_utils.get_const_exons_by_gene(const_exons_gff_filename, output_dir, # Treat all exons as constitutive all_constitutive=True, min_size=min_exon_size) filter_reads = not no_bam_filter if filter_reads: print "Filtering BAM reads" else: print "Turning off filtering of BAM reads" for bam_filename in bams_to_process: t1 = time.time() output_filename = os.path.join(output_dir, "%s.insert_len" \ %(os.path.basename(bam_filename))) if not os.path.isfile(bam_filename): print "Cannot find BAM file %s" %(bam_filename) print "Quitting..." sys.exit(1) print "Fetching reads in constitutive exons" mapped_bam_filename = exon_utils.map_bam2gff(bam_filename, const_exons_gff_filename, output_dir) if mapped_bam_filename == None: raise Exception, "Error: Insert length computation failed." # Load mapped BAM filename mapped_bam = pysam.Samfile(mapped_bam_filename, "rb") ### ### TODO: Rewrite this so that you only pair reads within an interval ### paired_reads = sam_utils.pair_sam_reads(mapped_bam, filter_reads=filter_reads) num_paired_reads = len(paired_reads) if num_paired_reads == 0: print "WARNING: no paired mates in %s. Skipping...\n"\ "Are you sure the read IDs match? If your BAM paired flags are "\ "unset, try using --no-bam-filter." \ %(bam_filename) continue print "Using %d paired mates" %(num_paired_reads) interval_to_paired_dists = compute_inserts_from_paired_mates(paired_reads) summarize_insert_len_dist(interval_to_paired_dists, output_filename, sd_max=sd_max) t2 = time.time() print "Insert length computation took %.2f seconds." %(t2 - t1) # def pair_reads_from_bed_intervals(bed_stream): # """ # Match up read mates with each other, indexed by the BED interval # that they fall in. # Return a dictionary of BED region mapping to a set of read pairs. # Arguments: # - bed_filename: file with BED reads and the region they map to. # Returns. # """ # return # def compute_insert_len(bam_filename, gff_filename, output_dir, # min_exon_size): # """ # Compute insert length distribution and output it to the given # directory. # """ # print "Computing insert length distribution of %s" %(bam_filename) # print " - Using gene models from: %s" %(gff_filename) # print " - Outputting to: %s" %(output_dir) # print " - Minimum exon size used: %d" %(min_exon_size) # if not os.path.isdir(output_dir): # print "Making directory: %s" %(output_dir) # os.makedirs(output_dir) # output_filename = os.path.join(output_dir, # "%s.insert_len" %(os.path.basename(bam_filename))) # # Load BAM file with reads # bamfile = sam_utils.load_bam_reads(bam_filename) # # Load the genes from the GFF # print "Loading genes from GFF..." # t1 = time.time() # gff_genes = gene_utils.load_genes_from_gff(gff_filename) # t2 = time.time() # print " - Loading genes from GFF took %.2f seconds" %(t2 - t1) # insert_lengths = [] # t1 = time.time() # relevant_region = 0 # for gene_id, gene_info in gff_genes.iteritems(): # gene_obj = gene_info["gene_object"] # # Get all the constitutive parts # const_parts = gene_obj.get_const_parts() # chrom = gene_obj.chrom # # Consider only the large constitutive parts # for part in const_parts: # if part.len >= min_exon_size: # # Get all the reads that land in the coordinates of the exon # try: # exon_reads = bamfile.fetch(chrom, part.start, part.end) # except ValueError: # print "Could not fetch from region: ", chrom, part.start, part.end # continue # # Pair all the paired-end reads that land there # paired_reads = sam_utils.pair_sam_reads(exon_reads) # num_paired_reads = len(paired_reads) # if num_paired_reads == 0: # continue # print "Found %d region" %(relevant_region) # relevant_region += 1 # # Compute the insert length of each read # for read_pair_id, read_pair in paired_reads.iteritems(): # if len(read_pair) != 2: # # Skip non-paired reads # continue # left_read, right_read = read_pair # insert_len = right_read.pos - left_read.pos + 1 # if insert_len > 0: # insert_lengths.append(insert_len) # else: # print "Negative or zero insert length ignored..." # # Output results to file # output_file = open(output_filename, 'w') # insert_length_str = "\n".join(map(str, insert_lengths)) # output_file.write(insert_length_str) # output_file.close() # t2 = time.time() # print "Insert length computation took %.2f seconds." %(t2 - t1) def output_insert_len_dist(interval_to_paired_dists, output_file): """ Output insert length distribution indexed by regions. """ header = "#%s\t%s\n" %("region", "insert_len") output_file.write(header) for region, insert_lens in interval_to_paired_dists.iteritems(): if len(insert_lens) == 0: continue str_lens = ",".join([str(l) for l in insert_lens]) output_line = "%s\t%s\n" %(region, str_lens) output_file.write(output_line) def compute_insert_len_stats(insert_dist): """ Return insert length statistics. """ # Compute mean and standard deviation of insert # length distribution mu = mean(insert_dist) sdev = std(insert_dist) # Compute dispersion (d), where # # d = sdev / sqrt(mean) # # dispersion measures how variable # the insert length distribution is # about the mean dispersion = sdev / sqrt(float(mu)) # Number of read pairs used num_pairs = len(insert_dist) return mu, sdev, dispersion, num_pairs def summarize_insert_len_dist(interval_to_paired_dists, output_filename, sd_max=2): """ Summarize insert len distributions. """ print "Summarizing insert length distribution.." print " - Output file: %s" %(output_filename) output_file = open(output_filename, "w") print "Removing values %d-many deviations outside the mean" \ %(sd_max) # Filter insert length distribution based on sd_max filtered_interval_to_dist = filter_insert_len(interval_to_paired_dists, sd_max) filtered_insert_dist = get_insert_dist_array(filtered_interval_to_dist) if len(filtered_insert_dist) == 0: print "Error: Could not find any properly mated pairs to " \ "compute insert length with. Are you sure your BAM reads " \ "are properly paired and map the chromosome headers in the " \ "constitutive exon file?" sys.exit(1) mu, sdev, dispersion, num_pairs = \ compute_insert_len_stats(filtered_insert_dist) print "mean\tsdev\tdispersion" print "%.1f\t%.1f\t%.1f" \ %(mu, sdev, dispersion) min_insert = min(filtered_insert_dist) max_insert = max(filtered_insert_dist) print "min insert: %d" %(min_insert) print "max insert: %d" %(max_insert) # Write headers header_line = "#%s=%.1f,%s=%.1f,%s=%.1f,%s=%d\n" \ %("mean", mu, "sdev", sdev, "dispersion", dispersion, "num_pairs", num_pairs) output_file.write(header_line) # Write raw insert lengths indexed by region output_insert_len_dist(filtered_interval_to_dist, output_file) output_file.close() def greeting(): print "Utility for computing insert length distributions from paired-end " \ "BAM files." print "Part of MISO (Mixture of Isoforms model)\n" print "See --help for usage.\n" def main(): from optparse import OptionParser parser = OptionParser() parser.add_option("--compute-insert-len", dest="compute_insert_len", nargs=2, default=None, help="Compute insert length for given sample. Takes as input " "(1) a comma-separated list of sorted, indexed BAM files with headers " "(or a single BAM filename), (2) a GFF file with constitutive exons. " "Outputs the insert length distribution into the output directory.") parser.add_option("--no-bam-filter", dest="no_bam_filter", action="store_true", default=False, help="If provided, this ignores the BAM file flags that state whether the read was paired " "or not, and instead uses only the read IDs to pair up the mates. Use this if your " "paired-end BAM was the result of a samtools merge operation.") parser.add_option("--min-exon-size", dest="min_exon_size", nargs=1, type="int", default=500, help="Minimum size of constitutive exon (in nucleotides) that should be used " "in the computation. Default is 500 bp.") parser.add_option("--sd-max", dest="sd_max", nargs=1, default=2, type="int", help="Number of standard deviations used to define outliers. By default, set " "to 2, meaning that any points at least 2*sigma away from the mean of the " "insert length distribution will be discarded.") parser.add_option("--output-dir", dest="output_dir", nargs=1, default=None, help="Output directory.") (options, args) = parser.parse_args() if options.compute_insert_len is None: greeting() return if options.output_dir is None: greeting() print "Error: need --output-dir." return output_dir = os.path.abspath(os.path.expanduser(options.output_dir)) sd_max = options.sd_max if options.compute_insert_len != None: bams_to_process = [os.path.abspath(os.path.expanduser(f)) for f in \ options.compute_insert_len[0].split(",")] gff_filename = os.path.abspath(os.path.expanduser(options.compute_insert_len[1])) compute_insert_len(bams_to_process, gff_filename, output_dir, options.min_exon_size, no_bam_filter=options.no_bam_filter, sd_max=sd_max) if __name__ == "__main__": main()
Xinglab/rmats2sashimiplot
src/MISO/misopy/pe_utils.py
Python
gpl-2.0
19,818
[ "pysam" ]
044a1b8ef86b9bf18a189ecc37186e9a296deb74335c943d664b106e886adf48
"""Code for checking for local names and superfluous import statements. This code provides searches for local symbols in the AST, assignments and such things. """ # This file is part of the Snakefood open source package. # See http://furius.ca/snakefood/ for licensing details. # stdlib imports import compiler __all__ = ('get_names_from_ast', 'filter_unused_imports', 'NamesVisitor', 'AssignVisitor', 'AllVisitor') def get_names_from_ast(ast): "Find all the names being referenced/used." vis = NamesVisitor() compiler.walk(ast, vis) dotted_names, simple_names = vis.finalize() return (dotted_names, simple_names) def filter_unused_imports(ast, found_imports): """ Given the ast and the list of found imports in the file, find out which of the imports are not used and return two lists: a list of used imports, and a list of unused imports. """ used_imports, unused_imports = [], [] # Find all the names being referenced/used. dotted_names, simple_names = get_names_from_ast(ast) # Find all the names being exported via __all__. vis = AllVisitor() compiler.walk(ast, vis) exported = vis.finalize() # Check that all imports have been referenced at least once. usednames = set(x[0] for x in dotted_names) usednames.update(x[0] for x in exported) used_imports = [] for x in found_imports: _, _, lname, lineno, _, _ = x if lname is not None and lname not in usednames: unused_imports.append(x) else: used_imports.append(x) return used_imports, unused_imports class Visitor(object): "Base class for our visitors." def continue_(self, node): for child in node.getChildNodes(): self.visit(child) class NamesVisitor(Visitor): """AST visitor that finds all the identifier references that are defined, including dotted references. This includes all free names and names with attribute references. """ def __init__(self): self.dotted = [] self.simple = [] self.attributes = [] def visitName(self, node): self.attributes.append(node.name) self.attributes.reverse() attribs = self.attributes for i in xrange(1, len(attribs)+1): self.dotted.append(('.'.join(attribs[0:i]), node.lineno)) self.simple.append((attribs[0], node.lineno)) self.attributes = [] def visitGetattr(self, node): self.attributes.append(node.attrname) self.continue_(node) def finalize(self): return self.dotted, self.simple class AssignVisitor(Visitor): """AST visitor that builds a list of all potential names that are being assigned to. This is used later to heuristically figure out if a name being refered to is never assigned to nor in the imports.""" def __init__(self): self.assnames = [] self.in_class = False def visitAssName(self, node): self.assnames.append((node.name, node.lineno)) self.continue_(node) def visitClass(self, node): self.assnames.append((node.name, node.lineno)) prev, self.in_class = self.in_class, True self.continue_(node) self.in_class = prev def visitFunction(self, node): # Avoid method definitions. if not self.in_class: self.assnames.append((node.name, node.lineno)) self.continue_(node) def finalize(self): return self.assnames class AllVisitor(Visitor): """AST visitor that find an __all__ directive and accumulates the list of constants in it.""" def __init__(self): self.all = [] self.in_assign = False self.in_all = False def visitAssign(self, node): prev, self.in_assign = self.in_assign, True self.continue_(node) self.in_assign = prev def visitAssName(self, node): if self.in_assign and node.name == '__all__': self.in_all = True self.continue_(node) def visitConst(self, node): if self.in_assign and self.in_all: self.all.append((node.value, node.lineno)) self.continue_(node) def finalize(self): return self.all
GreatFruitOmsk/snakefood
lib/python/snakefood/local.py
Python
gpl-2.0
4,247
[ "VisIt" ]
e305eaa39611663c1710a4b5d49c7d03e7b60c25f07f9787134c1f4461dd429e
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class VotcaCsg(CMakePackage): """Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) is a package intended to reduce the amount of routine work when doing systematic coarse-graining of various systems. The core is written in C++. This package contains the VOTCA coarse-graining engine. """ homepage = "http://www.votca.org" url = "https://github.com/votca/csg/tarball/v1.4" git = "https://github.com/votca/csg.git" maintainers = ['junghans'] version('master', branch='master') version('stable', branch='stable') version('1.6.3', sha256='35456b1f3116364b10ada37d99798294bd2d3df2e670cef3936251f88036ef88') version('1.6.2', sha256='96b244b282005259832ed6ec0dc22dafe132dcfc3d73dcd8e53b62f40befb545') version('1.6.1', sha256='ed12bcb1ccdf71f54e21cdcc9803add4b8ebdc6b8263cb5b0034f5db01e31dbb') version('1.6', sha256='8cf6a4ac3ef7347c720a44d8a676f8cbd1462e162f6113de39f27b89354465ea') version('1.5.1', sha256='7fca1261bd267bf38d2edd26259730fed3126c0c3fd91fb81940dbe17bb568fd') version('1.5', sha256='160387cdc51f87dd20ff2e2eed97086beee415d48f3c92f4199f6109068c8ff4') version('1.4.1', sha256='41dccaecadd0165c011bec36a113629e27745a5a133d1a042efe4356acdb5450') version('1.4', sha256='c13e7febd792de8c3d426203f089bd4d33b8067f9db5e8840e4579c88b61146e') depends_on("cmake@2.8:", type='build') for v in ["1.4", "1.4.1", "1.5", "1.5.1", "1.6", "1.6.1", "1.6.2", "1.6.3", "master", "stable"]: depends_on('votca-tools@%s' % v, when="@%s:%s.0" % (v, v)) depends_on("boost") depends_on("gromacs~mpi@5.1:2019.9999") depends_on("hdf5~mpi")
iulian787/spack
var/spack/repos/builtin/packages/votca-csg/package.py
Python
lgpl-2.1
1,911
[ "Gromacs" ]
6385154c910e7725aa9faf2744066b07886556b457c2bb46875d1d42b867a362
from catkit import Gratoms import numpy as np import ase import re try: from math import gcd except ImportError: from fractions import gcd def running_mean(array, N=5): """Calculate the running mean of array for N instances. Parameters ---------- array : array_like | ndarray (N,) Array of values to have a average taken from. N : int Number of values to take an average with. Returns ------- running_mean : ndarray (N + 1,) Mean value of the running average. """ length = len(array) if length < N: N = length cumsum = np.cumsum(np.insert(array, 0, 0)) running_mean = (cumsum[N:] - cumsum[:-N]) / float(N) return running_mean def to_gratoms(atoms, edges=None): """Convert and atom object to a gratoms object.""" gratoms = Gratoms( numbers=atoms.numbers, positions=atoms.positions, pbc=atoms.pbc, cell=atoms.cell, edges=edges ) if atoms.constraints: gratoms.set_constraint(atoms.constraints) return gratoms def get_atomic_numbers(formula, return_count=False): """Return the atomic numbers associated with a chemical formula. Parameters ---------- formula : string A chemical formula to parse into atomic numbers. return_count : bool Return the count of each element in the formula. Returns ------- numbers : ndarray (n,) Element numbers in associated species. counts : ndarray (n,) Count of each element in a species. """ parse = re.findall('[A-Z][a-z]?|[0-9]+', formula) values = {} for i, e in enumerate(parse): if e.isdigit(): values[parse[i - 1]] += int(e) - 1 else: if e not in values: values[e] = 1 else: values[e] += 1 numbers = np.array([ ase.data.chemical_symbols.index(k) for k in values.keys()]) srt = np.argsort(numbers) numbers = numbers[srt] if return_count: counts = np.array([v for v in values.values()])[srt] return numbers, counts return numbers def get_reference_energies(species, energies): """Get reference energies for the elements in a set of molecules. Parameters ---------- species : list (n,) Chemical formulas for each molecular species. energies : list (n,) Total energies associated with each species. Returns ------- elements : ndarray (n,) Atomic elements associated with all species. references : ndarray (n,) Reference energies associated with each element. """ if not isinstance(energies, np.ndarray): energies = np.array(energies) A = np.zeros((len(species), len(species))) elements = np.zeros(len(species), dtype=int) n = 0 # Construct the elements array as they appear for i, s in enumerate(species): num, cnt = get_atomic_numbers(s, True) for j in num[~np.in1d(num, elements)]: elements[n] = j n += 1 A[i][np.in1d(elements, num)] = cnt references = np.linalg.solve(A, energies) srt = np.argsort(elements) references = references[srt] elements = elements[srt] return elements, references def parse_slice(slice_name): """Return a correctly parsed slice from input of varying types.""" if isinstance(slice_name, (slice)): _slice = slice_name elif isinstance(slice_name, type(None)): _slice = slice(None) elif isinstance(slice_name, int): i = int(slice_name) _slice = slice(i, i + 1) elif isinstance(slice_name, str): if slice_name.isdigit(): i = int(slice_name) _slice = slice(i, i + 1) else: split = slice_name.split(':') split = [int(_) if _.lstrip('-').isdigit() else None for _ in split] _slice = slice(*split) return _slice def ext_gcd(a, b): """Extension of greatest common divisor.""" if b == 0: return 1, 0 elif a % b == 0: return 0, 1 else: x, y = ext_gcd(b, a % b) return y, x - y * (a // b) def list_gcd(values): """Return the greatest common divisor of a list of values.""" if isinstance(values[0], float): values = np.array(values, dtype=int) gcd_func = np.frompyfunc(gcd, 2, 1) _gcd = np.ufunc.reduce(gcd_func, values) return _gcd
jboes/CatKit
catkit/gen/utils/utilities.py
Python
gpl-3.0
4,498
[ "ASE" ]
01bdb9f6f7ec38a07802e86795703de8abb174de6b1a9bd43e4438ac13255c59
# -*- coding: utf-8 -*- # Copyright 2012 splinter authors. All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. from .fake_webapp import EXAMPLE_APP class StatusCodeTest(object): def test_should_visit_index_of_example_app_and_get_200_status_code(self): self.browser.visit(EXAMPLE_APP) self.assertEqual(200, self.browser.status_code) self.assertEqual("200 - OK", str(self.browser.status_code)) def test_should_visit_error_of_example_app_and_not_get_200_status_code(self): self.browser.visit(EXAMPLE_APP + 'error.html') self.assertNotEqual(200, self.browser.status_code) self.assertEqual('404 - Not Found', str(self.browser.status_code))
bmcculley/splinter
tests/status_code.py
Python
bsd-3-clause
767
[ "VisIt" ]
6aa7c8921e1133a26457405ed4a243ad19ec7b847fea47a4c5b7056a29117366
#!/usr/bin/env python # # Author: Qiming Sun <osirpt.sun@gmail.com> # ''' This example shows how to specify auxiliary basis for density fitting integrals. The format and input convention of auxbasis are the same to the AO basis. See also examples/gto/04-input_basis.py ''' import tempfile from pyscf import gto, scf, df # # If auxbasis is not specified, default optimal auxiliary basis (if possible) # or even-tempered gaussian functions will be generated as auxbasis # mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis={'N1':'ccpvdz', 'N2':'tzp'}) mf = scf.RHF(mol).density_fit() mf.kernel() print('Default auxbasis', mf.with_df.auxmol.basis) # # The default basis is generated in the function df.make_auxbasis. It returns # a basis dict for the DF auxiliary basis. In the real calculations, you can # first generate the default basis then make modification. # auxbasis = df.make_auxbasis(mol) print(mf.with_df.auxmol.basis == auxbasis) auxbasis['N2'] = 'ccpvdz jkfit' mf = scf.RHF(mol).density_fit(auxbasis=auxbasis) mf.kernel() # # Input with key argument auxbasis='xxx' in .density_fit function # This auxbasis will be used for all elements in the system. # mol = gto.M(atom='N1 0 0 0; N2 0 0 1.2', basis='ccpvdz') mf = scf.RHF(mol).density_fit(auxbasis='weigend') mf.kernel() # # The DF basis can be assigned to with_df.auxbasis attribute. # Like the AO basis input, DF basis can be specified separately for each element. # mf = scf.RHF(mol).density_fit() mf.with_df.auxbasis = {'default': 'weigend', 'N2': 'ahlrichs'} mf.kernel() # # Combined basis set is also supported in DF basis input. # mf = scf.RHF(mol).density_fit() mf.with_df.auxbasis = ('weigend','sto3g') mf.kernel() # # Even-tempered Gaussian DF basis can be generated based on the AO basis. # In the following example, the exponents of auxbasis are # alpha = a * 1.7^i i = 0..N # where a and N are determined by the smallest and largest exponets of AO basis. # mf = scf.RHF(mol).density_fit() mf.with_df.auxbasis = df.aug_etb(mol, beta=1.7) mf.kernel()
gkc1000/pyscf
examples/df/01-auxbasis.py
Python
apache-2.0
2,034
[ "Gaussian", "PySCF" ]
c3392b6feed2d9c7c63ff83a37453f9e4b3241f056a51e1ac41fb56231b9c444
import os import vtk import csv import math from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy import numpy as np import sklearn.utils #----------------------------------------------------------------------- # SPECIFIC FUNCTIONS #----------------------------------------------------------------------- def check_images(ifile_image1, ifile_image2): """Check whether the extent, spacing and origin of two images are the same.""" # read images print "Checking images", ifile_image1, "and", ifile_image2 image1 = readmetaimage(ifile_image1) image2 = readmetaimage(ifile_image2) # compare image extents extent1 = image1.GetExtent() extent2 = image2.GetExtent() if (extent1[0] == extent2[0] and extent1[1] == extent2[1] and extent1[2] == extent2[2] and extent1[3] == extent2[3] and extent1[4] == extent2[4] and extent1[5] == extent2[5]): match = True print 'Extents match' else: match = False print ('Greyscale image and binary mask have different dimensions.\n' + 'Fix this before proceeding with the benchmark.') # compare image spacings spacing1 = image1.GetSpacing() spacing2 = image2.GetSpacing() if (spacing1[0] == spacing2[0] and spacing1[1] == spacing2[1] and spacing1[2] == spacing2[2]): match = True print 'Spacings match' else: match = False print ('Greyscale image and binary mask have different spacings.\n' + 'Fix this before proceeding with the benchmark.') # compare image origins origin1 = image1.GetOrigin() origin2 = image2.GetOrigin() if (origin1[0] == origin2[0] and origin1[1] == origin2[1] and origin1[2] == origin2[2]): match = True print 'Origin match' else: match = False print ('Greyscale image and binary mask have different origins.\n' + 'Fix this before proceeding with the benchmark.') return match def clip_mitral(surface, ifile_plane): """Clip atrium at level of mitral valve.""" # read plane information from csv-file planeinfo = np.genfromtxt(ifile_plane, delimiter=',') normal = planeinfo[0].flatten().tolist() point = planeinfo[1].flatten().tolist() insideout = int(planeinfo[2, 0].flatten()) # clip atrium at level of mitral valve clippedsurface = planeclip(surface, point, normal, insideout) clippedsurface = extractlargestregion(clippedsurface) return clippedsurface def clip_vein_endpoint(surface, ifile_sufix, targetdistance): """Clip vein the targetdistance away from the body.""" regionslabels = getregionslabels() # extract the body from the surface # including all points (alloff=1) to avoid holes after appending body = pointthreshold(surface, 'autolabels', regionslabels['body'], regionslabels['laa'], 1) body = extractlargestregion(body) # initialize appender with the body appender = vtk.vtkAppendPolyData() appender.AddInput(body) for k in range(1,5): index = 'pv' + str(k) # extract vein # excluding some points (alloff=0) # to avoid overlapping edges after appending vein = pointthreshold(surface, 'autolabels', regionslabels[index], regionslabels[index], 0) # load the centreline and the clipoint cl = readvtp(os.path.join(ifile_sufix, 'clvein' + str(k) + '.vtp')) clippointid = int(np.loadtxt(os.path.join(ifile_sufix, 'clippointid' + str(k) + '.csv'))) clippoint0 = cl.GetPoint(clippointid) clipnormal = (np.array(cl.GetPoint(clippointid + 1)) - np.array(cl.GetPoint(clippointid - 1))) abscissasarray = cl.GetPointData().GetArray('Abscissas') startabscissa = abscissasarray.GetValue(clippointid) currentabscissa = 0 currentid = clippointid # find clip point while ((currentabscissa < targetdistance) and (currentabscissa >= 0) and (currentid >= 0)): currentid -= 1 currentabscissa = startabscissa - abscissasarray.GetValue(currentid) if currentid > 0: currentid = currentid + 1 else: # vein ended before target distance # then clip 2 mm before end of centreline (5x0.4 mm) from end point currentid = 4 # clip and append clippoint1 = cl.GetPoint(currentid) clippedvein = planeclip(vein, clippoint1, clipnormal, 0) # keep region closest to ostium point clippedvein = extractclosestpointregion(clippedvein, clippoint0) # clip generates new points to make a flat cut. The values may be # interpolated. we want all values to rounded to a certain label value. clippedvein = roundpointarray(clippedvein, 'autolabels') appender.AddInput(clippedvein) # collect body + veins appender.Update() clippedsurface = appender.GetOutput() clippedsurface = cleanpolydata(clippedsurface) return clippedsurface def compute_dice(ifile_image, surfacetarget, ofile=''): """Generate image from surfacetarget and compute Dice-metric with respect to ifile_image.""" # load the whole image refimage = readmetaimage(ifile_image) orispacing = refimage.GetSpacing() spacing = [orispacing[0], orispacing[1], orispacing[2]] bounds = refimage.GetBounds() # make reference and target image # body label is generated with the whole surface indexes = ['laa','pv1','pv2','pv3','pv4'] targetimage = imagefordice(surfacetarget, spacing, bounds, indexes, 'autolabels') if ofile: print "saving", ofile writemetaimage(targetimage, ofile) # extract each label and compute metric body and pvs (no laa) regionslabels = getregionslabels() # initialise metric dictionary metric_all = {'body': [0.], 'pvs': [0.]} # body metric refimagelabel = imagethresholdbetween(refimage, regionslabels['body'] - 0.5, regionslabels['body'] + 0.5) targetimagelabel = imagethresholdbetween(targetimage, regionslabels['body'] - 0.5, regionslabels['body'] + 0.5) # compute metric metric = dicemetric(refimagelabel, targetimagelabel) metric_all['body'] = [metric] # pvs metric metric = [0., 0., 0., 0.] for k in range(0, 4): index = 'pv' + str(k + 1) refimagelabel = imagethresholdbetween(refimage, regionslabels[index] - 0.5, regionslabels[index] + 0.5) targetimagelabel = imagethresholdbetween(targetimage, regionslabels[index] - 0.5, regionslabels[index] + 0.5) # compute metric metric[k] = dicemetric(refimagelabel, targetimagelabel) metric_all['pvs'] = metric return metric_all def compute_s2s_error(surface, surfacetarget, nsamples, ofile=''): """Compute the symmetric surface-to-surface distance (s2s-metric) for surface and surfacetarget. Resample the s2s-array to nsamples.""" # cap the surfaces to improve surface to surface distance on clipped areas surfacecap = capsurface(surface,'autolabels') edges = extractboundaryedge(surfacecap) if edges.GetNumberOfPoints() > 0: surfacecap = fillholes(surfacecap) surfacetargetcap = capsurface(surfacetarget, 'autolabels') edges = extractboundaryedge(surfacetargetcap) if edges.GetNumberOfPoints() > 0: surfacetargetcap = fillholes(surfacetargetcap) # compute distances seg2gtsurf = surface2surfacedistance(surfacecap, surfacetargetcap, 'S2S') gt2segsurf = surface2surfacedistance(surfacetargetcap, surfacecap, 'S2S') if ofile: writevtp(seg2gtsurf, ofile + 'seg2gt.vtp') writevtp(gt2segsurf, ofile + 'gt2seg.vtp') # to have ~ same amount of samples per case # extract body and pvs # re sample to nsamples per case indexes = ['body', 'pvs'] rfrom = {'body': 36, 'pvs': 76} rto = {'body': 36, 'pvs': 79} # initialise metric dictionary s2s_all = {'body': [0.], 'pvs': [0.]} for index in indexes: # extracting each region # including all points seg2gtsurf = pointthreshold(seg2gtsurf, 'autolabels', rfrom[index], rto[index], 1) gt2segsurf = pointthreshold(gt2segsurf, 'autolabels', rfrom[index], rto[index], 1) # turning distance array into numpy if (seg2gtsurf.GetPointData().GetArray('S2S') != None): seg2gtarray = vtk_to_numpy(seg2gtsurf.GetPointData(). GetArray('S2S')) else: seg2gtarray = [] if (gt2segsurf.GetPointData().GetArray('S2S') != None): gt2segarray = vtk_to_numpy(gt2segsurf.GetPointData(). GetArray('S2S')) else: gt2segarray = [] # resample to nsamples (unless array smaller than that) if len(seg2gtarray) > nsamples: seg2gtarray = resamplearray(seg2gtarray, nsamples) if len(gt2segarray) > nsamples: gt2segarray = resamplearray(gt2segarray, nsamples) # concatenate error to have symmetric metric superarray = np.concatenate([seg2gtarray, gt2segarray]) s2s_all[index] = superarray return s2s_all def labels2mesh(image, label, radius=1.): """Generate mesh from label image using Marching Cubes algorithm.""" # threshold all values above label image = imagethresholdupper(image, label) image = imageopenclose(image, 0, 1, radius) # after threshold the image is binary surface = marchingcubes(image, 1, 1) surface = smoothtaubin(surface) surfaceout = cleanpolydata(surface) return surfaceout def transfer_gtlabels(surface, target, arrayname): """Project labels in array with arrayname from surface to target.""" # labels regionslabels = getregionslabels() indexes = ['pv1', 'pv2', 'pv3', 'pv4', 'laa'] # cleaning target = cleanpolydata(target) numberofpoints = target.GetNumberOfPoints() # create array gtlabelsarray = vtk.vtkDoubleArray() gtlabelsarray.SetName(arrayname) gtlabelsarray.SetNumberOfTuples(numberofpoints) target.GetPointData().AddArray(gtlabelsarray) # initialize with body label gtlabelsarray.FillComponent(0, regionslabels['body']) # get labels from surface gtlabelsurface = surface.GetPointData().GetArray(arrayname) # initiate locator locator = vtk.vtkPointLocator() locator.SetDataSet(surface) locator.BuildLocator() # go through each point of target surface for i in range(numberofpoints): # determine closest point on surface point = target.GetPoint(i) closestpointid = locator.FindClosestPoint(point) # get label of point value = gtlabelsurface.GetValue(closestpointid) # assign label to target point gtlabelsarray.SetValue(i, value) # check that there is only one region per pv/laa label for index in indexes: # for each region, check if there are other regions on the surface # with the same label. If so, keep largest region # and relabel small regions to body label target = filldisconnectedregion(target, arrayname, regionslabels[index], regionslabels['body']) # for each region, fill small patches (i.e. body label) # with corresponding region label target = fillpatch(target, arrayname, regionslabels[index], regionslabels['body']) # relabel isolated points in the body with vein label (e.g. close to ostia) target = fillpatch(target, arrayname, regionslabels['body'], regionslabels['body']) return target #----------------------------------------------------------------------- # GENERAL FUNCTIONS #----------------------------------------------------------------------- def addvectors(point1, point2): """Add two vectors.""" return [point1[0] + point2[0], point1[1] + point2[1], point1[2] + point2[2]] def cellthreshold(polydata, arrayname, start=0, end=1): """Extract those cells from polydata whose celldata values are within a specified range.""" threshold = vtk.vtkThreshold() threshold.SetInput(polydata) threshold.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_CELLS, arrayname) threshold.ThresholdBetween(start, end) threshold.Update() surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(threshold.GetOutput()) surfer.Update() return surfer.GetOutput() def cleanpolydata(polydata): """Apply VTK mesh cleaning filter to polydata.""" cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(polydata) cleaner.Update() return cleaner.GetOutput() def dicemetric(image_reference, image_target): """Compute overlap between two images.""" # for overlap image: add two images and extract region with value a + b image_overlap = imagesum(image_reference, image_target) overlap = imagethresholdupper(image_overlap, 2) # image scalars to numpy array # this allows us to have quick access to pixel data reference_array = vtk_to_numpy(image_reference.GetPointData().GetScalars()) target_array = vtk_to_numpy(image_target.GetPointData().GetScalars()) overlap_array = vtk_to_numpy(overlap.GetPointData().GetScalars()) # to compute dice metric, count non-zero pixels pix_reference = np.sum(reference_array) pix_target = np.sum(target_array) pix_overlap = np.sum(overlap_array) if (pix_target + pix_reference ) > 0: DM = 2.0 * pix_overlap / (pix_target + pix_reference) else: DM = 0 print 'Dice metric', DM return DM def mindistancetopolydata(reference, polydata): """Compute minimum distance between two polydata.""" refdist = 1000000 # initiate point locator locator = vtk.vtkPointLocator() locator.SetDataSet(reference) locator.BuildLocator() # go through each point of polydata for i in range(polydata.GetNumberOfPoints()): point = polydata.GetPoint(i) # determine closest point on target. closestpointid = locator.FindClosestPoint(point) dist = euclideandistance(point, reference.GetPoint(closestpointid)) if dist < refdist: refdist = dist return refdist def euclideandistance(point1, point2): """Compute Euclidean distance between two points.""" return math.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2 + (point1[2] - point2[2])**2) def extractboundaryedge(polydata): """Extract boundary edges of a surface mesh.""" edge = vtk.vtkFeatureEdges() edge.SetInput(polydata) edge.FeatureEdgesOff() edge.NonManifoldEdgesOff() edge.Update() return edge.GetOutput() def extractconnectedregion(polydata, regionid): """Run connectivity filter to assign regionsids and return region with given regionid.""" # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(polydata) surfer.Update() # clean before connectivity filter # to avoid artificial regionIds cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(surfer.GetOutput()) cleaner.Update() # extract all regions connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(cleaner.GetOutput()) connect.SetExtractionModeToAllRegions() connect.ColorRegionsOn() connect.Update() # threshold especified region surface = pointthreshold(connect.GetOutput(), 'RegionId', float(regionid), float(regionid)) return surface def extractclosestpointregion(polydata, point=[0, 0, 0]): """Extract region closest to specified point.""" # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(polydata) surfer.Update() # clean before connectivity filter # to avoid artificial regionIds cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(surfer.GetOutput()) cleaner.Update() # extract regions closest to point connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(cleaner.GetOutput()) connect.SetExtractionModeToClosestPointRegion() connect.SetClosestPoint(point) connect.FullScalarConnectivityOn() connect.Update() return connect.GetOutput() def extractlargestregion(polydata): """Extract largest of several disconnected regions.""" # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(polydata) surfer.Update() # clean before connectivity filter # to avoid artificial regionIds cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(surfer.GetOutput()) cleaner.Update() # extract largest region connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(cleaner.GetOutput()) connect.SetExtractionModeToLargestRegion() connect.Update() # cleaning phantom points cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(connect.GetOutput()) cleaner.Update() return cleaner.GetOutput() def fillholes(polydata, holesize=1000000): """Fill holes in surface. Use holesize to specify the maximum 'radius' of the holes to be filled.""" filler = vtk.vtkFillHolesFilter() filler.SetInput(polydata) filler.SetHoleSize(holesize) filler.Update() return filler.GetOutput() def fillpatch(surface, arrayname, value, patchvalue): """Replace value for patchvalue in specified array of surface.""" # extract main body body = pointthreshold(surface, arrayname, patchvalue, patchvalue) mainbody = extractlargestregion(body) areamainbody = surfacearea(mainbody) edgesmainbody = extractboundaryedge(mainbody) # extract subpart submesh = pointthreshold(surface, arrayname, value, value, 0) # assuming the patch label is known patches = pointthreshold(surface, arrayname, patchvalue, patchvalue, 0) # if there is more than one edge, smaller edges should be patches edges = extractboundaryedge(submesh) if edges.GetNumberOfPoints() > 0: # the edge closest to the body is the ostium # hence it should not be a patch smallestdistr = findadjoiningregionid(edgesmainbody,edges) nedges = getregionsrange(edges) # loop again to fill the patch for r in range(int(nedges[1]) + 1): # taking centroid of edge smalledge = extractconnectedregion(edges, r) centroid = pointsetcentreofmass(smalledge) if (r != smallestdistr): patch = extractclosestpointregion(patches, centroid) # check patch is smaller than body, based on surface area areapatch = surfacearea(patch) if areapatch < 0.5 * areamainbody: transferlabels(surface, patch, arrayname, value) return surface def filldisconnectedregion(targetsurface, arrayname, label, rlabel): """Find disconnected regions with the same label. Replace its label to the second closest label.""" # extract label region subpd = pointthreshold(targetsurface, arrayname, label, label, 1) if subpd.GetNumberOfPoints() > 0: # find largest regionid regionid = findlargestregionid(subpd) regionsrange = getregionsrange(subpd) # loop through all regions to relabel small regions if regionsrange[1] > 0.0: for j in range(int(regionsrange[1]) + 1): # for other regions, replace value if j != regionid: subsubpd = extractconnectedregion(subpd, j) transferlabels(targetsurface, subsubpd, arrayname, rlabel) return targetsurface def findadjoiningregionid(reference, target): """Find the regionid in target closest to any reference region.""" nregions = getregionsrange(target) smallestdist = 1000000 smallestdistr = 0 # iterate over regions to find the adjoining regions if nregions > 0: for r in range(int(nregions[1]) + 1): # taking centroid of region smallregion = extractconnectedregion(target, r) # find region closest to reference currentdist = mindistancetopolydata(reference, smallregion) if currentdist < smallestdist: smallestdist = currentdist smallestdistr = r return smallestdistr def findlargestregionid(polydata): """Get id of largest of several disconnected regions.""" # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(polydata) surfer.Update() # clean before connectivity filter # to avoid artificial regionIds cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(surfer.GetOutput()) cleaner.Update() # extract all connected regions connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(cleaner.GetOutput()) connect.SetExtractionModeToAllRegions() connect.ColorRegionsOn() connect.Update() # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(connect.GetOutput()) surfer.Update() # compute range regions = surfer.GetOutput().GetPointData().GetArray('RegionId') regionsrange = regions.GetRange() maxpoints = 0 largestregionid = regionsrange[0] # if more than one region, find the largest if (regionsrange[1] > 0.0): for j in range(int(regionsrange[0]), int(regionsrange[1]) + 1): outsurf = pointthreshold(surfer.GetOutput(), 'RegionId', j, j) numberofpoints = outsurf.GetNumberOfPoints() if (numberofpoints> maxpoints): maxpoints = numberofpoints largestregionid = j return largestregionid def getregionsrange(polydata): """Return range of connected regions.""" # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(polydata) surfer.Update() # clean before connectivity filter # to avoid artificial regionIds cleaner = vtk.vtkCleanPolyData() cleaner.SetInput(surfer.GetOutput()) cleaner.Update() # extract all connected regions connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(cleaner.GetOutput()) connect.SetExtractionModeToAllRegions() connect.ColorRegionsOn() connect.Update() # extract surface surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(connect.GetOutput()) surfer.Update() # get range regions = surfer.GetOutput().GetPointData().GetArray('RegionId') regionsrange = regions.GetRange() return regionsrange def imagefordice(surface, spacing, bounds, indexes, arrayname): """Compute image from surface using imagestencil. Values of labels on surface are preserved.""" # labels regionslabels = getregionslabels() # cap whole surface with flat covers surfacecap = capsurface(surface) # if we still have edges, run fill holes edges = extractboundaryedge(surfacecap) if edges.GetNumberOfPoints() > 0: surfacecap = fillholes(surfacecap) # make image with the whole surface wholeimage = mesh2image(surfacecap, spacing, bounds, 1) # each label for index in indexes: vein = pointthreshold(surface, arrayname, regionslabels[index], regionslabels[index], 0) # cap vein with flat covers veincap = capsurface(vein) # if we still have edges, run fill holes edges = extractboundaryedge(veincap) if edges.GetNumberOfPoints() > 0: veincap = fillholes(veincap) # generate image using imagestencil veinimage = mesh2image(veincap, spacing, bounds, 1) # add the images wholeimage = imagesum(wholeimage, veinimage) # pixels with value 2 correspond to vein region wholeimage = imagereplacevalue(wholeimage, 2, regionslabels[index]) # pixels remaining with value 1 correspond to body wholeimage = imagereplacevalue(wholeimage, 1, regionslabels['body']) return wholeimage def getregionslabels(): """Return dictionary linking regionids to anatomical locations.""" regionslabels = {'body': 36, 'laa': 37, 'pv2': 76, 'pv1': 77, 'pv3': 78, 'pv4': 79} return regionslabels def mesh2image(pd, spacing, bounds, value=255): """Generates an image in which pixels inside the surface set to value.""" # start with white image dim, origin = bounds_dim_origin(bounds,spacing) whiteimage = image_from_value(spacing,dim,origin,value) # polygonal data to image stencil pol2stenc = vtk.vtkPolyDataToImageStencil() pol2stenc.SetInput(pd) pol2stenc.SetOutputOrigin(origin) pol2stenc.SetOutputSpacing(spacing) pol2stenc.SetOutputWholeExtent(0, dim[0] , 0, dim[1] , 0, dim[2] ) pol2stenc.Update() # cut the corresponding white image and set the background imgstenc = vtk.vtkImageStencil() imgstenc.SetInput(whiteimage) imgstenc.SetStencil(pol2stenc.GetOutput()) imgstenc.ReverseStencilOff() imgstenc.SetBackgroundValue(0) imgstenc.Update() return imgstenc.GetOutput() def bounds_dim_origin(bounds,spacing): """Compute origin and extent based on bounds.""" # compute dimensions dim = [0, 0, 0] origin = [0, 0, 0] for i in range(0, 3): dim[i] = int(math.ceil((bounds[i * 2 + 1] - bounds[i * 2]) / spacing[i])) # origin is the lower bound (double) for i in range(0, 3): origin[i] = bounds[i * 2] return dim,origin def image_from_value(spacing, dim, origin, value=255): """Generate a white image with a defined spacing and bounds.""" # initialise image image = vtk.vtkImageData() image.SetSpacing(spacing) image.SetDimensions(dim) image.SetExtent(0, dim[0], 0, dim[1], 0, dim[2]) image.SetOrigin(origin) image.SetScalarTypeToUnsignedChar() image.SetNumberOfScalarComponents(1) image.AllocateScalars() imagescalars = image.GetPointData().GetScalars() # Fill component is much faster than visiting each element to set value imagescalars.FillComponent(0, value) image.Update() return image def capsurface(polydata, arrayname=''): """Cap holes in surface with a flat cover.""" # generates a flat cover for a convex hole defined by edges fedges = extractboundaryedge(polydata) # find each connected edge connect = vtk.vtkPolyDataConnectivityFilter() connect.SetInput(fedges) connect.Update() ncontours = connect.GetNumberOfExtractedRegions() append = vtk.vtkAppendPolyData() append.AddInput(polydata) # generate each flat cover for i in range(ncontours): connect.AddSpecifiedRegion(i) connect.SetExtractionModeToSpecifiedRegions() connect.Update() edges = connect.GetOutput() cover = vtk.vtkPolyData() generatecover(edges, cover, arrayname) # append to original polydata append.AddInput(cover) connect.DeleteSpecifiedRegion(i) append.Update() outsurface = cleanpolydata(append.GetOutput()) return outsurface def generatecover(edges, cover, arrayname=''): """Create caps for capping a surface with holes.""" # create the building blocks of polydata. polys = vtk.vtkCellArray() points = vtk.vtkPoints() surfilt = vtk.vtkCleanPolyData() surfilt.SetInput( edges ) surfilt.Update() points.DeepCopy(surfilt.GetOutput().GetPoints()) npoints = points.GetNumberOfPoints() if arrayname: # keep pre existing array array = surfilt.GetOutput().GetPointData().GetArray(arrayname) arraynp = vtk_to_numpy(array) array.InsertNextValue(np.mean(arraynp)) # add centroid centr = np.zeros(3) for i in range( npoints ): pt = np.zeros(3) points.GetPoint(i,pt) centr = centr + pt centr = centr / npoints cntpt = points.InsertNextPoint(centr) # add cells for i in range(surfilt.GetOutput().GetNumberOfCells()): cell = surfilt.GetOutput().GetCell(i) polys.InsertNextCell(3) polys.InsertCellPoint(cell.GetPointId(0)) polys.InsertCellPoint(cell.GetPointId(1)) polys.InsertCellPoint(cntpt) # assign the pieces to the polydata cover.SetPoints(points) cover.SetPolys(polys) if arrayname: cover.GetPointData().AddArray(array) def imageopenclose(image, openvalue, closevalue, kernelsize): """Performs opening and closing morphological operations with a 3D ellipsoidal kernel.""" openClose = vtk.vtkImageOpenClose3D() openClose.SetInput(image) openClose.SetOpenValue(openvalue) openClose.SetCloseValue(closevalue) openClose.SetKernelSize(kernelsize, kernelsize, kernelsize) openClose.ReleaseDataFlagOff() openClose.GetOutput() openClose.GetCloseValue() openClose.GetOpenValue() return openClose.GetOutput() def imagereplacevalue(image, const1, const2): """Replaces the scalar value in a image with another.""" kfilter = vtk.vtkImageMathematics() kfilter.SetInput1(image) kfilter.SetConstantC(const1) kfilter.SetConstantK(const2) kfilter.SetOperationToReplaceCByK() kfilter.Update() return kfilter.GetOutput() def imagesum(image1, image2): """Adds two images.""" sumfilter = vtk.vtkImageMathematics() sumfilter.SetInput1(image1) sumfilter.SetInput2(image2) sumfilter.SetOperationToAdd() sumfilter.Update() return sumfilter.GetOutput() def imagethresholdbetween(image, t1, t2, invalue=1.0): """Thresholds an image between t1 and t2.""" tfilter = vtk.vtkImageThreshold() tfilter.SetInput(image) tfilter.ThresholdBetween(t1, t2) tfilter.SetOutValue(0.0) tfilter.SetInValue(invalue) tfilter.Update() return tfilter.GetOutput() def imagethresholdupper(image, t, invalue=1.0): """Thresholds values equal or greater than t.""" tfilter = vtk.vtkImageThreshold() tfilter.SetInput(image) tfilter.ThresholdByUpper(t) tfilter.SetOutValue(0.0) tfilter.SetInValue(invalue) tfilter.Update() return tfilter.GetOutput() def marchingcubes(image, startlabel, endlabel): """Generates object boundaries from labelled volumes using Marching Cubes algorithm.""" discretecubes = vtk.vtkDiscreteMarchingCubes() discretecubes.SetInput(image) discretecubes.GenerateValues(endlabel - startlabel + 1, startlabel, endlabel) discretecubes.Update() return discretecubes.GetOutput() def planeclip(surface, point, normal, insideout=1): """Clip a surface using the plane perpendicular to normal and centred at point.""" clipplane = vtk.vtkPlane() clipplane.SetOrigin(point) clipplane.SetNormal(normal) clipper = vtk.vtkClipPolyData() clipper.SetInput(surface) clipper.SetClipFunction(clipplane) if insideout: clipper.InsideOutOn() else: clipper.InsideOutOff() clipper.Update() return clipper.GetOutput() def pointsetcentreofmass(polydata): """Compute the centre of mass of a polydata.""" centre = [0, 0, 0] for i in range(polydata.GetNumberOfPoints()): point = [polydata.GetPoints().GetPoint(i)[0], polydata.GetPoints().GetPoint(i)[1], polydata.GetPoints().GetPoint(i)[2]] centre = addvectors(centre, point) return dividevector(centre, polydata.GetNumberOfPoints()) def dividevector(point, n): """Divide vector by scalar value.""" nr = float(n) return [point[0]/nr, point[1]/nr, point[2]/nr] def pointthreshold(polydata, arrayname, start=0, end=1, alloff=0): """Threshold between start and end values in array. By default, threshold excludes points whose neighbours do not satisfy the threshold value. Enabling the flag 'alloff' disables this setting to include all points.""" threshold = vtk.vtkThreshold() threshold.SetInput(polydata) threshold.SetInputArrayToProcess(0, 0, 0, vtk.vtkDataObject.FIELD_ASSOCIATION_POINTS, arrayname) threshold.ThresholdBetween(start, end) if (alloff): threshold.AllScalarsOff() threshold.Update() surfer = vtk.vtkDataSetSurfaceFilter() surfer.SetInput(threshold.GetOutput()) surfer.Update() return surfer.GetOutput() def readmetaimage(filename): """Read a metaimage.""" reader = vtk.vtkMetaImageReader() reader.SetFileName(filename) reader.Update() return reader.GetOutput() def readvtp(filename, dataarrays=True): """Read polydata in XML format.""" reader = vtk.vtkXMLPolyDataReader() reader.SetFileName(filename) reader.Update() if not dataarrays: for i in range(reader.GetNumberOfPointArrays()): arrayname = reader.GetPointArrayName(i) reader.SetPointArrayStatus(arrayname, 0) for i in range(reader.GetNumberOfCellArrays()): arrayname = reader.GetCellArrayName(i) reader.SetPointArrayStatus(arrayname, 0) reader.Update() return reader.GetOutput() def resamplearray(x, n): """Resample an array to n samples using a bootstrapping technique.""" y = sklearn.utils.resample(x, n_samples=n) return y def roundpointarray(polydata, name): """Round values in point array.""" # get original array array = polydata.GetPointData().GetArray(name) # round labels for i in range(polydata.GetNumberOfPoints()): value = array.GetValue(i) array.SetValue(i, round(value)) return polydata def smoothtaubin(polydata, iterations=15, angle=120, passband=0.001): """Execute volume reserving smoothing.""" smoother = vtk.vtkWindowedSincPolyDataFilter() smoother.SetInput(polydata) smoother.SetNumberOfIterations(iterations) smoother.BoundarySmoothingOff() smoother.FeatureEdgeSmoothingOff() smoother.SetFeatureAngle(angle) smoother.SetPassBand(passband) smoother.NonManifoldSmoothingOn() smoother.NormalizeCoordinatesOn() smoother.Update() return smoother.GetOutput() def surfacearea(polydata): """Compute surface area of polydata.""" properties = vtk.vtkMassProperties() properties.SetInput(polydata) properties.Update() return properties.GetSurfaceArea() def surface2surfacedistance(ref, target, arrayname): """Compute distance between two surfaces. Output is added as point array.""" # adapted from vtkvmtkSurfaceDistance # initialise locator = vtk.vtkCellLocator() genericcell = vtk.vtkGenericCell() cellid = vtk.mutable(0) point = [0., 0., 0.] closestpoint = [0., 0., 0.] subid = vtk.mutable(0) distance2 = vtk.mutable(0) # create array distarray = vtk.vtkDoubleArray() distarray.SetName(arrayname) distarray.SetNumberOfTuples(target.GetNumberOfPoints()) target.GetPointData().AddArray(distarray) # build locator locator.SetDataSet(ref) locator.BuildLocator() # compute distance for i in range(target.GetNumberOfPoints()): point = target.GetPoint(i) locator.FindClosestPoint(point, closestpoint, genericcell, cellid, subid, distance2) distance = math.sqrt(distance2) # add value to array distarray.SetValue(i, distance) target.Update() return target def transferlabels(target, reference, arrayname, value): """Project array from reference surface to target surface using closest point.""" # initiate point locator locator = vtk.vtkPointLocator() locator.SetDataSet(target) locator.BuildLocator() # get array from target array = target.GetPointData().GetArray(arrayname) # go through each point of reference target for i in range(reference.GetNumberOfPoints()): point = reference.GetPoint(i) # determine closest point on target. closestpointid = locator.FindClosestPoint(point) array.SetValue(closestpointid, value) return target def visualise(surface, reference, case, arrayname, mini, maxi): """Visualise surface with colormap based on arrayname. Reference surface is visualise with alpha = 0.5.""" #Create a lookup table to map point data to colors lut = vtk.vtkLookupTable() lut.SetNumberOfTableValues(255) lut.SetValueRange(0, 255) # qualitative data from colorbrewer lut.SetTableValue(0, 0, 0, 0, 1) #black lut.SetTableValue(mini, 1, 1, 1, 1) # white lut.SetTableValue(mini + 1, 77/255., 175/255., 74/255. , 1) # green lut.SetTableValue(maxi - 3, 152/255., 78/255., 163/255., 1) # purple lut.SetTableValue(maxi - 2, 255/255., 127/255., 0., 1) # orange lut.SetTableValue(maxi - 1, 55/255., 126/255., 184/255., 1) # blue lut.SetTableValue(maxi, 166/255., 86/255., 40/255., 1) # brown lut.Build() # create a text actor txt = vtk.vtkTextActor() txt.SetInput(case) txtprop=txt.GetTextProperty() txtprop.SetFontFamilyToArial() txtprop.SetFontSize(18) txtprop.SetColor(0, 0, 0) txt.SetDisplayPosition(20, 30) # create a rendering window, renderer, and renderwindowinteractor ren = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren) iren = vtk.vtkRenderWindowInteractor() style = vtk.vtkInteractorStyleTrackballCamera() iren.SetInteractorStyle(style) iren.SetRenderWindow(renWin) # surface mapper and actor surfacemapper = vtk.vtkPolyDataMapper() surfacemapper.SetInput(surface) surfacemapper.SetScalarModeToUsePointFieldData() surfacemapper.SelectColorArray(arrayname) surfacemapper.SetLookupTable(lut) surfacemapper.SetScalarRange(0, 255) surfaceactor = vtk.vtkActor() surfaceactor.SetMapper(surfacemapper) # refsurface mapper and actor refmapper = vtk.vtkPolyDataMapper() refmapper.SetInput(reference) refmapper.SetScalarModeToUsePointFieldData() refmapper.SelectColorArray(arrayname) refmapper.SetLookupTable(lut) refmapper.SetScalarRange(0, 255) refactor = vtk.vtkActor() refactor.GetProperty().SetOpacity(0.5) refactor.SetMapper(refmapper) # assign actors to the renderer ren.AddActor(refactor) ren.AddActor(surfaceactor) ren.AddActor(txt) # set the background and size, zoom in and render ren.SetBackground(1, 1, 1) renWin.SetSize(1280, 960) ren.ResetCamera() ren.GetActiveCamera().Zoom(1) # enable user interface interactor iren.Initialize() renWin.Render() iren.Start() def writearray2csv(array, ofile, label=''): """Write array to csv.""" f = open(ofile, 'wb') for i in range(len(array)): if label: line = str(label[i]) + ', ' + str(array[i]) + '\n' else: line = str(array[i]) + '\n' f.write(line) f.close() def writevtk(surface, filename): """Write vtkPolyData file.""" writer = vtk.vtkPolyDataWriter() writer.SetInput(surface) writer.SetFileTypeToASCII() writer.SetFileName(filename) writer.Write() def writevtp(surface, filename): """Write vtkPolyData file in XML format.""" writer = vtk.vtkXMLPolyDataWriter() writer.SetInput(surface) writer.SetFileName(filename) writer.Write() def writemetaimage(image, filename): """Write image file in mhd format.""" writer = vtk.vtkMetaImageWriter() writer.SetFileName(filename) writer.SetInput(image) writer.Write()
catactg/lasc
code/lasc_benchmark_tools.py
Python
bsd-2-clause
40,887
[ "VTK" ]
edb021a96aea31aaf0347cd43c431b933d98fd1243010d63ec44aeb39f426572
#!/usr/bin/env python import json import logging import os import string import subprocess import sys import time import uuid from random import choice from twisted.internet import reactor, defer from twisted.internet.task import deferLater from twisted.internet.defer import CancelledError from twisted.python import log from twisted.web import server, resource, http from twisted.web.resource import Resource from twisted.web.server import NOT_DONE_YET from twisted.web.static import File from vtk.web import upload try: import argparse except ImportError: import _argparse as argparse sample_config_file = """ Here is a sample of what a configuration file could look like: { ## =============================== ## General launcher configuration ## =============================== "configuration": { "host" : "localhost", "port" : 8080, "endpoint": "paraview", # SessionManager Endpoint "content": "/.../www", # Optional: Directory shared over HTTP "proxy_file" : "/.../proxy-mapping.txt", # Proxy-Mapping file for Apache "sessionURL" : "ws://${host}:${port}/ws", # ws url used by the client to connect to the started process "timeout" : 25, # Wait time in second after process start "log_dir" : "/.../viz-logs", # Directory for log files "upload_dir" : "/.../data", # If launcher should act as upload server, where to put files "fields" : ["file", "host", "port", "updir"] # List of fields that should be send back to client }, ## =============================== ## Useful session vars for client ## =============================== "sessionData" : { "updir": "/Home" }, # Tells client which path to updateFileBrowser after uploads ## =============================== ## Resources list for applications ## =============================== "resources" : [ { "host" : "localhost", "port_range" : [9001, 9003] } ], ## =============================== ## Set of properties for cmd line ## =============================== "properties" : { "vtkpython" : "/.../VTK/build/bin/vtkpython", "pvpython" : "/.../ParaView/build/bin/pvpython", "vtk_python_path": "/.../VTK/build/Wrapping/Python/vtk/web", "pv_python_path": "/.../ParaView/build/lib/site-packages/paraview/web", "plugins_path": "/.../ParaView/build/lib", "dataDir": "/.../path/to/data/directory" }, ## =============================== ## Application list with cmd lines ## =============================== "apps" : { "cone" : { "cmd" : [ "${vtkpython}", "${vtk_python_path}/vtk_web_cone.py", "--port", "$port" ], "ready_line" : "Starting factory" }, "graph" : { "cmd" : [ "${vtkpython}", "${vtk_python_path}/vtk_web_graph.py", "--port", "$port", "--vertices", "${numberOfVertices}", "--edges", "${numberOfEdges}" ], "ready_line" : "Starting factory" }, "phylotree" : { "cmd" : [ "${vtkpython}", "${vtk_python_path}/vtk_web_phylogenetic_tree.py", "--port", "$port", "--tree", "${dataDir}/visomics/${treeFile}", "--table", "${dataDir}/visomics/${tableFile}" ], "ready_line" : "Starting factory" }, "filebrowser" : { "cmd" : [ "${vtkpython}", "${vtk_python_path}/vtk_web_filebrowser.py", "--port", "${port}", "--data-dir", "${dataDir}" ], "ready_line" : "Starting factory" }, "data_prober": { "cmd": [ "${pvpython}", "-dr", "${pv_python_path}/pv_web_data_prober.py", "--port", "${port}", "--data-dir", "${dataDir}", "-f" ], "ready_line" : "Starting factory" }, "visualizer": { "cmd": [ "${pvpython}", "-dr", "${pv_python_path}/pv_web_visualizer.py", "--plugins", "${plugins_path}/libPointSprite_Plugin.so", "--port", "${port}", "--data-dir", "${dataDir}", "--load-file", "${dataDir}/${fileToLoad}", "--authKey", "${secret}", "-f" ], "ready_line" : "Starting factory" }, "loader": { "cmd": [ "${pvpython}", "-dr", "${pv_python_path}/pv_web_file_loader.py", "--port", "${port}", "--data-dir", "${dataDir}", "--load-file", "${dataDir}/${fileToLoad}", "-f" ], "ready_line" : "Starting factory" }, "launcher" : { "cmd": [ "/.../ParaView/Web/Applications/Parallel/server/launcher.sh", "${port}", "${client}", "${resources}", "${file}" ], "ready_line" : "Starting factory" }, "your_app": { "cmd": [ "your_shell_script.sh", "--resource-host", "${host}", "--resource-port", "${port}", "--session-id", "${id}", "--generated-password", "${secret}", "--application-key", "${application}" ], "ready_line": "Output line from your shell script indicating process is ready" } } """ # ============================================================================= # Helper module methods # ============================================================================= def generatePassword(): return ''.join(choice(string.letters + string.digits) for _ in xrange(16)) # ----------------------------------------------------------------------------- def validateKeySet(obj, expected_keys, object_name): all_key_found = True for key in expected_keys: if not obj.has_key(key): print "ERROR: %s is missing %s key." % (object_name, key) all_key_found = False return all_key_found # ----------------------------------------------------------------------------- def replaceVariables(template_str, variable_list): for key_pair in variable_list: item_template = string.Template(template_str) template_str = item_template.safe_substitute(key_pair) if "$" in template_str: logging.error("Some properties could not be resolved: " + template_str) return template_str # ----------------------------------------------------------------------------- def replaceList(template_list, variable_list): result_list = [] for str in template_list: result_list.append(replaceVariables(str, variable_list)) return result_list # ----------------------------------------------------------------------------- def filterResponse(obj, public_keys): public_keys.extend(['id', 'sessionURL', 'sessionManagerURL']) filtered_output = {} for field in obj: if field in public_keys: filtered_output[field] = obj[field] return filtered_output # ----------------------------------------------------------------------------- def extractSessionId(request): path = request.path.split('/') if len(path) < 3: return None return str(path[2]) # ============================================================================= # Session manager # ============================================================================= class SessionManager(object): def __init__(self, config, mapping): self.sessions = {} self.config = config self.resources = ResourceManager(config["resources"]) self.mapping = mapping def createSession(self, options): # Assign id and store options id = str(uuid.uuid1()) # Assign resource to session host, port = self.resources.getNextResource() # Do we have resources if host: options['id'] = id options['host'] = host options['port'] = port if not options.has_key('secret'): options['secret'] = generatePassword() options['sessionURL'] = replaceVariables(self.config['configuration']['sessionURL'], [options, self.config['properties']]) options['cmd'] = replaceList(self.config['apps'][options['application']]['cmd'], [options, self.config['properties']]) if self.config.has_key('sessionData') : for key in self.config['sessionData'] : options[key] = replaceVariables(self.config['sessionData'][key], [options, self.config['properties']]) self.sessions[id] = options self.mapping.update(self.sessions) return options return None def deleteSession(self, id): host = self.sessions[id]['host'] port = self.sessions[id]['port'] self.resources.freeResource(host, port) del self.sessions[id] self.mapping.update(self.sessions) def getSession(self, id): if self.sessions.has_key(id): return self.sessions[id] return None # ============================================================================= # Proxy manager # ============================================================================= class ProxyMappingManager(object): def update(sessions): pass class ProxyMappingManagerTXT(ProxyMappingManager): def __init__(self, file_path, pattern="%s %s:%d\n"): self.file_path = file_path self.pattern = pattern def update(self, sessions): with open(self.file_path, "w") as map_file: for id in sessions: map_file.write(self.pattern % (id, sessions[id]['host'], sessions[id]['port'])) # ============================================================================= # Resource manager # ============================================================================= class ResourceManager(object): """ Class that provides methods to keep track on available resources (host/port) """ def __init__(self, resourceList): self.resources = {} for resource in resourceList: host = resource['host'] portList = range(resource['port_range'][0],resource['port_range'][1]+1) if self.resources.has_key(host): self.resources[host]['available'].extend(portList) else: self.resources[host] = { 'available': portList, 'used': []} def getNextResource(self): """ Return a (host, port) pair if any available otherwise will return None """ # find host with max availibility winner = None availibilityCount = 0 for host in self.resources: if availibilityCount < len(self.resources[host]['available']): availibilityCount = len(self.resources[host]['available']) winner = host if winner: port = self.resources[winner]['available'].pop() self.resources[winner]['used'].append(port) return (winner, port) return (None, None) def freeResource(self, host, port): """ Free a previously reserved resource """ if self.resources.has_key(host) and port in self.resources[host]['used']: self.resources[host]['used'].remove(port) self.resources[host]['available'].append(port) # ============================================================================= # Process manager # ============================================================================= class ProcessManager(object): def __init__(self, configuration): self.config = configuration self.log_dir = configuration['configuration']['log_dir'] self.processes = {} def __del__(self): for id in self.processes: self.processes[id].terminate() def _getLogFilePath(self, id): return "%s%s%s.txt" % (self.log_dir, os.sep, id) def startProcess(self, session): proc = None # Create output log file logFilePath = self._getLogFilePath(session['id']) with open(logFilePath, "a+", 0) as log_file: try: proc = subprocess.Popen(session['cmd'], stdout=log_file, stderr=log_file) self.processes[session['id']] = proc except: logging.error("The command line failed") logging.error(' '.join(map(str, session['cmd']))) return None return proc def stopProcess(self, id): proc = self.processes[id] del self.processes[id] try: proc.terminate() except: pass # we tried def listEndedProcess(self): session_to_release = [] for id in self.processes: if self.processes[id].poll() is not None: session_to_release.append(id) return session_to_release def isRunning(self, id): return self.processes[id].poll() is None # ======================================================================== # Look for ready line in process output. Return True if found, False # otherwise. If no ready_line is configured and process is running return # False. This will then rely on the timout time. # ======================================================================== def isReady(self, session, count = 0): id = session['id'] # The process has to be running to be ready! if not self.isRunning(id) and count < 60: return False # Give up after 60 seconds if still not running if not self.isRunning(id): return True application = self.config['apps'][session['application']] ready_line = application.get('ready_line', None) # If no ready_line is configured and the process is running then thats # enough. if not ready_line: return False ready = False # Check the output for ready_line logFilePath = self._getLogFilePath(session['id']) with open(logFilePath, "r", 0) as log_file: for line in log_file.readlines(): if ready_line in line: ready = True break return ready # =========================================================================== # Class to implement requests to POST, GET and DELETE methods # =========================================================================== class LauncherResource(resource.Resource, object): def __init__(self, options, config): super(LauncherResource, self).__init__() self._options = options self._config = config self.time_to_wait = int(config['configuration']['timeout']) self.field_filter = config['configuration']['fields'] self.session_manager = SessionManager(config,ProxyMappingManagerTXT(config['configuration']['proxy_file'])) self.process_manager = ProcessManager(config) def getChild(self, path, request): return self def __del__(self): logging.warning("Server factory shutting down. Stopping all processes") # ======================================================================== # Handle POST request # ======================================================================== def render_POST(self, request): payload = json.loads(request.content.getvalue()) # Make sure the request has all the expected keys if not validateKeySet(payload, ["application"], "Launch request"): request.setResponseCode(http.BAD_REQUEST) return json.dumps({"error": "The request is not complete"}) # Try to free any available resource id_to_free = self.process_manager.listEndedProcess() for id in id_to_free: self.session_manager.deleteSession(id) self.process_manager.stopProcess(id) # Create new session session = self.session_manager.createSession(payload) # No resource available if not session: request.setResponseCode(http.SERVICE_UNAVAILABLE) return json.dumps({"error": "All the resources are currently taken"}) # Start process proc = self.process_manager.startProcess(session) if not proc: request.setResponseCode(http.SERVICE_UNAVAILABLE) return json.dumps({"error": "The process did not properly start. %s" % str(session['cmd'])}) # local function to act as errback for Deferred objects. def errback(error): # Filter out CancelledError and propagate rest if error.type != CancelledError: return error # Deferred object set to timeout request if process doesn't start in time timeout_deferred = deferLater(reactor, self.time_to_wait, lambda: request) timeout_deferred.addCallback(self._delayedRenderTimeout, session) timeout_deferred.addErrback(errback) # Make sure other deferred is canceled once one has been fired request.notifyFinish().addCallback(lambda x: timeout_deferred.cancel()) # If a ready_line is configured create a Deferred object to wait for # ready line to be produced if 'ready_line' in self._config['apps'][session['application']]: ready_deferred = self._waitForReady(session, request) ready_deferred.addCallback(self._delayedRenderReady, session) ready_deferred.addErrback(errback) # Make sure other deferred is canceled once one has been fired request.notifyFinish().addCallback(lambda x: ready_deferred.cancel()) return NOT_DONE_YET # ======================================================================== # Wait for session to be ready. Rather than blocking keep using callLater(...) # to schedule self in reactor. Return a Deferred object whose callback will # be triggered when the session is ready # ======================================================================== def _waitForReady(self, session, request, count=0, d=None): if not d: d = defer.Deferred() if not 'startTimedOut' in session and \ not self.process_manager.isReady(session, count + 1): reactor.callLater(1, self._waitForReady, session, request, count + 1, d) else: d.callback(request) return d # ======================================================================== # Called when the timeout out expires. Check if process is now ready # and send response to client. # ======================================================================== def _delayedRenderTimeout(self, request, session): ready = self.process_manager.isReady(session, 0) if ready: request.write(json.dumps(filterResponse(session, self.field_filter))) request.setResponseCode(http.OK) else: request.write(json.dumps({"error": "Session did not start before timeout expired. Check session logs."})) # Mark the session as timed out and clean up the process session['startTimedOut'] = True self.session_manager.deleteSession(session['id']) self.process_manager.stopProcess(session['id']) request.setResponseCode(http.SERVICE_UNAVAILABLE) request.finish() # ======================================================================== # Called when the process is ready ( the ready line has been read from the # process output). # ======================================================================== def _delayedRenderReady(self, request, session): filterkeys = self.field_filter if session['secret'] in session['cmd']: filterkeys = self.field_filter + [ 'secret' ] request.write(json.dumps(filterResponse(session, filterkeys))) request.setResponseCode(http.OK) request.finish() # ========================================================================= # Handle GET request # ========================================================================= def render_GET(self, request): id = extractSessionId(request) if not id: message = "id not provided in GET request" logging.error(message) request.setResponseCode(http.BAD_REQUEST) return json.dumps({"error":message}) logging.info("GET request received for id: %s" % id) session = self.session_manager.getSession(id) if not session: message = "No session with id: %s" % id logging.error(message) request.setResponseCode(http.NOT_FOUND) return json.dumps({"error":message}) # Return session meta-data request.setResponseCode(http.OK) return json.dumps(filterResponse(session, self.field_filter)) # ========================================================================= # Handle DELETE request # ========================================================================= def render_DELETE(self, request): id = extractSessionId(request) if not id: message = "id not provided in DELETE request" logging.error(message) request.setResponseCode(http.BAD_REQUEST) return json.dumps({"error":message}) logging.info("DELETE request received for id: %s" % id) session = self.session_manager.getSession(id) if not session: message = "No session with id: %s" % id logging.error(message) request.setResponseCode(http.NOT_FOUND) return json.dumps({"error":message}) # Remove session self.session_manager.deleteSession(id) self.process_manager.stopProcess(id) message = "Deleted session with id: %s" % id logging.info(message) request.setResponseCode(http.OK) return session # ============================================================================= # Start the web server # ============================================================================= def startWebServer(options, config): # Extract properties from config log_dir = str(config["configuration"]["log_dir"]) content = str(config["configuration"]["content"]) endpoint = str(config["configuration"]["endpoint"]) host = str(config["configuration"]["host"]) port = int(config["configuration"]["port"]) # Setup logging logFileName = log_dir + os.sep + "launcherLog.log" formatting = '%(asctime)s:%(levelname)s:%(name)s:%(message)s' logging.basicConfig(level=logging.DEBUG, filename=logFileName, filemode='w', format=formatting) observer = log.PythonLoggingObserver() observer.start() if options.debug: console = logging.StreamHandler(sys.stdout) console.setLevel(logging.INFO) formatter = logging.Formatter(formatting) console.setFormatter(formatter) logging.getLogger('').addHandler(console) # Initialize web resource web_resource = File(content) if (len(content) > 0) else resource.Resource() # Attach launcher web_resource.putChild(endpoint, LauncherResource(options, config)) # Check if launcher should act as a file upload server as well if config["configuration"].has_key("upload_dir"): from upload import UploadPage updir = replaceVariables(config['configuration']['upload_dir'], [config['properties']]) uploadResource = UploadPage(updir) web_resource.putChild("upload", uploadResource) site = server.Site(web_resource) reactor.listenTCP(port, site, interface=host) reactor.run() # ============================================================================= # Parse config file # ============================================================================= def parseConfig(options): # Read values from the configuration file try: config = json.loads(open(options.config[0]).read()) except: message = "ERROR: Unable to read config file.\n" message += str(sys.exc_info()[1]) + "\n" + str(sys.exc_info()[2]) print message print sample_config_file sys.exit(2) expected_keys = ["configuration", "apps", "properties", "resources"] if not validateKeySet(config, expected_keys, "Config file"): print sample_config_file sys.exit(2) expected_keys = ["endpoint", "host", "port", "proxy_file", "sessionURL", "timeout", "log_dir", "fields"] if not validateKeySet(config["configuration"], expected_keys, "file.configuration"): print sample_config_file sys.exit(2) if not config["configuration"].has_key("content"): config["configuration"]["content"] = "" return config # ============================================================================= # Setup default arguments to be parsed # -d, --debug # -t, --proxyFileType Type of proxy file (txt, dbm) # ============================================================================= def add_arguments(parser): parser.add_argument("config", type=str, nargs=1, help="configuration file for the launcher") parser.add_argument("-d", "--debug", help="log debugging messages to stdout", action="store_true") return parser # ============================================================================= # Parse arguments # ============================================================================= def start(argv=None, description="VTKWeb Launcher"): parser = argparse.ArgumentParser(description=description) add_arguments(parser) args = parser.parse_args(argv) config = parseConfig(args) startWebServer(args, config) # ============================================================================= # Main # ============================================================================= if __name__ == "__main__": start()
ashray/VTK-EVM
Web/Python/vtk/web/launcher.py
Python
bsd-3-clause
26,500
[ "ParaView", "VTK" ]
bffc798cbc661f94eaa2d5f03893f3ed6361a056b0984b70c0ff0e7500248b5f
#!/usr/bin/env python #pylint: disable=missing-docstring #################################################################################################### # DO NOT MODIFY THIS HEADER # # MOOSE - Multiphysics Object Oriented Simulation Environment # # # # (c) 2010 Battelle Energy Alliance, LLC # # ALL RIGHTS RESERVED # # # # Prepared by Battelle Energy Alliance, LLC # # Under Contract No. DE-AC07-05ID14517 # # With the U. S. Department of Energy # # # # See COPYRIGHT for full restrictions # #################################################################################################### import os import unittest import subprocess import shutil import tempfile import mooseutils import MooseDocs class TestSQA(unittest.TestCase): """ Test the build works across directories. """ SITE_DIR = tempfile.mkdtemp(dir=os.path.join(os.getenv('HOME'), '.local', 'share', 'moose')) WORKING_DIR = os.getcwd() def setUp(self): """ Runs prior to each test. """ os.chdir(os.path.join(MooseDocs.ROOT_DIR, 'test', 'docs')) if not os.path.exists(self.SITE_DIR): os.makedirs(self.SITE_DIR) def tearDown(self): """ Runs after each test. """ os.chdir(self.WORKING_DIR) shutil.rmtree(self.SITE_DIR) def testBuild(self): """ Test that sqa demo is working. """ exe = mooseutils.find_moose_executable(os.path.join(MooseDocs.MOOSE_DIR, 'test')) self.assertTrue(os.path.isfile(exe), "The moose_test executable does not exist.") c = ['./moosedocs.py', 'build', '--config-file', 'sqa.yml', '--clean', '--site-dir', self.SITE_DIR] proc = subprocess.Popen(c, cwd=os.path.join(MooseDocs.MOOSE_DIR, 'test', 'docs'), stdout=subprocess.PIPE) out = proc.stdout.read() self.assertIn(out, 'WARNINGS: 0 ERRORS: 4\n') with open(os.path.join(self.SITE_DIR, 'sqa', 'test_srs', 'index.html'), 'r') as fid: html = fid.read() self.assertIn('Testing testing testing', html) self.assertIn('Missing Template Item: project_description', html) self.assertIn('Missing Template Item: system_scope', html) self.assertIn('<span class="moose-collection-name">F1.50</span>', html) self.assertIn('<div class="collapsible-header moose-group-header">Transient Analysis</div>', html) self.assertIn('<li id="requirement-F1.10">', html) with open(os.path.join(self.SITE_DIR, 'sqa', 'test_rtm', 'index.html'), 'r') as fid: html = fid.read() self.assertIn('<div class="collapsible-header moose-group-header">Transient Analysis</div>', html) self.assertIn('<a href="../test_srs/index.html">F1.10</a>', html) self.assertIn('<span class="moose-sqa-error">F9.99</span>', html) with open(os.path.join(self.SITE_DIR, 'sqa', 'test_v_and_v', 'index.html'), 'r') as fid: html = fid.read() self.assertIn('<a href="validation/V1-01/index.html">V1.01</a>', html) link = os.path.join(MooseDocs.ROOT_DIR, 'test/docs/content/sqa/test_v_and_v/validation/V1-02.md') self.assertIn('<a class="moose-bad-link" href="{}">V1.02</a>'.format(link), html) self.assertIn('<span class="new badge danger" data-badge-caption="danger">1', html) self.assertIn('<span class="new badge error" data-badge-caption="error">1', html) with open(os.path.join(self.SITE_DIR, 'sqa', 'index.html'), 'r') as fid: html = fid.read() self.assertIn('<span class="moose-page-status" data-filename="test_srs/index.html">', html) self.assertIn('<span class="new badge pass" data-badge-caption="pass">', html) if __name__ == '__main__': unittest.main(verbosity=2)
Chuban/moose
python/MooseDocs/tests/sqa/test_sqa.py
Python
lgpl-2.1
4,670
[ "MOOSE" ]
8601f0bf50599a5d859a7ab893d12a3bee96fccbd693d9a8c357848a1c3f7cd1
######################################################################## # File: RegisterOperation.py # Author: Krzysztof.Ciba@NOSPAMgmail.com # Date: 2013/03/19 13:55:14 ######################################################################## """ :mod: RegisterFile ================== .. module: RegisterFile :synopsis: register operation handler .. moduleauthor:: Krzysztof.Ciba@NOSPAMgmail.com RegisterFile operation handler """ __RCSID__ = "$Id $" # # # @file RegisterOperation.py # @author Krzysztof.Ciba@NOSPAMgmail.com # @date 2013/03/19 13:55:24 # @brief Definition of RegisterOperation class. # # imports from DIRAC import S_OK, S_ERROR from DIRAC.FrameworkSystem.Client.MonitoringClient import gMonitor from DIRAC.RequestManagementSystem.private.OperationHandlerBase import OperationHandlerBase from DIRAC.DataManagementSystem.Client.DataManager import DataManager ######################################################################## class RegisterFile( OperationHandlerBase ): """ .. class:: RegisterOperation RegisterFile operation handler """ def __init__( self, operation = None, csPath = None ): """c'tor :param self: self reference :param Operation operation: Operation instance :param str csPath: CS path for this handler """ OperationHandlerBase.__init__( self, operation, csPath ) # # RegisterFile specific monitor info gMonitor.registerActivity( "RegisterAtt", "Attempted file registrations", "RequestExecutingAgent", "Files/min", gMonitor.OP_SUM ) gMonitor.registerActivity( "RegisterOK", "Successful file registrations", "RequestExecutingAgent", "Files/min", gMonitor.OP_SUM ) gMonitor.registerActivity( "RegisterFail", "Failed file registrations", "RequestExecutingAgent", "Files/min", gMonitor.OP_SUM ) def __call__( self ): """ call me maybe """ # # counter for failed files failedFiles = 0 # # catalog(s) to use catalogs = self.operation.Catalog if catalogs: catalogs = [ cat.strip() for cat in catalogs.split( ',' ) ] dm = DataManager( catalogs = catalogs ) # # get waiting files waitingFiles = self.getWaitingFilesList() # # loop over files for opFile in waitingFiles: gMonitor.addMark( "RegisterAtt", 1 ) # # get LFN lfn = opFile.LFN # # and others fileTuple = ( lfn , opFile.PFN, opFile.Size, self.operation.targetSEList[0], opFile.GUID, opFile.Checksum ) # # call DataManager registerFile = dm.registerFile( fileTuple ) # # check results if not registerFile["OK"] or lfn in registerFile["Value"]["Failed"]: gMonitor.addMark( "RegisterFail", 1 ) # self.dataLoggingClient().addFileRecord( lfn, "RegisterFail", ','.join( catalogs ) if catalogs else "all catalogs", "", "RegisterFile" ) reason = registerFile.get( "Message", registerFile.get( "Value", {} ).get( "Failed", {} ).get( lfn, 'Unknown' ) ) errorStr = "failed to register LFN %s: %s" % ( lfn, reason ) opFile.Error = errorStr self.log.warn( errorStr ) failedFiles += 1 else: gMonitor.addMark( "RegisterOK", 1 ) # self.dataLoggingClient().addFileRecord( lfn, "Register", ','.join( catalogs ) if catalogs else "all catalogs", "", "RegisterFile" ) self.log.info( "file %s has been registered at %s" % ( lfn, ','.join( catalogs ) if catalogs else "all catalogs" ) ) opFile.Status = "Done" # # final check if failedFiles: self.log.info( "all files processed, %s files failed to register" % failedFiles ) self.operation.Error = "some files failed to register" return S_ERROR( self.operation.Error ) return S_OK()
miloszz/DIRAC
DataManagementSystem/Agent/RequestOperations/RegisterFile.py
Python
gpl-3.0
3,811
[ "DIRAC" ]
766bcba8b68a4ce1daae2c0a75c1272b3787c7c2f03c0d0db874fc0be4679e68
from openanalysis.base_data_structures import UnionFind, PriorityQueue import openanalysis.tree_growth as TreeGrowth def kruskal_mst(G): """ Finds Minimum Spanning Tree of graph by Kruskal's Algorithm :param G: networkx graph :return: iterator through edges of Minimum spanning Tree """ edge_list = sorted(G.edges(data=True), key=lambda x: x[2]['weight']) subtrees = UnionFind() for u, v, w in edge_list: if subtrees[u] != subtrees[v]: yield (u, v, w) subtrees.union(u, v) def prim(G): """ Finds Minimum Spanning Tree of graph by Prim's Algorithm :param G: networkx graph :return: iterator through edges of Minimum spanning Tree """ V = G.nodes() # Set of all vertices of G while V: # We pop the nodes as soon as they are visited, # so this means "until all the nodes are visited" u = V.pop(0) # Now remove the first vertex and start building the tree visited = {u} # Set of visited nodes stringe_heap = [] # Store the stringe nodes with weights import heapq for v in G.neighbors(u): heapq.heappush(stringe_heap, (G.edge[u][v]['weight'], u, v)) # Now build the min heap storing (weight,source,dest) tuples # Tuples are sorted by their first element # Now start popping from heap,and build MST while stringe_heap: weight, u_star, v_star = heapq.heappop(stringe_heap) if v_star in visited: # No need to do anything since v_star is already visited continue visited.add(v_star) # Mark dest as visited V.remove(v_star) yield (u_star, v_star, {'weight': weight}) # yield the edge for w_star in G.neighbors(v_star): # Update strige heap with neighbour edges of v_star if w_star not in visited: heapq.heappush(stringe_heap, (G.edge[v_star][w_star]['weight'], v_star, w_star)) def dfs(G, root=None): """ Iterates through edges of DFS tree of G :param G: networkx Graph :param root: node to start DFS from. If it is none, DFS is done for all components of G else DFS is done for components connected with root :return: Iterator of edges of DFS tree """ visited = set() if root is None: nodes = G.nodes() # nodes to visit else: nodes = [root] for start in nodes: if start in visited: continue visited.add(start) stack = [(start, child) for child in sorted(G.neighbors(start), reverse=True)] while stack: parent, child = stack.pop() if child not in visited: visited.add(child) yield (parent, child) stack += [(child, grandchild) for grandchild in sorted(G.neighbors(child), reverse=True)] def bfs(G, root=None): """ Iterates through edges of DFS tree of G :param G: networkx Graph :param root: node to start DFS from. If it is none, DFS is done for all components of G else DFS is done for components connected with root :return: Iterator of edges of DFS tree """ visited = set() if root is None: nodes = G.nodes() else: nodes = [root] for start in nodes: if start in visited: continue visited.add(start) Q = [start] while Q: current = Q.pop(0) for n in sorted(G.neighbors(current)): if n not in visited: visited.add(n) Q.append(n) yield (current, n) def dijkstra(G, source=None): """ Returns edges of Single source shortest path starting form source :param G: networkx Graph :param source: source to compute the distances from :return: Iterator through edges of SSSP Tree """ if source is None: source = G.nodes()[0] V = G.nodes() dist, prev = {}, {} Q = PriorityQueue() for v in V: dist[v] = float("inf") prev[v] = None Q.add_task(task=v, priority=dist[v]) dist[source] = 0 Q.update_task(task=source, new_priority=dist[source]) visited = set() for i in range(0, len(G.nodes())): u_star = Q.remove_min() if prev[u_star] is not None: yield (u_star, prev[u_star]) visited.add(u_star) for u in G.neighbors(u_star): if u not in visited and dist[u_star] + G.edge[u][u_star]['weight'] < dist[u]: dist[u] = dist[u_star] + G.edge[u][u_star]['weight'] prev[u] = u_star Q.update_task(u, dist[u]) if __name__ == "__main__": TreeGrowth.tree_growth_visualizer(bfs)
OpenWeavers/openanalysis
analysistest/tree_growth.py
Python
gpl-3.0
4,835
[ "VisIt" ]
6fe6e8705028547f2047fb40792b41707849fc1291ae220460335102edf7fa80
# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by 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. # ============================================================================== """Tests for `tf.data.experimental.SqlDataset`.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.kernel_tests import sql_dataset_test_base from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import test_util from tensorflow.python.platform import test @test_util.run_all_in_graph_and_eager_modes class SqlDatasetTest(sql_dataset_test_base.SqlDatasetTestBase): # Test that SqlDataset can read from a database table. def testReadResultSet(self): for _ in range(2): # Run twice to verify statelessness of db operations. dataset = self._createSqlDataset( query="SELECT first_name, last_name, motto FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string), num_repeats=2) self.assertDatasetProduces( dataset, expected_output=[(b"John", b"Doe", b"Hi!"), (b"Jane", b"Moe", b"Hi again!")] * 2, num_test_iterations=2) # Test that SqlDataset works on a join query. def testReadResultSetJoinQuery(self): get_next = self.getNext( self._createSqlDataset( query="SELECT students.first_name, state, motto FROM students " "INNER JOIN people " "ON students.first_name = people.first_name " "AND students.last_name = people.last_name", output_types=(dtypes.string, dtypes.string, dtypes.string))) self.assertEqual((b"John", b"California", b"Hi!"), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that SqlDataset can read a database entry with a null-terminator # in the middle of the text and place the entry in a `string` tensor. def testReadResultSetNullTerminator(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, favorite_nonsense_word " "FROM students ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) self.assertEqual((b"John", b"Doe", b"n\0nsense"), self.evaluate(get_next())) self.assertEqual((b"Jane", b"Moe", b"nonsense\0"), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that SqlDataset works when used on two different queries. # Because the output types of the dataset must be determined at graph-creation # time, the two queries must have the same number and types of columns. def testReadResultSetReuseSqlDataset(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, motto FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) self.assertEqual((b"John", b"Doe", b"Hi!"), self.evaluate(get_next())) self.assertEqual((b"Jane", b"Moe", b"Hi again!"), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, state FROM people " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) self.assertEqual((b"John", b"Doe", b"California"), self.evaluate(get_next())) self.assertEqual((b"Benjamin", b"Franklin", b"Pennsylvania"), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that an `OutOfRangeError` is raised on the first call to # `get_next_str_only` if result set is empty. def testReadEmptyResultSet(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, motto FROM students " "WHERE first_name = 'Nonexistent'", output_types=(dtypes.string, dtypes.string, dtypes.string))) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that an error is raised when `driver_name` is invalid. def testReadResultSetWithInvalidDriverName(self): with self.assertRaises(errors.InvalidArgumentError): dataset = self._createSqlDataset( driver_name="sqlfake", query="SELECT first_name, last_name, motto FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string)) self.assertDatasetProduces(dataset, expected_output=[]) # Test that an error is raised when a column name in `query` is nonexistent def testReadResultSetWithInvalidColumnName(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, fake_column FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) with self.assertRaises(errors.UnknownError): self.evaluate(get_next()) # Test that an error is raised when there is a syntax error in `query`. def testReadResultSetOfQueryWithSyntaxError(self): get_next = self.getNext( self._createSqlDataset( query="SELEmispellECT first_name, last_name, motto FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) with self.assertRaises(errors.UnknownError): self.evaluate(get_next()) # Test that an error is raised when the number of columns in `query` # does not match the length of `, output_types`. def testReadResultSetWithMismatchBetweenColumnsAndOutputTypes(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.string, dtypes.string))) with self.assertRaises(errors.InvalidArgumentError): self.evaluate(get_next()) # Test that no results are returned when `query` is an insert query rather # than a select query. In particular, the error refers to the number of # output types passed to the op not matching the number of columns in the # result set of the query (namely, 0 for an insert statement.) def testReadResultSetOfInsertQuery(self): get_next = self.getNext( self._createSqlDataset( query="INSERT INTO students (first_name, last_name, motto) " "VALUES ('Foo', 'Bar', 'Baz'), ('Fizz', 'Buzz', 'Fizzbuzz')", output_types=(dtypes.string, dtypes.string, dtypes.string))) with self.assertRaises(errors.InvalidArgumentError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table and # place it in an `int8` tensor. def testReadResultSetInt8(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int8))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a negative or 0-valued integer from a # SQLite database table and place it in an `int8` tensor. def testReadResultSetInt8NegativeAndZero(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, income, favorite_negative_number " "FROM students " "WHERE first_name = 'John' ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int8, dtypes.int8))) self.assertEqual((b"John", 0, -2), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a large (positive or negative) integer from # a SQLite database table and place it in an `int8` tensor. def testReadResultSetInt8MaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT desk_number, favorite_negative_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.int8, dtypes.int8))) self.assertEqual((9, -2), self.evaluate(get_next())) # Max and min values of int8 self.assertEqual((127, -128), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table and # place it in an `int16` tensor. def testReadResultSetInt16(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int16))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a negative or 0-valued integer from a # SQLite database table and place it in an `int16` tensor. def testReadResultSetInt16NegativeAndZero(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, income, favorite_negative_number " "FROM students " "WHERE first_name = 'John' ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int16, dtypes.int16))) self.assertEqual((b"John", 0, -2), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a large (positive or negative) integer from # a SQLite database table and place it in an `int16` tensor. def testReadResultSetInt16MaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, favorite_medium_sized_number " "FROM students ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int16))) # Max value of int16 self.assertEqual((b"John", 32767), self.evaluate(get_next())) # Min value of int16 self.assertEqual((b"Jane", -32768), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table and # place it in an `int32` tensor. def testReadResultSetInt32(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int32))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) # Test that `SqlDataset` can read a negative or 0-valued integer from a # SQLite database table and place it in an `int32` tensor. def testReadResultSetInt32NegativeAndZero(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, income FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int32))) self.assertEqual((b"John", 0), self.evaluate(get_next())) self.assertEqual((b"Jane", -20000), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a large (positive or negative) integer from # a SQLite database table and place it in an `int32` tensor. def testReadResultSetInt32MaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, favorite_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int32))) # Max value of int32 self.assertEqual((b"John", 2147483647), self.evaluate(get_next())) # Min value of int32 self.assertEqual((b"Jane", -2147483648), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a numeric `varchar` from a SQLite database # table and place it in an `int32` tensor. def testReadResultSetInt32VarCharColumnAsInt(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, school_id FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int32))) self.assertEqual((b"John", 123), self.evaluate(get_next())) self.assertEqual((b"Jane", 1000), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table # and place it in an `int64` tensor. def testReadResultSetInt64(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int64))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a negative or 0-valued integer from a # SQLite database table and place it in an `int64` tensor. def testReadResultSetInt64NegativeAndZero(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, income FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int64))) self.assertEqual((b"John", 0), self.evaluate(get_next())) self.assertEqual((b"Jane", -20000), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a large (positive or negative) integer from # a SQLite database table and place it in an `int64` tensor. def testReadResultSetInt64MaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, favorite_big_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.int64))) # Max value of int64 self.assertEqual((b"John", 9223372036854775807), self.evaluate(get_next())) # Min value of int64 self.assertEqual((b"Jane", -9223372036854775808), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table and # place it in a `uint8` tensor. def testReadResultSetUInt8(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.uint8))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read the minimum and maximum uint8 values from a # SQLite database table and place them in `uint8` tensors. def testReadResultSetUInt8MinAndMaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, brownie_points FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.uint8))) # Min value of uint8 self.assertEqual((b"John", 0), self.evaluate(get_next())) # Max value of uint8 self.assertEqual((b"Jane", 255), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer from a SQLite database table # and place it in a `uint16` tensor. def testReadResultSetUInt16(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, desk_number FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.uint16))) self.assertEqual((b"John", 9), self.evaluate(get_next())) self.assertEqual((b"Jane", 127), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read the minimum and maximum uint16 values from a # SQLite database table and place them in `uint16` tensors. def testReadResultSetUInt16MinAndMaxValues(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, account_balance FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.uint16))) # Min value of uint16 self.assertEqual((b"John", 0), self.evaluate(get_next())) # Max value of uint16 self.assertEqual((b"Jane", 65535), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a 0-valued and 1-valued integer from a # SQLite database table and place them as `True` and `False` respectively # in `bool` tensors. def testReadResultSetBool(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, registration_complete FROM students " "ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.bool))) self.assertEqual((b"John", True), self.evaluate(get_next())) self.assertEqual((b"Jane", False), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read an integer that is not 0-valued or 1-valued # from a SQLite database table and place it as `True` in a `bool` tensor. def testReadResultSetBoolNotZeroOrOne(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, favorite_medium_sized_number " "FROM students ORDER BY first_name DESC", output_types=(dtypes.string, dtypes.bool))) self.assertEqual((b"John", True), self.evaluate(get_next())) self.assertEqual((b"Jane", True), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a float from a SQLite database table # and place it in a `float64` tensor. def testReadResultSetFloat64(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, victories FROM townspeople " "ORDER BY first_name", output_types=(dtypes.string, dtypes.string, dtypes.float64))) self.assertEqual((b"George", b"Washington", 20.0), self.evaluate(get_next())) self.assertEqual((b"John", b"Adams", -19.95), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a float from a SQLite database table beyond # the precision of 64-bit IEEE, without throwing an error. Test that # `SqlDataset` identifies such a value as equal to itself. def testReadResultSetFloat64OverlyPrecise(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, accolades FROM townspeople " "ORDER BY first_name", output_types=(dtypes.string, dtypes.string, dtypes.float64))) self.assertEqual( (b"George", b"Washington", 1331241.321342132321324589798264627463827647382647382643874), self.evaluate(get_next())) self.assertEqual( (b"John", b"Adams", 1331241321342132321324589798264627463827647382647382643874.0), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) # Test that `SqlDataset` can read a float from a SQLite database table, # representing the largest integer representable as a 64-bit IEEE float # such that the previous integer is also representable as a 64-bit IEEE float. # Test that `SqlDataset` can distinguish these two numbers. def testReadResultSetFloat64LargestConsecutiveWholeNumbersNotEqual(self): get_next = self.getNext( self._createSqlDataset( query="SELECT first_name, last_name, triumphs FROM townspeople " "ORDER BY first_name", output_types=(dtypes.string, dtypes.string, dtypes.float64))) self.assertNotEqual((b"George", b"Washington", 9007199254740992.0), self.evaluate(get_next())) self.assertNotEqual((b"John", b"Adams", 9007199254740991.0), self.evaluate(get_next())) with self.assertRaises(errors.OutOfRangeError): self.evaluate(get_next()) if __name__ == "__main__": test.main()
kevin-coder/tensorflow-fork
tensorflow/python/data/experimental/kernel_tests/sql_dataset_test.py
Python
apache-2.0
22,191
[ "MOE" ]
35e3e76c1d08656f0f5db9fd7f6609988b6909377413bde625fcc75b258bda44
# # Copyright (C) 2015-2021 University of Oxford # # This file is part of msprime. # # msprime is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # msprime is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with msprime. If not, see <http://www.gnu.org/licenses/>. # """ Module responsible for defining and running ancestry simulations. """ from __future__ import annotations import collections.abc import copy import dataclasses import inspect import json import logging import math import struct import sys from typing import ClassVar from typing import Union import numpy as np import tskit from . import core from . import demography as demog from . import intervals from . import mutations from . import provenance from msprime import _msprime logger: logging.Logger = logging.getLogger(__name__) def _model_factory(model): """ Returns a simulation model corresponding to the specified model. - If model is None, the default simulation model is returned. - If model is a string, return the corresponding model instance. - If model is an instance of AncestryModel, return a copy of it. - Otherwise raise a type error. """ model_map = { "hudson": StandardCoalescent(), "smc": SmcApproxCoalescent(), "smc_prime": SmcPrimeApproxCoalescent(), "dtwf": DiscreteTimeWrightFisher(), "wf_ped": WrightFisherPedigree(), } if model is None: model_instance = StandardCoalescent() elif isinstance(model, str): lower_model = model.lower() if lower_model not in model_map: raise ValueError( "Model '{}' unknown. Choose from {}".format( model, list(model_map.keys()) ) ) model_instance = model_map[lower_model] elif not isinstance(model, AncestryModel): raise TypeError( "Simulation model must be a string or an instance of AncestryModel" ) else: model_instance = model return model_instance def _parse_model_change_events(events): """ Parses the specified list of events provided in model_arg[1:] into AncestryModelChange events. There are two different forms supported, and model descriptions are anything supported by model_factory. """ err = ( "Simulation model change events must be either a two-tuple " "(time, model), describing the time of the model change and " "the new model or be an instance of AncestryModelChange." ) model_change_events = [] for event in events: if isinstance(event, (tuple, list)): if len(event) != 2: raise ValueError(err) t = event[0] if t is not None: try: t = float(t) except (TypeError, ValueError): raise ValueError( "Model change times must be either a floating point " "value or None" ) event = AncestryModelChange(t, _model_factory(event[1])) elif isinstance(event, AncestryModelChange): # We don't want to modify our inputs, so take a deep copy. event = copy.copy(event) event.model = _model_factory(event.model) else: raise TypeError(err) model_change_events.append(event) return model_change_events def _parse_model_arg(model_arg): """ Parses the specified model argument from the simulate function, returning the initial model and any model change events. """ err = ( "The model argument must be either (a) a value that can be " "interpreted as a simulation model or (b) a list in which " "the first element is a model description and the remaining " "elements are model change events. These can either be described " "by a (time, model) tuple or AncestryModelChange instances." ) if isinstance(model_arg, (list, tuple)): if len(model_arg) < 1: raise ValueError(err) model = _model_factory(model_arg[0]) model_change_events = _parse_model_change_events(model_arg[1:]) else: model = _model_factory(model_arg) model_change_events = [] return model, model_change_events def _filter_events(demographic_events): """ Returns a tuple (demographic_events, model_change_events) which separates out the AncestryModelChange events from the list. This is to support the pre-1.0 syntax for model changes, where they were included in the demographic_events parameter. """ filtered_events = [] model_change_events = [] for event in demographic_events: if isinstance(event, AncestryModelChange): model_change_events.append(event) else: filtered_events.append(event) # Make sure any model references are resolved. model_change_events = _parse_model_change_events(model_change_events) return filtered_events, model_change_events def _check_population_configurations(population_configurations): err = ( "Population configurations must be a list of PopulationConfiguration instances" ) for config in population_configurations: if not isinstance(config, demog.PopulationConfiguration): raise TypeError(err) # This class is only used in the 0.x interface. Sample = collections.namedtuple("Sample", ["population", "time"]) def _samples_factory(sample_size, samples, population_configurations): """ Returns a list of Sample objects, given the specified inputs. """ the_samples = [] if sample_size is not None: if samples is not None: raise ValueError("Cannot specify sample size and samples simultaneously.") if population_configurations is not None: raise ValueError( "Cannot specify sample size and population_configurations " "simultaneously." ) s = Sample(population=0, time=0.0) the_samples = [s for _ in range(sample_size)] # If we have population configurations we may have embedded sample_size # values telling us how many samples to take from each population. if population_configurations is not None: _check_population_configurations(population_configurations) if samples is None: the_samples = [] for j, conf in enumerate(population_configurations): if conf.sample_size is not None: the_samples += [Sample(j, 0) for _ in range(conf.sample_size)] else: for conf in population_configurations: if conf.sample_size is not None: raise ValueError( "Cannot specify population configuration sample size" " and samples simultaneously" ) the_samples = samples elif samples is not None: the_samples = samples return the_samples def _demography_factory( Ne, population_configurations, migration_matrix, demographic_events ): demography = demog.Demography.from_old_style( population_configurations, migration_matrix=migration_matrix, demographic_events=demographic_events, Ne=Ne, ignore_sample_size=True, ) return demography.validate() def _build_initial_tables(*, sequence_length, samples, ploidy, demography, pedigree): # NOTE: this is only used in the simulate() codepath. tables = tskit.TableCollection(sequence_length) if pedigree is None: for index, (population, time) in enumerate(samples): tables.nodes.add_row( flags=tskit.NODE_IS_SAMPLE, time=time, population=population, ) if population < 0: raise ValueError(f"Negative population ID in sample at index {index}") if population >= demography.num_populations: raise ValueError( f"Invalid population reference '{population}' in sample " f"at index {index}" ) else: # TODO This should be removed - pedigree code path should only be callable # from sim_ancestry for parents, time, is_sample in zip( pedigree.parents, pedigree.time, pedigree.is_sample ): # We encode the parents in the metadata for now, but see # https://github.com/tskit-dev/tskit/issues/852 encoded_parents = struct.pack("=ii", *parents) ind_id = tables.individuals.add_row(0, metadata=encoded_parents) node_flags = tskit.NODE_IS_SAMPLE if is_sample else 0 for _ in range(ploidy): tables.nodes.add_row(node_flags, time, population=0, individual=ind_id) # This is for the simulate() code path so we don't add metadata schemas # and insert the user metadata in directly as encoded JSON, as before. for population in demography.populations: encoded_metadata = b"" if population.extra_metadata is not None: encoded_metadata = json.dumps(population.extra_metadata).encode() tables.populations.add_row(encoded_metadata) return tables def _parse_simulate( sample_size=None, *, Ne=1, length=None, recombination_rate=None, recombination_map=None, population_configurations=None, pedigree=None, migration_matrix=None, samples=None, demographic_events=None, model=None, record_migrations=False, from_ts=None, start_time=None, end_time=None, record_full_arg=False, num_labels=None, random_seed=None, ): """ Argument parser for the simulate frontend. Interprets all the parameters and returns an appropriate instance of Simulator. """ if Ne <= 0: raise ValueError("Population size must be positive") samples_specified = ( sample_size is None and population_configurations is None and samples is None and from_ts is None ) if samples_specified: raise ValueError( "Either sample_size, samples, population_configurations or from_ts must " "be specified" ) samples = _samples_factory(sample_size, samples, population_configurations) model, model_change_events = _parse_model_arg(model) if demographic_events is not None: demographic_events, old_style_model_change_events = _filter_events( demographic_events ) if len(old_style_model_change_events) > 0: if len(model_change_events) > 0: raise ValueError( "Cannot specify AncestryModelChange events using both new-style " "and pre 1.0 syntax" ) model_change_events = old_style_model_change_events demography = _demography_factory( Ne, population_configurations, migration_matrix, demographic_events ) # The logic for checking from_ts and recombination map is bound together # in a complicated way, so we can factor them out into separate functions. if from_ts is None: if len(samples) < 2: raise ValueError("Sample size must be >= 2") else: if len(samples) > 0: raise ValueError("Cannot specify samples with from_ts") if not isinstance(from_ts, tskit.TreeSequence): raise TypeError("from_ts must be a TreeSequence instance.") if demography.num_populations != from_ts.num_populations: raise ValueError( "Mismatch in the number of populations in from_ts and simulation " "parameters. The number of populations in the simulation must be " "equal to the number of populations in from_ts" ) discrete_genome = False if recombination_map is None: # Default to 1 if no from_ts; otherwise default to the sequence length # of from_ts if from_ts is None: the_length = 1 if length is None else length else: the_length = from_ts.sequence_length if length is None else length the_rate = 0 if recombination_rate is None else recombination_rate if the_length <= 0: raise ValueError("Cannot provide non-positive sequence length") if the_rate < 0: raise ValueError("Cannot provide negative recombination rate") recombination_map = intervals.RateMap.uniform(the_length, the_rate) else: if isinstance(recombination_map, intervals.RecombinationMap): if recombination_map._is_discrete: logger.info("Emulating v0.x discrete sites simulation") discrete_genome = True # Convert from the legacy RecombinationMap class recombination_map = recombination_map.map elif not isinstance(recombination_map, intervals.RateMap): raise TypeError("RateMap instance required.") if length is not None or recombination_rate is not None: raise ValueError( "Cannot specify length/recombination_rate along with " "a recombination map" ) if from_ts is not None: if recombination_map.sequence_length != from_ts.sequence_length: raise ValueError( "Recombination map and from_ts must have identical " "sequence_length" ) if num_labels is not None and num_labels < 1: raise ValueError("Must have at least one structured coalescent label") if from_ts is None: tables = _build_initial_tables( sequence_length=recombination_map.sequence_length, samples=samples, # FIXME not clear how this is all working now. We shouldn't have # the pedigree as a parameter here at all which would probably # simplify things. ploidy=2, demography=demography, pedigree=pedigree, ) else: tables = from_ts.tables # It's useful to call _parse_simulate outside the context of the main # entry point - so we want to get good seeds in this case too. random_seed = _parse_random_seed(random_seed) random_generator = _msprime.RandomGenerator(random_seed) sim = Simulator( tables=tables, recombination_map=recombination_map, model=model, store_migrations=record_migrations, store_full_arg=record_full_arg, start_time=start_time, end_time=end_time, num_labels=num_labels, demography=demography, model_change_events=model_change_events, # Defaults for the values that are not supported through simulate() gene_conversion_map=intervals.RateMap.uniform( recombination_map.sequence_length, 0 ), gene_conversion_tract_length=0, discrete_genome=discrete_genome, ploidy=2, random_generator=random_generator, ) return sim def _parse_random_seed(seed): """ Parse the specified random seed value. If no seed is provided, generate a high-quality random seed. """ if seed is None: seed = core.get_random_seed() seed = int(seed) return seed def _parse_replicate_index(*, replicate_index, random_seed, num_replicates): """ Parse the replicate_index value, and ensure that its value makes sense in the context of the other parameters. """ if replicate_index is None: return None if random_seed is None: raise ValueError("Cannot specify the replicate_index without a random_seed") if num_replicates is not None: raise ValueError("Cannot specify the replicate_index as well as num_replicates") replicate_index = int(replicate_index) if replicate_index < 0: raise ValueError("Cannot specify negative replicate_index.") return replicate_index def _build_provenance(command, random_seed, frame): """ Builds a provenance dictionary suitable for use as the basis of tree sequence provenance in replicate simulations. Uses the specified stack frame to determine the values of the arguments passed in, with a few exceptions. """ argspec = inspect.getargvalues(frame) # num_replicates is excluded as provenance is per replicate # replicate index is excluded as it is inserted for each replicate parameters = { "command": command, **{ arg: argspec.locals[arg] for arg in argspec.args if arg not in ["num_replicates", "replicate_index"] }, } parameters["random_seed"] = random_seed return provenance.get_provenance_dict(parameters) def simulate( sample_size=None, *, Ne=1, length=None, recombination_rate=None, recombination_map=None, mutation_rate=None, population_configurations=None, pedigree=None, migration_matrix=None, demographic_events=None, samples=None, model=None, record_migrations=False, random_seed=None, replicate_index=None, mutation_generator=None, num_replicates=None, from_ts=None, start_time=None, end_time=None, record_full_arg=False, num_labels=None, record_provenance=True, ): """ Simulates the coalescent with recombination under the specified model parameters and returns the resulting :class:`tskit.TreeSequence`. Note that Ne is the effective diploid population size (so the effective number of genomes in the population is 2*Ne), but ``sample_size`` is the number of (monoploid) genomes sampled. :param int sample_size: The number of sampled monoploid genomes. If not specified or None, this defaults to the sum of the subpopulation sample sizes. Either ``sample_size``, ``population_configurations`` or ``samples`` must be specified. :param float Ne: The effective (diploid) population size. This defaults to 1 if not specified. :param float length: The length of the simulated region in bases. This parameter cannot be used along with ``recombination_map``. Defaults to 1 if not specified. :param float recombination_rate: The rate of recombination per base per generation. This parameter cannot be used along with ``recombination_map``. Defaults to 0 if not specified. :param recombination_map: The map describing the changing rates of recombination along the simulated chromosome. This parameter cannot be used along with the ``recombination_rate`` or ``length`` parameters, as these values are encoded within the map. Defaults to a uniform rate as described in the ``recombination_rate`` parameter if not specified. :type recombination_map: :class:`.RecombinationMap` :param float mutation_rate: The rate of infinite sites mutations per unit of sequence length per generation. If not specified, no mutations are generated. This option only allows for infinite sites mutations with a binary (i.e., 0/1) alphabet. For more control over the mutational process, please use the :func:`.mutate` function. :param list population_configurations: The list of :class:`.PopulationConfiguration` instances describing the sampling configuration, relative sizes and growth rates of the populations to be simulated. If this is not specified, a single population with a sample of size ``sample_size`` is assumed. :type population_configurations: list or None. :param list migration_matrix: The matrix describing the rates of migration between all pairs of populations. If :math:`N` populations are defined in the ``population_configurations`` parameter, then the migration matrix must be an :math:`N \\times N` matrix with 0 on the diagonal, consisting of :math:`N` lists of length :math:`N` or an :math:`N \\times N` numpy array. The :math:`[j, k]^{th}` element of the migration matrix gives the expected number of migrants moving from population :math:`k` to population :math:`j` per generation, divided by the size of population :math:`j`. When simulating from the discrete-time Wright-Fisher model (``model = "dtwf"``), the row sums of the migration matrix must not exceed 1. There are no sum constraints for migration rates in continuous-time models. :param list demographic_events: The list of demographic events to simulate. Demographic events describe changes to the populations in the past. Events should be supplied in non-decreasing order of time in the past. Events with the same time value will be applied sequentially in the order that they were supplied before the simulation algorithm continues with the next time step. :param list samples: The list specifying the location and time of all samples. This parameter may be used to specify historical samples, and cannot be used in conjunction with the ``sample_size`` parameter. Each sample is a (``population``, ``time``) pair such that the sample in position ``j`` in the list of samples is drawn in the specified population at the specfied time. Time is measured in generations ago, as elsewhere. :param int random_seed: The random seed. If this is `None`, a random seed will be automatically generated. Valid random seeds must be between 1 and :math:`2^{32} - 1`. :param int num_replicates: The number of replicates of the specified parameters to simulate. If this is not specified or None, no replication is performed and a :class:`tskit.TreeSequence` object returned. If `num_replicates` is provided, the specified number of replicates is performed, and an iterator over the resulting :class:`tskit.TreeSequence` objects returned. :param tskit.TreeSequence from_ts: If specified, initialise the simulation from the root segments of this tree sequence and return the completed tree sequence. Please see :ref:`here <sec_ancestry_initial_state>` for details on the required properties of this tree sequence and its interactions with other parameters. (Default: None). :param float start_time: If specified, set the initial time that the simulation starts to this value. If not specified, the start time is zero if performing a simulation of a set of samples, or is the time of the oldest node if simulating from an existing tree sequence (see the ``from_ts`` parameter). :param float end_time: If specified, terminate the simulation at the specified time. In the returned tree sequence, all rootward paths from samples with time < end_time will end in a node with one child with time equal to end_time. Sample nodes with time >= end_time will also be present in the output tree sequence. If not specified or ``None``, run the simulation until all samples have an MRCA at all positions in the genome. :param bool record_full_arg: If True, record all intermediate nodes arising from common ancestor and recombination events in the output tree sequence. This will result in unary nodes (i.e., nodes in marginal trees that have only one child). Defaults to False. :param model: The simulation model to use. This can either be a string (e.g., ``"smc_prime"``) or an instance of a simulation model class (e.g, ``msprime.DiscreteTimeWrightFisher()``. Please see the :ref:`sec_ancestry_models` section for more details on specifying ancestry models. :type model: str or simulation model instance :param bool record_provenance: If True, record all configuration and parameters required to recreate the tree sequence. These can be accessed via ``TreeSequence.provenances()``). :return: The :class:`tskit.TreeSequence` object representing the results of the simulation if no replication is performed, or an iterator over the independent replicates simulated if the `num_replicates` parameter has been used. :rtype: :class:`tskit.TreeSequence` or an iterator over :class:`tskit.TreeSequence` replicates. """ replicate_index = _parse_replicate_index( random_seed=random_seed, num_replicates=num_replicates, replicate_index=replicate_index, ) random_seed = _parse_random_seed(random_seed) provenance_dict = None if record_provenance: frame = inspect.currentframe() provenance_dict = _build_provenance("simulate", random_seed, frame) if mutation_generator is not None: # This error was added in version 0.6.1. raise ValueError( "mutation_generator is not longer supported. Please use " "msprime.mutate instead" ) if mutation_rate is not None: # There is ambiguity in how we should throw mutations onto partially # built tree sequences: on the whole thing, or must the newly added # topology? Before or after start_time? We avoid this complexity by # asking the user to use mutate(), which should have the required # flexibility. if from_ts is not None: raise ValueError( "Cannot specify mutation rate combined with from_ts. Please use " "msprime.mutate on the final tree sequence instead" ) # There is ambiguity in how the start_time argument should interact with # the mutation generator: should we throw mutations down on the whole # tree or just the (partial) edges after start_time? To avoid complicating # things here, make the user use mutate() which should have the flexibility # to do whatever is needed. if start_time is not None and start_time > 0: raise ValueError( "Cannot specify mutation rate combined with a non-zero " "start_time. Please use msprime.mutate on the returned " "tree sequence instead" ) mutation_rate = float(mutation_rate) sim = _parse_simulate( sample_size=sample_size, Ne=Ne, length=length, recombination_rate=recombination_rate, recombination_map=recombination_map, population_configurations=population_configurations, pedigree=pedigree, migration_matrix=migration_matrix, demographic_events=demographic_events, samples=samples, model=model, record_migrations=record_migrations, from_ts=from_ts, start_time=start_time, end_time=end_time, record_full_arg=record_full_arg, num_labels=num_labels, random_seed=random_seed, ) return _wrap_replicates( sim, num_replicates=num_replicates, replicate_index=replicate_index, provenance_dict=provenance_dict, mutation_rate=mutation_rate, ) def _wrap_replicates( simulator, *, num_replicates, replicate_index, provenance_dict, mutation_rate=None, ): """ Wrapper for the logic used to run replicate simulations for the two frontends. """ if num_replicates is None and replicate_index is None: # Default single-replicate case. replicate_index = 0 if replicate_index is not None: num_replicates = replicate_index + 1 iterator = simulator.run_replicates( num_replicates, mutation_rate=mutation_rate, provenance_dict=provenance_dict, ) if replicate_index is not None: deque = collections.deque(iterator, maxlen=1) return deque.pop() else: return iterator def _parse_rate_map(rate_param, sequence_length, name): """ Parse the specified input rate parameter value into a rate map. """ # Note: in the future we might have another clause here where we # allow for a different map per population. This could be # accepted as either a list of N rate maps, or a dictionary mapping # population names to maps. # See https://github.com/tskit-dev/msprime/issues/1095 msg_head = f"Error in parsing rate map for {name}: " if isinstance(rate_param, intervals.RateMap): rate_map = rate_param if rate_map.sequence_length != sequence_length: raise ValueError(msg_head + "sequence_length must match") else: rate_param = 0 if rate_param is None else float(rate_param) rate_map = intervals.RateMap.uniform(sequence_length, rate_param) return rate_map def _insert_sample_sets(sample_sets, demography, default_ploidy, tables): """ Insert the samples described in the specified {population_id: num_samples} map into the specified set of tables. """ for sample_set in sample_sets: n = sample_set.num_samples population = demography[sample_set.population] time = population.sampling_time if sample_set.time is None else sample_set.time ploidy = default_ploidy if sample_set.ploidy is None else sample_set.ploidy logger.info( f"Sampling {n} individuals with ploidy {ploidy} in population " f"{population.id} (name='{population.name}') at time {time}" ) node_individual = len(tables.individuals) + np.repeat( np.arange(n, dtype=np.int32), ploidy ) ind_flags = np.zeros(n, dtype=np.uint32) tables.individuals.append_columns(flags=ind_flags) N = n * ploidy tables.nodes.append_columns( flags=np.full(N, tskit.NODE_IS_SAMPLE, dtype=np.uint32), time=np.full(N, time), population=np.full(N, population.id, dtype=np.int32), individual=node_individual, ) def _parse_sample_sets(sample_sets, demography): # Don't modify the inputs. sample_sets = copy.deepcopy(sample_sets) for sample_set in sample_sets: if not isinstance(sample_set, SampleSet): raise TypeError("msprime.SampleSet object required") if not core.isinteger(sample_set.num_samples): raise TypeError( "The number of samples to draw from a population must be an integer" ) sample_set.num_samples = int(sample_set.num_samples) if sample_set.num_samples < 0: raise ValueError("Number of samples cannot be negative") if sample_set.population is None: if demography.num_populations == 1: sample_set.population = 0 else: raise ValueError( "Must specify a SampleSet population in multipopulation models" ) if sum(sample_set.num_samples for sample_set in sample_sets) == 0: raise ValueError("Zero samples specified") return sample_sets def _parse_samples(samples, demography, ploidy, tables): """ Parse the specified "samples" value for sim_ancestry and insert them into the specified tables. """ if isinstance(samples, collections.abc.Sequence): sample_sets = samples elif isinstance(samples, collections.abc.Mapping): sample_sets = [ SampleSet(num_samples, population) for population, num_samples in samples.items() ] elif core.isinteger(samples): if len(tables.populations) != 1: raise ValueError( "Numeric samples can only be used in single population models. " "Please use Demography.sample() to generate a list of samples " "for your model, which can be used instead." ) sample_sets = [SampleSet(samples)] else: raise TypeError( f"The value '{samples}' cannot be interpreted as sample specification. " "Samples must either be a single integer, a dict that maps populations " "to the number of samples for that population, or a list of SampleSet " "objects. Please see the online documentation for more details on " "the different forms." ) sample_sets = _parse_sample_sets(sample_sets, demography) _insert_sample_sets(sample_sets, demography, ploidy, tables) def _parse_sim_ancestry( samples=None, *, sequence_length=None, recombination_rate=None, gene_conversion_rate=None, gene_conversion_tract_length=None, discrete_genome=None, population_size=None, demography=None, ploidy=None, model=None, initial_state=None, start_time=None, end_time=None, record_migrations=None, record_full_arg=None, num_labels=None, random_seed=None, init_for_debugger=False, ): """ Argument parser for the sim_ancestry frontend. Interprets all the parameters and returns an appropriate instance of Simulator. """ # As a general rule we try to cast any input value to the required types # early and in a way that provides an interpretable traceback. # Simple defaults. start_time = 0 if start_time is None else float(start_time) end_time = math.inf if end_time is None else float(end_time) discrete_genome = core._parse_flag(discrete_genome, default=True) record_full_arg = core._parse_flag(record_full_arg, default=False) record_migrations = core._parse_flag(record_migrations, default=False) if initial_state is not None: if isinstance(initial_state, tskit.TreeSequence): initial_state = initial_state.dump_tables() elif not isinstance(initial_state, tskit.TableCollection): raise TypeError( "initial_state must either be a TreeSequence or TableCollection instance" ) if sequence_length is None: # These are all the cases in which we derive the sequence_length # from somewhere else. if initial_state is not None: sequence_length = initial_state.sequence_length elif recombination_rate is None and gene_conversion_rate is None: # In this case, we're doing single-locus simulations, so a sequence # length of 1 makes sense. sequence_length = 1 elif isinstance(recombination_rate, intervals.RateMap): sequence_length = recombination_rate.sequence_length elif isinstance(gene_conversion_rate, intervals.RateMap): sequence_length = gene_conversion_rate.sequence_length else: raise ValueError( "A sequence_length value must be specified. This can be either " "via the the sequence_length parameter itself, of implicitly " "through using a RateMap instance for the recombination_rate " "or gene_conversion_rate parameters, or via the initial_state " "tables. " ) else: sequence_length = float(sequence_length) assert sequence_length is not None if discrete_genome and math.floor(sequence_length) != sequence_length: raise ValueError("Must have integer sequence length with discrete_genome=True") recombination_map = _parse_rate_map( recombination_rate, sequence_length, "recombination" ) gene_conversion_map = _parse_rate_map( gene_conversion_rate, sequence_length, "gene conversion" ) if gene_conversion_tract_length is None: if gene_conversion_rate is None: # It doesn't matter what the tract_length is, just set a # value to keep the low-level code happy. gene_conversion_tract_length = 1 else: raise ValueError( "Must specify tract length when simulating gene conversion" ) else: if gene_conversion_rate is None: raise ValueError( "Must specify gene conversion rate along with tract length" ) gene_conversion_tract_length = float(gene_conversion_tract_length) # Default to diploid ploidy = 2 if ploidy is None else ploidy if not core.isinteger(ploidy): raise TypeError("ploidy must be an integer") ploidy = int(ploidy) if ploidy < 1: raise ValueError("ploidy must be >= 1") model, model_change_events = _parse_model_arg(model) is_dtwf = isinstance(model, DiscreteTimeWrightFisher) # Check the demography. If no demography is specified, we default to a # single-population model with a given population size. If an initial # state is provided, we default to using that number of populations. if demography is None: if is_dtwf: # A default size of 1 isn't so smart for DTWF and almost certainly # an error. if population_size is None: raise ValueError( "When using the DTWF model, the population size must be set " "explicitly, either using the population_size or demography " "arguments." ) num_populations = 1 if initial_state is None else len(initial_state.populations) population_size = 1 if population_size is None else float(population_size) demography = demog.Demography.isolated_model( [population_size] * num_populations ) elif isinstance(demography, demog.Demography): if population_size is not None: raise ValueError("Cannot specify demography and population size") else: raise TypeError("demography argument must be an instance of msprime.Demography") demography = demography.validate() if initial_state is None: if samples is None and not init_for_debugger: raise ValueError( "Either the samples or initial_state arguments must be provided" ) initial_state = tskit.TableCollection(sequence_length) demography.insert_populations(initial_state) if not init_for_debugger: _parse_samples(samples, demography, ploidy, initial_state) else: if samples is not None: raise ValueError("Cannot specify both samples and initial_state") if sequence_length != initial_state.sequence_length: raise ValueError( "The initial_state sequence length must be consistent with the" "value derived from either the sequence_length, " "recombination_rate or gene_conversion_rate parameters." ) if len(initial_state.populations) == 0: raise ValueError( "initial_state tables must define at least one population." ) # It's useful to call _parse_sim_ancestry outside the context of the main # entry point - so we want to get good seeds in this case too. random_seed = _parse_random_seed(random_seed) random_generator = _msprime.RandomGenerator(random_seed) return Simulator( tables=initial_state, recombination_map=recombination_map, gene_conversion_map=gene_conversion_map, gene_conversion_tract_length=gene_conversion_tract_length, discrete_genome=discrete_genome, ploidy=ploidy, demography=demography, model=model, model_change_events=model_change_events, store_migrations=record_migrations, store_full_arg=record_full_arg, start_time=start_time, end_time=end_time, num_labels=num_labels, random_generator=random_generator, ) def sim_ancestry( samples=None, *, demography=None, sequence_length=None, discrete_genome=None, recombination_rate=None, gene_conversion_rate=None, gene_conversion_tract_length=None, population_size=None, ploidy=None, model=None, initial_state=None, start_time=None, end_time=None, record_migrations=None, record_full_arg=None, num_labels=None, random_seed=None, num_replicates=None, replicate_index=None, record_provenance=None, ): """ Simulates an ancestral process described by a given model, demography and samples, and return a :class:`tskit.TreeSequence` (or a sequence of replicate tree sequences). :param samples: The sampled individuals as either an integer, specifying the number of individuals to sample in a single-population model; or a list of :class:`.SampleSet` objects defining the properties of groups of similar samples; or as a mapping in which the keys are population identifiers (either an integer ID or string name) and the values are the number of samples to take from the corresponding population at its default sampling time. It is important to note that samples correspond to *individuals* here, and each sampled individual is usually associated with :math:`k` sample *nodes* (or genomes) when ``ploidy`` = :math:`k`. See :ref:`sec_ancestry_samples` for further details. Either ``samples`` or ``initial_state`` must be specified. :param demography: The demographic model to simulate, describing the extant and ancestral populations, their population sizes and growth rates, their migration rates, and demographic events affecting the populations over time. See the :ref:`sec_demography` section for details on how to specify demographic models and :ref:`sec_ancestry_samples` for details on how to specify the populations that samples are drawn from. If not specified (or None) we default to a single population with constant size 1 (see also the ``population_size`` parameter). :param int ploidy: The number of monoploid genomes per sample individual (Default=2). See :ref:`sec_ancestry_ploidy` for usage examples. :param float sequence_length: The length of the genome sequence to simulate. See :ref:`sec_ancestry_genome_length` for usage examples for this parameter and how it interacts with other parameters. :param bool discrete_genome: If True (the default) simulation occurs in discrete genome coordinates such that recombination and gene conversion breakpoints always occur at integer positions. Thus, multiple (e.g.) recombinations can occur at the same genome position. If ``discrete_genome`` is False simulations are performed using continuous genome coordinates. In this case multiple events at precisely the same genome location are very unlikely (but technically possible). See :ref:`sec_ancestry_discrete_genome` for usage examples. :param recombination_rate: The rate of recombination along the sequence; can be either a single value (specifying a single rate over the entire sequence) or an instance of :class:`RateMap`. See :ref:`sec_ancestry_recombination` for usage examples for this parameter and how it interacts with other parameters. :param gene_conversion_rate: The rate of gene conversion along the sequence; can be a single value (specifying a single rate over the entire sequence). Currently an instance of :class:`RateMap` is not supported. If provided, a value for ``gene_conversion_tract_length`` must also be specified. See :ref:`sec_ancestry_gene_conversion` for usage examples for this parameter and how it interacts with other parameters. :param gene_conversion_tract_length: The mean length of the gene conversion tracts. For discrete genomes the tract lengths are geometrically distributed with mean ``gene_conversion_tract_length``, which must be greater than or equal to 1. For continuous genomes the tract lengths are exponentially distributed with mean ``gene_conversion_tract_length``, which must be larger than 0. :param population_size: The size of the default single population :class:`.Demography`. If not specified, defaults to 1. Cannot be specified along with the ``demography`` parameter. See the :ref:`sec_demography` section for more details on demographic models and population sizes and the :ref:`sec_ancestry_population_size` section for usage examples. :param int random_seed: The random seed. If this is not specified or `None`, a high-quality random seed will be automatically generated. Valid random seeds must be between 1 and :math:`2^{32} - 1`. See :ref:`sec_ancestry_random_seed` for usage examples. :param int num_replicates: The number of replicates of the specified parameters to simulate. If this is not specified or `None`, no replication is performed and a :class:`tskit.TreeSequence` object returned. If `num_replicates` is provided, the specified number of replicates is performed, and an iterator over the resulting :class:`tskit.TreeSequence` objects returned. See :ref:`sec_ancestry_replication` for examples. :param bool record_full_arg: If True, record all intermediate nodes arising from common ancestor and recombination events in the output tree sequence. This will result in unary nodes (i.e., nodes in marginal trees that have only one child). Defaults to False. See :ref:`sec_ancestry_full_arg` for examples. :param bool record_migrations: If True, record all migration events that occur in the :ref:`tskit:sec_migration_table_definition` of the output tree sequence. Defaults to False. See :ref:`sec_ancestry_record_migrations` for examples. :param tskit.TreeSequence initial_state: If specified, initialise the simulation from the root segments of this tree sequence and return the completed tree sequence. Please see :ref:`sec_ancestry_initial_state` for details of the required properties of this tree sequence and its interactions with other parameters. (Default: None). :param float start_time: If specified, set the initial time that the simulation starts to this value. If not specified, the start time is zero if performing a simulation of a set of samples, or is the time of the oldest node if simulating from an existing tree sequence (see the ``initial_state`` parameter). See :ref:`sec_ancestry_start_time` for examples. :param float end_time: If specified, terminate the simulation at the specified time. In the returned tree sequence, all rootward paths from samples with time < ``end_time`` will end in a node with one child with time equal to end_time. Any sample nodes with time >= ``end_time`` will also be present in the output tree sequence. If not specified or ``None``, run the simulation until all samples have an MRCA at all positions in the genome. See :ref:`sec_ancestry_end_time` for examples. :param model: The ancestry model to use. This can either be a string (e.g., ``"smc_prime"``) or an instance of an ancestry model class (e.g, ``msprime.DiscreteTimeWrightFisher()``. Please see the :ref:`sec_ancestry_models` section for more details on specifying ancestry models. :type model: str or .AncestryModel :return: The :class:`tskit.TreeSequence` object representing the results of the simulation if no replication is performed, or an iterator over the independent replicates simulated if the `num_replicates` parameter has been used. :rtype: :class:`tskit.TreeSequence` or an iterator over :class:`tskit.TreeSequence` replicates. """ record_provenance = True if record_provenance is None else record_provenance replicate_index = _parse_replicate_index( random_seed=random_seed, num_replicates=num_replicates, replicate_index=replicate_index, ) random_seed = _parse_random_seed(random_seed) provenance_dict = None if record_provenance: frame = inspect.currentframe() provenance_dict = _build_provenance("sim_ancestry", random_seed, frame) sim = _parse_sim_ancestry( samples=samples, sequence_length=sequence_length, recombination_rate=recombination_rate, gene_conversion_rate=gene_conversion_rate, gene_conversion_tract_length=gene_conversion_tract_length, discrete_genome=discrete_genome, population_size=population_size, demography=demography, ploidy=ploidy, model=model, initial_state=initial_state, start_time=start_time, end_time=end_time, record_migrations=record_migrations, record_full_arg=record_full_arg, num_labels=num_labels, random_seed=random_seed, ) return _wrap_replicates( sim, num_replicates=num_replicates, replicate_index=replicate_index, provenance_dict=provenance_dict, ) class Simulator(_msprime.Simulator): """ Class to simulate trees under a variety of population models. Note: this class is not intended to be instantiated directly and is only for internal library use. The interface may change arbitrarily between versions. """ def __init__( self, *, tables, recombination_map, gene_conversion_map, gene_conversion_tract_length, discrete_genome, ploidy, demography, model_change_events, random_generator, model=None, store_migrations=False, store_full_arg=False, start_time=None, end_time=None, num_labels=None, ): # We always need at least n segments, so no point in making # allocation any smaller than this. num_samples = len(tables.nodes) block_size = 64 * 1024 segment_block_size = max(block_size, num_samples) avl_node_block_size = block_size node_mapping_block_size = block_size if num_labels is None: num_labels = self._choose_num_labels(model, model_change_events) # Now, convert the high-level values into their low-level # counterparts. ll_simulation_model = model.get_ll_representation() ll_population_configuration = [pop.asdict() for pop in demography.populations] ll_demographic_events = [ event.get_ll_representation() for event in demography.events ] ll_recomb_map = recombination_map.asdict() ll_tables = _msprime.LightweightTableCollection(tables.sequence_length) ll_tables.fromdict(tables.asdict()) # FIXME support arbitrary gene conversion maps. # https://github.com/tskit-dev/msprime/issues/1212 assert len(gene_conversion_map.rate) == 1 gene_conversion_rate = gene_conversion_map.rate[0] start_time = -1 if start_time is None else start_time super().__init__( tables=ll_tables, recombination_map=ll_recomb_map, start_time=start_time, random_generator=random_generator, model=ll_simulation_model, migration_matrix=demography.migration_matrix, population_configuration=ll_population_configuration, demographic_events=ll_demographic_events, store_migrations=store_migrations, store_full_arg=store_full_arg, num_labels=num_labels, segment_block_size=segment_block_size, avl_node_block_size=avl_node_block_size, node_mapping_block_size=node_mapping_block_size, gene_conversion_rate=gene_conversion_rate, gene_conversion_tract_length=gene_conversion_tract_length, discrete_genome=discrete_genome, ploidy=ploidy, ) # highlevel attributes used externally that have no lowlevel equivalent self.end_time = end_time self.model_change_events = model_change_events self.demography = demography # Temporary, until we add the low-level infrastructure for the gc map # when we'll take the same approach as the recombination map. self.gene_conversion_map = gene_conversion_map def copy_tables(self): """ Returns a copy of the underlying table collection. This is useful for testing and avoids using the LightweightTableCollection object, which is returned by self.tables. """ return tskit.TableCollection.fromdict(self.tables.asdict()) @property def sample_configuration(self): """ Returns a list of the number of samples in each of the populations. """ tables = self.copy_tables() num_samples = [0 for _ in tables.populations] for node in tables.nodes: if (node.flags & tskit.NODE_IS_SAMPLE) != 0: num_samples[node.population] += 1 return num_samples @property def recombination_map(self): return intervals.RateMap(**super().recombination_map) def _choose_num_labels(self, model, model_change_events): """ Choose the number of labels appropriately, given the simulation models that will be simulated. """ num_labels = 1 models = [model] + [event.model for event in model_change_events] for model in models: if isinstance(model, SweepGenicSelection): num_labels = 2 return num_labels def _run_until(self, end_time, event_chunk=None, debug_func=None): # This is a pretty big default event chunk so that we don't spend # too much time going back and forth into Python. We could imagine # doing something a bit more sophisticated where we try to tune the # number of events so that we end up with roughly 10 second slices # (say). if event_chunk is None: event_chunk = 10 ** 4 if event_chunk <= 0: raise ValueError("Must have at least 1 event per chunk") logger.info("Running model %s until max time: %f", self.model, end_time) while super().run(end_time, event_chunk) == _msprime.EXIT_MAX_EVENTS: logger.debug("time=%g ancestors=%d", self.time, self.num_ancestors) if debug_func is not None: debug_func(self) def run(self, event_chunk=None, debug_func=None): """ Runs the simulation until complete coalescence has occurred. """ for event in self.model_change_events: # If the event time is a callable, we compute the end_time # as a function of the current simulation time. current_time = self.time model_start_time = event.time if callable(event.time): model_start_time = event.time(current_time) # If model_start_time is None, we run until the current # model completes. Note that when event.time is a callable # it can also return None for this behaviour. if model_start_time is None: model_start_time = np.inf if model_start_time < current_time: raise ValueError( "Model start times out of order or not computed correctly. " f"current time = {current_time}; start_time = {model_start_time}" ) self._run_until(model_start_time, event_chunk, debug_func) logger.info( "model %s ended at time=%g nodes=%d edges=%d", self.model, self.time, self.num_nodes, self.num_edges, ) if self.time > model_start_time: raise NotImplementedError( "The previously running model does not support ending early " "and the requested model change cannot be performed. Please " "open an issue on GitHub if this functionality is something " "you require" ) ll_new_model = event.model.get_ll_representation() self.model = ll_new_model end_time = np.inf if self.end_time is None else self.end_time self._run_until(end_time, event_chunk, debug_func) self.finalise_tables() logger.info( "Completed at time=%g nodes=%d edges=%d", self.time, self.num_nodes, self.num_edges, ) def run_replicates( self, num_replicates, *, mutation_rate=None, provenance_dict=None, ): """ Sequentially yield the specified number of simulation replicates. """ encoded_provenance = None # The JSON is modified for each replicate to insert the replicate number. # To avoid repeatedly encoding the same JSON (which can take milliseconds) # we insert a replaceable string. placeholder = "@@_REPLICATE_INDEX_@@" if provenance_dict is not None: provenance_dict["parameters"]["replicate_index"] = placeholder encoded_provenance = provenance.json_encode_provenance( provenance_dict, num_replicates ) for replicate_index in range(num_replicates): self.run() if mutation_rate is not None: # This is only called from simulate() or the ms interface, # so does not need any further parameters. mutations._simple_mutate( tables=self.tables, random_generator=self.random_generator, sequence_length=self.sequence_length, rate=mutation_rate, ) tables = tskit.TableCollection.fromdict(self.tables.asdict()) replicate_provenance = None if encoded_provenance is not None: replicate_provenance = encoded_provenance.replace( f'"{placeholder}"', str(replicate_index) ) tables.provenances.add_row(replicate_provenance) yield tables.tree_sequence() self.reset() @dataclasses.dataclass class SampleSet: """ TODO document """ num_samples: int population: Union[int, str, None] = None time: Union[float, None] = None ploidy: Union[int, None] = None def asdict(self): return dataclasses.asdict(self) # TODO update the documentation here to state that using this class is # deprecated, and users should use the model=[...] notation instead. @dataclasses.dataclass class AncestryModelChange: """ An event representing a change of underlying :ref:`ancestry model <sec_ancestry_models>`. :param float time: The time at which the ancestry model changes to the new model, in generations. After this time, all internal tree nodes, edges and migrations are the result of the new model. If time is set to None (the default), the model change will occur immediately after the previous model has completed. If time is a callable, the time at which the model changes is the result of calling this function with the time that the previous model started with as a parameter. :param model: The new ancestry model to use. This can either be a string (e.g., ``"smc_prime"``) or an instance of an ancestry model class (e.g, ``msprime.DiscreteTimeWrightFisher()``. Please see the :ref:`sec_ancestry_models` section for more details on specifying these models. If this is None (the default) the model is changed to the standard coalescent. :type model: str or .AncestryModel """ time: Union[float, None] = None model: Union[str, AncestryModel, None] = None def asdict(self): return dataclasses.asdict(self) class SimulationModelChange(AncestryModelChange): """ Deprecated 0.x way to describe an :class:`AncestryModelChange`. """ @dataclasses.dataclass class AncestryModel: """ Abstract superclass of all ancestry models. """ name: ClassVar[str] def get_ll_representation(self): return {"name": self.name} def asdict(self): return dataclasses.asdict(self) class StandardCoalescent(AncestryModel): """ The classical coalescent with recombination model (i.e., Hudson's algorithm). The string ``"hudson"`` can be used to refer to this model. This is the default simulation model. """ name = "hudson" class SmcApproxCoalescent(AncestryModel): """ The original SMC model defined by McVean and Cardin. This model is implemented using a naive rejection sampling approach and so it may not be any more efficient to simulate than the standard Hudson model. The string ``"smc"`` can be used to refer to this model. """ name = "smc" class SmcPrimeApproxCoalescent(AncestryModel): """ The SMC' model defined by Marjoram and Wall as an improvement on the original SMC. model is implemented using a naive rejection sampling approach and so it may not be any more efficient to simulate than the standard Hudson model. The string ``"smc_prime"`` can be used to refer to this model. """ name = "smc_prime" class DiscreteTimeWrightFisher(AncestryModel): """ A discrete backwards-time Wright-Fisher model, with diploid back-and-forth recombination. The string ``"dtwf"`` can be used to refer to this model. Wright-Fisher simulations are performed very similarly to coalescent simulations, with all parameters denoting the same quantities in both models. Because events occur at discrete times however, the order in which they occur matters. Each generation consists of the following ordered events: - Migration events. As in the Hudson coalescent, these move single extant lineages between populations. Because migration events occur before lineages choose parents, migrant lineages choose parents from their new population in the same generation. - Demographic events. All events with `previous_generation < event_time <= current_generation` are carried out here. - Lineages draw parents. Each (monoploid) extant lineage draws a parent from their current population. - Diploid recombination. Each parent is diploid, so all child lineages recombine back-and-forth into the same two parental genome copies. These become two independent lineages in the next generation. - Historical sampling events. All historical samples with `previous_generation < sample_time <= current_generation` are inserted. """ name = "dtwf" class WrightFisherPedigree(AncestryModel): # TODO Complete documentation. # TODO Since the pedigree is a necessary parameter for this simulation # model and it cannot be used with any other model we should make it a # parametric model where the parameter is the pedigree. This would # streamline a bunch of logic. """ Backwards-time simulations through a pre-specified pedigree, with diploid individuals and back-and-forth recombination. The string ``"wf_ped"`` can be used to refer to this model. """ name = "wf_ped" class ParametricAncestryModel(AncestryModel): """ The superclass of ancestry models that require extra parameters. """ def get_ll_representation(self): d = super().get_ll_representation() d.update(self.__dict__) return d @dataclasses.dataclass class BetaCoalescent(ParametricAncestryModel): """ A Lambda-coalescent with multiple mergers in the haploid cases, or a Xi-coalescent with simultaneous multiple mergers in the polyploid case. There are two main differences between the Beta-coalescent and the standard coalescent. Firstly, the number of lineages that take part in each common ancestor event is random, with distribution determined by moments of the :math:`Beta(2 - \\alpha, \\alpha)`-distribution. In particular, when there are :math:`n` lineages, each set of :math:`k \\leq n` of them participates in a common ancestor event at rate .. math:: \\frac{1}{B(2 - \\alpha, \\alpha)} \\int_0^1 x^{k - \\alpha - 1} (1 - x)^{n - k + \\alpha - 1} dx, where :math:`B(2 - \\alpha, \\alpha)` is the Beta-function. If ploidy = 1, then all participating lineages merge into one common ancestor, corresponding to haploid, single-parent reproduction. If ploidy = :math:`p > 1`, all participating lineages split randomly into :math:`2 p` groups, corresponding to two-parent reproduction with :math:`p` copies of each chromosome per parent. All lineages within each group merge simultaneously. Secondly, the number of generations between common ancestor events predicted by the Beta-coalescent is proportional to :math:`N^{\\alpha - 1}`, where :math:`N` is the population size. Specifically, the mean number of generations until two lineages undergo a common ancestor event is .. math:: G = \\frac{m^{\\alpha} N^{\\alpha - 1}}{\\alpha B(2 - \\alpha, \\alpha)}, if ploidy = 1, and .. math:: G = \\frac{2 p m^{\\alpha} (N / 2)^{\\alpha - 1}} {\\alpha B(2 - \\alpha, \\alpha)}, if ploidy = :math:`p > 1`, where :math:`m` is the mean number of juveniles per family given by .. math:: m = 2 + \\frac{2^{\\alpha}}{3^{\\alpha - 1} (\\alpha - 1)}, if ploidy > 1, and .. math:: m = 1 + \\frac{1}{2^{\\alpha - 1} (\\alpha - 1)}, if ploidy = 1. In the polyploid case we divide the population size :math:`N` by two because we assume the :math:`N` polyploid individuals form :math:`N / 2` two-parent families in which reproduction takes place. .. warning:: The number of generations between common ancestor events :math:`G` depends both on the population size :math:`N` and :math:`\\alpha`, and can be dramatically shorter than in the case of the standard coalescent. For :math:`\\alpha \\approx 1` that is due to insensitivity of :math:`G` to :math:`N` --- see :ref:`sec_ancestry_models_multiple_mergers` for an illustration. For :math:`\\alpha \\approx 2`, :math:`G` is almost linear in :math:`N`, but can nevertheless be small because :math:`B(2 - \\alpha, \\alpha) \\rightarrow \\infty` as :math:`\\alpha \\rightarrow 2`. As a result, population sizes must often be many orders of magnitude larger than census population sizes to obtain realistic amounts of diversity in simulated samples. See `Schweinsberg (2003) <https://www.sciencedirect.com/science/article/pii/S0304414903000280>`_ for the derivation of the common ancestor event rate, as well as the number of generations between common ancestor events. Note however that Schweinsberg (2003) only covers the haploid case. For details of the diploid extension, see `Blath et al. (2013) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527250/>`_, and `Birkner et al. (2018) <https://projecteuclid.org/euclid.ejp/1527818427>`_ for a diploid version of the Schweinsberg (2003) model specifically. The general polyploid model is analogous to the diploid case, with :math:`2 p` available copies of parental chromsomes per common ancestor event, and hence up to :math:`2 p` simultaneous mergers. :param float alpha: Determines the degree of skewness in the family size distribution, and must satisfy :math:`1 < \\alpha < 2`. Smaller values of :math:`\\alpha` correspond to greater skewness, and :math:`\\alpha = 2` would coincide with the standard coalescent. :param float truncation_point: The maximum number of juveniles :math:`K` born to one family as a fraction of the population size :math:`N`. Must satisfy :math:`0 < K \\leq \\inf`. Determines the maximum fraction of the population replaced by offspring in one reproduction event, :math:`\\tau`, via :math:`\\tau = K / (K + m)`, where :math:`m` is the mean juvenile number above. The default is :math:`K = \\inf`, which corresponds to the standard Beta-coalescent with :math:`\\tau = 1`. When :math:`K < \\inf`, the number of lineages participating in a common ancestor event is determined by moments of the Beta:math:`(2 - \\alpha, \\alpha)` distribution conditioned on not exceeding :math:`\\tau`, and the Beta-function in the expression for :math:`G` is replaced by the incomplete Beta-function :math:`B(\\tau; 2 - \\alpha, \\alpha)`. """ name = "beta" alpha: Union[float, None] = None truncation_point: float = sys.float_info.max @dataclasses.dataclass class DiracCoalescent(ParametricAncestryModel): """ A Lambda-coalescent with multiple mergers in the haploid cases, or a Xi-coalescent with simultaneous multiple mergers in the polyploid case. The Dirac-coalescent is an implementation of the model of `Blath et al. (2013) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3527250/>`_ The simulation proceeds similarly to the standard coalescent. In addition to binary common ancestor events at rate :math:`n (n - 1) / 2` when there are :math:`n` lineages, potential multiple merger events take place at rate :math:`c > 0`. Each lineage participates in each multiple merger event independently with probability :math:`0 < \\psi \\leq 1`. If ploidy = 1, then all participating lineages merge into one common ancestor, corresponding to haploid, single-parent reproduction. If ploidy = :math:`p > 1`, all participating lineages split randomly into :math:`2 p` groups, corresponding to two-parent reproduction with :math:`p` copies of each chromosome per parent. All lineages within each group merge simultaneously. .. warning:: The Dirac-coalescent is obtained as a scaling limit of Moran models, rather than Wright-Fisher models. As a consequence, the number of generations between coalescence events is proportional to :math:`N^2`, rather than :math:`N` generations as in the standard coalescent. See :ref:`sec_ancestry_models_multiple_mergers` for an illustration of how this affects simulation output in practice. :param float c: Determines the rate of potential multiple merger events. We require :math:`c > 0`. :param float psi: Determines the fraction of the population replaced by offspring in one large reproduction event, i.e. one reproduction event giving rise to potential multiple mergers when viewed backwards in time. We require :math:`0 < \\psi \\leq 1`. """ name = "dirac" psi: Union[float, None] = None c: Union[float, None] = None @dataclasses.dataclass class SweepGenicSelection(ParametricAncestryModel): """ A selective sweep that has occured in the history of the sample. This will lead to a burst of rapid coalescence near the selected site. The strength of selection during the sweep is determined by the parameter :math:`s`. Here we define s such that the fitness of the three genotypes at our benefical locus are :math:`W_{bb}=1`, :math:`W_{Bb}=1 + s/2`, :math:`W_{BB}=1 + s`. Thus fitness of the heterozygote is intermediate to the two homozygotes. The model is one of a a structured coalescent where selective backgrounds are defined as in `Braverman et al. (1995) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1206652/>`_ The implementation details here follow closely to those in discoal, `Kern and Schrider (2016) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167068/>`_ See :ref:`sec_ancestry_models_selective_sweeps` for a basic usage and example and :ref:`sec_ancestry_models_sweep_types` for details on how to specify different types of sweeps. .. warning:: If the effective strength of selection (:math:`2Ns`) is sufficiently large the time difference between successive events can be smaller than the finite precision available, leading to zero length branches in the output trees. As this is not allowed by tskit, an error will be raised. .. warning:: Currently models with more than one population and a selective sweep are not implemented. Further population size change during the sweep is not yet possible in msprime. :param float position: the location of the beneficial allele along the chromosome. :param float start_frequency: population frequency of the benefical allele at the start of the selective sweep. E.g., for a *de novo* allele in a diploid population of size N, start frequency would be :math:`1/2N`. :param float end_frequency: population frequency of the beneficial allele at the end of the selective sweep. :param float s: :math:`s` is the selection coefficient of the beneficial mutation. :param float dt: dt is the small increment of time for stepping through the sweep phase of the model. a good rule of thumb is for this to be approximately :math:`1/40N` or smaller. """ name = "sweep_genic_selection" position: Union[float, None] = None start_frequency: Union[float, None] = None end_frequency: Union[float, None] = None s: Union[float, None] = None dt: Union[float, None] = None
jeromekelleher/msprime
msprime/ancestry.py
Python
gpl-3.0
74,467
[ "DIRAC" ]
d1ab078ed1d32b8e43cffbc6b6c4dcf822859a6255b454fc2ab28fbe2f0f926f
import pandas as pd import nmrpystar import mdtraj as md t0 = md.load("./Trajectories/1am7_1.dcd", top="./1am7_fixed.pdb")[0:50] t1 = md.load("./Trajectories/1am7_1.dcd", top="./1am7_fixed.pdb")[-50:] prediction0 = md.nmr.chemical_shifts_shiftx2(t0).mean(1) # Average over time dimensions prediction1 = md.nmr.chemical_shifts_shiftx2(t1).mean(1) # Average over time dimensions parsed = nmrpystar.parse(open("./16664.str").read()) print(parsed.status) q = parsed.value.saves["assigned_chem_shift_list_1"].loops[1] x = pd.DataFrame(q.rows, columns=q.keys) x = x[["Atom_chem_shift.Seq_ID", "Atom_chem_shift.Atom_ID", "Atom_chem_shift.Val"]] x.rename(columns={"Atom_chem_shift.Seq_ID":"resSeq", "Atom_chem_shift.Atom_ID":"name", "Atom_chem_shift.Val":"value"}, inplace=True) # Need to make dtypes match to do eventual comparison. x["resSeq"] = x["resSeq"].astype('int') x["value"] = x["value"].astype('float') expt = x.set_index(["resSeq", "name"]).value prediction0.name = "value" prediction1.name = "value" delta0 = (expt - prediction0).dropna() rms0 = (delta0 ** 2.).reset_index().groupby("name").value.mean() ** 0.5 delta1 = (expt - prediction1).dropna() rms1 = (delta1 ** 2.).reset_index().groupby("name").value.mean() ** 0.5
hainm/open-forcefield-group
nmr/code/compare_shifts_T4_test.py
Python
gpl-2.0
1,238
[ "MDTraj" ]
cd8339b37bbcf30079aa864c19dca216197d8f14456688da4d00c6d6726c6f29
# Version: 0.15+dev """The Versioneer - like a rocketeer, but for versions. The Versioneer ============== * like a rocketeer, but for versions! * https://github.com/warner/python-versioneer * Brian Warner * License: Public Domain * Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, and pypy * [![Latest Version] (https://pypip.in/version/versioneer/badge.svg?style=flat) ](https://pypi.python.org/pypi/versioneer/) * [![Build Status] (https://travis-ci.org/warner/python-versioneer.png?branch=master) ](https://travis-ci.org/warner/python-versioneer) This is a tool for managing a recorded version number in distutils-based python projects. The goal is to remove the tedious and error-prone "update the embedded version string" step from your release process. Making a new release should be as easy as recording a new tag in your version-control system, and maybe making new tarballs. ## Quick Install * `pip install versioneer` to somewhere to your $PATH * add a `[versioneer]` section to your setup.cfg (see below) * run `versioneer install` in your source tree, commit the results ## Version Identifiers Source trees come from a variety of places: * a version-control system checkout (mostly used by developers) * a nightly tarball, produced by build automation * a snapshot tarball, produced by a web-based VCS browser, like github's "tarball from tag" feature * a release tarball, produced by "setup.py sdist", distributed through PyPI Within each source tree, the version identifier (either a string or a number, this tool is format-agnostic) can come from a variety of places: * ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows about recent "tags" and an absolute revision-id * the name of the directory into which the tarball was unpacked * an expanded VCS keyword ($Id$, etc) * a `_version.py` created by some earlier build step For released software, the version identifier is closely related to a VCS tag. Some projects use tag names that include more than just the version string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool needs to strip the tag prefix to extract the version identifier. For unreleased software (between tags), the version identifier should provide enough information to help developers recreate the same tree, while also giving them an idea of roughly how old the tree is (after version 1.2, before version 1.3). Many VCS systems can report a description that captures this, for example `git describe --tags --dirty --always` reports things like "0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the 0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has uncommitted changes. The version identifier is used for multiple purposes: * to allow the module to self-identify its version: `myproject.__version__` * to choose a name and prefix for a 'setup.py sdist' tarball ## Theory of Operation Versioneer works by adding a special `_version.py` file into your source tree, where your `__init__.py` can import it. This `_version.py` knows how to dynamically ask the VCS tool for version information at import time. `_version.py` also contains `$Revision$` markers, and the installation process marks `_version.py` to have this marker rewritten with a tag name during the `git archive` command. As a result, generated tarballs will contain enough information to get the proper version. To allow `setup.py` to compute a version too, a `versioneer.py` is added to the top level of your source tree, next to `setup.py` and the `setup.cfg` that configures it. This overrides several distutils/setuptools commands to compute the version when invoked, and changes `setup.py build` and `setup.py sdist` to replace `_version.py` with a small static file that contains just the generated version data. ## Installation First, decide on values for the following configuration variables: * `VCS`: the version control system you use. Currently accepts "git". * `style`: the style of version string to be produced. See "Styles" below for details. Defaults to "pep440", which looks like `TAG[+DISTANCE.gSHORTHASH[.dirty]]`. * `versionfile_source`: A project-relative pathname into which the generated version strings should be written. This is usually a `_version.py` next to your project's main `__init__.py` file, so it can be imported at runtime. If your project uses `src/myproject/__init__.py`, this should be `src/myproject/_version.py`. This file should be checked in to your VCS as usual: the copy created below by `setup.py setup_versioneer` will include code that parses expanded VCS keywords in generated tarballs. The 'build' and 'sdist' commands will replace it with a copy that has just the calculated version string. This must be set even if your project does not have any modules (and will therefore never import `_version.py`), since "setup.py sdist" -based trees still need somewhere to record the pre-calculated version strings. Anywhere in the source tree should do. If there is a `__init__.py` next to your `_version.py`, the `setup.py setup_versioneer` command (described below) will append some `__version__`-setting assignments, if they aren't already present. * `versionfile_build`: Like `versionfile_source`, but relative to the build directory instead of the source directory. These will differ when your setup.py uses 'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`, then you will probably have `versionfile_build='myproject/_version.py'` and `versionfile_source='src/myproject/_version.py'`. If this is set to None, then `setup.py build` will not attempt to rewrite any `_version.py` in the built tree. If your project does not have any libraries (e.g. if it only builds a script), then you should use `versionfile_build = None` and override `distutils.command.build_scripts` to explicitly insert a copy of `versioneer.get_version()` into your generated script. * `tag_prefix`: a string, like 'PROJECTNAME-', which appears at the start of all VCS tags. If your tags look like 'myproject-1.2.0', then you should use tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this should be an empty string, using either `tag_prefix=` or `tag_prefix=''`. * `parentdir_prefix`: a optional string, frequently the same as tag_prefix, which appears at the start of all unpacked tarball filenames. If your tarball unpacks into 'myproject-1.2.0', this should be 'myproject-'. To disable this feature, just omit the field from your `setup.cfg`. This tool provides one script, named `versioneer`. That script has one mode, "install", which writes a copy of `versioneer.py` into the current directory and runs `versioneer.py setup` to finish the installation. To versioneer-enable your project: * 1: Modify your `setup.cfg`, adding a section named `[versioneer]` and populating it with the configuration values you decided earlier (note that the option names are not case-sensitive): ```` [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- ```` * 2: Run `versioneer install`. This will do the following: * copy `versioneer.py` into the top of your source tree * create `_version.py` in the right place (`versionfile_source`) * modify your `__init__.py` (if one exists next to `_version.py`) to define `__version__` (by calling a function from `_version.py`) * modify your `MANIFEST.in` to include both `versioneer.py` and the generated `_version.py` in sdist tarballs `versioneer install` will complain about any problems it finds with your `setup.py` or `setup.cfg`. Run it multiple times until you have fixed all the problems. * 3: add a `import versioneer` to your setup.py, and add the following arguments to the setup() call: version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), * 4: commit these changes to your VCS. To make sure you won't forget, `versioneer install` will mark everything it touched for addition using `git add`. Don't forget to add `setup.py` and `setup.cfg` too. ## Post-Installation Usage Once established, all uses of your tree from a VCS checkout should get the current version string. All generated tarballs should include an embedded version string (so users who unpack them will not need a VCS tool installed). If you distribute your project through PyPI, then the release process should boil down to two steps: * 1: git tag 1.0 * 2: python setup.py register sdist upload If you distribute it through github (i.e. users use github to generate tarballs with `git archive`), the process is: * 1: git tag 1.0 * 2: git push; git push --tags Versioneer will report "0+untagged.NUMCOMMITS.gHASH" until your tree has at least one tag in its history. ## Version-String Flavors Code which uses Versioneer can learn about its version string at runtime by importing `_version` from your main `__init__.py` file and running the `get_versions()` function. From the "outside" (e.g. in `setup.py`), you can import the top-level `versioneer.py` and run `get_versions()`. Both functions return a dictionary with different flavors of version information: * `['version']`: A condensed version string, rendered using the selected style. This is the most commonly used value for the project's version string. The default "pep440" style yields strings like `0.11`, `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section below for alternative styles. * `['full-revisionid']`: detailed revision identifier. For Git, this is the full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". * `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that this is only accurate if run in a VCS checkout, otherwise it is likely to be False or None * `['error']`: if the version string could not be computed, this will be set to a string describing the problem, otherwise it will be None. It may be useful to throw an exception in setup.py if this is set, to avoid e.g. creating tarballs with a version string of "unknown". Some variants are more useful than others. Including `full-revisionid` in a bug report should allow developers to reconstruct the exact code being tested (or indicate the presence of local changes that should be shared with the developers). `version` is suitable for display in an "about" box or a CLI `--version` output: it can be easily compared against release notes and lists of bugs fixed in various releases. The installer adds the following text to your `__init__.py` to place a basic version in `YOURPROJECT.__version__`: from ._version import get_versions __version__ = get_versions()['version'] del get_versions ## Styles The setup.cfg `style=` configuration controls how the VCS information is rendered into a version string. The default style, "pep440", produces a PEP440-compliant string, equal to the un-prefixed tag name for actual releases, and containing an additional "local version" section with more detail for in-between builds. For Git, this is TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags --dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and that this commit is two revisions ("+2") beyond the "0.11" tag. For released software (exactly equal to a known tag), the identifier will only contain the stripped tag, e.g. "0.11". Other styles are available. See details.md in the Versioneer source tree for descriptions. ## Debugging Versioneer tries to avoid fatal errors: if something goes wrong, it will tend to return a version of "0+unknown". To investigate the problem, run `setup.py version`, which will run the version-lookup code in a verbose mode, and will display the full contents of `get_versions()` (including the `error` string, which may help identify what went wrong). ## Updating Versioneer To upgrade your project to a new release of Versioneer, do the following: * install the new Versioneer (`pip install -U versioneer` or equivalent) * edit `setup.cfg`, if necessary, to include any new configuration settings indicated by the release notes * re-run `versioneer install` in your source tree, to replace `SRC/_version.py` * commit any changed files ### Upgrading to 0.15 Starting with this version, Versioneer is configured with a `[versioneer]` section in your `setup.cfg` file. Earlier versions required the `setup.py` to set attributes on the `versioneer` module immediately after import. The new version will refuse to run (raising an exception during import) until you have provided the necessary `setup.cfg` section. In addition, the Versioneer package provides an executable named `versioneer`, and the installation process is driven by running `versioneer install`. In 0.14 and earlier, the executable was named `versioneer-installer` and was run without an argument. ### Upgrading to 0.14 0.14 changes the format of the version string. 0.13 and earlier used hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a plus-separated "local version" section strings, with dot-separated components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old format, but should be ok with the new one. ### Upgrading from 0.11 to 0.12 Nothing special. ### Upgrading from 0.10 to 0.11 You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running `setup.py setup_versioneer`. This will enable the use of additional version-control systems (SVN, etc) in the future. ## Future Directions This tool is designed to make it easily extended to other version-control systems: all VCS-specific components are in separate directories like src/git/ . The top-level `versioneer.py` script is assembled from these components by running make-versioneer.py . In the future, make-versioneer.py will take a VCS name as an argument, and will construct a version of `versioneer.py` that is specific to the given VCS. It might also take the configuration arguments that are currently provided manually during installation by editing setup.py . Alternatively, it might go the other direction and include code from all supported VCS systems, reducing the number of intermediate scripts. ## License To make Versioneer easier to embed, all its code is dedicated to the public domain. The `_version.py` that it creates is also in the public domain. Specifically, both are released under the Creative Commons "Public Domain Dedication" license (CC0-1.0), as described in https://creativecommons.org/publicdomain/zero/1.0/ . """ from __future__ import print_function try: import configparser except ImportError: import ConfigParser as configparser import errno import json import os import re import subprocess import sys class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_root(): """Get the project root directory. We require that all commands are run from the project root, i.e. the directory that contains setup.py, setup.cfg, and versioneer.py . """ root = os.path.realpath(os.path.abspath(os.getcwd())) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): # allow 'python path/to/setup.py COMMAND' root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): err = ("Versioneer was unable to run the project root directory. " "Versioneer requires setup.py to be executed from " "its immediate directory (like 'python setup.py COMMAND'), " "or in a way that lets it use sys.argv[0] to find the root " "(like 'python path/to/setup.py COMMAND').") raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools # tree) execute all dependencies in a single python process, so # "versioneer" may be imported multiple times, and python's shared # module-import table will cache the first one. So we can't use # os.path.dirname(__file__), as that will find whichever # versioneer.py was first imported, even in later projects. me = os.path.realpath(os.path.abspath(__file__)) if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]: print("Warning: build in %s is using versioneer.py from %s" % (os.path.dirname(me), versioneer_py)) except NameError: pass return root def get_config_from_root(root): """Read the project setup.cfg file to determine Versioneer config.""" # This might raise EnvironmentError (if setup.cfg is missing), or # configparser.NoSectionError (if it lacks a [versioneer] section), or # configparser.NoOptionError (if it lacks "VCS="). See the docstring at # the top of versioneer.py for instructions on writing your setup.cfg . setup_cfg = os.path.join(root, "setup.cfg") parser = configparser.SafeConfigParser() with open(setup_cfg, "r") as f: parser.readfp(f) VCS = parser.get("versioneer", "VCS") # mandatory def get(parser, name): if parser.has_option("versioneer", name): return parser.get("versioneer", name) return None cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = get(parser, "style") or "" cfg.versionfile_source = get(parser, "versionfile_source") cfg.versionfile_build = get(parser, "versionfile_build") cfg.tag_prefix = get(parser, "tag_prefix") if cfg.tag_prefix in ("''", '""'): cfg.tag_prefix = "" cfg.parentdir_prefix = get(parser, "parentdir_prefix") cfg.verbose = get(parser, "verbose") return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" # these dictionaries contain VCS-specific tools LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %s" % dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %s" % (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %s (error)" % dispcmd) return None return stdout LONG_VERSION_PY['git'] = r''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build # directories (produced by setup.py build) will contain a much shorter file # that just contains the computed version number. # This file is released into the public domain. Generated by # versioneer-0.15+dev (https://github.com/warner/python-versioneer) """Git implementation of _version.py.""" import errno import os import re import subprocess import sys def get_keywords(): """Get the keywords needed to look up the version information.""" # these strings will be replaced by git during git-archive. # setup.py/versioneer.py will grep for the variable names, so they must # each be defined on a line of their own. _version.py will just call # get_keywords(). git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" keywords = {"refnames": git_refnames, "full": git_full} return keywords class VersioneerConfig: """Container for Versioneer configuration parameters.""" def get_config(): """Create, populate and return the VersioneerConfig() object.""" # these strings are filled in when 'setup.py versioneer' creates # _version.py cfg = VersioneerConfig() cfg.VCS = "git" cfg.style = "%(STYLE)s" cfg.tag_prefix = "%(TAG_PREFIX)s" cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" cfg.verbose = False return cfg class NotThisMethod(Exception): """Exception raised if a method is not valid for the current scenario.""" LONG_VERSION_PY = {} HANDLERS = {} def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f return decorate def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): """Call the given command(s).""" assert isinstance(commands, list) p = None for c in commands: try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, stderr=(subprocess.PIPE if hide_stderr else None)) break except EnvironmentError: e = sys.exc_info()[1] if e.errno == errno.ENOENT: continue if verbose: print("unable to run %%s" %% dispcmd) print(e) return None else: if verbose: print("unable to find command, tried %%s" %% (commands,)) return None stdout = p.communicate()[0].strip() if sys.version_info[0] >= 3: stdout = stdout.decode() if p.returncode != 0: if verbose: print("unable to run %%s (error)" %% dispcmd) return None return stdout def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%%s', but '%%s' doesn't start with " "prefix '%%s'" %% (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = [r.strip() for r in refnames.strip("()").split(",")] # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %%d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%%s', no digits" %% ",".join(set(refs) - tags)) if verbose: print("likely tags: %%s" %% ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %%s" %% r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "branch": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "branch": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %%s" %% root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM). Note, for git v1.7 # and below, it is necessary to run "git update-index --refresh" first. describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%%s*" %% tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # abbrev-ref available with git >= 1.7 branch_name = run_command(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root).strip() if branch_name == 'HEAD': branches = run_command(GITS, ["branch", "--contains"], cwd=root).split('\n') branches = [branch[2:] for branch in branches if branch[4:5] != '('] if 'master' in branches: branch_name = 'master' elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] branch_name = branch_name.replace(' ', '.').replace('(', '').replace(')', '') pieces['branch'] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%%s'" %% describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%%s' doesn't start with prefix '%%s'" print(fmt %% (full_tag, tag_prefix)) pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" %% (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits return pieces # Default matches v1.2.x, maint/1.2.x, 1.2.x, 1.x etc. default_maint_branch_regexp = ".*([0-9]+\.)+x$" def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%%d" %% pieces["distance"] else: # exception #1 rendered = "0.post.dev%%d" %% pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%%s" %% pieces["short"] else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%%s" %% pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%%d" %% pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def add_one_to_version(version_string, number_index_to_increment=-1): """ Add one to a version string at the given numeric indices. >>> add_one_to_version('v1.2.3') 'v1.2.4' """ # Break up the tag by number groups (preserving multi-digit # numbers as multidigit) parts = re.split("([0-9]+)", version_string) digit_parts = [(i, part) for i, part in enumerate(parts) if part.isdigit()] # Deal with negative indexing. increment_at_index = ((number_index_to_increment + len(digit_parts)) %% len(digit_parts)) for n_seen, (i, part) in enumerate(digit_parts): if n_seen == increment_at_index: parts[i] = str(int(part) + 1) elif n_seen > increment_at_index: parts[i] = '0' return ''.join(parts) def render_pep440_branch_based(pieces): # [TAG+1 of minor number][.devDISTANCE][+gHEX]. The git short is # included for dirty. # exceptions: # 1: no tags. 0.0.0.devDISTANCE[+gHEX] master = pieces.get('branch') == 'master' maint = re.match(default_maint_branch_regexp, pieces.get('branch') or '') # If we are on a tag, just pep440-pre it. if pieces["closest-tag"] and not (pieces["distance"] or pieces["dirty"]): rendered = pieces["closest-tag"] else: # Put a default closest-tag in. if not pieces["closest-tag"]: pieces["closest-tag"] = '0.0.0' if pieces["distance"] or pieces["dirty"]: if maint: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post%%d" %% pieces["distance"] else: rendered = add_one_to_version(pieces["closest-tag"]) if pieces["distance"]: rendered += ".dev%%d" %% pieces["distance"] # Put the branch name in if it isn't master nor a # maintenance branch. plus = '+' if not (master or maint): rendered += "%%s%%s" %% (plus, pieces.get('branch') or 'unknown_branch') plus = '_' if pieces["dirty"]: rendered += "%%sg%%s" %% (plus, pieces["short"]) else: rendered = pieces["closest-tag"] return rendered STYLES = {'default': render_pep440, 'pep440': render_pep440, 'pep440-pre': render_pep440_pre, 'pep440-post': render_pep440_post, 'pep440-old': render_pep440_old, 'git-describe': render_git_describe, 'git-describe-long': render_git_describe_long, 'pep440-old': render_pep440_old, 'pep440-branch-based': render_pep440_branch_based, } def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style: style = 'default' renderer = STYLES.get(style) if not renderer: raise ValueError("unknown style '%%s'" %% style) rendered = renderer(pieces) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} def get_versions(): """Get version information or return default if unable to do so.""" # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have # __file__, we can work backwards from there to the root. Some # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which # case we can only use expanded keywords. cfg = get_config() verbose = cfg.verbose try: return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass try: root = os.path.realpath(__file__) # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. for i in cfg.versionfile_source.split('/'): root = os.path.dirname(root) except NameError: return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to find root of source tree"} try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) return render(pieces, cfg.style) except NotThisMethod: pass try: if cfg.parentdir_prefix: return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) except NotThisMethod: pass return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} ''' @register_vcs_handler("git", "get_keywords") def git_get_keywords(versionfile_abs): """Extract version information from the given file.""" # the code embedded in _version.py can just fetch the value of these # keywords. When used from setup.py, we don't want to import _version.py, # so we do it with a regexp instead. This function is not used from # _version.py. keywords = {} try: f = open(versionfile_abs, "r") for line in f.readlines(): if line.strip().startswith("git_refnames ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["refnames"] = mo.group(1) if line.strip().startswith("git_full ="): mo = re.search(r'=\s*"(.*)"', line) if mo: keywords["full"] = mo.group(1) f.close() except EnvironmentError: pass return keywords @register_vcs_handler("git", "keywords") def git_versions_from_keywords(keywords, tag_prefix, verbose): """Get version information from git keywords.""" if not keywords: raise NotThisMethod("no keywords at all, weird") refnames = keywords["refnames"].strip() if refnames.startswith("$Format"): if verbose: print("keywords are unexpanded, not using") raise NotThisMethod("unexpanded keywords, not a git-archive tarball") refs = [r.strip() for r in refnames.strip("()").split(",")] # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d # expansion behaves like git log --decorate=short and strips out the # refs/heads/ and refs/tags/ prefixes that would let us distinguish # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". tags = set([r for r in refs if re.search(r'\d', r)]) if verbose: print("discarding '%s', no digits" % ",".join(set(refs) - tags)) if verbose: print("likely tags: %s" % ",".join(sorted(tags))) for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): r = ref[len(tag_prefix):] if verbose: print("picking %s" % r) return {"version": r, "full-revisionid": keywords["full"].strip(), "dirty": False, "error": None, "branch": None } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") return {"version": "0+unknown", "full-revisionid": keywords["full"].strip(), "dirty": False, "error": "no suitable tags", "branch": None} @register_vcs_handler("git", "pieces_from_vcs") def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): """Get version from 'git describe' in the root of the source tree. This only gets called if the git-archive 'subst' keywords were *not* expanded, and _version.py hasn't already been rewritten with a short version string, meaning we're inside a checked out source tree. """ if not os.path.exists(os.path.join(root, ".git")): if verbose: print("no .git in %s" % root) raise NotThisMethod("no .git directory") GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM). Note, for git v1.7 # and below, it is necessary to run "git update-index --refresh" first. describe_out = run_command(GITS, ["describe", "--tags", "--dirty", "--always", "--long", "--match", "%s*" % tag_prefix], cwd=root) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") describe_out = describe_out.strip() full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) if full_out is None: raise NotThisMethod("'git rev-parse' failed") full_out = full_out.strip() pieces = {} pieces["long"] = full_out pieces["short"] = full_out[:7] # maybe improved later pieces["error"] = None # abbrev-ref available with git >= 1.7 branch_name = run_command(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root).strip() if branch_name == 'HEAD': branches = run_command(GITS, ["branch", "--contains"], cwd=root).split('\n') branches = [branch[2:] for branch in branches if branch[4:5] != '('] if 'master' in branches: branch_name = 'master' elif not branches: branch_name = None else: # Pick the first branch that is returned. Good or bad. branch_name = branches[0] branch_name = branch_name.replace(' ', '.').replace('(', '').replace(')', '') pieces['branch'] = branch_name # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] # TAG might have hyphens. git_describe = describe_out # look for -dirty suffix dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: git_describe = git_describe[:git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? pieces["error"] = ("unable to parse git-describe output: '%s'" % describe_out) return pieces # tag full_tag = mo.group(1) if not full_tag.startswith(tag_prefix): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" % (full_tag, tag_prefix)) return pieces pieces["closest-tag"] = full_tag[len(tag_prefix):] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) # commit: short hex revision ID pieces["short"] = mo.group(3) else: # HEX: no tags pieces["closest-tag"] = None count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int('3') # total number of commits return pieces def do_vcs_install(manifest_in, versionfile_source, ipy): """Git-specific installation logic for Versioneer. For Git, this means creating/changing .gitattributes to mark _version.py for export-time keyword substitution. """ GITS = ["git"] if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] files = [manifest_in, versionfile_source] if ipy: files.append(ipy) try: me = __file__ if me.endswith(".pyc") or me.endswith(".pyo"): me = os.path.splitext(me)[0] + ".py" versioneer_file = os.path.relpath(me) except NameError: versioneer_file = "versioneer.py" files.append(versioneer_file) present = False try: f = open(".gitattributes", "r") for line in f.readlines(): if line.strip().startswith(versionfile_source): if "export-subst" in line.strip().split()[1:]: present = True f.close() except EnvironmentError: pass if not present: f = open(".gitattributes", "a+") f.write("%s export-subst\n" % versionfile_source) f.close() files.append(".gitattributes") run_command(GITS, ["add", "--"] + files) def versions_from_parentdir(parentdir_prefix, root, verbose): """Try to determine the version from the parent directory name. Source tarballs conventionally unpack into a directory that includes both the project name and a version string. """ dirname = os.path.basename(root) if not dirname.startswith(parentdir_prefix): if verbose: print("guessing rootdir is '%s', but '%s' doesn't start with " "prefix '%s'" % (root, dirname, parentdir_prefix)) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") return {"version": dirname[len(parentdir_prefix):], "full-revisionid": None, "dirty": False, "error": None} SHORT_VERSION_PY = """ # This file was generated by 'versioneer.py' (0.15+dev) from # revision-control system data, or from the parent directory name of an # unpacked source archive. Distribution tarballs contain a pre-generated copy # of this file. import json import sys version_json = ''' %s ''' # END VERSION_JSON def get_versions(): return json.loads(version_json) """ def versions_from_file(filename): """Try to determine the version from _version.py if present.""" try: with open(filename) as f: contents = f.read() except EnvironmentError: raise NotThisMethod("unable to read _version.py") mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) print("set %s to '%s'" % (filename, versions["version"])) # Default matches v1.2.x, maint/1.2.x, 1.2.x, 1.x etc. default_maint_branch_regexp = ".*([0-9]+\.)+x$" def plus_or_dot(pieces): """Return a + if we don't already have one, else return a .""" if "+" in pieces.get("closest-tag", ""): return "." return "+" def render_pep440(pieces): """Build up version string, with post-release "local version identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty Exceptions: 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += plus_or_dot(pieces) rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" else: # exception #1 rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered def render_pep440_pre(pieces): """TAG[.post.devDISTANCE] -- No -dirty. Exceptions: 1: no tags. 0.post.devDISTANCE """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post.dev%d" % pieces["distance"] else: # exception #1 rendered = "0.post.dev%d" % pieces["distance"] return rendered def render_pep440_post(pieces): """TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that .dev0 sorts backwards (a dirty tree will appear "older" than the corresponding clean one), but you shouldn't be releasing software with -dirty anyways. Exceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += plus_or_dot(pieces) rendered += "g%s" % pieces["short"] else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" rendered += "+g%s" % pieces["short"] return rendered def render_pep440_old(pieces): """TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. Eexceptions: 1: no tags. 0.postDISTANCE[.dev0] """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"] or pieces["dirty"]: rendered += ".post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" else: # exception #1 rendered = "0.post%d" % pieces["distance"] if pieces["dirty"]: rendered += ".dev0" return rendered def render_git_describe(pieces): """TAG[-DISTANCE-gHEX][-dirty]. Like 'git describe --tags --dirty --always'. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def render_git_describe_long(pieces): """TAG-DISTANCE-gHEX[-dirty]. Like 'git describe --tags --dirty --always -long'. The distance/hash is unconditional. Exceptions: 1: no tags. HEX[-dirty] (note: no 'g' prefix) """ if pieces["closest-tag"]: rendered = pieces["closest-tag"] rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) else: # exception #1 rendered = pieces["short"] if pieces["dirty"]: rendered += "-dirty" return rendered def add_one_to_version(version_string, number_index_to_increment=-1): """ Add one to a version string at the given numeric indices. >>> add_one_to_version('v1.2.3') 'v1.2.4' """ # Break up the tag by number groups (preserving multi-digit # numbers as multidigit) parts = re.split("([0-9]+)", version_string) digit_parts = [(i, part) for i, part in enumerate(parts) if part.isdigit()] # Deal with negative indexing. increment_at_index = ((number_index_to_increment + len(digit_parts)) % len(digit_parts)) for n_seen, (i, part) in enumerate(digit_parts): if n_seen == increment_at_index: parts[i] = str(int(part) + 1) elif n_seen > increment_at_index: parts[i] = '0' return ''.join(parts) def render_pep440_branch_based(pieces): # [TAG+1 of minor number][.devDISTANCE][+gHEX]. The git short is # included for dirty. # exceptions: # 1: no tags. 0.0.0.devDISTANCE[+gHEX] master = pieces.get('branch') == 'master' maint = re.match(default_maint_branch_regexp, pieces.get('branch') or '') # If we are on a tag, just pep440-pre it. if pieces["closest-tag"] and not (pieces["distance"] or pieces["dirty"]): rendered = pieces["closest-tag"] else: # Put a default closest-tag in. if not pieces["closest-tag"]: pieces["closest-tag"] = '0.0.0' if pieces["distance"] or pieces["dirty"]: if maint: rendered = pieces["closest-tag"] if pieces["distance"]: rendered += ".post%d" % pieces["distance"] else: rendered = add_one_to_version(pieces["closest-tag"]) if pieces["distance"]: rendered += ".dev%d" % pieces["distance"] # Put the branch name in if it isn't master nor a # maintenance branch. plus = '+' if not (master or maint): rendered += "%s%s" % (plus, pieces.get('branch') or 'unknown_branch') plus = '_' if pieces["dirty"]: rendered += "%sg%s" % (plus, pieces["short"]) else: rendered = pieces["closest-tag"] return rendered STYLES = {'default': render_pep440, 'pep440': render_pep440, 'pep440-pre': render_pep440_pre, 'pep440-post': render_pep440_post, 'pep440-old': render_pep440_old, 'git-describe': render_git_describe, 'git-describe-long': render_git_describe_long, 'pep440-old': render_pep440_old, 'pep440-branch-based': render_pep440_branch_based, } def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: return {"version": "unknown", "full-revisionid": pieces.get("long"), "dirty": None, "error": pieces["error"]} if not style: style = 'default' renderer = STYLES.get(style) if not renderer: raise ValueError("unknown style '%s'" % style) rendered = renderer(pieces) return {"version": rendered, "full-revisionid": pieces["long"], "dirty": pieces["dirty"], "error": None} class VersioneerBadRootError(Exception): """The project root directory is unknown or missing key files.""" def get_versions(verbose=False): """Get the project version from whatever source is available. Returns dict with two keys: 'version' and 'full'. """ if "versioneer" in sys.modules: # see the discussion in cmdclass.py:get_cmdclass() del sys.modules["versioneer"] root = get_root() cfg = get_config_from_root(root) assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose assert cfg.versionfile_source is not None, \ "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) # extract version from first of: _version.py, VCS command (e.g. 'git # describe'), parentdir. This is meant to work for developers using a # source checkout, for users of a tarball created by 'setup.py sdist', # and for users of a tarball/zipball created by 'git archive' or github's # download-from-tag feature or the equivalent in other VCSes. get_keywords_f = handlers.get("get_keywords") from_keywords_f = handlers.get("keywords") if get_keywords_f and from_keywords_f: try: keywords = get_keywords_f(versionfile_abs) ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) if verbose: print("got version from expanded keyword %s" % ver) return ver except NotThisMethod: pass try: ver = versions_from_file(versionfile_abs) if verbose: print("got version from file %s %s" % (versionfile_abs, ver)) return ver except NotThisMethod: pass from_vcs_f = handlers.get("pieces_from_vcs") if from_vcs_f: try: pieces = from_vcs_f(cfg.tag_prefix, root, verbose) ver = render(pieces, cfg.style) if verbose: print("got version from VCS %s" % ver) return ver except NotThisMethod: pass try: if cfg.parentdir_prefix: ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) if verbose: print("got version from parentdir %s" % ver) return ver except NotThisMethod: pass if verbose: print("unable to compute version") return {"version": "0+unknown", "full-revisionid": None, "dirty": None, "error": "unable to compute version"} def get_version(): """Get the short version string for this project.""" return get_versions()["version"] def get_cmdclass(): """Get the custom setuptools/distutils subclasses used by Versioneer.""" if "versioneer" in sys.modules: del sys.modules["versioneer"] # this fixes the "python setup.py develop" case (also 'install' and # 'easy_install .'), in which subdependencies of the main project are # built (using setup.py bdist_egg) in the same python process. Assume # a main project A and a dependency B, which use different versions # of Versioneer. A's setup.py imports A's Versioneer, leaving it in # sys.modules by the time B's setup.py is executed, causing B to run # with the wrong versioneer. Setuptools wraps the sub-dep builds in a # sandbox that restores sys.modules to it's pre-build state, so the # parent is protected against the child's "import versioneer". By # removing ourselves from sys.modules here, before the child build # happens, we protect the child from the parent's versioneer too. # Also see https://github.com/warner/python-versioneer/issues/52 cmds = {} # we add "version" to both distutils and setuptools from distutils.core import Command class cmd_version(Command): description = "report generated version string" user_options = [] boolean_options = [] def initialize_options(self): pass def finalize_options(self): pass def run(self): vers = get_versions(verbose=True) print("Version: %s" % vers["version"]) print(" full-revisionid: %s" % vers.get("full-revisionid")) print(" dirty: %s" % vers.get("dirty")) if vers["error"]: print(" error: %s" % vers["error"]) cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools # # most invocation pathways end up running build_py: # distutils/build -> build_py # distutils/install -> distutils/build ->.. # setuptools/bdist_wheel -> distutils/install ->.. # setuptools/bdist_egg -> distutils/install_lib -> build_py # setuptools/install -> bdist_egg ->.. # setuptools/develop -> ? # we override different "build_py" commands for both environments if "setuptools" in sys.modules: from setuptools.command.build_py import build_py as _build_py else: from distutils.command.build_py import build_py as _build_py class cmd_build_py(_build_py): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() _build_py.run(self) # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) cmds["build_py"] = cmd_build_py if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe class cmd_build_exe(_build_exe): def run(self): root = get_root() cfg = get_config_from_root(root) versions = get_versions() target_versionfile = cfg.versionfile_source print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) _build_exe.run(self) os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) cmds["build_exe"] = cmd_build_exe del cmds["build_py"] # we override different "sdist" commands for both environments if "setuptools" in sys.modules: from setuptools.command.sdist import sdist as _sdist else: from distutils.command.sdist import sdist as _sdist class cmd_sdist(_sdist): def run(self): versions = get_versions() self._versioneer_generated_versions = versions # unless we update this, the command will keep using the old # version self.distribution.metadata.version = versions["version"] return _sdist.run(self) def make_release_tree(self, base_dir, files): root = get_root() cfg = get_config_from_root(root) _sdist.make_release_tree(self, base_dir, files) # now locate _version.py in the new base_dir directory # (remembering that it may be a hardlink) and replace it with an # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, self._versioneer_generated_versions) cmds["sdist"] = cmd_sdist return cmds CONFIG_ERROR = """ setup.cfg is missing the necessary Versioneer configuration. You need a section like: [versioneer] VCS = git style = pep440 versionfile_source = src/myproject/_version.py versionfile_build = myproject/_version.py tag_prefix = parentdir_prefix = myproject- You will also need to edit your setup.py to use the results: import versioneer setup(version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), ...) Please read the docstring in ./versioneer.py for configuration instructions, edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. """ SAMPLE_CONFIG = """ # See the docstring in versioneer.py for instructions. Note that you must # re-run 'versioneer.py setup' after changing this section, and commit the # resulting files. [versioneer] #VCS = git #style = pep440 #versionfile_source = #versionfile_build = #tag_prefix = #parentdir_prefix = """ INIT_PY_SNIPPET = """ from ._version import get_versions __version__ = get_versions()['version'] del get_versions """ def do_setup(): """Main VCS-independent setup function for installing Versioneer.""" root = get_root() try: cfg = get_config_from_root(root) except (EnvironmentError, configparser.NoSectionError, configparser.NoOptionError) as e: if isinstance(e, (EnvironmentError, configparser.NoSectionError)): print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) return 1 print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] f.write(LONG % {"DOLLAR": "$", "STYLE": cfg.style, "TAG_PREFIX": cfg.tag_prefix, "PARENTDIR_PREFIX": cfg.parentdir_prefix, "VERSIONFILE_SOURCE": cfg.versionfile_source, }) ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: old = f.read() except EnvironmentError: old = "" if INIT_PY_SNIPPET not in old: print(" appending to %s" % ipy) with open(ipy, "a") as f: f.write(INIT_PY_SNIPPET) else: print(" %s unmodified" % ipy) else: print(" %s doesn't exist, ok" % ipy) ipy = None # Make sure both the top-level "versioneer.py" and versionfile_source # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so # they'll be copied into source distributions. Pip won't be able to # install the package without this. manifest_in = os.path.join(root, "MANIFEST.in") simple_includes = set() try: with open(manifest_in, "r") as f: for line in f: if line.startswith("include "): for include in line.split()[1:]: simple_includes.add(include) except EnvironmentError: pass # That doesn't cover everything MANIFEST.in can do # (http://docs.python.org/2/distutils/sourcedist.html#commands), so # it might give some false negatives. Appending redundant 'include' # lines is safe, though. if "versioneer.py" not in simple_includes: print(" appending 'versioneer.py' to MANIFEST.in") with open(manifest_in, "a") as f: f.write("include versioneer.py\n") else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: print(" appending versionfile_source ('%s') to MANIFEST.in" % cfg.versionfile_source) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: print(" versionfile_source already in MANIFEST.in") # Make VCS-specific changes. For git, this means creating/changing # .gitattributes to mark _version.py for export-time keyword # substitution. do_vcs_install(manifest_in, cfg.versionfile_source, ipy) return 0 def scan_setup_py(): """Validate the contents of setup.py against Versioneer's expectations.""" found = set() setters = False errors = 0 with open("setup.py", "r") as f: for line in f.readlines(): if "import versioneer" in line: found.add("import") if "versioneer.get_cmdclass()" in line: found.add("cmdclass") if "versioneer.get_version()" in line: found.add("get_version") if "versioneer.VCS" in line: setters = True if "versioneer.versionfile_source" in line: setters = True if len(found) != 3: print("") print("Your setup.py appears to be missing some important items") print("(but I might be wrong). Please make sure it has something") print("roughly like the following:") print("") print(" import versioneer") print(" setup( version=versioneer.get_version(),") print(" cmdclass=versioneer.get_cmdclass(), ...)") print("") errors += 1 if setters: print("You should remove lines like 'versioneer.VCS = ' and") print("'versioneer.versionfile_source = ' . This configuration") print("now lives in setup.cfg, and should be removed from setup.py") print("") errors += 1 return errors if __name__ == "__main__": cmd = sys.argv[1] if cmd == "setup": errors = do_setup() errors += scan_setup_py() if errors: sys.exit(1)
lbdreyer/nc-time-axis
versioneer.py
Python
bsd-3-clause
72,477
[ "Brian" ]
d2ac4c3371c86ea58ebbfb3371c9bf54bc72b5314b5c2103c459ebb813e369ea
# -*- coding: utf-8 -*- # Author: Vincent Dubourg <vincent.dubourg@gmail.com> # (mostly translation, see implementation details) # Jan Hendrik Metzen <jhm@informatik.uni-bremen.de> # (converting to a object-oriented, more modular design) # Licence: BSD 3 clause """ The built-in correlation models submodule for the gaussian_process module. """ from abc import ABCMeta, abstractmethod import numpy as np from sklearn.utils import check_array from sklearn.externals.six import with_metaclass MACHINE_EPSILON = np.finfo(np.double).eps def l1_cross_differences(X): """ Computes the nonzero componentwise differences between the vectors in X. Parameters ---------- X: array_like An array with shape (n_samples, n_features) Returns ------- D: array with shape (n_samples * (n_samples - 1) / 2, n_features) The array of componentwise differences. ij: arrays with shape (n_samples * (n_samples - 1) / 2, 2) The indices i and j of the vectors in X associated to the cross- distances in D: D[k] = np.abs(X[ij[k, 0]] - Y[ij[k, 1]]). """ X = check_array(X) n_samples, n_features = X.shape n_nonzero_cross_diff = n_samples * (n_samples - 1) // 2 ij = np.zeros((n_nonzero_cross_diff, 2), dtype=np.int) D = np.zeros((n_nonzero_cross_diff, n_features)) ll_1 = 0 for k in range(n_samples - 1): ll_0 = ll_1 ll_1 = ll_0 + n_samples - k - 1 ij[ll_0:ll_1, 0] = k ij[ll_0:ll_1, 1] = np.arange(k + 1, n_samples) D[ll_0:ll_1] = X[k] - X[(k + 1):n_samples] return D, ij.astype(np.int) class StationaryCorrelation(with_metaclass(ABCMeta, object)): """ Base-class for stationary correlation models for Gaussian Processes. Stationary correlation models dependent only on the relative distance and not on the absolute positions of the respective datapoints. We can thus work internally solely on these distances. """ def __init__(self): pass def fit(self, X, nugget=10. * MACHINE_EPSILON): """ Fits the correlation model for training data X Parameters ---------- X : array_like, shape=(n_samples, n_features) An array of training datapoints at which observations were made, i.e., where the outputs y are known nugget : double or ndarray, optional The Gaussian Process nugget parameter The nugget is added to the diagonal of the assumed training covariance; in this way it acts as a Tikhonov regularization in the problem. In the special case of the squared exponential correlation function, the nugget mathematically represents the variance of the input values. Default assumes a nugget close to machine precision for the sake of robustness (nugget = 10. * MACHINE_EPSILON). """ self.X = X self.nugget = nugget self.n_samples = X.shape[0] # Calculate array with shape (n_eval, n_features) giving the # componentwise distances between locations x and x' at which the # correlation model should be evaluated. self.D, self.ij = l1_cross_differences(self.X) if (np.min(np.sum(self.D, axis=1)) == 0. and not isinstance(self, PureNugget)): raise Exception("Multiple input features cannot have the same" " value.") def __call__(self, theta, X=None): """ Compute correlation for given correlation parameter(s) theta. Parameters ---------- theta : array_like An array with giving the autocorrelation parameter(s). Dimensionality depends on the specific correlation model; often shape (1,) corresponds to an isotropic correlation model and shape (n_features,) to a anisotropic one. X : array_like, shape(n_eval, n_features) An array containing the n_eval query points whose correlation with the training datapoints shall be computed. If None, autocorrelation of the training datapoints is computed instead. Returns ------- r : array_like, shape=(n_eval, n_samples) if X != None (n_samples, n_samples) if X == None An array containing the values of the correlation model. """ theta = np.asarray(theta, dtype=np.float) if X is not None: # Get pairwise componentwise L1-differences to the input training # set d = X[:, np.newaxis, :] - self.X[np.newaxis, :, :] d = d.reshape((-1, X.shape[1])) else: # No external datapoints given; auto-correlation of training set # is used instead d = self.D if d.ndim > 1: n_features = d.shape[1] else: n_features = 1 # Compute the correlation for the respective correlation model (handled # by subclass) r = self._compute_corr(theta, d, n_features) if X is not None: # Convert to 2d matrix return r.reshape(-1, self.n_samples) else: # Auto-correlation computed only for upper triangular part of # matrix. Fill diagonal with 1+nugget and the lower triangular # by exploiting symmetry of matrix R = np.eye(self.n_samples) * (1. + self.nugget) R[self.ij[:, 0], self.ij[:, 1]] = r R[self.ij[:, 1], self.ij[:, 0]] = r return R def log_prior(self, theta): """ Returns the (log) prior probability of parameters theta. The prior is assumed to be uniform over the parameter space. NOTE: The returned quantity is an improper prior as its integral over the parameter space is not equal to 1. Parameters ---------- theta : array_like, shape=(1,) or (n_features,) An array with shape 1 (isotropic) or n_features (anisotropic) giving the autocorrelation parameter(s). Returns ------- log_p : float The (log) prior probability of parameters theta. An improper probability. """ return 0 @abstractmethod def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) or (n_features,) An array with shape 1 (isotropic) or n_features (anisotropic) giving the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ class AbsoluteExponential(StationaryCorrelation): """ Absolute exponential autocorrelation model. Absolute exponential autocorrelation model (Ornstein-Uhlenbeck stochastic process):: n theta, d --> r(theta, d) = exp( sum - theta_i * d_i ) i = 1 """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) or (n_features,) An array with shape 1 (isotropic) or n_features (anisotropic) giving the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) d = np.abs(d) if theta.size == 1: return np.exp(- theta[0] * np.sum(d, axis=1)) elif theta.size != n_features: raise ValueError("Length of theta must be 1 or %s" % n_features) else: return np.exp(- np.sum(theta.reshape(1, n_features) * d, axis=1)) class SquaredExponential(StationaryCorrelation): """ Squared exponential correlation model. Squared exponential correlation model (Radial Basis Function). (Infinitely differentiable stochastic process, very smooth):: n theta, d --> r(theta, d) = exp( sum - theta_i * (d_i)^2 ) i = 1 """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) [isotropic] (n_features,) [anisotropic] or (k*n_features,) [factor analysis distance] An array encoding the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) return np.exp(-self._quadratic_activation(theta, d, n_features)) def _quadratic_activation(self, theta, d, n_features): """ Utility function for computing quadratic activation. Computes the activation activ=d.T * M * d where M is a covariance matrix of size n*n. The hyperparameters theta specify * an isotropic covariance matrix, i.e., M = theta * I with I being the identity, if theta has shape 1 * an automatic relevance determination model if theta has shape n, in which the characteristic length scales of each dimension are learned separately: M = diag(theta) * a factor analysis distance model if theta has shape k*n for k> 1, in which a low-rank approximation of the full matrix M is learned. This low-rank approximation approximates the covariance matrix as low-rank matrix plus a diagonal matrix: M = Lambda * Lambda.T + diag(l), where Lambda is a n*(k-1) matrix and l specifies the diagonal matrix. Parameters ---------- theta : array_like, shape=(1,) [isotropic] (n_features,) [anisotropic] or (k*n_features,) [factor analysis distance] An array encoding the autocorrelation parameter(s). In the case of the factor analysis distance, M is approximated by M = Lambda * Lambda.T + diag(l), where l is encoded in the last n entries of theta and Lambda is encoded row-wise in the first entries of theta. Note that Lambda may contain negative entries while theta is strictly positive; because of this, the entries of Lambda are set to the logarithm with basis 10 of the corresponding entries in theta. array_like, shape=(n_eval, n_features) An array giving the componentwise differences of x and x' at which the quadratic activation should be evaluated. Returns ------- a : array_like, shape=(n_eval, ) An array with the activation values for the respective componentwise differences d. """ if theta.size == 1: # case where M is isotropic: M = diag(theta[0]) return theta[0] * np.sum(d ** 2, axis=1) elif theta.size == n_features: # anisotropic but diagonal case (ARD) return np.sum(theta.reshape(1, n_features) * d ** 2, axis=1) elif theta.size % n_features == 0: # Factor analysis case: M = lambda*lambda.T + diag(l) theta = theta.reshape((1, theta.size)) M = np.diag(theta[0, :n_features]) # the diagonal matrix part l # The low-rank matrix contribution which allows accounting for # correlations in the feature dimensions # NOTE: these components of theta are passed through a log-function # to allow negative values in Lambda Lambda = np.log10(theta[0, n_features:].reshape((n_features, -1))) M += Lambda.dot(Lambda.T) return np.sum(d.dot(M) * d, -1) else: raise ValueError("Length of theta must be 1 or a multiple of %s." % n_features) class Matern_1_5(SquaredExponential): """ Matern correlation model for nu=1.5. Sample paths are once differentiable. Given by:: r(theta, dx) = (1 + np.sqrt(3*activ))*exp(-np.sqrt(3*activ)) where activ=dx.T * M * dx and M is a covariance matrix of size n*n. See Rasmussen and Williams 2006, pp84 for details regarding the different variants of the Matern kernel. """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) [isotropic] (n_features,) [anisotropic] or (k*n_features,) [factor analysis distance] An array encoding the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) activ = self._quadratic_activation(theta, d, n_features) tmp = np.sqrt(3 * activ) # temporary variable for preventing # recomputation return (1 + tmp) * np.exp(-tmp) class Matern_2_5(SquaredExponential): """ Matern correlation model for nu=2.5. Sample paths are twice differentiable. Given by:: r(theta, dx) = (1 + np.sqrt(5*activ) + 5/3*activ)*exp(-np.sqrt(5*activ)) where activ=dx.T * M * dx and M is a covariance matrix of size n*n. See Rasmussen and Williams 2006, pp84 for details regarding the different variants of the Matern kernel. """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) [isotropic] (n_features,) [anisotropic] or (k*n_features,) [factor analysis distance] An array encoding the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) activ = self._quadratic_activation(theta, d, n_features) tmp = np.sqrt(5 * activ) # temporary variable for preventing # recomputation return (1 + tmp + 5.0 / 3.0 * activ) * np.exp(-tmp) class GeneralizedExponential(StationaryCorrelation): """ Generalized exponential correlation model. Generalized exponential correlation model. (Useful when one does not know the smoothness of the function to be predicted.):: n theta, d --> r(theta, d) = exp( sum - theta_i * |d_i|^p ) i = 1 """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1+1,) or (n_features+1,) An array with shape 1+1 (isotropic) or n_features+1 (anisotropic) giving the autocorrelation parameter(s) (theta, p). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) lth = theta.size if n_features > 1 and lth == 2: theta = np.hstack([np.repeat(theta[0], n_features), theta[1]]) elif lth != n_features + 1: raise Exception("Length of theta must be 2 or %s" % (n_features + 1)) else: theta = theta.reshape(1, lth) td = theta[:, 0:-1].reshape(1, n_features) \ * np.abs(d) ** theta[:, -1] return np.exp(- np.sum(td, 1)) class PureNugget(StationaryCorrelation): """ Spatial independence correlation model (pure nugget). Useful when one wants to solve an ordinary least squares problem!:: n theta, d --> r(theta, dx) = 1 if sum |d_i| == 0 i = 1 0 otherwise """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like None. d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like An array with shape (n_eval, ) with the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) n_eval = d.shape[0] r = np.zeros(n_eval) r[np.all(d == 0., axis=1)] = 1. return r class Cubic(StationaryCorrelation): """ Cubic correlation model. Cubic correlation model:: theta, d --> r(theta, d) = n prod max(0, 1 - 3(theta_j*d_ij)^2 + 2(theta_j*d_ij)^3) , i = 1,...,m j = 1 """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) or (n_features,) An array with shape 1 (isotropic) or n_features (anisotropic) giving the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) lth = theta.size if lth == 1: td = np.abs(d) * theta elif lth != n_features: raise Exception("Length of theta must be 1 or " + str(n_features)) else: td = np.abs(d) * theta.reshape(1, n_features) td[td > 1.] = 1. ss = 1. - td ** 2. * (3. - 2. * td) return np.prod(ss, 1) class Linear(StationaryCorrelation): """ Linear correlation model. Linear correlation model:: theta, d --> r(theta, d) = n prod max(0, 1 - theta_j*d_ij) , i = 1,...,m j = 1 """ def _compute_corr(self, theta, d, n_features): """ Correlation for given pairwise, component-wise L1-differences. Parameters ---------- theta : array_like, shape=(1,) or (n_features,) An array with shape 1 (isotropic) or n_features (anisotropic) giving the autocorrelation parameter(s). d : array_like, shape=(n_eval, n_features) An array with the pairwise, component-wise L1-differences of x and x' at which the correlation model should be evaluated. Returns ------- r : array_like, shape=(n_eval, ) An array containing the values of the autocorrelation model. """ d = np.asarray(d, dtype=np.float) lth = theta.size if lth == 1: td = np.abs(d) * theta elif lth != n_features: raise Exception("Length of theta must be 1 or %s" % n_features) else: td = np.abs(d) * theta.reshape(1, n_features) td[td > 1.] = 1. ss = 1. - td return np.prod(ss, 1)
jmetzen/skgp
skgp/correlation_models/stationary.py
Python
bsd-3-clause
21,469
[ "Gaussian" ]
0a3c490339b97cdfd030e23b4712f7a631c718713df2536ba33453e8603f803e
############################################################################## # Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class PyNetcdf4(PythonPackage): """Python interface to the netCDF Library.""" homepage = "https://github.com/Unidata/netcdf4-python" url = "https://pypi.io/packages/source/n/netCDF4/netCDF4-1.2.7.tar.gz" version('1.2.7', '77b357d78f9658dd973dee901f6d86f8') version('1.2.3.1', '24fc0101c7c441709c230e76af611d53') depends_on('py-setuptools', type='build') depends_on('py-cython@0.19:', type='build') depends_on('py-numpy@1.7:', type=('build', 'run')) depends_on('netcdf') depends_on('hdf5@1.8.0:')
skosukhin/spack
var/spack/repos/builtin/packages/py-netcdf4/package.py
Python
lgpl-2.1
1,807
[ "NetCDF" ]
6ccbe3b8324d5977cf80fbbbc29b05765efcb5d03a3f3ebe066524c6ecb1bdcf
############################### # This file is part of PyLaDa. # # Copyright (C) 2013 National Renewable Energy Lab # # PyLaDa is a high throughput computational platform for Physics. It aims to make it easier to submit # large numbers of jobs on supercomputers. It provides a python interface to physical input, such as # crystal structures, as well as to a number of DFT (VASP, CRYSTAL) and atomic potential programs. It # is able to organise and launch computational jobs on PBS and SLURM. # # PyLaDa is free software: you can redistribute it and/or modify it under the terms of the GNU General # Public License as published by the Free Software Foundation, either version 3 of the License, or (at # your option) any later version. # # PyLaDa is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even # the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along with PyLaDa. If not, see # <http://www.gnu.org/licenses/>. ############################### """ Mixin classes for extraction objects. """ __docformat__ = 'restructuredtext en' from ...tools.extract import search_factory OutcarSearchMixin = search_factory('OutcarSearchMixin', 'OUTCAR', __name__) class IOMixin(OutcarSearchMixin): """ A mixin base clase which controls file IO. Defines special property with file-like behaviors. Makes it easier to change the behavior of the extraction class. """ def __init__(self, directory=None, OUTCAR=None, FUNCCAR=None, CONTCAR=None): """ Initializes the extraction class. :Parameters: directory : str or None path to the directory where the VASP output is located. If none, will use current working directory. Can also be the path to the OUTCAR file itself. OUTCAR : str or None If given, this name will be used, rather than files.OUTCAR. CONTCAR : str or None If given, this name will be used, rather than files.CONTCAR. """ from .. import files object.__init__(self) self.OUTCAR = OUTCAR if OUTCAR is not None else files.OUTCAR """ Filename of the OUTCAR file from VASP. """ self.CONTCAR = CONTCAR if CONTCAR is not None else files.CONTCAR """ Filename of the CONTCAR file from VASP. """ OutcarSearchMixin.__init__(self) def __contcar__(self): """ Returns path to FUNCCAR file. :raise IOError: if the FUNCCAR file does not exist. """ from os.path import exists, join path = join(self.directory, self.CONTCAR) if not exists(path): raise IOError("Path {0} does not exist.\n".format(path)) return open(path, 'r') @property def is_running(self): """ True if program is running on this functional. A file '.pylada_is_running' is created in the output folder when it is set-up to run CRYSTAL_. The same file is removed when CRYSTAL_ returns (more specifically, when the :py:class:`pylada.process.ProgramProcess` is polled). Hence, this file serves as a marker of those jobs which are currently running. """ from os.path import join, exists is_run = exists(join(self.directory, '.pylada_is_running')) return is_run
pylada/pylada-light
src/pylada/vasp/extract/mixin.py
Python
gpl-3.0
3,547
[ "CRYSTAL", "VASP" ]
ea02425fddce7d2eb97ca3e8ed0df1f9198a3abc53ca3d78e38d7bf5758cc4a3
# -*- coding: utf-8 -*- #******************************************************************* # * File: merge.py # * Description: # * Author: HarshaRani # * E-mail: hrani@ncbs.res.in # ********************************************************************/ # ********************************************************************** #** This program is part of 'MOOSE', the #** Messaging Object Oriented Simulation Environment, #** also known as GENESIS 3 base code. #** copyright (C) 2003-2017 Upinder S. Bhalla. and NCBS #Created : Friday Dec 16 23:19:00 2016(+0530) #Version #Last-Updated: Thursday Jan 12 17:30:33 2017(+0530) # By: Harsha #**********************************************************************/ # This program is used to merge models # -- Model B is merged to modelA #Rules are #--In this models are mergered at group level (if exists) import sys import os #from . import _moose as moose import moose import mtypes from moose.chemUtil.chemConnectUtil import * from moose.chemUtil.graphUtils import * def mergeChemModel(A,B): """ Merges two model or the path """ modelA,loadedA = loadModels(A) modelB,loadedB = loadModels(B) if not loadedA or not loadedB: if not loadedA: modelB = moose.Shell('/') if not loadedB: modelA = moose.Shell('/') else: directory, bfname = os.path.split(B) global grpNotcopiedyet,poolListina poolListina = {} grpNotcopiedyet = [] dictComptA = dict( [ (i.name,i) for i in moose.wildcardFind(modelA+'/##[ISA=ChemCompt]') ] ) dictComptB = dict( [ (i.name,i) for i in moose.wildcardFind(modelB+'/##[ISA=ChemCompt]') ] ) poolNotcopiedyet = [] for key in list(dictComptB.keys()): if key not in dictComptA: # if compartmentname from modelB does not exist in modelA, then copy copy = moose.copy(dictComptB[key],moose.element(modelA)) else: #if compartmentname from modelB exist in modelA, #volume is not same, then change volume of ModelB same as ModelA if abs(dictComptA[key].volume - dictComptB[key].volume): #hack for now while (abs(dictComptA[key].volume - dictComptB[key].volume) != 0.0): dictComptB[key].volume = float(dictComptA[key].volume) dictComptA = dict( [ (i.name,i) for i in moose.wildcardFind(modelA+'/##[ISA=ChemCompt]') ] ) #Mergering pool poolMerge(dictComptA[key],dictComptB[key],poolNotcopiedyet) if grpNotcopiedyet: # objA = moose.element(comptA).parent.name # if not moose.exists(objA+'/'+comptB.name+'/'+bpath.name): # print bpath # moose.copy(bpath,moose.element(objA+'/'+comptB.name)) pass comptAdict = comptList(modelA) poolListina = {} poolListina = updatePoolList(comptAdict) funcNotallowed = [] R_Duplicated, R_Notcopiedyet,R_Daggling = [], [], [] E_Duplicated, E_Notcopiedyet,E_Daggling = [], [], [] for key in list(dictComptB.keys()): funcNotallowed = [] funcNotallowed = functionMerge(dictComptA,dictComptB,key) poolListina = updatePoolList(dictComptA) R_Duplicated,R_Notcopiedyet,R_Daggling = reacMerge(dictComptA,dictComptB,key,poolListina) poolListina = updatePoolList(dictComptA) E_Duplicated,E_Notcopiedyet,E_Daggling = enzymeMerge(dictComptA,dictComptB,key,poolListina) print("\n Model is merged to %s" %modelA) if funcNotallowed: print( "\nPool already connected to a function, this function is not to connect to same pool, since no two function are allowed to connect to same pool:") for fl in list(funcNotallowed): print("\t [Pool]: %s [Function]: %s \n" %(str(fl.parent.name), str(fl.path))) if R_Duplicated or E_Duplicated: print ("Reaction / Enzyme are Duplicate" "\n 1. The once whoes substrate / product names are different for a give reaction name " "\n 2. its compartment to which it belongs to may be is different" "\n Models have to decide to keep or delete these reaction/enzyme") if E_Duplicated: print("Reaction: ") for rd in list(R_Duplicated): print ("%s " %str(rd.name)) if E_Duplicated: print ("Enzyme:") for ed in list(E_Duplicated): print ("%s " %str(ed.name)) if R_Notcopiedyet or E_Notcopiedyet: print ("\nThese Reaction/Enzyme in model are not dagging but while copying the associated substrate or product is missing") if R_Notcopiedyet: print("Reaction: ") for rd in list(R_Notcopiedyet): print ("%s " %str(rd.name)) if E_Notcopiedyet: print ("Enzyme:") for ed in list(E_Notcopiedyet): print ("%s " %str(ed.name)) if R_Daggling or E_Daggling: print ("\n Daggling reaction/enzyme are not not allowed in moose, these are not merged") if R_Daggling: print("Reaction: ") for rd in list(R_Daggling): print ("%s " %str(rd.name)) if E_Daggling: print ("Enzyme:") for ed in list(E_Daggling): print ("%s " %str(ed.name)) def functionMerge(comptA,comptB,key): funcNotallowed = [] comptApath = moose.element(comptA[key]).path comptBpath = moose.element(comptB[key]).path funcListina = moose.wildcardFind(comptApath+'/##[ISA=PoolBase]') funcListinb = moose.wildcardFind(comptBpath+'/##[ISA=Function]') objA = moose.element(comptApath).parent.name objB = moose.element(comptBpath).parent.name #For function mergering pool name is taken as reference funcNotcopiedyet = [] for fb in funcListinb: if fb.parent.className in ['ZombiePool','Pool','ZombieBufPool','BufPool']: objA = moose.element(comptApath).parent.name fbpath = fb.path #funcpath = fbpath[fbpath.find(findCompartment(fb).name)-1:len(fbpath)] funcparentB = fb.parent.path funcpath = fbpath.replace(objB,objA) funcparentA = funcparentB.replace(objB,objA) tt = moose.element(funcparentA).neighbors['setN'] if tt: funcNotallowed.append(fb) else: if len(moose.element(fb.path+'/x').neighbors["input"]): #inputB = moose.element(fb.path+'/x').neighbors["input"] inputB = subprdList(moose.element(fb.path+'/x'),"input") inputB_expr = fb.expr if moose.exists(funcpath): #inputA = moose.element(objA+funcpath+'/x').neighbors["input"] inputA = subprdList(moose.element(funcpath+'/x'),"input") inputA_expr = moose.element(funcpath).expr hassameExpr = False if inputA_expr == inputB_expr: hassameExpr = True hassameLen,hassameS,hassameVols = same_len_name_vol(inputA,inputB) if not all((hassameLen,hassameS,hassameVols,hassameExpr)): fb.name = fb.name+'_duplicatedF' createFunction(fb,inputB,objB,objA) else: #function doesnot exist then copy if len(inputB): volinput = [] for inb in inputB: volinput.append(findCompartment(moose.element(inb)).volume) if len(set(volinput)) == 1: # If all the input connected belongs to one compartment then copy createFunction(fb,inputB,objB,objA) else: # moose doesn't allow function input to come from different compartment funcNotallowed.append(fb) return funcNotallowed def createFunction(fb,inputB,objB,objA): fapath1 = fb.path.replace(objB,objA) fapath = fapath1.replace('[0]','') if not moose.exists(fapath): # if fb.parent.className in ['CubeMesh','CyclMesh']: # des = moose.Function('/'+objA+'/'+fb.parent.name+'/'+fb.name) # elif fb.parent.className in ['Pool','ZombiePool','BufPool','ZombieBufPool']: # for akey in list(poolListina[findCompartment(fb).name]): # if fb.parent.name == akey.name: # des = moose.Function(akey.path+'/'+fb.name) des = moose.Function(fapath) moose.connect(des, 'valueOut', moose.element(fapath).parent,'setN' ) for src in inputB: pool = ((src.path).replace(objB,objA)).replace('[0]','') numVariables = des.numVars expr = "" expr = (des.expr+'+'+'x'+str(numVariables)) expr = expr.lstrip("0 +") expr = expr.replace(" ","") des.expr = expr moose.connect( pool, 'nOut', des.x[numVariables], 'input' ) #if fb.expr != des.expr: # print "Function ",des, " which is duplicated from modelB, expression is different, this is tricky in moose to know what those constants are connected to " # print "ModelB ", fb, fb.expr, "\nModelA ",des, des.expr def comptList(modelpath): comptdict = {} for ca in moose.wildcardFind(modelpath+'/##[ISA=ChemCompt]'): comptdict[ca.name] = ca return comptdict def loadModels(filename): """ load models into moose if file, if moosepath itself it passes back the path and delete solver if exist """ modelpath = '/' loaded = False if os.path.isfile(filename) : modelpath = filename[filename.rfind('/'): filename.rfind('.')] ext = os.path.splitext(filename)[1] filename = filename.strip() modeltype = mtypes.getType(filename) subtype = mtypes.getSubtype(filename, modeltype) if subtype == 'kkit' or modeltype == "cspace": moose.loadModel(filename,modelpath) loaded = True elif subtype == 'sbml': #moose.ReadSBML() pass else: print("This file is not supported for mergering") modelpath = moose.Shell('/') elif moose.exists(filename): modelpath = filename loaded = True ## default is 'ee' solver while loading the model using moose.loadModel, ## yet deleteSolver is called just to be assured if loaded: deleteSolver(modelpath) return modelpath,loaded def deleteSolver(modelRoot): compts = moose.wildcardFind(modelRoot+'/##[ISA=ChemCompt]') for compt in compts: if moose.exists(compt.path+'/stoich'): st = moose.element(compt.path+'/stoich') st_ksolve = st.ksolve moose.delete(st) if moose.exists((st_ksolve).path): moose.delete(st_ksolve) def poolMerge(comptA,comptB,poolNotcopiedyet): aCmptGrp = moose.wildcardFind(comptA.path+'/#[TYPE=Neutral]') aCmptGrp = aCmptGrp +(moose.element(comptA.path),) bCmptGrp = moose.wildcardFind(comptB.path+'/#[TYPE=Neutral]') bCmptGrp = bCmptGrp +(moose.element(comptB.path),) objA = moose.element(comptA.path).parent.name objB = moose.element(comptB.path).parent.name for bpath in bCmptGrp: grp_cmpt = ((bpath.path).replace(objB,objA)).replace('[0]','') if moose.exists(grp_cmpt) : if moose.element(grp_cmpt).className != bpath.className: grp_cmpt = grp_cmpt+'_grp' bpath.name = bpath.name+"_grp" l = moose.Neutral(grp_cmpt) else: moose.Neutral(grp_cmpt) apath = moose.element(bpath.path.replace(objB,objA)) bpoollist = moose.wildcardFind(bpath.path+'/#[ISA=PoolBase]') apoollist = moose.wildcardFind(apath.path+'/#[ISA=PoolBase]') for bpool in bpoollist: if bpool.name not in [apool.name for apool in apoollist]: copied = copy_deleteUnlyingPoolObj(bpool,apath) if copied == False: #hold it for later, this pool may be under enzyme, as cplx poolNotcopiedyet.append(bpool) def copy_deleteUnlyingPoolObj(pool,path): # check if this pool is under compartement or under enzyme?(which is enzyme_cplx) # if enzyme_cplx then don't copy untill this perticular enzyme is copied # case: This enzyme_cplx might exist in modelA if enzyme exist # which will automatically copie's the pool copied = False if pool.parent.className not in ["Enz","ZombieEnz"]: poolcopied = moose.copy(pool,path) copied = True # deleting function and enzyme which gets copied if exist under pool # This is done to ensure daggling function / enzyme not copied. funclist = [] for types in ['setConc','setN','increment']: funclist.extend(moose.element(poolcopied).neighbors[types]) for fl in funclist: moose.delete(fl) enzlist = moose.element(poolcopied).neighbors['reac'] for el in list(set(enzlist)): moose.delete(el.path) return copied def updatePoolList(comptAdict): for key,value in list(comptAdict.items()): plist = moose.wildcardFind(value.path+'/##[ISA=PoolBase]') poolListina[key] = plist return poolListina def enzymeMerge(comptA,comptB,key,poolListina): war_msg = "" RE_Duplicated, RE_Notcopiedyet, RE_Daggling = [], [], [] comptApath = moose.element(comptA[key]).path comptBpath = moose.element(comptB[key]).path objA = moose.element(comptApath).parent.name objB = moose.element(comptBpath).parent.name enzyListina = moose.wildcardFind(comptApath+'/##[ISA=EnzBase]') enzyListinb = moose.wildcardFind(comptBpath+'/##[ISA=EnzBase]') for eb in enzyListinb: eBsubname, eBprdname = [],[] eBsubname = subprdList(eb,"sub") eBprdname = subprdList(eb,"prd") allexists, allexistp = False, False allclean = False poolinAlist = poolListina[findCompartment(eb).name] for pA in poolinAlist: if eb.parent.name == pA.name: eapath = eb.parent.path.replace(objB,objA) if not moose.exists(eapath+'/'+eb.name): #This will take care # -- If same enzparent name but different enzyme name # -- or different parent/enzyme name if eBsubname and eBprdname: allexists = checkexist(eBsubname,objB,objA) allexistp = checkexist(eBprdname,objB,objA) if allexists and allexistp: enzPool = moose.element(pA.path) eapath = eb.parent.path.replace(objB,objA) enz = moose.element(moose.copy(eb,moose.element(eapath))) enzPool = enz.parent if eb.className in ["ZombieEnz","Enz"]: moose.connect(moose.element(enz),"enz",enzPool,"reac") if eb.className in ["ZombieMMenz","MMenz"]: moose.connect(enzPool,"nOut",enz,"enzDest") connectObj(enz,eBsubname,"sub",comptA,war_msg) connectObj(enz,eBprdname,"prd",comptA,war_msg) allclean = True else: # didn't find sub or prd for this Enzyme #print ("didn't find sub or prd for this reaction" ) RE_Notcopiedyet.append(eb) else: # -- it is dagging reaction RE_Daggling.append(eb) #print ("This reaction \""+eb.path+"\" has no substrate/product daggling reaction are not copied") #war_msg = war_msg+"\nThis reaction \""+eb.path+"\" has no substrate/product daggling reaction are not copied" else: #Same Enzyme name # -- Same substrate and product including same volume then don't copy # -- different substrate/product or if sub/prd's volume is different then DUPLICATE the Enzyme allclean = False #ea = moose.element('/'+obj+'/'+enzcompartment.name+'/'+enzparent.name+'/'+eb.name) #ea = moose.element(pA.path+'/'+eb.name) ea = moose.element(eb.path.replace(objB,objA)) eAsubname = subprdList(ea,"sub") eBsubname = subprdList(eb,"sub") hasSamenoofsublen,hasSameS,hasSamevols = same_len_name_vol(eAsubname,eBsubname) eAprdname = subprdList(ea,"prd") eBprdname = subprdList(eb,"prd") hasSamenoofprdlen,hasSameP,hasSamevolp = same_len_name_vol(eAprdname,eBprdname) if not all((hasSamenoofsublen,hasSameS,hasSamevols,hasSamenoofprdlen,hasSameP,hasSamevolp)): # May be different substrate or product or volume of Sub/prd may be different, # Duplicating the enzyme if eBsubname and eBprdname: allexists,allexistp = False,False allexists = checkexist(eBsubname,objB,objA) allexistp = checkexist(eBprdname,objB,objA) if allexists and allexistp: eb.name = eb.name+"_duplicated" if eb.className in ["ZombieEnz","Enz"]: eapath = eb.parent.path.replace(objB,objA) enz = moose.copy(eb,moose.element(eapath)) moose.connect(enz, 'enz', eapath, 'reac' ) if eb.className in ["ZombieMMenz","MMenz"]: eapath = eb.parent.path.replace(objB,objA) enz = moose.copy(eb.name,moose.element(eapath)) enzinfo = moose.Annotator(enz.path+'/info') moose.connect(moose.element(enz).parent,"nOut",moose.element(enz),"enzDest") #moose.connect(moose.element(enz),"enz",moose.element(enz).parent,"reac") #moose.connect( cplxItem, 'reac', enz, 'cplx' ) connectObj(enz,eBsubname,"sub",comptA,war_msg) connectObj(enz,eBprdname,"prd",comptA,war_msg) RE_Duplicated.append(enz) allclean = True else: allclean = False else: allclean = True if not allclean: # didn't find sub or prd for this enzyme # -- it may be connected Enzyme cplx if eBsubname and eBprdname: RE_Notcopiedyet.append(eb) #print ("This Enzyme \""+eb.path+"\" has no substrate/product must be connect to cplx") #war_msg = war_msg+ "\nThis Enzyme \""+rb.path+"\" has no substrate/product must be connect to cplx" else: RE_Daggling.append(eb) #print ("This enzyme \""+eb.path+"\" has no substrate/product daggling reaction are not copied") #war_msg = war_msg+"\nThis reaction \""+eb.path+"\" has no substrate/product daggling reaction are not copied" return RE_Duplicated,RE_Notcopiedyet,RE_Daggling def reacMerge(comptA,comptB,key,poolListina): RE_Duplicated, RE_Notcopiedyet, RE_Daggling = [], [], [] war_msg = "" comptApath = moose.element(comptA[key]).path comptBpath = moose.element(comptB[key]).path objA = moose.element(comptApath).parent.name objB = moose.element(comptBpath).parent.name reacListina = moose.wildcardFind(comptApath+'/##[ISA=ReacBase]') reacListinb = moose.wildcardFind(comptBpath+'/##[ISA=ReacBase]') for rb in reacListinb: rBsubname, rBprdname = [],[] rBsubname = subprdList(rb,"sub") rBprdname = subprdList(rb,"prd") allexists, allexistp = False, False allclean = False if rb.name not in [ra.name for ra in reacListina]: # reaction name not found then copy # And assuming that pools are copied earlier EXPECT POOL CPLX #To be assured the it takes correct compartment name incase reaction sub's #belongs to different compt key = findCompartment(rb).name if rBsubname and rBprdname: allexists = checkexist(rBsubname,objB,objA) allexistp = checkexist(rBprdname,objB,objA) if allexists and allexistp: rapath = rb.parent.path.replace(objB,objA) reac = moose.copy(rb,moose.element(rapath)) connectObj(reac,rBsubname,"sub",comptA,war_msg) connectObj(reac,rBprdname,"prd",comptA,war_msg) allclean = True else: # didn't find sub or prd for this reaction # -- it may be connected Enzyme cplx RE_Notcopiedyet.append(rb) else: # -- it is dagging reaction RE_Daggling.append(rb) #print ("This reaction \""+rb.path+"\" has no substrate/product daggling reaction are not copied") #war_msg = war_msg+"\nThis reaction \""+rb.path+"\" has no substrate/product daggling reaction are not copied" else: #Same reaction name # -- Same substrate and product including same volume then don't copy # -- different substrate/product or if sub/prd's volume is different then DUPLICATE the reaction allclean = False for ra in reacListina: if rb.name == ra.name: rAsubname = subprdList(ra,"sub") rBsubname = subprdList(rb,"sub") hasSamenoofsublen,hasSameS,hasSamevols = same_len_name_vol(rAsubname,rBsubname) rAprdname = subprdList(ra,"prd") rBprdname = subprdList(rb,"prd") hasSamenoofprdlen,hasSameP,hasSamevolp = same_len_name_vol(rAprdname,rBprdname) if not all((hasSamenoofsublen,hasSameS,hasSamevols,hasSamenoofprdlen,hasSameP,hasSamevolp)): # May be different substrate or product or volume of Sub/prd may be different, # Duplicating the reaction if rBsubname and rBprdname: allexists,allexistp = False,False allexists = checkexist(rBsubname,objB,objA) allexistp = checkexist(rBprdname,objB,objA) if allexists and allexistp: rb.name = rb.name+"_duplicated" #reac = moose.Reac(comptA[key].path+'/'+rb.name+"_duplicated") rapath = rb.parent.path.replace(objB,objA) reac = moose.copy(rb,moose.element(rapath)) connectObj(reac,rBsubname,"sub",comptA,war_msg) connectObj(reac,rBprdname,"prd",comptA,war_msg) RE_Duplicated.append(reac) allclean = True else: allclean = False else: allclean = True if not allclean: # didn't find sub or prd for this reaction # -- it may be connected Enzyme cplx if rBsubname and rBprdname: RE_Notcopiedyet.append(rb) #print ("This reaction \""+rb.path+"\" has no substrate/product must be connect to cplx") #war_msg = war_msg+ "\nThis reaction \""+rb.path+"\" has no substrate/product must be connect to cplx" else: RE_Daggling.append(rb) #print ("This reaction \""+rb.path+"\" has no substrate/product daggling reaction are not copied") #war_msg = war_msg+"\nThis reaction \""+rb.path+"\" has no substrate/product daggling reaction are not copied" return RE_Duplicated,RE_Notcopiedyet,RE_Daggling def subprdList(reac,subprd): #print "Reac ",reac rtype = moose.element(reac).neighbors[subprd] rname = [] for rs in rtype: rname.append(moose.element(rs)) return rname def same_len_name_vol(rA,rB): uaS = set(rA) ubS = set(rB) aS = set([uas.name for uas in uaS]) bS = set([ubs.name for ubs in ubS]) hassameLen = False hassameSP = False hassamevol = False hassamevollist = [] if (len(rA) == len(rB) ): hassameLen = True if not (len (aS.union(bS) - aS.intersection(bS))): hassameSP = True if rB and rA: rAdict = dict( [ (i.name,i) for i in (rA) ] ) rBdict = dict( [ (i.name,i) for i in (rB) ] ) for key,bpath in rBdict.items(): apath = rAdict[key] comptA = moose.element(findCompartment(apath)) comptB = moose.element(findCompartment(bpath)) if not abs(comptA.volume -comptB.volume): hassamevollist.append(True) else: hassamevollist.append(False) if len(set(hassamevollist))==1: for x in set(hassamevollist): hassamevol = x return ( hassameLen,hassameSP,hassamevol) def connectObj(reac,spList,spType,comptA,war_msg): #It should not come here unless the sub/prd is connected to enzyme cplx pool allclean = False for rsp in spList: for akey in list(poolListina[findCompartment(rsp).name]): if rsp.name == akey.name: if moose.exists(akey.path): moose.connect(moose.element(reac), spType, moose.element(akey), 'reac', 'OneToOne') allclean = True else: #It should not come here unless the sub/prd is connected to enzyme cplx pool #print ("This pool \""+rsp.name+"\" doesnot exists in this "+comptName+" compartment to connect to this reaction \""+reac.name+"\"") #war_msg = war_msg+ "This pool \""+rsp.name+"\" doesnot exists in this "+comptName+" compartment to connect to this reaction \""+reac.name+"\"" allclean = False return allclean def checkexist(spList,objB,objA): allexistL = [] allexist = False for rsp in spList: found = False rspPath = rsp.path.replace(objB,objA) if moose.exists(rspPath): found = True allexistL.append(found) if len(set(allexistL))==1: for x in set(allexistL): allexist = x return allexist def findCompartment(element): while not mooseIsInstance(element,["CubeMesh","CyclMesh"]): element = element.parent return element def mooseIsInstance(element, classNames): return moose.element(element).__class__.__name__ in classNames if __name__ == "__main__": modelA = '/home/harsha/genesis_files/gfile/acc92.g' modelB = '/home/harsha/genesis_files/gfile/acc50.g' mergered = mergeChemModel(modelA,modelB)
dharmasam9/moose-core
python/moose/merge/merge.py
Python
gpl-3.0
28,641
[ "MOOSE" ]
5a90cbb2de812274a85d94af91c33fdce76fb2f796d48fdf06b770c3f9c6b2ce
# # Copyright (C) 2013,2014,2015,2016 The ESPResSo project # # This file is part of ESPResSo. # # ESPResSo is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # ESPResSo is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # # This is a modified version of the lj liquid script, which simulates # a two component lj liquid. # By switching the lj interaction between the two components # from atractive to purely repulsive, de-mixing can be achieved. # 1. Setup and equilibrate LJ liquid from __future__ import print_function import espressomd import os import numpy as np from espressomd import visualization from threading import Thread n_part = 200 density = 0.4442 skin = 0.4 time_step = 0.01 eq_tstep = 0.01 temperature = 0.728 box_l = np.power(n_part/density, 1.0/3.0) warm_steps = 100 warm_n_time = 2000 min_dist = 0.87 # integration sampling_interval = 10 equilibration_interval = 1000 sampling_iterations = 10000 equilibration_iterations= 10 # Interaction parameters (Lennard Jones) ############################################################# lj_eps = 1.0 lj_sig = 1.0 lj_cut = 2.5*lj_sig lj_cap = 20 # This is the cutoff of the interaction between species 0 and 1. # By setting it to 2**(1./6.) *lj_sig, it can be made purely repulsive lj_cut_mixed =2.5 * lj_sig lj_cut_mixed =2**(1./6.) * lj_sig # System setup ############################################################# system = espressomd.System(box_l=[1.0, 1.0, 1.0]) if not os.path.exists('data') : os.mkdir('data') system.time_step = time_step system.cell_system.skin = skin system.box_l = [box_l, box_l, box_l] # Here, lj interactions need to be setup for both components # as well as for the mixed case of component 0 interacting with # component 1. # component 0 system.non_bonded_inter[0, 0].lennard_jones.set_params( epsilon=lj_eps, sigma=lj_sig, cutoff=lj_cut, shift="auto") # component 1 system.non_bonded_inter[1, 1].lennard_jones.set_params( epsilon=lj_eps, sigma=lj_sig, cutoff=lj_cut, shift="auto") # mixed case system.non_bonded_inter[0, 1].lennard_jones.set_params( epsilon=lj_eps, sigma=lj_sig, cutoff=lj_cut_mixed, shift="auto") system.force_cap = lj_cap print("LJ-parameters:") print(system.non_bonded_inter[0, 0].lennard_jones.get_params()) # Thermostat system.thermostat.set_langevin(kT=temperature, gamma=1.0) # Particle setup ############################################################# volume = box_l * box_l * box_l for i in range(n_part): system.part.add(id=i, pos=np.random.random(3) * system.box_l) # Every 2nd particle should be of component 1 if i%2==1: system.part[i].type=1 ############################################################# # Warmup Integration # ############################################################# print(""" Start warmup integration: At maximum {} times {} steps Stop if minimal distance is larger than {} """.strip().format(warm_n_time, warm_steps, min_dist)) i = 0 act_min_dist = system.analysis.mindist() while i < warm_n_time and act_min_dist < min_dist : system.integrator.run(warm_steps) act_min_dist = system.analysis.mindist() print("run {} at time = {} (LJ cap= {} ) min dist = {}".strip().format(i, system.time, lj_cap, act_min_dist)) i+=1 lj_cap += 1.0 system.force_cap = lj_cap system.force_cap = 0 def loop(): while True: system.integrator.run(100) visualizer.update() visualizer = visualization.mayaviLive(system) #Start simulation in seperate thread t = Thread(target=loop) t.daemon = True t.start() #Start blocking visualizer visualizer.start()
KonradBreitsprecher/espresso
doc/tutorials/01-lennard_jones/scripts/two-component-visualization.py
Python
gpl-3.0
4,208
[ "ESPResSo" ]
c8224aab5f4eb379a8d166f0de41aff6a0de293101f1549c25e8cd648f6160e0
# This code is part of Ansible, but is an independent component. # This particular file snippet, and this file snippet only, is BSD licensed. # Modules you write using this snippet, which is embedded dynamically by Ansible # still belong to the author of the module, and may assign their own license # to the complete work. # # Copyright (c), Michael DeHaan <michael.dehaan@gmail.com>, 2012-2013 # Copyright (c), Toshio Kuratomi <tkuratomi@ansible.com> 2016 # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE # USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # BOOLEANS_TRUE = ['y', 'yes', 'on', '1', 'true', 1, True] BOOLEANS_FALSE = ['n', 'no', 'off', '0', 'false', 0, False] BOOLEANS = BOOLEANS_TRUE + BOOLEANS_FALSE SIZE_RANGES = { 'Y': 1<<80, 'Z': 1<<70, 'E': 1<<60, 'P': 1<<50, 'T': 1<<40, 'G': 1<<30, 'M': 1<<20, 'K': 1<<10, 'B': 1 } # ansible modules can be written in any language. To simplify # development of Python modules, the functions available here can # be used to do many common tasks import locale import os import re import pipes import shlex import subprocess import sys import types import time import select import shutil import stat import tempfile import traceback import grp import pwd import platform import errno import datetime from itertools import repeat, chain try: import syslog HAS_SYSLOG=True except ImportError: HAS_SYSLOG=False try: from systemd import journal has_journal = True except ImportError: has_journal = False HAVE_SELINUX=False try: import selinux HAVE_SELINUX=True except ImportError: pass # Python2 & 3 way to get NoneType NoneType = type(None) try: from collections import Sequence, Mapping except ImportError: # python2.5 Sequence = (list, tuple) Mapping = (dict,) # Note: When getting Sequence from collections, it matches with strings. If # this matters, make sure to check for strings before checking for sequencetype try: from collections.abc import KeysView SEQUENCETYPE = (Sequence, KeysView) except: SEQUENCETYPE = Sequence try: import json # Detect the python-json library which is incompatible # Look for simplejson if that's the case try: if not isinstance(json.loads, types.FunctionType) or not isinstance(json.dumps, types.FunctionType): raise ImportError except AttributeError: raise ImportError except ImportError: try: import simplejson as json except ImportError: print('\n{"msg": "Error: ansible requires the stdlib json or simplejson module, neither was found!", "failed": true}') sys.exit(1) except SyntaxError: print('\n{"msg": "SyntaxError: probably due to installed simplejson being for a different python version", "failed": true}') sys.exit(1) AVAILABLE_HASH_ALGORITHMS = dict() try: import hashlib # python 2.7.9+ and 2.7.0+ for attribute in ('available_algorithms', 'algorithms'): algorithms = getattr(hashlib, attribute, None) if algorithms: break if algorithms is None: # python 2.5+ algorithms = ('md5', 'sha1', 'sha224', 'sha256', 'sha384', 'sha512') for algorithm in algorithms: AVAILABLE_HASH_ALGORITHMS[algorithm] = getattr(hashlib, algorithm) except ImportError: import sha AVAILABLE_HASH_ALGORITHMS = {'sha1': sha.sha} try: import md5 AVAILABLE_HASH_ALGORITHMS['md5'] = md5.md5 except ImportError: pass from ansible.module_utils.pycompat24 import get_exception, literal_eval from ansible.module_utils.six import (PY2, PY3, b, binary_type, integer_types, iteritems, text_type, string_types) from ansible.module_utils.six.moves import map, reduce from ansible.module_utils._text import to_native, to_bytes, to_text PASSWORD_MATCH = re.compile(r'^(?:.+[-_\s])?pass(?:[-_\s]?(?:word|phrase|wrd|wd)?)(?:[-_\s].+)?$', re.I) _NUMBERTYPES = tuple(list(integer_types) + [float]) # Deprecated compat. Only kept in case another module used these names Using # ansible.module_utils.six is preferred NUMBERTYPES = _NUMBERTYPES imap = map try: # Python 2 unicode except NameError: # Python 3 unicode = text_type try: # Python 2.6+ bytes except NameError: # Python 2.4 bytes = binary_type try: # Python 2 basestring except NameError: # Python 3 basestring = string_types _literal_eval = literal_eval # End of deprecated names # Internal global holding passed in params. This is consulted in case # multiple AnsibleModules are created. Otherwise each AnsibleModule would # attempt to read from stdin. Other code should not use this directly as it # is an internal implementation detail _ANSIBLE_ARGS = None FILE_COMMON_ARGUMENTS=dict( src = dict(), mode = dict(type='raw'), owner = dict(), group = dict(), seuser = dict(), serole = dict(), selevel = dict(), setype = dict(), follow = dict(type='bool', default=False), # not taken by the file module, but other modules call file so it must ignore them. content = dict(no_log=True), backup = dict(), force = dict(), remote_src = dict(), # used by assemble regexp = dict(), # used by assemble delimiter = dict(), # used by assemble directory_mode = dict(), # used by copy unsafe_writes = dict(type='bool'), # should be available to any module using atomic_move ) PASSWD_ARG_RE = re.compile(r'^[-]{0,2}pass[-]?(word|wd)?') # Can't use 07777 on Python 3, can't use 0o7777 on Python 2.4 PERM_BITS = int('07777', 8) # file mode permission bits EXEC_PERM_BITS = int('00111', 8) # execute permission bits DEFAULT_PERM = int('0666', 8) # default file permission bits def get_platform(): ''' what's the platform? example: Linux is a platform. ''' return platform.system() def get_distribution(): ''' return the distribution name ''' if platform.system() == 'Linux': try: supported_dists = platform._supported_dists + ('arch','alpine') distribution = platform.linux_distribution(supported_dists=supported_dists)[0].capitalize() if not distribution and os.path.isfile('/etc/system-release'): distribution = platform.linux_distribution(supported_dists=['system'])[0].capitalize() if 'Amazon' in distribution: distribution = 'Amazon' else: distribution = 'OtherLinux' except: # FIXME: MethodMissing, I assume? distribution = platform.dist()[0].capitalize() else: distribution = None return distribution def get_distribution_version(): ''' return the distribution version ''' if platform.system() == 'Linux': try: distribution_version = platform.linux_distribution()[1] if not distribution_version and os.path.isfile('/etc/system-release'): distribution_version = platform.linux_distribution(supported_dists=['system'])[1] except: # FIXME: MethodMissing, I assume? distribution_version = platform.dist()[1] else: distribution_version = None return distribution_version def get_all_subclasses(cls): ''' used by modules like Hardware or Network fact classes to retrieve all subclasses of a given class. __subclasses__ return only direct sub classes. This one go down into the class tree. ''' # Retrieve direct subclasses subclasses = cls.__subclasses__() to_visit = list(subclasses) # Then visit all subclasses while to_visit: for sc in to_visit: # The current class is now visited, so remove it from list to_visit.remove(sc) # Appending all subclasses to visit and keep a reference of available class for ssc in sc.__subclasses__(): subclasses.append(ssc) to_visit.append(ssc) return subclasses def load_platform_subclass(cls, *args, **kwargs): ''' used by modules like User to have different implementations based on detected platform. See User module for an example. ''' this_platform = get_platform() distribution = get_distribution() subclass = None # get the most specific superclass for this platform if distribution is not None: for sc in get_all_subclasses(cls): if sc.distribution is not None and sc.distribution == distribution and sc.platform == this_platform: subclass = sc if subclass is None: for sc in get_all_subclasses(cls): if sc.platform == this_platform and sc.distribution is None: subclass = sc if subclass is None: subclass = cls return super(cls, subclass).__new__(subclass) def json_dict_unicode_to_bytes(d, encoding='utf-8', errors='surrogate_or_strict'): ''' Recursively convert dict keys and values to byte str Specialized for json return because this only handles, lists, tuples, and dict container types (the containers that the json module returns) ''' if isinstance(d, text_type): return to_bytes(d, encoding=encoding, errors=errors) elif isinstance(d, dict): return dict(map(json_dict_unicode_to_bytes, iteritems(d), repeat(encoding), repeat(errors))) elif isinstance(d, list): return list(map(json_dict_unicode_to_bytes, d, repeat(encoding), repeat(errors))) elif isinstance(d, tuple): return tuple(map(json_dict_unicode_to_bytes, d, repeat(encoding), repeat(errors))) else: return d def json_dict_bytes_to_unicode(d, encoding='utf-8', errors='surrogate_or_strict'): ''' Recursively convert dict keys and values to byte str Specialized for json return because this only handles, lists, tuples, and dict container types (the containers that the json module returns) ''' if isinstance(d, binary_type): # Warning, can traceback return to_text(d, encoding=encoding, errors=errors) elif isinstance(d, dict): return dict(map(json_dict_bytes_to_unicode, iteritems(d), repeat(encoding), repeat(errors))) elif isinstance(d, list): return list(map(json_dict_bytes_to_unicode, d, repeat(encoding), repeat(errors))) elif isinstance(d, tuple): return tuple(map(json_dict_bytes_to_unicode, d, repeat(encoding), repeat(errors))) else: return d def return_values(obj): """ Return native stringified values from datastructures. For use with removing sensitive values pre-jsonification.""" if isinstance(obj, (text_type, binary_type)): if obj: yield to_native(obj, errors='surrogate_or_strict') return elif isinstance(obj, SEQUENCETYPE): for element in obj: for subelement in return_values(element): yield subelement elif isinstance(obj, Mapping): for element in obj.items(): for subelement in return_values(element[1]): yield subelement elif isinstance(obj, (bool, NoneType)): # This must come before int because bools are also ints return elif isinstance(obj, NUMBERTYPES): yield to_native(obj, nonstring='simplerepr') else: raise TypeError('Unknown parameter type: %s, %s' % (type(obj), obj)) def remove_values(value, no_log_strings): """ Remove strings in no_log_strings from value. If value is a container type, then remove a lot more""" if isinstance(value, (text_type, binary_type)): # Need native str type native_str_value = value if isinstance(value, text_type): value_is_text = True if PY2: native_str_value = to_bytes(value, encoding='utf-8', errors='surrogate_or_strict') elif isinstance(value, binary_type): value_is_text = False if PY3: native_str_value = to_text(value, encoding='utf-8', errors='surrogate_or_strict') if native_str_value in no_log_strings: return 'VALUE_SPECIFIED_IN_NO_LOG_PARAMETER' for omit_me in no_log_strings: native_str_value = native_str_value.replace(omit_me, '*' * 8) if value_is_text and isinstance(native_str_value, binary_type): value = to_text(native_str_value, encoding='utf-8', errors='surrogate_or_replace') elif not value_is_text and isinstance(native_str_value, text_type): value = to_bytes(native_str_value, encoding='utf-8', errors='surrogate_or_replace') else: value = native_str_value elif isinstance(value, SEQUENCETYPE): return [remove_values(elem, no_log_strings) for elem in value] elif isinstance(value, Mapping): return dict((k, remove_values(v, no_log_strings)) for k, v in value.items()) elif isinstance(value, tuple(chain(NUMBERTYPES, (bool, NoneType)))): stringy_value = to_native(value, encoding='utf-8', errors='surrogate_or_strict') if stringy_value in no_log_strings: return 'VALUE_SPECIFIED_IN_NO_LOG_PARAMETER' for omit_me in no_log_strings: if omit_me in stringy_value: return 'VALUE_SPECIFIED_IN_NO_LOG_PARAMETER' elif isinstance(value, datetime.datetime): value = value.isoformat() else: raise TypeError('Value of unknown type: %s, %s' % (type(value), value)) return value def heuristic_log_sanitize(data, no_log_values=None): ''' Remove strings that look like passwords from log messages ''' # Currently filters: # user:pass@foo/whatever and http://username:pass@wherever/foo # This code has false positives and consumes parts of logs that are # not passwds # begin: start of a passwd containing string # end: end of a passwd containing string # sep: char between user and passwd # prev_begin: where in the overall string to start a search for # a passwd # sep_search_end: where in the string to end a search for the sep data = to_native(data) output = [] begin = len(data) prev_begin = begin sep = 1 while sep: # Find the potential end of a passwd try: end = data.rindex('@', 0, begin) except ValueError: # No passwd in the rest of the data output.insert(0, data[0:begin]) break # Search for the beginning of a passwd sep = None sep_search_end = end while not sep: # URL-style username+password try: begin = data.rindex('://', 0, sep_search_end) except ValueError: # No url style in the data, check for ssh style in the # rest of the string begin = 0 # Search for separator try: sep = data.index(':', begin + 3, end) except ValueError: # No separator; choices: if begin == 0: # Searched the whole string so there's no password # here. Return the remaining data output.insert(0, data[0:begin]) break # Search for a different beginning of the password field. sep_search_end = begin continue if sep: # Password was found; remove it. output.insert(0, data[end:prev_begin]) output.insert(0, '********') output.insert(0, data[begin:sep + 1]) prev_begin = begin output = ''.join(output) if no_log_values: output = remove_values(output, no_log_values) return output def bytes_to_human(size, isbits=False, unit=None): base = 'Bytes' if isbits: base = 'bits' suffix = '' for suffix, limit in sorted(iteritems(SIZE_RANGES), key=lambda item: -item[1]): if (unit is None and size >= limit) or unit is not None and unit.upper() == suffix[0]: break if limit != 1: suffix += base[0] else: suffix = base return '%.2f %s' % (float(size)/ limit, suffix) def human_to_bytes(number, default_unit=None, isbits=False): ''' Convert number in string format into bytes (ex: '2K' => 2048) or using unit argument ex: human_to_bytes('10M') <=> human_to_bytes(10, 'M') ''' m = re.search('^\s*(\d*\.?\d*)\s*([A-Za-z]+)?', str(number), flags=re.IGNORECASE) if m is None: raise ValueError("human_to_bytes() can't interpret following string: %s" % str(number)) try: num = float(m.group(1)) except: raise ValueError("human_to_bytes() can't interpret following number: %s (original input string: %s)" % (m.group(1), number)) unit = m.group(2) if unit is None: unit = default_unit if unit is None: ''' No unit given, returning raw number ''' return int(round(num)) range_key = unit[0].upper() try: limit = SIZE_RANGES[range_key] except: raise ValueError("human_to_bytes() failed to convert %s (unit = %s). The suffix must be one of %s" % (number, unit, ", ".join(SIZE_RANGES.keys()))) # default value unit_class = 'B' unit_class_name = 'byte' # handling bits case if isbits: unit_class = 'b' unit_class_name = 'bit' # check unit value if more than one character (KB, MB) if len(unit) > 1: expect_message = 'expect %s%s or %s' % (range_key, unit_class, range_key) if range_key == 'B': expect_message = 'expect %s or %s' % (unit_class, unit_class_name) if unit_class_name in unit.lower(): pass elif unit[1] != unit_class: raise ValueError("human_to_bytes() failed to convert %s. Value is not a valid string (%s)" % (number, expect_message)) return int(round(num * limit)) def is_executable(path): '''is the given path executable? Limitations: * Does not account for FSACLs. * Most times we really want to know "Can the current user execute this file" This function does not tell us that, only if an execute bit is set. ''' # These are all bitfields so first bitwise-or all the permissions we're # looking for, then bitwise-and with the file's mode to determine if any # execute bits are set. return ((stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) & os.stat(path)[stat.ST_MODE]) def _load_params(): ''' read the modules parameters and store them globally. This function may be needed for certain very dynamic custom modules which want to process the parameters that are being handed the module. Since this is so closely tied to the implementation of modules we cannot guarantee API stability for it (it may change between versions) however we will try not to break it gratuitously. It is certainly more future-proof to call this function and consume its outputs than to implement the logic inside it as a copy in your own code. ''' global _ANSIBLE_ARGS if _ANSIBLE_ARGS is not None: buffer = _ANSIBLE_ARGS else: # debug overrides to read args from file or cmdline # Avoid tracebacks when locale is non-utf8 # We control the args and we pass them as utf8 if len(sys.argv) > 1: if os.path.isfile(sys.argv[1]): fd = open(sys.argv[1], 'rb') buffer = fd.read() fd.close() else: buffer = sys.argv[1] if PY3: buffer = buffer.encode('utf-8', errors='surrogateescape') # default case, read from stdin else: if PY2: buffer = sys.stdin.read() else: buffer = sys.stdin.buffer.read() _ANSIBLE_ARGS = buffer try: params = json.loads(buffer.decode('utf-8')) except ValueError: # This helper used too early for fail_json to work. print('\n{"msg": "Error: Module unable to decode valid JSON on stdin. Unable to figure out what parameters were passed", "failed": true}') sys.exit(1) if PY2: params = json_dict_unicode_to_bytes(params) try: return params['ANSIBLE_MODULE_ARGS'] except KeyError: # This helper does not have access to fail_json so we have to print # json output on our own. print('\n{"msg": "Error: Module unable to locate ANSIBLE_MODULE_ARGS in json data from stdin. Unable to figure out what parameters were passed", "failed": true}') sys.exit(1) def env_fallback(*args, **kwargs): ''' Load value from environment ''' for arg in args: if arg in os.environ: return os.environ[arg] else: raise AnsibleFallbackNotFound def _lenient_lowercase(lst): """Lowercase elements of a list. If an element is not a string, pass it through untouched. """ lowered = [] for value in lst: try: lowered.append(value.lower()) except AttributeError: lowered.append(value) return lowered class AnsibleFallbackNotFound(Exception): pass class AnsibleModule(object): def __init__(self, argument_spec, bypass_checks=False, no_log=False, check_invalid_arguments=True, mutually_exclusive=None, required_together=None, required_one_of=None, add_file_common_args=False, supports_check_mode=False, required_if=None): ''' common code for quickly building an ansible module in Python (although you can write modules in anything that can return JSON) see library/* for examples ''' self._name = os.path.basename(__file__) #initialize name until we can parse from options self.argument_spec = argument_spec self.supports_check_mode = supports_check_mode self.check_mode = False self.no_log = no_log self.cleanup_files = [] self._debug = False self._diff = False self._verbosity = 0 # May be used to set modifications to the environment for any # run_command invocation self.run_command_environ_update = {} self.aliases = {} self._legal_inputs = ['_ansible_check_mode', '_ansible_no_log', '_ansible_debug', '_ansible_diff', '_ansible_verbosity', '_ansible_selinux_special_fs', '_ansible_module_name', '_ansible_version', '_ansible_syslog_facility'] if add_file_common_args: for k, v in FILE_COMMON_ARGUMENTS.items(): if k not in self.argument_spec: self.argument_spec[k] = v self._load_params() self._set_fallbacks() # append to legal_inputs and then possibly check against them try: self.aliases = self._handle_aliases() except Exception: e = get_exception() # Use exceptions here because it isn't safe to call fail_json until no_log is processed print('\n{"failed": true, "msg": "Module alias error: %s"}' % str(e)) sys.exit(1) # Save parameter values that should never be logged self.no_log_values = set() # Use the argspec to determine which args are no_log for arg_name, arg_opts in self.argument_spec.items(): if arg_opts.get('no_log', False): # Find the value for the no_log'd param no_log_object = self.params.get(arg_name, None) if no_log_object: self.no_log_values.update(return_values(no_log_object)) # check the locale as set by the current environment, and reset to # a known valid (LANG=C) if it's an invalid/unavailable locale self._check_locale() self._check_arguments(check_invalid_arguments) # check exclusive early if not bypass_checks: self._check_mutually_exclusive(mutually_exclusive) self._set_defaults(pre=True) self._CHECK_ARGUMENT_TYPES_DISPATCHER = { 'str': self._check_type_str, 'list': self._check_type_list, 'dict': self._check_type_dict, 'bool': self._check_type_bool, 'int': self._check_type_int, 'float': self._check_type_float, 'path': self._check_type_path, 'raw': self._check_type_raw, 'jsonarg': self._check_type_jsonarg, 'json': self._check_type_jsonarg, 'bytes': self._check_type_bytes, 'bits': self._check_type_bits, } if not bypass_checks: self._check_required_arguments() self._check_argument_types() self._check_argument_values() self._check_required_together(required_together) self._check_required_one_of(required_one_of) self._check_required_if(required_if) self._set_defaults(pre=False) if not self.no_log: self._log_invocation() # finally, make sure we're in a sane working dir self._set_cwd() def load_file_common_arguments(self, params): ''' many modules deal with files, this encapsulates common options that the file module accepts such that it is directly available to all modules and they can share code. ''' path = params.get('path', params.get('dest', None)) if path is None: return {} else: path = os.path.expanduser(os.path.expandvars(path)) b_path = to_bytes(path, errors='surrogate_or_strict') # if the path is a symlink, and we're following links, get # the target of the link instead for testing if params.get('follow', False) and os.path.islink(b_path): b_path = os.path.realpath(b_path) path = to_native(b_path) mode = params.get('mode', None) owner = params.get('owner', None) group = params.get('group', None) # selinux related options seuser = params.get('seuser', None) serole = params.get('serole', None) setype = params.get('setype', None) selevel = params.get('selevel', None) secontext = [seuser, serole, setype] if self.selinux_mls_enabled(): secontext.append(selevel) default_secontext = self.selinux_default_context(path) for i in range(len(default_secontext)): if i is not None and secontext[i] == '_default': secontext[i] = default_secontext[i] return dict( path=path, mode=mode, owner=owner, group=group, seuser=seuser, serole=serole, setype=setype, selevel=selevel, secontext=secontext, ) # Detect whether using selinux that is MLS-aware. # While this means you can set the level/range with # selinux.lsetfilecon(), it may or may not mean that you # will get the selevel as part of the context returned # by selinux.lgetfilecon(). def selinux_mls_enabled(self): if not HAVE_SELINUX: return False if selinux.is_selinux_mls_enabled() == 1: return True else: return False def selinux_enabled(self): if not HAVE_SELINUX: seenabled = self.get_bin_path('selinuxenabled') if seenabled is not None: (rc,out,err) = self.run_command(seenabled) if rc == 0: self.fail_json(msg="Aborting, target uses selinux but python bindings (libselinux-python) aren't installed!") return False if selinux.is_selinux_enabled() == 1: return True else: return False # Determine whether we need a placeholder for selevel/mls def selinux_initial_context(self): context = [None, None, None] if self.selinux_mls_enabled(): context.append(None) return context # If selinux fails to find a default, return an array of None def selinux_default_context(self, path, mode=0): context = self.selinux_initial_context() if not HAVE_SELINUX or not self.selinux_enabled(): return context try: ret = selinux.matchpathcon(to_native(path, errors='surrogate_or_strict'), mode) except OSError: return context if ret[0] == -1: return context # Limit split to 4 because the selevel, the last in the list, # may contain ':' characters context = ret[1].split(':', 3) return context def selinux_context(self, path): context = self.selinux_initial_context() if not HAVE_SELINUX or not self.selinux_enabled(): return context try: ret = selinux.lgetfilecon_raw(to_native(path, errors='surrogate_or_strict')) except OSError: e = get_exception() if e.errno == errno.ENOENT: self.fail_json(path=path, msg='path %s does not exist' % path) else: self.fail_json(path=path, msg='failed to retrieve selinux context') if ret[0] == -1: return context # Limit split to 4 because the selevel, the last in the list, # may contain ':' characters context = ret[1].split(':', 3) return context def user_and_group(self, filename): filename = os.path.expanduser(os.path.expandvars(filename)) b_filename = to_bytes(filename, errors='surrogate_or_strict') st = os.lstat(b_filename) uid = st.st_uid gid = st.st_gid return (uid, gid) def find_mount_point(self, path): path = os.path.realpath(os.path.expanduser(os.path.expandvars(path))) while not os.path.ismount(path): path = os.path.dirname(path) return path def is_special_selinux_path(self, path): """ Returns a tuple containing (True, selinux_context) if the given path is on a NFS or other 'special' fs mount point, otherwise the return will be (False, None). """ try: f = open('/proc/mounts', 'r') mount_data = f.readlines() f.close() except: return (False, None) path_mount_point = self.find_mount_point(path) for line in mount_data: (device, mount_point, fstype, options, rest) = line.split(' ', 4) if path_mount_point == mount_point: for fs in self._selinux_special_fs: if fs in fstype: special_context = self.selinux_context(path_mount_point) return (True, special_context) return (False, None) def set_default_selinux_context(self, path, changed): if not HAVE_SELINUX or not self.selinux_enabled(): return changed context = self.selinux_default_context(path) return self.set_context_if_different(path, context, False) def set_context_if_different(self, path, context, changed, diff=None): if not HAVE_SELINUX or not self.selinux_enabled(): return changed cur_context = self.selinux_context(path) new_context = list(cur_context) # Iterate over the current context instead of the # argument context, which may have selevel. (is_special_se, sp_context) = self.is_special_selinux_path(path) if is_special_se: new_context = sp_context else: for i in range(len(cur_context)): if len(context) > i: if context[i] is not None and context[i] != cur_context[i]: new_context[i] = context[i] elif context[i] is None: new_context[i] = cur_context[i] if cur_context != new_context: if diff is not None: if 'before' not in diff: diff['before'] = {} diff['before']['secontext'] = cur_context if 'after' not in diff: diff['after'] = {} diff['after']['secontext'] = new_context try: if self.check_mode: return True rc = selinux.lsetfilecon(to_native(path), str(':'.join(new_context))) except OSError: e = get_exception() self.fail_json(path=path, msg='invalid selinux context: %s' % str(e), new_context=new_context, cur_context=cur_context, input_was=context) if rc != 0: self.fail_json(path=path, msg='set selinux context failed') changed = True return changed def set_owner_if_different(self, path, owner, changed, diff=None): path = os.path.expanduser(os.path.expandvars(path)) b_path = to_bytes(path, errors='surrogate_or_strict') if owner is None: return changed orig_uid, orig_gid = self.user_and_group(path) try: uid = int(owner) except ValueError: try: uid = pwd.getpwnam(owner).pw_uid except KeyError: self.fail_json(path=path, msg='chown failed: failed to look up user %s' % owner) if orig_uid != uid: if diff is not None: if 'before' not in diff: diff['before'] = {} diff['before']['owner'] = orig_uid if 'after' not in diff: diff['after'] = {} diff['after']['owner'] = uid if self.check_mode: return True try: os.lchown(b_path, uid, -1) except OSError: self.fail_json(path=path, msg='chown failed') changed = True return changed def set_group_if_different(self, path, group, changed, diff=None): path = os.path.expanduser(os.path.expandvars(path)) b_path = to_bytes(path, errors='surrogate_or_strict') if group is None: return changed orig_uid, orig_gid = self.user_and_group(b_path) try: gid = int(group) except ValueError: try: gid = grp.getgrnam(group).gr_gid except KeyError: self.fail_json(path=path, msg='chgrp failed: failed to look up group %s' % group) if orig_gid != gid: if diff is not None: if 'before' not in diff: diff['before'] = {} diff['before']['group'] = orig_gid if 'after' not in diff: diff['after'] = {} diff['after']['group'] = gid if self.check_mode: return True try: os.lchown(b_path, -1, gid) except OSError: self.fail_json(path=path, msg='chgrp failed') changed = True return changed def set_mode_if_different(self, path, mode, changed, diff=None): b_path = to_bytes(path, errors='surrogate_or_strict') b_path = os.path.expanduser(os.path.expandvars(b_path)) path_stat = os.lstat(b_path) if mode is None: return changed if not isinstance(mode, int): try: mode = int(mode, 8) except Exception: try: mode = self._symbolic_mode_to_octal(path_stat, mode) except Exception: e = get_exception() self.fail_json(path=path, msg="mode must be in octal or symbolic form", details=str(e)) if mode != stat.S_IMODE(mode): # prevent mode from having extra info orbeing invalid long number self.fail_json(path=path, msg="Invalid mode supplied, only permission info is allowed", details=mode) prev_mode = stat.S_IMODE(path_stat.st_mode) if prev_mode != mode: if diff is not None: if 'before' not in diff: diff['before'] = {} diff['before']['mode'] = '0%03o' % prev_mode if 'after' not in diff: diff['after'] = {} diff['after']['mode'] = '0%03o' % mode if self.check_mode: return True # FIXME: comparison against string above will cause this to be executed # every time try: if hasattr(os, 'lchmod'): os.lchmod(b_path, mode) else: if not os.path.islink(b_path): os.chmod(b_path, mode) else: # Attempt to set the perms of the symlink but be # careful not to change the perms of the underlying # file while trying underlying_stat = os.stat(b_path) os.chmod(b_path, mode) new_underlying_stat = os.stat(b_path) if underlying_stat.st_mode != new_underlying_stat.st_mode: os.chmod(b_path, stat.S_IMODE(underlying_stat.st_mode)) except OSError: e = get_exception() if os.path.islink(b_path) and e.errno == errno.EPERM: # Can't set mode on symbolic links pass elif e.errno in (errno.ENOENT, errno.ELOOP): # Can't set mode on broken symbolic links pass else: raise e except Exception: e = get_exception() self.fail_json(path=path, msg='chmod failed', details=str(e)) path_stat = os.lstat(b_path) new_mode = stat.S_IMODE(path_stat.st_mode) if new_mode != prev_mode: changed = True return changed def _symbolic_mode_to_octal(self, path_stat, symbolic_mode): new_mode = stat.S_IMODE(path_stat.st_mode) mode_re = re.compile(r'^(?P<users>[ugoa]+)(?P<operator>[-+=])(?P<perms>[rwxXst-]*|[ugo])$') for mode in symbolic_mode.split(','): match = mode_re.match(mode) if match: users = match.group('users') operator = match.group('operator') perms = match.group('perms') if users == 'a': users = 'ugo' for user in users: mode_to_apply = self._get_octal_mode_from_symbolic_perms(path_stat, user, perms) new_mode = self._apply_operation_to_mode(user, operator, mode_to_apply, new_mode) else: raise ValueError("bad symbolic permission for mode: %s" % mode) return new_mode def _apply_operation_to_mode(self, user, operator, mode_to_apply, current_mode): if operator == '=': if user == 'u': mask = stat.S_IRWXU | stat.S_ISUID elif user == 'g': mask = stat.S_IRWXG | stat.S_ISGID elif user == 'o': mask = stat.S_IRWXO | stat.S_ISVTX # mask out u, g, or o permissions from current_mode and apply new permissions inverse_mask = mask ^ PERM_BITS new_mode = (current_mode & inverse_mask) | mode_to_apply elif operator == '+': new_mode = current_mode | mode_to_apply elif operator == '-': new_mode = current_mode - (current_mode & mode_to_apply) return new_mode def _get_octal_mode_from_symbolic_perms(self, path_stat, user, perms): prev_mode = stat.S_IMODE(path_stat.st_mode) is_directory = stat.S_ISDIR(path_stat.st_mode) has_x_permissions = (prev_mode & EXEC_PERM_BITS) > 0 apply_X_permission = is_directory or has_x_permissions # Permission bits constants documented at: # http://docs.python.org/2/library/stat.html#stat.S_ISUID if apply_X_permission: X_perms = { 'u': {'X': stat.S_IXUSR}, 'g': {'X': stat.S_IXGRP}, 'o': {'X': stat.S_IXOTH} } else: X_perms = { 'u': {'X': 0}, 'g': {'X': 0}, 'o': {'X': 0} } user_perms_to_modes = { 'u': { 'r': stat.S_IRUSR, 'w': stat.S_IWUSR, 'x': stat.S_IXUSR, 's': stat.S_ISUID, 't': 0, 'u': prev_mode & stat.S_IRWXU, 'g': (prev_mode & stat.S_IRWXG) << 3, 'o': (prev_mode & stat.S_IRWXO) << 6 }, 'g': { 'r': stat.S_IRGRP, 'w': stat.S_IWGRP, 'x': stat.S_IXGRP, 's': stat.S_ISGID, 't': 0, 'u': (prev_mode & stat.S_IRWXU) >> 3, 'g': prev_mode & stat.S_IRWXG, 'o': (prev_mode & stat.S_IRWXO) << 3 }, 'o': { 'r': stat.S_IROTH, 'w': stat.S_IWOTH, 'x': stat.S_IXOTH, 's': 0, 't': stat.S_ISVTX, 'u': (prev_mode & stat.S_IRWXU) >> 6, 'g': (prev_mode & stat.S_IRWXG) >> 3, 'o': prev_mode & stat.S_IRWXO } } # Insert X_perms into user_perms_to_modes for key, value in X_perms.items(): user_perms_to_modes[key].update(value) or_reduce = lambda mode, perm: mode | user_perms_to_modes[user][perm] return reduce(or_reduce, perms, 0) def set_fs_attributes_if_different(self, file_args, changed, diff=None): # set modes owners and context as needed changed = self.set_context_if_different( file_args['path'], file_args['secontext'], changed, diff ) changed = self.set_owner_if_different( file_args['path'], file_args['owner'], changed, diff ) changed = self.set_group_if_different( file_args['path'], file_args['group'], changed, diff ) changed = self.set_mode_if_different( file_args['path'], file_args['mode'], changed, diff ) return changed def set_directory_attributes_if_different(self, file_args, changed, diff=None): return self.set_fs_attributes_if_different(file_args, changed, diff) def set_file_attributes_if_different(self, file_args, changed, diff=None): return self.set_fs_attributes_if_different(file_args, changed, diff) def add_path_info(self, kwargs): ''' for results that are files, supplement the info about the file in the return path with stats about the file path. ''' path = kwargs.get('path', kwargs.get('dest', None)) if path is None: return kwargs b_path = to_bytes(path, errors='surrogate_or_strict') if os.path.exists(b_path): (uid, gid) = self.user_and_group(path) kwargs['uid'] = uid kwargs['gid'] = gid try: user = pwd.getpwuid(uid)[0] except KeyError: user = str(uid) try: group = grp.getgrgid(gid)[0] except KeyError: group = str(gid) kwargs['owner'] = user kwargs['group'] = group st = os.lstat(b_path) kwargs['mode'] = '0%03o' % stat.S_IMODE(st[stat.ST_MODE]) # secontext not yet supported if os.path.islink(b_path): kwargs['state'] = 'link' elif os.path.isdir(b_path): kwargs['state'] = 'directory' elif os.stat(b_path).st_nlink > 1: kwargs['state'] = 'hard' else: kwargs['state'] = 'file' if HAVE_SELINUX and self.selinux_enabled(): kwargs['secontext'] = ':'.join(self.selinux_context(path)) kwargs['size'] = st[stat.ST_SIZE] else: kwargs['state'] = 'absent' return kwargs def _check_locale(self): ''' Uses the locale module to test the currently set locale (per the LANG and LC_CTYPE environment settings) ''' try: # setting the locale to '' uses the default locale # as it would be returned by locale.getdefaultlocale() locale.setlocale(locale.LC_ALL, '') except locale.Error: # fallback to the 'C' locale, which may cause unicode # issues but is preferable to simply failing because # of an unknown locale locale.setlocale(locale.LC_ALL, 'C') os.environ['LANG'] = 'C' os.environ['LC_ALL'] = 'C' os.environ['LC_MESSAGES'] = 'C' except Exception: e = get_exception() self.fail_json(msg="An unknown error was encountered while attempting to validate the locale: %s" % e) def _handle_aliases(self): # this uses exceptions as it happens before we can safely call fail_json aliases_results = {} #alias:canon for (k,v) in self.argument_spec.items(): self._legal_inputs.append(k) aliases = v.get('aliases', None) default = v.get('default', None) required = v.get('required', False) if default is not None and required: # not alias specific but this is a good place to check this raise Exception("internal error: required and default are mutually exclusive for %s" % k) if aliases is None: continue if not isinstance(aliases, SEQUENCETYPE) or isinstance(aliases, (binary_type, text_type)): raise Exception('internal error: aliases must be a list or tuple') for alias in aliases: self._legal_inputs.append(alias) aliases_results[alias] = k if alias in self.params: self.params[k] = self.params[alias] return aliases_results def _check_arguments(self, check_invalid_arguments): self._syslog_facility = 'LOG_USER' for (k,v) in list(self.params.items()): if k == '_ansible_check_mode' and v: self.check_mode = True elif k == '_ansible_no_log': self.no_log = self.boolean(v) elif k == '_ansible_debug': self._debug = self.boolean(v) elif k == '_ansible_diff': self._diff = self.boolean(v) elif k == '_ansible_verbosity': self._verbosity = v elif k == '_ansible_selinux_special_fs': self._selinux_special_fs = v elif k == '_ansible_syslog_facility': self._syslog_facility = v elif k == '_ansible_version': self.ansible_version = v elif k == '_ansible_module_name': self._name = v elif check_invalid_arguments and k not in self._legal_inputs: self.fail_json(msg="unsupported parameter for module: %s" % k) #clean up internal params: if k.startswith('_ansible_'): del self.params[k] if self.check_mode and not self.supports_check_mode: self.exit_json(skipped=True, msg="remote module (%s) does not support check mode" % self._name) def _count_terms(self, check): count = 0 for term in check: if term in self.params: count += 1 return count def _check_mutually_exclusive(self, spec): if spec is None: return for check in spec: count = self._count_terms(check) if count > 1: self.fail_json(msg="parameters are mutually exclusive: %s" % (check,)) def _check_required_one_of(self, spec): if spec is None: return for check in spec: count = self._count_terms(check) if count == 0: self.fail_json(msg="one of the following is required: %s" % ','.join(check)) def _check_required_together(self, spec): if spec is None: return for check in spec: counts = [ self._count_terms([field]) for field in check ] non_zero = [ c for c in counts if c > 0 ] if len(non_zero) > 0: if 0 in counts: self.fail_json(msg="parameters are required together: %s" % (check,)) def _check_required_arguments(self): ''' ensure all required arguments are present ''' missing = [] for (k,v) in self.argument_spec.items(): required = v.get('required', False) if required and k not in self.params: missing.append(k) if len(missing) > 0: self.fail_json(msg="missing required arguments: %s" % ",".join(missing)) def _check_required_if(self, spec): ''' ensure that parameters which conditionally required are present ''' if spec is None: return for (key, val, requirements) in spec: missing = [] if key in self.params and self.params[key] == val: for check in requirements: count = self._count_terms((check,)) if count == 0: missing.append(check) if len(missing) > 0: self.fail_json(msg="%s is %s but the following are missing: %s" % (key, val, ','.join(missing))) def _check_argument_values(self): ''' ensure all arguments have the requested values, and there are no stray arguments ''' for (k,v) in self.argument_spec.items(): choices = v.get('choices',None) if choices is None: continue if isinstance(choices, SEQUENCETYPE) and not isinstance(choices, (binary_type, text_type)): if k in self.params: if self.params[k] not in choices: # PyYaml converts certain strings to bools. If we can unambiguously convert back, do so before checking # the value. If we can't figure this out, module author is responsible. lowered_choices = None if self.params[k] == 'False': lowered_choices = _lenient_lowercase(choices) FALSEY = frozenset(BOOLEANS_FALSE) overlap = FALSEY.intersection(choices) if len(overlap) == 1: # Extract from a set (self.params[k],) = overlap if self.params[k] == 'True': if lowered_choices is None: lowered_choices = _lenient_lowercase(choices) TRUTHY = frozenset(BOOLEANS_TRUE) overlap = TRUTHY.intersection(choices) if len(overlap) == 1: (self.params[k],) = overlap if self.params[k] not in choices: choices_str=",".join([to_native(c) for c in choices]) msg="value of %s must be one of: %s, got: %s" % (k, choices_str, self.params[k]) self.fail_json(msg=msg) else: self.fail_json(msg="internal error: choices for argument %s are not iterable: %s" % (k, choices)) def safe_eval(self, value, locals=None, include_exceptions=False): # do not allow method calls to modules if not isinstance(value, string_types): # already templated to a datavaluestructure, perhaps? if include_exceptions: return (value, None) return value if re.search(r'\w\.\w+\(', value): if include_exceptions: return (value, None) return value # do not allow imports if re.search(r'import \w+', value): if include_exceptions: return (value, None) return value try: result = literal_eval(value) if include_exceptions: return (result, None) else: return result except Exception: e = get_exception() if include_exceptions: return (value, e) return value def _check_type_str(self, value): if isinstance(value, string_types): return value # Note: This could throw a unicode error if value's __str__() method # returns non-ascii. Have to port utils.to_bytes() if that happens return str(value) def _check_type_list(self, value): if isinstance(value, list): return value if isinstance(value, string_types): return value.split(",") elif isinstance(value, int) or isinstance(value, float): return [ str(value) ] raise TypeError('%s cannot be converted to a list' % type(value)) def _check_type_dict(self, value): if isinstance(value, dict): return value if isinstance(value, string_types): if value.startswith("{"): try: return json.loads(value) except: (result, exc) = self.safe_eval(value, dict(), include_exceptions=True) if exc is not None: raise TypeError('unable to evaluate string as dictionary') return result elif '=' in value: fields = [] field_buffer = [] in_quote = False in_escape = False for c in value.strip(): if in_escape: field_buffer.append(c) in_escape = False elif c == '\\': in_escape = True elif not in_quote and c in ('\'', '"'): in_quote = c elif in_quote and in_quote == c: in_quote = False elif not in_quote and c in (',', ' '): field = ''.join(field_buffer) if field: fields.append(field) field_buffer = [] else: field_buffer.append(c) field = ''.join(field_buffer) if field: fields.append(field) return dict(x.split("=", 1) for x in fields) else: raise TypeError("dictionary requested, could not parse JSON or key=value") raise TypeError('%s cannot be converted to a dict' % type(value)) def _check_type_bool(self, value): if isinstance(value, bool): return value if isinstance(value, string_types) or isinstance(value, int): return self.boolean(value) raise TypeError('%s cannot be converted to a bool' % type(value)) def _check_type_int(self, value): if isinstance(value, int): return value if isinstance(value, string_types): return int(value) raise TypeError('%s cannot be converted to an int' % type(value)) def _check_type_float(self, value): if isinstance(value, float): return value if isinstance(value, (binary_type, text_type, int)): return float(value) raise TypeError('%s cannot be converted to a float' % type(value)) def _check_type_path(self, value): value = self._check_type_str(value) return os.path.expanduser(os.path.expandvars(value)) def _check_type_jsonarg(self, value): # Return a jsonified string. Sometimes the controller turns a json # string into a dict/list so transform it back into json here if isinstance(value, (text_type, binary_type)): return value.strip() else: if isinstance(value, (list, tuple, dict)): return json.dumps(value) raise TypeError('%s cannot be converted to a json string' % type(value)) def _check_type_raw(self, value): return value def _check_type_bytes(self, value): try: self.human_to_bytes(value) except ValueError: raise TypeError('%s cannot be converted to a Byte value' % type(value)) def _check_type_bits(self, value): try: self.human_to_bytes(value, isbits=True) except ValueError: raise TypeError('%s cannot be converted to a Bit value' % type(value)) def _check_argument_types(self): ''' ensure all arguments have the requested type ''' for (k, v) in self.argument_spec.items(): wanted = v.get('type', None) if k not in self.params: continue if wanted is None: # Mostly we want to default to str. # For values set to None explicitly, return None instead as # that allows a user to unset a parameter if self.params[k] is None: continue wanted = 'str' value = self.params[k] if value is None: continue try: type_checker = self._CHECK_ARGUMENT_TYPES_DISPATCHER[wanted] except KeyError: self.fail_json(msg="implementation error: unknown type %s requested for %s" % (wanted, k)) try: self.params[k] = type_checker(value) except (TypeError, ValueError): self.fail_json(msg="argument %s is of type %s and we were unable to convert to %s" % (k, type(value), wanted)) def _set_defaults(self, pre=True): for (k,v) in self.argument_spec.items(): default = v.get('default', None) if pre == True: # this prevents setting defaults on required items if default is not None and k not in self.params: self.params[k] = default else: # make sure things without a default still get set None if k not in self.params: self.params[k] = default def _set_fallbacks(self): for k,v in self.argument_spec.items(): fallback = v.get('fallback', (None,)) fallback_strategy = fallback[0] fallback_args = [] fallback_kwargs = {} if k not in self.params and fallback_strategy is not None: for item in fallback[1:]: if isinstance(item, dict): fallback_kwargs = item else: fallback_args = item try: self.params[k] = fallback_strategy(*fallback_args, **fallback_kwargs) except AnsibleFallbackNotFound: continue def _load_params(self): ''' read the input and set the params attribute. This method is for backwards compatibility. The guts of the function were moved out in 2.1 so that custom modules could read the parameters. ''' # debug overrides to read args from file or cmdline self.params = _load_params() def _log_to_syslog(self, msg): if HAS_SYSLOG: module = 'ansible-%s' % self._name facility = getattr(syslog, self._syslog_facility, syslog.LOG_USER) syslog.openlog(str(module), 0, facility) syslog.syslog(syslog.LOG_INFO, msg) def debug(self, msg): if self._debug: self.log(msg) def log(self, msg, log_args=None): if not self.no_log: if log_args is None: log_args = dict() module = 'ansible-%s' % self._name if isinstance(module, binary_type): module = module.decode('utf-8', 'replace') # 6655 - allow for accented characters if not isinstance(msg, (binary_type, text_type)): raise TypeError("msg should be a string (got %s)" % type(msg)) # We want journal to always take text type # syslog takes bytes on py2, text type on py3 if isinstance(msg, binary_type): journal_msg = remove_values(msg.decode('utf-8', 'replace'), self.no_log_values) else: # TODO: surrogateescape is a danger here on Py3 journal_msg = remove_values(msg, self.no_log_values) if PY3: syslog_msg = journal_msg else: syslog_msg = journal_msg.encode('utf-8', 'replace') if has_journal: journal_args = [("MODULE", os.path.basename(__file__))] for arg in log_args: journal_args.append((arg.upper(), str(log_args[arg]))) try: journal.send(u"%s %s" % (module, journal_msg), **dict(journal_args)) except IOError: # fall back to syslog since logging to journal failed self._log_to_syslog(syslog_msg) else: self._log_to_syslog(syslog_msg) def _log_invocation(self): ''' log that ansible ran the module ''' # TODO: generalize a separate log function and make log_invocation use it # Sanitize possible password argument when logging. log_args = dict() for param in self.params: canon = self.aliases.get(param, param) arg_opts = self.argument_spec.get(canon, {}) no_log = arg_opts.get('no_log', False) arg_type = arg_opts.get('type', 'str') if self.boolean(no_log): log_args[param] = 'NOT_LOGGING_PARAMETER' # try to capture all passwords/passphrase named fields elif arg_type != 'bool' and PASSWORD_MATCH.search(param): log_args[param] = 'NOT_LOGGING_PASSWORD' else: param_val = self.params[param] if not isinstance(param_val, (text_type, binary_type)): param_val = str(param_val) elif isinstance(param_val, text_type): param_val = param_val.encode('utf-8') log_args[param] = heuristic_log_sanitize(param_val, self.no_log_values) msg = [] for arg in log_args: arg_val = log_args[arg] if not isinstance(arg_val, (text_type, binary_type)): arg_val = str(arg_val) elif isinstance(arg_val, text_type): arg_val = arg_val.encode('utf-8') msg.append('%s=%s' % (arg, arg_val)) if msg: msg = 'Invoked with %s' % ' '.join(msg) else: msg = 'Invoked' self.log(msg, log_args=log_args) def _set_cwd(self): try: cwd = os.getcwd() if not os.access(cwd, os.F_OK|os.R_OK): raise return cwd except: # we don't have access to the cwd, probably because of sudo. # Try and move to a neutral location to prevent errors for cwd in [os.path.expandvars('$HOME'), tempfile.gettempdir()]: try: if os.access(cwd, os.F_OK|os.R_OK): os.chdir(cwd) return cwd except: pass # we won't error here, as it may *not* be a problem, # and we don't want to break modules unnecessarily return None def get_bin_path(self, arg, required=False, opt_dirs=[]): ''' find system executable in PATH. Optional arguments: - required: if executable is not found and required is true, fail_json - opt_dirs: optional list of directories to search in addition to PATH if found return full path; otherwise return None ''' sbin_paths = ['/sbin', '/usr/sbin', '/usr/local/sbin'] paths = [] for d in opt_dirs: if d is not None and os.path.exists(d): paths.append(d) paths += os.environ.get('PATH', '').split(os.pathsep) bin_path = None # mangle PATH to include /sbin dirs for p in sbin_paths: if p not in paths and os.path.exists(p): paths.append(p) for d in paths: if not d: continue path = os.path.join(d, arg) if os.path.exists(path) and is_executable(path): bin_path = path break if required and bin_path is None: self.fail_json(msg='Failed to find required executable %s' % arg) return bin_path def boolean(self, arg): ''' return a bool for the arg ''' if arg is None or isinstance(arg, bool): return arg if isinstance(arg, string_types): arg = arg.lower() if arg in BOOLEANS_TRUE: return True elif arg in BOOLEANS_FALSE: return False else: self.fail_json(msg='Boolean %s not in either boolean list' % arg) def jsonify(self, data): for encoding in ("utf-8", "latin-1"): try: return json.dumps(data, encoding=encoding) # Old systems using old simplejson module does not support encoding keyword. except TypeError: try: new_data = json_dict_bytes_to_unicode(data, encoding=encoding) except UnicodeDecodeError: continue return json.dumps(new_data) except UnicodeDecodeError: continue self.fail_json(msg='Invalid unicode encoding encountered') def from_json(self, data): return json.loads(data) def add_cleanup_file(self, path): if path not in self.cleanup_files: self.cleanup_files.append(path) def do_cleanup_files(self): for path in self.cleanup_files: self.cleanup(path) def exit_json(self, **kwargs): ''' return from the module, without error ''' self.add_path_info(kwargs) if not 'changed' in kwargs: kwargs['changed'] = False if 'invocation' not in kwargs: kwargs['invocation'] = {'module_args': self.params} kwargs = remove_values(kwargs, self.no_log_values) self.do_cleanup_files() print('\n%s' % self.jsonify(kwargs)) sys.exit(0) def fail_json(self, **kwargs): ''' return from the module, with an error message ''' self.add_path_info(kwargs) assert 'msg' in kwargs, "implementation error -- msg to explain the error is required" kwargs['failed'] = True if 'invocation' not in kwargs: kwargs['invocation'] = {'module_args': self.params} kwargs = remove_values(kwargs, self.no_log_values) self.do_cleanup_files() print('\n%s' % self.jsonify(kwargs)) sys.exit(1) def fail_on_missing_params(self, required_params=None): ''' This is for checking for required params when we can not check via argspec because we need more information than is simply given in the argspec. ''' if not required_params: return missing_params = [] for required_param in required_params: if not self.params.get(required_param): missing_params.append(required_param) if missing_params: self.fail_json(msg="missing required arguments: %s" % ','.join(missing_params)) def digest_from_file(self, filename, algorithm): ''' Return hex digest of local file for a digest_method specified by name, or None if file is not present. ''' if not os.path.exists(filename): return None if os.path.isdir(filename): self.fail_json(msg="attempted to take checksum of directory: %s" % filename) # preserve old behaviour where the third parameter was a hash algorithm object if hasattr(algorithm, 'hexdigest'): digest_method = algorithm else: try: digest_method = AVAILABLE_HASH_ALGORITHMS[algorithm]() except KeyError: self.fail_json(msg="Could not hash file '%s' with algorithm '%s'. Available algorithms: %s" % (filename, algorithm, ', '.join(AVAILABLE_HASH_ALGORITHMS))) blocksize = 64 * 1024 infile = open(filename, 'rb') block = infile.read(blocksize) while block: digest_method.update(block) block = infile.read(blocksize) infile.close() return digest_method.hexdigest() def md5(self, filename): ''' Return MD5 hex digest of local file using digest_from_file(). Do not use this function unless you have no other choice for: 1) Optional backwards compatibility 2) Compatibility with a third party protocol This function will not work on systems complying with FIPS-140-2. Most uses of this function can use the module.sha1 function instead. ''' if 'md5' not in AVAILABLE_HASH_ALGORITHMS: raise ValueError('MD5 not available. Possibly running in FIPS mode') return self.digest_from_file(filename, 'md5') def sha1(self, filename): ''' Return SHA1 hex digest of local file using digest_from_file(). ''' return self.digest_from_file(filename, 'sha1') def sha256(self, filename): ''' Return SHA-256 hex digest of local file using digest_from_file(). ''' return self.digest_from_file(filename, 'sha256') def backup_local(self, fn): '''make a date-marked backup of the specified file, return True or False on success or failure''' backupdest = '' if os.path.exists(fn): # backups named basename-YYYY-MM-DD@HH:MM:SS~ ext = time.strftime("%Y-%m-%d@%H:%M:%S~", time.localtime(time.time())) backupdest = '%s.%s.%s' % (fn, os.getpid(), ext) try: shutil.copy2(fn, backupdest) except (shutil.Error, IOError): e = get_exception() self.fail_json(msg='Could not make backup of %s to %s: %s' % (fn, backupdest, e)) return backupdest def cleanup(self, tmpfile): if os.path.exists(tmpfile): try: os.unlink(tmpfile) except OSError: e = get_exception() sys.stderr.write("could not cleanup %s: %s" % (tmpfile, e)) def atomic_move(self, src, dest, unsafe_writes=False): '''atomically move src to dest, copying attributes from dest, returns true on success it uses os.rename to ensure this as it is an atomic operation, rest of the function is to work around limitations, corner cases and ensure selinux context is saved if possible''' context = None dest_stat = None b_src = to_bytes(src, errors='surrogate_or_strict') b_dest = to_bytes(dest, errors='surrogate_or_strict') if os.path.exists(b_dest): try: dest_stat = os.stat(b_dest) os.chmod(b_src, dest_stat.st_mode & PERM_BITS) os.chown(b_src, dest_stat.st_uid, dest_stat.st_gid) except OSError: e = get_exception() if e.errno != errno.EPERM: raise if self.selinux_enabled(): context = self.selinux_context(dest) else: if self.selinux_enabled(): context = self.selinux_default_context(dest) creating = not os.path.exists(b_dest) try: # Optimistically try a rename, solves some corner cases and can avoid useless work, throws exception if not atomic. os.rename(b_src, b_dest) except (IOError, OSError): e = get_exception() if e.errno not in [errno.EPERM, errno.EXDEV, errno.EACCES, errno.ETXTBSY, errno.EBUSY]: # only try workarounds for errno 18 (cross device), 1 (not permitted), 13 (permission denied) # and 26 (text file busy) which happens on vagrant synced folders and other 'exotic' non posix file systems self.fail_json(msg='Could not replace file: %s to %s: %s' % (src, dest, e)) else: b_dest_dir = os.path.dirname(b_dest) # Use bytes here. In the shippable CI, this fails with # a UnicodeError with surrogateescape'd strings for an unknown # reason (doesn't happen in a local Ubuntu16.04 VM) native_dest_dir = b_dest_dir native_suffix = os.path.basename(b_dest) native_prefix = b('.ansible_tmp') try: tmp_dest_fd, tmp_dest_name = tempfile.mkstemp( prefix=native_prefix, dir=native_dest_dir, suffix=native_suffix) except (OSError, IOError): e = get_exception() self.fail_json(msg='The destination directory (%s) is not writable by the current user. Error was: %s' % (os.path.dirname(dest), e)) except TypeError: # We expect that this is happening because python3.4.x and # below can't handle byte strings in mkstemp(). Traceback # would end in something like: # file = _os.path.join(dir, pre + name + suf) # TypeError: can't concat bytes to str self.fail_json(msg='Failed creating temp file for atomic move. This usually happens when using Python3 less than Python3.5. Please use Python2.x or Python3.5 or greater.', exception=sys.exc_info()) b_tmp_dest_name = to_bytes(tmp_dest_name, errors='surrogate_or_strict') try: try: # close tmp file handle before file operations to prevent text file busy errors on vboxfs synced folders (windows host) os.close(tmp_dest_fd) # leaves tmp file behind when sudo and not root try: shutil.move(b_src, b_tmp_dest_name) except OSError: # cleanup will happen by 'rm' of tempdir # copy2 will preserve some metadata shutil.copy2(b_src, b_tmp_dest_name) if self.selinux_enabled(): self.set_context_if_different( b_tmp_dest_name, context, False) try: tmp_stat = os.stat(b_tmp_dest_name) if dest_stat and (tmp_stat.st_uid != dest_stat.st_uid or tmp_stat.st_gid != dest_stat.st_gid): os.chown(b_tmp_dest_name, dest_stat.st_uid, dest_stat.st_gid) except OSError: e = get_exception() if e.errno != errno.EPERM: raise try: os.rename(b_tmp_dest_name, b_dest) except (shutil.Error, OSError, IOError): e = get_exception() if unsafe_writes: self._unsafe_writes(b_tmp_dest_name, b_dest, e) else: self.fail_json(msg='Could not replace file: %s to %s: %s' % (src, dest, e)) except (shutil.Error, OSError, IOError): e = get_exception() self.fail_json(msg='Could not replace file: %s to %s: %s' % (src, dest, e)) finally: self.cleanup(b_tmp_dest_name) if creating: # make sure the file has the correct permissions # based on the current value of umask umask = os.umask(0) os.umask(umask) os.chmod(b_dest, DEFAULT_PERM & ~umask) try: os.chown(b_dest, os.geteuid(), os.getegid()) except OSError: # We're okay with trying our best here. If the user is not # root (or old Unices) they won't be able to chown. pass if self.selinux_enabled(): # rename might not preserve context self.set_context_if_different(dest, context, False) def _unsafe_writes(self, src, dest, exception): # sadly there are some situations where we cannot ensure atomicity, but only if # the user insists and we get the appropriate error we update the file unsafely if exception.errno == errno.EBUSY: #TODO: issue warning that this is an unsafe operation, but doing it cause user insists try: try: out_dest = open(dest, 'wb') in_src = open(src, 'rb') shutil.copyfileobj(in_src, out_dest) finally: # assuring closed files in 2.4 compatible way if out_dest: out_dest.close() if in_src: in_src.close() except (shutil.Error, OSError, IOError): e = get_exception() self.fail_json(msg='Could not write data to file (%s) from (%s): %s' % (dest, src, e)) else: self.fail_json(msg='Could not replace file: %s to %s: %s' % (src, dest, exception)) def run_command(self, args, check_rc=False, close_fds=True, executable=None, data=None, binary_data=False, path_prefix=None, cwd=None, use_unsafe_shell=False, prompt_regex=None, environ_update=None, umask=None, encoding='utf-8', errors='surrogate_or_strict'): ''' Execute a command, returns rc, stdout, and stderr. :arg args: is the command to run * If args is a list, the command will be run with shell=False. * If args is a string and use_unsafe_shell=False it will split args to a list and run with shell=False * If args is a string and use_unsafe_shell=True it runs with shell=True. :kw check_rc: Whether to call fail_json in case of non zero RC. Default False :kw close_fds: See documentation for subprocess.Popen(). Default True :kw executable: See documentation for subprocess.Popen(). Default None :kw data: If given, information to write to the stdin of the command :kw binary_data: If False, append a newline to the data. Default False :kw path_prefix: If given, additional path to find the command in. This adds to the PATH environment vairable so helper commands in the same directory can also be found :kw cwd: If given, working directory to run the command inside :kw use_unsafe_shell: See `args` parameter. Default False :kw prompt_regex: Regex string (not a compiled regex) which can be used to detect prompts in the stdout which would otherwise cause the execution to hang (especially if no input data is specified) :kw environ_update: dictionary to *update* os.environ with :kw umask: Umask to be used when running the command. Default None :kw encoding: Since we return native strings, on python3 we need to know the encoding to use to transform from bytes to text. If you want to always get bytes back, use encoding=None. The default is "utf-8". This does not affect transformation of strings given as args. :kw errors: Since we return native strings, on python3 we need to transform stdout and stderr from bytes to text. If the bytes are undecodable in the ``encoding`` specified, then use this error handler to deal with them. The default is ``surrogate_or_strict`` which means that the bytes will be decoded using the surrogateescape error handler if available (available on all python3 versions we support) otherwise a UnicodeError traceback will be raised. This does not affect transformations of strings given as args. :returns: A 3-tuple of return code (integer), stdout (native string), and stderr (native string). On python2, stdout and stderr are both byte strings. On python3, stdout and stderr are text strings converted according to the encoding and errors parameters. If you want byte strings on python3, use encoding=None to turn decoding to text off. ''' shell = False if isinstance(args, list): if use_unsafe_shell: args = " ".join([pipes.quote(x) for x in args]) shell = True elif isinstance(args, (binary_type, text_type)) and use_unsafe_shell: shell = True elif isinstance(args, (binary_type, text_type)): # On python2.6 and below, shlex has problems with text type # On python3, shlex needs a text type. if PY2: args = to_bytes(args, errors='surrogate_or_strict') elif PY3: args = to_text(args, errors='surrogateescape') args = shlex.split(args) else: msg = "Argument 'args' to run_command must be list or string" self.fail_json(rc=257, cmd=args, msg=msg) prompt_re = None if prompt_regex: if isinstance(prompt_regex, text_type): if PY3: prompt_regex = to_bytes(prompt_regex, errors='surrogateescape') elif PY2: prompt_regex = to_bytes(prompt_regex, errors='surrogate_or_strict') try: prompt_re = re.compile(prompt_regex, re.MULTILINE) except re.error: self.fail_json(msg="invalid prompt regular expression given to run_command") # expand things like $HOME and ~ if not shell: args = [ os.path.expanduser(os.path.expandvars(x)) for x in args if x is not None ] rc = 0 msg = None st_in = None # Manipulate the environ we'll send to the new process old_env_vals = {} # We can set this from both an attribute and per call for key, val in self.run_command_environ_update.items(): old_env_vals[key] = os.environ.get(key, None) os.environ[key] = val if environ_update: for key, val in environ_update.items(): old_env_vals[key] = os.environ.get(key, None) os.environ[key] = val if path_prefix: old_env_vals['PATH'] = os.environ['PATH'] os.environ['PATH'] = "%s:%s" % (path_prefix, os.environ['PATH']) # If using test-module and explode, the remote lib path will resemble ... # /tmp/test_module_scratch/debug_dir/ansible/module_utils/basic.py # If using ansible or ansible-playbook with a remote system ... # /tmp/ansible_vmweLQ/ansible_modlib.zip/ansible/module_utils/basic.py # Clean out python paths set by ansiballz if 'PYTHONPATH' in os.environ: pypaths = os.environ['PYTHONPATH'].split(':') pypaths = [x for x in pypaths \ if not x.endswith('/ansible_modlib.zip') \ and not x.endswith('/debug_dir')] os.environ['PYTHONPATH'] = ':'.join(pypaths) if not os.environ['PYTHONPATH']: del os.environ['PYTHONPATH'] # create a printable version of the command for use # in reporting later, which strips out things like # passwords from the args list to_clean_args = args if PY2: if isinstance(args, text_type): to_clean_args = to_bytes(args) else: if isinstance(args, binary_type): to_clean_args = to_text(args) if isinstance(args, (text_type, binary_type)): to_clean_args = shlex.split(to_clean_args) clean_args = [] is_passwd = False for arg in to_clean_args: if is_passwd: is_passwd = False clean_args.append('********') continue if PASSWD_ARG_RE.match(arg): sep_idx = arg.find('=') if sep_idx > -1: clean_args.append('%s=********' % arg[:sep_idx]) continue else: is_passwd = True arg = heuristic_log_sanitize(arg, self.no_log_values) clean_args.append(arg) clean_args = ' '.join(pipes.quote(arg) for arg in clean_args) if data: st_in = subprocess.PIPE kwargs = dict( executable=executable, shell=shell, close_fds=close_fds, stdin=st_in, stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) # store the pwd prev_dir = os.getcwd() # make sure we're in the right working directory if cwd and os.path.isdir(cwd): cwd = os.path.abspath(os.path.expanduser(cwd)) kwargs['cwd'] = cwd try: os.chdir(cwd) except (OSError, IOError): e = get_exception() self.fail_json(rc=e.errno, msg="Could not open %s, %s" % (cwd, str(e))) old_umask = None if umask: old_umask = os.umask(umask) try: if self._debug: self.log('Executing: ' + clean_args) cmd = subprocess.Popen(args, **kwargs) # the communication logic here is essentially taken from that # of the _communicate() function in ssh.py stdout = b('') stderr = b('') rpipes = [cmd.stdout, cmd.stderr] if data: if not binary_data: data += '\n' if isinstance(data, text_type): data = to_bytes(data) cmd.stdin.write(data) cmd.stdin.close() while True: rfd, wfd, efd = select.select(rpipes, [], rpipes, 1) if cmd.stdout in rfd: dat = os.read(cmd.stdout.fileno(), 9000) stdout += dat if dat == b(''): rpipes.remove(cmd.stdout) if cmd.stderr in rfd: dat = os.read(cmd.stderr.fileno(), 9000) stderr += dat if dat == b(''): rpipes.remove(cmd.stderr) # if we're checking for prompts, do it now if prompt_re: if prompt_re.search(stdout) and not data: if encoding: stdout = to_native(stdout, encoding=encoding, errors=errors) else: stdout = stdout return (257, stdout, "A prompt was encountered while running a command, but no input data was specified") # only break out if no pipes are left to read or # the pipes are completely read and # the process is terminated if (not rpipes or not rfd) and cmd.poll() is not None: break # No pipes are left to read but process is not yet terminated # Only then it is safe to wait for the process to be finished # NOTE: Actually cmd.poll() is always None here if rpipes is empty elif not rpipes and cmd.poll() == None: cmd.wait() # The process is terminated. Since no pipes to read from are # left, there is no need to call select() again. break cmd.stdout.close() cmd.stderr.close() rc = cmd.returncode except (OSError, IOError): e = get_exception() self.log("Error Executing CMD:%s Exception:%s" % (clean_args, to_native(e))) self.fail_json(rc=e.errno, msg=to_native(e), cmd=clean_args) except Exception: e = get_exception() self.log("Error Executing CMD:%s Exception:%s" % (clean_args,to_native(traceback.format_exc()))) self.fail_json(rc=257, msg=to_native(e), exception=traceback.format_exc(), cmd=clean_args) # Restore env settings for key, val in old_env_vals.items(): if val is None: del os.environ[key] else: os.environ[key] = val if old_umask: os.umask(old_umask) if rc != 0 and check_rc: msg = heuristic_log_sanitize(stderr.rstrip(), self.no_log_values) self.fail_json(cmd=clean_args, rc=rc, stdout=stdout, stderr=stderr, msg=msg) # reset the pwd os.chdir(prev_dir) if encoding is not None: return (rc, to_native(stdout, encoding=encoding, errors=errors), to_native(stderr, encoding=encoding, errors=errors)) return (rc, stdout, stderr) def append_to_file(self, filename, str): filename = os.path.expandvars(os.path.expanduser(filename)) fh = open(filename, 'a') fh.write(str) fh.close() def bytes_to_human(self, size): return bytes_to_human(size) # for backwards compatibility pretty_bytes = bytes_to_human def human_to_bytes(self, number, isbits=False): return human_to_bytes(number, isbits) # # Backwards compat # # In 2.0, moved from inside the module to the toplevel is_executable = is_executable def get_module_path(): return os.path.dirname(os.path.realpath(__file__))
eerorika/ansible
lib/ansible/module_utils/basic.py
Python
gpl-3.0
92,348
[ "VisIt" ]
35b968e0aafdd8e1954440b7470a6fbe706ca4d92d907c8060d06ec82b4e972e
from collections import namedtuple from ua_parser import user_agent_parser from .compat import string_types MOBILE_DEVICE_FAMILIES = ( 'iPhone', 'iPod', 'Generic Smartphone', 'Generic Feature Phone', 'PlayStation Vita', 'iOS-Device' ) PC_OS_FAMILIES = ( 'Windows 95', 'Windows 98', 'Windows ME', 'Solaris', ) MOBILE_OS_FAMILIES = ( 'Windows Phone', 'Windows Phone OS', # Earlier versions of ua-parser returns Windows Phone OS 'Symbian OS', 'Bada', 'Windows CE', 'Windows Mobile', 'Maemo', ) MOBILE_BROWSER_FAMILIES = ( 'Opera Mobile', 'Opera Mini', ) TABLET_DEVICE_FAMILIES = ( 'iPad', 'BlackBerry Playbook', 'Blackberry Playbook', # Earlier versions of ua-parser returns "Blackberry" instead of "BlackBerry" 'Kindle', 'Kindle Fire', 'Kindle Fire HD', 'Galaxy Tab', 'Xoom', 'Dell Streak', ) TOUCH_CAPABLE_OS_FAMILIES = ( 'iOS', 'Android', 'Windows Phone', 'Windows Phone OS', 'Windows RT', 'Windows CE', 'Windows Mobile', 'Firefox OS', 'MeeGo', ) TOUCH_CAPABLE_DEVICE_FAMILIES = ( 'BlackBerry Playbook', 'Blackberry Playbook', 'Kindle Fire', ) EMAIL_PROGRAM_FAMILIES = { 'Outlook', 'Windows Live Mail', 'AirMail', 'Apple Mail', 'Outlook', 'Thunderbird', 'Lightning', 'ThunderBrowse', 'Windows Live Mail', 'The Bat!', 'Lotus Notes', 'IBM Notes', 'Barca', 'MailBar', 'kmail2', 'YahooMobileMail' } def verify_attribute(attribute): if isinstance(attribute, string_types) and attribute.isdigit(): return int(attribute) return attribute def parse_version(major=None, minor=None, patch=None, patch_minor=None): # Returns version number tuple, attributes will be integer if they're numbers major = verify_attribute(major) minor = verify_attribute(minor) patch = verify_attribute(patch) patch_minor = verify_attribute(patch_minor) return tuple( filter(lambda x: x is not None, (major, minor, patch, patch_minor)) ) Browser = namedtuple('Browser', ['family', 'version', 'version_string']) def parse_browser(family, major=None, minor=None, patch=None, patch_minor=None): # Returns a browser object version = parse_version(major, minor, patch) version_string = '.'.join([str(v) for v in version]) return Browser(family, version, version_string) OperatingSystem = namedtuple('OperatingSystem', ['family', 'version', 'version_string']) def parse_operating_system(family, major=None, minor=None, patch=None, patch_minor=None): version = parse_version(major, minor, patch) version_string = '.'.join([str(v) for v in version]) return OperatingSystem(family, version, version_string) Device = namedtuple('Device', ['family', 'brand', 'model']) def parse_device(family, brand, model): return Device(family, brand, model) class UserAgent(object): def __init__(self, user_agent_string): ua_dict = user_agent_parser.Parse(user_agent_string) self.ua_string = user_agent_string self.os = parse_operating_system(**ua_dict['os']) self.browser = parse_browser(**ua_dict['user_agent']) self.device = parse_device(**ua_dict['device']) def __str__(self): device = self.is_pc and "PC" or self.device.family os = ("%s %s" % (self.os.family, self.os.version_string)).strip() browser = ("%s %s" % (self.browser.family, self.browser.version_string)).strip() return " / ".join([device, os, browser]) def __unicode__(self): return unicode(str(self)) def _is_android_tablet(self): # Newer Android tablets don't have "Mobile" in their user agent string, # older ones like Galaxy Tab still have "Mobile" though they're not if ('Mobile Safari' not in self.ua_string and self.browser.family != "Firefox Mobile"): return True return False def _is_blackberry_touch_capable_device(self): # A helper to determine whether a BB phone has touch capabilities # Blackberry Bold Touch series begins with 99XX if 'Blackberry 99' in self.device.family: return True if 'Blackberry 95' in self.device.family: # BB Storm devices return True if 'Blackberry 95' in self.device.family: # BB Torch devices return True return False @property def is_tablet(self): if self.device.family in TABLET_DEVICE_FAMILIES: return True if (self.os.family == 'Android' and self._is_android_tablet()): return True if self.os.family.startswith('Windows RT'): return True if self.os.family == 'Firefox OS' and 'Mobile' not in self.browser.family: return True return False @property def is_mobile(self): # First check for mobile device and mobile browser families if self.device.family in MOBILE_DEVICE_FAMILIES: return True if self.browser.family in MOBILE_BROWSER_FAMILIES: return True # Device is considered Mobile OS is Android and not tablet # This is not fool proof but would have to suffice for now if ((self.os.family == 'Android' or self.os.family == 'Firefox OS') and not self.is_tablet): return True if self.os.family == 'BlackBerry OS' and self.device.family != 'Blackberry Playbook': return True if self.os.family in MOBILE_OS_FAMILIES: return True # TODO: remove after https://github.com/tobie/ua-parser/issues/126 is closed if 'J2ME' in self.ua_string or 'MIDP' in self.ua_string: return True # This is here mainly to detect Google's Mobile Spider if 'iPhone;' in self.ua_string: return True if 'Googlebot-Mobile' in self.ua_string: return True # Mobile Spiders should be identified as mobile if self.device.family == 'Spider' and 'Mobile' in self.browser.family: return True # Nokia mobile if 'NokiaBrowser' in self.ua_string and 'Mobile' in self.ua_string: return True return False @property def is_touch_capable(self): # TODO: detect touch capable Nokia devices if self.os.family in TOUCH_CAPABLE_OS_FAMILIES: return True if self.device.family in TOUCH_CAPABLE_DEVICE_FAMILIES: return True if self.os.family.startswith('Windows 8') and 'Touch' in self.ua_string: return True if 'BlackBerry' in self.os.family and self._is_blackberry_touch_capable_device(): return True return False @property def is_pc(self): # Returns True for "PC" devices (Windows, Mac and Linux) if 'Windows NT' in self.ua_string or self.os.family in PC_OS_FAMILIES: return True # TODO: remove after https://github.com/tobie/ua-parser/issues/127 is closed if self.os.family == 'Mac OS X' and 'Silk' not in self.ua_string: return True # Maemo has 'Linux' and 'X11' in UA, but it is not for PC if 'Maemo' in self.ua_string: return False if 'Chrome OS' in self.os.family: return True if 'Linux' in self.ua_string and 'X11' in self.ua_string: return True return False @property def is_bot(self): return True if self.device.family == 'Spider' else False @property def is_email_client(self): if self.browser.family in EMAIL_PROGRAM_FAMILIES: return True return False def parse(user_agent_string): return UserAgent(user_agent_string)
public-ink/public-ink
server/appengine/lib/user_agents/parsers.py
Python
gpl-3.0
7,813
[ "Galaxy" ]
73c17577e931d663d462d0fcd8f3dfcfde47b2c7a3befe24dd74303aa663c27f
# -*- coding: utf-8 -*- """ Bok choy acceptance tests for Entrance exams in the LMS """ from __future__ import absolute_import from textwrap import dedent from common.test.acceptance.fixtures.course import CourseFixture, XBlockFixtureDesc from common.test.acceptance.pages.common.auto_auth import AutoAuthPage from common.test.acceptance.pages.lms.courseware import CoursewarePage from common.test.acceptance.tests.helpers import UniqueCourseTest class EntranceExamTest(UniqueCourseTest): """ Base class for tests of Entrance Exams in the LMS. """ USERNAME = "joe_student" EMAIL = "joe@example.com" def setUp(self): super(EntranceExamTest, self).setUp() self.xqueue_grade_response = None self.courseware_page = CoursewarePage(self.browser, self.course_id) # Install a course with a hierarchy and problems course_fixture = CourseFixture( self.course_info['org'], self.course_info['number'], self.course_info['run'], self.course_info['display_name'], settings={ 'entrance_exam_enabled': 'true', 'entrance_exam_minimum_score_pct': '50' } ) problem = self.get_problem() course_fixture.add_children( XBlockFixtureDesc('chapter', 'Test Section').add_children( XBlockFixtureDesc('sequential', 'Test Subsection').add_children(problem) ) ).install() entrance_exam_subsection = None outline = course_fixture.studio_course_outline_as_json for child in outline['child_info']['children']: if child.get('display_name') == "Entrance Exam": entrance_exam_subsection = child['child_info']['children'][0] if entrance_exam_subsection: course_fixture.create_xblock(entrance_exam_subsection['id'], problem) # Auto-auth register for the course. AutoAuthPage(self.browser, username=self.USERNAME, email=self.EMAIL, course_id=self.course_id, staff=False).visit() def get_problem(self): """ Subclasses should override this to complete the fixture """ raise NotImplementedError() class EntranceExamPassTest(EntranceExamTest): """ Tests the scenario when a student passes entrance exam. """ def get_problem(self): """ Create a multiple choice problem """ xml = dedent(""" <problem> <multiplechoiceresponse> <label>What is height of eiffel tower without the antenna?.</label> <choicegroup type="MultipleChoice"> <choice correct="false">324 meters<choicehint>Antenna is 24 meters high</choicehint></choice> <choice correct="true">300 meters</choice> <choice correct="false">224 meters</choice> <choice correct="false">400 meters</choice> </choicegroup> </multiplechoiceresponse> </problem> """) return XBlockFixtureDesc('problem', 'HEIGHT OF EIFFEL TOWER', data=xml)
ESOedX/edx-platform
common/test/acceptance/tests/lms/test_lms_entrance_exams.py
Python
agpl-3.0
3,073
[ "VisIt" ]
28c92e9240ca453de8a900b6a829f306dfa0e3daed83238f069ae15a092c3e0a
# -*- coding: utf-8 -*- # Copyright: (c) 2019, Ansible Project # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type import copy import json import os import pytest import re import shutil import tarfile import yaml from io import BytesIO, StringIO from units.compat.mock import MagicMock import ansible.module_utils.six.moves.urllib.error as urllib_error from ansible import context from ansible.cli.galaxy import GalaxyCLI from ansible.errors import AnsibleError from ansible.galaxy import collection, api from ansible.module_utils._text import to_bytes, to_native, to_text from ansible.utils import context_objects as co from ansible.utils.display import Display def call_galaxy_cli(args): orig = co.GlobalCLIArgs._Singleton__instance co.GlobalCLIArgs._Singleton__instance = None try: GalaxyCLI(args=['ansible-galaxy', 'collection'] + args).run() finally: co.GlobalCLIArgs._Singleton__instance = orig def artifact_json(namespace, name, version, dependencies, server): json_str = json.dumps({ 'artifact': { 'filename': '%s-%s-%s.tar.gz' % (namespace, name, version), 'sha256': '2d76f3b8c4bab1072848107fb3914c345f71a12a1722f25c08f5d3f51f4ab5fd', 'size': 1234, }, 'download_url': '%s/download/%s-%s-%s.tar.gz' % (server, namespace, name, version), 'metadata': { 'namespace': namespace, 'name': name, 'dependencies': dependencies, }, 'version': version }) return to_text(json_str) def artifact_versions_json(namespace, name, versions, galaxy_api, available_api_versions=None): results = [] available_api_versions = available_api_versions or {} api_version = 'v2' if 'v3' in available_api_versions: api_version = 'v3' for version in versions: results.append({ 'href': '%s/api/%s/%s/%s/versions/%s/' % (galaxy_api.api_server, api_version, namespace, name, version), 'version': version, }) if api_version == 'v2': json_str = json.dumps({ 'count': len(versions), 'next': None, 'previous': None, 'results': results }) if api_version == 'v3': response = {'meta': {'count': len(versions)}, 'data': results, 'links': {'first': None, 'last': None, 'next': None, 'previous': None}, } json_str = json.dumps(response) return to_text(json_str) def error_json(galaxy_api, errors_to_return=None, available_api_versions=None): errors_to_return = errors_to_return or [] available_api_versions = available_api_versions or {} response = {} api_version = 'v2' if 'v3' in available_api_versions: api_version = 'v3' if api_version == 'v2': assert len(errors_to_return) <= 1 if errors_to_return: response = errors_to_return[0] if api_version == 'v3': response['errors'] = errors_to_return json_str = json.dumps(response) return to_text(json_str) @pytest.fixture(autouse='function') def reset_cli_args(): co.GlobalCLIArgs._Singleton__instance = None yield co.GlobalCLIArgs._Singleton__instance = None @pytest.fixture() def collection_artifact(request, tmp_path_factory): test_dir = to_text(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) namespace = 'ansible_namespace' collection = 'collection' skeleton_path = os.path.join(os.path.dirname(os.path.split(__file__)[0]), 'cli', 'test_data', 'collection_skeleton') collection_path = os.path.join(test_dir, namespace, collection) call_galaxy_cli(['init', '%s.%s' % (namespace, collection), '-c', '--init-path', test_dir, '--collection-skeleton', skeleton_path]) dependencies = getattr(request, 'param', None) if dependencies: galaxy_yml = os.path.join(collection_path, 'galaxy.yml') with open(galaxy_yml, 'rb+') as galaxy_obj: existing_yaml = yaml.safe_load(galaxy_obj) existing_yaml['dependencies'] = dependencies galaxy_obj.seek(0) galaxy_obj.write(to_bytes(yaml.safe_dump(existing_yaml))) galaxy_obj.truncate() call_galaxy_cli(['build', collection_path, '--output-path', test_dir]) collection_tar = os.path.join(test_dir, '%s-%s-0.1.0.tar.gz' % (namespace, collection)) return to_bytes(collection_path), to_bytes(collection_tar) @pytest.fixture() def galaxy_server(): context.CLIARGS._store = {'ignore_certs': False} galaxy_api = api.GalaxyAPI(None, 'test_server', 'https://galaxy.ansible.com') return galaxy_api def test_build_requirement_from_path(collection_artifact): actual = collection.CollectionRequirement.from_path(collection_artifact[0], True) assert actual.namespace == u'ansible_namespace' assert actual.name == u'collection' assert actual.b_path == collection_artifact[0] assert actual.api is None assert actual.skip is True assert actual.versions == set([u'*']) assert actual.latest_version == u'*' assert actual.dependencies == {} def test_build_requirement_from_path_with_manifest(collection_artifact): manifest_path = os.path.join(collection_artifact[0], b'MANIFEST.json') manifest_value = json.dumps({ 'collection_info': { 'namespace': 'namespace', 'name': 'name', 'version': '1.1.1', 'dependencies': { 'ansible_namespace.collection': '*' } } }) with open(manifest_path, 'wb') as manifest_obj: manifest_obj.write(to_bytes(manifest_value)) actual = collection.CollectionRequirement.from_path(collection_artifact[0], True) # While the folder name suggests a different collection, we treat MANIFEST.json as the source of truth. assert actual.namespace == u'namespace' assert actual.name == u'name' assert actual.b_path == collection_artifact[0] assert actual.api is None assert actual.skip is True assert actual.versions == set([u'1.1.1']) assert actual.latest_version == u'1.1.1' assert actual.dependencies == {'ansible_namespace.collection': '*'} def test_build_requirement_from_path_invalid_manifest(collection_artifact): manifest_path = os.path.join(collection_artifact[0], b'MANIFEST.json') with open(manifest_path, 'wb') as manifest_obj: manifest_obj.write(b"not json") expected = "Collection file at '%s' does not contain a valid json string." % to_native(manifest_path) with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_path(collection_artifact[0], True) def test_build_requirement_from_tar(collection_artifact): actual = collection.CollectionRequirement.from_tar(collection_artifact[1], True, True) assert actual.namespace == u'ansible_namespace' assert actual.name == u'collection' assert actual.b_path == collection_artifact[1] assert actual.api is None assert actual.skip is False assert actual.versions == set([u'0.1.0']) assert actual.latest_version == u'0.1.0' assert actual.dependencies == {} def test_build_requirement_from_tar_fail_not_tar(tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) test_file = os.path.join(test_dir, b'fake.tar.gz') with open(test_file, 'wb') as test_obj: test_obj.write(b"\x00\x01\x02\x03") expected = "Collection artifact at '%s' is not a valid tar file." % to_native(test_file) with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_tar(test_file, True, True) def test_build_requirement_from_tar_no_manifest(tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) json_data = to_bytes(json.dumps( { 'files': [], 'format': 1, } )) tar_path = os.path.join(test_dir, b'ansible-collections.tar.gz') with tarfile.open(tar_path, 'w:gz') as tfile: b_io = BytesIO(json_data) tar_info = tarfile.TarInfo('FILES.json') tar_info.size = len(json_data) tar_info.mode = 0o0644 tfile.addfile(tarinfo=tar_info, fileobj=b_io) expected = "Collection at '%s' does not contain the required file MANIFEST.json." % to_native(tar_path) with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_tar(tar_path, True, True) def test_build_requirement_from_tar_no_files(tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) json_data = to_bytes(json.dumps( { 'collection_info': {}, } )) tar_path = os.path.join(test_dir, b'ansible-collections.tar.gz') with tarfile.open(tar_path, 'w:gz') as tfile: b_io = BytesIO(json_data) tar_info = tarfile.TarInfo('MANIFEST.json') tar_info.size = len(json_data) tar_info.mode = 0o0644 tfile.addfile(tarinfo=tar_info, fileobj=b_io) expected = "Collection at '%s' does not contain the required file FILES.json." % to_native(tar_path) with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_tar(tar_path, True, True) def test_build_requirement_from_tar_invalid_manifest(tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) json_data = b"not a json" tar_path = os.path.join(test_dir, b'ansible-collections.tar.gz') with tarfile.open(tar_path, 'w:gz') as tfile: b_io = BytesIO(json_data) tar_info = tarfile.TarInfo('MANIFEST.json') tar_info.size = len(json_data) tar_info.mode = 0o0644 tfile.addfile(tarinfo=tar_info, fileobj=b_io) expected = "Collection tar file member MANIFEST.json does not contain a valid json string." with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_tar(tar_path, True, True) def test_build_requirement_from_name(galaxy_server, monkeypatch): mock_get_versions = MagicMock() mock_get_versions.return_value = ['2.1.9', '2.1.10'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '*', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'2.1.9', u'2.1.10']) assert actual.latest_version == u'2.1.10' assert actual.dependencies is None assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection') def test_build_requirement_from_name_with_prerelease(galaxy_server, monkeypatch): mock_get_versions = MagicMock() mock_get_versions.return_value = ['1.0.1', '2.0.1-beta.1', '2.0.1'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '*', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'1.0.1', u'2.0.1']) assert actual.latest_version == u'2.0.1' assert actual.dependencies is None assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection') def test_build_requirment_from_name_with_prerelease_explicit(galaxy_server, monkeypatch): mock_get_info = MagicMock() mock_get_info.return_value = api.CollectionVersionMetadata('namespace', 'collection', '2.0.1-beta.1', None, None, {}) monkeypatch.setattr(galaxy_server, 'get_collection_version_metadata', mock_get_info) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '2.0.1-beta.1', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'2.0.1-beta.1']) assert actual.latest_version == u'2.0.1-beta.1' assert actual.dependencies == {} assert mock_get_info.call_count == 1 assert mock_get_info.mock_calls[0][1] == ('namespace', 'collection', '2.0.1-beta.1') def test_build_requirement_from_name_second_server(galaxy_server, monkeypatch): mock_get_versions = MagicMock() mock_get_versions.return_value = ['1.0.1', '1.0.2', '1.0.3'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) broken_server = copy.copy(galaxy_server) broken_server.api_server = 'https://broken.com/' mock_404 = MagicMock() mock_404.side_effect = api.GalaxyError(urllib_error.HTTPError('https://galaxy.server.com', 404, 'msg', {}, StringIO()), "custom msg") monkeypatch.setattr(broken_server, 'get_collection_versions', mock_404) actual = collection.CollectionRequirement.from_name('namespace.collection', [broken_server, galaxy_server], '>1.0.1', False, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None # assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'1.0.2', u'1.0.3']) assert actual.latest_version == u'1.0.3' assert actual.dependencies is None assert mock_404.call_count == 1 assert mock_404.mock_calls[0][1] == ('namespace', 'collection') assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection') def test_build_requirement_from_name_missing(galaxy_server, monkeypatch): mock_open = MagicMock() mock_open.side_effect = api.GalaxyError(urllib_error.HTTPError('https://galaxy.server.com', 404, 'msg', {}, StringIO()), "") monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_open) expected = "Failed to find collection namespace.collection:*" with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server, galaxy_server], '*', False, True) def test_build_requirement_from_name_401_unauthorized(galaxy_server, monkeypatch): mock_open = MagicMock() mock_open.side_effect = api.GalaxyError(urllib_error.HTTPError('https://galaxy.server.com', 401, 'msg', {}, StringIO()), "error") monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_open) expected = "error (HTTP Code: 401, Message: msg)" with pytest.raises(api.GalaxyError, match=re.escape(expected)): collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server, galaxy_server], '*', False) def test_build_requirement_from_name_single_version(galaxy_server, monkeypatch): mock_get_info = MagicMock() mock_get_info.return_value = api.CollectionVersionMetadata('namespace', 'collection', '2.0.0', None, None, {}) monkeypatch.setattr(galaxy_server, 'get_collection_version_metadata', mock_get_info) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '2.0.0', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'2.0.0']) assert actual.latest_version == u'2.0.0' assert actual.dependencies == {} assert mock_get_info.call_count == 1 assert mock_get_info.mock_calls[0][1] == ('namespace', 'collection', '2.0.0') def test_build_requirement_from_name_multiple_versions_one_match(galaxy_server, monkeypatch): mock_get_versions = MagicMock() mock_get_versions.return_value = ['2.0.0', '2.0.1', '2.0.2'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) mock_get_info = MagicMock() mock_get_info.return_value = api.CollectionVersionMetadata('namespace', 'collection', '2.0.1', None, None, {}) monkeypatch.setattr(galaxy_server, 'get_collection_version_metadata', mock_get_info) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '>=2.0.1,<2.0.2', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'2.0.1']) assert actual.latest_version == u'2.0.1' assert actual.dependencies == {} assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection') assert mock_get_info.call_count == 1 assert mock_get_info.mock_calls[0][1] == ('namespace', 'collection', '2.0.1') def test_build_requirement_from_name_multiple_version_results(galaxy_server, monkeypatch): mock_get_versions = MagicMock() mock_get_versions.return_value = ['2.0.0', '2.0.1', '2.0.2', '2.0.3', '2.0.4', '2.0.5'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) actual = collection.CollectionRequirement.from_name('namespace.collection', [galaxy_server], '!=2.0.2', True, True) assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.b_path is None assert actual.api == galaxy_server assert actual.skip is False assert actual.versions == set([u'2.0.0', u'2.0.1', u'2.0.3', u'2.0.4', u'2.0.5']) assert actual.latest_version == u'2.0.5' assert actual.dependencies is None assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection') @pytest.mark.parametrize('versions, requirement, expected_filter, expected_latest', [ [['1.0.0', '1.0.1'], '*', ['1.0.0', '1.0.1'], '1.0.1'], [['1.0.0', '1.0.5', '1.1.0'], '>1.0.0,<1.1.0', ['1.0.5'], '1.0.5'], [['1.0.0', '1.0.5', '1.1.0'], '>1.0.0,<=1.0.5', ['1.0.5'], '1.0.5'], [['1.0.0', '1.0.5', '1.1.0'], '>=1.1.0', ['1.1.0'], '1.1.0'], [['1.0.0', '1.0.5', '1.1.0'], '!=1.1.0', ['1.0.0', '1.0.5'], '1.0.5'], [['1.0.0', '1.0.5', '1.1.0'], '==1.0.5', ['1.0.5'], '1.0.5'], [['1.0.0', '1.0.5', '1.1.0'], '1.0.5', ['1.0.5'], '1.0.5'], [['1.0.0', '2.0.0', '3.0.0'], '>=2', ['2.0.0', '3.0.0'], '3.0.0'], ]) def test_add_collection_requirements(versions, requirement, expected_filter, expected_latest): req = collection.CollectionRequirement('namespace', 'name', None, 'https://galaxy.com', versions, requirement, False) assert req.versions == set(expected_filter) assert req.latest_version == expected_latest def test_add_collection_requirement_to_unknown_installed_version(): req = collection.CollectionRequirement('namespace', 'name', None, 'https://galaxy.com', ['*'], '*', False, skip=True) expected = "Cannot meet requirement namespace.name:1.0.0 as it is already installed at version 'unknown'." with pytest.raises(AnsibleError, match=expected): req.add_requirement(str(req), '1.0.0') def test_add_collection_wildcard_requirement_to_unknown_installed_version(): req = collection.CollectionRequirement('namespace', 'name', None, 'https://galaxy.com', ['*'], '*', False, skip=True) req.add_requirement(str(req), '*') assert req.versions == set('*') assert req.latest_version == '*' def test_add_collection_requirement_with_conflict(galaxy_server): expected = "Cannot meet requirement ==1.0.2 for dependency namespace.name from source '%s'. Available versions " \ "before last requirement added: 1.0.0, 1.0.1\n" \ "Requirements from:\n" \ "\tbase - 'namespace.name:==1.0.2'" % galaxy_server.api_server with pytest.raises(AnsibleError, match=expected): collection.CollectionRequirement('namespace', 'name', None, galaxy_server, ['1.0.0', '1.0.1'], '==1.0.2', False) def test_add_requirement_to_existing_collection_with_conflict(galaxy_server): req = collection.CollectionRequirement('namespace', 'name', None, galaxy_server, ['1.0.0', '1.0.1'], '*', False) expected = "Cannot meet dependency requirement 'namespace.name:1.0.2' for collection namespace.collection2 from " \ "source '%s'. Available versions before last requirement added: 1.0.0, 1.0.1\n" \ "Requirements from:\n" \ "\tbase - 'namespace.name:*'\n" \ "\tnamespace.collection2 - 'namespace.name:1.0.2'" % galaxy_server.api_server with pytest.raises(AnsibleError, match=re.escape(expected)): req.add_requirement('namespace.collection2', '1.0.2') def test_add_requirement_to_installed_collection_with_conflict(): source = 'https://galaxy.ansible.com' req = collection.CollectionRequirement('namespace', 'name', None, source, ['1.0.0', '1.0.1'], '*', False, skip=True) expected = "Cannot meet requirement namespace.name:1.0.2 as it is already installed at version '1.0.1'. " \ "Use --force to overwrite" with pytest.raises(AnsibleError, match=re.escape(expected)): req.add_requirement(None, '1.0.2') def test_add_requirement_to_installed_collection_with_conflict_as_dep(): source = 'https://galaxy.ansible.com' req = collection.CollectionRequirement('namespace', 'name', None, source, ['1.0.0', '1.0.1'], '*', False, skip=True) expected = "Cannot meet requirement namespace.name:1.0.2 as it is already installed at version '1.0.1'. " \ "Use --force-with-deps to overwrite" with pytest.raises(AnsibleError, match=re.escape(expected)): req.add_requirement('namespace.collection2', '1.0.2') def test_install_skipped_collection(monkeypatch): mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) req = collection.CollectionRequirement('namespace', 'name', None, 'source', ['1.0.0'], '*', False, skip=True) req.install(None, None) assert mock_display.call_count == 1 assert mock_display.mock_calls[0][1][0] == "Skipping 'namespace.name' as it is already installed" def test_install_collection(collection_artifact, monkeypatch): mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) collection_tar = collection_artifact[1] output_path = os.path.join(os.path.split(collection_tar)[0], b'output') collection_path = os.path.join(output_path, b'ansible_namespace', b'collection') os.makedirs(os.path.join(collection_path, b'delete_me')) # Create a folder to verify the install cleans out the dir temp_path = os.path.join(os.path.split(collection_tar)[0], b'temp') os.makedirs(temp_path) req = collection.CollectionRequirement.from_tar(collection_tar, True, True) req.install(to_text(output_path), temp_path) # Ensure the temp directory is empty, nothing is left behind assert os.listdir(temp_path) == [] actual_files = os.listdir(collection_path) actual_files.sort() assert actual_files == [b'FILES.json', b'MANIFEST.json', b'README.md', b'docs', b'playbooks', b'plugins', b'roles'] assert mock_display.call_count == 1 assert mock_display.mock_calls[0][1][0] == "Installing 'ansible_namespace.collection:0.1.0' to '%s'" \ % to_text(collection_path) def test_install_collection_with_download(galaxy_server, collection_artifact, monkeypatch): collection_tar = collection_artifact[1] output_path = os.path.join(os.path.split(collection_tar)[0], b'output') collection_path = os.path.join(output_path, b'ansible_namespace', b'collection') mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) mock_download = MagicMock() mock_download.return_value = collection_tar monkeypatch.setattr(collection, '_download_file', mock_download) monkeypatch.setattr(galaxy_server, '_available_api_versions', {'v2': 'v2/'}) temp_path = os.path.join(os.path.split(collection_tar)[0], b'temp') os.makedirs(temp_path) meta = api.CollectionVersionMetadata('ansible_namespace', 'collection', '0.1.0', 'https://downloadme.com', 'myhash', {}) req = collection.CollectionRequirement('ansible_namespace', 'collection', None, galaxy_server, ['0.1.0'], '*', False, metadata=meta) req.install(to_text(output_path), temp_path) # Ensure the temp directory is empty, nothing is left behind assert os.listdir(temp_path) == [] actual_files = os.listdir(collection_path) actual_files.sort() assert actual_files == [b'FILES.json', b'MANIFEST.json', b'README.md', b'docs', b'playbooks', b'plugins', b'roles'] assert mock_display.call_count == 1 assert mock_display.mock_calls[0][1][0] == "Installing 'ansible_namespace.collection:0.1.0' to '%s'" \ % to_text(collection_path) assert mock_download.call_count == 1 assert mock_download.mock_calls[0][1][0] == 'https://downloadme.com' assert mock_download.mock_calls[0][1][1] == temp_path assert mock_download.mock_calls[0][1][2] == 'myhash' assert mock_download.mock_calls[0][1][3] is True def test_install_collections_from_tar(collection_artifact, monkeypatch): collection_path, collection_tar = collection_artifact temp_path = os.path.split(collection_tar)[0] shutil.rmtree(collection_path) mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) collection.install_collections([(to_text(collection_tar), '*', None,)], to_text(temp_path), [u'https://galaxy.ansible.com'], True, False, False, False, False) assert os.path.isdir(collection_path) actual_files = os.listdir(collection_path) actual_files.sort() assert actual_files == [b'FILES.json', b'MANIFEST.json', b'README.md', b'docs', b'playbooks', b'plugins', b'roles'] with open(os.path.join(collection_path, b'MANIFEST.json'), 'rb') as manifest_obj: actual_manifest = json.loads(to_text(manifest_obj.read())) assert actual_manifest['collection_info']['namespace'] == 'ansible_namespace' assert actual_manifest['collection_info']['name'] == 'collection' assert actual_manifest['collection_info']['version'] == '0.1.0' # Filter out the progress cursor display calls. display_msgs = [m[1][0] for m in mock_display.mock_calls if 'newline' not in m[2]] assert len(display_msgs) == 3 assert display_msgs[0] == "Process install dependency map" assert display_msgs[1] == "Starting collection install process" assert display_msgs[2] == "Installing 'ansible_namespace.collection:0.1.0' to '%s'" % to_text(collection_path) def test_install_collections_existing_without_force(collection_artifact, monkeypatch): collection_path, collection_tar = collection_artifact temp_path = os.path.split(collection_tar)[0] mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) # If we don't delete collection_path it will think the original build skeleton is installed so we expect a skip collection.install_collections([(to_text(collection_tar), '*', None,)], to_text(temp_path), [u'https://galaxy.ansible.com'], True, False, False, False, False) assert os.path.isdir(collection_path) actual_files = os.listdir(collection_path) actual_files.sort() assert actual_files == [b'README.md', b'docs', b'galaxy.yml', b'playbooks', b'plugins', b'roles'] # Filter out the progress cursor display calls. display_msgs = [m[1][0] for m in mock_display.mock_calls if 'newline' not in m[2]] assert len(display_msgs) == 4 # Msg1 is the warning about not MANIFEST.json, cannot really check message as it has line breaks which varies based # on the path size assert display_msgs[1] == "Process install dependency map" assert display_msgs[2] == "Starting collection install process" assert display_msgs[3] == "Skipping 'ansible_namespace.collection' as it is already installed" # Makes sure we don't get stuck in some recursive loop @pytest.mark.parametrize('collection_artifact', [ {'ansible_namespace.collection': '>=0.0.1'}, ], indirect=True) def test_install_collection_with_circular_dependency(collection_artifact, monkeypatch): collection_path, collection_tar = collection_artifact temp_path = os.path.split(collection_tar)[0] shutil.rmtree(collection_path) mock_display = MagicMock() monkeypatch.setattr(Display, 'display', mock_display) collection.install_collections([(to_text(collection_tar), '*', None,)], to_text(temp_path), [u'https://galaxy.ansible.com'], True, False, False, False, False) assert os.path.isdir(collection_path) actual_files = os.listdir(collection_path) actual_files.sort() assert actual_files == [b'FILES.json', b'MANIFEST.json', b'README.md', b'docs', b'playbooks', b'plugins', b'roles'] with open(os.path.join(collection_path, b'MANIFEST.json'), 'rb') as manifest_obj: actual_manifest = json.loads(to_text(manifest_obj.read())) assert actual_manifest['collection_info']['namespace'] == 'ansible_namespace' assert actual_manifest['collection_info']['name'] == 'collection' assert actual_manifest['collection_info']['version'] == '0.1.0' # Filter out the progress cursor display calls. display_msgs = [m[1][0] for m in mock_display.mock_calls if 'newline' not in m[2]] assert len(display_msgs) == 3 assert display_msgs[0] == "Process install dependency map" assert display_msgs[1] == "Starting collection install process" assert display_msgs[2] == "Installing 'ansible_namespace.collection:0.1.0' to '%s'" % to_text(collection_path)
kvar/ansible
test/units/galaxy/test_collection_install.py
Python
gpl-3.0
31,481
[ "Galaxy" ]
d3401cbce8e96cc32304bc7a52cec337f2c4d04116954ac62d727e3589c89c2c
#!/usr/bin/env python # -*- coding: utf-8 -*- # # king_phisher/client/graphs.py # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the name of the project nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import collections import string from king_phisher import color from king_phisher import ipaddress from king_phisher import its from king_phisher import ua_parser from king_phisher import utilities from king_phisher.client import client_rpc from king_phisher.client import gui_utilities from king_phisher.client.widget import extras from king_phisher.constants import ColorHexCode from king_phisher.constants import OSFamily from boltons import iterutils from gi.repository import Gtk from smoke_zephyr.requirements import check_requirements from smoke_zephyr.utilities import unique try: import matplotlib matplotlib.rcParams['backend'] = 'GTK3Cairo' from matplotlib import dates from matplotlib import patches from matplotlib import pyplot from matplotlib import ticker from matplotlib import lines from matplotlib.backends.backend_gtk3cairo import FigureCanvasGTK3Cairo as FigureCanvas from matplotlib.backends.backend_gtk3cairo import FigureManagerGTK3Cairo as FigureManager from matplotlib.backends.backend_gtk3 import NavigationToolbar2GTK3 as NavigationToolbar except ImportError: has_matplotlib = False """Whether the :py:mod:`matplotlib` module is available.""" else: if not its.frozen and check_requirements(['matplotlib>=1.5.1']): has_matplotlib = False else: has_matplotlib = True try: import mpl_toolkits.basemap except ImportError: has_matplotlib_basemap = False """Whether the :py:mod:`mpl_toolkits.basemap` module is available.""" else: if not its.frozen and check_requirements(['basemap>=1.0.7']): has_matplotlib_basemap = False else: has_matplotlib_basemap = True EXPORTED_GRAPHS = {} MPL_COLOR_NULL = 'darkcyan' __all__ = ('export_graph_provider', 'get_graph', 'get_graphs', 'CampaignGraph') def export_graph_provider(cls): """ Decorator to mark classes as valid graph providers. This decorator also sets the :py:attr:`~.CampaignGraph.name` attribute. :param class cls: The class to mark as a graph provider. :return: The *cls* parameter is returned. """ if not issubclass(cls, CampaignGraph): raise RuntimeError("{0} is not a subclass of CampaignGraph".format(cls.__name__)) if not cls.is_available: return None graph_name = cls.__name__[13:] cls.name = graph_name EXPORTED_GRAPHS[graph_name] = cls return cls def get_graph(graph_name): """ Return the graph providing class for *graph_name*. The class providing the specified graph must have been previously exported using :py:func:`.export_graph_provider`. :param str graph_name: The name of the graph provider. :return: The graph provider class. :rtype: :py:class:`.CampaignGraph` """ return EXPORTED_GRAPHS.get(graph_name) def get_graphs(): """ Get a list of all registered graph providers. :return: All registered graph providers. :rtype: list """ return sorted(EXPORTED_GRAPHS.keys()) class GraphBase(object): """ A basic graph provider for using :py:mod:`matplotlib` to create graph representations of campaign data. This class is meant to be subclassed by real providers. """ name = 'Unknown' """The name of the graph provider.""" name_human = 'Unknown' """The human readable name of the graph provider used for UI identification.""" graph_title = 'Unknown' """The title that will be given to the graph.""" table_subscriptions = [] """A list of tables from which information is needed to produce the graph.""" is_available = True def __init__(self, application, size_request=None, style_context=None): """ :param tuple size_request: The size to set for the canvas. """ self.application = application self.style_context = style_context self.config = application.config """A reference to the King Phisher client configuration.""" self.figure, _ = pyplot.subplots() self.figure.set_facecolor(self.get_color('bg', ColorHexCode.WHITE)) self.axes = self.figure.get_axes() self.canvas = FigureCanvas(self.figure) self.manager = None self.minimum_size = (380, 200) """An absolute minimum size for the canvas.""" if size_request is not None: self.resize(*size_request) self.canvas.mpl_connect('button_press_event', self.mpl_signal_canvas_button_pressed) self.canvas.show() self.navigation_toolbar = NavigationToolbar(self.canvas, self.application.get_active_window()) self.popup_menu = Gtk.Menu.new() menu_item = Gtk.MenuItem.new_with_label('Export') menu_item.connect('activate', self.signal_activate_popup_menu_export) self.popup_menu.append(menu_item) menu_item = Gtk.MenuItem.new_with_label('Refresh') menu_item.connect('activate', self.signal_activate_popup_refresh) self.popup_menu.append(menu_item) menu_item = Gtk.CheckMenuItem.new_with_label('Show Toolbar') menu_item.connect('toggled', self.signal_toggled_popup_menu_show_toolbar) self._menu_item_show_toolbar = menu_item self.popup_menu.append(menu_item) self.popup_menu.show_all() self.navigation_toolbar.hide() self._legend = None @property def rpc(self): return self.application.rpc @staticmethod def _ax_hide_ticks(ax): for tick in ax.yaxis.get_major_ticks(): tick.tick1On = False tick.tick2On = False @staticmethod def _ax_set_spine_color(ax, spine_color): for pos in ('top', 'right', 'bottom', 'left'): ax.spines[pos].set_color(spine_color) def add_legend_patch(self, legend_rows, fontsize=None): if self._legend is not None: self._legend.remove() self._legend = None fontsize = fontsize or self.fontsize_scale legend_bbox = self.figure.legend( tuple(patches.Patch(color=patch_color) for patch_color, _ in legend_rows), tuple(label for _, label in legend_rows), borderaxespad=1.25, fontsize=fontsize, frameon=True, handlelength=1.5, handletextpad=0.75, labelspacing=0.3, loc='lower right' ) legend_bbox.legendPatch.set_linewidth(0) self._legend = legend_bbox def get_color(self, color_name, default): """ Get a color by its style name such as 'fg' for foreground. If the specified color does not exist, default will be returned. The underlying logic for this function is provided by :py:func:`~.gui_utilities.gtk_style_context_get_color`. :param str color_name: The style name of the color. :param default: The default color to return if the specified one was not found. :return: The desired color if it was found. :rtype: tuple """ color_name = 'theme_color_graph_' + color_name sc_color = gui_utilities.gtk_style_context_get_color(self.style_context, color_name, default) return (sc_color.red, sc_color.green, sc_color.blue) def make_window(self): """ Create a window from the figure manager. :return: The graph in a new, dedicated window. :rtype: :py:class:`Gtk.Window` """ if self.manager is None: self.manager = FigureManager(self.canvas, 0) self.navigation_toolbar.destroy() self.navigation_toolbar = self.manager.toolbar self._menu_item_show_toolbar.set_active(True) window = self.manager.window window.set_transient_for(self.application.get_active_window()) window.set_title(self.graph_title) return window @property def fontsize_scale(self): scale = self.markersize_scale if scale < 5: fontsize = 'xx-small' elif scale < 7: fontsize = 'x-small' elif scale < 9: fontsize = 'small' else: fontsize = 'medium' return fontsize @property def markersize_scale(self): bbox = self.axes[0].get_window_extent().transformed(self.figure.dpi_scale_trans.inverted()) return bbox.width * self.figure.dpi * 0.01 def mpl_signal_canvas_button_pressed(self, event): if event.button != 3: return self.popup_menu.popup(None, None, None, None, event.button, Gtk.get_current_event_time()) return True def signal_activate_popup_menu_export(self, action): dialog = extras.FileChooserDialog('Export Graph', self.application.get_active_window()) file_name = self.config['campaign_name'] + '.png' response = dialog.run_quick_save(file_name) dialog.destroy() if not response: return destination_file = response['target_path'] self.figure.savefig(destination_file, format='png') def signal_activate_popup_refresh(self, event): self.refresh() def signal_toggled_popup_menu_show_toolbar(self, widget): if widget.get_property('active'): self.navigation_toolbar.show() else: self.navigation_toolbar.hide() def resize(self, width=0, height=0): """ Attempt to resize the canvas. Regardless of the parameters the canvas will never be resized to be smaller than :py:attr:`.minimum_size`. :param int width: The desired width of the canvas. :param int height: The desired height of the canvas. """ min_width, min_height = self.minimum_size width = max(width, min_width) height = max(height, min_height) self.canvas.set_size_request(width, height) class CampaignGraph(GraphBase): """ Graph format used for the graphs generated in the dashboard and in the create graphs tab. """ def _load_graph(self, info_cache): raise NotImplementedError() def load_graph(self): """Load the graph information via :py:meth:`.refresh`.""" self.refresh() def refresh(self, info_cache=None, stop_event=None): """ Refresh the graph data by retrieving the information from the remote server. :param dict info_cache: An optional cache of data tables. :param stop_event: An optional object indicating that the operation should stop. :type stop_event: :py:class:`threading.Event` :return: A dictionary of cached tables from the server. :rtype: dict """ info_cache = (info_cache or {}) if not self.rpc: return info_cache for table in self.table_subscriptions: if stop_event and stop_event.is_set(): return info_cache if not table in info_cache: query_filter = None if 'campaign_id' in client_rpc.database_table_objects[table].__slots__: query_filter = {'campaign_id': self.config['campaign_id']} info_cache[table] = tuple(self.rpc.remote_table(table, query_filter=query_filter)) for ax in self.axes: ax.clear() if self._legend is not None: self._legend.remove() self._legend = None self._load_graph(info_cache) self.figure.suptitle( self.graph_title, color=self.get_color('fg', ColorHexCode.BLACK), size=14, weight='bold', y=0.97 ) self.canvas.draw() return info_cache class CampaignBarGraph(CampaignGraph): yticklabel_fmt = "{0:,}" def __init__(self, *args, **kwargs): super(CampaignBarGraph, self).__init__(*args, **kwargs) self.figure.subplots_adjust(top=0.85, right=0.85, bottom=0.05, left=0.225) ax = self.axes[0] ax.tick_params( axis='both', top='off', right='off', bottom='off', left='off', labelbottom='off' ) ax.invert_yaxis() self.axes.append(ax.twinx()) def _barh(self, ax, bars, height, max_bars=None): # define the necessary colors color_bg = self.get_color('bg', ColorHexCode.WHITE) color_bar_bg = self.get_color('bar_bg', ColorHexCode.GRAY) color_bar_fg = self.get_color('bar_fg', ColorHexCode.BLACK) ax.set_axis_bgcolor(color_bg) self.resize(height=60 + 20 * len(bars)) # draw the foreground / filled bar bar_container = ax.barh( range(len(bars)), bars, height=height, color=color_bar_fg, linewidth=0 ) # draw the background / unfilled bar largest_bar = (max(bars) if len(bars) else 0) ax.barh( range(len(bars)), [largest_bar - bar for bar in bars], left=bars, height=height, color=color_bar_bg, linewidth=0 ) return bar_container def _load_graph(self, info_cache): raise NotImplementedError() def _graph_null_bar(self, title): return self.graph_bar([0], 1, [''], xlabel=title) def graph_bar(self, bars, max_bars, yticklabels, xlabel=None): """ Create a horizontal bar graph with better defaults for the standard use cases. :param list bars: The values of the bars to graph. :param int max_bars: The number to treat as the logical maximum number of plotted bars. :param list yticklabels: The labels to use on the x-axis. :param str xlabel: The label to give to the y-axis. :return: The bars created using :py:mod:`matplotlib` :rtype: `matplotlib.container.BarContainer` """ height = 0.275 color_bg = self.get_color('bg', ColorHexCode.WHITE) color_fg = self.get_color('fg', ColorHexCode.BLACK) ax1, ax2 = self.axes # primary axis bar_container = self._barh(ax1, bars, height, max_bars) yticks = [float(y) + (height / 2) for y in range(len(bars))] # this makes the top bar shorter than the rest # ax1.set_ybound(0, max(len(bars), max_bars)) ax1.set_yticks(yticks) ax1.set_yticklabels(yticklabels, color=color_fg, size=10) ax2.set_yticks(yticks) ax2.set_yticklabels([self.yticklabel_fmt.format(bar) for bar in bars], color=color_fg, size=12) ax2.set_ylim(ax1.get_ylim()) # remove the y-axis tick marks self._ax_hide_ticks(ax1) self._ax_hide_ticks(ax2) self._ax_set_spine_color(ax1, color_bg) self._ax_set_spine_color(ax2, color_bg) if xlabel: ax1.set_xlabel(xlabel, color=color_fg, size=12) return bar_container class CampaignLineGraph(CampaignGraph): def __init__(self, *args, **kwargs): super(CampaignLineGraph, self).__init__(*args, **kwargs) def _load_graph(self, info_cache): raise NotImplementedError() class CampaignPieGraph(CampaignGraph): def __init__(self, *args, **kwargs): super(CampaignPieGraph, self).__init__(*args, **kwargs) self.figure.subplots_adjust(top=0.85, right=0.75, bottom=0.05, left=0.05) def _load_graph(self, info_cache): raise NotImplementedError() def _graph_null_pie(self, title): ax = self.axes[0] ax.pie( (100,), autopct='%1.0f%%', colors=(self.get_color('pie_low', ColorHexCode.GRAY),), labels=(title,), shadow=True, startangle=225, textprops={'color': self.get_color('fg', ColorHexCode.BLACK)} ) ax.axis('equal') return def graph_pie(self, parts, autopct=None, labels=None, legend_labels=None): colors = color.get_scale( self.get_color('pie_low', ColorHexCode.BLACK), self.get_color('pie_high', ColorHexCode.GRAY), len(parts), ascending=False ) ax = self.axes[0] pie = ax.pie( parts, autopct=autopct, colors=colors, explode=[0.1] + ([0] * (len(parts) - 1)), labels=labels or tuple("{0:.1f}%".format(p) for p in parts), labeldistance=1.15, shadow=True, startangle=45, textprops={'color': self.get_color('fg', ColorHexCode.BLACK)}, wedgeprops={'linewidth': 0} ) ax.axis('equal') if legend_labels is not None: self.add_legend_patch(tuple(zip(colors, legend_labels)), fontsize='x-small') return pie @export_graph_provider class CampaignGraphDepartmentComparison(CampaignBarGraph): """Display a graph which compares the different departments.""" graph_title = 'Department Comparison' name_human = 'Bar - Department Comparison' table_subscriptions = ('company_departments', 'messages', 'visits') yticklabel_fmt = "{0:.01f}%" def _load_graph(self, info_cache): departments = info_cache['company_departments'] departments = dict((department.id, department.name) for department in departments) messages = info_cache['messages'] message_departments = dict((message.id, departments[message.company_department_id]) for message in messages if message.company_department_id is not None) if not len(message_departments): self._graph_null_bar('') return messages = [message for message in messages if message.id in message_departments] visits = info_cache['visits'] visits = [visit for visit in visits if visit.message_id in message_departments] visits = unique(visits, key=lambda visit: visit.message_id) department_visits = collections.Counter() department_visits.update(message_departments[visit.message_id] for visit in visits) department_totals = collections.Counter() department_totals.update(message_departments[message.id] for message in messages) department_scores = dict((department, (float(department_visits[department]) / float(total)) * 100) for department, total in department_totals.items()) department_scores = sorted(department_scores.items(), key=lambda x: (x[1], x[0]), reverse=True) department_scores = collections.OrderedDict(department_scores) yticklabels, bars = zip(*department_scores.items()) self.graph_bar(bars, len(yticklabels), yticklabels) return @export_graph_provider class CampaignGraphOverview(CampaignBarGraph): """Display a graph which represents an overview of the campaign.""" graph_title = 'Campaign Overview' name_human = 'Bar - Campaign Overview' table_subscriptions = ('credentials', 'visits') def _load_graph(self, info_cache): rpc = self.rpc visits = info_cache['visits'] creds = info_cache['credentials'] messages_count = rpc('db/table/count', 'messages', query_filter={'campaign_id': self.config['campaign_id']}) messages_not_opened = rpc('db/table/count', 'messages', query_filter={'campaign_id': self.config['campaign_id'], 'opened': None}) bars = [] bars.append(messages_count) bars.append(messages_count - messages_not_opened) bars.append(len(visits)) bars.append(len(unique(visits, key=lambda visit: visit.message_id))) if len(creds): bars.append(len(creds)) bars.append(len(unique(creds, key=lambda cred: cred.message_id))) yticklabels = ('Messages', 'Opened', 'Visits', 'Unique\nVisits', 'Credentials', 'Unique\nCredentials') self.graph_bar(bars, len(yticklabels), yticklabels[:len(bars)]) return @export_graph_provider class CampaignGraphVisitorInfo(CampaignBarGraph): """Display a graph which shows the different operating systems seen from visitors.""" graph_title = 'Campaign Visitor OS Information' name_human = 'Bar - Visitor OS Information' table_subscriptions = ('visits',) def _load_graph(self, info_cache): visits = info_cache['visits'] operating_systems = collections.Counter() for visit in visits: user_agent = None if visit.visitor_details: user_agent = ua_parser.parse_user_agent(visit.visitor_details) operating_systems.update([user_agent.os_name if user_agent and user_agent.os_name else 'Unknown OS']) os_names = sorted(operating_systems.keys()) bars = [operating_systems[os_name] for os_name in os_names] self.graph_bar(bars, len(OSFamily), os_names) return @export_graph_provider class CampaignGraphVisitorInfoPie(CampaignPieGraph): """Display a graph which compares the different operating systems seen from visitors.""" graph_title = 'Campaign Visitor OS Information' name_human = 'Pie - Visitor OS Information' table_subscriptions = ('visits',) def _load_graph(self, info_cache): visits = info_cache['visits'] if not len(visits): self._graph_null_pie('No Visitor Information') return operating_systems = collections.Counter() for visit in visits: ua = ua_parser.parse_user_agent(visit.visitor_details) operating_systems.update([ua.os_name or 'Unknown OS' if ua else 'Unknown OS']) (os_names, count) = tuple(zip(*reversed(sorted(operating_systems.items(), key=lambda item: item[1])))) self.graph_pie(count, labels=tuple("{0:,}".format(os) for os in count), legend_labels=os_names) return @export_graph_provider class CampaignGraphVisitsTimeline(CampaignLineGraph): """Display a graph which represents the visits of a campaign over time.""" graph_title = 'Campaign Visits Timeline' name_human = 'Line - Visits Timeline' table_subscriptions = ('visits',) def _load_graph(self, info_cache): # define the necessary colors color_bg = self.get_color('bg', ColorHexCode.WHITE) color_fg = self.get_color('fg', ColorHexCode.BLACK) color_line_bg = self.get_color('line_bg', ColorHexCode.WHITE) color_line_fg = self.get_color('line_fg', ColorHexCode.BLACK) visits = info_cache['visits'] first_visits = [utilities.datetime_utc_to_local(visit.first_visit) for visit in visits] ax = self.axes[0] ax.tick_params( axis='both', which='both', colors=color_fg, top='off', bottom='off' ) ax.set_axis_bgcolor(color_line_bg) ax.set_ylabel('Number of Visits', color=self.get_color('fg', ColorHexCode.WHITE), size=10) self._ax_hide_ticks(ax) self._ax_set_spine_color(ax, color_bg) if not len(first_visits): ax.set_yticks((0,)) ax.set_xticks((0,)) return first_visits.sort() ax.plot_date( first_visits, range(1, len(first_visits) + 1), '-', color=color_line_fg, linewidth=6 ) self.figure.autofmt_xdate() self.figure.subplots_adjust(top=0.85, right=0.95, bottom=0.25, left=0.1) locator = dates.AutoDateLocator() ax.xaxis.set_major_locator(locator) ax.xaxis.set_major_formatter(dates.AutoDateFormatter(locator)) return @export_graph_provider class CampaignGraphMessageResults(CampaignPieGraph): """Display the percentage of messages which resulted in a visit.""" graph_title = 'Campaign Message Results' name_human = 'Pie - Message Results' table_subscriptions = ('credentials', 'visits') def _load_graph(self, info_cache): rpc = self.rpc messages_count = rpc('db/table/count', 'messages', query_filter={'campaign_id': self.config['campaign_id']}) if not messages_count: self._graph_null_pie('No Messages Sent') return visits_count = len(unique(info_cache['visits'], key=lambda visit: visit.message_id)) credentials_count = len(unique(info_cache['credentials'], key=lambda cred: cred.message_id)) if not credentials_count <= visits_count <= messages_count: raise ValueError('credential visit and message counts are inconsistent') labels = ['Without Visit', 'With Visit', 'With Credentials'] sizes = [] sizes.append((float(messages_count - visits_count) / float(messages_count)) * 100) sizes.append((float(visits_count - credentials_count) / float(messages_count)) * 100) sizes.append((float(credentials_count) / float(messages_count)) * 100) if not credentials_count: labels.pop() sizes.pop() if not visits_count: labels.pop() sizes.pop() self.graph_pie(sizes, legend_labels=labels) return class CampaignGraphVisitsMap(CampaignGraph): """A base class to display a map which shows the locations of visit origins.""" graph_title = 'Campaign Visit Locations' table_subscriptions = ('credentials', 'visits') is_available = has_matplotlib_basemap draw_states = False def _load_graph(self, info_cache): visits = unique(info_cache['visits'], key=lambda visit: visit.message_id) cred_ips = set(cred.message_id for cred in info_cache['credentials']) cred_ips = set([visit.visitor_ip for visit in visits if visit.message_id in cred_ips]) color_fg = self.get_color('fg', ColorHexCode.BLACK) color_land = self.get_color('map_land', ColorHexCode.GRAY) color_water = self.get_color('map_water', ColorHexCode.WHITE) ax = self.axes[0] bm = mpl_toolkits.basemap.Basemap(resolution='c', ax=ax, **self.basemap_args) if self.draw_states: bm.drawstates() bm.drawcoastlines() bm.drawcountries() bm.fillcontinents(color=color_land, lake_color=color_water) parallels = bm.drawparallels( (-60, -30, 0, 30, 60), labels=(1, 1, 0, 0) ) self._map_set_line_color(parallels, color_fg) meridians = bm.drawmeridians( (0, 90, 180, 270), labels=(0, 0, 0, 1) ) self._map_set_line_color(meridians, color_fg) bm.drawmapboundary( fill_color=color_water, linewidth=0 ) if not visits: return ctr = collections.Counter() ctr.update([visit.visitor_ip for visit in visits]) base_markersize = self.markersize_scale base_markersize = max(base_markersize, 3.05) base_markersize = min(base_markersize, 9) self._plot_visitor_map_points(bm, ctr, base_markersize, cred_ips) self.add_legend_patch(((self.color_with_creds, 'With Credentials'), (self.color_without_creds, 'Without Credentials'))) return def _resolve_geolocations(self, all_ips): geo_locations = {} public_ips = [] for visitor_ip in all_ips: ip = ipaddress.ip_address(visitor_ip) if ip.is_private or ip.is_loopback: continue public_ips.append(visitor_ip) public_ips.sort() for ip_chunk in iterutils.chunked(public_ips, 100): geo_locations.update(self.rpc.geoip_lookup_multi(ip_chunk)) return geo_locations def _plot_visitor_map_points(self, bm, ctr, base_markersize, cred_ips): o_high = float(max(ctr.values())) o_low = float(min(ctr.values())) color_with_creds = self.color_with_creds color_without_creds = self.color_without_creds geo_locations = self._resolve_geolocations(ctr.keys()) for visitor_ip, geo_location in geo_locations.items(): if not (geo_location.coordinates.longitude and geo_location.coordinates.latitude): continue occurrences = ctr[visitor_ip] pts = bm(geo_location.coordinates.longitude, geo_location.coordinates.latitude) if o_high == o_low: markersize = 2.0 else: markersize = 1.0 + (float(occurrences) - o_low) / (o_high - o_low) markersize = markersize * base_markersize bm.plot( pts[0], pts[1], 'o', markeredgewidth=0, markerfacecolor=(color_with_creds if visitor_ip in cred_ips else color_without_creds), markersize=markersize ) return def _map_set_line_color(self, map_lines, line_color): for sub_lines, texts in map_lines.values(): for line in sub_lines: line.set_color(line_color) for text in texts: text.set_color(line_color) @property def color_with_creds(self): return self.get_color('map_marker1', ColorHexCode.RED) @property def color_without_creds(self): return self.get_color('map_marker2', ColorHexCode.YELLOW) @export_graph_provider class CampaignGraphVisitsMapUSA(CampaignGraphVisitsMap): """Display a map of the USA which shows the locations of visit origins.""" name_human = 'Map - Visit Locations (USA)' draw_states = True basemap_args = dict(projection='lcc', lat_1=30, lon_0=-90, llcrnrlon=-122.5, llcrnrlat=12.5, urcrnrlon=-45, urcrnrlat=50) @export_graph_provider class CampaignGraphVisitsMapWorld(CampaignGraphVisitsMap): """Display a map of the world which shows the locations of visit origins.""" name_human = 'Map - Visit Locations (World)' basemap_args = dict(projection='kav7', lon_0=0) @export_graph_provider class CampaignGraphPasswordComplexityPie(CampaignPieGraph): """Display a graph which displays the number of passwords which meet standard complexity requirements.""" graph_title = 'Campaign Password Complexity' name_human = 'Pie - Password Complexity' table_subscriptions = ('credentials',) def _load_graph(self, info_cache): passwords = set(cred.password for cred in info_cache['credentials']) if not len(passwords): self._graph_null_pie('No Credential Information') return ctr = collections.Counter() ctr.update(self._check_complexity(password) for password in passwords) self.graph_pie((ctr[True], ctr[False]), autopct='%1.1f%%', legend_labels=('Complex', 'Not Complex')) return def _check_complexity(self, password): if len(password) < 8: return False met = 0 for char_set in (string.ascii_uppercase, string.ascii_lowercase, string.digits, string.punctuation): for char in password: if char in char_set: met += 1 break return met >= 3 class CampaignCompGraph(GraphBase): """ Display selected campaigns data by order of campaign start date.""" graph_title = 'Campaign Comparison Graph' name_human = 'Graph' def __init__(self, *args, **kwargs): super(CampaignCompGraph, self).__init__(*args, **kwargs) ax = self.axes[0] self.axes.append(ax.twinx()) ax2 = self.axes[1] self._config_axes(ax, ax2) self._campaigns = [] def _calc(self, stats, key, comp_key='messages'): return 0 if stats[comp_key] == 0 else (float(stats[key]) / stats[comp_key]) * 100 def _config_axes(self, ax, ax2): # define the necessary colors color_bg = self.get_color('bg', ColorHexCode.WHITE) color_fg = self.get_color('fg', ColorHexCode.BLACK) color_line_bg = self.get_color('line_bg', ColorHexCode.WHITE) ax.tick_params( axis='both', which='both', colors=color_fg, top='off', bottom='off' ) ax2.tick_params( axis='both', which='both', colors=color_fg, top='off', bottom='off' ) ax.set_axis_bgcolor(color_line_bg) ax2.set_axis_bgcolor(color_line_bg) title = pyplot.title('Campaign Comparison', color=color_fg, size=self.markersize_scale * 1.75, loc='left') title.set_position([0.075, 1.05]) ax.set_ylabel('Percent Visits/Credentials', color=color_fg, size=self.markersize_scale * 1.5) ax.set_xlabel('Campaign Name', color=color_fg, size=self.markersize_scale * 1.5) self._ax_hide_ticks(ax) self._ax_hide_ticks(ax2) ax2.set_ylabel('Messages', color=color_fg, size=self.markersize_scale * 1.25, rotation=270, labelpad=20) self._ax_set_spine_color(ax, color_bg) self._ax_set_spine_color(ax2, color_bg) ax2.get_yaxis().set_major_locator(ticker.MaxNLocator(integer=True)) ax.tick_params(axis='x', labelsize=10, pad=5) def load_graph(self, campaigns): """ Load the information to compare the specified and paint it to the canvas. Campaigns are graphed on the X-axis in the order that they are provided. No sorting of campaigns is done by this method. :param tuple campaigns: A tuple containing campaign IDs to compare. """ ax = self.axes[0] ax2 = self.axes[1] ax.clear() ax2.clear() self._config_axes(ax, ax2) rpc = self.rpc ellipsize = lambda text: (text if len(text) < 20 else text[:17] + '...') visits_line_color = self.get_color('line_fg', ColorHexCode.RED) creds_line_color = self.get_color('map_marker1', ColorHexCode.BLACK) messages_color = '#046D8B' trained_color = '#77c67f' ax.grid(True) ax.set_xticks(range(len(campaigns))) ax.set_xticklabels([ellipsize(rpc.remote_table_row('campaigns', cid).name) for cid in campaigns]) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(self.markersize_scale * 1.25) labels = ax.get_xticklabels() pyplot.setp(labels, rotation=15) self._campaigns = campaigns campaigns = [rpc('/campaign/stats', cid) for cid in campaigns] ax2.plot([stats['messages'] for stats in campaigns], label='Messages', color=messages_color, lw=3) if sum(stats['messages-trained'] for stats in campaigns): ax.plot([self._calc(stats, 'messages-trained', 'visits-unique') for stats in campaigns], label='Trained (Visited)', color=trained_color, lw=3) ax.plot([self._calc(stats, 'messages-trained') for stats in campaigns], label='Trained (All)', color=trained_color, lw=3, ls='dashed') ax.plot([self._calc(stats, 'visits') for stats in campaigns], label='Visits', color=visits_line_color, lw=3) ax.plot([self._calc(stats, 'visits-unique') for stats in campaigns], label='Unique Visits', color=visits_line_color, lw=3, ls='dashed') if sum(stats['credentials'] for stats in campaigns): ax.plot([self._calc(stats, 'credentials') for stats in campaigns], label='Credentials', color=creds_line_color, lw=3) ax.plot([self._calc(stats, 'credentials-unique') for stats in campaigns], label='Unique Credentials', color=creds_line_color, lw=3, ls='dashed') ax.set_ylim((0, 100)) ax2.set_ylim(bottom=0) self.canvas.set_size_request(500 + 50 * (len(campaigns) - 1), 500) legend_patch = [ (visits_line_color, 'solid', 'Visits'), (visits_line_color, 'dotted', 'Unique Visits') ] if sum(stats['credentials'] for stats in campaigns): legend_patch.extend([ (creds_line_color, 'solid', 'Credentials'), (creds_line_color, 'dotted', 'Unique Credentials') ]) if sum(stats['messages-trained'] for stats in campaigns): legend_patch.extend([ (trained_color, 'solid', 'Trained (Visited)'), (trained_color, 'dotted', 'Trained (All)') ]) legend_patch.append( (messages_color, 'solid', 'Messages') ) self.add_legend_patch(legend_patch) pyplot.tight_layout() def add_legend_patch(self, legend_rows, fontsize=None): if self._legend is not None: self._legend.remove() self._legend = None legend_bbox = self.figure.legend( tuple(lines.Line2D([], [], color=patch_color, lw=3, ls=style) for patch_color, style, _ in legend_rows), tuple(label for _, _, label in legend_rows), borderaxespad=1, columnspacing=1.5, fontsize=self.fontsize_scale, ncol=3, frameon=True, handlelength=2, handletextpad=0.5, labelspacing=0.5, loc='upper right' ) legend_bbox.get_frame().set_facecolor(self.get_color('line_bg', ColorHexCode.GRAY)) for text in legend_bbox.get_texts(): text.set_color('white') legend_bbox.legendPatch.set_linewidth(0) self._legend = legend_bbox def refresh(self): self.load_graph(self._campaigns)
hdemeyer/king-phisher
king_phisher/client/graphs.py
Python
bsd-3-clause
33,983
[ "VisIt" ]
8904e023ac5166cf81128057504e09fa41f93a7b8b3202f90eb933ecaba58511
from __future__ import division, absolute_import, print_function import os import sys import types import re import warnings from numpy.core.numerictypes import issubclass_, issubsctype, issubdtype from numpy.core import ndarray, ufunc, asarray import numpy as np # getargspec and formatargspec were removed in Python 3.6 from numpy.compat import getargspec, formatargspec __all__ = [ 'issubclass_', 'issubsctype', 'issubdtype', 'deprecate', 'deprecate_with_doc', 'get_include', 'info', 'source', 'who', 'lookfor', 'byte_bounds', 'safe_eval' ] def get_include(): """ Return the directory that contains the NumPy \\*.h header files. Extension modules that need to compile against NumPy should use this function to locate the appropriate include directory. Notes ----- When using ``distutils``, for example in ``setup.py``. :: import numpy as np ... Extension('extension_name', ... include_dirs=[np.get_include()]) ... """ import numpy if numpy.show_config is None: # running from numpy source directory d = os.path.join(os.path.dirname(numpy.__file__), 'core', 'include') else: # using installed numpy core headers import numpy.core as core d = os.path.join(os.path.dirname(core.__file__), 'include') return d def _set_function_name(func, name): func.__name__ = name return func class _Deprecate(object): """ Decorator class to deprecate old functions. Refer to `deprecate` for details. See Also -------- deprecate """ def __init__(self, old_name=None, new_name=None, message=None): self.old_name = old_name self.new_name = new_name self.message = message def __call__(self, func, *args, **kwargs): """ Decorator call. Refer to ``decorate``. """ old_name = self.old_name new_name = self.new_name message = self.message import warnings if old_name is None: try: old_name = func.__name__ except AttributeError: old_name = func.__name__ if new_name is None: depdoc = "`%s` is deprecated!" % old_name else: depdoc = "`%s` is deprecated, use `%s` instead!" % \ (old_name, new_name) if message is not None: depdoc += "\n" + message def newfunc(*args,**kwds): """`arrayrange` is deprecated, use `arange` instead!""" warnings.warn(depdoc, DeprecationWarning, stacklevel=2) return func(*args, **kwds) newfunc = _set_function_name(newfunc, old_name) doc = func.__doc__ if doc is None: doc = depdoc else: doc = '\n\n'.join([depdoc, doc]) newfunc.__doc__ = doc try: d = func.__dict__ except AttributeError: pass else: newfunc.__dict__.update(d) return newfunc def deprecate(*args, **kwargs): """ Issues a DeprecationWarning, adds warning to `old_name`'s docstring, rebinds ``old_name.__name__`` and returns the new function object. This function may also be used as a decorator. Parameters ---------- func : function The function to be deprecated. old_name : str, optional The name of the function to be deprecated. Default is None, in which case the name of `func` is used. new_name : str, optional The new name for the function. Default is None, in which case the deprecation message is that `old_name` is deprecated. If given, the deprecation message is that `old_name` is deprecated and `new_name` should be used instead. message : str, optional Additional explanation of the deprecation. Displayed in the docstring after the warning. Returns ------- old_func : function The deprecated function. Examples -------- Note that ``olduint`` returns a value after printing Deprecation Warning: >>> olduint = np.deprecate(np.uint) >>> olduint(6) /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114: DeprecationWarning: uint32 is deprecated warnings.warn(str1, DeprecationWarning, stacklevel=2) 6 """ # Deprecate may be run as a function or as a decorator # If run as a function, we initialise the decorator class # and execute its __call__ method. if args: fn = args[0] args = args[1:] # backward compatibility -- can be removed # after next release if 'newname' in kwargs: kwargs['new_name'] = kwargs.pop('newname') if 'oldname' in kwargs: kwargs['old_name'] = kwargs.pop('oldname') return _Deprecate(*args, **kwargs)(fn) else: return _Deprecate(*args, **kwargs) deprecate_with_doc = lambda msg: _Deprecate(message=msg) #-------------------------------------------- # Determine if two arrays can share memory #-------------------------------------------- def byte_bounds(a): """ Returns pointers to the end-points of an array. Parameters ---------- a : ndarray Input array. It must conform to the Python-side of the array interface. Returns ------- (low, high) : tuple of 2 integers The first integer is the first byte of the array, the second integer is just past the last byte of the array. If `a` is not contiguous it will not use every byte between the (`low`, `high`) values. Examples -------- >>> I = np.eye(2, dtype='f'); I.dtype dtype('float32') >>> low, high = np.byte_bounds(I) >>> high - low == I.size*I.itemsize True >>> I = np.eye(2, dtype='G'); I.dtype dtype('complex192') >>> low, high = np.byte_bounds(I) >>> high - low == I.size*I.itemsize True """ ai = a.__array_interface__ a_data = ai['data'][0] astrides = ai['strides'] ashape = ai['shape'] bytes_a = asarray(a).dtype.itemsize a_low = a_high = a_data if astrides is None: # contiguous case a_high += a.size * bytes_a else: for shape, stride in zip(ashape, astrides): if stride < 0: a_low += (shape-1)*stride else: a_high += (shape-1)*stride a_high += bytes_a return a_low, a_high #----------------------------------------------------------------------------- # Function for output and information on the variables used. #----------------------------------------------------------------------------- def who(vardict=None): """ Print the NumPy arrays in the given dictionary. If there is no dictionary passed in or `vardict` is None then returns NumPy arrays in the globals() dictionary (all NumPy arrays in the namespace). Parameters ---------- vardict : dict, optional A dictionary possibly containing ndarrays. Default is globals(). Returns ------- out : None Returns 'None'. Notes ----- Prints out the name, shape, bytes and type of all of the ndarrays present in `vardict`. Examples -------- >>> a = np.arange(10) >>> b = np.ones(20) >>> np.who() Name Shape Bytes Type =========================================================== a 10 40 int32 b 20 160 float64 Upper bound on total bytes = 200 >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str', ... 'idx':5} >>> np.who(d) Name Shape Bytes Type =========================================================== y 3 24 float64 x 2 16 float64 Upper bound on total bytes = 40 """ if vardict is None: frame = sys._getframe().f_back vardict = frame.f_globals sta = [] cache = {} for name in vardict.keys(): if isinstance(vardict[name], ndarray): var = vardict[name] idv = id(var) if idv in cache.keys(): namestr = name + " (%s)" % cache[idv] original = 0 else: cache[idv] = name namestr = name original = 1 shapestr = " x ".join(map(str, var.shape)) bytestr = str(var.nbytes) sta.append([namestr, shapestr, bytestr, var.dtype.name, original]) maxname = 0 maxshape = 0 maxbyte = 0 totalbytes = 0 for k in range(len(sta)): val = sta[k] if maxname < len(val[0]): maxname = len(val[0]) if maxshape < len(val[1]): maxshape = len(val[1]) if maxbyte < len(val[2]): maxbyte = len(val[2]) if val[4]: totalbytes += int(val[2]) if len(sta) > 0: sp1 = max(10, maxname) sp2 = max(10, maxshape) sp3 = max(10, maxbyte) prval = "Name %s Shape %s Bytes %s Type" % (sp1*' ', sp2*' ', sp3*' ') print(prval + "\n" + "="*(len(prval)+5) + "\n") for k in range(len(sta)): val = sta[k] print("%s %s %s %s %s %s %s" % (val[0], ' '*(sp1-len(val[0])+4), val[1], ' '*(sp2-len(val[1])+5), val[2], ' '*(sp3-len(val[2])+5), val[3])) print("\nUpper bound on total bytes = %d" % totalbytes) return #----------------------------------------------------------------------------- # NOTE: pydoc defines a help function which works similarly to this # except it uses a pager to take over the screen. # combine name and arguments and split to multiple lines of width # characters. End lines on a comma and begin argument list indented with # the rest of the arguments. def _split_line(name, arguments, width): firstwidth = len(name) k = firstwidth newstr = name sepstr = ", " arglist = arguments.split(sepstr) for argument in arglist: if k == firstwidth: addstr = "" else: addstr = sepstr k = k + len(argument) + len(addstr) if k > width: k = firstwidth + 1 + len(argument) newstr = newstr + ",\n" + " "*(firstwidth+2) + argument else: newstr = newstr + addstr + argument return newstr _namedict = None _dictlist = None # Traverse all module directories underneath globals # to see if something is defined def _makenamedict(module='numpy'): module = __import__(module, globals(), locals(), []) thedict = {module.__name__:module.__dict__} dictlist = [module.__name__] totraverse = [module.__dict__] while True: if len(totraverse) == 0: break thisdict = totraverse.pop(0) for x in thisdict.keys(): if isinstance(thisdict[x], types.ModuleType): modname = thisdict[x].__name__ if modname not in dictlist: moddict = thisdict[x].__dict__ dictlist.append(modname) totraverse.append(moddict) thedict[modname] = moddict return thedict, dictlist def _info(obj, output=sys.stdout): """Provide information about ndarray obj. Parameters ---------- obj : ndarray Must be ndarray, not checked. output Where printed output goes. Notes ----- Copied over from the numarray module prior to its removal. Adapted somewhat as only numpy is an option now. Called by info. """ extra = "" tic = "" bp = lambda x: x cls = getattr(obj, '__class__', type(obj)) nm = getattr(cls, '__name__', cls) strides = obj.strides endian = obj.dtype.byteorder print("class: ", nm, file=output) print("shape: ", obj.shape, file=output) print("strides: ", strides, file=output) print("itemsize: ", obj.itemsize, file=output) print("aligned: ", bp(obj.flags.aligned), file=output) print("contiguous: ", bp(obj.flags.contiguous), file=output) print("fortran: ", obj.flags.fortran, file=output) print( "data pointer: %s%s" % (hex(obj.ctypes._as_parameter_.value), extra), file=output ) print("byteorder: ", end=' ', file=output) if endian in ['|', '=']: print("%s%s%s" % (tic, sys.byteorder, tic), file=output) byteswap = False elif endian == '>': print("%sbig%s" % (tic, tic), file=output) byteswap = sys.byteorder != "big" else: print("%slittle%s" % (tic, tic), file=output) byteswap = sys.byteorder != "little" print("byteswap: ", bp(byteswap), file=output) print("type: %s" % obj.dtype, file=output) def info(object=None, maxwidth=76, output=sys.stdout, toplevel='numpy'): """ Get help information for a function, class, or module. Parameters ---------- object : object or str, optional Input object or name to get information about. If `object` is a numpy object, its docstring is given. If it is a string, available modules are searched for matching objects. If None, information about `info` itself is returned. maxwidth : int, optional Printing width. output : file like object, optional File like object that the output is written to, default is ``stdout``. The object has to be opened in 'w' or 'a' mode. toplevel : str, optional Start search at this level. See Also -------- source, lookfor Notes ----- When used interactively with an object, ``np.info(obj)`` is equivalent to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython prompt. Examples -------- >>> np.info(np.polyval) # doctest: +SKIP polyval(p, x) Evaluate the polynomial p at x. ... When using a string for `object` it is possible to get multiple results. >>> np.info('fft') # doctest: +SKIP *** Found in numpy *** Core FFT routines ... *** Found in numpy.fft *** fft(a, n=None, axis=-1) ... *** Repeat reference found in numpy.fft.fftpack *** *** Total of 3 references found. *** """ global _namedict, _dictlist # Local import to speed up numpy's import time. import pydoc import inspect if (hasattr(object, '_ppimport_importer') or hasattr(object, '_ppimport_module')): object = object._ppimport_module elif hasattr(object, '_ppimport_attr'): object = object._ppimport_attr if object is None: info(info) elif isinstance(object, ndarray): _info(object, output=output) elif isinstance(object, str): if _namedict is None: _namedict, _dictlist = _makenamedict(toplevel) numfound = 0 objlist = [] for namestr in _dictlist: try: obj = _namedict[namestr][object] if id(obj) in objlist: print("\n " "*** Repeat reference found in %s *** " % namestr, file=output ) else: objlist.append(id(obj)) print(" *** Found in %s ***" % namestr, file=output) info(obj) print("-"*maxwidth, file=output) numfound += 1 except KeyError: pass if numfound == 0: print("Help for %s not found." % object, file=output) else: print("\n " "*** Total of %d references found. ***" % numfound, file=output ) elif inspect.isfunction(object): name = object.__name__ arguments = formatargspec(*getargspec(object)) if len(name+arguments) > maxwidth: argstr = _split_line(name, arguments, maxwidth) else: argstr = name + arguments print(" " + argstr + "\n", file=output) print(inspect.getdoc(object), file=output) elif inspect.isclass(object): name = object.__name__ arguments = "()" try: if hasattr(object, '__init__'): arguments = formatargspec( *getargspec(object.__init__.__func__) ) arglist = arguments.split(', ') if len(arglist) > 1: arglist[1] = "("+arglist[1] arguments = ", ".join(arglist[1:]) except Exception: pass if len(name+arguments) > maxwidth: argstr = _split_line(name, arguments, maxwidth) else: argstr = name + arguments print(" " + argstr + "\n", file=output) doc1 = inspect.getdoc(object) if doc1 is None: if hasattr(object, '__init__'): print(inspect.getdoc(object.__init__), file=output) else: print(inspect.getdoc(object), file=output) methods = pydoc.allmethods(object) if methods != []: print("\n\nMethods:\n", file=output) for meth in methods: if meth[0] == '_': continue thisobj = getattr(object, meth, None) if thisobj is not None: methstr, other = pydoc.splitdoc( inspect.getdoc(thisobj) or "None" ) print(" %s -- %s" % (meth, methstr), file=output) elif (sys.version_info[0] < 3 and isinstance(object, types.InstanceType)): # check for __call__ method # types.InstanceType is the type of the instances of oldstyle classes print("Instance of class: ", object.__class__.__name__, file=output) print(file=output) if hasattr(object, '__call__'): arguments = formatargspec( *getargspec(object.__call__.__func__) ) arglist = arguments.split(', ') if len(arglist) > 1: arglist[1] = "("+arglist[1] arguments = ", ".join(arglist[1:]) else: arguments = "()" if hasattr(object, 'name'): name = "%s" % object.name else: name = "<name>" if len(name+arguments) > maxwidth: argstr = _split_line(name, arguments, maxwidth) else: argstr = name + arguments print(" " + argstr + "\n", file=output) doc = inspect.getdoc(object.__call__) if doc is not None: print(inspect.getdoc(object.__call__), file=output) print(inspect.getdoc(object), file=output) else: print(inspect.getdoc(object), file=output) elif inspect.ismethod(object): name = object.__name__ arguments = formatargspec( *getargspec(object.__func__) ) arglist = arguments.split(', ') if len(arglist) > 1: arglist[1] = "("+arglist[1] arguments = ", ".join(arglist[1:]) else: arguments = "()" if len(name+arguments) > maxwidth: argstr = _split_line(name, arguments, maxwidth) else: argstr = name + arguments print(" " + argstr + "\n", file=output) print(inspect.getdoc(object), file=output) elif hasattr(object, '__doc__'): print(inspect.getdoc(object), file=output) def source(object, output=sys.stdout): """ Print or write to a file the source code for a NumPy object. The source code is only returned for objects written in Python. Many functions and classes are defined in C and will therefore not return useful information. Parameters ---------- object : numpy object Input object. This can be any object (function, class, module, ...). output : file object, optional If `output` not supplied then source code is printed to screen (sys.stdout). File object must be created with either write 'w' or append 'a' modes. See Also -------- lookfor, info Examples -------- >>> np.source(np.interp) #doctest: +SKIP In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py def interp(x, xp, fp, left=None, right=None): \"\"\".... (full docstring printed)\"\"\" if isinstance(x, (float, int, number)): return compiled_interp([x], xp, fp, left, right).item() else: return compiled_interp(x, xp, fp, left, right) The source code is only returned for objects written in Python. >>> np.source(np.array) #doctest: +SKIP Not available for this object. """ # Local import to speed up numpy's import time. import inspect try: print("In file: %s\n" % inspect.getsourcefile(object), file=output) print(inspect.getsource(object), file=output) except Exception: print("Not available for this object.", file=output) # Cache for lookfor: {id(module): {name: (docstring, kind, index), ...}...} # where kind: "func", "class", "module", "object" # and index: index in breadth-first namespace traversal _lookfor_caches = {} # regexp whose match indicates that the string may contain a function # signature _function_signature_re = re.compile(r"[a-z0-9_]+\(.*[,=].*\)", re.I) def lookfor(what, module=None, import_modules=True, regenerate=False, output=None): """ Do a keyword search on docstrings. A list of of objects that matched the search is displayed, sorted by relevance. All given keywords need to be found in the docstring for it to be returned as a result, but the order does not matter. Parameters ---------- what : str String containing words to look for. module : str or list, optional Name of module(s) whose docstrings to go through. import_modules : bool, optional Whether to import sub-modules in packages. Default is True. regenerate : bool, optional Whether to re-generate the docstring cache. Default is False. output : file-like, optional File-like object to write the output to. If omitted, use a pager. See Also -------- source, info Notes ----- Relevance is determined only roughly, by checking if the keywords occur in the function name, at the start of a docstring, etc. Examples -------- >>> np.lookfor('binary representation') Search results for 'binary representation' ------------------------------------------ numpy.binary_repr Return the binary representation of the input number as a string. numpy.core.setup_common.long_double_representation Given a binary dump as given by GNU od -b, look for long double numpy.base_repr Return a string representation of a number in the given base system. ... """ import pydoc # Cache cache = _lookfor_generate_cache(module, import_modules, regenerate) # Search # XXX: maybe using a real stemming search engine would be better? found = [] whats = str(what).lower().split() if not whats: return for name, (docstring, kind, index) in cache.items(): if kind in ('module', 'object'): # don't show modules or objects continue ok = True doc = docstring.lower() for w in whats: if w not in doc: ok = False break if ok: found.append(name) # Relevance sort # XXX: this is full Harrison-Stetson heuristics now, # XXX: it probably could be improved kind_relevance = {'func': 1000, 'class': 1000, 'module': -1000, 'object': -1000} def relevance(name, docstr, kind, index): r = 0 # do the keywords occur within the start of the docstring? first_doc = "\n".join(docstr.lower().strip().split("\n")[:3]) r += sum([200 for w in whats if w in first_doc]) # do the keywords occur in the function name? r += sum([30 for w in whats if w in name]) # is the full name long? r += -len(name) * 5 # is the object of bad type? r += kind_relevance.get(kind, -1000) # is the object deep in namespace hierarchy? r += -name.count('.') * 10 r += max(-index / 100, -100) return r def relevance_value(a): return relevance(a, *cache[a]) found.sort(key=relevance_value) # Pretty-print s = "Search results for '%s'" % (' '.join(whats)) help_text = [s, "-"*len(s)] for name in found[::-1]: doc, kind, ix = cache[name] doclines = [line.strip() for line in doc.strip().split("\n") if line.strip()] # find a suitable short description try: first_doc = doclines[0].strip() if _function_signature_re.search(first_doc): first_doc = doclines[1].strip() except IndexError: first_doc = "" help_text.append("%s\n %s" % (name, first_doc)) if not found: help_text.append("Nothing found.") # Output if output is not None: output.write("\n".join(help_text)) elif len(help_text) > 10: pager = pydoc.getpager() pager("\n".join(help_text)) else: print("\n".join(help_text)) def _lookfor_generate_cache(module, import_modules, regenerate): """ Generate docstring cache for given module. Parameters ---------- module : str, None, module Module for which to generate docstring cache import_modules : bool Whether to import sub-modules in packages. regenerate : bool Re-generate the docstring cache Returns ------- cache : dict {obj_full_name: (docstring, kind, index), ...} Docstring cache for the module, either cached one (regenerate=False) or newly generated. """ global _lookfor_caches # Local import to speed up numpy's import time. import inspect if sys.version_info[0] >= 3: # In Python3 stderr, stdout are text files. from io import StringIO else: from StringIO import StringIO if module is None: module = "numpy" if isinstance(module, str): try: __import__(module) except ImportError: return {} module = sys.modules[module] elif isinstance(module, list) or isinstance(module, tuple): cache = {} for mod in module: cache.update(_lookfor_generate_cache(mod, import_modules, regenerate)) return cache if id(module) in _lookfor_caches and not regenerate: return _lookfor_caches[id(module)] # walk items and collect docstrings cache = {} _lookfor_caches[id(module)] = cache seen = {} index = 0 stack = [(module.__name__, module)] while stack: name, item = stack.pop(0) if id(item) in seen: continue seen[id(item)] = True index += 1 kind = "object" if inspect.ismodule(item): kind = "module" try: _all = item.__all__ except AttributeError: _all = None # import sub-packages if import_modules and hasattr(item, '__path__'): for pth in item.__path__: for mod_path in os.listdir(pth): this_py = os.path.join(pth, mod_path) init_py = os.path.join(pth, mod_path, '__init__.py') if (os.path.isfile(this_py) and mod_path.endswith('.py')): to_import = mod_path[:-3] elif os.path.isfile(init_py): to_import = mod_path else: continue if to_import == '__init__': continue try: old_stdout = sys.stdout old_stderr = sys.stderr try: sys.stdout = StringIO() sys.stderr = StringIO() __import__("%s.%s" % (name, to_import)) finally: sys.stdout = old_stdout sys.stderr = old_stderr # Catch SystemExit, too except BaseException: continue for n, v in _getmembers(item): try: item_name = getattr(v, '__name__', "%s.%s" % (name, n)) mod_name = getattr(v, '__module__', None) except NameError: # ref. SWIG's global cvars # NameError: Unknown C global variable item_name = "%s.%s" % (name, n) mod_name = None if '.' not in item_name and mod_name: item_name = "%s.%s" % (mod_name, item_name) if not item_name.startswith(name + '.'): # don't crawl "foreign" objects if isinstance(v, ufunc): # ... unless they are ufuncs pass else: continue elif not (inspect.ismodule(v) or _all is None or n in _all): continue stack.append(("%s.%s" % (name, n), v)) elif inspect.isclass(item): kind = "class" for n, v in _getmembers(item): stack.append(("%s.%s" % (name, n), v)) elif hasattr(item, "__call__"): kind = "func" try: doc = inspect.getdoc(item) except NameError: # ref SWIG's NameError: Unknown C global variable doc = None if doc is not None: cache[name] = (doc, kind, index) return cache def _getmembers(item): import inspect try: members = inspect.getmembers(item) except Exception: members = [(x, getattr(item, x)) for x in dir(item) if hasattr(item, x)] return members #----------------------------------------------------------------------------- # The following SafeEval class and company are adapted from Michael Spencer's # ASPN Python Cookbook recipe: # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/364469 # Accordingly it is mostly Copyright 2006 by Michael Spencer. # The recipe, like most of the other ASPN Python Cookbook recipes was made # available under the Python license. # http://www.python.org/license # It has been modified to: # * handle unary -/+ # * support True/False/None # * raise SyntaxError instead of a custom exception. class SafeEval(object): """ Object to evaluate constant string expressions. This includes strings with lists, dicts and tuples using the abstract syntax tree created by ``compiler.parse``. .. deprecated:: 1.10.0 See Also -------- safe_eval """ def __init__(self): # 2014-10-15, 1.10 warnings.warn("SafeEval is deprecated in 1.10 and will be removed.", DeprecationWarning, stacklevel=2) def visit(self, node): cls = node.__class__ meth = getattr(self, 'visit' + cls.__name__, self.default) return meth(node) def default(self, node): raise SyntaxError("Unsupported source construct: %s" % node.__class__) def visitExpression(self, node): return self.visit(node.body) def visitNum(self, node): return node.n def visitStr(self, node): return node.s def visitBytes(self, node): return node.s def visitDict(self, node,**kw): return dict([(self.visit(k), self.visit(v)) for k, v in zip(node.keys, node.values)]) def visitTuple(self, node): return tuple([self.visit(i) for i in node.elts]) def visitList(self, node): return [self.visit(i) for i in node.elts] def visitUnaryOp(self, node): import ast if isinstance(node.op, ast.UAdd): return +self.visit(node.operand) elif isinstance(node.op, ast.USub): return -self.visit(node.operand) else: raise SyntaxError("Unknown unary op: %r" % node.op) def visitName(self, node): if node.id == 'False': return False elif node.id == 'True': return True elif node.id == 'None': return None else: raise SyntaxError("Unknown name: %s" % node.id) def visitNameConstant(self, node): return node.value def safe_eval(source): """ Protected string evaluation. Evaluate a string containing a Python literal expression without allowing the execution of arbitrary non-literal code. Parameters ---------- source : str The string to evaluate. Returns ------- obj : object The result of evaluating `source`. Raises ------ SyntaxError If the code has invalid Python syntax, or if it contains non-literal code. Examples -------- >>> np.safe_eval('1') 1 >>> np.safe_eval('[1, 2, 3]') [1, 2, 3] >>> np.safe_eval('{"foo": ("bar", 10.0)}') {'foo': ('bar', 10.0)} >>> np.safe_eval('import os') Traceback (most recent call last): ... SyntaxError: invalid syntax >>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()') Traceback (most recent call last): ... SyntaxError: Unsupported source construct: compiler.ast.CallFunc """ # Local import to speed up numpy's import time. import ast return ast.literal_eval(source) def _median_nancheck(data, result, axis, out): """ Utility function to check median result from data for NaN values at the end and return NaN in that case. Input result can also be a MaskedArray. Parameters ---------- data : array Input data to median function result : Array or MaskedArray Result of median function axis : {int, sequence of int, None}, optional Axis or axes along which the median was computed. out : ndarray, optional Output array in which to place the result. Returns ------- median : scalar or ndarray Median or NaN in axes which contained NaN in the input. """ if data.size == 0: return result data = np.rollaxis(data, axis, data.ndim) n = np.isnan(data[..., -1]) # masked NaN values are ok if np.ma.isMaskedArray(n): n = n.filled(False) if result.ndim == 0: if n == True: warnings.warn("Invalid value encountered in median", RuntimeWarning, stacklevel=3) if out is not None: out[...] = data.dtype.type(np.nan) result = out else: result = data.dtype.type(np.nan) elif np.count_nonzero(n.ravel()) > 0: warnings.warn("Invalid value encountered in median for" + " %d results" % np.count_nonzero(n.ravel()), RuntimeWarning, stacklevel=3) result[n] = np.nan return result #-----------------------------------------------------------------------------
bringingheavendown/numpy
numpy/lib/utils.py
Python
bsd-3-clause
36,347
[ "VisIt" ]
6f16d31c5e703b59247056c0474d5d02edd68620f3b59e43013ccd1e07541760
# coding: utf-8 from __future__ import unicode_literals import binascii import collections import email import getpass import io import itertools import optparse import os import re import shlex import shutil import socket import struct import subprocess import sys import xml.etree.ElementTree try: import urllib.request as compat_urllib_request except ImportError: # Python 2 import urllib2 as compat_urllib_request try: import urllib.error as compat_urllib_error except ImportError: # Python 2 import urllib2 as compat_urllib_error try: import urllib.parse as compat_urllib_parse except ImportError: # Python 2 import urllib as compat_urllib_parse try: from urllib.parse import urlparse as compat_urllib_parse_urlparse except ImportError: # Python 2 from urlparse import urlparse as compat_urllib_parse_urlparse try: import urllib.parse as compat_urlparse except ImportError: # Python 2 import urlparse as compat_urlparse try: import urllib.response as compat_urllib_response except ImportError: # Python 2 import urllib as compat_urllib_response try: import http.cookiejar as compat_cookiejar except ImportError: # Python 2 import cookielib as compat_cookiejar try: import http.cookies as compat_cookies except ImportError: # Python 2 import Cookie as compat_cookies try: import html.entities as compat_html_entities except ImportError: # Python 2 import htmlentitydefs as compat_html_entities try: # Python >= 3.3 compat_html_entities_html5 = compat_html_entities.html5 except AttributeError: # Copied from CPython 3.5.1 html/entities.py compat_html_entities_html5 = { 'Aacute': '\xc1', 'aacute': '\xe1', 'Aacute;': '\xc1', 'aacute;': '\xe1', 'Abreve;': '\u0102', 'abreve;': '\u0103', 'ac;': '\u223e', 'acd;': '\u223f', 'acE;': '\u223e\u0333', 'Acirc': '\xc2', 'acirc': '\xe2', 'Acirc;': '\xc2', 'acirc;': '\xe2', 'acute': '\xb4', 'acute;': '\xb4', 'Acy;': '\u0410', 'acy;': '\u0430', 'AElig': '\xc6', 'aelig': '\xe6', 'AElig;': '\xc6', 'aelig;': '\xe6', 'af;': '\u2061', 'Afr;': '\U0001d504', 'afr;': '\U0001d51e', 'Agrave': '\xc0', 'agrave': '\xe0', 'Agrave;': '\xc0', 'agrave;': '\xe0', 'alefsym;': '\u2135', 'aleph;': '\u2135', 'Alpha;': '\u0391', 'alpha;': '\u03b1', 'Amacr;': '\u0100', 'amacr;': '\u0101', 'amalg;': '\u2a3f', 'AMP': '&', 'amp': '&', 'AMP;': '&', 'amp;': '&', 'And;': '\u2a53', 'and;': '\u2227', 'andand;': '\u2a55', 'andd;': '\u2a5c', 'andslope;': '\u2a58', 'andv;': '\u2a5a', 'ang;': '\u2220', 'ange;': '\u29a4', 'angle;': '\u2220', 'angmsd;': '\u2221', 'angmsdaa;': '\u29a8', 'angmsdab;': '\u29a9', 'angmsdac;': '\u29aa', 'angmsdad;': '\u29ab', 'angmsdae;': '\u29ac', 'angmsdaf;': '\u29ad', 'angmsdag;': '\u29ae', 'angmsdah;': '\u29af', 'angrt;': '\u221f', 'angrtvb;': '\u22be', 'angrtvbd;': '\u299d', 'angsph;': '\u2222', 'angst;': '\xc5', 'angzarr;': '\u237c', 'Aogon;': '\u0104', 'aogon;': '\u0105', 'Aopf;': '\U0001d538', 'aopf;': '\U0001d552', 'ap;': '\u2248', 'apacir;': '\u2a6f', 'apE;': '\u2a70', 'ape;': '\u224a', 'apid;': '\u224b', 'apos;': "'", 'ApplyFunction;': '\u2061', 'approx;': '\u2248', 'approxeq;': '\u224a', 'Aring': '\xc5', 'aring': '\xe5', 'Aring;': '\xc5', 'aring;': '\xe5', 'Ascr;': '\U0001d49c', 'ascr;': '\U0001d4b6', 'Assign;': '\u2254', 'ast;': '*', 'asymp;': '\u2248', 'asympeq;': '\u224d', 'Atilde': '\xc3', 'atilde': '\xe3', 'Atilde;': '\xc3', 'atilde;': '\xe3', 'Auml': '\xc4', 'auml': '\xe4', 'Auml;': '\xc4', 'auml;': '\xe4', 'awconint;': '\u2233', 'awint;': '\u2a11', 'backcong;': '\u224c', 'backepsilon;': '\u03f6', 'backprime;': '\u2035', 'backsim;': '\u223d', 'backsimeq;': '\u22cd', 'Backslash;': '\u2216', 'Barv;': '\u2ae7', 'barvee;': '\u22bd', 'Barwed;': '\u2306', 'barwed;': '\u2305', 'barwedge;': '\u2305', 'bbrk;': '\u23b5', 'bbrktbrk;': '\u23b6', 'bcong;': '\u224c', 'Bcy;': '\u0411', 'bcy;': '\u0431', 'bdquo;': '\u201e', 'becaus;': '\u2235', 'Because;': '\u2235', 'because;': '\u2235', 'bemptyv;': '\u29b0', 'bepsi;': '\u03f6', 'bernou;': '\u212c', 'Bernoullis;': '\u212c', 'Beta;': '\u0392', 'beta;': '\u03b2', 'beth;': '\u2136', 'between;': '\u226c', 'Bfr;': '\U0001d505', 'bfr;': '\U0001d51f', 'bigcap;': '\u22c2', 'bigcirc;': '\u25ef', 'bigcup;': '\u22c3', 'bigodot;': '\u2a00', 'bigoplus;': '\u2a01', 'bigotimes;': '\u2a02', 'bigsqcup;': '\u2a06', 'bigstar;': '\u2605', 'bigtriangledown;': '\u25bd', 'bigtriangleup;': '\u25b3', 'biguplus;': '\u2a04', 'bigvee;': '\u22c1', 'bigwedge;': '\u22c0', 'bkarow;': '\u290d', 'blacklozenge;': '\u29eb', 'blacksquare;': '\u25aa', 'blacktriangle;': '\u25b4', 'blacktriangledown;': '\u25be', 'blacktriangleleft;': '\u25c2', 'blacktriangleright;': '\u25b8', 'blank;': '\u2423', 'blk12;': '\u2592', 'blk14;': '\u2591', 'blk34;': '\u2593', 'block;': '\u2588', 'bne;': '=\u20e5', 'bnequiv;': '\u2261\u20e5', 'bNot;': '\u2aed', 'bnot;': '\u2310', 'Bopf;': '\U0001d539', 'bopf;': '\U0001d553', 'bot;': '\u22a5', 'bottom;': '\u22a5', 'bowtie;': '\u22c8', 'boxbox;': '\u29c9', 'boxDL;': '\u2557', 'boxDl;': '\u2556', 'boxdL;': '\u2555', 'boxdl;': '\u2510', 'boxDR;': '\u2554', 'boxDr;': '\u2553', 'boxdR;': '\u2552', 'boxdr;': '\u250c', 'boxH;': '\u2550', 'boxh;': '\u2500', 'boxHD;': '\u2566', 'boxHd;': '\u2564', 'boxhD;': '\u2565', 'boxhd;': '\u252c', 'boxHU;': '\u2569', 'boxHu;': '\u2567', 'boxhU;': '\u2568', 'boxhu;': '\u2534', 'boxminus;': '\u229f', 'boxplus;': '\u229e', 'boxtimes;': '\u22a0', 'boxUL;': '\u255d', 'boxUl;': '\u255c', 'boxuL;': '\u255b', 'boxul;': '\u2518', 'boxUR;': '\u255a', 'boxUr;': '\u2559', 'boxuR;': '\u2558', 'boxur;': '\u2514', 'boxV;': '\u2551', 'boxv;': '\u2502', 'boxVH;': '\u256c', 'boxVh;': '\u256b', 'boxvH;': '\u256a', 'boxvh;': '\u253c', 'boxVL;': '\u2563', 'boxVl;': '\u2562', 'boxvL;': '\u2561', 'boxvl;': '\u2524', 'boxVR;': '\u2560', 'boxVr;': '\u255f', 'boxvR;': '\u255e', 'boxvr;': '\u251c', 'bprime;': '\u2035', 'Breve;': '\u02d8', 'breve;': '\u02d8', 'brvbar': '\xa6', 'brvbar;': '\xa6', 'Bscr;': '\u212c', 'bscr;': '\U0001d4b7', 'bsemi;': '\u204f', 'bsim;': '\u223d', 'bsime;': '\u22cd', 'bsol;': '\\', 'bsolb;': '\u29c5', 'bsolhsub;': '\u27c8', 'bull;': '\u2022', 'bullet;': '\u2022', 'bump;': '\u224e', 'bumpE;': '\u2aae', 'bumpe;': '\u224f', 'Bumpeq;': '\u224e', 'bumpeq;': '\u224f', 'Cacute;': '\u0106', 'cacute;': '\u0107', 'Cap;': '\u22d2', 'cap;': '\u2229', 'capand;': '\u2a44', 'capbrcup;': '\u2a49', 'capcap;': '\u2a4b', 'capcup;': '\u2a47', 'capdot;': '\u2a40', 'CapitalDifferentialD;': '\u2145', 'caps;': '\u2229\ufe00', 'caret;': '\u2041', 'caron;': '\u02c7', 'Cayleys;': '\u212d', 'ccaps;': '\u2a4d', 'Ccaron;': '\u010c', 'ccaron;': '\u010d', 'Ccedil': '\xc7', 'ccedil': '\xe7', 'Ccedil;': '\xc7', 'ccedil;': '\xe7', 'Ccirc;': '\u0108', 'ccirc;': '\u0109', 'Cconint;': '\u2230', 'ccups;': '\u2a4c', 'ccupssm;': '\u2a50', 'Cdot;': '\u010a', 'cdot;': '\u010b', 'cedil': '\xb8', 'cedil;': '\xb8', 'Cedilla;': '\xb8', 'cemptyv;': '\u29b2', 'cent': '\xa2', 'cent;': '\xa2', 'CenterDot;': '\xb7', 'centerdot;': '\xb7', 'Cfr;': '\u212d', 'cfr;': '\U0001d520', 'CHcy;': '\u0427', 'chcy;': '\u0447', 'check;': '\u2713', 'checkmark;': '\u2713', 'Chi;': '\u03a7', 'chi;': '\u03c7', 'cir;': '\u25cb', 'circ;': '\u02c6', 'circeq;': '\u2257', 'circlearrowleft;': '\u21ba', 'circlearrowright;': '\u21bb', 'circledast;': '\u229b', 'circledcirc;': '\u229a', 'circleddash;': '\u229d', 'CircleDot;': '\u2299', 'circledR;': '\xae', 'circledS;': '\u24c8', 'CircleMinus;': '\u2296', 'CirclePlus;': '\u2295', 'CircleTimes;': '\u2297', 'cirE;': '\u29c3', 'cire;': '\u2257', 'cirfnint;': '\u2a10', 'cirmid;': '\u2aef', 'cirscir;': '\u29c2', 'ClockwiseContourIntegral;': '\u2232', 'CloseCurlyDoubleQuote;': '\u201d', 'CloseCurlyQuote;': '\u2019', 'clubs;': '\u2663', 'clubsuit;': '\u2663', 'Colon;': '\u2237', 'colon;': ':', 'Colone;': '\u2a74', 'colone;': '\u2254', 'coloneq;': '\u2254', 'comma;': ',', 'commat;': '@', 'comp;': '\u2201', 'compfn;': '\u2218', 'complement;': '\u2201', 'complexes;': '\u2102', 'cong;': '\u2245', 'congdot;': '\u2a6d', 'Congruent;': '\u2261', 'Conint;': '\u222f', 'conint;': '\u222e', 'ContourIntegral;': '\u222e', 'Copf;': '\u2102', 'copf;': '\U0001d554', 'coprod;': '\u2210', 'Coproduct;': '\u2210', 'COPY': '\xa9', 'copy': '\xa9', 'COPY;': '\xa9', 'copy;': '\xa9', 'copysr;': '\u2117', 'CounterClockwiseContourIntegral;': '\u2233', 'crarr;': '\u21b5', 'Cross;': '\u2a2f', 'cross;': '\u2717', 'Cscr;': '\U0001d49e', 'cscr;': '\U0001d4b8', 'csub;': '\u2acf', 'csube;': '\u2ad1', 'csup;': '\u2ad0', 'csupe;': '\u2ad2', 'ctdot;': '\u22ef', 'cudarrl;': '\u2938', 'cudarrr;': '\u2935', 'cuepr;': '\u22de', 'cuesc;': '\u22df', 'cularr;': '\u21b6', 'cularrp;': '\u293d', 'Cup;': '\u22d3', 'cup;': '\u222a', 'cupbrcap;': '\u2a48', 'CupCap;': '\u224d', 'cupcap;': '\u2a46', 'cupcup;': '\u2a4a', 'cupdot;': '\u228d', 'cupor;': '\u2a45', 'cups;': '\u222a\ufe00', 'curarr;': '\u21b7', 'curarrm;': '\u293c', 'curlyeqprec;': '\u22de', 'curlyeqsucc;': '\u22df', 'curlyvee;': '\u22ce', 'curlywedge;': '\u22cf', 'curren': '\xa4', 'curren;': '\xa4', 'curvearrowleft;': '\u21b6', 'curvearrowright;': '\u21b7', 'cuvee;': '\u22ce', 'cuwed;': '\u22cf', 'cwconint;': '\u2232', 'cwint;': '\u2231', 'cylcty;': '\u232d', 'Dagger;': '\u2021', 'dagger;': '\u2020', 'daleth;': '\u2138', 'Darr;': '\u21a1', 'dArr;': '\u21d3', 'darr;': '\u2193', 'dash;': '\u2010', 'Dashv;': '\u2ae4', 'dashv;': '\u22a3', 'dbkarow;': '\u290f', 'dblac;': '\u02dd', 'Dcaron;': '\u010e', 'dcaron;': '\u010f', 'Dcy;': '\u0414', 'dcy;': '\u0434', 'DD;': '\u2145', 'dd;': '\u2146', 'ddagger;': '\u2021', 'ddarr;': '\u21ca', 'DDotrahd;': '\u2911', 'ddotseq;': '\u2a77', 'deg': '\xb0', 'deg;': '\xb0', 'Del;': '\u2207', 'Delta;': '\u0394', 'delta;': '\u03b4', 'demptyv;': '\u29b1', 'dfisht;': '\u297f', 'Dfr;': '\U0001d507', 'dfr;': '\U0001d521', 'dHar;': '\u2965', 'dharl;': '\u21c3', 'dharr;': '\u21c2', 'DiacriticalAcute;': '\xb4', 'DiacriticalDot;': '\u02d9', 'DiacriticalDoubleAcute;': '\u02dd', 'DiacriticalGrave;': '`', 'DiacriticalTilde;': '\u02dc', 'diam;': '\u22c4', 'Diamond;': '\u22c4', 'diamond;': '\u22c4', 'diamondsuit;': '\u2666', 'diams;': '\u2666', 'die;': '\xa8', 'DifferentialD;': '\u2146', 'digamma;': '\u03dd', 'disin;': '\u22f2', 'div;': '\xf7', 'divide': '\xf7', 'divide;': '\xf7', 'divideontimes;': '\u22c7', 'divonx;': '\u22c7', 'DJcy;': '\u0402', 'djcy;': '\u0452', 'dlcorn;': '\u231e', 'dlcrop;': '\u230d', 'dollar;': '$', 'Dopf;': '\U0001d53b', 'dopf;': '\U0001d555', 'Dot;': '\xa8', 'dot;': '\u02d9', 'DotDot;': '\u20dc', 'doteq;': '\u2250', 'doteqdot;': '\u2251', 'DotEqual;': '\u2250', 'dotminus;': '\u2238', 'dotplus;': '\u2214', 'dotsquare;': '\u22a1', 'doublebarwedge;': '\u2306', 'DoubleContourIntegral;': '\u222f', 'DoubleDot;': '\xa8', 'DoubleDownArrow;': '\u21d3', 'DoubleLeftArrow;': '\u21d0', 'DoubleLeftRightArrow;': '\u21d4', 'DoubleLeftTee;': '\u2ae4', 'DoubleLongLeftArrow;': '\u27f8', 'DoubleLongLeftRightArrow;': '\u27fa', 'DoubleLongRightArrow;': '\u27f9', 'DoubleRightArrow;': '\u21d2', 'DoubleRightTee;': '\u22a8', 'DoubleUpArrow;': '\u21d1', 'DoubleUpDownArrow;': '\u21d5', 'DoubleVerticalBar;': '\u2225', 'DownArrow;': '\u2193', 'Downarrow;': '\u21d3', 'downarrow;': '\u2193', 'DownArrowBar;': '\u2913', 'DownArrowUpArrow;': '\u21f5', 'DownBreve;': '\u0311', 'downdownarrows;': '\u21ca', 'downharpoonleft;': '\u21c3', 'downharpoonright;': '\u21c2', 'DownLeftRightVector;': '\u2950', 'DownLeftTeeVector;': '\u295e', 'DownLeftVector;': '\u21bd', 'DownLeftVectorBar;': '\u2956', 'DownRightTeeVector;': '\u295f', 'DownRightVector;': '\u21c1', 'DownRightVectorBar;': '\u2957', 'DownTee;': '\u22a4', 'DownTeeArrow;': '\u21a7', 'drbkarow;': '\u2910', 'drcorn;': '\u231f', 'drcrop;': '\u230c', 'Dscr;': '\U0001d49f', 'dscr;': '\U0001d4b9', 'DScy;': '\u0405', 'dscy;': '\u0455', 'dsol;': '\u29f6', 'Dstrok;': '\u0110', 'dstrok;': '\u0111', 'dtdot;': '\u22f1', 'dtri;': '\u25bf', 'dtrif;': '\u25be', 'duarr;': '\u21f5', 'duhar;': '\u296f', 'dwangle;': '\u29a6', 'DZcy;': '\u040f', 'dzcy;': '\u045f', 'dzigrarr;': '\u27ff', 'Eacute': '\xc9', 'eacute': '\xe9', 'Eacute;': '\xc9', 'eacute;': '\xe9', 'easter;': '\u2a6e', 'Ecaron;': '\u011a', 'ecaron;': '\u011b', 'ecir;': '\u2256', 'Ecirc': '\xca', 'ecirc': '\xea', 'Ecirc;': '\xca', 'ecirc;': '\xea', 'ecolon;': '\u2255', 'Ecy;': '\u042d', 'ecy;': '\u044d', 'eDDot;': '\u2a77', 'Edot;': '\u0116', 'eDot;': '\u2251', 'edot;': '\u0117', 'ee;': '\u2147', 'efDot;': '\u2252', 'Efr;': '\U0001d508', 'efr;': '\U0001d522', 'eg;': '\u2a9a', 'Egrave': '\xc8', 'egrave': '\xe8', 'Egrave;': '\xc8', 'egrave;': '\xe8', 'egs;': '\u2a96', 'egsdot;': '\u2a98', 'el;': '\u2a99', 'Element;': '\u2208', 'elinters;': '\u23e7', 'ell;': '\u2113', 'els;': '\u2a95', 'elsdot;': '\u2a97', 'Emacr;': '\u0112', 'emacr;': '\u0113', 'empty;': '\u2205', 'emptyset;': '\u2205', 'EmptySmallSquare;': '\u25fb', 'emptyv;': '\u2205', 'EmptyVerySmallSquare;': '\u25ab', 'emsp13;': '\u2004', 'emsp14;': '\u2005', 'emsp;': '\u2003', 'ENG;': '\u014a', 'eng;': '\u014b', 'ensp;': '\u2002', 'Eogon;': '\u0118', 'eogon;': '\u0119', 'Eopf;': '\U0001d53c', 'eopf;': '\U0001d556', 'epar;': '\u22d5', 'eparsl;': '\u29e3', 'eplus;': '\u2a71', 'epsi;': '\u03b5', 'Epsilon;': '\u0395', 'epsilon;': '\u03b5', 'epsiv;': '\u03f5', 'eqcirc;': '\u2256', 'eqcolon;': '\u2255', 'eqsim;': '\u2242', 'eqslantgtr;': '\u2a96', 'eqslantless;': '\u2a95', 'Equal;': '\u2a75', 'equals;': '=', 'EqualTilde;': '\u2242', 'equest;': '\u225f', 'Equilibrium;': '\u21cc', 'equiv;': '\u2261', 'equivDD;': '\u2a78', 'eqvparsl;': '\u29e5', 'erarr;': '\u2971', 'erDot;': '\u2253', 'Escr;': '\u2130', 'escr;': '\u212f', 'esdot;': '\u2250', 'Esim;': '\u2a73', 'esim;': '\u2242', 'Eta;': '\u0397', 'eta;': '\u03b7', 'ETH': '\xd0', 'eth': '\xf0', 'ETH;': '\xd0', 'eth;': '\xf0', 'Euml': '\xcb', 'euml': '\xeb', 'Euml;': '\xcb', 'euml;': '\xeb', 'euro;': '\u20ac', 'excl;': '!', 'exist;': '\u2203', 'Exists;': '\u2203', 'expectation;': '\u2130', 'ExponentialE;': '\u2147', 'exponentiale;': '\u2147', 'fallingdotseq;': '\u2252', 'Fcy;': '\u0424', 'fcy;': '\u0444', 'female;': '\u2640', 'ffilig;': '\ufb03', 'fflig;': '\ufb00', 'ffllig;': '\ufb04', 'Ffr;': '\U0001d509', 'ffr;': '\U0001d523', 'filig;': '\ufb01', 'FilledSmallSquare;': '\u25fc', 'FilledVerySmallSquare;': '\u25aa', 'fjlig;': 'fj', 'flat;': '\u266d', 'fllig;': '\ufb02', 'fltns;': '\u25b1', 'fnof;': '\u0192', 'Fopf;': '\U0001d53d', 'fopf;': '\U0001d557', 'ForAll;': '\u2200', 'forall;': '\u2200', 'fork;': '\u22d4', 'forkv;': '\u2ad9', 'Fouriertrf;': '\u2131', 'fpartint;': '\u2a0d', 'frac12': '\xbd', 'frac12;': '\xbd', 'frac13;': '\u2153', 'frac14': '\xbc', 'frac14;': '\xbc', 'frac15;': '\u2155', 'frac16;': '\u2159', 'frac18;': '\u215b', 'frac23;': '\u2154', 'frac25;': '\u2156', 'frac34': '\xbe', 'frac34;': '\xbe', 'frac35;': '\u2157', 'frac38;': '\u215c', 'frac45;': '\u2158', 'frac56;': '\u215a', 'frac58;': '\u215d', 'frac78;': '\u215e', 'frasl;': '\u2044', 'frown;': '\u2322', 'Fscr;': '\u2131', 'fscr;': '\U0001d4bb', 'gacute;': '\u01f5', 'Gamma;': '\u0393', 'gamma;': '\u03b3', 'Gammad;': '\u03dc', 'gammad;': '\u03dd', 'gap;': '\u2a86', 'Gbreve;': '\u011e', 'gbreve;': '\u011f', 'Gcedil;': '\u0122', 'Gcirc;': '\u011c', 'gcirc;': '\u011d', 'Gcy;': '\u0413', 'gcy;': '\u0433', 'Gdot;': '\u0120', 'gdot;': '\u0121', 'gE;': '\u2267', 'ge;': '\u2265', 'gEl;': '\u2a8c', 'gel;': '\u22db', 'geq;': '\u2265', 'geqq;': '\u2267', 'geqslant;': '\u2a7e', 'ges;': '\u2a7e', 'gescc;': '\u2aa9', 'gesdot;': '\u2a80', 'gesdoto;': '\u2a82', 'gesdotol;': '\u2a84', 'gesl;': '\u22db\ufe00', 'gesles;': '\u2a94', 'Gfr;': '\U0001d50a', 'gfr;': '\U0001d524', 'Gg;': '\u22d9', 'gg;': '\u226b', 'ggg;': '\u22d9', 'gimel;': '\u2137', 'GJcy;': '\u0403', 'gjcy;': '\u0453', 'gl;': '\u2277', 'gla;': '\u2aa5', 'glE;': '\u2a92', 'glj;': '\u2aa4', 'gnap;': '\u2a8a', 'gnapprox;': '\u2a8a', 'gnE;': '\u2269', 'gne;': '\u2a88', 'gneq;': '\u2a88', 'gneqq;': '\u2269', 'gnsim;': '\u22e7', 'Gopf;': '\U0001d53e', 'gopf;': '\U0001d558', 'grave;': '`', 'GreaterEqual;': '\u2265', 'GreaterEqualLess;': '\u22db', 'GreaterFullEqual;': '\u2267', 'GreaterGreater;': '\u2aa2', 'GreaterLess;': '\u2277', 'GreaterSlantEqual;': '\u2a7e', 'GreaterTilde;': '\u2273', 'Gscr;': '\U0001d4a2', 'gscr;': '\u210a', 'gsim;': '\u2273', 'gsime;': '\u2a8e', 'gsiml;': '\u2a90', 'GT': '>', 'gt': '>', 'GT;': '>', 'Gt;': '\u226b', 'gt;': '>', 'gtcc;': '\u2aa7', 'gtcir;': '\u2a7a', 'gtdot;': '\u22d7', 'gtlPar;': '\u2995', 'gtquest;': '\u2a7c', 'gtrapprox;': '\u2a86', 'gtrarr;': '\u2978', 'gtrdot;': '\u22d7', 'gtreqless;': '\u22db', 'gtreqqless;': '\u2a8c', 'gtrless;': '\u2277', 'gtrsim;': '\u2273', 'gvertneqq;': '\u2269\ufe00', 'gvnE;': '\u2269\ufe00', 'Hacek;': '\u02c7', 'hairsp;': '\u200a', 'half;': '\xbd', 'hamilt;': '\u210b', 'HARDcy;': '\u042a', 'hardcy;': '\u044a', 'hArr;': '\u21d4', 'harr;': '\u2194', 'harrcir;': '\u2948', 'harrw;': '\u21ad', 'Hat;': '^', 'hbar;': '\u210f', 'Hcirc;': '\u0124', 'hcirc;': '\u0125', 'hearts;': '\u2665', 'heartsuit;': '\u2665', 'hellip;': '\u2026', 'hercon;': '\u22b9', 'Hfr;': '\u210c', 'hfr;': '\U0001d525', 'HilbertSpace;': '\u210b', 'hksearow;': '\u2925', 'hkswarow;': '\u2926', 'hoarr;': '\u21ff', 'homtht;': '\u223b', 'hookleftarrow;': '\u21a9', 'hookrightarrow;': '\u21aa', 'Hopf;': '\u210d', 'hopf;': '\U0001d559', 'horbar;': '\u2015', 'HorizontalLine;': '\u2500', 'Hscr;': '\u210b', 'hscr;': '\U0001d4bd', 'hslash;': '\u210f', 'Hstrok;': '\u0126', 'hstrok;': '\u0127', 'HumpDownHump;': '\u224e', 'HumpEqual;': '\u224f', 'hybull;': '\u2043', 'hyphen;': '\u2010', 'Iacute': '\xcd', 'iacute': '\xed', 'Iacute;': '\xcd', 'iacute;': '\xed', 'ic;': '\u2063', 'Icirc': '\xce', 'icirc': '\xee', 'Icirc;': '\xce', 'icirc;': '\xee', 'Icy;': '\u0418', 'icy;': '\u0438', 'Idot;': '\u0130', 'IEcy;': '\u0415', 'iecy;': '\u0435', 'iexcl': '\xa1', 'iexcl;': '\xa1', 'iff;': '\u21d4', 'Ifr;': '\u2111', 'ifr;': '\U0001d526', 'Igrave': '\xcc', 'igrave': '\xec', 'Igrave;': '\xcc', 'igrave;': '\xec', 'ii;': '\u2148', 'iiiint;': '\u2a0c', 'iiint;': '\u222d', 'iinfin;': '\u29dc', 'iiota;': '\u2129', 'IJlig;': '\u0132', 'ijlig;': '\u0133', 'Im;': '\u2111', 'Imacr;': '\u012a', 'imacr;': '\u012b', 'image;': '\u2111', 'ImaginaryI;': '\u2148', 'imagline;': '\u2110', 'imagpart;': '\u2111', 'imath;': '\u0131', 'imof;': '\u22b7', 'imped;': '\u01b5', 'Implies;': '\u21d2', 'in;': '\u2208', 'incare;': '\u2105', 'infin;': '\u221e', 'infintie;': '\u29dd', 'inodot;': '\u0131', 'Int;': '\u222c', 'int;': '\u222b', 'intcal;': '\u22ba', 'integers;': '\u2124', 'Integral;': '\u222b', 'intercal;': '\u22ba', 'Intersection;': '\u22c2', 'intlarhk;': '\u2a17', 'intprod;': '\u2a3c', 'InvisibleComma;': '\u2063', 'InvisibleTimes;': '\u2062', 'IOcy;': '\u0401', 'iocy;': '\u0451', 'Iogon;': '\u012e', 'iogon;': '\u012f', 'Iopf;': '\U0001d540', 'iopf;': '\U0001d55a', 'Iota;': '\u0399', 'iota;': '\u03b9', 'iprod;': '\u2a3c', 'iquest': '\xbf', 'iquest;': '\xbf', 'Iscr;': '\u2110', 'iscr;': '\U0001d4be', 'isin;': '\u2208', 'isindot;': '\u22f5', 'isinE;': '\u22f9', 'isins;': '\u22f4', 'isinsv;': '\u22f3', 'isinv;': '\u2208', 'it;': '\u2062', 'Itilde;': '\u0128', 'itilde;': '\u0129', 'Iukcy;': '\u0406', 'iukcy;': '\u0456', 'Iuml': '\xcf', 'iuml': '\xef', 'Iuml;': '\xcf', 'iuml;': '\xef', 'Jcirc;': '\u0134', 'jcirc;': '\u0135', 'Jcy;': '\u0419', 'jcy;': '\u0439', 'Jfr;': '\U0001d50d', 'jfr;': '\U0001d527', 'jmath;': '\u0237', 'Jopf;': '\U0001d541', 'jopf;': '\U0001d55b', 'Jscr;': '\U0001d4a5', 'jscr;': '\U0001d4bf', 'Jsercy;': '\u0408', 'jsercy;': '\u0458', 'Jukcy;': '\u0404', 'jukcy;': '\u0454', 'Kappa;': '\u039a', 'kappa;': '\u03ba', 'kappav;': '\u03f0', 'Kcedil;': '\u0136', 'kcedil;': '\u0137', 'Kcy;': '\u041a', 'kcy;': '\u043a', 'Kfr;': '\U0001d50e', 'kfr;': '\U0001d528', 'kgreen;': '\u0138', 'KHcy;': '\u0425', 'khcy;': '\u0445', 'KJcy;': '\u040c', 'kjcy;': '\u045c', 'Kopf;': '\U0001d542', 'kopf;': '\U0001d55c', 'Kscr;': '\U0001d4a6', 'kscr;': '\U0001d4c0', 'lAarr;': '\u21da', 'Lacute;': '\u0139', 'lacute;': '\u013a', 'laemptyv;': '\u29b4', 'lagran;': '\u2112', 'Lambda;': '\u039b', 'lambda;': '\u03bb', 'Lang;': '\u27ea', 'lang;': '\u27e8', 'langd;': '\u2991', 'langle;': '\u27e8', 'lap;': '\u2a85', 'Laplacetrf;': '\u2112', 'laquo': '\xab', 'laquo;': '\xab', 'Larr;': '\u219e', 'lArr;': '\u21d0', 'larr;': '\u2190', 'larrb;': '\u21e4', 'larrbfs;': '\u291f', 'larrfs;': '\u291d', 'larrhk;': '\u21a9', 'larrlp;': '\u21ab', 'larrpl;': '\u2939', 'larrsim;': '\u2973', 'larrtl;': '\u21a2', 'lat;': '\u2aab', 'lAtail;': '\u291b', 'latail;': '\u2919', 'late;': '\u2aad', 'lates;': '\u2aad\ufe00', 'lBarr;': '\u290e', 'lbarr;': '\u290c', 'lbbrk;': '\u2772', 'lbrace;': '{', 'lbrack;': '[', 'lbrke;': '\u298b', 'lbrksld;': '\u298f', 'lbrkslu;': '\u298d', 'Lcaron;': '\u013d', 'lcaron;': '\u013e', 'Lcedil;': '\u013b', 'lcedil;': '\u013c', 'lceil;': '\u2308', 'lcub;': '{', 'Lcy;': '\u041b', 'lcy;': '\u043b', 'ldca;': '\u2936', 'ldquo;': '\u201c', 'ldquor;': '\u201e', 'ldrdhar;': '\u2967', 'ldrushar;': '\u294b', 'ldsh;': '\u21b2', 'lE;': '\u2266', 'le;': '\u2264', 'LeftAngleBracket;': '\u27e8', 'LeftArrow;': '\u2190', 'Leftarrow;': '\u21d0', 'leftarrow;': '\u2190', 'LeftArrowBar;': '\u21e4', 'LeftArrowRightArrow;': '\u21c6', 'leftarrowtail;': '\u21a2', 'LeftCeiling;': '\u2308', 'LeftDoubleBracket;': '\u27e6', 'LeftDownTeeVector;': '\u2961', 'LeftDownVector;': '\u21c3', 'LeftDownVectorBar;': '\u2959', 'LeftFloor;': '\u230a', 'leftharpoondown;': '\u21bd', 'leftharpoonup;': '\u21bc', 'leftleftarrows;': '\u21c7', 'LeftRightArrow;': '\u2194', 'Leftrightarrow;': '\u21d4', 'leftrightarrow;': '\u2194', 'leftrightarrows;': '\u21c6', 'leftrightharpoons;': '\u21cb', 'leftrightsquigarrow;': '\u21ad', 'LeftRightVector;': '\u294e', 'LeftTee;': '\u22a3', 'LeftTeeArrow;': '\u21a4', 'LeftTeeVector;': '\u295a', 'leftthreetimes;': '\u22cb', 'LeftTriangle;': '\u22b2', 'LeftTriangleBar;': '\u29cf', 'LeftTriangleEqual;': '\u22b4', 'LeftUpDownVector;': '\u2951', 'LeftUpTeeVector;': '\u2960', 'LeftUpVector;': '\u21bf', 'LeftUpVectorBar;': '\u2958', 'LeftVector;': '\u21bc', 'LeftVectorBar;': '\u2952', 'lEg;': '\u2a8b', 'leg;': '\u22da', 'leq;': '\u2264', 'leqq;': '\u2266', 'leqslant;': '\u2a7d', 'les;': '\u2a7d', 'lescc;': '\u2aa8', 'lesdot;': '\u2a7f', 'lesdoto;': '\u2a81', 'lesdotor;': '\u2a83', 'lesg;': '\u22da\ufe00', 'lesges;': '\u2a93', 'lessapprox;': '\u2a85', 'lessdot;': '\u22d6', 'lesseqgtr;': '\u22da', 'lesseqqgtr;': '\u2a8b', 'LessEqualGreater;': '\u22da', 'LessFullEqual;': '\u2266', 'LessGreater;': '\u2276', 'lessgtr;': '\u2276', 'LessLess;': '\u2aa1', 'lesssim;': '\u2272', 'LessSlantEqual;': '\u2a7d', 'LessTilde;': '\u2272', 'lfisht;': '\u297c', 'lfloor;': '\u230a', 'Lfr;': '\U0001d50f', 'lfr;': '\U0001d529', 'lg;': '\u2276', 'lgE;': '\u2a91', 'lHar;': '\u2962', 'lhard;': '\u21bd', 'lharu;': '\u21bc', 'lharul;': '\u296a', 'lhblk;': '\u2584', 'LJcy;': '\u0409', 'ljcy;': '\u0459', 'Ll;': '\u22d8', 'll;': '\u226a', 'llarr;': '\u21c7', 'llcorner;': '\u231e', 'Lleftarrow;': '\u21da', 'llhard;': '\u296b', 'lltri;': '\u25fa', 'Lmidot;': '\u013f', 'lmidot;': '\u0140', 'lmoust;': '\u23b0', 'lmoustache;': '\u23b0', 'lnap;': '\u2a89', 'lnapprox;': '\u2a89', 'lnE;': '\u2268', 'lne;': '\u2a87', 'lneq;': '\u2a87', 'lneqq;': '\u2268', 'lnsim;': '\u22e6', 'loang;': '\u27ec', 'loarr;': '\u21fd', 'lobrk;': '\u27e6', 'LongLeftArrow;': '\u27f5', 'Longleftarrow;': '\u27f8', 'longleftarrow;': '\u27f5', 'LongLeftRightArrow;': '\u27f7', 'Longleftrightarrow;': '\u27fa', 'longleftrightarrow;': '\u27f7', 'longmapsto;': '\u27fc', 'LongRightArrow;': '\u27f6', 'Longrightarrow;': '\u27f9', 'longrightarrow;': '\u27f6', 'looparrowleft;': '\u21ab', 'looparrowright;': '\u21ac', 'lopar;': '\u2985', 'Lopf;': '\U0001d543', 'lopf;': '\U0001d55d', 'loplus;': '\u2a2d', 'lotimes;': '\u2a34', 'lowast;': '\u2217', 'lowbar;': '_', 'LowerLeftArrow;': '\u2199', 'LowerRightArrow;': '\u2198', 'loz;': '\u25ca', 'lozenge;': '\u25ca', 'lozf;': '\u29eb', 'lpar;': '(', 'lparlt;': '\u2993', 'lrarr;': '\u21c6', 'lrcorner;': '\u231f', 'lrhar;': '\u21cb', 'lrhard;': '\u296d', 'lrm;': '\u200e', 'lrtri;': '\u22bf', 'lsaquo;': '\u2039', 'Lscr;': '\u2112', 'lscr;': '\U0001d4c1', 'Lsh;': '\u21b0', 'lsh;': '\u21b0', 'lsim;': '\u2272', 'lsime;': '\u2a8d', 'lsimg;': '\u2a8f', 'lsqb;': '[', 'lsquo;': '\u2018', 'lsquor;': '\u201a', 'Lstrok;': '\u0141', 'lstrok;': '\u0142', 'LT': '<', 'lt': '<', 'LT;': '<', 'Lt;': '\u226a', 'lt;': '<', 'ltcc;': '\u2aa6', 'ltcir;': '\u2a79', 'ltdot;': '\u22d6', 'lthree;': '\u22cb', 'ltimes;': '\u22c9', 'ltlarr;': '\u2976', 'ltquest;': '\u2a7b', 'ltri;': '\u25c3', 'ltrie;': '\u22b4', 'ltrif;': '\u25c2', 'ltrPar;': '\u2996', 'lurdshar;': '\u294a', 'luruhar;': '\u2966', 'lvertneqq;': '\u2268\ufe00', 'lvnE;': '\u2268\ufe00', 'macr': '\xaf', 'macr;': '\xaf', 'male;': '\u2642', 'malt;': '\u2720', 'maltese;': '\u2720', 'Map;': '\u2905', 'map;': '\u21a6', 'mapsto;': '\u21a6', 'mapstodown;': '\u21a7', 'mapstoleft;': '\u21a4', 'mapstoup;': '\u21a5', 'marker;': '\u25ae', 'mcomma;': '\u2a29', 'Mcy;': '\u041c', 'mcy;': '\u043c', 'mdash;': '\u2014', 'mDDot;': '\u223a', 'measuredangle;': '\u2221', 'MediumSpace;': '\u205f', 'Mellintrf;': '\u2133', 'Mfr;': '\U0001d510', 'mfr;': '\U0001d52a', 'mho;': '\u2127', 'micro': '\xb5', 'micro;': '\xb5', 'mid;': '\u2223', 'midast;': '*', 'midcir;': '\u2af0', 'middot': '\xb7', 'middot;': '\xb7', 'minus;': '\u2212', 'minusb;': '\u229f', 'minusd;': '\u2238', 'minusdu;': '\u2a2a', 'MinusPlus;': '\u2213', 'mlcp;': '\u2adb', 'mldr;': '\u2026', 'mnplus;': '\u2213', 'models;': '\u22a7', 'Mopf;': '\U0001d544', 'mopf;': '\U0001d55e', 'mp;': '\u2213', 'Mscr;': '\u2133', 'mscr;': '\U0001d4c2', 'mstpos;': '\u223e', 'Mu;': '\u039c', 'mu;': '\u03bc', 'multimap;': '\u22b8', 'mumap;': '\u22b8', 'nabla;': '\u2207', 'Nacute;': '\u0143', 'nacute;': '\u0144', 'nang;': '\u2220\u20d2', 'nap;': '\u2249', 'napE;': '\u2a70\u0338', 'napid;': '\u224b\u0338', 'napos;': '\u0149', 'napprox;': '\u2249', 'natur;': '\u266e', 'natural;': '\u266e', 'naturals;': '\u2115', 'nbsp': '\xa0', 'nbsp;': '\xa0', 'nbump;': '\u224e\u0338', 'nbumpe;': '\u224f\u0338', 'ncap;': '\u2a43', 'Ncaron;': '\u0147', 'ncaron;': '\u0148', 'Ncedil;': '\u0145', 'ncedil;': '\u0146', 'ncong;': '\u2247', 'ncongdot;': '\u2a6d\u0338', 'ncup;': '\u2a42', 'Ncy;': '\u041d', 'ncy;': '\u043d', 'ndash;': '\u2013', 'ne;': '\u2260', 'nearhk;': '\u2924', 'neArr;': '\u21d7', 'nearr;': '\u2197', 'nearrow;': '\u2197', 'nedot;': '\u2250\u0338', 'NegativeMediumSpace;': '\u200b', 'NegativeThickSpace;': '\u200b', 'NegativeThinSpace;': '\u200b', 'NegativeVeryThinSpace;': '\u200b', 'nequiv;': '\u2262', 'nesear;': '\u2928', 'nesim;': '\u2242\u0338', 'NestedGreaterGreater;': '\u226b', 'NestedLessLess;': '\u226a', 'NewLine;': '\n', 'nexist;': '\u2204', 'nexists;': '\u2204', 'Nfr;': '\U0001d511', 'nfr;': '\U0001d52b', 'ngE;': '\u2267\u0338', 'nge;': '\u2271', 'ngeq;': '\u2271', 'ngeqq;': '\u2267\u0338', 'ngeqslant;': '\u2a7e\u0338', 'nges;': '\u2a7e\u0338', 'nGg;': '\u22d9\u0338', 'ngsim;': '\u2275', 'nGt;': '\u226b\u20d2', 'ngt;': '\u226f', 'ngtr;': '\u226f', 'nGtv;': '\u226b\u0338', 'nhArr;': '\u21ce', 'nharr;': '\u21ae', 'nhpar;': '\u2af2', 'ni;': '\u220b', 'nis;': '\u22fc', 'nisd;': '\u22fa', 'niv;': '\u220b', 'NJcy;': '\u040a', 'njcy;': '\u045a', 'nlArr;': '\u21cd', 'nlarr;': '\u219a', 'nldr;': '\u2025', 'nlE;': '\u2266\u0338', 'nle;': '\u2270', 'nLeftarrow;': '\u21cd', 'nleftarrow;': '\u219a', 'nLeftrightarrow;': '\u21ce', 'nleftrightarrow;': '\u21ae', 'nleq;': '\u2270', 'nleqq;': '\u2266\u0338', 'nleqslant;': '\u2a7d\u0338', 'nles;': '\u2a7d\u0338', 'nless;': '\u226e', 'nLl;': '\u22d8\u0338', 'nlsim;': '\u2274', 'nLt;': '\u226a\u20d2', 'nlt;': '\u226e', 'nltri;': '\u22ea', 'nltrie;': '\u22ec', 'nLtv;': '\u226a\u0338', 'nmid;': '\u2224', 'NoBreak;': '\u2060', 'NonBreakingSpace;': '\xa0', 'Nopf;': '\u2115', 'nopf;': '\U0001d55f', 'not': '\xac', 'Not;': '\u2aec', 'not;': '\xac', 'NotCongruent;': '\u2262', 'NotCupCap;': '\u226d', 'NotDoubleVerticalBar;': '\u2226', 'NotElement;': '\u2209', 'NotEqual;': '\u2260', 'NotEqualTilde;': '\u2242\u0338', 'NotExists;': '\u2204', 'NotGreater;': '\u226f', 'NotGreaterEqual;': '\u2271', 'NotGreaterFullEqual;': '\u2267\u0338', 'NotGreaterGreater;': '\u226b\u0338', 'NotGreaterLess;': '\u2279', 'NotGreaterSlantEqual;': '\u2a7e\u0338', 'NotGreaterTilde;': '\u2275', 'NotHumpDownHump;': '\u224e\u0338', 'NotHumpEqual;': '\u224f\u0338', 'notin;': '\u2209', 'notindot;': '\u22f5\u0338', 'notinE;': '\u22f9\u0338', 'notinva;': '\u2209', 'notinvb;': '\u22f7', 'notinvc;': '\u22f6', 'NotLeftTriangle;': '\u22ea', 'NotLeftTriangleBar;': '\u29cf\u0338', 'NotLeftTriangleEqual;': '\u22ec', 'NotLess;': '\u226e', 'NotLessEqual;': '\u2270', 'NotLessGreater;': '\u2278', 'NotLessLess;': '\u226a\u0338', 'NotLessSlantEqual;': '\u2a7d\u0338', 'NotLessTilde;': '\u2274', 'NotNestedGreaterGreater;': '\u2aa2\u0338', 'NotNestedLessLess;': '\u2aa1\u0338', 'notni;': '\u220c', 'notniva;': '\u220c', 'notnivb;': '\u22fe', 'notnivc;': '\u22fd', 'NotPrecedes;': '\u2280', 'NotPrecedesEqual;': '\u2aaf\u0338', 'NotPrecedesSlantEqual;': '\u22e0', 'NotReverseElement;': '\u220c', 'NotRightTriangle;': '\u22eb', 'NotRightTriangleBar;': '\u29d0\u0338', 'NotRightTriangleEqual;': '\u22ed', 'NotSquareSubset;': '\u228f\u0338', 'NotSquareSubsetEqual;': '\u22e2', 'NotSquareSuperset;': '\u2290\u0338', 'NotSquareSupersetEqual;': '\u22e3', 'NotSubset;': '\u2282\u20d2', 'NotSubsetEqual;': '\u2288', 'NotSucceeds;': '\u2281', 'NotSucceedsEqual;': '\u2ab0\u0338', 'NotSucceedsSlantEqual;': '\u22e1', 'NotSucceedsTilde;': '\u227f\u0338', 'NotSuperset;': '\u2283\u20d2', 'NotSupersetEqual;': '\u2289', 'NotTilde;': '\u2241', 'NotTildeEqual;': '\u2244', 'NotTildeFullEqual;': '\u2247', 'NotTildeTilde;': '\u2249', 'NotVerticalBar;': '\u2224', 'npar;': '\u2226', 'nparallel;': '\u2226', 'nparsl;': '\u2afd\u20e5', 'npart;': '\u2202\u0338', 'npolint;': '\u2a14', 'npr;': '\u2280', 'nprcue;': '\u22e0', 'npre;': '\u2aaf\u0338', 'nprec;': '\u2280', 'npreceq;': '\u2aaf\u0338', 'nrArr;': '\u21cf', 'nrarr;': '\u219b', 'nrarrc;': '\u2933\u0338', 'nrarrw;': '\u219d\u0338', 'nRightarrow;': '\u21cf', 'nrightarrow;': '\u219b', 'nrtri;': '\u22eb', 'nrtrie;': '\u22ed', 'nsc;': '\u2281', 'nsccue;': '\u22e1', 'nsce;': '\u2ab0\u0338', 'Nscr;': '\U0001d4a9', 'nscr;': '\U0001d4c3', 'nshortmid;': '\u2224', 'nshortparallel;': '\u2226', 'nsim;': '\u2241', 'nsime;': '\u2244', 'nsimeq;': '\u2244', 'nsmid;': '\u2224', 'nspar;': '\u2226', 'nsqsube;': '\u22e2', 'nsqsupe;': '\u22e3', 'nsub;': '\u2284', 'nsubE;': '\u2ac5\u0338', 'nsube;': '\u2288', 'nsubset;': '\u2282\u20d2', 'nsubseteq;': '\u2288', 'nsubseteqq;': '\u2ac5\u0338', 'nsucc;': '\u2281', 'nsucceq;': '\u2ab0\u0338', 'nsup;': '\u2285', 'nsupE;': '\u2ac6\u0338', 'nsupe;': '\u2289', 'nsupset;': '\u2283\u20d2', 'nsupseteq;': '\u2289', 'nsupseteqq;': '\u2ac6\u0338', 'ntgl;': '\u2279', 'Ntilde': '\xd1', 'ntilde': '\xf1', 'Ntilde;': '\xd1', 'ntilde;': '\xf1', 'ntlg;': '\u2278', 'ntriangleleft;': '\u22ea', 'ntrianglelefteq;': '\u22ec', 'ntriangleright;': '\u22eb', 'ntrianglerighteq;': '\u22ed', 'Nu;': '\u039d', 'nu;': '\u03bd', 'num;': '#', 'numero;': '\u2116', 'numsp;': '\u2007', 'nvap;': '\u224d\u20d2', 'nVDash;': '\u22af', 'nVdash;': '\u22ae', 'nvDash;': '\u22ad', 'nvdash;': '\u22ac', 'nvge;': '\u2265\u20d2', 'nvgt;': '>\u20d2', 'nvHarr;': '\u2904', 'nvinfin;': '\u29de', 'nvlArr;': '\u2902', 'nvle;': '\u2264\u20d2', 'nvlt;': '<\u20d2', 'nvltrie;': '\u22b4\u20d2', 'nvrArr;': '\u2903', 'nvrtrie;': '\u22b5\u20d2', 'nvsim;': '\u223c\u20d2', 'nwarhk;': '\u2923', 'nwArr;': '\u21d6', 'nwarr;': '\u2196', 'nwarrow;': '\u2196', 'nwnear;': '\u2927', 'Oacute': '\xd3', 'oacute': '\xf3', 'Oacute;': '\xd3', 'oacute;': '\xf3', 'oast;': '\u229b', 'ocir;': '\u229a', 'Ocirc': '\xd4', 'ocirc': '\xf4', 'Ocirc;': '\xd4', 'ocirc;': '\xf4', 'Ocy;': '\u041e', 'ocy;': '\u043e', 'odash;': '\u229d', 'Odblac;': '\u0150', 'odblac;': '\u0151', 'odiv;': '\u2a38', 'odot;': '\u2299', 'odsold;': '\u29bc', 'OElig;': '\u0152', 'oelig;': '\u0153', 'ofcir;': '\u29bf', 'Ofr;': '\U0001d512', 'ofr;': '\U0001d52c', 'ogon;': '\u02db', 'Ograve': '\xd2', 'ograve': '\xf2', 'Ograve;': '\xd2', 'ograve;': '\xf2', 'ogt;': '\u29c1', 'ohbar;': '\u29b5', 'ohm;': '\u03a9', 'oint;': '\u222e', 'olarr;': '\u21ba', 'olcir;': '\u29be', 'olcross;': '\u29bb', 'oline;': '\u203e', 'olt;': '\u29c0', 'Omacr;': '\u014c', 'omacr;': '\u014d', 'Omega;': '\u03a9', 'omega;': '\u03c9', 'Omicron;': '\u039f', 'omicron;': '\u03bf', 'omid;': '\u29b6', 'ominus;': '\u2296', 'Oopf;': '\U0001d546', 'oopf;': '\U0001d560', 'opar;': '\u29b7', 'OpenCurlyDoubleQuote;': '\u201c', 'OpenCurlyQuote;': '\u2018', 'operp;': '\u29b9', 'oplus;': '\u2295', 'Or;': '\u2a54', 'or;': '\u2228', 'orarr;': '\u21bb', 'ord;': '\u2a5d', 'order;': '\u2134', 'orderof;': '\u2134', 'ordf': '\xaa', 'ordf;': '\xaa', 'ordm': '\xba', 'ordm;': '\xba', 'origof;': '\u22b6', 'oror;': '\u2a56', 'orslope;': '\u2a57', 'orv;': '\u2a5b', 'oS;': '\u24c8', 'Oscr;': '\U0001d4aa', 'oscr;': '\u2134', 'Oslash': '\xd8', 'oslash': '\xf8', 'Oslash;': '\xd8', 'oslash;': '\xf8', 'osol;': '\u2298', 'Otilde': '\xd5', 'otilde': '\xf5', 'Otilde;': '\xd5', 'otilde;': '\xf5', 'Otimes;': '\u2a37', 'otimes;': '\u2297', 'otimesas;': '\u2a36', 'Ouml': '\xd6', 'ouml': '\xf6', 'Ouml;': '\xd6', 'ouml;': '\xf6', 'ovbar;': '\u233d', 'OverBar;': '\u203e', 'OverBrace;': '\u23de', 'OverBracket;': '\u23b4', 'OverParenthesis;': '\u23dc', 'par;': '\u2225', 'para': '\xb6', 'para;': '\xb6', 'parallel;': '\u2225', 'parsim;': '\u2af3', 'parsl;': '\u2afd', 'part;': '\u2202', 'PartialD;': '\u2202', 'Pcy;': '\u041f', 'pcy;': '\u043f', 'percnt;': '%', 'period;': '.', 'permil;': '\u2030', 'perp;': '\u22a5', 'pertenk;': '\u2031', 'Pfr;': '\U0001d513', 'pfr;': '\U0001d52d', 'Phi;': '\u03a6', 'phi;': '\u03c6', 'phiv;': '\u03d5', 'phmmat;': '\u2133', 'phone;': '\u260e', 'Pi;': '\u03a0', 'pi;': '\u03c0', 'pitchfork;': '\u22d4', 'piv;': '\u03d6', 'planck;': '\u210f', 'planckh;': '\u210e', 'plankv;': '\u210f', 'plus;': '+', 'plusacir;': '\u2a23', 'plusb;': '\u229e', 'pluscir;': '\u2a22', 'plusdo;': '\u2214', 'plusdu;': '\u2a25', 'pluse;': '\u2a72', 'PlusMinus;': '\xb1', 'plusmn': '\xb1', 'plusmn;': '\xb1', 'plussim;': '\u2a26', 'plustwo;': '\u2a27', 'pm;': '\xb1', 'Poincareplane;': '\u210c', 'pointint;': '\u2a15', 'Popf;': '\u2119', 'popf;': '\U0001d561', 'pound': '\xa3', 'pound;': '\xa3', 'Pr;': '\u2abb', 'pr;': '\u227a', 'prap;': '\u2ab7', 'prcue;': '\u227c', 'prE;': '\u2ab3', 'pre;': '\u2aaf', 'prec;': '\u227a', 'precapprox;': '\u2ab7', 'preccurlyeq;': '\u227c', 'Precedes;': '\u227a', 'PrecedesEqual;': '\u2aaf', 'PrecedesSlantEqual;': '\u227c', 'PrecedesTilde;': '\u227e', 'preceq;': '\u2aaf', 'precnapprox;': '\u2ab9', 'precneqq;': '\u2ab5', 'precnsim;': '\u22e8', 'precsim;': '\u227e', 'Prime;': '\u2033', 'prime;': '\u2032', 'primes;': '\u2119', 'prnap;': '\u2ab9', 'prnE;': '\u2ab5', 'prnsim;': '\u22e8', 'prod;': '\u220f', 'Product;': '\u220f', 'profalar;': '\u232e', 'profline;': '\u2312', 'profsurf;': '\u2313', 'prop;': '\u221d', 'Proportion;': '\u2237', 'Proportional;': '\u221d', 'propto;': '\u221d', 'prsim;': '\u227e', 'prurel;': '\u22b0', 'Pscr;': '\U0001d4ab', 'pscr;': '\U0001d4c5', 'Psi;': '\u03a8', 'psi;': '\u03c8', 'puncsp;': '\u2008', 'Qfr;': '\U0001d514', 'qfr;': '\U0001d52e', 'qint;': '\u2a0c', 'Qopf;': '\u211a', 'qopf;': '\U0001d562', 'qprime;': '\u2057', 'Qscr;': '\U0001d4ac', 'qscr;': '\U0001d4c6', 'quaternions;': '\u210d', 'quatint;': '\u2a16', 'quest;': '?', 'questeq;': '\u225f', 'QUOT': '"', 'quot': '"', 'QUOT;': '"', 'quot;': '"', 'rAarr;': '\u21db', 'race;': '\u223d\u0331', 'Racute;': '\u0154', 'racute;': '\u0155', 'radic;': '\u221a', 'raemptyv;': '\u29b3', 'Rang;': '\u27eb', 'rang;': '\u27e9', 'rangd;': '\u2992', 'range;': '\u29a5', 'rangle;': '\u27e9', 'raquo': '\xbb', 'raquo;': '\xbb', 'Rarr;': '\u21a0', 'rArr;': '\u21d2', 'rarr;': '\u2192', 'rarrap;': '\u2975', 'rarrb;': '\u21e5', 'rarrbfs;': '\u2920', 'rarrc;': '\u2933', 'rarrfs;': '\u291e', 'rarrhk;': '\u21aa', 'rarrlp;': '\u21ac', 'rarrpl;': '\u2945', 'rarrsim;': '\u2974', 'Rarrtl;': '\u2916', 'rarrtl;': '\u21a3', 'rarrw;': '\u219d', 'rAtail;': '\u291c', 'ratail;': '\u291a', 'ratio;': '\u2236', 'rationals;': '\u211a', 'RBarr;': '\u2910', 'rBarr;': '\u290f', 'rbarr;': '\u290d', 'rbbrk;': '\u2773', 'rbrace;': '}', 'rbrack;': ']', 'rbrke;': '\u298c', 'rbrksld;': '\u298e', 'rbrkslu;': '\u2990', 'Rcaron;': '\u0158', 'rcaron;': '\u0159', 'Rcedil;': '\u0156', 'rcedil;': '\u0157', 'rceil;': '\u2309', 'rcub;': '}', 'Rcy;': '\u0420', 'rcy;': '\u0440', 'rdca;': '\u2937', 'rdldhar;': '\u2969', 'rdquo;': '\u201d', 'rdquor;': '\u201d', 'rdsh;': '\u21b3', 'Re;': '\u211c', 'real;': '\u211c', 'realine;': '\u211b', 'realpart;': '\u211c', 'reals;': '\u211d', 'rect;': '\u25ad', 'REG': '\xae', 'reg': '\xae', 'REG;': '\xae', 'reg;': '\xae', 'ReverseElement;': '\u220b', 'ReverseEquilibrium;': '\u21cb', 'ReverseUpEquilibrium;': '\u296f', 'rfisht;': '\u297d', 'rfloor;': '\u230b', 'Rfr;': '\u211c', 'rfr;': '\U0001d52f', 'rHar;': '\u2964', 'rhard;': '\u21c1', 'rharu;': '\u21c0', 'rharul;': '\u296c', 'Rho;': '\u03a1', 'rho;': '\u03c1', 'rhov;': '\u03f1', 'RightAngleBracket;': '\u27e9', 'RightArrow;': '\u2192', 'Rightarrow;': '\u21d2', 'rightarrow;': '\u2192', 'RightArrowBar;': '\u21e5', 'RightArrowLeftArrow;': '\u21c4', 'rightarrowtail;': '\u21a3', 'RightCeiling;': '\u2309', 'RightDoubleBracket;': '\u27e7', 'RightDownTeeVector;': '\u295d', 'RightDownVector;': '\u21c2', 'RightDownVectorBar;': '\u2955', 'RightFloor;': '\u230b', 'rightharpoondown;': '\u21c1', 'rightharpoonup;': '\u21c0', 'rightleftarrows;': '\u21c4', 'rightleftharpoons;': '\u21cc', 'rightrightarrows;': '\u21c9', 'rightsquigarrow;': '\u219d', 'RightTee;': '\u22a2', 'RightTeeArrow;': '\u21a6', 'RightTeeVector;': '\u295b', 'rightthreetimes;': '\u22cc', 'RightTriangle;': '\u22b3', 'RightTriangleBar;': '\u29d0', 'RightTriangleEqual;': '\u22b5', 'RightUpDownVector;': '\u294f', 'RightUpTeeVector;': '\u295c', 'RightUpVector;': '\u21be', 'RightUpVectorBar;': '\u2954', 'RightVector;': '\u21c0', 'RightVectorBar;': '\u2953', 'ring;': '\u02da', 'risingdotseq;': '\u2253', 'rlarr;': '\u21c4', 'rlhar;': '\u21cc', 'rlm;': '\u200f', 'rmoust;': '\u23b1', 'rmoustache;': '\u23b1', 'rnmid;': '\u2aee', 'roang;': '\u27ed', 'roarr;': '\u21fe', 'robrk;': '\u27e7', 'ropar;': '\u2986', 'Ropf;': '\u211d', 'ropf;': '\U0001d563', 'roplus;': '\u2a2e', 'rotimes;': '\u2a35', 'RoundImplies;': '\u2970', 'rpar;': ')', 'rpargt;': '\u2994', 'rppolint;': '\u2a12', 'rrarr;': '\u21c9', 'Rrightarrow;': '\u21db', 'rsaquo;': '\u203a', 'Rscr;': '\u211b', 'rscr;': '\U0001d4c7', 'Rsh;': '\u21b1', 'rsh;': '\u21b1', 'rsqb;': ']', 'rsquo;': '\u2019', 'rsquor;': '\u2019', 'rthree;': '\u22cc', 'rtimes;': '\u22ca', 'rtri;': '\u25b9', 'rtrie;': '\u22b5', 'rtrif;': '\u25b8', 'rtriltri;': '\u29ce', 'RuleDelayed;': '\u29f4', 'ruluhar;': '\u2968', 'rx;': '\u211e', 'Sacute;': '\u015a', 'sacute;': '\u015b', 'sbquo;': '\u201a', 'Sc;': '\u2abc', 'sc;': '\u227b', 'scap;': '\u2ab8', 'Scaron;': '\u0160', 'scaron;': '\u0161', 'sccue;': '\u227d', 'scE;': '\u2ab4', 'sce;': '\u2ab0', 'Scedil;': '\u015e', 'scedil;': '\u015f', 'Scirc;': '\u015c', 'scirc;': '\u015d', 'scnap;': '\u2aba', 'scnE;': '\u2ab6', 'scnsim;': '\u22e9', 'scpolint;': '\u2a13', 'scsim;': '\u227f', 'Scy;': '\u0421', 'scy;': '\u0441', 'sdot;': '\u22c5', 'sdotb;': '\u22a1', 'sdote;': '\u2a66', 'searhk;': '\u2925', 'seArr;': '\u21d8', 'searr;': '\u2198', 'searrow;': '\u2198', 'sect': '\xa7', 'sect;': '\xa7', 'semi;': ';', 'seswar;': '\u2929', 'setminus;': '\u2216', 'setmn;': '\u2216', 'sext;': '\u2736', 'Sfr;': '\U0001d516', 'sfr;': '\U0001d530', 'sfrown;': '\u2322', 'sharp;': '\u266f', 'SHCHcy;': '\u0429', 'shchcy;': '\u0449', 'SHcy;': '\u0428', 'shcy;': '\u0448', 'ShortDownArrow;': '\u2193', 'ShortLeftArrow;': '\u2190', 'shortmid;': '\u2223', 'shortparallel;': '\u2225', 'ShortRightArrow;': '\u2192', 'ShortUpArrow;': '\u2191', 'shy': '\xad', 'shy;': '\xad', 'Sigma;': '\u03a3', 'sigma;': '\u03c3', 'sigmaf;': '\u03c2', 'sigmav;': '\u03c2', 'sim;': '\u223c', 'simdot;': '\u2a6a', 'sime;': '\u2243', 'simeq;': '\u2243', 'simg;': '\u2a9e', 'simgE;': '\u2aa0', 'siml;': '\u2a9d', 'simlE;': '\u2a9f', 'simne;': '\u2246', 'simplus;': '\u2a24', 'simrarr;': '\u2972', 'slarr;': '\u2190', 'SmallCircle;': '\u2218', 'smallsetminus;': '\u2216', 'smashp;': '\u2a33', 'smeparsl;': '\u29e4', 'smid;': '\u2223', 'smile;': '\u2323', 'smt;': '\u2aaa', 'smte;': '\u2aac', 'smtes;': '\u2aac\ufe00', 'SOFTcy;': '\u042c', 'softcy;': '\u044c', 'sol;': '/', 'solb;': '\u29c4', 'solbar;': '\u233f', 'Sopf;': '\U0001d54a', 'sopf;': '\U0001d564', 'spades;': '\u2660', 'spadesuit;': '\u2660', 'spar;': '\u2225', 'sqcap;': '\u2293', 'sqcaps;': '\u2293\ufe00', 'sqcup;': '\u2294', 'sqcups;': '\u2294\ufe00', 'Sqrt;': '\u221a', 'sqsub;': '\u228f', 'sqsube;': '\u2291', 'sqsubset;': '\u228f', 'sqsubseteq;': '\u2291', 'sqsup;': '\u2290', 'sqsupe;': '\u2292', 'sqsupset;': '\u2290', 'sqsupseteq;': '\u2292', 'squ;': '\u25a1', 'Square;': '\u25a1', 'square;': '\u25a1', 'SquareIntersection;': '\u2293', 'SquareSubset;': '\u228f', 'SquareSubsetEqual;': '\u2291', 'SquareSuperset;': '\u2290', 'SquareSupersetEqual;': '\u2292', 'SquareUnion;': '\u2294', 'squarf;': '\u25aa', 'squf;': '\u25aa', 'srarr;': '\u2192', 'Sscr;': '\U0001d4ae', 'sscr;': '\U0001d4c8', 'ssetmn;': '\u2216', 'ssmile;': '\u2323', 'sstarf;': '\u22c6', 'Star;': '\u22c6', 'star;': '\u2606', 'starf;': '\u2605', 'straightepsilon;': '\u03f5', 'straightphi;': '\u03d5', 'strns;': '\xaf', 'Sub;': '\u22d0', 'sub;': '\u2282', 'subdot;': '\u2abd', 'subE;': '\u2ac5', 'sube;': '\u2286', 'subedot;': '\u2ac3', 'submult;': '\u2ac1', 'subnE;': '\u2acb', 'subne;': '\u228a', 'subplus;': '\u2abf', 'subrarr;': '\u2979', 'Subset;': '\u22d0', 'subset;': '\u2282', 'subseteq;': '\u2286', 'subseteqq;': '\u2ac5', 'SubsetEqual;': '\u2286', 'subsetneq;': '\u228a', 'subsetneqq;': '\u2acb', 'subsim;': '\u2ac7', 'subsub;': '\u2ad5', 'subsup;': '\u2ad3', 'succ;': '\u227b', 'succapprox;': '\u2ab8', 'succcurlyeq;': '\u227d', 'Succeeds;': '\u227b', 'SucceedsEqual;': '\u2ab0', 'SucceedsSlantEqual;': '\u227d', 'SucceedsTilde;': '\u227f', 'succeq;': '\u2ab0', 'succnapprox;': '\u2aba', 'succneqq;': '\u2ab6', 'succnsim;': '\u22e9', 'succsim;': '\u227f', 'SuchThat;': '\u220b', 'Sum;': '\u2211', 'sum;': '\u2211', 'sung;': '\u266a', 'sup1': '\xb9', 'sup1;': '\xb9', 'sup2': '\xb2', 'sup2;': '\xb2', 'sup3': '\xb3', 'sup3;': '\xb3', 'Sup;': '\u22d1', 'sup;': '\u2283', 'supdot;': '\u2abe', 'supdsub;': '\u2ad8', 'supE;': '\u2ac6', 'supe;': '\u2287', 'supedot;': '\u2ac4', 'Superset;': '\u2283', 'SupersetEqual;': '\u2287', 'suphsol;': '\u27c9', 'suphsub;': '\u2ad7', 'suplarr;': '\u297b', 'supmult;': '\u2ac2', 'supnE;': '\u2acc', 'supne;': '\u228b', 'supplus;': '\u2ac0', 'Supset;': '\u22d1', 'supset;': '\u2283', 'supseteq;': '\u2287', 'supseteqq;': '\u2ac6', 'supsetneq;': '\u228b', 'supsetneqq;': '\u2acc', 'supsim;': '\u2ac8', 'supsub;': '\u2ad4', 'supsup;': '\u2ad6', 'swarhk;': '\u2926', 'swArr;': '\u21d9', 'swarr;': '\u2199', 'swarrow;': '\u2199', 'swnwar;': '\u292a', 'szlig': '\xdf', 'szlig;': '\xdf', 'Tab;': '\t', 'target;': '\u2316', 'Tau;': '\u03a4', 'tau;': '\u03c4', 'tbrk;': '\u23b4', 'Tcaron;': '\u0164', 'tcaron;': '\u0165', 'Tcedil;': '\u0162', 'tcedil;': '\u0163', 'Tcy;': '\u0422', 'tcy;': '\u0442', 'tdot;': '\u20db', 'telrec;': '\u2315', 'Tfr;': '\U0001d517', 'tfr;': '\U0001d531', 'there4;': '\u2234', 'Therefore;': '\u2234', 'therefore;': '\u2234', 'Theta;': '\u0398', 'theta;': '\u03b8', 'thetasym;': '\u03d1', 'thetav;': '\u03d1', 'thickapprox;': '\u2248', 'thicksim;': '\u223c', 'ThickSpace;': '\u205f\u200a', 'thinsp;': '\u2009', 'ThinSpace;': '\u2009', 'thkap;': '\u2248', 'thksim;': '\u223c', 'THORN': '\xde', 'thorn': '\xfe', 'THORN;': '\xde', 'thorn;': '\xfe', 'Tilde;': '\u223c', 'tilde;': '\u02dc', 'TildeEqual;': '\u2243', 'TildeFullEqual;': '\u2245', 'TildeTilde;': '\u2248', 'times': '\xd7', 'times;': '\xd7', 'timesb;': '\u22a0', 'timesbar;': '\u2a31', 'timesd;': '\u2a30', 'tint;': '\u222d', 'toea;': '\u2928', 'top;': '\u22a4', 'topbot;': '\u2336', 'topcir;': '\u2af1', 'Topf;': '\U0001d54b', 'topf;': '\U0001d565', 'topfork;': '\u2ada', 'tosa;': '\u2929', 'tprime;': '\u2034', 'TRADE;': '\u2122', 'trade;': '\u2122', 'triangle;': '\u25b5', 'triangledown;': '\u25bf', 'triangleleft;': '\u25c3', 'trianglelefteq;': '\u22b4', 'triangleq;': '\u225c', 'triangleright;': '\u25b9', 'trianglerighteq;': '\u22b5', 'tridot;': '\u25ec', 'trie;': '\u225c', 'triminus;': '\u2a3a', 'TripleDot;': '\u20db', 'triplus;': '\u2a39', 'trisb;': '\u29cd', 'tritime;': '\u2a3b', 'trpezium;': '\u23e2', 'Tscr;': '\U0001d4af', 'tscr;': '\U0001d4c9', 'TScy;': '\u0426', 'tscy;': '\u0446', 'TSHcy;': '\u040b', 'tshcy;': '\u045b', 'Tstrok;': '\u0166', 'tstrok;': '\u0167', 'twixt;': '\u226c', 'twoheadleftarrow;': '\u219e', 'twoheadrightarrow;': '\u21a0', 'Uacute': '\xda', 'uacute': '\xfa', 'Uacute;': '\xda', 'uacute;': '\xfa', 'Uarr;': '\u219f', 'uArr;': '\u21d1', 'uarr;': '\u2191', 'Uarrocir;': '\u2949', 'Ubrcy;': '\u040e', 'ubrcy;': '\u045e', 'Ubreve;': '\u016c', 'ubreve;': '\u016d', 'Ucirc': '\xdb', 'ucirc': '\xfb', 'Ucirc;': '\xdb', 'ucirc;': '\xfb', 'Ucy;': '\u0423', 'ucy;': '\u0443', 'udarr;': '\u21c5', 'Udblac;': '\u0170', 'udblac;': '\u0171', 'udhar;': '\u296e', 'ufisht;': '\u297e', 'Ufr;': '\U0001d518', 'ufr;': '\U0001d532', 'Ugrave': '\xd9', 'ugrave': '\xf9', 'Ugrave;': '\xd9', 'ugrave;': '\xf9', 'uHar;': '\u2963', 'uharl;': '\u21bf', 'uharr;': '\u21be', 'uhblk;': '\u2580', 'ulcorn;': '\u231c', 'ulcorner;': '\u231c', 'ulcrop;': '\u230f', 'ultri;': '\u25f8', 'Umacr;': '\u016a', 'umacr;': '\u016b', 'uml': '\xa8', 'uml;': '\xa8', 'UnderBar;': '_', 'UnderBrace;': '\u23df', 'UnderBracket;': '\u23b5', 'UnderParenthesis;': '\u23dd', 'Union;': '\u22c3', 'UnionPlus;': '\u228e', 'Uogon;': '\u0172', 'uogon;': '\u0173', 'Uopf;': '\U0001d54c', 'uopf;': '\U0001d566', 'UpArrow;': '\u2191', 'Uparrow;': '\u21d1', 'uparrow;': '\u2191', 'UpArrowBar;': '\u2912', 'UpArrowDownArrow;': '\u21c5', 'UpDownArrow;': '\u2195', 'Updownarrow;': '\u21d5', 'updownarrow;': '\u2195', 'UpEquilibrium;': '\u296e', 'upharpoonleft;': '\u21bf', 'upharpoonright;': '\u21be', 'uplus;': '\u228e', 'UpperLeftArrow;': '\u2196', 'UpperRightArrow;': '\u2197', 'Upsi;': '\u03d2', 'upsi;': '\u03c5', 'upsih;': '\u03d2', 'Upsilon;': '\u03a5', 'upsilon;': '\u03c5', 'UpTee;': '\u22a5', 'UpTeeArrow;': '\u21a5', 'upuparrows;': '\u21c8', 'urcorn;': '\u231d', 'urcorner;': '\u231d', 'urcrop;': '\u230e', 'Uring;': '\u016e', 'uring;': '\u016f', 'urtri;': '\u25f9', 'Uscr;': '\U0001d4b0', 'uscr;': '\U0001d4ca', 'utdot;': '\u22f0', 'Utilde;': '\u0168', 'utilde;': '\u0169', 'utri;': '\u25b5', 'utrif;': '\u25b4', 'uuarr;': '\u21c8', 'Uuml': '\xdc', 'uuml': '\xfc', 'Uuml;': '\xdc', 'uuml;': '\xfc', 'uwangle;': '\u29a7', 'vangrt;': '\u299c', 'varepsilon;': '\u03f5', 'varkappa;': '\u03f0', 'varnothing;': '\u2205', 'varphi;': '\u03d5', 'varpi;': '\u03d6', 'varpropto;': '\u221d', 'vArr;': '\u21d5', 'varr;': '\u2195', 'varrho;': '\u03f1', 'varsigma;': '\u03c2', 'varsubsetneq;': '\u228a\ufe00', 'varsubsetneqq;': '\u2acb\ufe00', 'varsupsetneq;': '\u228b\ufe00', 'varsupsetneqq;': '\u2acc\ufe00', 'vartheta;': '\u03d1', 'vartriangleleft;': '\u22b2', 'vartriangleright;': '\u22b3', 'Vbar;': '\u2aeb', 'vBar;': '\u2ae8', 'vBarv;': '\u2ae9', 'Vcy;': '\u0412', 'vcy;': '\u0432', 'VDash;': '\u22ab', 'Vdash;': '\u22a9', 'vDash;': '\u22a8', 'vdash;': '\u22a2', 'Vdashl;': '\u2ae6', 'Vee;': '\u22c1', 'vee;': '\u2228', 'veebar;': '\u22bb', 'veeeq;': '\u225a', 'vellip;': '\u22ee', 'Verbar;': '\u2016', 'verbar;': '|', 'Vert;': '\u2016', 'vert;': '|', 'VerticalBar;': '\u2223', 'VerticalLine;': '|', 'VerticalSeparator;': '\u2758', 'VerticalTilde;': '\u2240', 'VeryThinSpace;': '\u200a', 'Vfr;': '\U0001d519', 'vfr;': '\U0001d533', 'vltri;': '\u22b2', 'vnsub;': '\u2282\u20d2', 'vnsup;': '\u2283\u20d2', 'Vopf;': '\U0001d54d', 'vopf;': '\U0001d567', 'vprop;': '\u221d', 'vrtri;': '\u22b3', 'Vscr;': '\U0001d4b1', 'vscr;': '\U0001d4cb', 'vsubnE;': '\u2acb\ufe00', 'vsubne;': '\u228a\ufe00', 'vsupnE;': '\u2acc\ufe00', 'vsupne;': '\u228b\ufe00', 'Vvdash;': '\u22aa', 'vzigzag;': '\u299a', 'Wcirc;': '\u0174', 'wcirc;': '\u0175', 'wedbar;': '\u2a5f', 'Wedge;': '\u22c0', 'wedge;': '\u2227', 'wedgeq;': '\u2259', 'weierp;': '\u2118', 'Wfr;': '\U0001d51a', 'wfr;': '\U0001d534', 'Wopf;': '\U0001d54e', 'wopf;': '\U0001d568', 'wp;': '\u2118', 'wr;': '\u2240', 'wreath;': '\u2240', 'Wscr;': '\U0001d4b2', 'wscr;': '\U0001d4cc', 'xcap;': '\u22c2', 'xcirc;': '\u25ef', 'xcup;': '\u22c3', 'xdtri;': '\u25bd', 'Xfr;': '\U0001d51b', 'xfr;': '\U0001d535', 'xhArr;': '\u27fa', 'xharr;': '\u27f7', 'Xi;': '\u039e', 'xi;': '\u03be', 'xlArr;': '\u27f8', 'xlarr;': '\u27f5', 'xmap;': '\u27fc', 'xnis;': '\u22fb', 'xodot;': '\u2a00', 'Xopf;': '\U0001d54f', 'xopf;': '\U0001d569', 'xoplus;': '\u2a01', 'xotime;': '\u2a02', 'xrArr;': '\u27f9', 'xrarr;': '\u27f6', 'Xscr;': '\U0001d4b3', 'xscr;': '\U0001d4cd', 'xsqcup;': '\u2a06', 'xuplus;': '\u2a04', 'xutri;': '\u25b3', 'xvee;': '\u22c1', 'xwedge;': '\u22c0', 'Yacute': '\xdd', 'yacute': '\xfd', 'Yacute;': '\xdd', 'yacute;': '\xfd', 'YAcy;': '\u042f', 'yacy;': '\u044f', 'Ycirc;': '\u0176', 'ycirc;': '\u0177', 'Ycy;': '\u042b', 'ycy;': '\u044b', 'yen': '\xa5', 'yen;': '\xa5', 'Yfr;': '\U0001d51c', 'yfr;': '\U0001d536', 'YIcy;': '\u0407', 'yicy;': '\u0457', 'Yopf;': '\U0001d550', 'yopf;': '\U0001d56a', 'Yscr;': '\U0001d4b4', 'yscr;': '\U0001d4ce', 'YUcy;': '\u042e', 'yucy;': '\u044e', 'yuml': '\xff', 'Yuml;': '\u0178', 'yuml;': '\xff', 'Zacute;': '\u0179', 'zacute;': '\u017a', 'Zcaron;': '\u017d', 'zcaron;': '\u017e', 'Zcy;': '\u0417', 'zcy;': '\u0437', 'Zdot;': '\u017b', 'zdot;': '\u017c', 'zeetrf;': '\u2128', 'ZeroWidthSpace;': '\u200b', 'Zeta;': '\u0396', 'zeta;': '\u03b6', 'Zfr;': '\u2128', 'zfr;': '\U0001d537', 'ZHcy;': '\u0416', 'zhcy;': '\u0436', 'zigrarr;': '\u21dd', 'Zopf;': '\u2124', 'zopf;': '\U0001d56b', 'Zscr;': '\U0001d4b5', 'zscr;': '\U0001d4cf', 'zwj;': '\u200d', 'zwnj;': '\u200c', } try: import http.client as compat_http_client except ImportError: # Python 2 import httplib as compat_http_client try: from urllib.error import HTTPError as compat_HTTPError except ImportError: # Python 2 from urllib2 import HTTPError as compat_HTTPError try: from urllib.request import urlretrieve as compat_urlretrieve except ImportError: # Python 2 from urllib import urlretrieve as compat_urlretrieve try: from html.parser import HTMLParser as compat_HTMLParser except ImportError: # Python 2 from HTMLParser import HTMLParser as compat_HTMLParser try: # Python 2 from HTMLParser import HTMLParseError as compat_HTMLParseError except ImportError: # Python <3.4 try: from html.parser import HTMLParseError as compat_HTMLParseError except ImportError: # Python >3.4 # HTMLParseError has been deprecated in Python 3.3 and removed in # Python 3.5. Introducing dummy exception for Python >3.5 for compatible # and uniform cross-version exceptiong handling class compat_HTMLParseError(Exception): pass try: from subprocess import DEVNULL compat_subprocess_get_DEVNULL = lambda: DEVNULL except ImportError: compat_subprocess_get_DEVNULL = lambda: open(os.path.devnull, 'w') try: import http.server as compat_http_server except ImportError: import BaseHTTPServer as compat_http_server try: compat_str = unicode # Python 2 except NameError: compat_str = str try: from urllib.parse import unquote_to_bytes as compat_urllib_parse_unquote_to_bytes from urllib.parse import unquote as compat_urllib_parse_unquote from urllib.parse import unquote_plus as compat_urllib_parse_unquote_plus except ImportError: # Python 2 _asciire = (compat_urllib_parse._asciire if hasattr(compat_urllib_parse, '_asciire') else re.compile(r'([\x00-\x7f]+)')) # HACK: The following are the correct unquote_to_bytes, unquote and unquote_plus # implementations from cpython 3.4.3's stdlib. Python 2's version # is apparently broken (see https://github.com/rg3/youtube-dl/pull/6244) def compat_urllib_parse_unquote_to_bytes(string): """unquote_to_bytes('abc%20def') -> b'abc def'.""" # Note: strings are encoded as UTF-8. This is only an issue if it contains # unescaped non-ASCII characters, which URIs should not. if not string: # Is it a string-like object? string.split return b'' if isinstance(string, compat_str): string = string.encode('utf-8') bits = string.split(b'%') if len(bits) == 1: return string res = [bits[0]] append = res.append for item in bits[1:]: try: append(compat_urllib_parse._hextochr[item[:2]]) append(item[2:]) except KeyError: append(b'%') append(item) return b''.join(res) def compat_urllib_parse_unquote(string, encoding='utf-8', errors='replace'): """Replace %xx escapes by their single-character equivalent. The optional encoding and errors parameters specify how to decode percent-encoded sequences into Unicode characters, as accepted by the bytes.decode() method. By default, percent-encoded sequences are decoded with UTF-8, and invalid sequences are replaced by a placeholder character. unquote('abc%20def') -> 'abc def'. """ if '%' not in string: string.split return string if encoding is None: encoding = 'utf-8' if errors is None: errors = 'replace' bits = _asciire.split(string) res = [bits[0]] append = res.append for i in range(1, len(bits), 2): append(compat_urllib_parse_unquote_to_bytes(bits[i]).decode(encoding, errors)) append(bits[i + 1]) return ''.join(res) def compat_urllib_parse_unquote_plus(string, encoding='utf-8', errors='replace'): """Like unquote(), but also replace plus signs by spaces, as required for unquoting HTML form values. unquote_plus('%7e/abc+def') -> '~/abc def' """ string = string.replace('+', ' ') return compat_urllib_parse_unquote(string, encoding, errors) try: from urllib.parse import urlencode as compat_urllib_parse_urlencode except ImportError: # Python 2 # Python 2 will choke in urlencode on mixture of byte and unicode strings. # Possible solutions are to either port it from python 3 with all # the friends or manually ensure input query contains only byte strings. # We will stick with latter thus recursively encoding the whole query. def compat_urllib_parse_urlencode(query, doseq=0, encoding='utf-8'): def encode_elem(e): if isinstance(e, dict): e = encode_dict(e) elif isinstance(e, (list, tuple,)): list_e = encode_list(e) e = tuple(list_e) if isinstance(e, tuple) else list_e elif isinstance(e, compat_str): e = e.encode(encoding) return e def encode_dict(d): return dict((encode_elem(k), encode_elem(v)) for k, v in d.items()) def encode_list(l): return [encode_elem(e) for e in l] return compat_urllib_parse.urlencode(encode_elem(query), doseq=doseq) try: from urllib.request import DataHandler as compat_urllib_request_DataHandler except ImportError: # Python < 3.4 # Ported from CPython 98774:1733b3bd46db, Lib/urllib/request.py class compat_urllib_request_DataHandler(compat_urllib_request.BaseHandler): def data_open(self, req): # data URLs as specified in RFC 2397. # # ignores POSTed data # # syntax: # dataurl := "data:" [ mediatype ] [ ";base64" ] "," data # mediatype := [ type "/" subtype ] *( ";" parameter ) # data := *urlchar # parameter := attribute "=" value url = req.get_full_url() scheme, data = url.split(':', 1) mediatype, data = data.split(',', 1) # even base64 encoded data URLs might be quoted so unquote in any case: data = compat_urllib_parse_unquote_to_bytes(data) if mediatype.endswith(';base64'): data = binascii.a2b_base64(data) mediatype = mediatype[:-7] if not mediatype: mediatype = 'text/plain;charset=US-ASCII' headers = email.message_from_string( 'Content-type: %s\nContent-length: %d\n' % (mediatype, len(data))) return compat_urllib_response.addinfourl(io.BytesIO(data), headers, url) try: compat_basestring = basestring # Python 2 except NameError: compat_basestring = str try: compat_chr = unichr # Python 2 except NameError: compat_chr = chr try: from xml.etree.ElementTree import ParseError as compat_xml_parse_error except ImportError: # Python 2.6 from xml.parsers.expat import ExpatError as compat_xml_parse_error etree = xml.etree.ElementTree class _TreeBuilder(etree.TreeBuilder): def doctype(self, name, pubid, system): pass if sys.version_info[0] >= 3: def compat_etree_fromstring(text): return etree.XML(text, parser=etree.XMLParser(target=_TreeBuilder())) else: # python 2.x tries to encode unicode strings with ascii (see the # XMLParser._fixtext method) try: _etree_iter = etree.Element.iter except AttributeError: # Python <=2.6 def _etree_iter(root): for el in root.findall('*'): yield el for sub in _etree_iter(el): yield sub # on 2.6 XML doesn't have a parser argument, function copied from CPython # 2.7 source def _XML(text, parser=None): if not parser: parser = etree.XMLParser(target=_TreeBuilder()) parser.feed(text) return parser.close() def _element_factory(*args, **kwargs): el = etree.Element(*args, **kwargs) for k, v in el.items(): if isinstance(v, bytes): el.set(k, v.decode('utf-8')) return el def compat_etree_fromstring(text): doc = _XML(text, parser=etree.XMLParser(target=_TreeBuilder(element_factory=_element_factory))) for el in _etree_iter(doc): if el.text is not None and isinstance(el.text, bytes): el.text = el.text.decode('utf-8') return doc if hasattr(etree, 'register_namespace'): compat_etree_register_namespace = etree.register_namespace else: def compat_etree_register_namespace(prefix, uri): """Register a namespace prefix. The registry is global, and any existing mapping for either the given prefix or the namespace URI will be removed. *prefix* is the namespace prefix, *uri* is a namespace uri. Tags and attributes in this namespace will be serialized with prefix if possible. ValueError is raised if prefix is reserved or is invalid. """ if re.match(r"ns\d+$", prefix): raise ValueError("Prefix format reserved for internal use") for k, v in list(etree._namespace_map.items()): if k == uri or v == prefix: del etree._namespace_map[k] etree._namespace_map[uri] = prefix if sys.version_info < (2, 7): # Here comes the crazy part: In 2.6, if the xpath is a unicode, # .//node does not match if a node is a direct child of . ! def compat_xpath(xpath): if isinstance(xpath, compat_str): xpath = xpath.encode('ascii') return xpath else: compat_xpath = lambda xpath: xpath try: from urllib.parse import parse_qs as compat_parse_qs except ImportError: # Python 2 # HACK: The following is the correct parse_qs implementation from cpython 3's stdlib. # Python 2's version is apparently totally broken def _parse_qsl(qs, keep_blank_values=False, strict_parsing=False, encoding='utf-8', errors='replace'): qs, _coerce_result = qs, compat_str pairs = [s2 for s1 in qs.split('&') for s2 in s1.split(';')] r = [] for name_value in pairs: if not name_value and not strict_parsing: continue nv = name_value.split('=', 1) if len(nv) != 2: if strict_parsing: raise ValueError('bad query field: %r' % (name_value,)) # Handle case of a control-name with no equal sign if keep_blank_values: nv.append('') else: continue if len(nv[1]) or keep_blank_values: name = nv[0].replace('+', ' ') name = compat_urllib_parse_unquote( name, encoding=encoding, errors=errors) name = _coerce_result(name) value = nv[1].replace('+', ' ') value = compat_urllib_parse_unquote( value, encoding=encoding, errors=errors) value = _coerce_result(value) r.append((name, value)) return r def compat_parse_qs(qs, keep_blank_values=False, strict_parsing=False, encoding='utf-8', errors='replace'): parsed_result = {} pairs = _parse_qsl(qs, keep_blank_values, strict_parsing, encoding=encoding, errors=errors) for name, value in pairs: if name in parsed_result: parsed_result[name].append(value) else: parsed_result[name] = [value] return parsed_result compat_os_name = os._name if os.name == 'java' else os.name if compat_os_name == 'nt': def compat_shlex_quote(s): return s if re.match(r'^[-_\w./]+$', s) else '"%s"' % s.replace('"', '\\"') else: try: from shlex import quote as compat_shlex_quote except ImportError: # Python < 3.3 def compat_shlex_quote(s): if re.match(r'^[-_\w./]+$', s): return s else: return "'" + s.replace("'", "'\"'\"'") + "'" try: args = shlex.split('中文') assert (isinstance(args, list) and isinstance(args[0], compat_str) and args[0] == '中文') compat_shlex_split = shlex.split except (AssertionError, UnicodeEncodeError): # Working around shlex issue with unicode strings on some python 2 # versions (see http://bugs.python.org/issue1548891) def compat_shlex_split(s, comments=False, posix=True): if isinstance(s, compat_str): s = s.encode('utf-8') return list(map(lambda s: s.decode('utf-8'), shlex.split(s, comments, posix))) def compat_ord(c): if type(c) is int: return c else: return ord(c) if sys.version_info >= (3, 0): compat_getenv = os.getenv compat_expanduser = os.path.expanduser def compat_setenv(key, value, env=os.environ): env[key] = value else: # Environment variables should be decoded with filesystem encoding. # Otherwise it will fail if any non-ASCII characters present (see #3854 #3217 #2918) def compat_getenv(key, default=None): from .utils import get_filesystem_encoding env = os.getenv(key, default) if env: env = env.decode(get_filesystem_encoding()) return env def compat_setenv(key, value, env=os.environ): def encode(v): from .utils import get_filesystem_encoding return v.encode(get_filesystem_encoding()) if isinstance(v, compat_str) else v env[encode(key)] = encode(value) # HACK: The default implementations of os.path.expanduser from cpython do not decode # environment variables with filesystem encoding. We will work around this by # providing adjusted implementations. # The following are os.path.expanduser implementations from cpython 2.7.8 stdlib # for different platforms with correct environment variables decoding. if compat_os_name == 'posix': def compat_expanduser(path): """Expand ~ and ~user constructions. If user or $HOME is unknown, do nothing.""" if not path.startswith('~'): return path i = path.find('/', 1) if i < 0: i = len(path) if i == 1: if 'HOME' not in os.environ: import pwd userhome = pwd.getpwuid(os.getuid()).pw_dir else: userhome = compat_getenv('HOME') else: import pwd try: pwent = pwd.getpwnam(path[1:i]) except KeyError: return path userhome = pwent.pw_dir userhome = userhome.rstrip('/') return (userhome + path[i:]) or '/' elif compat_os_name in ('nt', 'ce'): def compat_expanduser(path): """Expand ~ and ~user constructs. If user or $HOME is unknown, do nothing.""" if path[:1] != '~': return path i, n = 1, len(path) while i < n and path[i] not in '/\\': i = i + 1 if 'HOME' in os.environ: userhome = compat_getenv('HOME') elif 'USERPROFILE' in os.environ: userhome = compat_getenv('USERPROFILE') elif 'HOMEPATH' not in os.environ: return path else: try: drive = compat_getenv('HOMEDRIVE') except KeyError: drive = '' userhome = os.path.join(drive, compat_getenv('HOMEPATH')) if i != 1: # ~user userhome = os.path.join(os.path.dirname(userhome), path[1:i]) return userhome + path[i:] else: compat_expanduser = os.path.expanduser if sys.version_info < (3, 0): def compat_print(s): from .utils import preferredencoding print(s.encode(preferredencoding(), 'xmlcharrefreplace')) else: def compat_print(s): assert isinstance(s, compat_str) print(s) if sys.version_info < (3, 0) and sys.platform == 'win32': def compat_getpass(prompt, *args, **kwargs): if isinstance(prompt, compat_str): from .utils import preferredencoding prompt = prompt.encode(preferredencoding()) return getpass.getpass(prompt, *args, **kwargs) else: compat_getpass = getpass.getpass try: compat_input = raw_input except NameError: # Python 3 compat_input = input # Python < 2.6.5 require kwargs to be bytes try: def _testfunc(x): pass _testfunc(**{'x': 0}) except TypeError: def compat_kwargs(kwargs): return dict((bytes(k), v) for k, v in kwargs.items()) else: compat_kwargs = lambda kwargs: kwargs try: compat_numeric_types = (int, float, long, complex) except NameError: # Python 3 compat_numeric_types = (int, float, complex) if sys.version_info < (2, 7): def compat_socket_create_connection(address, timeout, source_address=None): host, port = address err = None for res in socket.getaddrinfo(host, port, 0, socket.SOCK_STREAM): af, socktype, proto, canonname, sa = res sock = None try: sock = socket.socket(af, socktype, proto) sock.settimeout(timeout) if source_address: sock.bind(source_address) sock.connect(sa) return sock except socket.error as _: err = _ if sock is not None: sock.close() if err is not None: raise err else: raise socket.error('getaddrinfo returns an empty list') else: compat_socket_create_connection = socket.create_connection # Fix https://github.com/rg3/youtube-dl/issues/4223 # See http://bugs.python.org/issue9161 for what is broken def workaround_optparse_bug9161(): op = optparse.OptionParser() og = optparse.OptionGroup(op, 'foo') try: og.add_option('-t') except TypeError: real_add_option = optparse.OptionGroup.add_option def _compat_add_option(self, *args, **kwargs): enc = lambda v: ( v.encode('ascii', 'replace') if isinstance(v, compat_str) else v) bargs = [enc(a) for a in args] bkwargs = dict( (k, enc(v)) for k, v in kwargs.items()) return real_add_option(self, *bargs, **bkwargs) optparse.OptionGroup.add_option = _compat_add_option if hasattr(shutil, 'get_terminal_size'): # Python >= 3.3 compat_get_terminal_size = shutil.get_terminal_size else: _terminal_size = collections.namedtuple('terminal_size', ['columns', 'lines']) def compat_get_terminal_size(fallback=(80, 24)): columns = compat_getenv('COLUMNS') if columns: columns = int(columns) else: columns = None lines = compat_getenv('LINES') if lines: lines = int(lines) else: lines = None if columns is None or lines is None or columns <= 0 or lines <= 0: try: sp = subprocess.Popen( ['stty', 'size'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = sp.communicate() _lines, _columns = map(int, out.split()) except Exception: _columns, _lines = _terminal_size(*fallback) if columns is None or columns <= 0: columns = _columns if lines is None or lines <= 0: lines = _lines return _terminal_size(columns, lines) try: itertools.count(start=0, step=1) compat_itertools_count = itertools.count except TypeError: # Python 2.6 def compat_itertools_count(start=0, step=1): n = start while True: yield n n += step if sys.version_info >= (3, 0): from tokenize import tokenize as compat_tokenize_tokenize else: from tokenize import generate_tokens as compat_tokenize_tokenize try: struct.pack('!I', 0) except TypeError: # In Python 2.6 and 2.7.x < 2.7.7, struct requires a bytes argument # See https://bugs.python.org/issue19099 def compat_struct_pack(spec, *args): if isinstance(spec, compat_str): spec = spec.encode('ascii') return struct.pack(spec, *args) def compat_struct_unpack(spec, *args): if isinstance(spec, compat_str): spec = spec.encode('ascii') return struct.unpack(spec, *args) else: compat_struct_pack = struct.pack compat_struct_unpack = struct.unpack try: from future_builtins import zip as compat_zip except ImportError: # not 2.6+ or is 3.x try: from itertools import izip as compat_zip # < 2.5 or 3.x except ImportError: compat_zip = zip __all__ = [ 'compat_HTMLParseError', 'compat_HTMLParser', 'compat_HTTPError', 'compat_basestring', 'compat_chr', 'compat_cookiejar', 'compat_cookies', 'compat_etree_fromstring', 'compat_etree_register_namespace', 'compat_expanduser', 'compat_get_terminal_size', 'compat_getenv', 'compat_getpass', 'compat_html_entities', 'compat_html_entities_html5', 'compat_http_client', 'compat_http_server', 'compat_input', 'compat_itertools_count', 'compat_kwargs', 'compat_numeric_types', 'compat_ord', 'compat_os_name', 'compat_parse_qs', 'compat_print', 'compat_setenv', 'compat_shlex_quote', 'compat_shlex_split', 'compat_socket_create_connection', 'compat_str', 'compat_struct_pack', 'compat_struct_unpack', 'compat_subprocess_get_DEVNULL', 'compat_tokenize_tokenize', 'compat_urllib_error', 'compat_urllib_parse', 'compat_urllib_parse_unquote', 'compat_urllib_parse_unquote_plus', 'compat_urllib_parse_unquote_to_bytes', 'compat_urllib_parse_urlencode', 'compat_urllib_parse_urlparse', 'compat_urllib_request', 'compat_urllib_request_DataHandler', 'compat_urllib_response', 'compat_urlparse', 'compat_urlretrieve', 'compat_xml_parse_error', 'compat_xpath', 'compat_zip', 'workaround_optparse_bug9161', ]
phihag/youtube-dl
youtube_dl/compat.py
Python
unlicense
90,595
[ "Bowtie" ]
664e6e37ddc2c9b857ec9b60f12794fcd46fedc16db5272bad3507cabfa44bfd
#DeepLearning Demo in Python3 # notes xrange is named range # print requires parenthisis () from numpy import exp, array, random, dot class NeuralNetwork(): def __init__(self): # Seed the random number generator, so it generates the same numbers # every time the program runs. random.seed(1) # We model a single neuron, with 3 input connections and 1 output connection. # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1 # and mean 0. self.synaptic_weights = 2 * random.random((3, 1)) - 1 # The Sigmoid function, which describes an S shaped curve. # We pass the weighted sum of the inputs through this function to # normalise them between 0 and 1. def __sigmoid(self, x): return 1 / (1 + exp(-x)) # The derivative of the Sigmoid function. # This is the gradient of the Sigmoid curve. # It indicates how confident we are about the existing weight. def __sigmoid_derivative(self, x): return x * (1 - x) # We train the neural network through a process of trial and error. # Adjusting the synaptic weights each time. def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations): for iteration in range(number_of_training_iterations): # Pass the training set through our neural network (a single neuron). output = self.think(training_set_inputs) # Calculate the error (The difference between the desired output # and the predicted output). error = training_set_outputs - output # Multiply the error by the input and again by the gradient of the Sigmoid curve. # This means less confident weights are adjusted more. # This means inputs, which are zero, do not cause changes to the weights. adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output)) # Adjust the weights. self.synaptic_weights += adjustment # The neural network thinks. def think(self, inputs): # Pass inputs through our neural network (our single neuron). return self.__sigmoid(dot(inputs, self.synaptic_weights)) if __name__ == "__main__": #Intialise a single neuron neural network. neural_network = NeuralNetwork() print ("Random starting synaptic weights: ") print (neural_network.synaptic_weights) # The training set. We have 4 examples, each consisting of 3 input values # and 1 output value. training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]]) training_set_outputs = array([[0, 1, 1, 0]]).T # Train the neural network using a training set. # Do it 10,000 times and make small adjustments each time. neural_network.train(training_set_inputs, training_set_outputs, 10000) print ("New synaptic weights after training: ") print (neural_network.synaptic_weights) # Test the neural network with a new situation. print ("Considering new situation [1, 0, 0] -> ?: ") print (neural_network.think(array([1, 0, 0])))
bluewitch/Code-Blue-Python
NeuralNetworkDemo.py
Python
mit
3,145
[ "NEURON" ]
8316f0d547ab9007a2ac1367120d18aeed2ee7c23fda64165e14b0acd9d02e3b
"""MDTraj: A modern, open library for the analysis of molecular dynamics trajectories MDTraj is a python library that allows users to manipulate molecular dynamics (MD) trajectories and perform a variety of analyses, including fast RMSD, solvent accessible surface area, hydrogen bonding, etc. A highlight of MDTraj is the wide variety of molecular dynamics trajectory file formats which are supported, including RCSB pdb, GROMACS xtc, tng, and trr, CHARMM / NAMD dcd, AMBER binpos, AMBER NetCDF, AMBER mdcrd, TINKER arc and MDTraj HDF5. """ from __future__ import print_function, absolute_import import sys from glob import glob DOCLINES = __doc__.split("\n") from setuptools import setup, Extension, find_packages sys.path.insert(0, '.') from basesetup import (write_version_py, build_ext, StaticLibrary, CompilerDetection, parse_setuppy_commands) try: # add an optional --disable-openmp to disable OpenMP support sys.argv.remove('--disable-openmp') disable_openmp = True except ValueError: disable_openmp = False ########################## VERSION = "1.9.6" ISRELEASED = True __version__ = VERSION ########################## CLASSIFIERS = """\ Development Status :: 5 - Production/Stable Intended Audience :: Science/Research Intended Audience :: Developers License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+) Programming Language :: C Programming Language :: Python Programming Language :: Python :: 3 Topic :: Scientific/Engineering :: Bio-Informatics Topic :: Scientific/Engineering :: Chemistry Operating System :: Microsoft :: Windows Operating System :: POSIX Operating System :: Unix Operating System :: MacOS """ # Global info about compiler compiler = CompilerDetection(disable_openmp) compiler.initialize() extra_cpp_libraries = [] if sys.platform == 'win32': extra_cpp_libraries.append('Ws2_32') # For determining if a path is relative (for dtr) extra_cpp_libraries.append('Shlwapi') ################################################################################ # Declaration of the compiled extension modules (cython + c) ################################################################################ def format_extensions(): compiler_args = compiler.compiler_args_warn xtc = Extension('mdtraj.formats.xtc', sources=['mdtraj/formats/xtc/src/xdrfile.c', 'mdtraj/formats/xtc/src/xdr_seek.c', 'mdtraj/formats/xtc/src/xdrfile_xtc.c', 'mdtraj/formats/xtc/xtc.pyx', ], include_dirs=['mdtraj/formats/xtc/include/', 'mdtraj/formats/xtc/'], extra_compile_args=compiler_args) trr = Extension('mdtraj.formats.trr', sources=['mdtraj/formats/xtc/src/xdrfile.c', 'mdtraj/formats/xtc/src/xdr_seek.c', 'mdtraj/formats/xtc/src/xdrfile_trr.c', 'mdtraj/formats/xtc/trr.pyx'], include_dirs=['mdtraj/formats/xtc/include/', 'mdtraj/formats/xtc/'], extra_compile_args=compiler_args) zlib_include_dirs = [] zlib_library_dirs = [] if sys.platform == 'win32': # Conda puts the zlib headers in ./Library/... on windows # If you're not using conda, good luck! zlib_include_dirs += ["{}/Library/include".format(sys.prefix)] zlib_library_dirs += ["{}/Library/lib".format(sys.prefix)] else: # On linux (and mac(?)) these paths should work for a standard # install of python+zlib or a conda install of python+zlib zlib_include_dirs += ["{}/include".format(sys.prefix)] zlib_library_dirs += ["{}/lib".format(sys.prefix)] tng = Extension('mdtraj.formats.tng', sources=glob('mdtraj/formats/tng/src/compression/*.c') + ['mdtraj/formats/tng/src/lib/tng_io.c', 'mdtraj/formats/tng/src/lib/md5.c', 'mdtraj/formats/tng/tng.pyx'], include_dirs=['mdtraj/formats/tng/include'] + zlib_include_dirs, define_macros=[('USE_ZLIB', 1)], library_dirs=zlib_library_dirs, libraries=['z'], ) dcd = Extension('mdtraj.formats.dcd', sources=['mdtraj/formats/dcd/src/dcdplugin.c', 'mdtraj/formats/dcd/dcd.pyx'], include_dirs=["mdtraj/formats/dcd/include/", 'mdtraj/formats/dcd/'], extra_compile_args=compiler_args) binpos = Extension('mdtraj.formats.binpos', sources=['mdtraj/formats/binpos/src/binposplugin.c', 'mdtraj/formats/binpos/binpos.pyx'], include_dirs=['mdtraj/formats/binpos/include/', 'mdtraj/formats/binpos/'], extra_compile_args=compiler_args) dtr = Extension('mdtraj.formats.dtr', sources=['mdtraj/formats/dtr/src/dtrplugin.cxx', 'mdtraj/formats/dtr/dtr.pyx'], include_dirs=['mdtraj/formats/dtr/include/', 'mdtraj/formats/dtr/'], define_macros=[('DESRES_READ_TIMESTEP2', 1)], language='c++', extra_compile_args=compiler_args, libraries=extra_cpp_libraries) return [xtc, trr, tng, dcd, binpos, dtr] def rmsd_extensions(): compiler_args = (compiler.compiler_args_openmp + compiler.compiler_args_sse2 + compiler.compiler_args_sse3 + compiler.compiler_args_opt + compiler.compiler_args_warn) compiler_libraries = compiler.compiler_libraries_openmp libtheobald = StaticLibrary( 'mdtraj.core.lib.libtheobald', sources=[ 'mdtraj/rmsd/src/theobald_rmsd.cpp', 'mdtraj/rmsd/src/center.cpp'], include_dirs=[ 'mdtraj/rmsd/include'], export_include=['mdtraj/rmsd/include/theobald_rmsd.h', 'mdtraj/rmsd/include/center.h'], language="c++", # don't enable OpenMP extra_compile_args=(compiler.compiler_args_sse2 + compiler.compiler_args_sse3 + compiler.compiler_args_opt)) rmsd = Extension('mdtraj._rmsd', sources=[ 'mdtraj/rmsd/src/theobald_rmsd.cpp', 'mdtraj/rmsd/src/rotation.cpp', 'mdtraj/rmsd/src/center.cpp', 'mdtraj/rmsd/_rmsd.pyx'], include_dirs=['mdtraj/rmsd/include'], extra_compile_args=compiler_args, libraries=compiler_libraries, language="c++") lprmsd = Extension('mdtraj._lprmsd', sources=[ 'mdtraj/rmsd/src/theobald_rmsd.cpp', 'mdtraj/rmsd/src/rotation.cpp', 'mdtraj/rmsd/src/center.cpp', 'mdtraj/rmsd/src/fancy_index.cpp', 'mdtraj/rmsd/src/Munkres.cpp', 'mdtraj/rmsd/src/euclidean_permutation.cpp', 'mdtraj/rmsd/_lprmsd.pyx'], language='c++', include_dirs=['mdtraj/rmsd/include'], extra_compile_args=compiler_args, libraries=compiler_libraries + extra_cpp_libraries) return rmsd, lprmsd, libtheobald def geometry_extensions(): compiler.initialize() compiler_args = ( compiler.compiler_args_openmp + compiler.compiler_args_sse2 + compiler.compiler_args_sse3 + compiler.compiler_args_opt + compiler.compiler_args_warn) define_macros = None compiler_libraries = compiler.compiler_libraries_openmp + extra_cpp_libraries return [ Extension('mdtraj.geometry._geometry', sources=['mdtraj/geometry/src/sasa.cpp', 'mdtraj/geometry/src/dssp.cpp', 'mdtraj/geometry/src/geometry.cpp', 'mdtraj/geometry/src/_geometry.pyx',], include_dirs=['mdtraj/geometry/include', 'mdtraj/geometry/src/kernels'], depends=['mdtraj/geometry/src/kernels/anglekernels.h', 'mdtraj/geometry/src/kernels/dihedralkernels.h', 'mdtraj/geometry/src/kernels/distancekernels.h'], define_macros=define_macros, extra_compile_args=compiler_args, libraries=compiler_libraries, language='c++'), Extension('mdtraj.geometry.drid', sources=["mdtraj/geometry/drid.pyx", "mdtraj/geometry/src/dridkernels.cpp", "mdtraj/geometry/src/moments.cpp"], include_dirs=["mdtraj/geometry/include"], define_macros=define_macros, extra_compile_args=compiler_args, libraries=compiler_libraries, language='c++'), Extension('mdtraj.geometry.neighbors', sources=["mdtraj/geometry/neighbors.pyx", "mdtraj/geometry/src/neighbors.cpp"], include_dirs=["mdtraj/geometry/include",], define_macros=define_macros, extra_compile_args=compiler_args, libraries=compiler_libraries, language='c++'), Extension('mdtraj.geometry.neighborlist', sources=["mdtraj/geometry/neighborlist.pyx", "mdtraj/geometry/src/neighborlist.cpp"], include_dirs=["mdtraj/geometry/include",], define_macros=define_macros, extra_compile_args=compiler_args, libraries=compiler_libraries, language='c++'), ] write_version_py(VERSION, ISRELEASED, 'mdtraj/version.py') metadata = \ dict(name='mdtraj', author='Robert McGibbon', author_email='rmcgibbo@gmail.com', description=DOCLINES[0], long_description="\n".join(DOCLINES[2:]), version=__version__, license='LGPLv2.1+', url='http://mdtraj.org', download_url = "https://github.com/rmcgibbo/mdtraj/releases/latest", platforms=['Linux', 'Mac OS-X', 'Unix', 'Windows'], classifiers=CLASSIFIERS.splitlines(), packages=find_packages(), cmdclass={'build_ext': build_ext}, install_requires=['numpy>=1.6', 'scipy', 'astunparse', 'pyparsing', ], package_data={'mdtraj.formats.pdb': ['data/*'], }, zip_safe=False, entry_points={'console_scripts': ['mdconvert = mdtraj.scripts.mdconvert:entry_point', 'mdinspect = mdtraj.scripts.mdinspect:entry_point']}, ) if __name__ == '__main__': # Don't use numpy if we are just - non-build actions are required to succeed # without NumPy for example when pip is used to install Scipy when # NumPy is not yet present in the system. run_build = parse_setuppy_commands() if run_build: extensions = format_extensions() extensions.extend(rmsd_extensions()) extensions.extend(geometry_extensions()) # most extensions use numpy, add headers for it. try: import Cython as _c from Cython.Build import cythonize if _c.__version__ < '0.29': raise ImportError("Too old") except ImportError as e: print('mdtrajs setup depends on Cython (>=0.29). Install it prior invoking setup.py') print(e) sys.exit(1) try: import numpy as np except ImportError: print('mdtrajs setup depends on NumPy. Install it prior invoking setup.py') sys.exit(1) for e in extensions: e.include_dirs.append(np.get_include()) metadata['ext_modules'] = cythonize(extensions, language_level=sys.version_info[0]) setup(**metadata)
dwhswenson/mdtraj
setup.py
Python
lgpl-2.1
12,458
[ "Amber", "CHARMM", "Gromacs", "MDTraj", "NAMD", "NetCDF", "TINKER" ]
bc62426570250cababc492aa92d10edf16ea93d7d1885adcd799da0d5ccf90f0
# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.conf import settings from django.conf.urls import include, url from django.conf.urls.static import static from django.contrib import admin from django.views.generic import TemplateView from django.views import defaults as default_views from ajax_select import urls as ajax_select_urls from arividam.siteconfig.views import RedirectUserView, SiteListView from arividam.djangocms_news.views import check_promoted, promote_news urlpatterns = [ #url(r'^$', TemplateView.as_view(template_name='pages/home.html'), name='home'), #url(r'^about/$', TemplateView.as_view(template_name='pages/about.html'), name='about'), url(r'^list-schools/$', SiteListView.as_view(), name='list-schools'), # Django Admin, use {% url 'admin:index' %} url(settings.ADMIN_URL, include(admin.site.urls)), # User management url(r'^users/', include('arividam.users.urls', namespace='users')), url(r'^accounts/', include('allauth.urls')), url(r'^ajax_select/', include(ajax_select_urls)), url(r'^messages/', include('postman.urls', namespace='postman', app_name='postman')), url(r'inbox/notifications/', include('notifications.urls', namespace='notifications')), url(r'^dashboard/', include('arividam.dashboard.urls', namespace='dashboard')), url(r'^redirect/', RedirectUserView.as_view()), url(r'^filer/', include('filer.urls')), url(r'^check_promoted/(\d+)/$', check_promoted), url(r'^promote_news/(\d+)/$', promote_news) # url(r'^notifications/', include('arividam.notifications.urls', namespace='notifications')), # Your stuff: custom urls includes go here ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) if settings.DEBUG: import debug_toolbar # This allows the error pages to be debugged during development, just visit # these url in browser to see how these error pages look like. urlpatterns += [ url(r'^400/$', default_views.bad_request, kwargs={'exception': Exception('Bad Request!')}), url(r'^403/$', default_views.permission_denied, kwargs={'exception': Exception('Permission Denied')}), url(r'^404/$', default_views.page_not_found, kwargs={'exception': Exception('Page not Found')}), url(r'^500/$', default_views.server_error), url(r'^__debug__/', include(debug_toolbar.urls)), ] #CMS catch-all patterns should be at the last urlpatterns = urlpatterns + [ url(r'^', include('cms.urls')), ]
c4sc/arividam
config/urls.py
Python
mit
2,517
[ "VisIt" ]
870c32c51dc5dd9da31c04969cc6a5d38707bce0a7e0acad35340b30303ee753
from __future__ import unicode_literals from frappe import _ def get_data(): return [ { "label": _("Issues"), "items": [ { "type": "doctype", "name": "Issue", "description": _("Support queries from customers."), "onboard": 1, }, { "type": "doctype", "name": "Issue Type", "description": _("Issue Type."), }, { "type": "doctype", "name": "Issue Priority", "description": _("Issue Priority."), } ] }, { "label": _("Warranty"), "items": [ { "type": "doctype", "name": "Warranty Claim", "description": _("Warranty Claim against Serial No."), }, { "type": "doctype", "name": "Serial No", "description": _("Single unit of an Item."), }, ] }, { "label": _("Service Level Agreement"), "items": [ { "type": "doctype", "name": "Service Level", "description": _("Service Level."), }, { "type": "doctype", "name": "Service Level Agreement", "description": _("Service Level Agreement."), } ] }, { "label": _("Maintenance"), "items": [ { "type": "doctype", "name": "Maintenance Schedule", }, { "type": "doctype", "name": "Maintenance Visit", }, ] }, { "label": _("Reports"), "icon": "fa fa-list", "items": [ { "type": "page", "name": "support-analytics", "label": _("Support Analytics"), "icon": "fa fa-bar-chart" }, { "type": "report", "name": "Minutes to First Response for Issues", "doctype": "Issue", "is_query_report": True }, { "type": "report", "name": "Support Hours", "doctype": "Issue", "is_query_report": True }, ] }, { "label": _("Settings"), "icon": "fa fa-list", "items": [ { "type": "doctype", "name": "Support Settings", "label": _("Support Settings"), }, ] }, ]
ebukoz/thrive
erpnext/config/support.py
Python
gpl-3.0
1,960
[ "VisIt" ]
9c045241c05d3fdac6cc972a35648978fb44c56dfc9da1b428ef2d55975b5031
# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT License. from __future__ import unicode_literals import unittest2 as unittest from pymatgen.electronic_structure.core import Orbital, Spin class SpinTest(unittest.TestCase): def test_init(self): self.assertEqual(int(Spin.up), 1) self.assertEqual(int(Spin.down), -1) def test_from_int(self): self.assertEqual(Spin(1), Spin.up) self.assertEqual(Spin(-1), Spin.down) self.assertRaises(ValueError, Spin, 0) def test_cached(self): self.assertEqual(id(Spin(1)), id(Spin.up)) class OrbitalTest(unittest.TestCase): def test_init(self): for orb in Orbital: self.assertEqual(Orbital(orb.value), orb) self.assertRaises(ValueError, Orbital, 100) def test_cached(self): self.assertEqual(id(Orbital(0)), id(Orbital.s)) if __name__ == '__main__': unittest.main()
aykol/pymatgen
pymatgen/electronic_structure/tests/test_core.py
Python
mit
969
[ "pymatgen" ]
18c380ab49da780ed1bb748f73d1ac45fc6b0ba863ef4f1dd9300dfc83588ce4
"""Base classes and utilities for readers and writers. Authors: * Brian Granger """ #----------------------------------------------------------------------------- # Copyright (C) 2008-2011 The IPython Development Team # # Distributed under the terms of the BSD License. The full license is in # the file COPYING, distributed as part of this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from base64 import encodestring, decodestring import pprint from . import py3compat str_to_bytes = py3compat.str_to_bytes #----------------------------------------------------------------------------- # Code #----------------------------------------------------------------------------- def restore_bytes(nb): """Restore bytes of image data from unicode-only formats. Base64 encoding is handled elsewhere. Bytes objects in the notebook are always b64-encoded. We DO NOT encode/decode around file formats. """ for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type == 'code': for output in cell.outputs: if 'png' in output: output.png = str_to_bytes(output.png, 'ascii') if 'jpeg' in output: output.jpeg = str_to_bytes(output.jpeg, 'ascii') return nb # output keys that are likely to have multiline values _multiline_outputs = ['text', 'html', 'svg', 'latex', 'javascript', 'json'] # FIXME: workaround for old splitlines() def _join_lines(lines): """join lines that have been written by splitlines() Has logic to protect against `splitlines()`, which should have been `splitlines(True)` """ if lines and lines[0].endswith(('\n', '\r')): # created by splitlines(True) return u''.join(lines) else: # created by splitlines() return u'\n'.join(lines) def rejoin_lines(nb): """rejoin multiline text into strings For reversing effects of ``split_lines(nb)``. This only rejoins lines that have been split, so if text objects were not split they will pass through unchanged. Used when reading JSON files that may have been passed through split_lines. """ for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type == 'code': if 'input' in cell and isinstance(cell.input, list): cell.input = _join_lines(cell.input) for output in cell.outputs: for key in _multiline_outputs: item = output.get(key, None) if isinstance(item, list): output[key] = _join_lines(item) else: # text, heading cell for key in ['source', 'rendered']: item = cell.get(key, None) if isinstance(item, list): cell[key] = _join_lines(item) return nb def split_lines(nb): """split likely multiline text into lists of strings For file output more friendly to line-based VCS. ``rejoin_lines(nb)`` will reverse the effects of ``split_lines(nb)``. Used when writing JSON files. """ for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type == 'code': if 'input' in cell and isinstance(cell.input, basestring): cell.input = cell.input.splitlines(True) for output in cell.outputs: for key in _multiline_outputs: item = output.get(key, None) if isinstance(item, basestring): output[key] = item.splitlines(True) else: # text, heading cell for key in ['source', 'rendered']: item = cell.get(key, None) if isinstance(item, basestring): cell[key] = item.splitlines(True) return nb # b64 encode/decode are never actually used, because all bytes objects in # the notebook are already b64-encoded, and we don't need/want to double-encode def base64_decode(nb): """Restore all bytes objects in the notebook from base64-encoded strings. Note: This is never used """ for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type == 'code': for output in cell.outputs: if 'png' in output: if isinstance(output.png, unicode): output.png = output.png.encode('ascii') output.png = decodestring(output.png) if 'jpeg' in output: if isinstance(output.jpeg, unicode): output.jpeg = output.jpeg.encode('ascii') output.jpeg = decodestring(output.jpeg) return nb def base64_encode(nb): """Base64 encode all bytes objects in the notebook. These will be b64-encoded unicode strings Note: This is never used """ for ws in nb.worksheets: for cell in ws.cells: if cell.cell_type == 'code': for output in cell.outputs: if 'png' in output: output.png = encodestring(output.png).decode('ascii') if 'jpeg' in output: output.jpeg = encodestring(output.jpeg).decode('ascii') return nb class NotebookReader(object): """A class for reading notebooks.""" def reads(self, s, **kwargs): """Read a notebook from a string.""" raise NotImplementedError("loads must be implemented in a subclass") def read(self, fp, **kwargs): """Read a notebook from a file like object""" nbs = fp.read() if not py3compat.PY3 and not isinstance(nbs, unicode): nbs = py3compat.str_to_unicode(nbs) return self.reads(nbs, **kwargs) class NotebookWriter(object): """A class for writing notebooks.""" def writes(self, nb, **kwargs): """Write a notebook to a string.""" raise NotImplementedError("loads must be implemented in a subclass") def write(self, nb, fp, **kwargs): """Write a notebook to a file like object""" nbs = self.writes(nb,**kwargs) if not py3compat.PY3 and not isinstance(nbs, unicode): # this branch is likely only taken for JSON on Python 2 nbs = py3compat.str_to_unicode(nbs) return fp.write(nbs)
maximsch2/SublimeIPythonNotebook
external/nbformat/rwbase.py
Python
gpl-3.0
6,753
[ "Brian" ]
1e39838cb52af16c42b0861ab2f5f2795501875bc8135c3d9e2d1bb12e17061a
""" TaskQueueDB class is a front-end to the task queues db """ __RCSID__ = "$Id" import types import random from DIRAC import gConfig, gLogger, S_OK, S_ERROR from DIRAC.WorkloadManagementSystem.private.SharesCorrector import SharesCorrector from DIRAC.WorkloadManagementSystem.private.Queues import maxCPUSegments from DIRAC.ConfigurationSystem.Client.Helpers.Operations import Operations from DIRAC.Core.Utilities import List from DIRAC.Core.Utilities.DictCache import DictCache from DIRAC.Core.Base.DB import DB from DIRAC.Core.Security import Properties, CS DEFAULT_GROUP_SHARE = 1000 TQ_MIN_SHARE = 0.001 singleValueDefFields = ( 'OwnerDN', 'OwnerGroup', 'Setup', 'CPUTime' ) multiValueDefFields = ( 'Sites', 'GridCEs', 'GridMiddlewares', 'BannedSites', 'Platforms', 'PilotTypes', 'SubmitPools', 'JobTypes', 'Tags' ) multiValueMatchFields = ( 'GridCE', 'Site', 'GridMiddleware', 'Platform', 'PilotType', 'SubmitPool', 'JobType', 'Tag' ) tagMatchFields = ( 'Tag', ) bannedJobMatchFields = ( 'Site', ) strictRequireMatchFields = ( 'SubmitPool', 'Platform', 'PilotType', 'Tag' ) mandatoryMatchFields = ( 'Setup', 'CPUTime' ) priorityIgnoredFields = ( 'Sites', 'BannedSites' ) class TaskQueueDB( DB ): def __init__( self ): random.seed() DB.__init__( self, 'TaskQueueDB', 'WorkloadManagement/TaskQueueDB' ) self.__maxJobsInTQ = 5000 self.__defaultCPUSegments = maxCPUSegments self.__maxMatchRetry = 3 self.__jobPriorityBoundaries = ( 0.001, 10 ) self.__groupShares = {} self.__deleteTQWithDelay = DictCache( self.__deleteTQIfEmpty ) self.__opsHelper = Operations() self.__ensureInsertionIsSingle = False self.__sharesCorrector = SharesCorrector( self.__opsHelper ) result = self.__initializeDB() if not result[ 'OK' ]: raise Exception( "Can't create tables: %s" % result[ 'Message' ] ) def enableAllTaskQueues( self ): """ Enable all Task queues """ return self.updateFields( "tq_TaskQueues", updateDict = { "Enabled" :"1" } ) def findOrphanJobs( self ): """ Find jobs that are not in any task queue """ result = self._query( "select JobID from tq_Jobs WHERE TQId not in (SELECT TQId from tq_TaskQueues)" ) if not result[ 'OK' ]: return result return S_OK( [ row[0] for row in result[ 'Value' ] ] ) def isSharesCorrectionEnabled( self ): return self.__getCSOption( "EnableSharesCorrection", False ) def __getCSOption( self, optionName, defValue ): return self.__opsHelper.getValue( "JobScheduling/%s" % optionName, defValue ) def getValidPilotTypes( self ): return self.__getCSOption( "AllPilotTypes", [ 'private' ] ) def __initializeDB( self ): """ Create the tables """ result = self._query( "show tables" ) if not result[ 'OK' ]: return result tablesInDB = [ t[0] for t in result[ 'Value' ] ] tablesToCreate = {} self.__tablesDesc = {} self.__tablesDesc[ 'tq_TaskQueues' ] = { 'Fields' : { 'TQId' : 'INTEGER(10) UNSIGNED AUTO_INCREMENT NOT NULL', 'OwnerDN' : 'VARCHAR(255) NOT NULL', 'OwnerGroup' : 'VARCHAR(32) NOT NULL', 'Setup' : 'VARCHAR(32) NOT NULL', 'CPUTime' : 'BIGINT(20) UNSIGNED NOT NULL', 'Priority' : 'FLOAT NOT NULL', 'Enabled' : 'TINYINT(1) NOT NULL DEFAULT 0' }, 'PrimaryKey' : 'TQId', 'Indexes': { 'TQOwner': [ 'OwnerDN', 'OwnerGroup', 'Setup', 'CPUTime' ] } } self.__tablesDesc[ 'tq_Jobs' ] = { 'Fields' : { 'TQId' : 'INTEGER(10) UNSIGNED NOT NULL', 'JobId' : 'INTEGER(11) UNSIGNED NOT NULL', 'Priority' : 'INTEGER UNSIGNED NOT NULL', 'RealPriority' : 'FLOAT NOT NULL' }, 'PrimaryKey' : 'JobId', 'Indexes': { 'TaskIndex': [ 'TQId' ] }, } for multiField in multiValueDefFields: tableName = 'tq_TQTo%s' % multiField self.__tablesDesc[ tableName ] = { 'Fields' : { 'TQId' : 'INTEGER UNSIGNED NOT NULL', 'Value' : 'VARCHAR(64) NOT NULL' }, 'Indexes': { 'TaskIndex': [ 'TQId' ], '%sIndex' % multiField: [ 'Value' ] }, } for tableName in self.__tablesDesc: if not tableName in tablesInDB: tablesToCreate[ tableName ] = self.__tablesDesc[ tableName ] return self._createTables( tablesToCreate ) def getGroupsInTQs( self ): cmdSQL = "SELECT DISTINCT( OwnerGroup ) FROM `tq_TaskQueues`" result = self._query( cmdSQL ) if not result[ 'OK' ]: return result return S_OK( [ row[0] for row in result[ 'Value' ] ] ) def forceRecreationOfTables( self ): dropSQL = "DROP TABLE IF EXISTS %s" % ", ".join( self.__tablesDesc ) result = self._update( dropSQL ) if not result[ 'OK' ]: return result return self._createTables( self.__tablesDesc ) def __strDict( self, dDict ): lines = [] keyLength = 0 for key in dDict: if len( key ) > keyLength: keyLength = len( key ) for key in sorted( dDict ): line = "%s: " % key line = line.ljust( keyLength + 2 ) value = dDict[ key ] if type( value ) in ( types.ListType, types.TupleType ): line += ','.join( list( value ) ) else: line += str( value ) lines.append( line ) return "{\n%s\n}" % "\n".join( lines ) def fitCPUTimeToSegments( self, cpuTime ): """ Fit the CPU time to the valid segments """ maxCPUSegments = self.__getCSOption( "taskQueueCPUTimeIntervals", self.__defaultCPUSegments ) try: maxCPUSegments = [ int( seg ) for seg in maxCPUSegments ] #Check segments in the CS last = 0 for cpuS in maxCPUSegments: if cpuS <= last: maxCPUSegments = self.__defaultCPUSegments break last = cpuS except: maxCPUSegments = self.__defaultCPUSegments #Map to a segment for iP in range( len( maxCPUSegments ) ): cpuSegment = maxCPUSegments[ iP ] if cpuTime <= cpuSegment: return cpuSegment return maxCPUSegments[-1] def _checkTaskQueueDefinition( self, tqDefDict ): """ Check a task queue definition dict is valid """ # Confine the LHCbPlatform legacy option here, use Platform everywhere else # until the LHCbPlatform is no more used in the TaskQueueDB if 'LHCbPlatforms' in tqDefDict and not "Platforms" in tqDefDict: tqDefDict['Platforms'] = tqDefDict['LHCbPlatforms'] if 'SystemConfigs' in tqDefDict and not "Platforms" in tqDefDict: tqDefDict['Platforms'] = tqDefDict['SystemConfigs'] for field in singleValueDefFields: if field not in tqDefDict: return S_ERROR( "Missing mandatory field '%s' in task queue definition" % field ) fieldValueType = type( tqDefDict[ field ] ) if field in [ "CPUTime" ]: if fieldValueType not in ( types.IntType, types.LongType ): return S_ERROR( "Mandatory field %s value type is not valid: %s" % ( field, fieldValueType ) ) else: if fieldValueType not in ( types.StringType, types.UnicodeType ): return S_ERROR( "Mandatory field %s value type is not valid: %s" % ( field, fieldValueType ) ) result = self._escapeString( tqDefDict[ field ] ) if not result[ 'OK' ]: return result tqDefDict[ field ] = result[ 'Value' ] for field in multiValueDefFields: if field not in tqDefDict: continue fieldValueType = type( tqDefDict[ field ] ) if fieldValueType not in ( types.ListType, types.TupleType ): return S_ERROR( "Multi value field %s value type is not valid: %s" % ( field, fieldValueType ) ) result = self._escapeValues( tqDefDict[ field ] ) if not result[ 'OK' ]: return result tqDefDict[ field ] = result[ 'Value' ] #FIXME: This is not used if 'PrivatePilots' in tqDefDict: validPilotTypes = self.getValidPilotTypes() for pilotType in tqDefDict[ 'PrivatePilots' ]: if pilotType not in validPilotTypes: return S_ERROR( "PilotType %s is invalid" % pilotType ) return S_OK( tqDefDict ) def _checkMatchDefinition( self, tqMatchDict ): """ Check a task queue match dict is valid """ def travelAndCheckType( value, validTypes, escapeValues = True ): valueType = type( value ) if valueType in ( types.ListType, types.TupleType ): for subValue in value: subValueType = type( subValue ) if subValueType not in validTypes: return S_ERROR( "List contained type %s is not valid -> %s" % ( subValueType, validTypes ) ) if escapeValues: return self._escapeValues( value ) return S_OK( value ) else: if valueType not in validTypes: return S_ERROR( "Type %s is not valid -> %s" % ( valueType, validTypes ) ) if escapeValues: return self._escapeString( value ) return S_OK( value ) # Confine the LHCbPlatform legacy option here, use Platform everywhere else # until the LHCbPlatform is no more used in the TaskQueueDB if 'LHCbPlatform' in tqMatchDict and not "Platform" in tqMatchDict: tqMatchDict['Platform'] = tqMatchDict['LHCbPlatform'] if 'SystemConfig' in tqMatchDict and not "Platform" in tqMatchDict: tqMatchDict['Platform'] = tqMatchDict['SystemConfig'] for field in singleValueDefFields: if field not in tqMatchDict: if field in mandatoryMatchFields: return S_ERROR( "Missing mandatory field '%s' in match request definition" % field ) continue fieldValue = tqMatchDict[ field ] if field in [ "CPUTime" ]: result = travelAndCheckType( fieldValue, ( types.IntType, types.LongType ), escapeValues = False ) else: result = travelAndCheckType( fieldValue, ( types.StringType, types.UnicodeType ) ) if not result[ 'OK' ]: return S_ERROR( "Match definition field %s failed : %s" % ( field, result[ 'Message' ] ) ) tqMatchDict[ field ] = result[ 'Value' ] #Check multivalue for multiField in multiValueMatchFields: for field in ( multiField, "Banned%s" % multiField ): if field in tqMatchDict: fieldValue = tqMatchDict[ field ] result = travelAndCheckType( fieldValue, ( types.StringType, types.UnicodeType ) ) if not result[ 'OK' ]: return S_ERROR( "Match definition field %s failed : %s" % ( field, result[ 'Message' ] ) ) tqMatchDict[ field ] = result[ 'Value' ] return S_OK( tqMatchDict ) def __createTaskQueue( self, tqDefDict, priority = 1, connObj = False ): """ Create a task queue Returns S_OK( tqId ) / S_ERROR """ if not connObj: result = self._getConnection() if not result[ 'OK' ]: return S_ERROR( "Can't create task queue: %s" % result[ 'Message' ] ) connObj = result[ 'Value' ] tqDefDict[ 'CPUTime' ] = self.fitCPUTimeToSegments( tqDefDict[ 'CPUTime' ] ) sqlSingleFields = [ 'TQId', 'Priority' ] sqlValues = [ "0", str( priority ) ] for field in singleValueDefFields: sqlSingleFields.append( field ) sqlValues.append( tqDefDict[ field ] ) #Insert the TQ Disabled sqlSingleFields.append( "Enabled" ) sqlValues.append( "0" ) cmd = "INSERT INTO tq_TaskQueues ( %s ) VALUES ( %s )" % ( ", ".join( sqlSingleFields ), ", ".join( [ str( v ) for v in sqlValues ] ) ) result = self._update( cmd, conn = connObj ) if not result[ 'OK' ]: self.log.error( "Can't insert TQ in DB", result[ 'Value' ] ) return result if 'lastRowId' in result: tqId = result['lastRowId'] else: result = self._query( "SELECT LAST_INSERT_ID()", conn = connObj ) if not result[ 'OK' ]: self.cleanOrphanedTaskQueues( connObj = connObj ) return S_ERROR( "Can't determine task queue id after insertion" ) tqId = result[ 'Value' ][0][0] for field in multiValueDefFields: if field not in tqDefDict: continue values = List.uniqueElements( [ value for value in tqDefDict[ field ] if value.strip() ] ) if not values: continue cmd = "INSERT INTO `tq_TQTo%s` ( TQId, Value ) VALUES " % field cmd += ", ".join( [ "( %s, %s )" % ( tqId, str( value ) ) for value in values ] ) result = self._update( cmd, conn = connObj ) if not result[ 'OK' ]: self.log.error( "Failed to insert %s condition" % field, result[ 'Message' ] ) self.cleanOrphanedTaskQueues( connObj = connObj ) return S_ERROR( "Can't insert values %s for field %s: %s" % ( str( values ), field, result[ 'Message' ] ) ) self.log.info( "Created TQ %s" % tqId ) return S_OK( tqId ) def cleanOrphanedTaskQueues( self, connObj = False ): """ Delete all empty task queues """ self.log.info( "Cleaning orphaned TQs" ) result = self._update( "DELETE FROM `tq_TaskQueues` WHERE Enabled >= 1 AND TQId not in ( SELECT DISTINCT TQId from `tq_Jobs` )", conn = connObj ) if not result[ 'OK' ]: return result for mvField in multiValueDefFields: result = self._update( "DELETE FROM `tq_TQTo%s` WHERE TQId not in ( SELECT DISTINCT TQId from `tq_TaskQueues` )" % mvField, conn = connObj ) if not result[ 'OK' ]: return result return S_OK() def __setTaskQueueEnabled( self, tqId, enabled = True, connObj = False ): if enabled: enabled = "+ 1" else: enabled = "- 1" upSQL = "UPDATE `tq_TaskQueues` SET Enabled = Enabled %s WHERE TQId=%d" % ( enabled, tqId ) result = self._update( upSQL, conn = connObj ) if not result[ 'OK' ]: self.log.error( "Error setting TQ state", "TQ %s State %s: %s" % ( tqId, enabled, result[ 'Message' ] ) ) return result updated = result['Value'] > 0 if updated: self.log.info( "Set enabled = %s for TQ %s" % ( enabled, tqId ) ) return S_OK( updated ) def __hackJobPriority( self, jobPriority ): jobPriority = min( max( int( jobPriority ), self.__jobPriorityBoundaries[0] ), self.__jobPriorityBoundaries[1] ) if jobPriority == self.__jobPriorityBoundaries[0]: return 10 ** ( -5 ) if jobPriority == self.__jobPriorityBoundaries[1]: return 10 ** 6 return jobPriority def insertJob( self, jobId, tqDefDict, jobPriority, skipTQDefCheck = False, numRetries = 10 ): """ Insert a job in a task queue Returns S_OK( tqId ) / S_ERROR """ try: long( jobId ) except ValueError: return S_ERROR( "JobId is not a number!" ) retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't insert job: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] if not skipTQDefCheck: tqDefDict = dict( tqDefDict ) retVal = self._checkTaskQueueDefinition( tqDefDict ) if not retVal[ 'OK' ]: self.log.error( "TQ definition check failed", retVal[ 'Message' ] ) return retVal tqDefDict = retVal[ 'Value' ] tqDefDict[ 'CPUTime' ] = self.fitCPUTimeToSegments( tqDefDict[ 'CPUTime' ] ) self.log.info( "Inserting job %s with requirements: %s" % ( jobId, self.__strDict( tqDefDict ) ) ) retVal = self.__findAndDisableTaskQueue( tqDefDict, skipDefinitionCheck = True, connObj = connObj ) if not retVal[ 'OK' ]: return retVal tqInfo = retVal[ 'Value' ] newTQ = False if not tqInfo[ 'found' ]: self.log.info( "Creating a TQ for job %s" % jobId ) retVal = self.__createTaskQueue( tqDefDict, 1, connObj = connObj ) if not retVal[ 'OK' ]: return retVal tqId = retVal[ 'Value' ] newTQ = True else: tqId = tqInfo[ 'tqId' ] self.log.info( "Found TQ %s for job %s requirements" % ( tqId, jobId ) ) try: result = self.__insertJobInTaskQueue( jobId, tqId, int( jobPriority ), checkTQExists = False, connObj = connObj ) if not result[ 'OK' ]: self.log.error( "Error inserting job in TQ", "Job %s TQ %s: %s" % ( jobId, tqId, result[ 'Message' ] ) ) return result if newTQ: self.recalculateTQSharesForEntity( tqDefDict[ 'OwnerDN' ], tqDefDict[ 'OwnerGroup' ], connObj = connObj ) finally: self.__setTaskQueueEnabled( tqId, True ) return S_OK() def __insertJobInTaskQueue( self, jobId, tqId, jobPriority, checkTQExists = True, connObj = False ): """ Insert a job in a given task queue """ self.log.info( "Inserting job %s in TQ %s with priority %s" % ( jobId, tqId, jobPriority ) ) if not connObj: result = self._getConnection() if not result[ 'OK' ]: return S_ERROR( "Can't insert job: %s" % result[ 'Message' ] ) connObj = result[ 'Value' ] if checkTQExists: result = self._query( "SELECT tqId FROM `tq_TaskQueues` WHERE TQId = %s" % tqId, conn = connObj ) if not result[ 'OK' ] or len ( result[ 'Value' ] ) == 0: return S_OK( "Can't find task queue with id %s: %s" % ( tqId, result[ 'Message' ] ) ) hackedPriority = self.__hackJobPriority( jobPriority ) result = self._update( "INSERT INTO tq_Jobs ( TQId, JobId, Priority, RealPriority ) VALUES ( %s, %s, %s, %f ) ON DUPLICATE KEY UPDATE TQId = %s, Priority = %s, RealPriority = %f" % ( tqId, jobId, jobPriority, hackedPriority, tqId, jobPriority, hackedPriority ), conn = connObj ) if not result[ 'OK' ]: return result return S_OK() def __generateTQFindSQL( self, tqDefDict, skipDefinitionCheck = False, connObj = False ): """ Find a task queue that has exactly the same requirements """ if not skipDefinitionCheck: tqDefDict = dict( tqDefDict ) result = self._checkTaskQueueDefinition( tqDefDict ) if not result[ 'OK' ]: return result tqDefDict = result[ 'Value' ] sqlCondList = [] for field in singleValueDefFields: sqlCondList.append( "`tq_TaskQueues`.%s = %s" % ( field, tqDefDict[ field ] ) ) #MAGIC SUBQUERIES TO ENSURE STRICT MATCH for field in multiValueDefFields: tableName = '`tq_TQTo%s`' % field if field in tqDefDict and tqDefDict[ field ]: firstQuery = "SELECT COUNT(%s.Value) FROM %s WHERE %s.TQId = `tq_TaskQueues`.TQId" % ( tableName, tableName, tableName ) grouping = "GROUP BY %s.TQId" % tableName valuesList = List.uniqueElements( [ value.strip() for value in tqDefDict[ field ] if value.strip() ] ) numValues = len( valuesList ) secondQuery = "%s AND %s.Value in (%s)" % ( firstQuery, tableName, ",".join( [ "%s" % str( value ) for value in valuesList ] ) ) sqlCondList.append( "%s = (%s %s)" % ( numValues, firstQuery, grouping ) ) sqlCondList.append( "%s = (%s %s)" % ( numValues, secondQuery, grouping ) ) else: sqlCondList.append( "`tq_TaskQueues`.TQId not in ( SELECT DISTINCT %s.TQId from %s )" % ( tableName, tableName ) ) #END MAGIC: That was easy ;) return S_OK( " AND ".join( sqlCondList ) ) def __findAndDisableTaskQueue( self, tqDefDict, skipDefinitionCheck = False, retries = 10, connObj = False ): """ Disable and find TQ """ for _ in range( retries ): result = self.__findSmallestTaskQueue( tqDefDict, skipDefinitionCheck = skipDefinitionCheck, connObj = connObj ) if not result[ 'OK' ]: return result data = result[ 'Value' ] if not data[ 'found' ]: return result if data[ 'enabled' ] < 1: gLogger.notice( "TaskQueue {tqId} seems to be already disabled ({enabled})".format( **data ) ) result = self.__setTaskQueueEnabled( data[ 'tqId' ], False ) if result[ 'OK' ]: return S_OK( data ) return S_ERROR( "Could not disable TQ" ) def __findSmallestTaskQueue( self, tqDefDict, skipDefinitionCheck = False, connObj = False ): """ Find a task queue that has exactly the same requirements """ result = self.__generateTQFindSQL( tqDefDict, skipDefinitionCheck = skipDefinitionCheck, connObj = connObj ) if not result[ 'OK' ]: return result sqlCmd = "SELECT COUNT( `tq_Jobs`.JobID ), `tq_TaskQueues`.TQId, `tq_TaskQueues`.Enabled FROM `tq_TaskQueues`, `tq_Jobs`" sqlCmd = "%s WHERE `tq_TaskQueues`.TQId = `tq_Jobs`.TQId AND %s GROUP BY `tq_Jobs`.TQId ORDER BY COUNT( `tq_Jobs`.JobID ) ASC" % ( sqlCmd, result[ 'Value' ] ) result = self._query( sqlCmd, conn = connObj ) if not result[ 'OK' ]: return S_ERROR( "Can't find task queue: %s" % result[ 'Message' ] ) data = result[ 'Value' ] if len( data ) == 0 or data[0][0] >= self.__maxJobsInTQ: return S_OK( { 'found' : False } ) return S_OK( { 'found' : True, 'tqId' : data[0][1], 'enabled' : data[0][2], 'jobs' : data[0][0] } ) def matchAndGetJob( self, tqMatchDict, numJobsPerTry = 50, numQueuesPerTry = 10, negativeCond = {} ): """ Match a job """ #Make a copy to avoid modification of original if escaping needs to be done tqMatchDict = dict( tqMatchDict ) self.log.info( "Starting match for requirements", self.__strDict( tqMatchDict ) ) retVal = self._checkMatchDefinition( tqMatchDict ) if not retVal[ 'OK' ]: self.log.error( "TQ match request check failed", retVal[ 'Message' ] ) return retVal retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't connect to DB: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] preJobSQL = "SELECT `tq_Jobs`.JobId, `tq_Jobs`.TQId FROM `tq_Jobs` WHERE `tq_Jobs`.TQId = %s AND `tq_Jobs`.Priority = %s" prioSQL = "SELECT `tq_Jobs`.Priority FROM `tq_Jobs` WHERE `tq_Jobs`.TQId = %s ORDER BY RAND() / `tq_Jobs`.RealPriority ASC LIMIT 1" postJobSQL = " ORDER BY `tq_Jobs`.JobId ASC LIMIT %s" % numJobsPerTry for _ in range( self.__maxMatchRetry ): if 'JobID' in tqMatchDict: # A certain JobID is required by the resource, so all TQ are to be considered retVal = self.matchAndGetTaskQueue( tqMatchDict, numQueuesToGet = 0, skipMatchDictDef = True, connObj = connObj ) preJobSQL = "%s AND `tq_Jobs`.JobId = %s " % ( preJobSQL, tqMatchDict['JobID'] ) else: retVal = self.matchAndGetTaskQueue( tqMatchDict, numQueuesToGet = numQueuesPerTry, skipMatchDictDef = True, negativeCond = negativeCond, connObj = connObj ) if not retVal[ 'OK' ]: return retVal tqList = retVal[ 'Value' ] if len( tqList ) == 0: self.log.info( "No TQ matches requirements" ) return S_OK( { 'matchFound' : False, 'tqMatch' : tqMatchDict } ) for tqId, tqOwnerDN, tqOwnerGroup in tqList: self.log.info( "Trying to extract jobs from TQ %s" % tqId ) retVal = self._query( prioSQL % tqId, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Can't retrieve winning priority for matching job: %s" % retVal[ 'Message' ] ) if len( retVal[ 'Value' ] ) == 0: continue prio = retVal[ 'Value' ][0][0] retVal = self._query( "%s %s" % ( preJobSQL % ( tqId, prio ), postJobSQL ), conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Can't begin transaction for matching job: %s" % retVal[ 'Message' ] ) jobTQList = [ ( row[0], row[1] ) for row in retVal[ 'Value' ] ] if len( jobTQList ) == 0: gLogger.info( "Task queue %s seems to be empty, triggering a cleaning" % tqId ) self.__deleteTQWithDelay.add( tqId, 300, ( tqId, tqOwnerDN, tqOwnerGroup ) ) while len( jobTQList ) > 0: jobId, tqId = jobTQList.pop( random.randint( 0, len( jobTQList ) - 1 ) ) self.log.info( "Trying to extract job %s from TQ %s" % ( jobId, tqId ) ) retVal = self.deleteJob( jobId, connObj = connObj ) if not retVal[ 'OK' ]: msgFix = "Could not take job" msgVar = " %s out from the TQ %s: %s" % ( jobId, tqId, retVal[ 'Message' ] ) self.log.error( msgFix, msgVar ) return S_ERROR( msgFix + msgVar ) if retVal[ 'Value' ] == True : self.log.info( "Extracted job %s with prio %s from TQ %s" % ( jobId, prio, tqId ) ) return S_OK( { 'matchFound' : True, 'jobId' : jobId, 'taskQueueId' : tqId, 'tqMatch' : tqMatchDict } ) self.log.info( "No jobs could be extracted from TQ %s" % tqId ) self.log.info( "Could not find a match after %s match retries" % self.__maxMatchRetry ) return S_ERROR( "Could not find a match after %s match retries" % self.__maxMatchRetry ) def matchAndGetTaskQueue( self, tqMatchDict, numQueuesToGet = 1, skipMatchDictDef = False, negativeCond = {}, connObj = False ): """ Get a queue that matches the requirements """ #Make a copy to avoid modification of original if escaping needs to be done tqMatchDict = dict( tqMatchDict ) if not skipMatchDictDef: retVal = self._checkMatchDefinition( tqMatchDict ) if not retVal[ 'OK' ]: return retVal retVal = self.__generateTQMatchSQL( tqMatchDict, numQueuesToGet = numQueuesToGet, negativeCond = negativeCond ) if not retVal[ 'OK' ]: return retVal matchSQL = retVal[ 'Value' ] retVal = self._query( matchSQL, conn = connObj ) if not retVal[ 'OK' ]: return retVal return S_OK( [ ( row[0], row[1], row[2] ) for row in retVal[ 'Value' ] ] ) def __generateSQLSubCond( self, sqlString, value, boolOp = 'OR' ): if type( value ) not in ( types.ListType, types.TupleType ): return sqlString % str( value ).strip() sqlORList = [] for v in value: sqlORList.append( sqlString % str( v ).strip() ) return "( %s )" % ( " %s " % boolOp ).join( sqlORList ) def __generateNotSQL( self, tableDict, negativeCond ): """ Generate negative conditions Can be a list of dicts or a dict: - list of dicts will be OR of conditional dicts - dicts will be normal conditional dict ( kay1 in ( v1, v2, ... ) AND key2 in ( v3, v4, ... ) ) """ condType = type( negativeCond ) if condType in ( types.ListType, types.TupleType ): sqlCond = [] for cD in negativeCond: sqlCond.append( self.__generateNotDictSQL( tableDict, cD ) ) return " ( %s )" % " OR ".join( sqlCond ) elif condType == types.DictType: return self.__generateNotDictSQL( tableDict, negativeCond ) raise RuntimeError( "negativeCond has to be either a list or a dict and it's %s" % condType ) def __generateNotDictSQL( self, tableDict, negativeCond ): """ Generate the negative sql condition from a standard condition dict not ( cond1 and cond2 ) = ( not cond1 or not cond 2 ) For instance: { 'Site': 'S1', 'JobType': [ 'T1', 'T2' ] } ( not 'S1' in Sites or ( not 'T1' in JobType and not 'T2' in JobType ) ) S2 T1 -> not False or ( not True and not False ) -> True or ... -> True -> Eligible S1 T3 -> not True or ( not False and not False ) -> False or (True and True ) -> True -> Eligible S1 T1 -> not True or ( not True and not False ) -> False or ( False and True ) -> False -> Nop """ condList = [] for field in negativeCond: if field in multiValueMatchFields: fullTableN = '`tq_TQTo%ss`' % field valList = negativeCond[ field ] if type( valList ) not in ( types.TupleType, types.ListType ): valList = ( valList, ) subList = [] for value in valList: value = self._escapeString( value )[ 'Value' ] sql = "%s NOT IN ( SELECT %s.Value FROM %s WHERE %s.TQId = tq.TQId )" % ( value, fullTableN, fullTableN, fullTableN ) subList.append( sql ) condList.append( "( %s )" % " AND ".join( subList ) ) elif field in singleValueDefFields: for value in negativeCond[field]: value = self._escapeString( value )[ 'Value' ] sql = "%s != tq.%s " % ( value, field ) condList.append( sql ) return "( %s )" % " OR ".join( condList ) def __generateTablesName( self, sqlTables, field ): fullTableName = 'tq_TQTo%ss' % field if fullTableName not in sqlTables: tableN = field.lower() sqlTables[ fullTableName ] = tableN return tableN, "`%s`" % fullTableName, return sqlTables[ fullTableName ], "`%s`" % fullTableName def __generateTQMatchSQL( self, tqMatchDict, numQueuesToGet = 1, negativeCond = {} ): """ Generate the SQL needed to match a task queue """ #Only enabled TQs sqlCondList = [] sqlTables = { "tq_TaskQueues" : "tq" } #If OwnerDN and OwnerGroup are defined only use those combinations that make sense if 'OwnerDN' in tqMatchDict and 'OwnerGroup' in tqMatchDict: groups = tqMatchDict[ 'OwnerGroup' ] if type( groups ) not in ( types.ListType, types.TupleType ): groups = [ groups ] dns = tqMatchDict[ 'OwnerDN' ] if type( dns ) not in ( types.ListType, types.TupleType ): dns = [ dns ] ownerConds = [] for group in groups: if Properties.JOB_SHARING in CS.getPropertiesForGroup( group.replace( '"', "" ) ): ownerConds.append( "tq.OwnerGroup = %s" % group ) else: for dn in dns: ownerConds.append( "( tq.OwnerDN = %s AND tq.OwnerGroup = %s )" % ( dn, group ) ) sqlCondList.append( " OR ".join( ownerConds ) ) else: #If not both are defined, just add the ones that are defined for field in ( 'OwnerGroup', 'OwnerDN' ): if field in tqMatchDict: sqlCondList.append( self.__generateSQLSubCond( "tq.%s = %%s" % field, tqMatchDict[ field ] ) ) #Type of single value conditions for field in ( 'CPUTime', 'Setup' ): if field in tqMatchDict: if field in ( 'CPUTime' ): sqlCondList.append( self.__generateSQLSubCond( "tq.%s <= %%s" % field, tqMatchDict[ field ] ) ) else: sqlCondList.append( self.__generateSQLSubCond( "tq.%s = %%s" % field, tqMatchDict[ field ] ) ) #Match multi value fields for field in multiValueMatchFields: #It has to be %ss , with an 's' at the end because the columns names # are plural and match options are singular if field in tqMatchDict and tqMatchDict[ field ]: _, fullTableN = self.__generateTablesName( sqlTables, field ) sqlMultiCondList = [] # if field != 'GridCE' or 'Site' in tqMatchDict: # Jobs for masked sites can be matched if they specified a GridCE # Site is removed from tqMatchDict if the Site is mask. In this case we want # that the GridCE matches explicitly so the COUNT can not be 0. In this case we skip this # condition sqlMultiCondList.append( "( SELECT COUNT(%s.Value) FROM %s WHERE %s.TQId = tq.TQId ) = 0" % ( fullTableN, fullTableN, fullTableN ) ) if field in tagMatchFields: if tqMatchDict[field] != '"Any"': csql = self.__generateTagSQLSubCond( fullTableN, tqMatchDict[field] ) else: csql = self.__generateSQLSubCond( "%%s IN ( SELECT %s.Value FROM %s WHERE %s.TQId = tq.TQId )" % ( fullTableN, fullTableN, fullTableN ), tqMatchDict[ field ] ) sqlMultiCondList.append( csql ) sqlCondList.append( "( %s )" % " OR ".join( sqlMultiCondList ) ) #In case of Site, check it's not in job banned sites if field in bannedJobMatchFields: fullTableN = '`tq_TQToBanned%ss`' % field csql = self.__generateSQLSubCond( "%%s not in ( SELECT %s.Value FROM %s WHERE %s.TQId = tq.TQId )" % ( fullTableN, fullTableN, fullTableN ), tqMatchDict[ field ], boolOp = 'OR' ) sqlCondList.append( csql ) #Resource banning bannedField = "Banned%s" % field if bannedField in tqMatchDict and tqMatchDict[ bannedField ]: fullTableN = '`tq_TQTo%ss`' % field csql = self.__generateSQLSubCond( "%%s not in ( SELECT %s.Value FROM %s WHERE %s.TQId = tq.TQId )" % ( fullTableN, fullTableN, fullTableN ), tqMatchDict[ bannedField ], boolOp = 'OR' ) sqlCondList.append( csql ) #For certain fields, the require is strict. If it is not in the tqMatchDict, the job cannot require it for field in strictRequireMatchFields: if field in tqMatchDict: continue fullTableN = '`tq_TQTo%ss`' % field sqlCondList.append( "( SELECT COUNT(%s.Value) FROM %s WHERE %s.TQId = tq.TQId ) = 0" % ( fullTableN, fullTableN, fullTableN ) ) # Add extra conditions if negativeCond: sqlCondList.append( self.__generateNotSQL( sqlTables, negativeCond ) ) #Generate the final query string tqSqlCmd = "SELECT tq.TQId, tq.OwnerDN, tq.OwnerGroup FROM `tq_TaskQueues` tq WHERE %s" % ( " AND ".join( sqlCondList ) ) #Apply priorities tqSqlCmd = "%s ORDER BY RAND() / tq.Priority ASC" % tqSqlCmd #Do we want a limit? if numQueuesToGet: tqSqlCmd = "%s LIMIT %s" % ( tqSqlCmd, numQueuesToGet ) return S_OK( tqSqlCmd ) def __generateTagSQLSubCond( self, tableName, tagMatchList ): """ Generate SQL condition where ALL the specified multiValue requirements must be present in the matching resource list """ sql1 = "SELECT COUNT(%s.Value) FROM %s WHERE %s.TQId=tq.TQId" % ( tableName, tableName, tableName ) if type( tagMatchList ) in [types.ListType, types.TupleType]: sql2 = sql1 + " AND %s.Value in ( %s )" % ( tableName, ','.join( [ "%s" % v for v in tagMatchList] ) ) else: sql2 = sql1 + " AND %s.Value=%s" % ( tableName, tagMatchList ) sql = '( '+sql1+' ) = ('+sql2+' )' return sql def deleteJob( self, jobId, connObj = False ): """ Delete a job from the task queues Return S_OK( True/False ) / S_ERROR """ if not connObj: retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't delete job: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] retVal = self._query( "SELECT t.TQId, t.OwnerDN, t.OwnerGroup FROM `tq_TaskQueues` t, `tq_Jobs` j WHERE j.JobId = %s AND t.TQId = j.TQId" % jobId, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Could not get job from task queue %s: %s" % ( jobId, retVal[ 'Message' ] ) ) data = retVal[ 'Value' ] if not data: return S_OK( False ) tqId, tqOwnerDN, tqOwnerGroup = data[0] self.log.info( "Deleting job %s" % jobId ) retVal = self._update( "DELETE FROM `tq_Jobs` WHERE JobId = %s" % jobId, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Could not delete job from task queue %s: %s" % ( jobId, retVal[ 'Message' ] ) ) if retVal['Value'] == 0: #No job deleted return S_OK( False ) #Always return S_OK() because job has already been taken out from the TQ self.__deleteTQWithDelay.add( tqId, 300, ( tqId, tqOwnerDN, tqOwnerGroup ) ) return S_OK( True ) def getTaskQueueForJob( self, jobId, connObj = False ): """ Return TaskQueue for a given Job Return S_OK( [TaskQueueID] ) / S_ERROR """ if not connObj: retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't get TQ for job: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] retVal = self._query( 'SELECT TQId FROM `tq_Jobs` WHERE JobId = %s ' % jobId, conn = connObj ) if not retVal[ 'OK' ]: return retVal if not retVal['Value']: return S_ERROR( 'Not in TaskQueues' ) return S_OK( retVal['Value'][0][0] ) def getTaskQueueForJobs( self, jobIDs, connObj = False ): """ Return TaskQueues for a given list of Jobs """ if not connObj: retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't get TQs for a job list: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] jobString = ','.join( [ str( x ) for x in jobIDs ] ) retVal = self._query( 'SELECT JobId,TQId FROM `tq_Jobs` WHERE JobId in (%s) ' % jobString, conn = connObj ) if not retVal[ 'OK' ]: return retVal if not retVal['Value']: return S_ERROR( 'Not in TaskQueues' ) resultDict = {} for jobID, TQID in retVal['Value']: resultDict[int( jobID )] = int( TQID ) return S_OK( resultDict ) def __getOwnerForTaskQueue( self, tqId, connObj = False ): retVal = self._query( "SELECT OwnerDN, OwnerGroup from `tq_TaskQueues` WHERE TQId=%s" % tqId, conn = connObj ) if not retVal[ 'OK' ]: return retVal data = retVal[ 'Value' ] if len( data ) == 0: return S_OK( False ) return S_OK( retVal[ 'Value' ][0] ) def __deleteTQIfEmpty( self, args ): ( tqId, tqOwnerDN, tqOwnerGroup ) = args retries = 3 while retries: retries -= 1 result = self.deleteTaskQueueIfEmpty( tqId, tqOwnerDN, tqOwnerGroup ) if result[ 'OK' ]: return gLogger.error( "Could not delete TQ %s: %s" % ( tqId, result[ 'Message' ] ) ) def deleteTaskQueueIfEmpty( self, tqId, tqOwnerDN = False, tqOwnerGroup = False, connObj = False ): """ Try to delete a task queue if its empty """ if not connObj: retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't insert job: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] if not tqOwnerDN or not tqOwnerGroup: retVal = self.__getOwnerForTaskQueue( tqId, connObj = connObj ) if not retVal[ 'OK' ]: return retVal data = retVal[ 'Value' ] if not data: return S_OK( False ) tqOwnerDN, tqOwnerGroup = data sqlCmd = "DELETE FROM `tq_TaskQueues` WHERE Enabled >= 1 AND `tq_TaskQueues`.TQId = %s" % tqId sqlCmd = "%s AND `tq_TaskQueues`.TQId not in ( SELECT DISTINCT TQId from `tq_Jobs` )" % sqlCmd retVal = self._update( sqlCmd, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Could not delete task queue %s: %s" % ( tqId, retVal[ 'Message' ] ) ) delTQ = retVal[ 'Value' ] if delTQ > 0: for mvField in multiValueDefFields: retVal = self._update( "DELETE FROM `tq_TQTo%s` WHERE TQId = %s" % ( mvField, tqId ), conn = connObj ) if not retVal[ 'OK' ]: return retVal self.recalculateTQSharesForEntity( tqOwnerDN, tqOwnerGroup, connObj = connObj ) self.log.info( "Deleted empty and enabled TQ %s" % tqId ) return S_OK( True ) return S_OK( False ) def deleteTaskQueue( self, tqId, tqOwnerDN = False, tqOwnerGroup = False, connObj = False ): """ Try to delete a task queue even if it has jobs """ self.log.info( "Deleting TQ %s" % tqId ) if not connObj: retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't insert job: %s" % retVal[ 'Message' ] ) connObj = retVal[ 'Value' ] if not tqOwnerDN or not tqOwnerGroup: retVal = self.__getOwnerForTaskQueue( tqId, connObj = connObj ) if not retVal[ 'OK' ]: return retVal data = retVal[ 'Value' ] if not data: return S_OK( False ) tqOwnerDN, tqOwnerGroup = data sqlCmd = "DELETE FROM `tq_TaskQueues` WHERE `tq_TaskQueues`.TQId = %s" % tqId retVal = self._update( sqlCmd, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Could not delete task queue %s: %s" % ( tqId, retVal[ 'Message' ] ) ) delTQ = retVal[ 'Value' ] sqlCmd = "DELETE FROM `tq_Jobs` WHERE `tq_Jobs`.TQId = %s" % tqId retVal = self._update( sqlCmd, conn = connObj ) if not retVal[ 'OK' ]: return S_ERROR( "Could not delete task queue %s: %s" % ( tqId, retVal[ 'Message' ] ) ) for field in multiValueDefFields: retVal = self._update( "DELETE FROM `tq_TQTo%s` WHERE TQId = %s" % ( field, tqId ), conn = connObj ) if not retVal[ 'OK' ]: return retVal if delTQ > 0: self.recalculateTQSharesForEntity( tqOwnerDN, tqOwnerGroup, connObj = connObj ) return S_OK( True ) return S_OK( False ) def getMatchingTaskQueues( self, tqMatchDict, negativeCond = False ): """ rename to have the same method as exposed in the Matcher """ return self.retrieveTaskQueuesThatMatch( tqMatchDict, negativeCond = negativeCond ) def getNumTaskQueues( self ): """ Get the number of task queues in the system """ sqlCmd = "SELECT COUNT( TQId ) FROM `tq_TaskQueues`" retVal = self._query( sqlCmd ) if not retVal[ 'OK' ]: return retVal return S_OK( retVal[ 'Value' ][0][0] ) def retrieveTaskQueuesThatMatch( self, tqMatchDict, negativeCond = False ): """ Get the info of the task queues that match a resource """ result = self.matchAndGetTaskQueue( tqMatchDict, numQueuesToGet = 0, negativeCond = negativeCond ) if not result[ 'OK' ]: return result return self.retrieveTaskQueues( [ tqTuple[0] for tqTuple in result[ 'Value' ] ] ) def retrieveTaskQueues( self, tqIdList = False ): """ Get all the task queues """ sqlSelectEntries = [ "`tq_TaskQueues`.TQId", "`tq_TaskQueues`.Priority", "COUNT( `tq_Jobs`.TQId )" ] sqlGroupEntries = [ "`tq_TaskQueues`.TQId", "`tq_TaskQueues`.Priority" ] for field in singleValueDefFields: sqlSelectEntries.append( "`tq_TaskQueues`.%s" % field ) sqlGroupEntries.append( "`tq_TaskQueues`.%s" % field ) sqlCmd = "SELECT %s FROM `tq_TaskQueues`, `tq_Jobs`" % ", ".join( sqlSelectEntries ) sqlTQCond = "" if tqIdList != False: if len( tqIdList ) == 0: return S_OK( {} ) else: sqlTQCond += " AND `tq_TaskQueues`.TQId in ( %s )" % ", ".join( [ str( id_ ) for id_ in tqIdList ] ) sqlCmd = "%s WHERE `tq_TaskQueues`.TQId = `tq_Jobs`.TQId %s GROUP BY %s" % ( sqlCmd, sqlTQCond, ", ".join( sqlGroupEntries ) ) retVal = self._query( sqlCmd ) if not retVal[ 'OK' ]: return S_ERROR( "Can't retrieve task queues info: %s" % retVal[ 'Message' ] ) tqData = {} for record in retVal[ 'Value' ]: tqId = record[0] tqData[ tqId ] = { 'Priority' : record[1], 'Jobs' : record[2] } record = record[3:] for iP in range( len( singleValueDefFields ) ): tqData[ tqId ][ singleValueDefFields[ iP ] ] = record[ iP ] tqNeedCleaning = False for field in multiValueDefFields: table = "`tq_TQTo%s`" % field sqlCmd = "SELECT %s.TQId, %s.Value FROM %s" % ( table, table, table ) retVal = self._query( sqlCmd ) if not retVal[ 'OK' ]: return S_ERROR( "Can't retrieve task queues field % info: %s" % ( field, retVal[ 'Message' ] ) ) for record in retVal[ 'Value' ]: tqId = record[0] value = record[1] if not tqId in tqData: if tqIdList == False or tqId in tqIdList: self.log.warn( "Task Queue %s is defined in field %s but does not exist, triggering a cleaning" % ( tqId, field ) ) tqNeedCleaning = True else: if field not in tqData[ tqId ]: tqData[ tqId ][ field ] = [] tqData[ tqId ][ field ].append( value ) if tqNeedCleaning: self.cleanOrphanedTaskQueues() return S_OK( tqData ) def __updateGlobalShares( self ): """ Update internal structure for shares """ #Update group shares self.__groupShares = self.getGroupShares() #Apply corrections if enabled if self.isSharesCorrectionEnabled(): result = self.getGroupsInTQs() if not result[ 'OK' ]: self.log.error( "Could not get groups in the TQs", result[ 'Message' ] ) activeGroups = result[ 'Value' ] newShares = {} for group in activeGroups: if group in self.__groupShares: newShares[ group ] = self.__groupShares[ group ] newShares = self.__sharesCorrector.correctShares( newShares ) for group in self.__groupShares: if group in newShares: self.__groupShares[ group ] = newShares[ group ] def recalculateTQSharesForAll( self ): """ Recalculate all priorities for TQ's """ if self.isSharesCorrectionEnabled(): self.log.info( "Updating correctors state" ) self.__sharesCorrector.update() self.__updateGlobalShares() self.log.info( "Recalculating shares for all TQs" ) retVal = self._getConnection() if not retVal[ 'OK' ]: return S_ERROR( "Can't insert job: %s" % retVal[ 'Message' ] ) result = self._query( "SELECT DISTINCT( OwnerGroup ) FROM `tq_TaskQueues`" ) if not result[ 'OK' ]: return result for group in [ r[0] for r in result[ 'Value' ] ]: self.recalculateTQSharesForEntity( "all", group ) return S_OK() def recalculateTQSharesForEntity( self, userDN, userGroup, connObj = False ): """ Recalculate the shares for a userDN/userGroup combo """ self.log.info( "Recalculating shares for %s@%s TQs" % ( userDN, userGroup ) ) if userGroup in self.__groupShares: share = self.__groupShares[ userGroup ] else: share = float( DEFAULT_GROUP_SHARE ) if Properties.JOB_SHARING in CS.getPropertiesForGroup( userGroup ): #If group has JobSharing just set prio for that entry, userDN is irrelevant return self.__setPrioritiesForEntity( userDN, userGroup, share, connObj = connObj ) selSQL = "SELECT OwnerDN, COUNT(OwnerDN) FROM `tq_TaskQueues` WHERE OwnerGroup='%s' GROUP BY OwnerDN" % ( userGroup ) result = self._query( selSQL, conn = connObj ) if not result[ 'OK' ]: return result #Get owners in this group and the amount of times they appear data = [ ( r[0], r[1] ) for r in result[ 'Value' ] if r ] numOwners = len( data ) #If there are no owners do now if numOwners == 0: return S_OK() #Split the share amongst the number of owners share /= numOwners entitiesShares = dict( [ ( row[0], share ) for row in data ] ) #If corrector is enabled let it work it's magic if self.isSharesCorrectionEnabled(): entitiesShares = self.__sharesCorrector.correctShares( entitiesShares, group = userGroup ) #Keep updating owners = dict( data ) #IF the user is already known and has more than 1 tq, the rest of the users don't need to be modified #(The number of owners didn't change) if userDN in owners and owners[ userDN ] > 1: return self.__setPrioritiesForEntity( userDN, userGroup, entitiesShares[ userDN ], connObj = connObj ) #Oops the number of owners may have changed so we recalculate the prio for all owners in the group for userDN in owners: self.__setPrioritiesForEntity( userDN, userGroup, entitiesShares[ userDN ], connObj = connObj ) return S_OK() def __setPrioritiesForEntity( self, userDN, userGroup, share, connObj = False, consolidationFunc = "AVG" ): """ Set the priority for a userDN/userGroup combo given a splitted share """ self.log.info( "Setting priorities to %s@%s TQs" % ( userDN, userGroup ) ) tqCond = [ "t.OwnerGroup='%s'" % userGroup ] allowBgTQs = gConfig.getValue( "/Registry/Groups/%s/AllowBackgroundTQs" % userGroup, False ) if Properties.JOB_SHARING not in CS.getPropertiesForGroup( userGroup ): tqCond.append( "t.OwnerDN='%s'" % userDN ) tqCond.append( "t.TQId = j.TQId" ) if consolidationFunc == 'AVG': selectSQL = "SELECT j.TQId, SUM( j.RealPriority )/COUNT(j.RealPriority) FROM `tq_TaskQueues` t, `tq_Jobs` j WHERE " elif consolidationFunc == 'SUM': selectSQL = "SELECT j.TQId, SUM( j.RealPriority ) FROM `tq_TaskQueues` t, `tq_Jobs` j WHERE " else: return S_ERROR( "Unknown consolidation func %s for setting priorities" % consolidationFunc ) selectSQL += " AND ".join( tqCond ) selectSQL += " GROUP BY t.TQId" result = self._query( selectSQL, conn = connObj ) if not result[ 'OK' ]: return result tqDict = dict( result[ 'Value' ] ) if len( tqDict ) == 0: return S_OK() #Calculate Sum of priorities totalPrio = 0 for k in tqDict: if tqDict[k] > 0.1 or not allowBgTQs: totalPrio += tqDict[ k ] #Update prio for each TQ for tqId in tqDict: if tqDict[ tqId ] > 0.1 or not allowBgTQs: prio = ( share / totalPrio ) * tqDict[ tqId ] else: prio = TQ_MIN_SHARE prio = max( prio, TQ_MIN_SHARE ) tqDict[ tqId ] = prio #Generate groups of TQs that will have the same prio=sum(prios) maomenos result = self.retrieveTaskQueues( list( tqDict ) ) if not result[ 'OK' ]: return result allTQsData = result[ 'Value' ] tqGroups = {} for tqid in allTQsData: tqData = allTQsData[ tqid ] for field in ( 'Jobs', 'Priority' ) + priorityIgnoredFields: if field in tqData: tqData.pop( field ) tqHash = [] for f in sorted( tqData ): tqHash.append( "%s:%s" % ( f, tqData[ f ] ) ) tqHash = "|".join( tqHash ) if tqHash not in tqGroups: tqGroups[ tqHash ] = [] tqGroups[ tqHash ].append( tqid ) tqGroups = [ tqGroups[ td ] for td in tqGroups ] #Do the grouping for tqGroup in tqGroups: totalPrio = 0 if len( tqGroup ) < 2: continue for tqid in tqGroup: totalPrio += tqDict[ tqid ] for tqid in tqGroup: tqDict[ tqid ] = totalPrio #Group by priorities prioDict = {} for tqId in tqDict: prio = tqDict[ tqId ] if prio not in prioDict: prioDict[ prio ] = [] prioDict[ prio ].append( tqId ) #Execute updates for prio in prioDict: tqList = ", ".join( [ str( tqId ) for tqId in prioDict[ prio ] ] ) updateSQL = "UPDATE `tq_TaskQueues` SET Priority=%.4f WHERE TQId in ( %s )" % ( prio, tqList ) self._update( updateSQL, conn = connObj ) return S_OK() def getGroupShares( self ): """ Get all the shares as a DICT """ result = gConfig.getSections( "/Registry/Groups" ) if result[ 'OK' ]: groups = result[ 'Value' ] else: groups = [] shares = {} for group in groups: shares[ group ] = gConfig.getValue( "/Registry/Groups/%s/JobShare" % group, DEFAULT_GROUP_SHARE ) return shares
marcelovilaca/DIRAC
WorkloadManagementSystem/DB/TaskQueueDB.py
Python
gpl-3.0
51,888
[ "DIRAC" ]
976429c6ec00ecff9e67e622c9328760278fb403d550836d682fbe6f93a1329e
#!/usr/bin/env python # -*- coding: utf-8 -*- import os import Tix import tkFileDialog, tkMessageBox import subprocess from datetime import date from fpdf import FPDF from utils import settings def main(_): '''Currently aimed at producing product testing results for ASE products but could change it to work for any product in the future. ''' # Create new external window. if not _.getExtWin(_, title=u"Production Analysis Report"): return options = 'selectColor gold' company_w = Tix.Select(_.extwin, label=u'公司', radio=True, orientation='vertical', options=options) # company_w.config(selectcolor='gold') company_w.add(u'台茂化工儀器原料行', text=u'台茂化工儀器原料行', background='purple') company_w.invoke(u'台茂化工儀器原料行') company_w.add(u'富茂工業原料行', text=u'富茂工業原料行', background='purple') company_w.add(u'永茂企業行', text=u'永茂企業行', background='purple') company_w.grid(row=0, column=0, columnspan=3, sticky='ew') pname_SV = Tix.StringVar() # pname_SV.trace('w', lambda: autofill()) product_w = Tix.ComboBox(_.extwin, label=u'品名', dropdown=True, editable=True, variable=pname_SV, command=lambda *args: autofill()) product_w.subwidget('entry').config(disabledforeground='black') # product_w.entry.configure(textvariable=pname_SV) ASE = _.dbm.get_cogroup(u'ASE') MPN_list = [] for ea in ASE.products: product_w.insert('end', ea.name) MPN_list.append(ea.MPN) product_w.grid(row=1, column=0, columnspan=3, sticky='ew') # ASE_w = Tix.LabelEntry(_.extwin, label=u'料號') # ASE_w.grid(row=2, column=0, sticky='ew') lot_w = Tix.LabelEntry(_.extwin, label=u'批號') lot_w.grid(row=3, column=0, columnspan=3, sticky='ew') qty_w = Tix.LabelEntry(_.extwin, label=u'數量') qty_w.grid(row=4, column=0, columnspan=3, sticky='ew') tester_w = Tix.LabelEntry(_.extwin, label=u'取樣人員') tester_w.grid(row=5, column=0, columnspan=3, sticky='ew') gridlabels = [u'檢 驗 項 目', u'規 格', u'檢 驗 結 果'] for i in range(3): Tix.Label(_.extwin, text=gridlabels[i]).grid(row=6, column=i) egrid = [] for i in range(8): egrid.append([]) for j in range(3): egrid[i].append(Tix.Entry(_.extwin, justify='center')) egrid[i][j].grid(row=i+10, column=j) # Restore previous entries for a particular product def autofill(): # Retrieve product record. MPN = MPN_list[product_w.slistbox.listbox.index('active')] p_rec = _.dbm.get_product(MPN) _dict = p_rec.json() if not _dict: return if _dict.get('amount'): qty_w.entry.delete(0, 'end') qty_w.entry.insert(0, _dict['amount']) if _dict.get('tester'): tester_w.entry.delete(0, 'end') tester_w.entry.insert(0, _dict['tester']) if _dict.get('lot_no'): lot_w.entry.delete(0, 'end') lot_w.entry.insert(0, _dict['lot_no']) if _dict.get('test_params'): tp = _dict['test_params'] for i in range(len(egrid)): for j in range(len(egrid[0])): egrid[i][j].delete(0, 'end') egrid[i][j].insert(0, tp[i][j]) submit_w = Tix.Button(_.extwin, text=u'提交') submit_w['command'] = lambda: submit() submit_w.grid(row=50, column=0, columnspan=3) def submit(): # Convert matrix of entry widgets into matrix of values. for i in range(8): for j in range(3): egrid[i][j] = egrid[i][j].get() # Retrieve product record. MPN = MPN_list[product_w.slistbox.listbox.index('active')] p_rec = _.dbm.get_product(MPN) _.dbm.session.commit() # Create dictioinary of values to pass to pdf writing method. _dict = dict( company=company_w['value'], product=product_w['selection'], ASE_pn=p_rec.ASE_PN, lot_no=lot_w.entry.get(), amount=qty_w.entry.get(), tester=tester_w.entry.get(), test_params=egrid, ) create_qc_pdf(**_dict) # Save options as JSON in product database record. del _dict['company'] del _dict['product'] del _dict['ASE_pn'] # Update previous json p_rec.json(_dict) _.dbm.session.commit() _.extwin.destroy() font = r'C:\Windows\Fonts\simfang.ttf' #font = r'C:\Windows\Fonts\simkai.ttf' #font = r'C:\Windows\Fonts\simhei.ttf' font = r'C:\Windows\Fonts\kaiu.ttf' # Shows superscript 3 but not 名 #font = r'C:\Windows\Fonts\fireflysung.ttf' # Shows superscript 3 but not 名 class myPDF(FPDF): form_number = u'FM0716A' # Left and right margin lm = 30 rm = 178 def header(self): lm = self.lm rm = self.rm mw = rm-lm C = 'C' try: if True: # Company logo left-top corner and smaller self.image(u'png/logo.png', x=12, y=10, w=34) else: # Company logo centered and the top self.image(u'png/logo.png', x=84, y=8, w=40) except IOError as e: print e try: self.image(u'png/signature1.png', x=48, y=240, w=24) except IOError as e: print e self.add_font(family=u'SimHei', style='B', fname=font, uni=True) # Only .ttf and not .ttc self.set_font(family=u'SimHei', style='B', size=16) # self.set_xy(lm, 25) # self.cell(mw, 10, u'台茂化工儀器原料行', align=C) # Client name self.set_font(u'SimHei', 'B', 16) self.set_xy(lm, 37) self.cell(mw, 8, u'產品檢驗報告', align=C) # Fill in headers self.set_font(u'SimHei', 'B', 13) self.set_fill_color(240,240,240) self.set_xy(30, 95) # Next cell auto-set to right self.cell(48, 10, txt=u'檢 驗 項 目', align=C, fill=True) self.cell(50, 10, txt=u'規 格', align=C, fill=True) self.cell(50, 10, txt=u'檢 驗 結 果', align=C, fill=True) self.set_xy(30, 210) self.cell(66-lm, 15, txt=u'結果研判:', align=C) self.cell(rm-66, 15, txt=u'符合規格', align=C) self.set_xy(30, 225) self.cell(104-lm, 10, txt=u'製表', align=C) self.cell(rm-104, 10, txt=u'檢驗人員', align=C) # Draw lines last, otherwise cell fill will overwrite. # Top table borders self.rect(lm, 50, mw, 40) # x, y, w, h for ea in [65,73,81]: self.line(lm, ea, rm, ea) # x1, y1, x2, y2 self.line(104, 65, 104, 90) # Middle table borders self.rect(lm, 95, mw, 205-95) # x, y, w, h for ea in range(105, 205, 10): self.line(lm, ea, rm, ea) # x1, y1, x2, y2 self.line(78, 95, 78, 205) self.line(128, 95, 128, 205) # Bottom table borders self.rect(lm, 210, mw, 50) # x, y, w, h for ea in [225,235]: self.line(lm, ea, rm, ea) # x1, y1, x2, y2 self.line(66, 210, 66, 225) self.line(104, 225, 104, 260) def xycell(self, x, y, *args, **kwargs): # Method to both set starting position and write cell in one command. # Preceed 'cell' parameters with x and y position parameters. self.set_xy(x, y) self.cell(*args, **kwargs) def footer(self): self.set_font(family=u'SimHei', style='B', size=12) # self.set_xy(155, -32) # self.cell(21, 5, txt=self.form_number, align='C') self.xycell(155, -32, 21, 5, txt=self.form_number, align='C') headers = ['company', 'product', 'ASE_pn', 'lot_no', 'exp_period', 'amount', 'tester', 'test_params'] def create_qc_pdf(**kwargs): try: kwargs['company'] = kwargs.get('company', u'台茂化工儀器原料行') kwargs['product'] = kwargs.get('product', u'product name?') kwargs['ASE_pn'] = kwargs.get('ASE_pn', u'ASE PN?') if not kwargs.get('lot_no'): kwargs['make_date'] = date.today() kwargs['test_date'] = date.today() kwargs['lot_no'] = u'lot number?' else: year = 2000 + int(kwargs['lot_no'][1:3]) month = int(kwargs['lot_no'][3:5]) day = int(kwargs['lot_no'][5:7]) kwargs['make_date'] = date(year, month, day) kwargs['test_date'] = date(year, month, day) kwargs['exp_period'] = kwargs.get('exp_period', u'一年') kwargs['amount'] = kwargs.get('amount', u'amount?') kwargs['tester'] = kwargs.get('tester', u'tester?') kwargs['test_params'] = kwargs.get('test_params', []) except Exception as e: print e return # Set placement and style of values tm_branch = dict(x=30, y=25, w=178-30, h=10, align='C') product_name = dict(x=31, y=50, w=104-31, h=15, align='L') product_ASE_pn = dict(x=105, y=50, w=104-31, h=15, align='L') make_date = dict(x=31, y=65, w=104-31, h=8, align='L') test_date = dict(x=31, y=73, w=104-31, h=8, align='L') exp_period = dict(x=31, y=81, w=104-31, h=9, align='L') lot_no = dict(x=105, y=65, w=104-31, h=8, align='L') amount = dict(x=105, y=73, w=104-31, h=8, align='L') tester = dict(x=105, y=81, w=104-31, h=9, align='L') # Create PDF FPDF = myPDF('P','mm','A4') FPDF.set_compression(False) FPDF.set_creator('TM_2014') FPDF.set_title(u'Quality inspection report for lot# {}'.format(kwargs['lot_no'])) FPDF.set_author(u'Taimau Chemicals') FPDF.set_subject(kwargs['lot_no']) # FPDF.set_subject(u'{} {}'.format(kwargs['product'], kwargs['lot_no']), isUTF8=True) FPDF.alias_nb_pages() FPDF.add_page() # Adding a page also creates a page break from last page FPDF.add_font(family=u'SimHei', style='', fname=font, uni=True) # Only .ttf and not .ttc FPDF.set_font(family=u'SimHei', style='', size=16) FPDF.xycell(txt=kwargs['company'], **tm_branch) FPDF.set_font(family=u'SimHei', style='B', size=13) FPDF.xycell(txt=u'產品: {}'.format(kwargs['product']), **product_name) FPDF.xycell(txt=u'料號: {}'.format(kwargs['ASE_pn']), **product_ASE_pn) FPDF.xycell(txt=u'製造日期: {}'.format(kwargs['make_date']), **make_date) FPDF.xycell(txt=u'檢驗日期: {}'.format(kwargs['test_date']), **test_date) FPDF.xycell(txt=u'保存期間: {}'.format(kwargs['exp_period']), **exp_period) FPDF.xycell(txt=u'批號: {}'.format(kwargs['lot_no']), **lot_no) FPDF.xycell(txt=u'生產數量: {}'.format(kwargs['amount']), **amount) FPDF.xycell(txt=u'取樣人員: {}'.format(kwargs['tester']), **tester) FPDF.set_left_margin(30) FPDF.set_xy(x=30, y=105) for (a, b, c) in kwargs['test_params']: if a+b+c == u'': break FPDF.cell(49, 10, txt=a, align='C') FPDF.cell(49, 10, txt=b, align='C') FPDF.cell(49, 10, txt=c, align='C') FPDF.ln() FPDF.cell(49) FPDF.cell(49, 10, txt=u'以下空白', align='C') initialfilename = u'QC_{}_{}'.format(kwargs['product'], kwargs['lot_no']) FILE_OPTS = dict( title = u'PDF name and location.', defaultextension = '.pdf', initialdir = os.path.expanduser('~') + '/Desktop/', initialfile = initialfilename, ) if settings.load().get(u'pdfpath'): FILE_OPTS['initialdir'] = settings.load()[u'pdfpath'] outfile = os.path.normpath(tkFileDialog.asksaveasfilename(**FILE_OPTS)) if os.path.exists(outfile): os.remove(outfile) if outfile and not os.path.exists(outfile): FPDF.output(name=outfile) try: subprocess.call(['start', outfile], shell=True) return except: pass try: print u'Trying alternate subprocess command.' subprocess.call(['start', '/D'] + list(os.path.split(outfile)), shell=True) return except UnicodeEncodeError: pass try: os.startfile(outfile) return except: pass print u'Failed to autoload PDF after creation.' return else: head = u'Cancelled' body = u'Canceled PDF creation.' tkMessageBox.showinfo(head, body) #if __name__ == '__main__': # create_qc_pdf(product=u'Nitrogen 60%', # company=u'台茂化工儀器原料行', # ASE_pn=u'2013-001-00816-000',)
Ripley6811/TAIMAU
src/pdf_tools/product_QC_report.py
Python
gpl-2.0
12,995
[ "ASE" ]
dcd3ef9579442273fa50f263072c9b94d46bf714a5cc3de8786800a3109bb3fe
## # Copyright 2013-2020 Ghent University # # This file is part of EasyBuild, # originally created by the HPC team of Ghent University (http://ugent.be/hpc/en), # with support of Ghent University (http://ugent.be/hpc), # the Flemish Supercomputer Centre (VSC) (https://www.vscentrum.be), # Flemish Research Foundation (FWO) (http://www.fwo.be/en) # and the Department of Economy, Science and Innovation (EWI) (http://www.ewi-vlaanderen.be/en). # # https://github.com/easybuilders/easybuild # # EasyBuild is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation v2. # # EasyBuild is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with EasyBuild. If not, see <http://www.gnu.org/licenses/>. ## """ EasyBuild support for building and installing PSI, implemented as an easyblock @author: Kenneth Hoste (Ghent University) @author: Ward Poelmans (Ghent University) """ from distutils.version import LooseVersion import glob import os import shutil import tempfile import easybuild.tools.environment as env from easybuild.easyblocks.generic.cmakemake import CMakeMake from easybuild.easyblocks.generic.configuremake import ConfigureMake from easybuild.framework.easyconfig import BUILD from easybuild.tools.build_log import EasyBuildError from easybuild.tools.modules import get_software_root from easybuild.tools.run import run_cmd class EB_PSI(CMakeMake): """ Support for building and installing PSI """ def __init__(self, *args, **kwargs): """Initialize class variables custom to PSI.""" super(EB_PSI, self).__init__(*args, **kwargs) self.psi_srcdir = None self.install_psi_objdir = None self.install_psi_srcdir = None @staticmethod def extra_options(): """Extra easyconfig parameters specific to PSI.""" extra_vars = CMakeMake.extra_options() extra_vars.update({ # always include running PSI unit tests (takes about 2h or less) 'runtest': ["tests TESTFLAGS='-u -q'", "Run tests included with PSI, without interruption.", BUILD], }) # Doesn't work with out-of-source build extra_vars['separate_build_dir'][0] = False return extra_vars def configure_step(self): """ Configure build outside of source directory. """ try: objdir = os.path.join(self.builddir, 'obj') os.makedirs(objdir) os.chdir(objdir) except OSError as err: raise EasyBuildError("Failed to prepare for configuration of PSI build: %s", err) env.setvar('F77FLAGS', os.getenv('F90FLAGS')) # In order to create new plugins with PSI, it needs to know the location of the source # and the obj dir after install. These env vars give that information to the configure script. self.psi_srcdir = os.path.basename(self.cfg['start_dir'].rstrip(os.sep)) self.install_psi_objdir = os.path.join(self.installdir, 'obj') self.install_psi_srcdir = os.path.join(self.installdir, self.psi_srcdir) env.setvar('PSI_OBJ_INSTALL_DIR', self.install_psi_objdir) env.setvar('PSI_SRC_INSTALL_DIR', self.install_psi_srcdir) # explicitely specify Python binary to use pythonroot = get_software_root('Python') if not pythonroot: raise EasyBuildError("Python module not loaded.") # pre 4.0b5, they were using autotools, on newer it's CMake if LooseVersion(self.version) <= LooseVersion("4.0b5") and self.name == "PSI": # Use EB Boost boostroot = get_software_root('Boost') if not boostroot: raise EasyBuildError("Boost module not loaded.") self.log.info("Using configure based build") env.setvar('PYTHON', os.path.join(pythonroot, 'bin', 'python')) env.setvar('USE_SYSTEM_BOOST', 'TRUE') if self.toolchain.options.get('usempi', None): # PSI doesn't require a Fortran compiler itself, but may require it to link to BLAS/LAPACK correctly # we should always specify the sequential Fortran compiler, # to avoid problems with -lmpi vs -lmpi_mt during linking fcompvar = 'F77_SEQ' else: fcompvar = 'F77' # update configure options # using multi-threaded BLAS/LAPACK is important for performance, # cfr. http://sirius.chem.vt.edu/psi4manual/latest/installfile.html#sec-install-iii opt_vars = [ ('cc', 'CC'), ('cxx', 'CXX'), ('fc', fcompvar), ('libdirs', 'LDFLAGS'), ('blas', 'LIBBLAS_MT'), ('lapack', 'LIBLAPACK_MT'), ] for (opt, var) in opt_vars: self.cfg.update('configopts', "--with-%s='%s'" % (opt, os.getenv(var))) # -DMPICH_IGNORE_CXX_SEEK dances around problem with order of stdio.h and mpi.h headers # both define SEEK_SET, this makes the one for MPI be ignored self.cfg.update('configopts', "--with-opt='%s -DMPICH_IGNORE_CXX_SEEK'" % os.getenv('CFLAGS')) # specify location of Boost self.cfg.update('configopts', "--with-boost=%s" % boostroot) # enable support for plugins self.cfg.update('configopts', "--with-plugins") ConfigureMake.configure_step(self, cmd_prefix=self.cfg['start_dir']) else: self.log.info("Using CMake based build") self.cfg.update('configopts', ' -DPYTHON_INTERPRETER=%s' % os.path.join(pythonroot, 'bin', 'python')) if self.name == 'PSI4' and LooseVersion(self.version) >= LooseVersion("1.2"): self.log.info("Remove the CMAKE_BUILD_TYPE test in PSI4 source and the downloaded dependencies!") self.log.info("Use PATCH_COMMAND in the corresponding CMakeLists.txt") self.cfg['build_type'] = 'EasyBuildRelease' if self.toolchain.options.get('usempi', None): self.cfg.update('configopts', " -DENABLE_MPI=ON") if get_software_root('imkl'): self.cfg.update('configopts', " -DENABLE_CSR=ON -DBLAS_TYPE=MKL") if self.name == 'PSI4': pcmsolverroot = get_software_root('PCMSolver') if pcmsolverroot: self.cfg.update('configopts', " -DENABLE_PCMSOLVER=ON") if LooseVersion(self.version) < LooseVersion("1.2"): self.cfg.update('configopts', " -DPCMSOLVER_ROOT=%s" % pcmsolverroot) else: self.cfg.update('configopts', " -DCMAKE_INSIST_FIND_PACKAGE_PCMSolver=ON " "-DPCMSolver_DIR=%s/share/cmake/PCMSolver" % pcmsolverroot) chempsroot = get_software_root('CheMPS2') if chempsroot: self.cfg.update('configopts', " -DENABLE_CHEMPS2=ON") if LooseVersion(self.version) < LooseVersion("1.2"): self.cfg.update('configopts', " -DCHEMPS2_ROOT=%s" % chempsroot) else: self.cfg.update('configopts', " -DCMAKE_INSIST_FIND_PACKAGE_CheMPS2=ON " "-DCheMPS2_DIR=%s/share/cmake/CheMPS2" % chempsroot) # Be aware, PSI4 wants exact versions of the following deps! built with CMake!! # If you want to use non-CMake build versions, the you have to provide the # corresponding Find<library-name>.cmake scripts # In PSI4 version 1.2.1, you can check the corresponding CMakeLists.txt file # in external/upstream/<library-name>/ if LooseVersion(self.version) >= LooseVersion("1.2"): for dep in ['libxc', 'Libint', 'pybind11', 'gau2grid']: deproot = get_software_root(dep) if deproot: self.cfg.update('configopts', " -DCMAKE_INSIST_FIND_PACKAGE_%s=ON" % dep) dep_dir = os.path.join(deproot, 'share', 'cmake', dep) self.cfg.update('configopts', " -D%s_DIR=%s " % (dep, dep_dir)) CMakeMake.configure_step(self, srcdir=self.cfg['start_dir']) def install_step(self): """Custom install procedure for PSI.""" super(EB_PSI, self).install_step() # the obj and unpacked sources must remain available for working with plugins try: for subdir in ['obj', self.psi_srcdir]: # copy symlinks as symlinks to work around broken symlinks shutil.copytree(os.path.join(self.builddir, subdir), os.path.join(self.installdir, subdir), symlinks=True) except OSError as err: raise EasyBuildError("Failed to copy obj and unpacked sources to install dir: %s", err) def test_step(self): """ Run the testsuite of PSI4 """ testdir = tempfile.mkdtemp() env.setvar('PSI_SCRATCH', testdir) if self.name == 'PSI4' and LooseVersion(self.version) >= LooseVersion("1.2"): if self.cfg['runtest']: paracmd = '' # Run ctest parallel, but limit to maximum 4 jobs (in case of slow disks) if self.cfg['parallel']: if self.cfg['parallel'] > 4: paracmd = '-j 4' else: paracmd = "-j %s" % self.cfg['parallel'] cmd = "ctest %s %s" % (paracmd, self.cfg['runtest']) run_cmd(cmd, log_all=True, simple=False) else: super(EB_PSI, self).test_step() try: shutil.rmtree(testdir) except OSError as err: raise EasyBuildError("Failed to remove test directory %s: %s", testdir, err) def sanity_check_step(self): """Custom sanity check for PSI.""" custom_paths = { 'files': ['bin/psi4'], 'dirs': ['include', ('share/psi', 'share/psi4')], } super(EB_PSI, self).sanity_check_step(custom_paths=custom_paths) def make_module_extra(self): """Custom variables for PSI module.""" txt = super(EB_PSI, self).make_module_extra() share_dir = os.path.join(self.installdir, 'share') if os.path.exists(share_dir): psi4datadir = glob.glob(os.path.join(share_dir, 'psi*')) if len(psi4datadir) == 1: txt += self.module_generator.set_environment('PSI4DATADIR', psi4datadir[0]) else: raise EasyBuildError("Failed to find exactly one PSI4 data dir: %s", psi4datadir) return txt
pescobar/easybuild-easyblocks
easybuild/easyblocks/p/psi.py
Python
gpl-2.0
11,207
[ "Psi4" ]
b45dea1850dc551137dd3b4580887c121668765844ac1c0fc929dacf4fcdd21e
import os import sys import argparse import time import glob from farmpy import lsf from farm_blast import blast, utils class Error (Exception): pass parser = argparse.ArgumentParser( description = 'Run BLAST in parallel on the farm', usage = '%(prog)s [options] <reference> <query>') parser.add_argument('--no_bsub', action='store_true', help=argparse.SUPPRESS) parser.add_argument('--fix_coords_in_blast_output', action='store_true', help=argparse.SUPPRESS) parser.add_argument('--split_bases_tolerance', type=int, default=1000, help=argparse.SUPPRESS) parser.add_argument('--test', action='store_true', help=argparse.SUPPRESS) common_blast_group = parser.add_argument_group('Common BLAST options') common_blast_group.add_argument('--blastall', action='store_true', help='Use blastall instead of the default blast+') common_blast_group.add_argument('-p', '--blast_type', help='Type of blast to run [%(default)s]', choices=sorted(list(blast.ref_not_protein_types)) + sorted(list(blast.ref_protein_types)), default='blastn') common_blast_group.add_argument('-e', '--evalue', help='Set the evalue cutoff') common_blast_group.add_argument('-W', '--word_size', help='Set the word size') common_blast_group.add_argument('--no_filter', action='store_true', help='Do not filter query sequence (equivalent to -F F in blastall, -dust no in blast+). By default, the query will be filtered') bsub_group = parser.add_argument_group('Bsub options') bsub_group.add_argument('-q', '--bsub_queue', help='Queue in which all jobs are run [%(default)s]', default = 'normal', metavar='Queue_name') bsub_group.add_argument('--blast_mem', type=float, help='Memory limit in GB for the farm jobs that run BLAST. Default is 0.5, except set to 5 if blastall tblastx is used. Defaults doubled if --no_filter used', metavar='FLOAT', default=None) bsub_group.add_argument('--bsub_name_prefix', help='Set the prefix of the names of the bsub jobs', default=None) advanced_opts_group = parser.add_argument_group('Advanced options') advanced_opts_group.add_argument('--act', action='store_true', help='Make ACT-friendly blast file, by concatenating all reference sequences together and all query sequences together before blasting.') advanced_opts_group.add_argument('--blast_options', help='Put any extra options to the blast call (i.e. blastall, blastn, blastx ...etc) in quotes. e.g. --blast_options "-r 2". Whatever you put in here is NOT sanity checked.', default = '', metavar='"options in quotes"') advanced_opts_group.add_argument('--debug', action='store_true', help='Just make scripts etc but do not run anything') advanced_opts_group.add_argument('--outdir', help='Name of output directory (must not exist already)', metavar='output directory', default=None) advanced_opts_group.add_argument('--split_bases', type=int, help='Number of bases in each split file of query. Default is 500000, except set to 200000 if blastall tblastx is used', metavar='INT', default=None) parser.add_argument('reference', help='Name of reference file. Does not need to be indexed already. If not indexed, can be any format from FASTA, FASTQ, GFF3, EMBL, Phylip, GBK', metavar='reference') parser.add_argument('query', help='Name of query file. Can be any format from FASTA, FASTQ, GFF3, EMBL, Phylip, GBK', metavar='query') def get_opts(args=None): return parser.parse_args(args=args) class Pipeline: def __init__(self, options, farm_blast_script): if options.outdir is None: if options.blastall: version = 'blastall' else: version = 'blast_plus' options.outdir = '.'.join(['Farm_blast', os.path.basename(options.reference), os.path.basename(options.query), version, options.blast_type, 'out']) self.outdir = os.path.abspath(options.outdir) self.reference = os.path.abspath(options.reference) self.query = os.path.abspath(options.query) self.bsub_queue = options.bsub_queue self.farm_blast_script = farm_blast_script self.test = options.test self.union_for_act = options.act self.blast = blast.Blast( self.reference, 'query.split.INDEX', outfile='tmp.array.out.INDEX', blastall=options.blastall, blast_type=options.blast_type, evalue=options.evalue, word_size=options.word_size, no_filter=options.no_filter, extra_options=options.blast_options ) self.setup_script = '01.setup.sh' self.start_array_script = '02.run_array.sh' self.combine_script = '03.combine.sh' if options.bsub_name_prefix is None: self.bsub_name_prefix = 'farm_blast:' + self.outdir else: self.bsub_name_prefix = options.bsub_name_prefix if options.no_bsub: self.no_bsub = True self.memory_units = 'MB' else: self.no_bsub = False self.memory_units = None self.debug = options.debug self.split_bases_tolerance = options.split_bases_tolerance self.files_to_delete = [ 'tmp.array.*', 'query.split.*', 'blast.out.tmp.gz', '02.array.id', '03.combine.sh.id', ] if not options.blast_mem: if self.blast.blastall and self.blast.blast_type == 'tblastx': self.array_mem = 5 else: self.array_mem = 0.5 if self.blast.no_filter: self.array_mem *= 2 else: self.array_mem = options.blast_mem if not options.split_bases: if self.blast.blastall and self.blast.blast_type == 'tblastx': self.split_bases = 200000 else: self.split_bases = 500000 else: self.split_bases = options.split_bases def _make_setup_script(self, script_name=None): if script_name is None: script_name = self.setup_script try: f = open(script_name, 'w') except: raise Error('Error opening setup script "' + script_name + '" for writing') print('set -e', file=f) if not self.blast.blast_db_exists() or self.union_for_act: if self.union_for_act: print('fastaq merge', self.reference, '- |', 'fastaq to_fasta -s - reference.fa', file=f) else: print('fastaq to_fasta -s', self.reference, 'reference.fa', file=f) self.reference = 'reference.fa' self.blast.reference = self.reference print(self.blast.format_database_command(), file=f) self.files_to_delete.append('reference.*') # blast strips off everything after the first whitespace, so do this # before chunking so names stay consistent with query fasta and in blast output if self.union_for_act: print('fastaq merge', self.query, '- |', 'fastaq to_fasta -s - - |', end=' ', file=f) else: print('fastaq to_fasta -s', self.query, '- |', end=' ', file=f) print('fastaq chunker --skip_all_Ns', '-', 'query.split', self.split_bases, self.split_bases_tolerance, file=f) f.close() def _make_setup_job(self): self.setup_job = lsf.Job( self.setup_script + '.o', self.setup_script + '.e', self.bsub_name_prefix + '.setup', self.bsub_queue, 1, 'bash ' + self.setup_script, memory_units=self.memory_units, ) def _make_array_job(self): self.array_job = lsf.Job( 'tmp.array.o', 'tmp.array.e', self.bsub_name_prefix + '.array', self.bsub_queue, self.array_mem, self.blast.get_run_command(), array_start=1, array_end=r'''$n''', memory_units=self.memory_units, max_array_size=100 ) def _make_start_array_job(self): self.start_array_job = lsf.Job( self.start_array_script + '.o', self.start_array_script + '.e', self.bsub_name_prefix + '.start_array', 'small', 0.1, 'bash ' + self.start_array_script, no_resources=True # we have to do this otherwise bmod fails! LSF bug? ) def _make_start_array_script(self, script_name=None): if script_name is None: script_name = self.start_array_script try: f = open(script_name, 'w') except: raise Error('Error writing script "' + script_name + '"') print('set -e', file=f) print(r'''n=`ls query.split.* | grep -v coords | wc -l`''', file=f) print(str(self.array_job) + r''' | awk '{print substr($2,2,length($2)-2)}' > 02.array.id''', file=f) print(r'''array_id=`cat 02.array.id` combine_id=`cat ''' + self.combine_script + r'''.id` bmod -w "done($array_id)" $combine_id''', file=f) f.close() def _make_combine_job(self): self.combine_job = lsf.Job( self.combine_script + '.o', self.combine_script + '.e', self.bsub_name_prefix + '.combine', self.bsub_queue, 0.5, 'bash ' + self.combine_script, memory_units=self.memory_units, threads=2 ) def _make_combine_script(self, script_name=None): if script_name is None: script_name = self.combine_script try: f = open(script_name, 'w') except: raise Error('Error writing script "' + script_name + '"') if not self.no_bsub: print('set -e', file=f) print(r'''cat tmp.array.e.* > 02.array.e cat tmp.array.o.* > 02.array.o cat tmp.array.out.* | gzip -9 -c > blast.out.tmp.gz''', file=f) if self.test: p = os.path.dirname(self.farm_blast_script) p = os.path.join(p, os.pardir) p = os.path.normpath(p) print('PYTHONPATH=' + p + ':$PYTHONPATH', file=f) if self.test: print(self.farm_blast_script, '--test --fix_coords_in_blast_output x x', file=f) else: print('farm_blast --fix_coords_in_blast_output x x', file=f) print('rm', ' '.join(self.files_to_delete), file=f) print('touch FINISHED', file=f) f.close() def run(self): '''Runs the whole blast pipeline''' # Here's the fun part: the first job splits the query file into # chunks. Don't know the number of chunks until that job has finished. # Therefore don't know the size of the job array until the chunking # job has finished. # # Here's what's going to happen: # 1. Submit chunking job. # 2. Submit job to submit an array, to run when Job1 finishes. # - this is NOT the array itself! # 3. Submit job that combines the results of the job array and tidies # up files. It is initially set to run when the job that starts # the array finishes. # 4. When the job that starts the array actually runs, it does this: # - figures out the size of the job array and submits it # - changes the dependency of the last combine job, so that # the combien job runs when the array has finished # 5. When the array finishes, Job3 will then run. try: os.mkdir(self.outdir) except: print('Error making output directory', self.outdir, file=sys.stderr) sys.exit(1) original_dir = os.getcwd() os.chdir(self.outdir) self._make_setup_script() self._make_setup_job() self._make_array_job() self._make_start_array_script() self._make_start_array_job() self._make_combine_script() self._make_combine_job() if self.debug: sys.exit() if self.no_bsub: self.setup_job.run_not_bsubbed() # get size of job array files_count = len(glob.glob('query.split.*')) - 1 self.array_job.array_end = files_count print(self.array_job) print(self.array_job.array_start) print(self.array_job.array_end) print(self.array_job._make_command_string().replace('\$LSB_JOBINDEX', '$LSB_JOBINDEX')) self.array_job.run_not_bsubbed() # a little hack here to make the farm_blast script run this_script = os.path.realpath(__file__) this_script_dir = os.path.dirname(this_script) module_root_dir = os.path.join(this_script_dir, '..') module_root_dir = os.path.normpath(module_root_dir) os.environ["PATH"] = os.path.join(module_root_dir, 'scripts:') + os.environ["PATH"] os.environ["PYTHONPATH"] = module_root_dir + ':' + os.environ["PYTHONPATH"] self.combine_job.run_not_bsubbed() else: self.setup_job.run() time.sleep(1) self.start_array_job.add_dependency(self.setup_job.job_id) self.start_array_job.run() time.sleep(1) self.combine_job.add_dependency(self.start_array_job.job_id) self.combine_job.run() time.sleep(1) try: f = open(self.combine_script + '.id', 'w') except: raise Error('Error opening file "' + self.combine_script + '.id' + '" for writing') print(self.combine_job.job_id, file=f) f.close() print('Jobs submitted to the farm.') print('Final job id is', self.combine_job.job_id) print('\nPipeline finished OK when this file is written:\n ', os.path.join(self.outdir, 'FINISHED')) print('\nFinal file will be called:\n ', os.path.join(self.outdir, 'blast.out.gz')) os.chdir(original_dir)
sanger-pathogens/Farm_blast
farm_blast/pipeline.py
Python
gpl-3.0
14,070
[ "BLAST" ]
df8790e397fadfc9efe28dcc628c912551a34c120c8d27bd9de8bbc0efc022f3
''' MFEM example 20p See c++ version in the MFEM library for more detail ''' import os import mfem.par as mfem from mfem.par import intArray from os.path import expanduser, join, dirname import numpy as np from numpy import sin, cos, exp, sqrt, pi from mpi4py import MPI num_procs = MPI.COMM_WORLD.size myid = MPI.COMM_WORLD.rank smyid = '{:0>6d}'.format(myid) m_ = 1.0 k_ = 1.0 def nicePrint(*s): MPI.COMM_WORLD.Barrier() for i in range(num_procs): MPI.COMM_WORLD.Barrier() if i == myid: print(str(myid)+': ' + ': '.join([str(ss) for ss in s])) MPI.COMM_WORLD.Barrier() def run(order=1, prob=0, nsteps=100, dt=0.1, sc=1.0, visualization=False): class GradT(mfem.Operator): def __init__(self): mfem.Operator.__init__(self, 1) def Mult(self, x, y): y.Set(1.0/m_, x) class NegGradV(mfem.TimeDependentOperator): def __init__(self): mfem.TimeDependentOperator.__init__(self, 1) def Mult(self, x, y): if prob == 1: y[0] = - k_ * sin(x[0]) elif prob == 2: y[0] = - k_ * x[0] * exp(-0.5 * x[0] * x[0]) elif prob == 3: y[0] = - k_ * (1.0 + 2.0 * x[0] * x[0]) * x[0] elif prob == 4: y[0] = - k_ * (1.0 - 0.25 * x[0] * x[0]) * x[0] else: y[0] = - k_ * x[0] def hamiltonian(q, p, t): h = 1.0 - 0.5 / m_ + 0.5 * p * p / m_ if prob == 1: h += k_ * (1.0 - cos(q)) elif prob == 2: h += k_ * (1.0 - exp(-0.5 * q * q)) elif prob == 3: h += 0.5 * k_ * (1.0 + q * q) * q * q elif prob == 4: h += 0.5 * k_ * (1.0 - 0.125 * q * q) * q * q else: h += 0.5 * k_ * q * q return h # 2. Create and Initialize the Symplectic Integration Solver siaSolver = mfem.SIAVSolver(order) P = GradT() F = NegGradV() siaSolver.Init(P, F) # 3. Set the initial conditions t = 0.0 q = mfem.Vector(1) p = mfem.Vector(1) e = mfem.Vector(nsteps+1) q[0] = sin(2*pi*myid/num_procs) p[0] = cos(2*pi*myid/num_procs) # 5. Create a Mesh for visualization in phase space nverts = 2*(nsteps+1)*num_procs if visualization else 0 nelems = nsteps*num_procs if visualization else 0 mesh = mfem.Mesh(2, nverts, nelems, 0, 3) part = mfem.intArray(nelems) # 6. Perform time-stepping e_mean = 0.0 for i in range(nsteps): if i == 0: e[0] = hamiltonian(q[0], p[0], t) e_mean += e[0] if visualization: for j in range(num_procs): mesh.AddVertex([0, 0, 0]) mesh.AddVertex([q[0], p[0], 0.0]) # 6b. Advance the state of the system t, dt = siaSolver.Step(q, p, t, dt) e[i+1] = hamiltonian(q[0], p[0], t) e_mean += e[i+1] # 6d. Add results to GLVis visualization if visualization: for j in range(num_procs): mesh.AddVertex([0, 0, t]) mesh.AddVertex([q[0], p[0], t]) mesh.AddQuad([2*i*num_procs + 2*j, 2*(i+1)*num_procs + 2*j, 2*(i+1)*num_procs + 2*j+1, 2*i*num_procs + 2*j+1]) part[num_procs*i + j] = j # this also works ;D # mesh.AddQuad(v.ToList()) #mesh.AddQuad(np.array(v.ToList(), dtype=np.int32)) # 7. Compute and display mean and standard deviation of the energy e_mean /= (nsteps + 1) e_var = 0.0 for i in range(nsteps+1): e_var += (e[i] - e_mean)**2 e_var /= (nsteps + 1) if myid == 0: print("Mean and standard deviation of the energy") nicePrint("{:g}".format(e_mean) + "\t" + "{:g}".format(sqrt(e_var))) # 9. Finalize the GLVis output if visualization: mesh.FinalizeQuadMesh(1) pmesh = mfem.ParMesh(MPI.COMM_WORLD, mesh, part.GetData()) fec = mfem.H1_FECollection(1, 2) fespace = mfem.ParFiniteElementSpace(pmesh, fec) energy = mfem.ParGridFunction(fespace) energy.Assign(0.0) for i in range(nsteps+1): energy[2*i+0] = e[i] energy[2*i+1] = e[i] sock = mfem.socketstream("localhost", 19916) sock.precision(8) sock << "parallel " << num_procs << " " << myid << "\n" sock << "solution\n" << pmesh << energy sock << "window_title 'Energy in Phase Space'\n" sock << "keys\n maac\n" << "axis_labels 'q' 'p' 't'\n" sock.flush() if __name__ == "__main__": from mfem.common.arg_parser import ArgParser parser = ArgParser(description='Ex20p (Sympletic ODE)') parser.add_argument('-m', '--mesh', default='star.mesh', action='store', type=str, help='Mesh file to use.') parser.add_argument("-p", "--problem-type", action='store', type=int, default=0, help=''.join(["Problem Type:\n", "\t 0 - Simple Harmonic Oscillator\n", "\t 1 - Pendulum\n", "\t 2 - Gaussian Potential Well\n", "\t 3 - Quartic Potential\n", "\t 4 - Negative Quartic Potential", ])) parser.add_argument('-o', '--order', action='store', default=1, type=int, help="Time integration order") parser.add_argument('-n', '--number-of-steps', action='store', default=100, type=int, help="Number of time steps") parser.add_argument('-dt', '--time-step', action='store', default=0.1, type=float, help="Time step size") parser.add_argument('-k', '--spring-constant', action='store', default=1, type=float, help="Sprint constant") parser.add_argument('-vis', '--visualization', action='store_true', default=True, help='Enable GLVis visualization') parser.add_argument('-no-gp', '--no-gnuplot', action='store_true', default=True, help='Disable GnuPlot visualization') args = parser.parse_args() if myid == 0: parser.print_options(args) prob = args.problem_type visualization = args.visualization order = args.order nsteps = args.number_of_steps dt = args.time_step sc = args.spring_constant np_gp = args.no_gnuplot run(order=order, prob=prob, nsteps=nsteps, dt=dt, sc=sc, visualization=visualization)
mfem/PyMFEM
examples/ex20p.py
Python
bsd-3-clause
7,072
[ "Gaussian" ]
1352ccf2e2fefac3bbb1bb76c7f155425a90728646d407fbd1553004ccf69eae
"""Galaxy (ansible-galaxy) plugin for integration tests.""" from __future__ import annotations import os import tempfile from ....config import ( IntegrationConfig, ) from ....docker_util import ( docker_cp_to, ) from ....containers import ( run_support_container, ) from . import ( CloudEnvironment, CloudEnvironmentConfig, CloudProvider, ) # We add BasicAuthentication, to make the tasks that deal with # direct API access easier to deal with across galaxy_ng and pulp SETTINGS = b''' CONTENT_ORIGIN = 'http://ansible-ci-pulp:80' ANSIBLE_API_HOSTNAME = 'http://ansible-ci-pulp:80' ANSIBLE_CONTENT_HOSTNAME = 'http://ansible-ci-pulp:80/pulp/content' TOKEN_AUTH_DISABLED = True GALAXY_REQUIRE_CONTENT_APPROVAL = False GALAXY_AUTHENTICATION_CLASSES = [ "rest_framework.authentication.SessionAuthentication", "rest_framework.authentication.TokenAuthentication", "rest_framework.authentication.BasicAuthentication", ] ''' SET_ADMIN_PASSWORD = b'''#!/usr/bin/execlineb -S0 foreground { redirfd -w 1 /dev/null redirfd -w 2 /dev/null export DJANGO_SETTINGS_MODULE pulpcore.app.settings export PULP_CONTENT_ORIGIN localhost s6-setuidgid postgres if { /usr/local/bin/django-admin reset-admin-password --password password } if { /usr/local/bin/pulpcore-manager create-group system:partner-engineers --users admin } } ''' # There are 2 overrides here: # 1. Change the gunicorn bind address from 127.0.0.1 to 0.0.0.0 now that Galaxy NG does not allow us to access the # Pulp API through it. # 2. Grant access allowing us to DELETE a namespace in Galaxy NG. This is as CI deletes and recreates repos and # distributions in Pulp which now breaks the namespace in Galaxy NG. Recreating it is the "simple" fix to get it # working again. # These may not be needed in the future, especially if 1 becomes configurable by an env var but for now they must be # done. OVERRIDES = b'''#!/usr/bin/execlineb -S0 foreground { sed -i "0,/\\"127.0.0.1:24817\\"/s//\\"0.0.0.0:24817\\"/" /etc/services.d/pulpcore-api/run } # This sed calls changes the first occurrence to "allow" which is conveniently the delete operation for a namespace. # https://github.com/ansible/galaxy_ng/blob/master/galaxy_ng/app/access_control/statements/standalone.py#L9-L11. backtick NG_PREFIX { python -c "import galaxy_ng; print(galaxy_ng.__path__[0], end='')" } importas ng_prefix NG_PREFIX foreground { sed -i "0,/\\"effect\\": \\"deny\\"/s//\\"effect\\": \\"allow\\"/" ${ng_prefix}/app/access_control/statements/standalone.py }''' class GalaxyProvider(CloudProvider): """ Galaxy plugin. Sets up pulp (ansible-galaxy) servers for tests. The pulp source itself resides at: https://github.com/pulp/pulp-oci-images """ def __init__(self, args): # type: (IntegrationConfig) -> None super().__init__(args) # Cannot use the latest container image as either galaxy_ng 4.2.0rc2 or pulp 0.5.0 has sporatic issues with # dropping published collections in CI. Try running the tests multiple times when updating. Will also need to # comment out the cache tests in 'test/integration/targets/ansible-galaxy-collection/tasks/install.yml' when # the newer update is available. self.pulp = os.environ.get( 'ANSIBLE_PULP_CONTAINER', 'quay.io/ansible/pulp-galaxy-ng:b79a7be64eff' ) self.uses_docker = True def setup(self): # type: () -> None """Setup cloud resource before delegation and reg cleanup callback.""" super().setup() galaxy_port = 80 pulp_host = 'ansible-ci-pulp' pulp_port = 24817 ports = [ galaxy_port, pulp_port, ] # Create the container, don't run it, we need to inject configs before it starts descriptor = run_support_container( self.args, self.platform, self.pulp, pulp_host, ports, start=False, allow_existing=True, ) if not descriptor: return if not descriptor.running: pulp_id = descriptor.container_id injected_files = { '/etc/pulp/settings.py': SETTINGS, '/etc/cont-init.d/111-postgres': SET_ADMIN_PASSWORD, '/etc/cont-init.d/000-ansible-test-overrides': OVERRIDES, } for path, content in injected_files.items(): with tempfile.NamedTemporaryFile() as temp_fd: temp_fd.write(content) temp_fd.flush() docker_cp_to(self.args, pulp_id, temp_fd.name, path) descriptor.start(self.args) self._set_cloud_config('PULP_HOST', pulp_host) self._set_cloud_config('PULP_PORT', str(pulp_port)) self._set_cloud_config('GALAXY_PORT', str(galaxy_port)) self._set_cloud_config('PULP_USER', 'admin') self._set_cloud_config('PULP_PASSWORD', 'password') class GalaxyEnvironment(CloudEnvironment): """Galaxy environment plugin. Updates integration test environment after delegation.""" def get_environment_config(self): # type: () -> CloudEnvironmentConfig """Return environment configuration for use in the test environment after delegation.""" pulp_user = str(self._get_cloud_config('PULP_USER')) pulp_password = str(self._get_cloud_config('PULP_PASSWORD')) pulp_host = self._get_cloud_config('PULP_HOST') galaxy_port = self._get_cloud_config('GALAXY_PORT') pulp_port = self._get_cloud_config('PULP_PORT') return CloudEnvironmentConfig( ansible_vars=dict( pulp_user=pulp_user, pulp_password=pulp_password, pulp_api='http://%s:%s' % (pulp_host, pulp_port), pulp_server='http://%s:%s/pulp_ansible/galaxy/' % (pulp_host, pulp_port), galaxy_ng_server='http://%s:%s/api/galaxy/' % (pulp_host, galaxy_port), ), env_vars=dict( PULP_USER=pulp_user, PULP_PASSWORD=pulp_password, PULP_SERVER='http://%s:%s/pulp_ansible/galaxy/api/' % (pulp_host, pulp_port), GALAXY_NG_SERVER='http://%s:%s/api/galaxy/' % (pulp_host, galaxy_port), ), )
mattclay/ansible
test/lib/ansible_test/_internal/commands/integration/cloud/galaxy.py
Python
gpl-3.0
6,384
[ "Galaxy" ]
ddfd8b27944d63c7bf5a949496e8f26636b2e72e93c2bb646c120af73188de40
# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import ray from bigdl.orca.cpu_info import schedule_workers import os import sys import logging log = logging.getLogger(__name__) class ClusterInfo: def ip_addr(self): return ray._private.services.get_node_ip_address() def set_cpu_affinity(self, core_list): proclist_str = f"[{','.join([str(i) for i in core_list])}]" os.environ["OMP_NUM_THREADS"] = str(len(core_list)) os.environ["OMP_SCHEDULE"] = "STATIC" os.environ["OMP_PROC_BIND"] = "CLOSE" # KMP_AFFINITY works on intel openmp (intel tensorlow, intel pytorch/ipex) # GOMP_CPU_AFFINITY works on gomp (stock pytorch) # os.sched_setaffinity works on other threads (stock tensorflow) os.environ["KMP_AFFINITY"] = f"verbose,granularity=fine,proclist={proclist_str},explicit" os.environ["GOMP_CPU_AFFINITY"] = proclist_str os.sched_setaffinity(0, set(core_list)) def disable_cpu_affinity(self, num_cores): os.environ["OMP_NUM_THREADS"] = str(num_cores) os.environ["KMP_AFFINITY"] = "disabled" os.environ["OMP_PROC_BIND"] = "FALSE" def run(self, func, *args, **kwargs): return func(*args, **kwargs) def make_worker(worker_cls): class Worker(worker_cls, ClusterInfo): pass return Worker class RayDLCluster: def __init__(self, num_workers, worker_cores, worker_cls=None, worker_param=None, cpu_binding=True, ): if not ray.is_initialized(): raise RuntimeError("Ray is not initialize. Please initialize ray.") self.num_workers = num_workers self.worker_cores = worker_cores self.worker_cls = make_worker(worker_cls) self.work_param = worker_param if sys.platform == 'linux': self.cpu_binding = cpu_binding else: if cpu_binding: log.warn(f"cpu_binding is only support in linux, detectiong os {sys.platform}, " "set cpu_binding to False") self.cpu_binding = False self.worker_class = ray.remote(num_cpus=self.worker_cores)(self.worker_cls) self.remote_workers = [self.worker_class.remote(**worker_param) for i in range(0, self.num_workers)] if self.cpu_binding: hosts = ray.get([worker.ip_addr.remote() for worker in self.remote_workers]) ip2workers = {} for ip, worker in zip(hosts, self.remote_workers): if ip not in ip2workers: ip2workers[ip] = [] ip2workers[ip].append(worker) ips = ip2workers.keys() cpu_binding_refs = [] for ip in ips: ref = ip2workers[ip][0].run.remote(schedule_workers, len(ip2workers[ip]), self.worker_cores) cpu_binding_refs.append(ref) cpu_bindings = ray.get(cpu_binding_refs) result = [] for ip, core_lists in zip(ips, cpu_bindings): for worker, core_list in zip(ip2workers[ip], core_lists): log.debug(f"Setting thread affinity for worker in {ip}: {core_list}") result.append(worker.set_cpu_affinity.remote(core_list)) ray.get(result) else: ray.get([worker.disable_cpu_affinity.remote(self.worker_cores) for worker in self.remote_workers]) def get_workers(self): return self.remote_workers
intel-analytics/BigDL
python/orca/src/bigdl/orca/learn/dl_cluster.py
Python
apache-2.0
4,240
[ "ORCA" ]
7a5ae963fa25a060e2ce793012b20f1594c29039b24832e095b9728d05c7c0e2