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+"10008502",1994-08-10,"10152037",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0081N6410","CNS",6411324.46256699,82651.8622267867,0.2833,0.085,1,300,0.5 +"10008502",1994-08-10,"10152038",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0081N6410","CNS",6411324.46256699,82651.8622267867,0.2833,0.085,1,300,1 +"10008502",1994-08-10,"10152047",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0086N6415","CNS",6412972.69926085,84824.3446515411,0.2833,0.085,1,300,1 +"10008502",1994-08-10,"10152051",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0086N6415","CNS",6412972.69926085,84824.3446515411,0.2833,0.085,2,300,2 +"10008502",1994-08-10,"10152057",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0086N6415","CNS",6416565.72874207,86198.15737288,0.2833,0.085,1,300,0.5 +"10008502",1994-08-10,"10152062",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0086N6415","CNS",6416565.72874207,86198.15737288,0.2833,0.085,1,300,1 +"10008503",1994-08-10,"10152138",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0101N6430","CNS",6432008.12515208,99748.945423955,0.2333,0.07,1,300,1 +"10008505",1994-08-10,"10152246",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0101N6450","CNS",6452353.0973712,101789.747199798,0.2667,0.08,1,300,2 +"10008505",1994-08-10,"10152256",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0101N6455","CNS",6454206.66533465,101975.881401933,0.2833,0.085,2,300,2 +"10008505",1994-08-10,"10152272",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6455765.92577638,105103.008621833,0.25,0.075,2,300,2 +"10008505",1994-08-10,"10152273",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6455765.92577638,105103.008621833,0.25,0.075,2,300,205 +"10008505",1994-08-10,"10152275",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6457413.74139436,107244.557466121,0.2667,0.08,2,300,2 +"10008505",1994-08-10,"10152276",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6457413.74139436,107244.557466121,0.2667,0.08,2,300,1 +"10008505",1994-08-10,"10152278",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6457413.74139436,107244.557466121,0.2667,0.08,1,300,2 +"10008505",1994-08-10,"10152282",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6457413.74139436,107244.557466121,0.2667,0.08,1,300,1 +"10008505",1994-08-10,"10152283",800,10,1,1994,1,1,1,"30",NA,"ESAS v5","E0106N6455","CNS",6457413.74139436,107244.557466121,0.2667,0.08,2,300,3 +"10008508",1994-08-10,"10152477",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0126N6470","CNS",6472450.03248414,125491.899367898,0.25,0.075,1,300,2 +"10008513",1994-08-11,"10152874",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0161N6515","CNS",6513950.38408955,161575.6333219,0.3333,0.1,1,300,2 +"10008567",1994-08-17,"10156841",800,10,1,1994,1,1,1,"3",NA,"ESAS v5","E0156N6245","CNS",6247596.9939365,158362.321584997,0.25,0.075,1,300,1 +"10008570",1994-08-19,"10157045",800,10,1,1994,1,8,1,"8",NA,"ESAS v5","E0141N6230","CNS",6230435.68811791,140315.520112438,0.2833,0.085,1,300,0 +"10008582",1994-08-20,"10158127",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6174730.33223647,212604.505904458,0.2833,0.085,1,300,2 +"10008582",1994-08-20,"10158128",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6174730.33223647,212604.505904458,0.2833,0.085,1,300,1 +"10008582",1994-08-20,"10158129",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6174730.33223647,212604.505904458,0.2833,0.085,1,300,4 +"10008582",1994-08-20,"10158130",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6174730.33223647,212604.505904458,0.2833,0.085,1,300,3 +"10008582",1994-08-20,"10158132",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6174730.33223647,212604.505904458,0.2833,0.085,1,300,1 +"10008582",1994-08-20,"10158142",800,10,1,1994,1,1,1,"5",NA,"ESAS v5","E0211N6175","SNS",6173023.64084548,210387.9365598,0.2333,0.07,1,300,6 +"10008621",1994-08-29,"10161433",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0301N6000","SNS",6000759.30178107,301754.043935832,0.25,0.075,2,300,4 +"10008621",1994-08-29,"10161434",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0301N6000","SNS",5998996.25925977,299491.174887844,0.2167,0.065,2,300,1 +"10008621",1994-08-29,"10161435",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0301N6000","SNS",5998996.25925977,299491.174887844,0.2167,0.065,1,300,1 +"10008621",1994-08-29,"10161437",800,10,1,1994,1,1,1,"9",NA,"ESAS v5","E0301N6000","SNS",5998996.25925977,299491.174887844,0.2167,0.065,2,300,3 +"10008805",1994-12-14,"10164055",800,10,3,1994,1,1,1,"6",NA,"ESAS v5","E0136N6230","CNS",6229957.32286982,133949.622610374,4.9812,1.4944,1,300,1 +"10008852",1995-01-21,"10164812",800,10,3,1994,1,1,1,"8",NA,"ESAS v5","E0136N6235","CNS",6234917.4478944,135742.754776555,4.2396,1.2719,1,300,2 +"10008852",1995-01-21,"10164814",800,10,3,1994,1,1,1,"8",NA,"ESAS v5","E0141N6240","CNS",6239549.76373882,142885.75659647,4.2396,1.2719,1,300,0.98786717752235 +"10009644",1995-12-13,"10194634",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0136N6235","CNS",6236981.71275183,137517.951966082,3.538,1.0614,1,300,6 +"10009644",1995-12-13,"10194635",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0141N6240","CNS",6238781.28608345,140580.252966222,3.538,1.0614,1,300,4 +"10009644",1995-12-13,"10194638",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0146N6240","CNS",6241812.60243518,147011.055771098,3.5558,1.0667,1,300,6 +"10009667",1995-12-22,"10194864",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0156N6245","CNS",6247291.06920529,157161.053690729,3.8505,1.1552,1,300,1 +"10009667",1995-12-22,"10194865",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0156N6245","CNS",6244814.07468153,154189.083665528,3.8505,1.1552,2,300,1 +"10009667",1995-12-22,"10194867",800,10,3,1995,1,1,1,"144",NA,"ESAS v5","E0151N6240","CNS",6242573.38530684,151083.299095394,3.7993,1.1398,2,300,56 +"10009778",1996-03-07,"10200313",800,10,4,1996,1,1,1,"6",NA,"ESAS v5","E0211N6370","CNS",6368099.76596694,213402.734835151,2.0503,0.6151,1,300,1 +"10009778",1996-03-07,"10200314",800,10,4,1996,1,1,1,"6",NA,"ESAS v5","E0216N6370","CNS",6370000.42266275,214175.735807962,2.0503,0.6151,1,300,4 +"10009830",1996-05-19,"10201258",800,10,5,1996,1,1,1,"120",NA,"ESAS v5","E0136N6235","CNS",6233977.29194071,137325.458366658,2.494,0.7482,2,300,4 +"10010204",1996-09-02,"10214723",800,10,1,1996,1,1,1,"11",NA,"ESAS v5","E0216N6375","CNS",6373353.47245517,214627.776462662,3.2005,0.9602,1,300,2 +"10010204",1996-09-02,"10214729",800,10,1,1996,1,1,1,"11",NA,"ESAS v5","E0216N6390","CNS",6391913.33124642,215989.163053858,2.8004,0.8401,1,300,1 +"10010204",1996-09-02,"10214730",800,10,1,1996,1,1,1,"11",NA,"ESAS v5","E0216N6395","CNS",6394605.99435203,215188.668342519,2.8004,0.8401,1,300,1 +"10010204",1996-09-02,"10214731",800,10,1,1996,1,1,1,"11",NA,"ESAS v5","E0216N6395","CNS",6397298.42537048,214395.4764716,2.8004,0.8401,2,300,1 +"10010269",1996-10-27,"10215391",800,10,2,1996,1,1,1,"11",NA,"ESAS v5","E0146N6415","CNS",6413450.02333019,146133.101026928,3.4266,1.028,2,300,1 +"10010269",1996-10-27,"10215392",800,10,2,1996,1,1,1,"11",NA,"ESAS v5","E0141N6415","CNS",6414392.29970122,142820.985791619,3.4266,1.028,2,300,3 +"10010269",1996-10-27,"10215393",800,10,2,1996,1,1,1,"11",NA,"ESAS v5","E0141N6415","CNS",6415348.46028967,139511.107859864,3.4266,1.028,2,300,2 +"10010704",1997-09-12,"10232137",800,10,1,1997,1,1,1,"120",NA,"ESAS v5","E0151N6515","CNS",6516267.67496202,152086.756029543,4.2635,1.2791,2,300,1 +"10011045",1998-01-24,"10235838",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0161N6280","CNS",6281680.65052075,159066.351032508,0.2759,0.0828,2,300,3 +"10011051",1998-01-25,"10236337",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0156N6265","CNS",6266829.85449148,154931.481047107,0.2693,0.0808,2,300,0.98786717752235 +"10011052",1998-01-25,"10236394",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0156N6260","CNS",6261911.20810787,155591.17804925,0.2676,0.0803,1,300,1 +"10011056",1998-01-25,"10236621",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0156N6250","CNS",6250416.17635154,158346.125755501,0.2231,0.0669,2,300,1 +"10011057",1998-01-25,"10236639",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0146N6245","CNS",6244292.78025188,148263.624906255,2.4238,0.7271,1,300,15 +"10011057",1998-01-25,"10236640",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0146N6245","CNS",6243139.50563804,146117.085103786,2.4545,0.7364,2,300,1066 +"10011057",1998-01-25,"10236641",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0146N6240","CNS",6242141.5123381,143863.709668111,2.4304,0.7291,2,300,48 +"10011058",1998-01-25,"10236642",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241107.69854315,141649.770310396,2.3417,0.7025,1,300,1 +"10011058",1998-01-25,"10236643",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6240538.80981219,139374.039580841,2.401,0.7203,2,300,1 +"10011058",1998-01-25,"10236644",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240249.82868304,136977.724029225,2.4283,0.7285,2,300,9 +"10011059",1998-01-26,"10236649",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0136N6235","CNS",6234664.46052666,137533.765850566,2.4403,0.7321,1,300,39 +"10011059",1998-01-26,"10236650",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6235","CNS",6233295.35280312,139568.220286854,2.4221,0.7266,2,300,1 +"10011061",1998-01-26,"10236713",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6240","CNS",6241406.73780858,146509.630113854,0.2517,0.0755,2,300,1 +"10011061",1998-01-26,"10236714",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6240","CNS",6241653.19074708,146505.115300577,0.2416,0.0725,1,300,4 +"10011061",1998-01-26,"10236715",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6240","CNS",6241893.99367849,146568.741617861,0.243,0.0729,2,300,1 +"10011061",1998-01-26,"10236722",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6245","CNS",6243632.64922065,146644.332766268,0.24,0.072,2,300,3 +"10011061",1998-01-26,"10236723",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6245","CNS",6243878.58814859,146646.02997697,0.24,0.07,2,300,6 +"10011061",1998-01-26,"10236724",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6245","CNS",6244050.17007518,146735.068380414,0.24,0.07,2,300,2 +"10011061",1998-01-26,"10236725",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6245","CNS",6244181.24057824,146908.011399933,0.2229,0.0669,2,300,1 +"10011061",1998-01-26,"10236746",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6245","CNS",6242912.43513396,149677.16021362,0.2603,0.0781,1,300,1 +"10011061",1998-01-26,"10236749",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6240","CNS",6242123.16017799,149618.509798048,0.2833,0.085,1,300,5 +"10011061",1998-01-26,"10236752",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6240","CNS",6241255.10110315,149565.865243445,0.271,0.0813,1,300,4 +"10011062",1998-01-26,"10236792",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0146N6230","CNS",6230724.94183182,148682.082530753,0.2616,0.0785,1,300,1 +"10011062",1998-01-26,"10236806",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0151N6225","CNS",6227534.84915141,149396.267868997,0.2691,0.0807,1,300,2 +"10011062",1998-01-26,"10236808",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0151N6225","CNS",6227304.81112922,149884.253268683,0.2556,0.0767,1,300,1 +"10011063",1998-01-26,"10236873",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6240","CNS",6240715.80825916,152602.768297552,0.2548,0.0764,1,300,1 +"10011063",1998-01-26,"10236874",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6240","CNS",6240971.33232054,152623.585412835,0.2673,0.0802,1,300,5 +"10011063",1998-01-26,"10236877",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6240","CNS",6241773.25255404,152663.978873972,0.2427,0.0728,1,300,1 +"10011064",1998-01-26,"10236934",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0156N6245","CNS",6244973.7671683,156083.514249485,0.2663,0.0799,1,300,1 +"10011067",1998-01-27,"10237071",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0131N6240","CNS",6240826.02869051,131951.957270318,0.255,0.0765,1,300,10 +"10011067",1998-01-27,"10237076",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0131N6240","CNS",6240598.66858499,133162.205766368,0.2412,0.0723,1,300,2 +"10011067",1998-01-27,"10237079",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240503.57669343,133878.195592665,0.2361,0.0708,1,300,2 +"10011067",1998-01-27,"10237080",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240505.58330807,134115.589914569,0.2418,0.0725,2,300,22 +"10011067",1998-01-27,"10237081",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240495.9642677,134358.227169711,0.2403,0.0721,2,300,1 +"10011067",1998-01-27,"10237082",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240486.35904267,134600.863813515,0.242,0.0726,2,300,1 +"10011067",1998-01-27,"10237083",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240498.98285907,134845.401829807,0.2412,0.0724,2,300,7 +"10011067",1998-01-27,"10237084",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240512.15082516,135083.739734162,0.2374,0.0712,1,300,38 +"10011067",1998-01-27,"10237085",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240536.43969601,135323.024338919,0.2376,0.0713,2,300,128 +"10011067",1998-01-27,"10237089",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6240581.36025449,136238.202327643,0.2066,0.062,1,300,1 +"10011067",1998-01-27,"10237101",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6240806.59233255,138853.741982995,0.2198,0.0659,1,300,0.98786717752235 +"10011067",1998-01-27,"10237110",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241189.70587688,140808.087597933,0.2344,0.0703,1,300,2 +"10011067",1998-01-27,"10237117",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242157.11600282,142074.818231674,0.2153,0.0646,2,300,3 +"10011067",1998-01-27,"10237118",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242336.0793019,142208.360720931,0.2222,0.0667,1,300,10 +"10011067",1998-01-27,"10237119",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242466.97413558,142381.520460777,0.2317,0.0695,1,300,3 +"10011067",1998-01-27,"10237120",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242607.42576222,142574.1819689,0.2316,0.0695,2,300,4 +"10011067",1998-01-27,"10237127",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242570.19165917,143419.293029627,0.2687,0.0806,2,300,9 +"10011067",1998-01-27,"10237128",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242303.58445135,143396.98657135,0.2635,0.0791,2,300,5 +"10011067",1998-01-27,"10237129",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6242033.86794989,143411.846590477,0.2576,0.0773,2,300,8 +"10011067",1998-01-27,"10237130",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241779.40570173,143378.083627073,0.2538,0.0761,2,300,1 +"10011067",1998-01-27,"10237149",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6235","CNS",6236895.23566632,142925.881135034,0.1894,0.0568,1,300,4 +"10011068",1998-01-27,"10237181",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6230","CNS",6230261.30652268,142396.342594126,0.2748,0.0825,1,300,1 +"10011068",1998-01-27,"10237184",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6230","CNS",6229443.10997771,142415.562204317,0.2361,0.0708,2,300,2 +"10011068",1998-01-27,"10237194",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6230","CNS",6229953.18494669,140199.895906532,0.2493,0.0748,1,300,4 +"10011068",1998-01-27,"10237199",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6230","CNS",6229844.87070056,139227.328718981,0.23,0.07,1,300,1 +"10011068",1998-01-27,"10237207",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0141N6230","CNS",6231786.72237486,139416.030879579,0.2326,0.0698,2,300,1 +"10011069",1998-01-27,"10237246",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241261.54656427,140221.374597316,0.25,0.07,1,300,12 +"10011069",1998-01-27,"10237248",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241223.54710654,139743.939863005,0.265,0.0795,2,300,2 +"10011069",1998-01-27,"10237250",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0141N6240","CNS",6241053.87551214,139236.625352985,0.267,0.0801,2,300,1 +"10011069",1998-01-27,"10237266",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6240","CNS",6238796.80700914,136829.089332219,0.264,0.0792,2,300,1 +"10011069",1998-01-27,"10237271",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0136N6235","CNS",6237504.59998662,136762.849116563,0.2681,0.0804,1,300,1 +"10011071",1998-01-27,"10237414",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0131N6245","CNS",6244796.99126334,133136.71863148,0.2484,0.0745,2,300,1 +"10011072",1998-01-27,"10237489",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6230","CNS",6232520.67234876,126944.127871846,0.2558,0.0767,2,300,1 +"10011072",1998-01-27,"10237504",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6235","CNS",6236497.42244398,127284.836047807,0.2455,0.0736,1,300,1 +"10011072",1998-01-27,"10237505",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6235","CNS",6236743.9357348,127281.348133033,0.2509,0.0753,1,300,1 +"10011073",1998-01-28,"10237532",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6240","CNS",6240484.28754988,126102.825975632,0.2468,0.074,2,300,4 +"10011073",1998-01-28,"10237536",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6240","CNS",6239838.41046327,125427.836897983,0.2205,0.0661,2,300,1 +"10011073",1998-01-28,"10237538",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6240","CNS",6239592.33073259,125043.922766534,0.2468,0.074,2,300,1 +"10011073",1998-01-28,"10237594",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0121N6225","CNS",6226021.01588867,123206.632130189,0.2614,0.0784,2,300,1 +"10011075",1998-01-28,"10237756",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0126N6220","CNS",6219067.20337297,127095.613185349,0.2764,0.0829,2,300,1 +"10011076",1998-01-28,"10237845",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0121N6235","CNS",6233788.14271787,120805.526764876,0.2538,0.0762,1,300,1 +"10011076",1998-01-28,"10237854",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0121N6235","CNS",6236042.09891616,121011.726374035,0.27,0.08,2,300,1 +"10011076",1998-01-28,"10237855",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0121N6235","CNS",6236242.46072822,120898.192716492,0.2569,0.0771,2,300,1 +"10011076",1998-01-28,"10237892",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0116N6230","CNS",6228708.92075516,117254.296290115,0.2401,0.072,1,300,1 +"10011077",1998-01-28,"10237919",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0116N6225","CNS",6222631.44426731,116366.25215636,0.2328,0.0698,2,300,3 +"10011079",1998-01-28,"10237976",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0111N6225","CNS",6227616.00530638,110870.426681298,0.2437,0.0731,1,300,1 +"10011080",1998-01-29,"10238029",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0106N6225","CNS",6226639.12038827,106486.393804371,0.2486,0.0746,2,300,1 +"10011081",1998-01-29,"10238059",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0116N6230","CNS",6228638.01404462,116170.366761291,2.6736,0.8021,2,300,3 +"10011082",1998-01-31,"10238109",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0201N6360","CNS",6358338.66767927,199959.866802295,0.2872,0.0862,2,300,1 +"10011083",1998-01-31,"10238141",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0206N6365","CNS",6365569.39462605,205009.571643214,0.2689,0.0807,2,300,1 +"10011083",1998-01-31,"10238187",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6375","CNS",6374553.71816236,212650.785623468,0.2438,0.0731,1,300,1 +"10011083",1998-01-31,"10238190",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6375","CNS",6375103.83762419,213087.318504546,0.2375,0.0713,1,300,1 +"10011083",1998-01-31,"10238193",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6375","CNS",6375662.16314243,213566.491854484,0.2396,0.0719,1,300,1 +"10011084",1998-01-31,"10238267",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0216N6390","CNS",6391516.07050007,215601.215214929,0.203,0.0609,1,300,1 +"10011084",1998-01-31,"10238299",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0216N6395","CNS",6397088.87247038,215465.446367345,0.1446,0.0434,1,300,2 +"10011084",1998-01-31,"10238301",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0216N6395","CNS",6397343.50992788,215339.552251303,0.158,0.0474,1,300,1 +"10011085",1998-01-31,"10238307",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398257.84943861,214888.710348059,0.1694,0.0508,1,300,4 +"10011085",1998-01-31,"10238308",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398398.81974883,214790.817316501,0.1712,0.0513,2,300,14 +"10011085",1998-01-31,"10238309",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398539.79262513,214692.931639375,0.1765,0.0529,2,300,50 +"10011085",1998-01-31,"10238310",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398681.18940018,214589.092063726,0.1711,0.0513,2,300,10 +"10011085",1998-01-31,"10238311",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398832.44065713,214503.929348807,0.1731,0.0519,2,300,18 +"10011085",1998-01-31,"10238312",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6398973.84269233,214400.105009943,0.1717,0.0515,2,300,32 +"10011085",1998-01-31,"10238313",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6399114.40404422,214308.209683756,0.1667,0.05,2,300,9 +"10011085",1998-01-31,"10238314",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6399245.11742517,214197.653140884,0.1722,0.0517,2,300,26 +"10011085",1998-01-31,"10238316",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6399527.09682744,214001.977812544,0.1765,0.053,2,300,6 +"10011085",1998-01-31,"10238317",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0216N6400","CNS",6399668.09037529,213904.151182349,0.1793,0.0538,2,300,4 +"10011085",1998-01-31,"10238318",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6400","CNS",6399820.62500353,213801.160435535,0.1792,0.0538,2,300,16 +"10011085",1998-01-31,"10238320",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6400","CNS",6400103.4709134,213593.626591639,0.164,0.0492,2,300,3 +"10011085",1998-01-31,"10238321",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6400","CNS",6400223.08934463,213482.334065874,0.1698,0.0509,2,300,2 +"10011085",1998-01-31,"10238322",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0211N6400","CNS",6400353.82694852,213371.83782991,0.1813,0.0544,2,300,18 +"10011087",1998-01-31,"10238519",800,10,3,1997,1,1,1,"9",NA,"ESAS v5","E0196N6410","CNS",6408854.7189192,197361.818011118,0.2477,0.0743,2,300,3 +"10011088",1998-02-01,"10238563",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0196N6410","CNS",6407934.2645432,197860.877517484,0.2875,0.0863,1,300,1 +"10011088",1998-02-01,"10238574",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0196N6410","CNS",6407807.76157834,194943.946431342,0.2433,0.073,1,300,0.921052631578947 +"10011090",1998-02-01,"10238768",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0176N6410","CNS",6408517.45930902,177380.466346159,0.2507,0.0752,2,300,23 +"10011090",1998-02-01,"10238770",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0176N6410","CNS",6408029.98973169,177323.433652978,0.2458,0.0737,2,300,2 +"10011091",1998-02-01,"10238826",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0166N6410","CNS",6408840.08131681,167371.210720651,0.2507,0.0752,2,300,8 +"10011092",1998-02-01,"10238969",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6415","CNS",6414343.50188087,157913.517713856,0.2526,0.0758,2,300,485 +"10011092",1998-02-01,"10238970",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6415","CNS",6414098.10020754,157904.589828102,0.2457,0.0737,1,300,1 +"10011092",1998-02-01,"10238976",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6412576.2172079,157775.01624182,0.2626,0.0788,2,300,1 +"10011092",1998-02-01,"10238982",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6411033.12914657,157631.679865641,0.2593,0.0778,1,300,1 +"10011092",1998-02-01,"10238985",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6410301.98408948,157545.485179376,0.2123,0.0637,2,300,11 +"10011092",1998-02-01,"10238986",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6410297.99592336,157329.512380977,0.2141,0.0642,2,300,2 +"10011092",1998-02-01,"10238987",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6410338.45359119,157117.324963829,0.217,0.0651,2,300,6 +"10011092",1998-02-01,"10238988",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6410400.63250198,156912.981862045,0.2217,0.0665,2,300,1 +"10011092",1998-02-01,"10238990",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6410","CNS",6410527.0531306,156480.525592094,0.2192,0.0658,2,300,2 +"10011093",1998-02-01,"10239031",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411362.52463388,147854.606542368,0.2162,0.0649,1,300,4 +"10011093",1998-02-01,"10239033",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411242.44521927,147436.675077736,0.2188,0.0656,1,300,2 +"10011093",1998-02-01,"10239034",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411194.05009668,147222.726778794,0.2145,0.0644,2,300,1 +"10011094",1998-02-04,"10239040",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6410117.68301163,144826.78314694,0.2708,0.0812,1,300,2 +"10011094",1998-02-04,"10239042",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6410412.06235441,145296.284048361,0.2769,0.0831,1,300,1 +"10011094",1998-02-04,"10239048",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411008.95890522,146781.062158846,0.2785,0.0835,2,300,1 +"10011094",1998-02-04,"10239049",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411131.94996008,147037.527315675,0.2741,0.0822,2,300,1 +"10011094",1998-02-04,"10239052",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6410","CNS",6411488.17686082,147697.859876696,0.2607,0.0782,1,300,1 +"10011094",1998-02-04,"10239061",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6415","CNS",6413781.9887919,147832.962476584,0.2456,0.0737,2,300,1 +"10011094",1998-02-04,"10239072",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6415","CNS",6416520.26049249,148132.810088893,0.2639,0.0792,2,300,1 +"10011094",1998-02-04,"10239075",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6415","CNS",6417286.13690412,148205.922493233,0.2447,0.0734,2,300,5 +"10011094",1998-02-04,"10239076",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6415","CNS",6417529.97495524,148233.277962086,0.2348,0.0704,1,300,2 +"10011094",1998-02-04,"10239085",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6419809.55194257,148403.22035693,0.2426,0.0728,2,300,8 +"10011094",1998-02-04,"10239087",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6420298.78867946,148440.140145647,0.2494,0.0748,2,300,1 +"10011094",1998-02-04,"10239090",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6421086.87925724,148515.224625535,0.2667,0.08,2,300,2 +"10011094",1998-02-04,"10239092",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6421608.39726702,148566.934756482,0.2404,0.0721,2,300,22 +"10011094",1998-02-04,"10239095",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6422343.03210385,148613.43615497,0.2601,0.078,2,300,1 +"10011094",1998-02-04,"10239096",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6420","CNS",6422600.057568,148618.055457439,0.2536,0.0761,2,300,11 +"10011094",1998-02-04,"10239099",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6425","CNS",6423354.82632808,148690.234034826,0.2568,0.077,2,300,2 +"10011094",1998-02-04,"10239100",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6425","CNS",6423612.37152755,148688.933418076,0.2508,0.0752,2,300,2 +"10011094",1998-02-04,"10239101",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0146N6425","CNS",6423855.68965522,148722.224305456,0.2436,0.0731,2,300,1 +"10011095",1998-02-07,"10239129",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0126N6420","CNS",6417725.38800638,128302.630382309,0.2558,0.0767,2,300,2 +"10011096",1998-02-09,"10239147",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0126N6420","CNS",6417902.50552093,128325.022219832,0.2447,0.0734,2,300,3 +"10011096",1998-02-09,"10239152",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0126N6420","CNS",6419168.38542438,128442.269756614,0.2548,0.0764,2,300,6 +"10011096",1998-02-09,"10239156",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0126N6420","CNS",6420157.20946908,128527.881711819,0.2667,0.08,2,300,1 +"10011096",1998-02-09,"10239226",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6425","CNS",6425907.0818838,139001.994546472,0.2578,0.0773,1,300,1 +"10011097",1998-02-09,"10239229",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0141N6425","CNS",6425161.930345,138946.72333365,0.2393,0.0718,2,300,1 +"10011097",1998-02-09,"10239262",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6415","CNS",6416904.62727093,138142.340250318,0.241,0.0723,2,300,2 +"10011097",1998-02-09,"10239263",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6415","CNS",6416670.33663882,138133.193789847,0.2437,0.0731,1,300,2 +"10011097",1998-02-09,"10239266",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6415","CNS",6415923.57681245,138095.822768953,0.2517,0.0755,2,300,2 +"10011097",1998-02-09,"10239267",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6415","CNS",6415679.25082729,138073.803388971,0.2564,0.0769,2,300,4 +"10011097",1998-02-09,"10239286",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6410","CNS",6410946.60229283,137665.373458937,0.2378,0.0713,2,300,1 +"10011097",1998-02-09,"10239293",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0136N6410","CNS",6409789.73721949,138328.739555659,0.2722,0.0817,1,300,1 +"10011097",1998-02-09,"10239295",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0141N6410","CNS",6409607.3190947,138864.037827717,0.2827,0.0848,2,300,1 +"10011097",1998-02-09,"10239296",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0141N6410","CNS",6409548.92185414,139140.638446312,0.2834,0.085,2,300,1 +"10011097",1998-02-09,"10239303",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0141N6410","CNS",6408951.91046584,140934.235739561,0.2727,0.0818,2,300,1 +"10011097",1998-02-09,"10239312",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0141N6410","CNS",6409351.16045557,143242.304822793,0.2723,0.0817,1,300,1 +"10011098",1998-02-09,"10239323",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6410","CNS",6408901.48735131,141877.232129235,0.208,0.0624,2,300,1 +"10011098",1998-02-09,"10239324",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6410","CNS",6408986.65692588,141674.929980301,0.2256,0.0677,2,300,2 +"10011098",1998-02-09,"10239329",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6410","CNS",6409524.78581527,140661.551063415,0.2237,0.0671,2,300,1 +"10011098",1998-02-09,"10239335",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6410","CNS",6409079.98689721,139626.344339186,0.2373,0.0712,2,300,1 +"10011098",1998-02-09,"10239336",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0141N6410","CNS",6409199.65536413,139415.170941547,0.2333,0.07,2,300,1 +"10011098",1998-02-09,"10239341",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0136N6410","CNS",6409684.97330838,138373.293638113,0.2458,0.0737,1,300,1 +"10011098",1998-02-09,"10239346",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0136N6410","CNS",6410185.42789298,137291.007897543,0.2369,0.0711,2,300,1 +"10011098",1998-02-09,"10239356",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0136N6410","CNS",6410897.10378154,134992.916415505,0.2389,0.0717,2,300,1 +"10011099",1998-02-10,"10239396",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0126N6415","CNS",6417454.04021762,125438.706604192,0.2271,0.0681,1,300,4 +"10011099",1998-02-10,"10239398",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0126N6415","CNS",6417508.94573251,124970.661455216,0.2412,0.0724,1,300,1 +"10011099",1998-02-10,"10239458",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0121N6425","CNS",6423407.9181508,118840.238147257,0.2247,0.0674,2,300,3 +"10011101",1998-02-10,"10239664",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0096N6420","CNS",6417711.53040828,98323.3051282114,0.2699,0.081,2,300,1 +"10011101",1998-02-10,"10239665",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0096N6420","CNS",6417979.69936383,98332.1460914948,0.2715,0.0814,2,300,3 +"10011102",1998-02-10,"10239699",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0101N6425","CNS",6426976.55834226,99280.768409767,0.2725,0.0818,2,300,1 +"10011102",1998-02-10,"10239700",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0101N6425","CNS",6427242.3498873,99313.3720857433,0.2746,0.0824,2,300,5 +"10011102",1998-02-10,"10239703",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0101N6430","CNS",6428056.16381731,99358.9824152882,0.2647,0.0794,1,300,1 +"10011102",1998-02-10,"10239735",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6426777.41586098,95465.9932007117,0.2322,0.0697,2,300,1 +"10011102",1998-02-10,"10239736",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6426579.12224427,95320.2298222815,0.2356,0.0707,1,300,1 +"10011102",1998-02-10,"10239738",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6426190.04988834,95065.3478287327,0.2362,0.0709,2,300,1 +"10011102",1998-02-10,"10239741",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6425643.81762176,94590.9178099051,0.2387,0.0716,2,300,5 +"10011102",1998-02-10,"10239745",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6424884.68049052,94004.9417627045,0.2377,0.0713,2,300,1 +"10011103",1998-02-10,"10239790",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6415","CNS",6415998.84049596,88331.5239252488,0.2202,0.0661,1,300,1 +"10011103",1998-02-10,"10239793",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6415","CNS",6415479.28980668,87929.97032781,0.225,0.0675,1,300,2 +"10011103",1998-02-10,"10239799",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6415","CNS",6414344.79680745,87182.8605423488,0.2316,0.0695,1,300,125 +"10011103",1998-02-10,"10239800",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6415","CNS",6414154.81014794,87067.2251414254,0.2232,0.0669,1,300,96 +"10011103",1998-02-10,"10239823",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6410680.81418572,83018.1227702086,0.2206,0.0662,2,300,2 +"10011103",1998-02-10,"10239825",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6410476.15739835,82611.9890762559,0.2309,0.0693,2,300,1 +"10011103",1998-02-10,"10239828",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6410168.00953508,82014.6089349578,0.22,0.07,1,300,1 +"10011103",1998-02-10,"10239830",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409958.50099049,81655.978113391,0.22,0.07,2,300,1 +"10011103",1998-02-10,"10239836",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409126.07698618,80600.7180206583,0.2248,0.0674,2,300,1 +"10011104",1998-02-10,"10239851",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0076N6405","CNS",6406927.62633315,78342.7051941801,0.2295,0.0688,1,300,1 +"10011104",1998-02-10,"10239854",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0076N6405","CNS",6406366.46158997,77916.6337280034,0.2438,0.0731,2,300,1 +"10011105",1998-02-12,"10239930",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405208.93498075,77186.9871126838,0.2102,0.0631,2,300,1 +"10011105",1998-02-12,"10239932",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405461.8358799,77556.8624528926,0.2084,0.0625,2,300,2 +"10011105",1998-02-12,"10239933",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405604.07498709,77698.2592828344,0.2035,0.0611,2,300,1 +"10011105",1998-02-12,"10239934",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405733.35067164,77856.3585957232,0.2035,0.061,2,300,1 +"10011105",1998-02-12,"10239937",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405948.53537904,78372.7616217159,0.189,0.0567,2,300,4 +"10011105",1998-02-12,"10239938",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405996.42402362,78558.4487477656,0.1864,0.0559,2,300,4 +"10011107",1998-02-12,"10240118",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0111N6420","CNS",6421269.1718925,113679.486106396,0.2565,0.0769,2,300,3 +"10011107",1998-02-12,"10240159",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0111N6430","CNS",6428911.10260942,111828.203511345,0.2478,0.0744,2,300,2 +"10011108",1998-02-12,"10240163",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0111N6430","CNS",6429048.75864375,110873.241263849,0.2318,0.0695,2,300,2 +"10011108",1998-02-12,"10240167",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0111N6430","CNS",6429153.34147622,109915.064594062,0.2415,0.0724,2,300,1 +"10011108",1998-02-12,"10240183",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0106N6430","CNS",6429471.74494175,106096.388016555,0.2402,0.0721,2,300,2 +"10011108",1998-02-12,"10240192",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0106N6430","CNS",6429048.87105211,104476.147882401,0.2527,0.0758,2,300,1 +"10011110",1998-02-12,"10240348",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6415","CNS",6415801.23879285,93132.3953915531,0.2747,0.0824,2,300,1 +"10011110",1998-02-12,"10240351",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6415","CNS",6416607.22433356,93256.048325008,0.2725,0.0817,2,300,31 +"10011110",1998-02-12,"10240352",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6415","CNS",6416873.59083178,93283.0278979921,0.2626,0.0788,1,300,17 +"10011110",1998-02-12,"10240353",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6415","CNS",6417141.16066143,93298.1280502793,0.2637,0.0791,2,300,2 +"10011110",1998-02-12,"10240354",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6415","CNS",6417410.53549426,93295.4114994756,0.2715,0.0814,2,300,1 +"10011110",1998-02-12,"10240360",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6420","CNS",6419005.72434718,93487.0154510066,0.2701,0.081,1,300,1 +"10011110",1998-02-12,"10240364",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6420","CNS",6420117.98848182,93575.7223804317,0.2811,0.0843,2,300,1 +"10011110",1998-02-12,"10240369",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6420","CNS",6421486.11956741,93684.392235159,0.264,0.0792,1,300,8 +"10011110",1998-02-12,"10240371",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6420","CNS",6422018.85045948,93738.386765985,0.2723,0.0817,1,300,4 +"10011110",1998-02-12,"10240372",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0091N6420","CNS",6422295.71309787,93772.442439065,0.2814,0.0844,2,300,1 +"10011110",1998-02-12,"10240373",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6420","CNS",6422570.17076851,93830.2260050267,0.2764,0.0829,1,300,1 +"10011110",1998-02-12,"10240374",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6422835.33382118,93869.0866175403,0.2612,0.0783,1,300,6 +"10011110",1998-02-12,"10240375",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0096N6425","CNS",6423081.90687582,93870.1092416264,0.26,0.08,1,300,2 +"10011111",1998-02-13,"10240415",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0081N6415","CNS",6415459.30318466,83447.285137955,0.2522,0.0757,1,300,1 +"10011111",1998-02-13,"10240416",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0081N6415","CNS",6415411.23705608,83694.5980869397,0.2469,0.0741,2,300,1 +"10011111",1998-02-13,"10240417",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415363.80180388,83935.9715296492,0.247,0.0741,2,300,1 +"10011111",1998-02-13,"10240418",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415360.76965279,84181.9425594903,0.2451,0.0735,2,300,1 +"10011111",1998-02-13,"10240419",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415413.85297915,84427.7077501273,0.2462,0.0739,2,300,1 +"10011111",1998-02-13,"10240420",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415478.66258173,84668.6687824448,0.2524,0.0757,2,300,8 +"10011111",1998-02-13,"10240421",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415589.71726019,84896.3781240933,0.2442,0.0732,2,300,7 +"10011111",1998-02-13,"10240422",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415749.46950536,85087.0599546382,0.234,0.0702,2,300,3 +"10011111",1998-02-13,"10240423",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6415924.00856688,85243.2188518229,0.2439,0.0732,2,300,8 +"10011111",1998-02-13,"10240428",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6416646.47161875,86176.4554139643,0.2538,0.0761,1,300,1 +"10011111",1998-02-13,"10240429",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6416830.31081358,86351.5035642803,0.251,0.0753,1,300,1 +"10011111",1998-02-13,"10240447",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6416517.73158931,88192.5857789397,0.2431,0.0729,2,300,1 +"10011111",1998-02-13,"10240461",800,10,4,1998,1,1,1,"9",NA,"ESAS v5","E0086N6415","CNS",6413030.59459945,87859.1114821357,0.2474,0.0742,2,300,1 +"10011112",1998-02-13,"10240487",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6405","CNS",6406342.27763531,87137.6818591531,0.2529,0.0759,2,300,1 +"10011112",1998-02-13,"10240494",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0086N6405","CNS",6404868.59729937,86854.2647365726,0.2711,0.0813,2,300,1 +"10011112",1998-02-13,"10240521",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6408883.75041462,82392.7042298201,0.2398,0.0719,1,300,3 +"10011112",1998-02-13,"10240522",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409128.50085883,82412.0697595083,0.2483,0.0745,2,300,8 +"10011112",1998-02-13,"10240523",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409376.33794874,82401.6755285097,0.2577,0.0773,2,300,1 +"10011112",1998-02-13,"10240524",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409630.95042943,82434.1002561657,0.2555,0.0767,2,300,2 +"10011112",1998-02-13,"10240525",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409884.32833546,82478.4278164137,0.2462,0.0738,2,300,1 +"10011112",1998-02-13,"10240545",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6410403.02888327,81587.8914577013,0.2455,0.0737,1,300,4 +"10011112",1998-02-13,"10240547",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409941.33678276,81389.5010744446,0.2667,0.08,2,300,3 +"10011112",1998-02-13,"10240548",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409760.78575931,81184.2261359286,0.2721,0.0816,2,300,3 +"10011112",1998-02-13,"10240549",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6409582.66914892,81063.4165431674,0.2721,0.0816,2,300,2 +"10011112",1998-02-13,"10240555",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6408542.20553214,79745.490311117,0.2806,0.0842,2,300,1 +"10011112",1998-02-13,"10240556",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0081N6410","CNS",6408372.20667203,79547.1895622932,0.2592,0.0778,2,300,1 +"10011112",1998-02-13,"10240560",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6407573.86604049,78813.6855984202,0.2723,0.0817,2,300,1 +"10011112",1998-02-13,"10240564",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6406692.98301003,78119.4374000346,0.2837,0.0851,2,300,2 +"10011112",1998-02-13,"10240565",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6406452.73649473,77949.7571939619,0.2808,0.0842,1,300,9 +"10011112",1998-02-13,"10240567",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405990.08497043,77654.3731728674,0.2733,0.082,2,300,4 +"10011112",1998-02-13,"10240570",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0076N6405","CNS",6405299.5788162,77178.4197880281,0.2838,0.0851,2,300,1 +"10011180",1998-01-07,"10241458",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6240","CNS",6239891.4880862,145628.970786819,3.641,1.0923,1,300,4 +"10011180",1998-01-07,"10241460",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0146N6240","CNS",6241790.00416722,148774.166807407,3.8396,1.1519,1,300,8 +"10011180",1998-01-07,"10241461",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6245","CNS",6243637.67826929,152155.245632566,3.8396,1.1519,2,300,9 +"10011183",1998-01-10,"10241551",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0156N6460","CNS",6460525.09598659,155726.080235053,3.6243,1.0873,2,300,1 +"10011183",1998-01-10,"10241554",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6465","CNS",6467107.31445097,153156.715755787,3.3663,1.0099,1,300,3 +"10011183",1998-01-10,"10241555",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0151N6470","CNS",6470202.32774285,151823.15277875,3.5271,1.0581,2,300,8 +"10011183",1998-01-10,"10241557",800,10,3,1997,1,1,1,"118",NA,"ESAS v5","E0156N6470","CNS",6471381.42816075,158813.820868791,3.5271,1.0581,1,300,1 +"10011211",1998-03-03,"10242145",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0156N6515","CNS",6516103.55445892,155176.641731848,3.4991,1.0497,1,300,1 +"10011215",1998-03-27,"10242331",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0216N6395","CNS",6397275.371137,216467.383396516,3.1939,0.9582,2,300,1 +"10011215",1998-03-27,"10242332",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0216N6400","CNS",6399886.35937076,214608.395954204,3.1569,0.9471,2,300,12 +"10011215",1998-03-27,"10242333",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0211N6400","CNS",6402473.91787524,212780.181595639,3.0571,0.9171,2,300,1 +"10011215",1998-03-27,"10242334",800,10,4,1998,1,1,1,"118",NA,"ESAS v5","E0211N6405","CNS",6404878.10247994,210875.455361095,2.9356,0.8807,2,300,1 +"10011283",1998-04-18,"10243068",800,10,5,1998,1,1,1,"118",NA,"ESAS v5","E0151N6515","CNS",6516851.21888659,153446.147489156,3.2603,0.9781,2,300,2 +"10011487",1998-07-06,"10245489",800,10,6,1998,1,1,1,"125",NA,"ESAS v5","E0146N6470","CNS",6469423.63629046,147862.54766966,3.3106,0.9932,1,300,1 +"10011852",1998-09-22,"10261096",800,10,1,1998,1,1,1,"124",NA,"ESAS v5","E0211N6365","CNS",6365531.26488414,212914.350398351,3.1938,0.9582,2,300,2 +"10011852",1998-09-22,"10261097",800,10,1,1998,1,1,1,"124",NA,"ESAS v5","E0216N6370","CNS",6368452.51733878,214266.428990897,3.1938,0.9582,2,300,2 +"10012336",2005-06-28,"10267844",800,10,6,2005,1,1,1,"166",NA,"ESAS v5","E0201N6350","CNS",6350164.21032125,201732.776829872,3.0231,0.9069,1,300,1 +"10012566",2010-08-07,"10275222",6270,10,1,2010,1,1,1,"190",NA,"ESAS v5","E0511N5725","SNS",5722862.57189512,513170.836785849,1.9,0.57,1,300,1 +"10012798",2011-02-06,"10279203",800,10,4,2011,1,1,2,"177",NA,"ESAS v5","E0391N6510","CNS",6509643.98095278,389931.00237292,1.8333,0.55,1,300,1 +"110000002",1992-10-02,"110000026",1680,110,2,1992,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0461N5690","SNS",5691104.89614671,463106.794655942,1.9,0.57,2,300,7 +"110000008",1992-10-22,"110000201",1680,110,2,1992,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0461N5685","SNS",5684432.34187691,463058.596028477,3.2,0.96,1,300,15 +"110000012",1992-11-09,"110000307",1730,110,2,1992,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5688755.40289086,509053.245218536,3.0333,0.91,1,300,4 +"110000022",1993-01-14,"110000498",1680,110,3,1992,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677633.27027789,491624.98048755,3.6,1.08,2,300,1 +"110000023",1993-01-26,"110000526",1730,110,3,1992,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677633.27027789,491624.98048755,3.4333,1.03,2,300,5 +"110000029",1993-02-15,"110000817",1680,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680972.00341846,490235.503446853,1.4667,0.44,1,300,2 +"110000030",1993-02-16,"110000857",1680,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677632.17780534,492322.898587152,1.9,0.57,1,300,2 +"110000030",1993-02-16,"110000877",5410,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5683207.82116716,485359.620365562,1.0667,0.32,1,300,5 +"110000030",1993-02-16,"110000879",1730,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5682093.78175163,486053.751289065,1.3,0.39,1,300,8 +"110000030",1993-02-16,"110000886",1730,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5682091.929866,486751.063155943,1.3333,0.4,1,300,2 +"110000030",1993-02-16,"110000908",5410,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0481N5685","SNS",5683221.49559123,481176.660731448,1.3333,0.4,1,300,2 +"110000031",1993-02-18,"110000932",1730,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677632.17780534,492322.898587152,2.1,0.63,1,300,1 +"110000035",1993-03-09,"110001168",1680,110,4,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0391N5665","SNS",5663185.19146746,392197.267383027,6.4,1.92,1,300,62 +"110000044",1993-04-23,"110001350",6270,110,5,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0481N5680","SNS",5677669.17947229,479062.468529688,5.2667,1.58,2,300,1 +"110000045",1993-04-26,"110001370",6270,110,5,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684309.66063007,489544.85584041,4.0667,1.22,1,300,2 +"110000061",1993-08-30,"110001697",5410,110,1,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0516N5695","SNS",5695445.13989166,515996.34571732,2.4667,0.74,1,300,1 +"110000064",1993-09-02,"110001743",6270,110,1,1993,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5692093.00482424,509743.280413043,3.1667,0.95,1,300,1 +"110000075",1993-12-17,"110002062",1680,110,3,1993,1,1,1,"11009",NA,"data update and replacement 06/11/2020","E0386N5665","SNS",5664572.45687962,388025.602206908,6.18,1.854,1,300,3 +"110000082",1994-02-07,"110002252",4960,110,4,1994,1,1,2,"11009",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5692281.57418521,511134.771652895,1.54,0.462,1,300,1 +"110000088",1994-03-01,"110002518",5410,110,4,1994,1,1,1,"11009",NA,"data update and replacement 06/11/2020","E0471N5690","SNS",5688454.26347412,471909.759145958,1.54,0.462,1,300,1 +"110000095",1994-05-09,"110002773",6270,110,5,1994,1,1,1,"11009",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5690420.77305645,507309.849257472,1.54,0.462,1,300,3 +"110000095",1994-05-09,"110002775",4960,110,5,1994,1,1,1,"11009",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5683024.14326899,484661.903627088,3.09,0.927,1,300,4 +"110000097",1994-10-18,"110002838",1680,110,2,1994,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0401N5690","SNS",5688974.01524462,400518.186895072,3.77,1.131,2,300,2 +"110000097",1994-10-18,"110002841",800,110,2,1994,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687271.45183501,393050.560355404,3.83,1.149,1,300,1 +"110000097",1994-10-18,"110002843",1680,110,2,1994,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0406N5685","SNS",5685921.04231035,405106.183649053,5.9,1.77,2,300,34 +"110000100",1994-11-17,"110002931",1680,110,2,1994,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670379.41360516,482972.859441777,2.47,0.741,1,300,16 +"110000100",1994-11-17,"110002959",1730,110,2,1994,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0506N5685","SNS",5683744.54563798,504066.308878284,3.71,1.113,1,300,21 +"110000111",1995-02-07,"110003294",1680,110,4,1995,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0476N5680","SNS",5682305.65003056,478500.349407589,2.43,0.729,1,300,7 +"110000113",1995-02-09,"110003363",1680,110,4,1995,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0471N5690","SNS",5687723.7245066,469932.073448597,3.39,1.017,2,300,1 +"110000138",1995-10-16,"110004183",800,110,2,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687288.52211218,392237.71787301,4.42,1.326,1,300,6 +"110000138",1995-10-16,"110004188",1680,110,2,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0416N5685","SNS",5683355.08462892,413892.676186531,6.32,1.896,1,300,6 +"110000139",1995-11-03,"110004223",800,110,2,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687122.48257314,391304.857553073,2.2,0.66,1,300,1 +"110000139",1995-11-03,"110004224",800,110,2,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687330.51508838,390263.75554493,2.27,0.681,1,300,3 +"110000140",1995-11-29,"110004260",5410,110,2,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687328.02327782,390379.89540418,3.05,0.915,1,300,1 +"110000144",1995-12-20,"110004371",5410,110,3,1995,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5691930.88699002,518327.280021376,1.6,0.48,1,300,11 +"110000145",1996-01-19,"110004389",800,110,3,1995,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687245.12453129,392079.974012098,3.7647,1.129,2,300,1 +"110000146",1996-02-06,"110004402",1730,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679045.68413244,489755.073479788,5.3686,1.611,1,300,5 +"110000149",1996-02-24,"110004536",5410,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0471N5680","SNS",5677706.2763369,471385.398098543,3.27,0.981,1,300,4 +"110000153",1996-02-28,"110004693",1730,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0471N5665","SNS",5667148.23579528,470160.868547357,4.08,1.224,1,300,2 +"110000153",1996-02-28,"110004702",1730,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5675237.82987738,485220.435658975,3.65,1.095,1,300,7 +"110000153",1996-02-28,"110004706",5410,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675779.05241315,491854.652985853,2.75,0.825,1,300,45 +"110000153",1996-02-28,"110004709",5410,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670794.85223941,483460.501093484,3.57,1.071,1,300,11 +"110000153",1996-02-28,"110004726",5410,110,4,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0476N5675","SNS",5675639.71440518,476959.564916985,4.04,1.212,1,300,1 +"110000155",1996-03-13,"110004761",1680,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0406N5690","SNS",5690532.36219264,406352.584394458,5.67,1.701,1,300,170 +"110000156",1996-03-18,"110004788",5320,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5689129.3618135,510909.552599177,1.1,0.33,1,300,28 +"110000156",1996-03-18,"110004789",5410,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5688757.13257351,509981.759572087,1.1,0.33,1,300,2 +"110000157",1996-03-19,"110004821",5320,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0526N5695","SNS",5696970.99275529,526421.032213729,1.16,0.348,1,300,21 +"110000157",1996-03-19,"110004832",1730,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0551N5695","SNS",5692881.2053685,549638.629959113,2.58,0.774,1,300,1 +"110000157",1996-03-19,"110004834",1730,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0556N5690","SNS",5689957.59654852,553845.065394166,2.65,0.795,1,300,1 +"110000159",1996-03-21,"110004858",800,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0476N5690","SNS",5689738.15308111,474353.846922841,3.14,0.942,2,300,1 +"110000159",1996-03-21,"110004865",4960,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0496N5695","SNS",5696905.40739808,494553.573863403,3.06,0.918,1,300,1 +"110000160",1996-03-22,"110004880",1680,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0356N5705","SNS",5707074.1368553,356890.554685705,2.42,0.726,1,300,53 +"110000163",1996-03-27,"110004983",1680,110,4,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0486N5700","SNS",5700818.9310193,484367.904067996,1.25,0.375,1,300,1 +"110000167",1996-07-31,"110005110",5410,110,6,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0481N5680","SNS",5678024.49507817,483134.844314625,4.5,1.35,1,300,2 +"110000167",1996-07-31,"110005127",800,110,6,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687288.52211218,392237.71787301,1.66,0.498,1,300,3 +"110000170",1996-08-23,"110005237",5320,110,1,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0441N5965","SNS",5964067.69584606,439876.626182277,3.23,0.969,1,300,11 +"110000170",1996-08-23,"110005238",5320,110,1,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0436N5960","SNS",5962436.48865313,437001.599710758,3.23,0.969,1,300,2 +"110000173",1996-08-26,"110005369",6270,110,1,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0416N5730","SNS",5728752.80984159,415349.79171889,3.44,1.032,1,300,1 +"110000174",1996-08-27,"110005409",800,110,1,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0461N5750","SNS",5748211.05265731,461341.241832992,3.46,1.038,1,300,5 +"110000178",1996-10-01,"110005555",1680,110,2,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0461N5695","SNS",5694440.36180221,463246.872288756,3.58,1.074,1,300,1 +"110000180",1996-10-03,"110005592",1680,110,2,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0501N5700","SNS",5700239.15780123,501737.090295285,0.6,0.18,1,300,48 +"110000181",1996-10-16,"110005633",800,110,2,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0391N5685","SNS",5687281.19065606,392586.068864932,2.48,0.744,1,300,6 +"110000181",1996-10-16,"110005648",1680,110,2,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0476N5685","SNS",5683798.35507659,476182.91844247,5.44,1.632,1,300,5 +"110000183",1996-12-16,"110005671",1680,110,3,1996,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0476N5685","SNS",5683788.55715833,478390.371432684,1.2,0.36,1,300,2 +"110000190",1997-01-29,"110005838",5410,110,3,1996,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0581N5690","SNS",5690726.73569419,583429.808338382,2.75,0.825,2,300,1 +"110000193",1997-02-05,"110005952",1680,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0471N5700","SNS",5699583.85051072,470465.228221185,2.77,0.831,1,300,41 +"110000195",1997-02-07,"110006013",5410,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0496N5690","SNS",5691342.35431332,497911.85032254,3.47,1.041,1,300,1 +"110000195",1997-02-07,"110006016",1680,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5690423.15514746,508818.253563258,3.74,1.122,1,300,2 +"110000196",1997-02-10,"110006020",5410,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5690430.35273226,512299.131890295,2.75,0.825,1,300,1 +"110000202",1997-02-27,"110006202",1680,110,4,1997,1,1,1,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5689877.53348279,513576.90848712,2.4,0.72,1,300,1 +"110000205",1997-03-16,"110006264",1730,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0381N5760","SNS",5762119.31142279,379628.330778682,2.4,0.72,2,300,9 +"110000205",1997-03-16,"110006264",5410,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0381N5760","SNS",5762119.31142279,379628.330778682,2.4,0.72,1,300,130 +"110000205",1997-03-16,"110006265",1730,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0381N5760","SNS",5760966.10578321,381318.09049787,1.53,0.459,1,300,1 +"110000208",1997-03-19,"110006351",5320,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0566N5695","SNS",5694743.66379883,566665.309530089,4.14,1.242,1,300,15 +"110000208",1997-03-19,"110006359",1680,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0581N5695","SNS",5693471.33779104,581181.007923897,3.61,1.083,1,300,10 +"110000208",1997-03-19,"110006360",1730,110,4,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0581N5695","SNS",5693510.6723058,583616.359975609,3.21,0.963,1,300,2 +"110000211",1997-04-30,"110006419",800,110,5,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0481N5680","SNS",5682116.5796009,479313.052099339,4.26,1.278,2,300,1 +"110000215",1997-08-22,"110006607",1730,110,1,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0361N5950","SNS",5950698.51628781,362644.958187978,2.7808,0.834,1,300,2 +"110000225",1997-09-10,"110007039",800,110,1,1997,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678313.79563276,491669.706303059,1.0717,0.322,1,300,1 +"110000230",1997-10-02,"110007196",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5676130.62268703,473199.612061355,3.6357,1.091,1,300,5 +"110000231",1997-10-03,"110007212",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5690751.74865198,505128.185855199,3.8641,1.159,1,300,14 +"110000231",1997-10-03,"110007216",800,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5688257.51712815,514109.353225767,1.3502,0.405,2,300,1 +"110000235",1997-10-13,"110007347",1730,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5689072.2387074,509599.467532012,2.4043,0.721,2,300,4 +"110000235",1997-10-13,"110007358",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684386.60913112,489994.230567328,0.7296,0.219,1,300,21 +"110000236",1997-10-14,"110007365",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677197.30461075,491641.717887486,2.0443,0.613,1,300,55 +"110000236",1997-10-14,"110007366",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677766.78914026,489750.493276246,1.8296,0.549,1,300,28 +"110000236",1997-10-14,"110007367",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5677883.01445184,487490.405835289,2.2566,0.677,1,300,40 +"110000236",1997-10-14,"110007377",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0466N5670","SNS",5671478.9539266,464855.049734906,2.6073,0.782,1,300,7 +"110000236",1997-10-14,"110007392",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0466N5675","SNS",5674854.52696514,464011.296718323,2.0597,0.618,1,300,30 +"110000236",1997-10-14,"110007422",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0466N5670","SNS",5671077.73083531,463856.927442999,3.2013,0.96,1,300,8 +"110000236",1997-10-14,"110007426",1680,110,2,1997,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0476N5675","SNS",5674800.37624804,474098.463401602,2.5591,0.768,1,300,2 +"110000246",1997-12-03,"110007772",1680,110,3,1997,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0516N5705","SNS",5707221.31146747,516274.53430458,1.1327,0.34,1,300,1 +"110000252",1998-01-26,"110007972",1730,110,3,1997,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0566N5695","SNS",5694192.96668232,566400.026477786,2.5188,0.756,1,300,4 +"110000257",1998-02-06,"110008099",1730,110,4,1998,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5688412.16612581,513304.65891343,1.8998,0.57,1,300,2 +"110000262",1998-02-16,"110008246",5410,110,4,1998,1,1,2,"11003",NA,"data update and replacement 06/11/2020","E0481N5685","SNS",5684729.41460204,482456.464011574,2.4027,0.721,1,300,1 +"110000266",1998-02-26,"110008353",1680,110,4,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0476N5685","SNS",5684428.96758525,475700.872333852,5.7177,1.715,1,300,7 +"110000266",1998-02-26,"110008362",1680,110,4,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0426N5695","SNS",5693484.88273516,425513.548983786,5.5657,1.67,1,300,45 +"110000271",1998-03-13,"110008516",1680,110,4,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0396N5665","SNS",5663277.15800194,394906.910744321,3.5522,1.066,1,300,55 +"110000273",1998-03-16,"110008553",1680,110,4,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0541N5780","SNS",5779387.94110111,542797.805374039,3.4153,1.025,1,300,7 +"110000273",1998-03-16,"110008574",1680,110,4,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0496N5740","SNS",5742424.75272687,495939.353502589,3.5382,1.061,1,300,27 +"110000292",1998-09-08,"110009223",6270,110,1,1998,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0501N5695","SNS",5693093.50389474,500156.557485223,0.8129,0.244,1,300,1 +"110000295",1998-09-18,"110009340",1680,110,1,1998,1,1,1,"11004",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5676788.0477376,493147.618830181,2.4092,0.723,1,300,1 +"110000298",1998-10-13,"110009433",5410,110,2,1998,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5690515.88515448,510237.92343392,3.3835,1.015,1,300,1 +"110000305",1998-11-04,"110009711",1680,110,2,1998,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0421N5685","SNS",5684572.98336194,422675.811566122,4.6776,1.403,1,300,13 +"110000306",1998-11-17,"110009762",1680,110,2,1998,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0416N5690","SNS",5689354.88694361,414040.11372957,5.6144,1.684,1,300,12 +"110000318",1999-02-09,"110010159",1730,110,4,1999,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0466N5690","SNS",5689207.01686268,463961.964476459,4.454,1.336,1,300,2 +"110000323",1999-03-08,"110010362",1680,110,4,1999,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5691186.41674286,514362.293913488,2.5347,0.76,2,300,1 +"110000323",1999-03-08,"110010368",1730,110,4,1999,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0516N5695","SNS",5695051.57292457,514937.767353365,3.9484,1.185,1,300,2 +"110000326",1999-03-11,"110010519",5410,110,4,1999,1,1,2,"11004",NA,"data update and replacement 06/11/2020","E0481N5710","SNS",5707878.54073444,483397.113711053,3.6264,1.088,1,300,2 +"110000326",1999-03-11,"110010567",5410,110,4,1999,1,1,2,"11004",NA,"data update and replacement 06/11/2020","E0481N5675","SNS",5675356.34691005,482870.094687006,2.2703,0.681,1,300,1 +"110000337",1999-05-20,"110010917",6270,110,5,1999,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677027.42877389,493799.453198086,1.5517,0.466,1,300,3 +"110000339",1999-08-19,"110010947",6270,110,1,1999,1,1,2,"11004",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5695362.21457244,504762.657641219,1.6068,0.482,1,300,1 +"110000339",1999-08-19,"110010965",6270,110,1,1999,1,1,2,"11004",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683416.61848075,489025.966750454,3.3588,1.008,1,300,2 +"110000346",1999-08-26,"110011375",6270,110,1,1999,1,1,2,"11004",NA,"data update and replacement 06/11/2020","E0401N5735","SNS",5734401.13395893,398971.699287092,2.9724,0.892,2,300,16 +"110000348",1999-10-25,"110011467",1730,110,2,1999,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691626.36115006,512499.209403923,2.8339,0.85,1,300,5 +"110000362",1999-12-16,"110011906",1730,110,3,1999,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0386N5665","SNS",5664684.57327226,386260.226661619,4.3742,1.312,1,300,1 +"110000372",2000-03-28,"110012282",1730,110,4,2000,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670365.81719646,480537.574702234,2.0169,0.605,1,300,1 +"110000387",2000-10-17,"110012998",1680,110,2,2000,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0476N5675","SNS",5674343.74526383,474678.195543344,3.7045,1.111,1,300,4 +"110000388",2000-10-18,"110013053",1680,110,2,2000,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0471N5700","SNS",5700906.47034395,473443.888788873,1.0165,0.305,1,300,15 +"110000394",2000-11-28,"110013256",1730,110,2,2000,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5691440.47748988,486264.85992883,3.4956,1.049,1,300,3 +"110000399",2001-01-09,"110013514",1730,110,3,2000,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0421N5690","SNS",5689104.53110853,423637.172943441,6.18677,1.856,1,300,1 +"110000406",2001-02-08,"110013849",5410,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0486N5700","SNS",5700325.40409174,484283.355687929,2.3503,0.705,1,300,2 +"110000406",2001-02-08,"110013882",1730,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5676091.90638139,491632.167088749,2.91913,0.876,1,300,2 +"110000410",2001-02-14,"110014082",5410,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5692522.40922405,514085.187066936,2.11651,0.635,1,300,1 +"110000410",2001-02-14,"110014090",5410,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5695701.80124045,506165.006267104,3.73184,1.12,1,300,3 +"110000418",2001-02-27,"110014476",1680,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5687241.90105878,486396.720338263,3.75954,1.128,1,300,2 +"110000418",2001-02-27,"110014491",1680,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5705","SNS",5702894.05217626,492119.660594317,4.2014,1.26,1,300,30 +"110000418",2001-02-27,"110014515",5410,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0521N5695","SNS",5695445.93480163,522153.562870648,3.50566,1.052,1,300,1 +"110000422",2001-03-27,"110014696",5410,110,4,2001,1,1,1,"11005",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5693015.78214483,504899.769264145,3.07491,0.922,1,300,1 +"110000423",2001-03-28,"110014772",1680,110,4,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0461N5735","SNS",5733809.40057906,462405.70009983,2.91454,0.874,1,300,21 +"110000425",2001-04-26,"110014844",6270,110,5,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5685406.13063382,486277.619185595,3.94028,1.182,1,300,2 +"110000427",2001-05-23,"110014959",6270,110,5,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5676212.47109455,493635.528517325,1.11514,0.335,1,300,4 +"110000431",2001-06-27,"110015131",6270,110,6,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0521N5695","SNS",5693987.04971565,522460.934047834,2.73076,0.819,1,300,2 +"110000432",2001-06-29,"110015172",1730,110,6,2001,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5688468.12417721,508850.601402119,3.92656,1.178,1,300,50 +"110000432",2001-06-29,"110015190",6270,110,6,2001,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5692209.19917737,512074.443694295,3.1963,0.959,1,300,1 +"110000436",2001-08-21,"110015415",4960,110,1,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0461N5695","SNS",5696880.20446267,460029.150187018,3.38632,1.016,1,300,15 +"110000437",2001-08-22,"110015429",6270,110,1,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0456N5775","SNS",5774923.36313215,458082.510824858,3.13177,0.94,1,300,2 +"110000437",2001-08-22,"110015450",6270,110,1,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0466N5830","SNS",5831231.44736254,465910.588840195,3.58767,1.076,1,300,2 +"110000439",2001-08-24,"110015589",6270,110,1,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0311N5935","SNS",5937171.28048121,311615.290978422,2.60148,0.78,1,300,1 +"110000444",2001-08-30,"110015833",6270,110,1,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0501N5700","SNS",5698320.52928651,499099.292066031,3.43596,1.031,1,300,2 +"110000445",2001-08-30,"110015841",6270,110,1,2001,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0471N5665","SNS",5667347.38869471,472127.954616728,4.13263,1.24,2,300,2 +"110000450",2001-10-29,"110016062",1680,110,2,2001,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670371.61325674,481069.962765788,3.59468,1.078,2,300,8 +"110000451",2001-10-30,"110016090",1730,110,2,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677623.34911019,493055.14376741,3.88442,1.165,1,300,9 +"110000459",2002-01-30,"110016438",1680,110,3,2001,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0521N5695","SNS",5697577.17421212,520101.827907415,4.26336,1.279,1,300,40 +"110000460",2002-02-04,"110016455",5410,110,4,2002,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691390.59903173,513379.175228657,2.58071,0.774,1,300,1 +"110000468",2002-03-25,"110016763",5410,110,4,2002,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0461N5690","SNS",5692344.20951324,463519.408374673,3.82289,1.147,1,300,1 +"110000469",2002-03-26,"110016805",1680,110,4,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681823.42797241,491750.375847615,3.76651,1.13,2,300,37 +"110000469",2002-03-26,"110016805",5410,110,4,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681823.42797241,491750.375847615,3.76651,1.13,1,300,6 +"110000469",2002-03-26,"110016808",1680,110,4,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0501N5685","SNS",5687410.76403174,501155.775156862,4.31199,1.294,1,300,10 +"110000469",2002-03-26,"110016818",4960,110,4,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0511N5705","SNS",5702665.28521732,509970.81475741,3.93683,1.181,1,300,1 +"110000469",2002-03-26,"110016848",1680,110,4,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5682530.10357355,489642.321715421,4.00504,1.202,1,300,9 +"110000470",2002-03-28,"110016868",1680,110,4,2002,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0471N5680","SNS",5679364.85515494,469763.753113368,2.6381,0.791,2,300,1 +"110000474",2002-05-28,"110017044",6270,110,5,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0501N5685","SNS",5686986.93994829,500422.514309094,2.84971,0.855,2,300,1 +"110000478",2002-08-02,"110017216",1730,110,1,2002,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679922.50490399,491974.036289754,3.01691,0.905,1,300,7 +"110000483",2002-09-25,"110017381",1680,110,1,2002,1,1,1,"11011",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5673450.30221983,487926.069328966,4.73821,1.421,1,300,2 +"110000483",2002-09-25,"110017403",1680,110,1,2002,1,1,1,"11011",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5699552.01866009,480843.58288704,1.14059,0.342,1,300,16 +"110000488",2002-11-27,"110017593",1730,110,2,2002,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5710542.00641969,496157.848659465,4.05736,1.217,1,300,1 +"110000489",2002-12-16,"110017643",1730,110,3,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677778.4703109,493490.35450671,2.78645,0.836,1,300,1 +"110000490",2002-12-17,"110017667",1680,110,3,2002,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5710654.58449541,502455.862587449,3.02659,0.908,1,300,11 +"110000494",2003-02-12,"110017831",1730,110,4,2003,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680881.30570146,491189.690108033,4.12642,1.238,1,300,7 +"110000496",2003-02-21,"110017878",1730,110,4,2003,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677699.55372746,493457.661916941,3.66389,1.099,1,300,2 +"110000497",2003-02-25,"110017930",5410,110,4,2003,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0456N5685","SNS",5686043.38462374,456604.004598521,3.60048,1.08,1,300,1 +"110000498",2003-02-26,"110018011",1680,110,4,2003,1,1,2,"11005",NA,"data update and replacement 06/11/2020","E0466N5725","SNS",5725713.36137634,465382.123521431,3.16704,0.95,1,300,18 +"110000506",2003-04-30,"110018327",1730,110,5,2003,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677939.82696242,493408.977523425,3.73688,1.121,1,300,2 +"110000507",2003-05-27,"110018370",1730,110,5,2003,1,1,1,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680338.00031879,492251.405820846,4.10018,1.23,1,300,2 +"110000509",2003-06-23,"110018441",5410,110,6,2003,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0476N5690","SNS",5688553.91346846,474098.292104446,3.9347,1.18,1,300,1 +"110000509",2003-06-23,"110018443",5410,110,6,2003,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0466N5690","SNS",5690344.79047462,468676.89639307,2.43912,0.732,1,300,2 +"110000509",2003-06-23,"110018453",5410,110,6,2003,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0466N5715","SNS",5716604.39226911,465386.61079045,4.61571,1.385,1,300,1 +"110000510",2003-06-24,"110018501",1730,110,6,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677111.09255063,493590.157370923,1.13034,0.339,2,300,3 +"110000512",2003-07-24,"110018549",800,110,6,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0466N5720","SNS",5721852.677622,466402.745796544,4.5238,1.357,1,300,1 +"110000513",2003-07-25,"110018577",6270,110,6,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0501N5690","SNS",5688349.26902433,500458.478219353,4.32223,1.297,1,300,1 +"110000513",2003-07-25,"110018578",6270,110,6,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5689769.23945594,504583.718488939,4.38487,1.315,1,300,1 +"110000514",2003-07-28,"110018645",6270,110,6,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678437.37164016,493074.249873928,4.22255,1.267,1,300,5 +"110000516",2003-08-27,"110018694",6270,110,1,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678484.34214014,492882.417413116,3.6879,1.106,1,300,1 +"110000518",2003-09-23,"110018779",5410,110,1,2003,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5674060.80922588,488924.48296577,4.36388,1.309,2,300,1 +"110000518",2003-09-23,"110018781",5410,110,1,2003,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670413.76029804,481100.450799882,4.24489,1.273,1,300,2 +"110000520",2003-10-14,"110018849",1680,110,2,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0511N5695","SNS",5694117.80760535,508939.806219326,3.82001,1.146,1,300,1 +"110000522",2003-10-16,"110018932",1680,110,2,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5720","SNS",5721101.71617216,498659.902611604,2.62881,0.789,1,300,2 +"110000522",2003-10-16,"110018945",1680,110,2,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5718509.24277848,487843.113768097,2.86413,0.859,1,300,30 +"110000522",2003-10-16,"110018954",1680,110,2,2003,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5705","SNS",5705243.52138725,481871.613446222,3.49254,1.048,1,300,10 +"110000525",2003-10-21,"110019044",5410,110,2,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691333.67269675,512416.329714944,3.48728,1.046,1,300,6 +"110000525",2003-10-21,"110019045",1680,110,2,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0511N5695","SNS",5693298.78587163,510431.476959295,2.09333,0.628,1,300,6 +"110000525",2003-10-21,"110019052",1680,110,2,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5705","SNS",5704345.94951258,490489.24619032,3.29722,0.989,2,300,14 +"110000525",2003-10-21,"110019054",1680,110,2,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5705","SNS",5706946.8032559,486717.496914903,3.22193,0.967,1,300,5 +"110000525",2003-10-21,"110019054",5410,110,2,2003,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5705","SNS",5706946.8032559,486717.496914903,3.22193,0.967,1,300,2 +"110000529",2003-11-25,"110019200",1680,110,2,2003,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5691156.78649559,507166.37824312,2.55882,0.768,2,300,1 +"110000534",2004-02-24,"110019344",1680,110,4,2004,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677678.69094351,493251.751008507,3.20359,0.961,1,300,7 +"110000535",2004-02-25,"110019390",1730,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675670.03623792,492610.267102068,3.85673,1.157,1,300,2 +"110000535",2004-02-25,"110019390",5320,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675670.03623792,492610.267102068,3.85673,1.157,1,300,6 +"110000535",2004-02-25,"110019405",1680,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0461N5675","SNS",5675688.88136298,463036.206372198,3.79,1.137,1,300,14 +"110000536",2004-03-29,"110019418",1680,110,4,2004,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681070.988281,491476.599441692,4.23299,1.27,1,300,23 +"110000537",2004-03-30,"110019460",4960,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670421.8671792,481938.614181992,4.14086,1.242,1,300,12 +"110000537",2004-03-30,"110019461",5320,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5669252.87885928,477991.741926965,4.10528,1.232,1,300,4 +"110000537",2004-03-30,"110019469",4960,110,4,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5674035.35656382,470185.116166483,4.17814,1.253,1,300,5 +"110000538",2004-03-31,"110019503",1730,110,4,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681959.59803307,491448.999613494,4.30428,1.291,1,300,3 +"110000540",2004-04-27,"110019578",6270,110,5,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675670.95650026,492744.669313822,3.6754,1.103,1,300,2 +"110000540",2004-04-27,"110019581",6270,110,5,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670466.27368255,481646.418621173,4.21257,1.264,1,300,2 +"110000540",2004-04-27,"110019612",4960,110,5,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5700","SNS",5699313.36389606,487493.561614636,3.72976,1.119,2,300,50 +"110000541",2004-04-28,"110019626",6270,110,5,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678044.38675356,493390.688360077,4.22577,1.268,1,300,2 +"110000541",2004-04-28,"110019636",6270,110,5,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0516N5695","SNS",5695238.59861588,516785.861562091,3.70726,1.112,1,300,2 +"110000542",2004-05-24,"110019681",6270,110,5,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679221.25842521,491013.03691126,0.59155,0.177,1,300,1 +"110000544",2004-05-26,"110019729",6270,110,5,2004,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679260.949105,492621.859153823,4.27798,1.283,1,300,20 +"110000544",2004-05-26,"110019743",6270,110,5,2004,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0476N5695","SNS",5695012.03631631,477884.825024477,4.28793,1.286,1,300,3 +"110000544",2004-05-26,"110019747",6270,110,5,2004,1,1,1,"11002",NA,"data update and replacement 06/11/2020","E0466N5680","SNS",5681121.31376939,468404.762801036,4.48847,1.347,1,300,6 +"110000550",2004-07-26,"110019960",6270,110,6,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0501N5695","SNS",5697554.46977367,501662.090184314,3.7684,1.131,2,300,1 +"110000551",2004-08-23,"110019985",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5673075.16674204,487159.094877936,4.0022,1.201,1,300,1 +"110000551",2004-08-23,"110019991",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5672966.89225737,473805.728655606,4.24812,1.274,2,300,1 +"110000552",2004-08-24,"110020015",4960,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5686159.23900833,497083.516931318,4.42623,1.328,1,300,1 +"110000552",2004-08-24,"110020016",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0501N5690","SNS",5687877.86607513,501236.326799871,4.54067,1.362,1,300,3 +"110000552",2004-08-24,"110020017",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5689641.06257309,505428.07626302,4.53116,1.359,1,300,11 +"110000552",2004-08-24,"110020018",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5690937.47307449,509718.825311968,4.46052,1.338,1,300,1 +"110000552",2004-08-24,"110020019",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5691809.56714989,514094.32330403,4.43814,1.331,1,300,5 +"110000552",2004-08-24,"110020033",4960,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5712404.52007081,499600.870283046,3.16585,0.95,1,300,1 +"110000552",2004-08-24,"110020049",6270,110,1,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678768.4554429,492510.065953691,3.87951,1.164,1,300,2 +"110000553",2004-08-25,"110020092",6270,110,1,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0476N5690","SNS",5690721.89218355,476297.581032259,3.37113,1.011,1,300,1 +"110000553",2004-08-25,"110020095",6270,110,1,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5685900.43129594,484906.878692018,4.02804,1.208,1,300,3 +"110000553",2004-08-25,"110020099",6270,110,1,2004,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677159.37029869,493233.617944096,1.86699,0.56,1,300,1 +"110000561",2004-12-13,"110020444",1730,110,3,2004,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677235.49770606,493710.085039071,1.84589,0.554,1,300,17 +"110000562",2004-12-14,"110020466",1730,110,3,2004,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5674041.19641878,490378.670079618,4.38509,1.316,1,300,1 +"110000568",2005-02-24,"110020670",1680,110,4,2005,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5720","SNS",5719354.03998984,477314.451355318,3.87463,1.162,1,300,500 +"110000568",2005-02-24,"110020688",1730,110,4,2005,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678217.56080167,492796.561617743,3.8514,1.155,1,300,1 +"110000569",2005-02-28,"110020690",1680,110,4,2005,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5673663.30582432,488147.703542087,4.60617,1.382,1,300,1 +"110000570",2005-03-30,"110020747",5410,110,4,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691134.10046917,512663.994468229,3.35103,1.005,1,300,3 +"110000572",2005-04-01,"110020823",1730,110,5,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678092.57476526,493110.97979236,3.76077,1.128,1,300,15 +"110000573",2005-04-25,"110020835",1730,110,5,2005,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691442.04136508,513487.483841607,3.06319,0.919,1,300,1 +"110000573",2005-04-25,"110020845",5410,110,5,2005,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0506N5710","SNS",5708505.28707203,504701.047037218,2.50228,0.751,1,300,1 +"110000574",2005-04-26,"110020904",5410,110,5,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677365.87635285,493497.996751225,2.75499,0.826,1,300,2 +"110000585",2005-06-23,"110021250",5410,110,6,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0471N5725","SNS",5725207.59291205,471571.768663998,1.4179,0.425,1,300,1 +"110000586",2005-06-24,"110021282",1730,110,6,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5675749.66187967,472894.254223748,4.604,1.381,1,300,29 +"110000590",2005-08-22,"110021460",1730,110,1,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5672478.6897875,474022.751944208,3.74674,1.124,1,300,12 +"110000590",2005-08-22,"110021479",6270,110,1,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5685509.3881352,488611.230244971,3.39197,1.018,1,300,1 +"110000591",2005-08-23,"110021542",6270,110,1,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684711.88700711,491541.273883645,3.48541,1.046,1,300,3 +"110000596",2005-09-22,"110021722",5410,110,1,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5705","SNS",5703591.27408296,482715.65309768,3.60805,1.082,1,300,2 +"110000597",2005-09-23,"110021770",6270,110,1,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5678873.479344,486767.716231539,3.817,1.145,2,300,1 +"110000598",2005-10-18,"110021777",5410,110,2,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5691464.56306737,485995.766082566,3.9234,1.177,1,300,1 +"110000598",2005-10-18,"110021782",1680,110,2,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5700","SNS",5698747.34452033,493390.723596159,3.23654,0.971,2,300,18 +"110000598",2005-10-18,"110021796",1680,110,2,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5700880.85065017,479739.256176242,3.96562,1.19,2,300,6 +"110000598",2005-10-18,"110021801",1680,110,2,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5682497.98934195,487130.935880448,3.79353,1.138,1,300,50 +"110000598",2005-10-18,"110021802",1680,110,2,2005,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679532.89979402,489695.560100267,3.7951,1.139,2,300,12 +"110000599",2005-10-19,"110021847",1680,110,2,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677346.22661829,492420.184649994,2.46009,0.738,2,300,4 +"110000603",2005-10-26,"110021952",1680,110,2,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5690790.99105412,513311.063942777,2.14413,0.643,1,300,1 +"110000607",2005-11-29,"110022078",1730,110,2,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680522.1703303,491817.474198676,2.65219,0.796,1,300,1 +"110000608",2005-11-30,"110022126",1680,110,2,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5674437.42496841,489639.365008508,4.1852,1.256,1,300,1 +"110000608",2005-11-30,"110022148",800,110,2,2005,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0476N5695","SNS",5694523.69211332,476182.436910225,4.52331,1.357,1,300,1 +"110000622",2006-04-27,"110022694",1730,110,5,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677941.11333159,493274.777405906,3.69989,1.11,1,300,2 +"110000624",2006-05-29,"110022769",1730,110,5,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677657.12263811,493593.701336527,3.42856,1.029,1,300,1 +"110000625",2006-06-28,"110022815",1730,110,6,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677440.51841826,493394.516326519,3.63965,1.092,1,300,23 +"110000626",2006-06-29,"110022889",1730,110,6,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5687292.34018262,495693.814553968,2.89558,0.869,1,300,4 +"110000627",2006-06-30,"110022901",1730,110,6,2006,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5685871.50815506,496593.226594062,3.70826,1.112,1,300,6 +"110000629",2006-07-27,"110022954",6270,110,6,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5685611.53215252,494941.005050005,3.38689,1.016,2,300,1 +"110000630",2006-07-28,"110023019",6270,110,6,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5680046.35356532,485278.51725303,3.15065,0.945,2,300,1 +"110000631",2006-08-22,"110023027",6270,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0501N5690","SNS",5689960.21820842,502559.093703531,1.81852,0.546,1,300,1 +"110000632",2006-08-23,"110023045",6270,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5705","SNS",5703951.34762715,472849.51191866,3.11681,0.935,1,300,3 +"110000632",2006-08-23,"110023051",6270,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5720","SNS",5720097.3650282,481599.275461879,2.25799,0.677,1,300,5 +"110000632",2006-08-23,"110023085",5410,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5815","SNS",5816046.11267428,470776.148729907,3.29982,0.99,1,300,6 +"110000633",2006-08-24,"110023100",5410,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0451N5900","SNS",5900924.63037377,451150.271203181,3.1005,0.93,1,300,5 +"110000633",2006-08-24,"110023103",1730,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0446N5910","SNS",5908849.40363309,447495.965918628,3.27546,0.983,1,300,19 +"110000633",2006-08-24,"110023124",5410,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0411N5875","SNS",5873913.84234684,409124.143051806,3.07069,0.921,1,300,1 +"110000638",2006-08-30,"110023298",4960,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0411N5675","SNS",5676539.2199272,413526.660718323,3.38652,1.016,1,300,4 +"110000639",2006-08-31,"110023356",6270,110,1,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5690","SNS",5689899.02728236,469191.637964821,3.89076,1.167,2,300,8 +"110000640",2006-09-04,"110023388",6270,110,1,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5697006.15958537,480022.809837312,4.1081,1.232,1,300,2 +"110000640",2006-09-04,"110023389",6270,110,1,2006,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5698080.92214036,482962.264055926,2.36354,0.709,2,300,2 +"110000643",2006-12-20,"110023466",1730,110,3,2006,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678101.45187512,493125.576899161,4.08723,1.226,1,300,9 +"110000646",2007-01-24,"110023599",5410,110,3,2006,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0466N5680","SNS",5680741.64297364,467584.566423721,3.86221,1.159,1,300,1 +"110000648",2007-01-26,"110023671",1730,110,3,2006,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677918.66652605,493432.957396976,3.61143,1.083,1,300,1 +"110000650",2007-02-22,"110023791",1680,110,4,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679409.24361267,492358.141188533,3.58486,1.075,1,300,2 +"110000651",2007-02-23,"110023835",1730,110,4,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5685821.58531766,491649.998721997,3.811,1.143,1,300,3 +"110000652",2007-03-22,"110023840",4960,110,4,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5674015.57717157,488757.324708332,4.39562,1.319,1,300,17 +"110000653",2007-04-24,"110023883",1680,110,5,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5690","SNS",5688732.9216143,471595.52806178,3.97365,1.192,2,300,7 +"110000653",2007-04-24,"110023919",6270,110,5,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5685","SNS",5687539.04431166,479639.166322066,3.87855,1.164,1,300,2 +"110000656",2007-05-30,"110024033",6270,110,5,2007,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0511N5700","SNS",5698694.6590799,511891.099198938,1.85012,0.555,2,300,3 +"110000656",2007-05-30,"110024049",6270,110,5,2007,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0516N5695","SNS",5692741.9521278,514642.109126514,3.41041,1.023,1,300,2 +"110000658",2007-06-01,"110024109",800,110,6,2007,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681163.00223016,490996.077187017,3.77575,1.133,1,300,1 +"110000659",2007-06-18,"110024179",5410,110,6,2007,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5678144.18751504,487559.642084116,4.37922,1.314,1,300,1 +"110000661",2007-06-20,"110024270",5410,110,6,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5684510.20208145,486119.020620647,4.10236,1.231,1,300,1 +"110000665",2007-08-29,"110024445",4960,110,1,2007,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0466N5705","SNS",5707104.20795758,468442.692372238,3.00145,0.9,1,300,14 +"110000680",2008-02-13,"110025020",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0471N5720","SNS",5718919.30199023,469466.361212697,4.26215,1.279,1,300,4 +"110000681",2008-02-25,"110025049",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0466N5690","SNS",5691666.13333662,468657.786899836,2.55821,0.767,1,300,2 +"110000681",2008-02-25,"110025058",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0461N5710","SNS",5710456.63395951,461617.105566838,3.40087,1.02,2,300,4 +"110000683",2008-03-26,"110025161",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5725","SNS",5725401.01069676,474070.62085449,3.94051,1.182,1,300,2 +"110000683",2008-03-26,"110025174",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5700","SNS",5702007.67859026,474943.411854502,3.64001,1.092,1,300,13 +"110000684",2008-03-27,"110025199",1680,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5710","SNS",5708133.68935383,493424.904390323,4.12353,1.237,1,300,3 +"110000684",2008-03-27,"110025220",5410,110,4,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5692334.46330446,514085.914607995,3.79958,1.14,1,300,6 +"110000687",2008-04-23,"110025325",800,110,5,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0476N5700","SNS",5700115.17508156,475322.596528681,2.82294,0.847,1,300,2 +"110000690",2008-05-22,"110025453",6270,110,5,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5672079.2756473,479744.137375554,4.07692,1.223,1,300,1 +"110000691",2008-05-23,"110025486",6270,110,5,2008,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5710","SNS",5711482.86820042,487751.641819345,3.75538,1.127,1,300,5 +"110000699",2008-07-23,"110025866",1730,110,6,2008,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5691133.21891375,485572.542024273,3.84893,1.155,2,300,11 +"110000700",2008-07-24,"110025878",1730,110,6,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5693052.41578541,480926.991751485,3.83808,1.151,1,300,5 +"110000702",2008-08-20,"110025941",6270,110,1,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683607.78333777,492105.289590026,3.34089,1.002,2,300,1 +"110000703",2008-08-21,"110025968",6270,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0506N5710","SNS",5708425.88536208,504199.284450327,3.47134,1.041,2,300,1 +"110000703",2008-08-21,"110026008",6270,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679282.08104844,489997.367689156,3.80815,1.142,2,300,2 +"110000704",2008-08-22,"110026010",1730,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678372.78923599,493134.663479923,3.76581,1.13,1,300,13 +"110000704",2008-08-22,"110026058",6270,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681915.80782989,491043.285498068,3.87865,1.164,1,300,3 +"110000704",2008-08-22,"110026059",6270,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678521.11149736,492831.878083969,3.79125,1.137,1,300,1 +"110000711",2008-09-16,"110026288",1680,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677405.85786337,493540.413006219,2.49967,0.75,1,300,2 +"110000711",2008-09-16,"110026288",6270,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677405.85786337,493540.413006219,2.49967,0.75,1,300,1 +"110000711",2008-09-16,"110026301",1680,110,1,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5684931.87978632,495104.946322897,3.79375,1.138,1,300,1 +"110000712",2008-09-17,"110026315",6270,110,1,2008,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677971.10456539,493301.615073012,3.58481,1.075,1,300,1 +"110000715",2008-10-27,"110026445",6270,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675836.8790437,492589.353045882,3.32296,0.997,1,300,1 +"110000715",2008-10-27,"110026466",1730,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681519.16584528,491474.486000314,2.09028,0.627,1,300,7 +"110000716",2008-10-28,"110026471",1680,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683513.22869338,489092.609542863,3.56576,1.07,1,300,6 +"110000716",2008-10-28,"110026495",1680,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0476N5705","SNS",5706242.86199905,477039.376741897,3.11062,0.933,1,300,32 +"110000716",2008-10-28,"110026505",1680,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5720741.2702704,486145.781516764,3.34995,1.005,1,300,28 +"110000717",2008-10-29,"110026521",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684107.02807309,490824.345389016,3.45132,1.035,1,300,45 +"110000717",2008-10-29,"110026527",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5705","SNS",5703799.52033321,490101.121951466,3.44839,1.035,1,300,7 +"110000717",2008-10-29,"110026536",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5710","SNS",5708609.57768457,485073.198220813,3.62169,1.087,1,300,25 +"110000717",2008-10-29,"110026539",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5715","SNS",5717155.98142227,486027.372157198,3.69214,1.108,1,300,6 +"110000717",2008-10-29,"110026546",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5715","SNS",5714184.98264634,490540.787383119,3.67012,1.101,1,300,7 +"110000719",2008-10-31,"110026598",1680,110,2,2008,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5690800.01696326,485420.191870573,3.61575,1.085,1,300,1 +"110000721",2008-11-26,"110026661",1730,110,2,2008,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677907.66703075,493338.868860325,3.53836,1.062,1,300,1 +"110000723",2008-12-16,"110026751",1680,110,3,2008,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5695098.38340978,480143.45193251,3.88766,1.166,1,300,3 +"110000730",2009-02-20,"110027080",1730,110,4,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5676952.0210002,493624.859129519,2.40569,0.722,1,300,2 +"110000731",2009-02-25,"110027099",1680,110,4,2009,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5720","SNS",5720827.56727816,492761.511500941,3.052519,0.916,1,300,17 +"110000731",2009-02-25,"110027118",1680,110,4,2009,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0496N5700","SNS",5702175.28203089,498541.260326328,1.221437,0.366,1,300,210 +"110000732",2009-02-26,"110027139",1680,110,4,2009,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0501N5685","SNS",5682808.82121027,500723.916024062,3.794466,1.138,1,300,5 +"110000732",2009-02-26,"110027139",5410,110,4,2009,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0501N5685","SNS",5682808.82121027,500723.916024062,3.794466,1.138,1,300,1 +"110000734",2009-03-20,"110027230",5320,110,4,2009,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0476N5700","SNS",5698442.16976522,476331.699383268,3.817659,1.145,1,300,7 +"110000742",2009-05-29,"110027533",6270,110,5,2009,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5686937.59045632,486244.596635759,3.852232,1.156,1,300,1 +"110000743",2009-06-23,"110027578",1730,110,6,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677674.65029467,492948.781435199,3.450288,1.035,1,300,60 +"110000743",2009-06-23,"110027579",1730,110,6,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680465.01554771,491405.837589382,3.261704,0.979,2,300,16 +"110000744",2009-06-24,"110027628",5410,110,6,2009,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677705.36795811,493261.696430602,3.426945,1.028,1,300,1 +"110000745",2009-06-25,"110027692",1730,110,6,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0506N5690","SNS",5688872.07528976,506845.485810919,2.964152,0.889,1,300,4 +"110000745",2009-06-25,"110027700",1730,110,6,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0526N5695","SNS",5695909.70998695,524351.173685831,3.159298,0.948,1,300,7 +"110000745",2009-06-25,"110027720",1730,110,6,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684209.90029565,491805.31314743,2.770023,0.831,1,300,14 +"110000749",2009-07-23,"110027890",5410,110,6,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5700","SNS",5701942.43084255,494357.52387618,3.040083,0.912,1,300,1 +"110000749",2009-07-23,"110027892",6270,110,6,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0501N5705","SNS",5705111.11543689,499563.852455808,3.032955,0.91,1,300,1 +"110000750",2009-07-24,"110027975",6270,110,6,2009,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680527.85301473,491741.313460923,3.22511,0.968,1,300,1 +"110000752",2009-08-05,"110027999",6270,110,1,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681916.61541025,491893.479992984,0.639155,0.192,1,300,3 +"110000752",2009-08-05,"110028007",6270,110,1,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5692724.58628058,508439.068763092,3.036468,0.911,1,300,2 +"110000752",2009-08-05,"110028008",6270,110,1,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5694005.28853667,505705.190535373,3.022072,0.907,1,300,7 +"110000752",2009-08-05,"110028024",6270,110,1,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681080.99524261,491477.732015933,3.380202,1.014,1,300,5 +"110000753",2009-08-21,"110028072",6270,110,1,2009,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678171.49824583,493136.90614511,3.376078,1.013,1,300,1 +"110000754",2009-08-24,"110028083",1730,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691232.87023042,511642.127548917,2.992103,0.898,1,300,8 +"110000754",2009-08-24,"110028091",5410,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5669915.87843019,476686.289179896,1.832338,0.55,1,300,3 +"110000755",2009-08-25,"110028108",6270,110,1,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5719858.38968537,485682.17541864,3.292453,0.988,2,300,1 +"110000756",2009-08-26,"110028146",6270,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680854.95694819,491649.703550201,3.403197,1.021,1,300,2 +"110000756",2009-08-26,"110028147",6270,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683837.72614919,489744.440371208,3.677277,1.103,1,300,4 +"110000756",2009-08-26,"110028153",6270,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5700","SNS",5699147.32389988,489621.351408364,3.441304,1.032,2,300,2 +"110000756",2009-08-26,"110028196",6270,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5682852.68816451,487155.253881495,3.472516,1.042,1,300,3 +"110000756",2009-08-26,"110028197",6270,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5679985.45358042,489188.895558021,3.593885,1.078,1,300,2 +"110000760",2009-09-21,"110028313",1680,110,1,2009,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5725","SNS",5726679.97560159,482007.420096552,3.247923,0.974,2,300,9 +"110000762",2009-09-30,"110028431",1680,110,1,2009,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0476N5685","SNS",5683580.22254197,474821.944321475,3.390261,1.017,1,300,15 +"110000763",2009-10-19,"110028490",1730,110,2,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678000.43395091,492991.101623948,2.549693,0.765,1,300,1 +"110000767",2009-10-23,"110028626",5410,110,2,2009,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677771.84738903,493451.403272051,3.379783,1.014,1,300,1 +"110000770",2009-12-14,"110028772",5410,110,3,2009,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5680155.75654975,491463.984966208,3.422133,1.027,1,300,1 +"110000774",2010-03-16,"110028906",1680,110,4,2010,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5689093.65501076,485553.547993489,3.447924,1.034,1,300,3 +"110000774",2010-03-16,"110028911",1730,110,4,2010,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0476N5700","SNS",5702408.24795603,476294.847331653,1.377987,0.413,1,300,2 +"110000774",2010-03-16,"110028917",1680,110,4,2010,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5715","SNS",5713157.33135394,495354.618603714,3.979582,1.194,1,300,27 +"110000774",2010-03-16,"110028919",1680,110,4,2010,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0501N5715","SNS",5714721.18030886,500861.429696253,2.50535,0.752,1,300,95 +"110000774",2010-03-16,"110028934",1680,110,4,2010,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5700","SNS",5702186.51026324,494916.090956338,3.800209,1.14,1,300,40 +"110000776",2010-03-18,"110028948",1730,110,4,2010,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683840.7407087,488835.458988465,3.717577,1.115,1,300,3 +"110000776",2010-03-18,"110028953",1730,110,4,2010,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0476N5700","SNS",5697949.24938868,476145.189187203,3.731291,1.119,1,300,1 +"110000776",2010-03-18,"110028974",1680,110,4,2010,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5725233.06926088,485837.269481899,3.691707,1.108,1,300,35 +"110000776",2010-03-18,"110028977",1680,110,4,2010,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5727267.05296935,487598.211416768,3.431582,1.029,1,300,120 +"110000777",2010-03-19,"110028994",5410,110,4,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678186.92636167,493243.138616211,4.085029,1.226,1,300,2 +"110000777",2010-03-19,"110029017",1680,110,4,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0471N5690","SNS",5688530.0755614,472282.782378321,3.459654,1.038,1,300,140 +"110000780",2010-04-21,"110029105",5410,110,5,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681322.14028946,491586.233027933,4.232509,1.27,1,300,12 +"110000781",2010-04-22,"110029155",1730,110,5,2010,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677966.45053509,493460.933548557,3.919351,1.176,2,300,6 +"110000782",2010-04-28,"110029191",6270,110,5,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0496N5680","SNS",5679823.78015675,494272.542656362,4.566613,1.37,1,300,1 +"110000782",2010-04-28,"110029198",6270,110,5,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0471N5680","SNS",5678294.49581484,471005.418126163,1.988068,0.596,1,300,1 +"110000787",2010-06-23,"110029406",5410,110,6,2010,1,1,1,"11007",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5687862.51806267,487235.60149066,4.082654,1.225,1,300,2 +"110000789",2010-07-27,"110029502",5410,110,6,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677866.52004146,493338.675823114,3.986474,1.196,1,300,2 +"110000789",2010-07-27,"110029503",6270,110,6,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681282.70468608,491228.738845424,4.064557,1.219,2,300,6 +"110000789",2010-07-27,"110029508",6270,110,6,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5695850.10050727,483607.339448265,3.788206,1.136,2,300,1 +"110000789",2010-07-27,"110029542",6270,110,6,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0476N5680","SNS",5678932.12647496,476829.56816992,4.672955,1.402,2,300,1 +"110000789",2010-07-27,"110029543",6270,110,6,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5680","SNS",5682590.54576633,479818.177676652,4.762966,1.429,1,300,3 +"110000792",2010-09-22,"110029641",5410,110,1,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0506N5710","SNS",5708483.36927339,505039.806409041,2.704182,0.811,1,300,4 +"110000792",2010-09-22,"110029673",5410,110,1,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5682400.48925156,489948.859176193,4.014287,1.204,1,300,6 +"110000796",2010-11-22,"110029819",1680,110,2,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0556N5690","SNS",5689605.66196365,554741.896533901,3.648579,1.095,1,300,4 +"110000797",2010-11-26,"110029865",5410,110,2,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5707584.68231371,496352.727553623,3.628684,1.089,1,300,1 +"110000800",2010-12-22,"110030035",1680,110,3,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683003.33571209,491102.148323451,1.473978,0.442,1,300,1 +"110000801",2011-01-18,"110030065",1680,110,3,2010,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5715","SNS",5716552.28148297,491432.994324851,3.5992,1.08,1,300,9 +"110000804",2011-01-21,"110030195",1680,110,3,2010,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0461N5685","SNS",5686165.54373039,461486.43654582,3.894369,1.168,1,300,1 +"110000805",2011-02-22,"110030235",1730,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5705","SNS",5706759.81690838,481139.623225547,3.891781,1.168,2,300,2 +"110000805",2011-02-22,"110030239",5410,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5719275.56803307,485310.730101781,3.173869,0.952,2,300,2 +"110000806",2011-02-23,"110030297",1680,110,4,2011,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5700316.20478333,481710.350296901,2.619896,0.786,1,300,4 +"110000807",2011-02-25,"110030356",5410,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683879.34374354,491291.322373709,3.343994,1.003,1,300,6 +"110000808",2011-03-18,"110030374",1680,110,4,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5720","SNS",5719895.14348047,494949.035348216,2.270692,0.681,1,300,45 +"110000808",2011-03-18,"110030379",5410,110,4,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5715","SNS",5713879.91471425,491352.87020714,4.143881,1.243,1,300,1 +"110000810",2011-03-22,"110030439",5320,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678564.23714065,493009.875480156,4.346901,1.304,1,300,2 +"110000810",2011-03-22,"110030459",1730,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5699268.75520125,482288.076681465,0.7934345,0.238,1,300,7 +"110000810",2011-03-22,"110030470",5410,110,4,2011,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5712201.78053492,494463.046639716,4.109325,1.233,1,300,6 +"110000811",2011-04-19,"110030523",5410,110,5,2011,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5705","SNS",5704816.40291777,482848.873677978,3.600211,1.08,1,300,12 +"110000811",2011-04-19,"110030532",6270,110,5,2011,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5680","SNS",5681282.2692433,487662.596763932,3.933522,1.18,1,300,2 +"110000812",2011-04-20,"110030578",6270,110,5,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5693392.03815601,481414.68316615,3.922279,1.177,1,300,4 +"110000813",2011-04-21,"110030585",1730,110,5,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5673799.20456531,488538.587162063,4.494006,1.348,1,300,5 +"110000813",2011-04-21,"110030587",1730,110,5,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5669802.74001011,480633.371788243,4.305731,1.292,1,300,8 +"110000813",2011-04-21,"110030587",5410,110,5,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5669802.74001011,480633.371788243,4.305731,1.292,1,300,1 +"110000813",2011-04-21,"110030595",1730,110,5,2011,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677120.85206574,489120.102650852,3.580674,1.074,1,300,2 +"110000817",2011-06-17,"110030681",1730,110,6,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5700","SNS",5700876.07989898,482205.086321111,3.469342,1.041,1,300,3 +"110000822",2011-09-16,"110030880",4960,110,1,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5681144.93826945,491820.21260608,3.730337,1.119,1,300,3 +"110000826",2011-11-08,"110031094",1680,110,2,2011,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5710","SNS",5710467.33156584,482491.752105811,4.002923,1.201,1,300,7 +"110000828",2011-11-10,"110031181",5410,110,2,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5675948.91461191,492789.325006013,3.264711,0.979,1,300,1 +"110000833",2012-01-25,"110031432",1680,110,3,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5706966.49776568,498085.134818623,3.531404,1.059,1,300,47 +"110000834",2012-01-26,"110031480",800,110,3,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5721469.98675021,486055.888303288,3.445028,1.034,2,300,1 +"110000835",2012-01-27,"110031500",1680,110,3,2011,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5730","SNS",5729440.49563609,481984.861644275,2.984138,0.895,1,300,4 +"110000836",2012-02-07,"110031520",5410,110,4,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677770.7859976,493411.830971171,3.596509,1.079,1,300,1 +"110000836",2012-02-07,"110031533",5410,110,4,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5715","SNS",5715669.06390644,483775.686965557,3.191647,0.957,2,300,2 +"110000836",2012-02-07,"110031537",5410,110,4,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5726592.2540237,487936.780531439,1.430871,0.429,1,300,1 +"110000836",2012-02-07,"110031543",5410,110,4,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5715","SNS",5714021.07023873,481441.426165285,3.616169,1.085,1,300,1 +"110000836",2012-02-07,"110031565",5410,110,4,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5695","SNS",5697040.3183219,478727.41423562,3.553503,1.066,1,300,14 +"110000840",2012-02-29,"110031678",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5700","SNS",5698120.52950789,485237.501220406,2.664979,0.533,1,200,5 +"110000841",2012-03-01,"110031698",800,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5722662.94785176,485028.014097865,2.182431,0.655,1,300,1 +"110000842",2012-03-02,"110031705",1730,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0481N5720","SNS",5718096.89820259,480330.153130502,3.757454,1.127,1,300,4 +"110000842",2012-03-02,"110031707",1730,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0481N5725","SNS",5725089.47204933,482995.225487601,3.690266,1.107,2,300,6 +"110000842",2012-03-02,"110031708",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5730","SNS",5728404.4164248,484521.118846455,3.617595,1.085,1,300,25 +"110000843",2012-03-08,"110031736",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5674725.98542486,471959.425791226,3.358752,1.008,1,300,60 +"110000843",2012-03-08,"110031747",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0476N5685","SNS",5685112.72801222,474373.001101041,3.298812,0.99,1,300,50 +"110000845",2012-03-30,"110031801",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5704845.59236311,494939.089106167,3.609337,1.083,1,300,15 +"110000845",2012-03-30,"110031813",1680,110,4,2012,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0496N5715","SNS",5713748.25344572,493948.59524626,3.273341,0.982,1,300,4 +"110000847",2012-04-11,"110031880",1730,110,5,2012,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678117.98162116,493239.558479739,3.816348,1.145,1,300,1 +"110000852",2012-05-07,"110032036",4960,110,5,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5693098.47293218,507122.390608459,2.728093,0.818,1,300,2 +"110000856",2012-07-05,"110032251",5410,110,6,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0471N5685","SNS",5686383.99201753,469491.087456386,3.756751,1.127,1,300,5 +"110000856",2012-07-05,"110032269",5410,110,6,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0476N5705","SNS",5703846.34717173,478007.041806975,3.522553,1.057,1,300,1 +"110000861",2012-10-22,"110032446",1680,110,2,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691584.4897145,512200.300795965,3.671348,1.101,1,300,7 +"110000866",2012-11-14,"110032707",5410,110,2,2012,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670179.71128101,480937.714637635,3.630679,1.089,1,300,2 +"110000867",2012-11-21,"110032748",800,110,2,2012,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5710342.09982665,494626.059023608,2.950409,0.885,2,300,1 +"110000871",2013-01-24,"110032894",1730,110,3,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5673028.654698,488990.364603024,0.7089909,0.213,1,300,3 +"110000871",2013-01-24,"110032908",1680,110,3,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5668309.1323616,480952.313084448,0.6441496,0.193,1,300,4 +"110000871",2013-01-24,"110033007",5410,110,3,2012,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5680","SNS",5679451.29717569,495299.446756811,0.7143665,0.214,1,300,5 +"110000877",2013-03-27,"110034005",800,110,4,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5721520.25036591,485977.769172563,0.605881,0.182,1,300,2 +"110000879",2013-03-29,"110034406",5410,110,4,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5709817.76090422,498986.201046842,0.5192854,0.156,1,300,1 +"110000880",2013-03-29,"110034517",1680,110,4,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0466N5705","SNS",5704175.34629393,468169.608101818,0.7734724,0.232,1,300,290 +"110000880",2013-03-29,"110034538",1680,110,4,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0461N5705","SNS",5703443.98355099,460495.523323694,0.6160418,0.185,1,300,35 +"110000880",2013-03-29,"110034562",1680,110,4,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5715","SNS",5717175.42540564,469831.757490803,0.6560108,0.197,1,300,8 +"110000885",2013-05-06,"110035333",4960,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0506N5695","SNS",5694818.82289728,506200.226886122,0.5749579,0.172,2,300,20 +"110000885",2013-05-06,"110035349",6270,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0496N5700","SNS",5698343.59360851,496969.525215679,0.6380467,0.191,1,300,7 +"110000885",2013-05-06,"110035357",6270,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5700","SNS",5699340.94175252,492030.483212033,0.6272681,0.188,1,300,17 +"110000885",2013-05-06,"110035370",4960,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5705","SNS",5703067.72451881,476063.259321973,0.7111684,0.213,1,300,65 +"110000885",2013-05-06,"110035390",6270,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5715","SNS",5715615.76352907,484100.13620858,0.7617091,0.229,1,300,5 +"110000886",2013-05-07,"110035406",5410,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5710","SNS",5710074.41399654,487490.536876199,0.5988683,0.18,1,300,1 +"110000886",2013-05-07,"110035452",4960,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0486N5710","SNS",5712580.50762581,486050.098760046,0.7426096,0.223,1,300,10 +"110000886",2013-05-07,"110035630",6270,110,5,2013,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0476N5720","SNS",5721637.05158489,478657.690432926,0.6784383,0.204,1,300,6 +"110000898",2013-09-30,"110037226",1680,110,1,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677958.77505394,493375.922622076,0.7008381,0.21,1,300,2 +"110000898",2013-09-30,"110037277",1680,110,1,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5705968.74041496,496364.12946453,0.7501106,0.225,1,300,20 +"110000898",2013-09-30,"110037285",1680,110,1,2013,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5709155.05507396,501531.440046179,0.7596633,0.228,2,300,1 +"110000901",2013-12-10,"110037773",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708881.09609785,494304.874634003,0.6816463,0.204,1,300,1 +"110000902",2013-12-11,"110037948",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5725","SNS",5726800.75168945,483438.942171015,0.7334629,0.22,1,300,1 +"110000905",2013-12-12,"110038245",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5726224.86650324,486753.45277242,0.3131431,0.094,1,300,1 +"110000905",2013-12-12,"110038257",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5722141.80451502,485248.277359139,0.4156153,0.125,1,300,1 +"110000905",2013-12-12,"110038274",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5727292.47950408,488124.371391899,0.2899083,0.087,1,300,1 +"110000906",2014-01-30,"110038494",5410,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5690014.79104072,487100.755765229,0.6912653,0.207,1,300,15 +"110000906",2014-01-30,"110038542",800,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5720931.36036486,485777.735173652,0.7351621,0.221,1,300,1 +"110000906",2014-01-30,"110038573",5410,110,3,2013,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5725775.79658321,486659.011119658,0.7842431,0.235,1,300,22 +"110000907",2014-02-04,"110038819",800,110,4,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708541.35170673,496044.925077174,0.6834841,0.205,1,300,1 +"110000907",2014-02-04,"110038822",800,110,4,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5709778.47391411,494412.893832201,0.6852533,0.206,1,300,1 +"110000909",2014-03-20,"110039235",800,110,4,2014,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0501N5715","SNS",5713029.53273304,499842.946084348,0.6808418,0.204,1,300,1 +"110000912",2014-04-02,"110039847",800,110,5,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5700","SNS",5702003.00715071,486426.245923674,0.6627041,0.199,1,300,1 +"110000913",2014-04-03,"110039994",800,110,5,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5721215.81606057,485870.15290245,0.6925722,0.208,2,300,1 +"110000915",2014-04-25,"110040220",6270,110,5,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5671237.93127477,480769.73475851,0.6184092,0.186,1,300,2 +"110000915",2014-04-25,"110040231",6270,110,5,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5668070.80080166,474824.658801376,0.6048455,0.181,1,300,3 +"110000915",2014-04-25,"110040331",6270,110,5,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0471N5695","SNS",5693058.52092935,468842.317808856,0.6726993,0.202,1,300,1 +"110000917",2014-06-26,"110040603",1730,110,6,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5680","SNS",5678746.83578425,494682.463202688,0.6326632,0.19,1,300,3 +"110000917",2014-06-26,"110040622",1730,110,6,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0506N5685","SNS",5685195.60501685,505057.176327762,0.6400701,0.192,2,300,13 +"110000918",2014-07-24,"110040813",1730,110,6,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5676693.7795674,493818.944133905,0.6768655,0.203,1,300,11 +"110000918",2014-07-24,"110040872",1730,110,6,2014,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0466N5670","SNS",5671356.96428448,463843.936954508,0.698143,0.209,1,300,8 +"110000919",2014-08-26,"110041063",4960,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5683346.63630968,491979.26179124,0.6148787,0.184,1,300,31 +"110000919",2014-08-26,"110041065",4960,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684360.95315168,492665.030047631,0.6137619,0.184,1,300,85 +"110000919",2014-08-26,"110041130",800,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5711361.35086666,499379.758766863,0.655746,0.197,1,300,1 +"110000919",2014-08-26,"110041137",800,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5712094.95977306,497253.3370754,0.6943405,0.208,1,300,1 +"110000919",2014-08-26,"110041143",800,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708530.15522038,496142.520548919,0.6617296,0.199,1,300,0.0158730158730159 +"110000921",2014-09-09,"110041405",800,110,1,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5711980.779339,499851.16305778,0.3257786,0.098,1,300,1 +"110000925",2014-10-29,"110042004",4960,110,2,2014,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5707942.68822513,500983.855143969,0.6472371,0.194,1,300,4 +"110000925",2014-10-29,"110042037",800,110,2,2014,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5710802.08515195,498648.091106002,0.7280699,0.218,1,300,1 +"110000925",2014-10-29,"110042170",1680,110,2,2014,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5677790.63609613,493543.479954768,0.6995897,0.21,1,300,97 +"110000926",2014-10-30,"110042199",4960,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5695","SNS",5694786.87462746,485066.674090843,0.7358818,0.221,1,300,7 +"110000926",2014-10-30,"110042237",5410,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5720336.14565784,485458.942387168,0.6997009,0.21,1,300,6 +"110000926",2014-10-30,"110042301",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5710","SNS",5709496.4591,479237.501014382,0.679831,0.204,1,300,1 +"110000926",2014-10-30,"110042306",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0476N5710","SNS",5711948.88504729,478410.086497381,0.6276863,0.188,1,300,3 +"110000926",2014-10-30,"110042315",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5715","SNS",5717344.61609917,480162.134581231,0.6315953,0.189,1,300,2 +"110000927",2014-10-31,"110042410",1680,110,2,2014,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0486N5685","SNS",5683719.5857181,488308.819501515,0.7249267,0.217,1,300,24 +"110000929",2014-11-18,"110042704",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5710","SNS",5709801.699189,502706.362430515,0.703722,0.211,2,300,1 +"110000930",2014-11-19,"110042920",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5727173.32787557,487721.836723302,0.5533425,0.166,1,300,1 +"110000930",2014-11-19,"110042929",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5721926.00421359,485648.314857831,0.6235227,0.187,2,300,9 +"110000930",2014-11-19,"110042956",1680,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5720","SNS",5722158.60183786,484833.701939311,0.5955777,0.179,1,300,1 +"110000930",2014-11-19,"110042997",1730,110,2,2014,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5715","SNS",5714711.48788609,479210.410698676,0.7640251,0.229,1,300,2 +"110000937",2015-02-25,"110043848",800,110,4,2015,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5725","SNS",5727566.27651145,488038.973236804,0.5198502,0.156,2,300,1 +"110000941",2015-04-22,"110044816",6270,110,5,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5705","SNS",5703835.04411453,485494.036959953,0.6196968,0.186,1,300,1 +"110000941",2015-04-22,"110044879",6270,110,5,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5688298.04043888,485715.247733112,0.6092963,0.183,2,300,2 +"110000943",2015-07-01,"110045148",1730,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678710.36949606,492688.262017128,0.7136468,0.214,1,300,5 +"110000943",2015-07-01,"110045172",1730,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5690","SNS",5687754.73082067,502163.032085973,0.5837561,0.175,1,300,22 +"110000943",2015-07-01,"110045188",1730,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5691088.73328044,511832.558234335,0.6831323,0.205,1,300,6 +"110000943",2015-07-01,"110045194",1730,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5691916.04416171,515889.934913622,0.6744529,0.202,1,300,19 +"110000943",2015-07-01,"110045204",6270,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0511N5695","SNS",5697450.69695019,513008.583583651,0.6642435,0.199,1,300,1 +"110000943",2015-07-01,"110045208",1730,110,6,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0511N5700","SNS",5699389.95488581,512893.828169288,0.6587287,0.198,1,300,12 +"110000946",2015-09-24,"110045844",4960,110,1,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677490.39379714,490053.754255704,0.6398923,0.192,1,300,1 +"110000946",2015-09-24,"110045846",6270,110,1,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5677463.01599175,488771.018587967,0.6397009,0.192,1,300,3 +"110000947",2015-09-25,"110046047",6270,110,1,2015,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684639.39783329,489820.971075605,0.7047175,0.211,2,300,9 +"110000947",2015-09-25,"110046265",6270,110,1,2015,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678869.66956053,492500.515118208,0.7040908,0.211,1,300,1 +"110000950",2016-01-21,"110046761",800,110,3,2015,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708492.58638858,497537.402560212,0.5209258,0.156,1,300,0.256410256410256 +"110000952",2016-02-16,"110047223",800,110,4,2016,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708723.85864555,495898.631866323,0.6746781,0.202,1,300,2 +"110000955",2016-03-17,"110047718",1730,110,4,2016,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5715","SNS",5713723.84626581,498153.725702906,0.5016352,0.15,1,300,4 +"110000955",2016-03-17,"110047743",800,110,4,2016,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708700.52573409,497601.391417589,0.660665,0.198,1,300,1 +"110000955",2016-03-17,"110047763",800,110,4,2016,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5708176.33287464,495047.579175814,0.6533529,0.196,1,300,1 +"110000955",2016-03-17,"110047764",800,110,4,2016,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5707844.57556552,495409.41826635,0.3273572,0.098,1,300,0.0219378427787934 +"110000958",2016-06-24,"110048330",5410,110,6,2016,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0501N5700","SNS",5700917.26567617,499714.113043052,0.6856082,0.206,1,300,1 +"110000959",2016-07-28,"110048387",6270,110,6,2016,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5685695.16155958,497738.750634049,0.7755792,0.233,2,300,5 +"110000959",2016-07-28,"110048407",5410,110,6,2016,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0511N5690","SNS",5690846.41983585,511887.347774055,0.2666949,0.08,1,300,1 +"110000959",2016-07-28,"110048415",6270,110,6,2016,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0516N5695","SNS",5693667.25716082,516124.304519892,0.7140885,0.214,1,300,4 +"110000959",2016-07-28,"110048473",6270,110,6,2016,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0506N5710","SNS",5708042.35340083,506465.667301631,0.6784222,0.204,1,300,1 +"110000960",2016-08-18,"110048685",6270,110,1,2016,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0511N5700","SNS",5700226.54045358,509470.236781051,0.6600706,0.198,2,300,1 +"110000963",2016-12-14,"110049318",1680,110,3,2016,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5705320.72088342,495898.243052603,0.6233507,0.187,1,300,5 +"110000963",2016-12-14,"110049364",800,110,3,2016,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5709028.38470636,498129.680658463,0.6556197,0.197,1,300,1 +"110000965",2017-03-24,"110049878",1680,110,4,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5690","SNS",5689350.83243913,493586.736633643,0.5718397,0.172,1,300,12 +"110000966",2017-04-27,"110050112",6270,110,5,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5690","SNS",5690248.11317175,495941.785099429,0.6034632,0.181,2,300,2 +"110000967",2017-05-22,"110050138",1730,110,5,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5680","SNS",5678323.97859214,493044.718232829,0.7077745,0.212,1,300,5 +"110000967",2017-05-22,"110050355",6270,110,5,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5690","SNS",5689692.94288018,492281.578134727,0.6741598,0.202,1,300,2 +"110000969",2017-07-27,"110050680",6270,110,6,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5702865.9663023,496261.696403457,0.5724039,0.172,1,300,1 +"110000969",2017-07-27,"110050685",6270,110,6,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0496N5705","SNS",5704569.68735474,498577.422941453,0.5771485,0.173,1,300,2 +"110000969",2017-07-27,"110050728",6270,110,6,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0506N5700","SNS",5699104.48615167,504749.431017211,0.7423139,0.223,1,300,1 +"110000969",2017-07-27,"110050745",6270,110,6,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0511N5695","SNS",5696016.78945947,512859.113329714,0.6993086,0.21,1,300,2 +"110000971",2017-08-31,"110051229",4960,110,1,2017,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0481N5695","SNS",5693196.82266132,479814.791358299,0.5381843,0.161,1,300,1 +"110000979",2018-07-02,"110052079",1730,110,6,2018,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5685","SNS",5684814.99534358,496621.167035508,0.6261472,0.188,1,300,11 +"110000981",2018-08-22,"110052350",6270,110,1,2018,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0486N5690","SNS",5687661.39286181,485118.118962232,0.6531932,0.196,1,300,3 +"110000981",2018-08-22,"110052522",6270,110,1,2018,1,1,2,"11007",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5669858.48761689,478104.006347973,0.6456119,0.194,1,300,1 +"110000988",2018-11-27,"110053710",1680,110,2,2018,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5705","SNS",5707119.23199869,490109.668091886,0.7113359,0.213,2,300,1 +"110000988",2018-11-27,"110053766",800,110,2,2018,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5710","SNS",5710619.61084879,492423.150517897,0.2916547,0.087,1,300,1 +"110000989",2018-12-18,"110053893",800,110,3,2018,1,1,2,"11010",NA,"data update and replacement 06/11/2020","E0496N5710","SNS",5709481.1066708,497921.497864269,0.6800454,0.204,1,300,2 +"110000991",2019-02-01,"110054321",800,110,4,2019,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0476N5675","SNS",5676002.24502292,475060.104195898,0.5665832,0.17,1,300,1 +"110000992",2019-02-25,"110054477",1730,110,4,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0486N5675","SNS",5674083.61330123,488666.820747919,0.7446718,0.223,1,300,2 +"110000992",2019-02-25,"110054520",1730,110,4,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0471N5675","SNS",5673614.52825787,469504.502019973,0.7557026,0.227,1,300,12 +"110000992",2019-02-25,"110054671",5410,110,4,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5685","SNS",5684059.32482022,489600.386818225,0.7048473,0.211,1,300,1 +"110000993",2019-02-27,"110054729",1680,110,4,2019,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0456N5705","SNS",5706376.58012478,455291.129567736,0.5379773,0.161,1,300,54 +"110000993",2019-02-27,"110054747",1680,110,4,2019,1,1,2,"11012",NA,"data update and replacement 06/11/2020","E0451N5700","SNS",5697714.68061189,453807.053977148,0.3408667,0.102,1,300,3 +"110000995",2019-03-29,"110055268",5410,110,4,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0471N5670","SNS",5672540.72279539,473418.537149877,0.6457251,0.194,1,300,3 +"110000995",2019-03-29,"110055302",1730,110,4,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0461N5675","SNS",5674084.10834584,462268.085529848,0.635051,0.191,1,300,1 +"110001000",2019-08-20,"110056040",800,110,1,2019,1,1,1,"11010",NA,"data update and replacement 06/11/2020","E0461N5695","SNS",5693881.26942721,459251.496081009,0.5564966,0.167,1,300,0.174603174603175 +"110001000",2019-08-20,"110056043",800,110,1,2019,1,1,1,"11010",NA,"data update and replacement 06/11/2020","E0461N5695","SNS",5694463.65843542,460863.17067146,0.580886,0.174,1,300,0.0317460317460317 +"110001001",2019-08-30,"110056114",6270,110,1,2019,1,1,2,"11002",NA,"data update and replacement 06/11/2020","E0481N5670","SNS",5670494.78048591,481144.569854118,0.68308,0.205,2,300,2 +"110001003",2019-09-10,"110056682",6270,110,1,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0491N5675","SNS",5677624.88425157,492749.526511181,0.5980684,0.179,1,300,2 +"110001008",2019-12-19,"110057604",5410,110,3,2019,1,1,2,"11013",NA,"data update and replacement 06/11/2020","E0471N5705","SNS",5705928.67495673,471445.732048974,0.5940717,0.178,1,300,2 +"120000393",1993-05-18,"120000393",800,120,5,1993,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0526N6125","SNS",6123934.16097889,526522.412482629,3.612212,1.083664,1,300,3 +"120000393",1993-05-18,"120000435",800,120,5,1993,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0696N6110","SNS",6108105.44897033,698714.1505414,4.620327,1.386098,1,300,1 +"120000461",1993-06-30,"120000461",5410,120,6,1993,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0451N6235","CNS",6235089.01729733,453124.124491065,4.9,1.46,1,300,1 +"120000461",1993-06-30,"120000496",5410,120,6,1993,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0426N6385","CNS",6386163.64367037,425417.429446877,3.7,1.1,1,300,1 +"120000824",1993-07-07,"120000825",5410,120,6,1993,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0221N6475","CNS",6476887.96851633,222761.965222024,2.3,0.7,1,300,3 +"120000872",1993-07-09,"120000879",5410,120,6,1993,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0171N6510","CNS",6511126.07283853,172810.547097508,4.1,1.24,1,300,5 +"120001361",1993-08-18,"120001373",5410,120,1,1993,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0246N6240","CNS",6240996.86720037,247656.958452142,4.52,1.35,1,300,7 +"120002297",1993-09-24,"120002302",1680,120,1,1993,1,1,1,"2305",NA,"data update and replacement 06/11/2020","E0586N5820","SNS",5818928.93750706,588219.341586511,4.69,1.41,1,300,3 +"120002297",1993-09-24,"120002305",1680,120,1,1993,1,1,1,"2305",NA,"data update and replacement 06/11/2020","E0601N5815","SNS",5814733.8462705,601430.405466464,7.12,2.14,1,300,2 +"120003198",1995-01-17,"120003203",1680,120,3,1994,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5930","SNS",5931854.43899167,669319.414463987,3.2,0.96,2,300,1 +"120003228",1995-01-18,"120003232",1680,120,3,1994,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0721N5940","SNS",5940762.38365881,722812.140639069,3.42,1.02,1,300,9 +"120003436",1995-02-07,"120003441",1680,120,4,1995,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0566N5760","SNS",5760557.48892892,566717.079751065,2.88,0.86,2,300,45 +"120003446",1995-02-08,"120003447",5410,120,4,1995,1,1,2,"121",NA,"data update and replacement 06/11/2020","E0611N5855","SNS",5857468.01605248,613342.804850046,2.6,0.78,1,300,90 +"120003494",1995-05-17,"120003494",6270,120,5,1995,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5815","SNS",5813351.48549796,587413.670295699,4.47,1.34,2,300,2 +"120004800",1997-04-07,"120004823",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0631N5910","SNS",5909689.00395718,629742.72017349,1.787746,0.5363239,1,300,47 +"120004849",1997-04-08,"120004853",1730,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0681N5935","SNS",5933113.34464305,679551.308448942,2.802337,0.8407012,1,300,1 +"120004849",1997-04-08,"120004870",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0721N5940","SNS",5942198.71283437,722029.59440627,2.758131,0.8274393,1,300,18 +"120004849",1997-04-08,"120004896",1730,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0726N5950","SNS",5948774.54057552,726570.318084921,1.488119,0.4464358,1,300,1 +"120004917",1997-04-09,"120004928",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0706N5935","SNS",5934467.58736327,705028.615356276,1.522826,0.4568477,1,300,2 +"120004917",1997-04-09,"120004968",4960,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0676N5940","SNS",5939115.76328188,675420.794908722,2.547875,0.7643625,1,300,1 +"120004978",1997-04-10,"120005024",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5910","SNS",5910729.11211325,639244.034400598,1.90421,0.5712629,2,300,2 +"120004978",1997-04-10,"120005025",5410,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5910","SNS",5909167.5077303,639973.106532932,2.140137,0.6420412,1,300,1 +"120005026",1997-04-11,"120005028",5410,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5900","SNS",5898333.87031652,643799.793275328,3.706,0.7412,1,200,1 +"120005026",1997-04-11,"120005029",5410,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5895381.05674257,644374.152625098,2.7795,0.5559,1,200,1 +"120005026",1997-04-11,"120005031",5410,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5890","SNS",5892050.51316043,645522.399837708,2.9648,0.59296,2,200,2 +"120005026",1997-04-11,"120005032",1730,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5890","SNS",5890182.85711319,644837.311217536,2.378017,0.4756033,1,200,8 +"120005049",1997-05-12,"120005110",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0716N5940","SNS",5941457.88027344,716604.084752439,1.987668,0.3975336,1,200,15 +"120005117",1997-05-13,"120005121",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5945","SNS",5943253.95570082,737173.603040232,1.950526,0.5851579,1,300,2 +"120005117",1997-05-13,"120005123",1730,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5940843.05372477,735912.537865462,1.950526,0.5851579,1,300,1 +"120005117",1997-05-13,"120005150",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0711N5935","SNS",5934882.00895918,709918.517193841,2.416957,0.725087,1,300,22 +"120005117",1997-05-13,"120005165",5410,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0701N5935","SNS",5933265.9617266,698921.132456454,2.365532,0.7096596,1,300,2 +"120005276",1997-04-30,"120005282",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0726N5940","SNS",5942242.95997141,724209.438340444,2.569036,0.7707107,1,300,3 +"120005276",1997-04-30,"120005303",1680,120,5,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5924425.43816833,670217.291995324,1.011593,0.3034779,1,300,4 +"120005385",1997-06-25,"120005410",800,120,6,1997,1,1,3,"92",NA,"data update and replacement 06/11/2020","E0641N5920","SNS",5919218.41387555,640633.578650046,1.998929,0.5996786,1,300,1 +"120005446",1997-07-22,"120005470",1680,120,6,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5942510.77349682,735001.965575626,2.47371,0.7421129,1,300,1 +"120005446",1997-07-22,"120005470",1730,120,6,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5942510.77349682,735001.965575626,2.47371,0.7421129,1,300,1 +"120005446",1997-07-22,"120005512",6270,120,6,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0706N5945","SNS",5944140.33289697,708444.386374599,2.437302,0.7311906,2,300,1 +"120005519",1997-07-23,"120005552",6270,120,6,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0681N5930","SNS",5932419.99116242,682355.095300396,2.306796,0.6920388,2,300,1 +"120005639",1997-09-09,"120005640",4960,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0651N5915","SNS",5914122.97516484,652812.759859067,2.456051,0.7368153,1,300,1 +"120005639",1997-09-09,"120005647",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0666N5920","SNS",5918036.15513307,665353.779140205,1.235333,0.3706,1,300,12 +"120005639",1997-09-09,"120005657",4960,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0676N5920","SNS",5921999.88319455,675585.618562347,1.360805,0.4082415,2,300,4 +"120005639",1997-09-09,"120005658",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0676N5920","SNS",5922265.87158655,677671.897521472,3.341526,1.002458,2,300,1 +"120005639",1997-09-09,"120005665",1730,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0691N5925","SNS",5922848.0807807,691141.232233185,2.161833,0.64855,1,300,49 +"120005639",1997-09-09,"120005669",1730,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0696N5925","SNS",5923945.4207673,695726.252746822,2.2236,0.66708,1,300,2 +"120005672",1997-09-11,"120005673",1730,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0701N5925","SNS",5926695.51073467,699347.491482871,1.976533,0.59296,1,300,7 +"120005744",1997-09-12,"120005744",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5910","SNS",5910184.21392221,642681.681512785,1.92712,0.578136,1,300,1 +"120005744",1997-09-12,"120005747",1730,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5905","SNS",5907517.03126023,644679.183218335,1.173567,0.35207,1,300,5 +"120005744",1997-09-12,"120005749",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5905","SNS",5904987.77244939,644792.144260928,1.297679,0.3893038,2,300,6 +"120005744",1997-09-12,"120005752",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5900","SNS",5900193.75401501,644083.374585406,1.654846,0.4964539,1,300,3 +"120005744",1997-09-12,"120005756",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5893999.10949193,645389.283254445,1.983705,0.5951114,1,300,3 +"120005744",1997-09-12,"120005765",5410,120,1,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5885","SNS",5883714.46106113,641952.409494968,1.665753,0.4997258,2,300,2 +"120005820",1997-10-21,"120005821",1680,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923026.26973134,670018.17963757,1.853,0.5559,1,300,87 +"120005820",1997-10-21,"120005821",1730,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923026.26973134,670018.17963757,1.853,0.5559,1,300,4 +"120005820",1997-10-21,"120005824",1680,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5920","SNS",5922353.70033901,670391.548240031,1.853,0.5559,1,300,8 +"120005820",1997-10-21,"120005826",1730,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923615.19027878,670385.011943518,1.57505,0.472515,1,300,2 +"120006021",1997-11-17,"120006024",1730,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871646.57424788,615607.555561423,1.01915,0.305745,1,300,3 +"120006021",1997-11-17,"120006025",1680,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872463.6537129,614778.497693128,1.605933,0.48178,1,300,5 +"120006021",1997-11-17,"120006045",5410,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0626N5905","SNS",5904245.31115241,624804.255728458,1.768678,0.5306032,2,300,1 +"120006103",1997-11-20,"120006114",5410,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5895535.48946909,643884.058915196,2.254483,0.676345,1,300,45 +"120006103",1997-11-20,"120006115",5410,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5893869.57715551,645266.402299017,2.161833,0.64855,1,300,450 +"120006103",1997-11-20,"120006119",1680,120,2,1997,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0646N5890","SNS",5890524.82237798,644475.562309827,1.050033,0.31501,1,300,25 +"120006206",1994-06-22,"120006228",5320,120,6,1994,1,1,2,"2253",NA,"data update and replacement 06/11/2020","E0226N6225","CNS",6223818.26359383,226644.578914434,3.104949,0.9314848,1,300,4 +"120006323",1994-06-24,"120006366",5410,120,6,1994,1,1,2,"2253",NA,"data update and replacement 06/11/2020","E0216N6335","CNS",6336062.29350088,217891.790382159,3.927548,1.178264,1,300,1 +"120006591",2002-03-04,"120006591",800,120,4,2002,1,1,3,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873390.54458661,619795.904575637,1.883883,0.565165,2,300,2 +"120006591",2002-03-04,"120006591",1680,120,4,2002,1,1,3,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873390.54458661,619795.904575637,1.883883,0.565165,1,300,12 +"120006592",2002-03-04,"120006631",1680,120,4,2002,1,1,3,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871587.26792956,616241.51775269,1.358029,0.4074087,1,300,2 +"120006592",2002-03-04,"120006634",5410,120,4,2002,1,1,3,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873532.84499994,620012.938472838,1.493832,0.4481495,1,300,6 +"120006666",2002-09-24,"120006667",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5817805.82093228,601098.182374281,1.585292,0.4755877,1,300,40 +"120006666",2002-09-24,"120006677",1730,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5815","SNS",5817435.59488005,584931.436473211,1.621243,0.4863728,1,300,3 +"120006666",2002-09-24,"120006699",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5822521.45339694,593370.033519347,1.507881,0.4523644,1,300,5 +"120006666",2002-09-24,"120006764",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5837606.96966262,606745.160876498,1.584081,0.4752243,1,300,8 +"120006808",2002-09-24,"120006817",1730,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5815","SNS",5817435.59488005,584931.436473211,1.621243,0.4863728,1,300,3 +"120006808",2002-09-24,"120006826",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5820","SNS",5821210.00883751,574289.474001022,1.98519,0.595557,1,300,15 +"120006926",2002-09-25,"120006926",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5835322.93638864,607879.578274143,1.446566,0.4339698,1,300,3 +"120006926",2002-09-25,"120006977",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5825381.95090521,607297.649929239,1.448595,0.4345786,1,300,2 +"120007072",2002-09-25,"120007079",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0596N5835","SNS",5835028.64854386,597726.533720264,1.446566,0.4339698,1,300,31 +"120007072",2002-09-25,"120007216",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5822714.4043073,604665.230948973,1.572311,0.4716933,1,300,4 +"120007218",2002-09-26,"120007358",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5827622.26027097,602492.548499702,1.620888,0.4862665,1,300,14 +"120007362",2002-09-26,"120007484",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5827592.87738356,600866.902213083,1.620888,0.4862665,1,300,8 +"120007489",2002-09-27,"120007495",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5825","SNS",5825031.82160924,598153.394686945,1.41423,0.4242691,1,300,1 +"120007489",2002-09-27,"120007509",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5824629.06379149,578297.551622609,1.414248,0.4242745,1,300,39 +"120007489",2002-09-27,"120007520",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0581N5820","SNS",5819807.25936645,581570.935524744,1.592547,0.4777641,1,300,40 +"120007489",2002-09-27,"120007522",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5820","SNS",5819857.05450344,584765.415246228,1.592547,0.4777641,1,300,40 +"120007489",2002-09-27,"120007531",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5820111.36629669,599140.09291598,1.592572,0.4777716,1,300,20 +"120007489",2002-09-27,"120007539",1680,120,1,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5815","SNS",5814927.52113345,603774.391531283,1.686097,0.5058291,1,300,1 +"120007551",2002-09-27,"120007572",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5824603.22219592,576879.219581031,1.414248,0.4242745,1,300,30 +"120007551",2002-09-27,"120007586",1680,120,1,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5820111.36629669,599140.09291598,1.592572,0.4777716,1,300,20 +"120008094",2002-10-24,"120008113",1680,120,2,2002,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0596N5825","SNS",5827563.56008857,594042.243805232,1.584343,0.4753028,1,300,8 +"120008258",2003-02-24,"120008262",5410,120,4,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5885","SNS",5884751.97366855,615264.794338781,3.049282,0.9147845,1,300,1 +"120008258",2003-02-24,"120008265",5410,120,4,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5895","SNS",5893419.46505223,618132.200549611,3.060196,0.9180588,1,300,1 +"120008299",2003-02-26,"120008312",1680,120,4,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872335.46251768,614301.976415833,0.9298683,0.2789605,1,300,6 +"120008441",2003-04-08,"120008446",1730,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830257.88736087,607692.675610909,1.481332,0.4443997,1,300,1 +"120008441",2003-04-08,"120008494",1730,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5822915.97141385,607242.066322913,1.584591,0.4753773,1,300,2 +"120008724",2003-04-09,"120008726",1680,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5832175.15436477,606422.992282846,1.571797,0.4715391,1,300,5 +"120008724",2003-04-09,"120008728",4960,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5830","SNS",5832175.25434742,603269.407264099,1.571797,0.4715391,1,300,20 +"120009308",2003-04-11,"120009326",1680,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0581N5825","SNS",5827206.86336255,580217.241224495,1.591063,0.477319,1,300,4 +"120009308",2003-04-11,"120009367",1680,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869596.42951098,616110.345049336,1.673363,0.5020088,1,300,93 +"120009308",2003-04-11,"120009367",6270,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869596.42951098,616110.345049336,1.673363,0.5020088,2,300,1 +"120009370",2003-04-11,"120009408",1680,120,5,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5845","SNS",5846808.47703642,592765.762956693,1.585845,0.4757534,1,300,8 +"120009692",2003-05-21,"120009698",1730,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5870306.74402358,616398.398850249,1.169481,0.3508442,1,300,1 +"120009922",2003-05-22,"120009965",1680,120,5,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5817722.18611434,602429.170325067,1.580998,0.4742994,2,300,11 +"120010107",2003-05-22,"120010155",1730,120,5,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5819998.14778335,606519.768725096,1.466958,0.4400875,1,300,1 +"120010543",2003-06-23,"120010549",5410,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5855","SNS",5852968.2536206,610236.513683382,1.478461,0.4435382,1,300,1 +"120010543",2003-06-23,"120010551",5410,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5850","SNS",5850015.05015552,610051.841480275,1.478461,0.4435382,1,300,1 +"120010629",2003-06-24,"120010656",5410,120,6,2003,1,1,2,"2361",NA,"data update and replacement 06/11/2020","E0581N5820","SNS",5819830.29613399,581818.117856352,1.612269,0.4836807,1,300,2 +"120010764",2003-06-24,"120010793",1730,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5820","SNS",5819889.83625512,585051.915244854,1.612269,0.4836807,1,300,3 +"120011038",2003-06-25,"120011084",1730,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5835741.53204013,608539.484060678,1.479996,0.4439989,1,300,12 +"120011328",2003-06-26,"120011351",5410,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5820","SNS",5821991.23586538,576471.306030633,1.508912,0.4526737,1,300,1 +"120011479",2003-06-27,"120011576",1730,120,6,2003,1,1,2,"2361",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871556.78555198,614971.598310233,1.445512,0.4336536,1,300,5 +"120011577",2003-06-27,"120011577",5410,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5850","SNS",5852480.71648721,611447.882551229,1.235333,0.3706,1,300,1 +"120011577",2003-06-27,"120011629",5410,120,6,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5855","SNS",5857356.35432848,607122.756417348,1.539353,0.4618058,1,300,1 +"120011675",2003-08-11,"120011693",6270,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5855","SNS",5853371.62099464,600110.856319614,1.110144,0.3330432,2,300,1 +"120011675",2003-08-11,"120011716",1730,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5840","SNS",5839483.66953714,576588.160123333,1.552663,0.4657988,1,300,3 +"120011675",2003-08-11,"120011737",4960,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5839877.74466905,608537.5108676,1.552663,0.4657988,1,300,4 +"120011871",2003-08-12,"120011879",5410,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5825","SNS",5825054.33348973,593743.812928941,1.615767,0.48473,1,300,3 +"120011871",2003-08-12,"120011910",4960,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5820","SNS",5820054.32733593,598242.15231832,1.550021,0.4650062,1,300,5 +"120011871",2003-08-12,"120011912",5410,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5820100.99720513,601351.50798342,1.550021,0.4650062,1,300,15 +"120011871",2003-08-12,"120011912",6270,120,1,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5820100.99720513,601351.50798342,1.550021,0.4650062,1,300,5 +"120012034",2003-08-12,"120012034",1730,120,1,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5825446.84626619,606703.761220187,1.615767,0.48473,1,300,20 +"120012034",2003-08-12,"120012189",6270,120,1,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5835","SNS",5837127.60556053,588920.375816977,1.507737,0.4523211,2,300,2 +"120012724",2003-08-15,"120012763",6270,120,1,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871564.88900942,618565.048413817,1.890984,0.5672951,1,300,1 +"120012724",2003-08-15,"120012764",6270,120,1,2003,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5870","SNS",5872382.37104564,619578.673422102,1.080917,0.324275,2,300,1 +"120012767",2003-11-04,"120012788",5410,120,2,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820607.54341158,604383.232527939,1.544174,0.4632521,1,300,7 +"120012949",2003-11-05,"120013010",4960,120,2,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5840","SNS",5839664.47778971,588310.732536259,1.546045,0.4638134,1,300,8 +"120013384",2003-11-07,"120013404",1530,120,2,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5825395.46560533,574878.890262168,1.332344,0.3997032,1,300,6 +"120013689",2004-02-17,"120013690",1680,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5818272.05873319,605841.823530905,1.596152,0.4788455,1,300,2 +"120013689",2004-02-17,"120013692",1680,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5818109.44889062,602643.793292117,1.596152,0.4788455,1,300,32 +"120013689",2004-02-17,"120013693",1680,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5818028.37205091,601044.77047986,1.596152,0.4788455,1,300,19 +"120013689",2004-02-17,"120013760",1680,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5820","SNS",5822255.29205155,574695.215765732,1.563852,0.4691556,1,300,2 +"120013689",2004-02-17,"120013785",5410,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5827567.79465126,607314.803210343,1.407999,0.4223998,1,300,4 +"120013800",2004-02-17,"120013896",5410,120,4,2004,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5827546.99214634,605902.514865572,1.407999,0.4223998,1,300,4 +"120013910",2004-02-18,"120013910",1680,120,4,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830299.73136825,607268.332419364,1.60533,0.4815989,1,300,19 +"120014419",2005-06-30,"120014487",5410,120,6,2005,1,1,2,"2369",NA,"data update and replacement 06/11/2020","E0776N6125","SNS",6126951.67844288,775915.516487873,1.412396,0.4237189,2,300,1 +"120014419",2005-06-30,"120014493",5410,120,6,2005,1,1,2,"2369",NA,"data update and replacement 06/11/2020","E0771N6120","SNS",6118792.74021136,773483.748096783,1.418623,0.425587,1,300,4 +"120014419",2005-06-30,"120014512",5410,120,6,2005,1,1,2,"2369",NA,"data update and replacement 06/11/2020","E0766N6090","SNS",6092621.9767582,765593.185622074,1.449932,0.4349796,1,300,1 +"120014858",2005-07-07,"120014890",5410,120,6,2005,1,1,2,"2370",NA,"data update and replacement 06/11/2020","E0636N6125","SNS",6125915.29981479,633903.745261685,1.381024,0.4143073,1,300,2 +"120015671",2005-07-16,"120015676",4960,120,6,2005,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0521N5900","SNS",5900164.33733174,523587.961074117,2.245753,0.6737258,1,300,3 +"120015805",2005-07-19,"120015822",800,120,6,2005,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0301N6000","SNS",5998963.50524538,298834.88172604,2.461038,0.7383113,2,300,3 +"120016079",2005-07-25,"120016092",4960,120,6,2005,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0266N6175","SNS",6174221.44072704,266814.818786285,1.426782,0.4280347,1,300,2 +"120016523",2005-10-03,"120016532",1680,120,2,2005,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5790","SNS",5791919.32730209,578076.357456181,2.034143,0.6102428,1,300,1 +"120016761",2005-10-05,"120016872",5410,120,2,2005,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5940","SNS",5940656.39298661,672627.613035293,2.296097,0.688829,1,300,2 +"120017043",2005-12-12,"120017047",5410,120,3,2005,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5770","SNS",5767781.87631733,578432.841974921,2.089136,0.6267407,1,300,1 +"120017127",2005-12-13,"120017147",5410,120,3,2005,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0561N5825","SNS",5826364.91568877,560624.836861369,2.129788,0.6389365,1,300,4 +"120017390",2006-02-20,"120017392",4960,120,4,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5775","SNS",5775809.89873289,586374.59411159,0.964931,0.2894793,1,300,2 +"120017404",2006-02-20,"120017408",1730,120,4,2006,1,2,2,"90",NA,"data update and replacement 06/11/2020","E0591N5780","SNS",5782330.83233061,592561.946127405,0.6303022,0.1890907,1,300,1 +"120017451",2006-02-21,"120017511",4960,120,4,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5842874.67038174,609536.598115681,1.701532,0.5104597,1,300,1 +"120017529",2006-02-22,"120017538",5410,120,4,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5805","SNS",5803836.29204914,602562.90642277,1.408632,0.4225896,1,300,1 +"120017529",2006-02-22,"120017560",1680,120,4,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5775","SNS",5775144.30172761,586803.777844323,1.5502,0.4650601,1,300,3 +"120017817",2006-04-11,"120017817",1680,120,5,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873611.60608187,620670.276848016,1.714191,0.5142574,1,300,3 +"120017817",2006-04-11,"120017820",1680,120,5,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5870696.74841915,616418.33581088,1.714191,0.5142574,1,300,7 +"120018277",2006-04-17,"120018279",1680,120,5,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5874596.63216083,621203.650966098,1.592187,0.3184375,1,200,7 +"120018277",2006-04-17,"120018282",6270,120,5,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871265.20782792,617872.649676143,1.542857,0.3085714,1,200,2 +"120018464",2006-07-24,"120018474",5410,120,6,2006,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0566N5925","SNS",5924667.17063892,567741.889403428,1.144334,0.3433001,1,300,1 +"120018964",2006-07-26,"120019036",800,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5945","SNS",5943906.22348334,602356.104078394,1.319982,0.3959947,1,300,1 +"120019300",2006-07-28,"120019371",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5875","SNS",5876791.45585744,613781.451318854,1.547747,0.464324,1,300,51 +"120019300",2006-07-28,"120019374",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872459.7816497,614709.418595226,1.266217,0.379865,2,300,45 +"120019300",2006-07-28,"120019376",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871633.76928571,617012.848374041,1.266217,0.379865,1,300,2 +"120019300",2006-07-28,"120019377",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872125.95378898,618163.293916867,1.273226,0.3819679,1,300,18 +"120019300",2006-07-28,"120019378",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5872710.41725156,619298.405038751,1.273226,0.3819679,2,300,20 +"120019300",2006-07-28,"120019379",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873295.1508516,620433.179693428,1.273226,0.3819679,1,300,2 +"120019300",2006-07-28,"120019380",6270,120,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5874013.71960996,621347.44583302,0.7639359,0.2291808,1,300,1 +"120019741",2007-04-09,"120019742",1680,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5870","SNS",5870937.16081137,618879.995423945,1.282449,0.3847348,1,300,6 +"120019741",2007-04-09,"120019768",1680,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5838253.15720764,608291.942103552,1.19639,0.358917,1,300,16 +"120019856",2007-04-09,"120019869",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5845","SNS",5845669.86640384,608785.261721421,0,0,1,300,8 +"120019955",2007-04-10,"120020019",1680,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5825205.66517132,602398.072399746,1.567881,0.4703644,1,300,6 +"120019955",2007-04-10,"120020069",1680,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5820","SNS",5820077.29329841,594604.962775743,1.60494,0.481482,1,300,10 +"120019955",2007-04-10,"120020092",1680,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5815","SNS",5817504.24207184,592238.798846658,1.858185,0.5574556,1,300,4 +"120019955",2007-04-10,"120020099",5410,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5817767.19049014,604560.436053988,1.825161,0.5475482,1,300,5 +"120020100",2007-04-10,"120020137",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5830","SNS",5828635.55438148,574829.254387951,1.494056,0.4482169,1,300,3 +"120020100",2007-04-10,"120020205",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5822169.23227217,607035.074235831,1.233233,0.3699699,1,300,70 +"120020100",2007-04-10,"120020237",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5815","SNS",5817504.24207184,592238.798846658,1.858185,0.5574556,1,300,4 +"120020100",2007-04-10,"120020244",5410,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5817767.19049014,604560.436053988,1.825161,0.5475482,1,300,5 +"120020389",2007-04-11,"120020518",5410,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5840","SNS",5839599.08007374,587276.052327874,1.518417,0.4555251,1,300,2 +"120020533",2007-04-12,"120020578",4960,120,5,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5831491.36358404,608419.729290571,1.538977,0.461693,1,300,15 +"120020649",2007-04-12,"120020668",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5827176.882448,576970.068630291,1.629261,0.4887783,2,300,1 +"120020649",2007-04-12,"120020763",1680,120,5,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869891.7252464,616310.388731944,1.326722,0.3980167,1,300,90 +"120020765",2007-06-27,"120020794",1730,120,6,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5822683.15245661,604868.672419547,0.9707558,0.2912267,1,300,10 +"120020915",2007-06-27,"120020988",4960,120,6,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5825","SNS",5827374.36708463,591994.66870875,1.491633,0.44749,1,300,5 +"120021133",2007-06-28,"120021269",5410,120,6,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5835","SNS",5837502.10276825,599806.526950564,1.863125,0.5589374,1,300,1 +"120021299",2007-06-28,"120021435",5410,120,6,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5835","SNS",5837502.10276825,599806.526950564,1.863125,0.5589374,2,300,1 +"120021679",2007-08-19,"120021687",1730,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5870","SNS",5872318.91336935,619443.371536889,0.9654606,0.2896382,1,300,1 +"120021679",2007-08-19,"120021688",6270,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871625.56878329,618769.301604951,0.9654606,0.2896382,2,300,1 +"120021679",2007-08-19,"120021728",6270,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5825849.98752115,607834.101601114,1.226429,0.3679287,1,300,1 +"120021679",2007-08-19,"120021779",6270,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820264.56976614,605358.688565791,1.267464,0.3802391,1,300,2 +"120022060",2007-08-20,"120022168",6270,120,1,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5835","SNS",5837485.19356644,601625.283644454,1.333731,0.4001193,2,300,1 +"120022060",2007-08-20,"120022172",6270,120,1,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5837578.6504955,606975.943447005,1.333731,0.4001193,1,300,4 +"120022060",2007-08-20,"120022187",6270,120,1,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0596N5840","SNS",5839825.43678955,594967.246241367,1.39941,0.4198229,2,300,1 +"120022396",2007-08-21,"120022530",6270,120,1,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5820","SNS",5819696.37810821,575343.527687906,1.417672,0.4253017,1,300,2 +"120022559",2007-08-22,"120022677",6270,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5835","SNS",5837534.39545568,574360.373152469,1.287835,0.3863505,1,300,1 +"120022737",2007-09-24,"120022738",4960,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871571.23392516,618234.606305884,1.979107,0.5937322,1,300,7 +"120022897",2007-09-26,"120022975",6270,120,1,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5815","SNS",5817104.23274677,594082.166313376,1.185309,0.3555928,1,300,1 +"120023080",2007-11-05,"120023105",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5845","SNS",5847016.10776826,606970.617035081,1.055783,0.3167348,1,300,1 +"120023144",2007-11-05,"120023170",1680,120,2,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5845","SNS",5845975.06697066,606792.82123564,1.055783,0.3167348,1,300,1 +"120023208",2007-11-06,"120023215",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5818312.97788114,602106.527679858,0.8187344,0.2456203,1,300,30 +"120023208",2007-11-06,"120023220",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5822400.05491549,601843.66775835,0.8187344,0.2456203,1,300,5 +"120023208",2007-11-06,"120023252",1730,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5850","SNS",5850532.87529654,607236.663014694,1.014645,0.3043935,1,300,1 +"120023208",2007-11-06,"120023254",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5850","SNS",5852489.10811533,607784.543681497,1.014645,0.3043935,1,300,130 +"120023208",2007-11-06,"120023260",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5860","SNS",5858357.13003975,609425.234102933,1.014645,0.3043935,1,300,81 +"120023208",2007-11-06,"120023265",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5863426.57372785,611294.69911007,1.087075,0.3261226,1,300,7 +"120023208",2007-11-06,"120023268",1680,120,2,2007,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5866625.34424405,612844.299958019,1.203734,0.3611203,1,300,16 +"120023276",2007-11-20,"120023277",1680,120,2,2007,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869814.41326846,616196.545254898,1.70583,0.1569364,1,300,55 +"120024789",2008-04-07,"120024852",1680,120,5,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5834830.44679394,608001.465306226,1.689799,0.5069398,1,300,13 +"120024861",2008-04-07,"120024861",1680,120,5,2008,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5870638.13453241,617624.899281847,2.2236,0.66708,1,300,18 +"120024861",2008-04-07,"120024926",1680,120,5,2008,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5832371.18255951,608051.007370001,1.419214,0.4257642,1,300,7 +"120024933",2008-04-08,"120025006",1680,120,5,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0581N5830","SNS",5832176.67625692,582545.872658081,1.513729,0.4541188,1,300,1 +"120024933",2008-04-08,"120025034",1680,120,5,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5835","SNS",5837259.67410093,594801.503157133,1.717778,0.5153335,1,300,33 +"120025074",2008-04-08,"120025083",1730,120,5,2008,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0586N5815","SNS",5817457.01454301,586977.675624657,1.752673,0.5258018,1,300,1 +"120025483",2008-04-10,"120025547",1680,120,5,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5830","SNS",5829843.35765273,576736.809805822,1.666395,0.4999184,1,300,3 +"120025483",2008-04-10,"120025586",1680,120,5,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5835","SNS",5837218.19524585,586097.833100173,1.799315,0.5397944,1,300,16 +"120026107",2008-06-24,"120026149",1730,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5822625.22319548,600734.105490124,1.452575,0.4357725,1,300,5 +"120026285",2008-06-25,"120026306",5410,120,6,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0571N5820","SNS",5818843.77476379,573485.995011257,1.523206,0.4569618,1,300,1 +"120026459",2008-06-25,"120026487",5410,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0581N5815","SNS",5817408.74614378,582115.284109546,1.452472,0.4357417,1,300,1 +"120026459",2008-06-25,"120026498",1730,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0596N5820","SNS",5817654.44730495,598139.674231885,1.452472,0.4357417,1,300,8 +"120026459",2008-06-25,"120026504",5410,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820049.28204556,605489.163652843,1.607784,0.4823351,1,300,2 +"120026459",2008-06-25,"120026510",5410,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5822358.75011661,600274.540623551,1.330932,0.3992795,1,300,1 +"120026459",2008-06-25,"120026515",5410,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5822401.21732141,593599.372925688,1.330932,0.3992795,1,300,1 +"120026459",2008-06-25,"120026517",5410,120,6,2008,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5822419.86348071,590929.462485748,1.330932,0.3992795,2,300,1 +"120026633",2008-06-26,"120026647",5410,120,6,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5830","SNS",5829870.8540404,594022.410992388,1.343682,0.4031046,1,300,4 +"120026633",2008-06-26,"120026649",5410,120,6,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5830","SNS",5829934.6281917,596717.338881477,1.343682,0.4031046,1,300,4 +"120026633",2008-06-26,"120026656",5410,120,6,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830168.44299428,606149.279971331,1.343682,0.4031046,1,300,6 +"120027247",2008-08-12,"120027365",6270,120,1,2008,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5824722.20863853,605461.92465615,1.99485,0.5984551,1,300,2 +"120027605",2008-08-14,"120027633",6270,120,1,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5835","SNS",5832662.59936961,602370.324461118,1.379046,0.4137139,1,300,5 +"120028181",2008-11-03,"120028202",1680,120,2,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5840050.43965955,604272.67481745,1.595463,0.4786389,1,300,1 +"120028181",2008-11-03,"120028207",1680,120,2,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0596N5840","SNS",5839807.72512871,596274.283822453,1.595463,0.4786389,1,300,6 +"120028591",2008-11-05,"120028617",1680,120,2,2008,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5815","SNS",5817314.24158979,591837.864893243,1.462826,0.4388478,1,300,8 +"120029229",2009-01-20,"120029240",5410,120,3,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830170.34995956,605980.979673256,1.294821,0.3884464,1,300,5 +"120029229",2009-01-20,"120029241",1680,120,3,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830196.49010342,607279.54254288,1.294821,0.3884464,1,300,11 +"120029419",2009-01-21,"120029498",800,120,3,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5830","SNS",5832351.47020904,591531.440111146,1.397449,0.4192346,2,300,1 +"120029754",2009-04-06,"120029755",1680,120,5,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5867227.29424314,613455.843963037,1.885006,0.5655019,1,300,30 +"120029754",2009-04-06,"120029818",4960,120,5,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5837607.63669094,606113.780706724,1.461617,0.438485,1,300,10 +"120030073",2009-04-08,"120030080",5410,120,5,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0596N5815","SNS",5817506.98524879,594888.875741672,1.522,0.4566001,1,300,1 +"120030157",2009-04-09,"120030198",800,120,5,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5823613.98840246,601090.783217842,1.443645,0.4330936,2,300,1 +"120030489",2009-06-22,"120030557",5410,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5840","SNS",5838118.76635788,601752.109891318,1.247633,0.3742899,1,300,1 +"120030832",2009-06-24,"120030888",5410,120,6,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5828569.1824418,607916.202029885,1.663455,0.4990365,1,300,6 +"120031104",2009-06-25,"120031104",5410,120,6,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820222.4031719,605777.091355612,1.346597,0.4039791,1,300,2 +"120031104",2009-06-25,"120031151",1730,120,6,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5825101.88823759,601744.375646163,1.325649,0.3976946,1,300,13 +"120031214",2009-06-25,"120031216",1730,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5820","SNS",5820164.81104984,603076.189485411,1.346597,0.4039791,1,300,16 +"120031324",2009-07-27,"120031324",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0626N5875","SNS",5876822.62460357,624558.623237989,0.8442016,0.2532605,1,300,28 +"120031324",2009-07-27,"120031325",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0626N5875","SNS",5876304.49219951,623889.721966143,0.8442016,0.2532605,1,300,20 +"120031324",2009-07-27,"120031327",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5875268.51559367,622551.554946042,0.8442016,0.2532605,1,300,10 +"120031324",2009-07-27,"120031329",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5874208.08499816,621154.692514802,0.9667093,0.2900128,1,300,36 +"120031324",2009-07-27,"120031330",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873640.78496441,620368.801455967,0.9667093,0.2900128,1,300,2 +"120031324",2009-07-27,"120031331",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873161.62604643,619541.390778076,0.9507489,0.2852247,2,300,10 +"120031324",2009-07-27,"120031333",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872289.19874174,617830.79927873,0.990091,0.2970273,1,300,6 +"120031324",2009-07-27,"120031335",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871410.44805154,616200.98120087,0.990091,0.2970273,2,300,2 +"120031324",2009-07-27,"120031336",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871622.43342651,615389.835771372,0.9265,0.27795,2,300,71 +"120031324",2009-07-27,"120031337",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872246.75652535,614736.857817234,0.9265,0.27795,2,300,9 +"120031324",2009-07-27,"120031356",6270,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5890","SNS",5890076.21792245,603214.909412566,1.207097,0.3621292,1,300,1 +"120031374",2009-07-28,"120031397",5410,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0581N5950","SNS",5949439.53275191,580253.493579496,1.334164,0.4002491,1,300,3 +"120031471",2009-07-29,"120031544",4960,120,6,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5945","SNS",5942671.93435789,612494.823404255,1.123744,0.3371232,1,300,4 +"120031561",2009-08-02,"120031570",6270,120,1,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0661N5985","SNS",5987182.81095209,660509.723658024,1.21108,0.363324,1,300,1 +"120031917",2009-08-06,"120031989",6270,120,1,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0571N5930","SNS",5928332.08872854,569638.019857649,1.210716,0.3632147,2,300,1 +"120032804",2009-10-07,"120032901",1680,120,2,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0601N5835","SNS",5835160.51179633,602995.219060615,1.444374,0.4333121,1,300,24 +"120032906",2009-10-07,"120032950",1680,120,2,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5825235.03514876,603423.384489223,1.375971,0.4127913,1,300,2 +"120033008",2009-10-08,"120033025",1680,120,2,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0581N5825","SNS",5824042.58746535,579734.071451922,1.473737,0.442121,1,300,60 +"120033008",2009-10-08,"120033045",1680,120,2,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5830","SNS",5831479.58828894,584845.904710084,1.401349,0.4204048,1,300,12 +"120033008",2009-10-08,"120033054",1680,120,2,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5840","SNS",5842500.34494847,591010.270767372,1.401349,0.4204048,1,300,240 +"120033008",2009-10-08,"120033056",1680,120,2,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5840","SNS",5841586.0054661,591440.293489868,1.698467,0.50954,2,300,26 +"120033282",2009-10-09,"120033347",1680,120,2,2009,1,1,2,"2393",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5818056.34120382,590158.658489282,1.75489,0.5264671,1,300,1 +"120033282",2009-10-09,"120033359",5410,120,2,2009,1,1,2,"2393",NA,"data update and replacement 06/11/2020","E0596N5830","SNS",5829100.72695501,597698.484065805,1.332818,0.3998454,2,300,3 +"120033374",2009-10-09,"120033394",5410,120,2,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5870203.87145757,616930.003382654,1.229094,0.3687281,1,300,1 +"120033395",2009-11-02,"120033423",1530,120,2,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5827159.11286239,577534.987536525,1.426649,0.4279948,1,300,30 +"120033929",2009-11-05,"120033972",1530,120,2,2009,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0591N5840","SNS",5840372.74235471,591000.95882638,1.353314,0.4059941,1,300,4 +"120034189",2009-11-06,"120034234",1680,120,2,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5837596.58857056,608140.769888682,1.56623,0.469869,1,300,4 +"120034259",2009-11-09,"120034280",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0526N5855","SNS",5853752.51962687,524152.434038922,1.543477,0.463043,1,300,4 +"120034259",2009-11-09,"120034289",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0511N5855","SNS",5853685.22543668,510190.276060034,1.563069,0.4689206,1,300,5 +"120034259",2009-11-09,"120034297",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0501N5855","SNS",5855600.05388916,499797.972472511,1.552136,0.4656408,1,300,5 +"120034259",2009-11-09,"120034305",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0501N5870","SNS",5868162.38252386,500020.553291853,1.578143,0.4734428,1,300,9 +"120034259",2009-11-09,"120034306",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0501N5870","SNS",5869192.50901936,500568.385261935,1.190309,0.3570928,1,300,70 +"120034259",2009-11-09,"120034318",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0516N5870","SNS",5872339.94819756,516168.411239678,1.542069,0.4626208,1,300,2 +"120034259",2009-11-09,"120034351",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0566N5875","SNS",5872739.64316401,567208.486114028,1.542069,0.4626208,1,300,6 +"120034358",2009-11-10,"120034453",1680,120,2,2009,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0561N5835","SNS",5835641.927546,560982.721535079,1.467328,0.4401985,1,300,5 +"120034804",2010-01-19,"120034891",1680,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5845","SNS",5843970.64092807,606170.416337295,1.387733,0.4163199,1,300,2 +"120034982",2010-01-20,"120035055",1680,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5824968.85408266,574481.473706027,1.632827,0.4898481,2,300,5 +"120034982",2010-01-20,"120035055",5410,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5825","SNS",5824968.85408266,574481.473706027,1.632827,0.4898481,1,300,1 +"120035081",2010-01-20,"120035127",800,120,3,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5832508.17610165,605257.506676204,1.453463,0.436039,2,300,1 +"120035081",2010-01-20,"120035161",1680,120,3,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0581N5820","SNS",5821921.77936744,581608.636210878,1.387589,0.4162766,1,300,2 +"120035180",2010-01-21,"120035223",1680,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820475.14765716,606707.308035134,1.488305,0.4464916,1,300,2 +"120035276",2010-01-21,"120035319",1680,120,3,2009,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5820475.14765716,606707.308035134,1.488305,0.4464916,1,300,2 +"120035372",2010-01-22,"120035385",1680,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5835","SNS",5834834.88050815,593173.077202283,1.485293,0.4455879,1,300,4 +"120035476",2010-01-25,"120035510",4960,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0501N5855","SNS",5853773.1483824,502698.759274212,1.620098,0.4860295,1,300,1 +"120035476",2010-01-25,"120035558",1730,120,3,2009,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0561N5875","SNS",5872640.64347324,559247.142045096,1.691707,0.507512,1,300,4 +"120036039",2010-02-23,"120036039",1730,120,4,2010,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0591N5840","SNS",5840746.59529036,591178.998705078,1.630122,0.4890366,1,300,1 +"120036483",2010-02-26,"120036527",5410,120,4,2010,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5834183.27921384,607771.401960187,1.680956,0.5042869,1,300,1 +"120036782",2010-03-02,"120036789",1680,120,4,2010,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0566N5815","SNS",5817317.87696481,566672.308421119,1.653957,0.4961871,1,300,11 +"120037521",2010-04-30,"120037568",6270,120,5,2010,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0711N5960","SNS",5961982.45147868,708914.981999736,1.956338,0.5869013,2,300,2 +"120038035",2010-05-27,"120038082",1680,120,5,2010,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0536N5950","SNS",5947831.94482519,534444.629930726,1.953813,0.586144,1,300,15 +"120038334",2010-06-21,"120038334",1730,120,6,2010,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0581N5770","SNS",5771817.44317011,581992.217969324,2.092,0.628,1,300,12 +"120038334",2010-06-21,"120038335",6270,120,6,2010,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0581N5770","SNS",5771008.30623135,580057.320229647,2.092,0.628,1,300,1 +"120038334",2010-06-21,"120038449",5410,120,6,2010,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0446N5770","SNS",5770543.37257588,448779.957407421,2.091,0.627,1,300,1 +"120038461",2010-06-22,"120038466",5410,120,6,2010,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0451N6010","SNS",6007674.55400673,451129.808256337,1.807744,0.5423233,1,300,2 +"120038893",2010-07-19,"120038904",6270,120,6,2010,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0566N5765","SNS",5764068.50807074,564543.481201947,1.727305,0.5181915,2,300,1 +"120039005",2010-07-20,"120039078",4960,120,6,2010,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0456N6135","SNS",6133335.16959476,454882.612540577,2.068354,0.6205061,1,300,3 +"120039138",2010-07-21,"120039257",4960,120,6,2010,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0551N5890","SNS",5890596.86254863,550353.690824008,1.946996,0.5840987,1,300,1 +"120039418",2010-07-23,"120039477",6270,120,6,2010,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0601N5900","SNS",5900893.41581931,599984.755100126,1.751226,0.5253677,2,300,1 +"120039533",2010-08-17,"120039540",6270,120,1,2010,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0556N5755","SNS",5753888.34472739,555177.028294135,1.437222,0.4311667,1,300,1 +"120040566",2010-09-22,"120040635",1680,120,1,2010,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0511N5845","SNS",5845141.32787961,511802.991075606,1.628221,0.4884664,1,300,6 +"120040878",2010-10-12,"120040899",1730,120,2,2010,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0451N6115","SNS",6117078.06185091,452278.661082744,2.194087,0.6582262,1,300,30 +"120043883",2011-10-06,"120043892",6270,120,2,2011,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5819928.30576474,589093.178205942,1.476113,0.442834,1,300,1 +"120044490",2011-11-02,"120044501",1680,120,2,2011,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5820","SNS",5822427.77478337,589912.430609139,1.502524,0.4507571,1,300,2 +"120044542",2011-11-03,"120044557",1680,120,2,2011,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0586N5825","SNS",5824788.64114171,585053.869189154,1.608396,0.4825189,1,300,19 +"120045417",2012-01-10,"120045444",800,120,3,2011,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0581N5825","SNS",5825941.51036454,583647.70443805,1.527879,0.4583638,2,300,1 +"120045634",2012-01-11,"120045718",800,120,3,2011,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0581N5825","SNS",5827197.30019115,583172.000825173,1.552274,0.4656823,2,300,1 +"120046132",2012-01-26,"120046156",1680,120,3,2011,1,2,2,"2384",NA,"data update and replacement 06/11/2020","E0516N5810","SNS",5812225.8110433,518026.282169446,1.529045,0.4587136,1,300,75 +"120046132",2012-01-26,"120046159",1680,120,3,2011,1,2,2,"2384",NA,"data update and replacement 06/11/2020","E0521N5815","SNS",5813937.42174648,522293.861962298,1.529045,0.4587136,1,300,2 +"120046414",2012-02-21,"120046447",1730,120,4,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0586N5830","SNS",5832335.39987928,586880.556803295,1.535134,0.4605402,1,300,1 +"120046756",2012-02-23,"120046804",1730,120,4,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5839215.42727612,608528.415454146,2.083997,0.625199,1,300,2 +"120047130",2012-03-07,"120047150",1680,120,4,2012,1,1,2,"2395",NA,"data update and replacement 06/11/2020","E0531N5780","SNS",5779741.11032054,532796.922862339,1.536952,0.4610856,1,300,190 +"120047631",2012-04-17,"120047714",1680,120,5,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0601N5825","SNS",5822669.34086927,602960.717031933,1.556917,0.4670752,1,300,47 +"120048217",2012-06-05,"120048625",6270,120,6,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5831884.93802852,608253.614087658,1.606819,0.4820458,1,300,1 +"120048217",2012-06-05,"120048674",4960,120,6,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5822450.13069106,605777.950955104,1.572284,0.4716852,1,300,3 +"120048695",2012-06-05,"120048695",6270,120,6,2012,1,1,2,"2384",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5837641.2289593,608046.306892032,2.118597,0.635579,1,300,1 +"120048813",2012-06-06,"120048841",6270,120,6,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0576N5830","SNS",5829378.55766472,577667.392083381,1.560329,0.4680987,2,300,1 +"120051812",2012-06-21,"120051852",1730,120,6,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0751N5935","SNS",5935034.38060515,751580.045430031,1.121937,0.3365812,1,300,18 +"120051812",2012-06-21,"120051853",1730,120,6,2012,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0751N5935","SNS",5934289.9944862,752423.044454364,1.121937,0.3365812,2,300,30 +"120054384",2012-02-10,"120054476",5410,120,4,2012,1,1,3,"2395",NA,"data update and replacement 06/11/2020","E0576N5765","SNS",5767026.36340424,574360.251097973,0.271,0.1626,1,600,4 +"120054384",2012-02-10,"120054729",1730,120,4,2012,1,1,3,"2395",NA,"data update and replacement 06/11/2020","E0586N5780","SNS",5781876.19758286,584353.005921166,0.195,0.117,1,600,3 +"120056034",2012-10-23,"120056040",1680,120,2,2012,1,1,4,"2366",NA,"data update and replacement 06/11/2020","E0711N6000","SNS",5999196.58435315,712543.485365793,0.3612302,0.2167381,1,600,6 +"120059757",2012-12-19,"120059869",1680,120,3,2012,1,4,1,"2384",NA,"data update and replacement 06/11/2020","E0601N5800","SNS",5801513.73609074,599560.503067876,0.2367868,0.1183934,1,500,8 +"120060613",2012-12-20,"120060634",1680,120,3,2012,1,4,1,"2366",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5824661.59293812,607785.379724487,0.2024748,0.1012374,2,500,9 +"120061973",2013-01-22,"120061973",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869440.72863618,615233.782409183,0.2565022,0.1539013,1,300,4 +"120061973",2013-01-22,"120061982",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5868523.7473759,614207.206526404,0.2587497,0.1552498,1,300,6 +"120061973",2013-01-22,"120061983",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5868575.59794971,613952.797359274,0.2587497,0.1552498,1,300,1 +"120061973",2013-01-22,"120062017",1730,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5865064.84324806,612786.43722383,0.2194917,0.131695,1,300,1 +"120061973",2013-01-22,"120062048",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0611N5860","SNS",5861856.74208171,612704.979852278,0.2081706,0.1249024,1,300,3 +"120061973",2013-01-22,"120062098",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0611N5855","SNS",5856731.26935701,611473.064366846,0.2398164,0.1438898,1,300,8 +"120062263",2013-01-23,"120062508",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0586N5775","SNS",5777161.42478324,588136.635012585,0.2352135,0.1411281,1,300,5 +"120062626",2013-01-24,"120062761",5410,120,3,2012,1,2,3,"2366",NA,"data update and replacement 06/11/2020","E0596N5790","SNS",5790125.33665405,594257.352864257,0.2451302,0.1470781,1,300,1 +"120063240",2013-01-22,"120063243",1680,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5868674.70570772,615312.585596097,0.2565022,0.1539013,1,300,2 +"120063240",2013-01-22,"120063245",1680,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5868317.58928515,615224.808728339,0.2587497,0.1552498,2,300,4 +"120063240",2013-01-22,"120063493",1730,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5842651.9131748,609971.612953097,0.2431589,0.1458953,1,300,2 +"120063240",2013-01-22,"120063495",5410,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5842822.99581579,609515.060818695,0.2431589,0.1458953,1,300,1 +"120063530",2013-01-23,"120063600",1730,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0576N5765","SNS",5764702.67396655,577941.469303841,0.2355261,0.1413157,1,300,1 +"120063530",2013-01-23,"120063673",1680,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0581N5770","SNS",5770531.26232689,582185.736153154,0.2566198,0.1539719,1,300,10 +"120064196",2013-01-25,"120064418",1730,120,3,2012,1,2,3,"2384",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5827772.82819604,608586.644802295,0.2560003,0.1536002,1,300,1 +"120066325",2013-02-18,"120066706",1730,120,4,2013,1,1,4,"2437",NA,"data update and replacement 06/11/2020","E0701N5990","SNS",5988292.99268264,699212.087567465,0.3546052,0.2127631,1,600,1 +"120070266",2013-05-12,"120070342",1730,120,5,2013,1,1,4,"2437",NA,"data update and replacement 06/11/2020","E0691N5985","SNS",5987274.34739395,693574.597489781,0.2831859,0.1699115,2,600,1 +"120070429",2013-05-12,"120070560",6270,120,5,2013,1,1,4,"2437",NA,"data update and replacement 06/11/2020","E0716N5995","SNS",5994478.48741323,714266.112552565,0.3360978,0.2016587,2,600,2 +"120071034",2000-03-06,"120071038",1680,120,4,2000,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0641N5885","SNS",5887503.7306654,641310.108094214,1.677536,0.5032608,2,300,23 +"120073980",2014-02-27,"120073980",1680,120,4,2014,1,5,2,"92",NA,"data update and replacement 06/11/2020","E0531N5810","SNS",5812222.36047356,531638.736269744,3.021661,1.812996,1,600,20 +"120074716",2016-03-14,"120074726",5410,120,4,2016,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5855","SNS",5854389.23206964,606095.656192176,1.567256,0.4701767,1,300,1 +"120075187",2016-03-17,"120075217",4960,120,4,2016,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0546N5800","SNS",5797943.83580853,548430.27805068,1.67624,0.5028719,1,300,2 +"120075187",2016-03-17,"120075221",5410,120,4,2016,1,1,2,"2366",NA,"data update and replacement 06/11/2020","E0541N5795","SNS",5797585.47270435,541714.953451948,1.67624,0.5028719,1,300,1 +"120075308",2016-03-17,"120075312",4960,120,4,2016,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0591N5795","SNS",5797581.58168802,589759.37183989,1.561155,0.4683466,1,300,1 +"120076262",2016-11-29,"120076286",1730,120,2,2016,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0536N5810","SNS",5809437.44015937,534666.393906673,1.563175,0.4689526,1,300,5 +"120076262",2016-11-29,"120076341",1530,120,2,2016,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0516N5840","SNS",5840786.33641689,513889.689603877,1.51776,0.4553281,1,300,10 +"120078726",2018-07-26,"120078807",4960,120,6,2018,1,1,3,"92",NA,"data update and replacement 06/11/2020","E0581N5930","SNS",5931161.48208788,583175.846274861,1.249159,0.374748,1,300,9 +"120079361",2018-08-21,"120079369",5410,120,1,2018,1,1,3,"2366",NA,"data update and replacement 06/11/2020","E0631N5985","SNS",5985372.98543506,631090.876247753,1,0,1,300,3 +"120079361",2018-08-21,"120079472",800,120,1,2018,1,1,3,"2366",NA,"data update and replacement 06/11/2020","E0566N5875","SNS",5872738.26135362,567109.435357976,1,0,1,300,0 +"140-14-270 - 2014-08-2014-08-13-7-1",2014-08-13,"140-14-270 - 2014-08-2014-08-13-7-1-137",6270,140,1,2014,8,1,1,"140-7",NA,"MWTL","E0561N5890","SNS",5892330.97696495,562406.639232924,4.947,5.54064,2,1252,1 +"140-14-270 - 2014-08-2014-08-16-10-1",2014-08-16,"140-14-270 - 2014-08-2014-08-16-10-1-629",6270,140,1,2014,8,1,1,"140-10",NA,"MWTL","E0561N5755","SNS",5752708.57325365,560136.685739143,4.682,5.24384,2,1252,1 +"140-14-270 - 2016-08-2016-08-23-3-1",2016-08-23,"140-14-270 - 2016-08-2016-08-23-3-1-640",6270,140,1,2016,8,1,1,"140-3",NA,"MWTL","E0576N5780","SNS",5779172.92934571,574045.67700429,4.811,5.38832,2,1252,1 +"140-14-270 - 2016-11-2016-11-03-6-2",2016-11-03,"140-14-270 - 2016-11-2016-11-03-6-2-440",800,140,2,2016,8,1,1,"140-6",NA,"MWTL","E0556N5920","SNS",5921807.64516294,556176.269826905,4.98,5.5776,2,1252,3 +"140-14-270 - 2017-01-2017-01-18-6-1",2017-01-18,"140-14-270 - 2017-01-2017-01-18-6-1-114",1730,140,3,2016,8,1,1,"140-6",NA,"MWTL","E0551N5855","SNS",5855807.85379792,553705.03814949,5.134,5.75008,2,1252,2 +"140-14-270 - 2017-04-2017-04-11-15-1",2017-04-11,"140-14-270 - 2017-04-2017-04-11-15-1-40",5410,140,5,2017,8,1,1,"140-15",NA,"MWTL","E0566N5755","SNS",5753028.3128844,565832.839287142,4.767,5.33904,2,1252,12 +"140-17-0266 - 2017-08-2017-08-02-3-2",2017-08-02,"140-17-0266 - 2017-08-2017-08-02-3-2-67",6270,140,1,2017,8,1,1,"140-3",NA,"MWTL","E0496N5725","SNS",5725679.67778763,497817.244730462,4.761,5.33232,2,1252,2 +"140-17-0266 - 2018-02-2018-02-20-3-2",2018-02-20,"140-17-0266 - 2018-02-2018-02-20-3-2-609",1730,140,4,2018,8,1,1,"140-3",NA,"MWTL","E0516N5715","SNS",5714337.08796101,516345.821176776,5.12,5.7344,2,1252,3 +"140-17-0266 - 2018-08-2018-08-29-14-1",2018-08-29,"140-17-0266 - 2018-08-2018-08-29-14-1-625",6270,140,1,2018,8,1,1,"140-14",NA,"MWTL","E0546N5750","SNS",5748908.1973044,546365.70125228,5.508,6.16896,2,1252,1 +"140-17-0266 - 2018-08-2018-08-29-14-1",2018-08-29,"140-17-0266 - 2018-08-2018-08-29-14-1-710",6270,140,1,2018,8,1,1,"140-14",NA,"MWTL","E0696N5935","SNS",5932999.26597497,695011.785528496,5.206,5.83072,2,1252,1 +"140-17-0266 - 2018-08-2018-08-29-3-2",2018-08-29,"140-17-0266 - 2018-08-2018-08-29-3-2-677",6270,140,1,2018,8,1,1,"140-3",NA,"MWTL","E0611N5895","SNS",5894456.01695633,610129.508726062,4.501,5.04112,2,1252,1 +"140-17-0266 - 2018-08-2018-08-29-3-2",2018-08-29,"140-17-0266 - 2018-08-2018-08-29-3-2-687",6270,140,1,2018,8,1,1,"140-3",NA,"MWTL","E0631N5915","SNS",5916707.01455919,633703.770255254,5.072,5.68064,2,1252,1 +"140-17-0266 - 2018-11-2018-11-18-14-1",2018-11-18,"140-17-0266 - 2018-11-2018-11-18-14-1-632",5410,140,2,2018,8,1,1,"140-14",NA,"MWTL","E0561N5760","SNS",5761501.18387381,561463.317212522,4.767,5.33904,2,1252,1 +"140-17-0266 - 2020-02-2020-02-14-16-2",2020-02-14,"140-17-0266 - 2020-02-2020-02-14-16-2-1828",5410,140,4,2020,8,1,1,"140-16",NA,"MWTL","E0536N5715","SNS",5715948.91215553,536613.014675542,5.081,5.69072,2,1252,1 +"140-17-0266 - 2020-02-2020-02-14-16-2",2020-02-14,"140-17-0266 - 2020-02-2020-02-14-16-2-1855",5410,140,4,2020,8,1,1,"140-16",NA,"MWTL","E0556N5740","SNS",5741186.66647443,558313.454827206,5.183,5.80496,2,1252,6 +"140-17-0266 - 2020-04-2020-05-20-14-1",2020-05-20,"140-17-0266 - 2020-04-2020-05-20-14-1-1628",1730,140,5,2020,8,1,1,"140-14",NA,"MWTL","E0566N5750","SNS",5747844.50240175,568231.098303179,4.537,5.08144,2,1252,56 +"140-17-0266 - 2020-06-2020-06-22-16-2",2020-06-22,"140-17-0266 - 2020-06-2020-06-22-16-2-1536",5410,140,6,2020,8,1,1,"140-16",NA,"MWTL","E0531N5900","SNS",5897780.61782485,531036.682387605,4.955,5.5496,2,1252,1 +"140000574",2003-11-18,"140000574",1680,140,2,2003,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0646N5925","SNS",5922645.00896952,644845.023624213,1.687365,0.5062095,1,300,1 +"140001563",2002-06-05,"140001613",1730,140,6,2002,1,1,2,"2385",NA,"data update and replacement 06/11/2020","E0581N5775","SNS",5777569.66964559,583366.464501124,0.2700833,0.081025,1,300,7 +"140003089",2002-06-07,"140003139",1730,140,6,2002,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0601N5805","SNS",5805600.70782402,603768.987458872,0.1432213,0.0429664,1,300,17 +"140003089",2002-06-07,"140003160",1730,140,6,2002,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0601N5800","SNS",5802423.24767474,602083.907196256,0.3503367,0.105101,1,300,2 +"140003912",2002-06-11,"140003959",1730,140,6,2002,1,1,2,"2361",NA,"data update and replacement 06/11/2020","E0606N5825","SNS",5826455.74479039,607195.243449994,0.283356,0.0850068,1,300,18 +"140005639",2002-06-13,"140005644",1730,140,6,2002,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873258.49202171,620346.935500275,0.225944,0.0677832,1,300,1 +"140005639",2002-06-13,"140006377",1730,140,6,2002,1,1,2,"2360",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871557.95176476,615253.212488302,0.3244087,0.0973226,1,300,1 +"140006451",2002-12-03,"140006456",1530,140,3,2002,1,1,2,"2361",NA,"data update and replacement 06/11/2020","E0581N5840","SNS",5837629.47823257,581617.149836968,2.875564,0.8626693,1,300,2 +"140006623",2004-12-02,"140006666",1680,140,3,2004,1,2,2,"2360",NA,"data update and replacement 06/11/2020","E0556N5750","SNS",5748270.9756751,557731.147888339,1.434051,0.4302153,1,300,15 +"140007030",2005-02-24,"140007040",1680,140,4,2005,1,2,2,"2360",NA,"data update and replacement 06/11/2020","E0566N5755","SNS",5753450.5275917,565541.613173168,1.36538,0.4096139,1,300,5 +"140007767",2006-03-13,"140007807",5410,140,4,2006,1,2,2,"2360",NA,"data update and replacement 06/11/2020","E0546N5745","SNS",5743726.1520609,548721.389249158,1.447863,0.4343588,1,300,1 +"140007767",2006-03-13,"140007854",1680,140,4,2006,1,2,2,"2360",NA,"data update and replacement 06/11/2020","E0561N5755","SNS",5756701.03432684,561165.600003779,1.696338,0.5089013,1,300,8 +"140007767",2006-03-13,"140007858",1680,140,4,2006,1,2,2,"2360",NA,"data update and replacement 06/11/2020","E0566N5755","SNS",5753848.79956908,566472.426001211,1.985661,0.5956983,1,300,4 +"150000054",1997-04-08,"150000073",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0711N5940","SNS",5939820.12838654,712994.148715153,2.571558,0.7714673,1,300,1 +"150000054",1997-04-08,"150000122",5410,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0706N5935","SNS",5934068.06600047,706495.819754035,2.327227,0.6981682,1,300,1 +"150000054",1997-04-08,"150000123",1730,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5932134.08898303,706015.729749365,2.094505,0.6283514,1,300,21 +"150000054",1997-04-08,"150000123",5410,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5932134.08898303,706015.729749365,2.094505,0.6283514,1,300,1 +"150000124",1997-04-09,"150000125",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0711N5935","SNS",5937464.1169935,709803.633445829,2.526818,0.7580455,1,300,11 +"150000124",1997-04-09,"150000137",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0706N5935","SNS",5932723.17933957,703981.508261774,2.188533,0.6565599,1,300,6 +"150000185",1997-04-10,"150000197",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0661N5925","SNS",5926479.91739542,660429.862988523,2.239042,0.6717125,1,300,22 +"150000185",1997-04-10,"150000214",4960,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5920","SNS",5920104.90437368,648352.004233558,2.31625,0.694875,1,300,24 +"150000233",1997-04-11,"150000236",5410,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5895381.05674257,644374.152625098,2.7795,0.5559,1,200,1 +"150000233",1997-04-11,"150000237",5410,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5893562.36399311,645889.541193647,1.668072,0.3336144,1,200,2 +"150000233",1997-04-11,"150000255",4960,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5875200.87212002,621508.344132431,1.976341,0.3952682,1,200,4 +"150000323",1997-05-13,"150000325",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0736N5945","SNS",5947154.45387769,736976.994606619,1.950526,0.5851579,1,300,2 +"150000323",1997-05-13,"150000330",5410,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0731N5940","SNS",5940057.47277535,733186.421690751,1.755474,0.5266421,1,300,1 +"150000323",1997-05-13,"150000356",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0711N5935","SNS",5934882.00895918,709918.517193841,2.416957,0.725087,1,300,22 +"150000459",1997-05-15,"150000477",1680,150,5,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0611N5875","SNS",5876433.551392,613753.552376463,2.50155,0.750465,1,300,23 +"150000481",1997-07-21,"150000484",4960,150,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0611N5880","SNS",5878831.35409656,612121.431928666,1.605576,0.4816727,1,300,2 +"150000579",1997-07-22,"150000594",6270,150,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0711N5940","SNS",5938790.55914882,710215.267955831,2.605411,0.7816232,2,300,1 +"150000579",1997-07-22,"150000604",1730,150,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5942510.77349682,735001.965575626,2.47371,0.7421129,1,300,1 +"150000658",1997-07-23,"150000688",6270,150,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0681N5930","SNS",5932419.99116242,682355.095300396,2.306796,0.6920388,1,300,1 +"150000846",1997-09-09,"150000847",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0651N5915","SNS",5914122.97516484,652812.759859067,2.456051,0.7368153,2,300,1 +"150000846",1997-09-09,"150000854",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0666N5920","SNS",5918036.15513307,665353.779140205,1.235333,0.3706,1,300,12 +"150000846",1997-09-09,"150000855",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0666N5920","SNS",5918907.10611705,666377.368829619,1.544167,0.46325,1,300,1 +"150000846",1997-09-09,"150000856",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0666N5920","SNS",5920024.40443066,666927.385098051,1.258286,0.3774858,1,300,2 +"150000846",1997-09-09,"150000862",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5922951.02890805,673562.712279138,0.6176667,0.1853,1,300,1 +"150000846",1997-09-09,"150000864",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0676N5920","SNS",5921999.88319455,675585.618562347,1.360805,0.4082415,1,300,1 +"150000846",1997-09-09,"150000865",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0676N5920","SNS",5922265.87158655,677671.897521472,3.341526,1.002458,1,300,1 +"150000846",1997-09-09,"150000872",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0691N5925","SNS",5922848.0807807,691141.232233185,2.161833,0.64855,1,300,70 +"150000846",1997-09-09,"150000873",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0691N5925","SNS",5922630.60792158,692647.277628398,0.9265,0.27795,1,300,3 +"150000846",1997-09-09,"150000876",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0696N5925","SNS",5923945.4207673,695726.252746822,2.2236,0.66708,1,300,2 +"150000846",1997-09-09,"150000878",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0696N5925","SNS",5926105.53369131,695971.335263869,0.8677812,0.2603344,1,300,5 +"150000879",1997-09-11,"150000881",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0701N5925","SNS",5926695.51073467,699347.491482871,1.976533,0.59296,1,300,4 +"150000879",1997-09-11,"150000934",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0641N5925","SNS",5927128.86139082,641516.565249644,1.664745,0.4994236,1,300,1 +"150000879",1997-09-11,"150000950",1730,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0636N5910","SNS",5909264.31084416,638755.392522052,1.834067,0.55022,1,300,17 +"150000952",1997-09-12,"150000960",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0646N5900","SNS",5900193.75401501,644083.374585406,1.654846,0.4964539,1,300,5 +"150000952",1997-09-12,"150000962",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0641N5895","SNS",5897050.94860202,643710.35932821,1.654846,0.4964539,1,300,1 +"150000952",1997-09-12,"150000964",4960,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5893999.10949193,645389.283254445,1.983705,0.5951114,1,300,1 +"150000952",1997-09-12,"150000964",5410,150,1,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5893999.10949193,645389.283254445,1.983705,0.5951114,1,300,4 +"150000988",1997-10-20,"150000988",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5875940.2444199,622793.170691893,2.530797,0.759239,1,300,36 +"150000988",1997-10-20,"150000992",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0631N5880","SNS",5880642.12243337,629592.486640291,2.490977,0.7472932,1,300,2 +"150000988",1997-10-20,"150000995",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0636N5880","SNS",5881467.51907474,635461.617071205,2.439783,0.731935,1,300,2 +"150000988",1997-10-20,"150000996",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0636N5880","SNS",5882619.39328267,636905.631328275,2.412314,0.7236943,1,300,23 +"150000988",1997-10-20,"150001014",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5905","SNS",5904331.24713734,645460.091431014,1.613647,0.4840941,1,300,1 +"150000988",1997-10-20,"150001021",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0656N5910","SNS",5912238.54587787,654653.221178634,2.439783,0.731935,1,300,3 +"150000988",1997-10-20,"150001026",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0661N5915","SNS",5916268.08126594,661969.028190523,2.069183,0.620755,1,300,6 +"150000988",1997-10-20,"150001029",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0666N5920","SNS",5918358.83289183,665825.162068894,1.655347,0.496604,1,300,1 +"150001034",1997-10-21,"150001034",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5920","SNS",5922481.8235842,669306.033631735,0,0,2,300,41 +"150001034",1997-10-21,"150001034",1730,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5920","SNS",5922481.8235842,669306.033631735,0,0,2,300,40 +"150001034",1997-10-21,"150001035",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923026.26973134,670018.17963757,0,0,1,300,26 +"150001034",1997-10-21,"150001035",1730,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923026.26973134,670018.17963757,0,0,1,300,3 +"150001034",1997-10-21,"150001036",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5925","SNS",5923957.74054851,670372.687062438,0,0,2,300,1 +"150001034",1997-10-21,"150001038",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0671N5920","SNS",5922353.70033901,670391.548240031,0,0,1,300,2 +"150001087",1997-10-22,"150001120",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0706N5940","SNS",5938035.68157037,704144.132128312,2.252964,0.6758892,1,300,1 +"150001214",1997-10-24,"150001214",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5905","SNS",5905121.77803117,644255.299178998,1.961528,0.5884584,1,300,1 +"150001214",1997-10-24,"150001216",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5900","SNS",5901366.65278751,644141.518122092,2.021033,0.6063098,1,300,5 +"150001214",1997-10-24,"150001218",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0641N5895","SNS",5897251.08159797,643553.950431716,1.616826,0.4850478,1,300,3 +"150001214",1997-10-24,"150001220",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5894119.69240835,645144.364574738,2.103309,0.6309927,1,300,7 +"150001240",1997-11-17,"150001240",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5873377.33424086,619667.510109597,2.31625,0.694875,1,300,6 +"150001240",1997-11-17,"150001243",1730,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5871646.57424788,615607.555561423,1.01915,0.305745,1,300,2 +"150001294",1997-11-19,"150001303",1730,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0696N5930","SNS",5930416.79098621,694274.970857286,2.425521,0.7276564,1,300,7 +"150001339",1997-11-20,"150001346",1680,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0641N5905","SNS",5903645.73028863,643817.786600104,2.586965,0.7760895,1,300,3 +"150001339",1997-11-20,"150001350",5410,150,2,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5895","SNS",5895535.48946909,643884.058915196,2.254483,0.676345,1,300,85 +"150001372",1999-01-11,"150001373",5410,150,3,1998,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872590.77663715,617465.268959649,2.81,0.84,1,300,4 +"150001384",1999-04-16,"150001392",1680,150,5,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0636N5885","SNS",5883308.45385911,636906.550748916,1.853,0.5559,1,300,1 +"150001384",1999-04-16,"150001405",4960,150,5,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0646N5905","SNS",5905238.65490433,644181.228602852,3.397167,1.01915,1,300,2521 +"150001406",1999-04-18,"150001406",1680,150,5,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0636N5905","SNS",5907206.50236597,637968.849716285,1.544167,0.46325,1,300,2 +"150001722",1998-08-28,"150001731",800,150,1,1998,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0606N5815","SNS",5814016.36528563,604149.4307573,4.337958,1.301387,2,300,1 +"150001722",1998-08-28,"150001731",6270,150,1,1998,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0606N5815","SNS",5814016.36528563,604149.4307573,4.337958,1.301387,2,300,2 +"150001758",1998-09-01,"150001760",4960,150,1,1998,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0466N6170","SNS",6170537.70300843,468686.993218275,4.669272,1.400782,1,300,1 +"150002062",1998-10-12,"150002062",800,150,2,1998,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5875","SNS",5874859.08590287,621062.781849771,1.235333,0.3706,2,300,1 +"150002062",1998-10-12,"150002066",1730,150,2,1998,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0626N5875","SNS",5877434.88915301,624261.83037856,2.947997,0.884399,1,300,8 +"160000001",1993-02-02,"160000006",800,160,4,1993,1,1,1,"2",NA,"data update and replacement 06/11/2020","E0211N6370","CNS",6371956.04834426,213687.647979223,3.67,1.1,1,300,111 +"160000246",1993-02-20,"160000249",800,160,4,1993,1,1,1,"2",NA,"data update and replacement 06/11/2020","E0216N6390","CNS",6392265.26503954,216115.859002811,3.83,1.15,1,300,3 +"160000246",1993-02-20,"160000250",800,160,4,1993,1,1,1,"2",NA,"data update and replacement 06/11/2020","E0216N6395","CNS",6394188.25456214,215251.187918528,3.83,1.15,2,300,7 +"160000261",1993-02-22,"160000282",800,160,4,1993,1,1,1,"2",NA,"data update and replacement 06/11/2020","E0181N6285","CNS",6283188.62629406,179739.815870495,3.67,1.1,2,300,7 +"160000261",1993-02-22,"160000284",800,160,4,1993,1,1,1,"2",NA,"data update and replacement 06/11/2020","E0181N6290","CNS",6290675.31817179,179284.414548431,3.67,1.1,2,300,2 +"160001226",1993-02-10,"160001226",800,160,4,1993,1,1,2,"2257",NA,"data update and replacement 06/11/2020","E0131N6445","CNS",6444329.29809272,130751.960653654,4,1.2,1,300,2.76 +"160001226",1993-02-10,"160001227",800,160,4,1993,1,1,2,"2257",NA,"data update and replacement 06/11/2020","E0131N6440","CNS",6440275.681076,130177.136811135,4,1.2,1,300,0.92 +"160001382",1994-03-24,"160001383",1680,160,4,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0606N5860","SNS",5860867.41696397,607877.993167109,1.88,0.56,1,300,6 +"160001411",1994-05-03,"160001428",4960,160,5,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0761N5965","SNS",5966650.24597348,763393.878082377,2.94,0.88,1,300,3 +"160001411",1994-05-03,"160001449",4960,160,5,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0836N6015","SNS",6014275.0451167,837948.677996443,3.83,1.15,1,300,1 +"160001561",1994-05-06,"160001615",800,160,5,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0206N6345","CNS",6345265.08889223,204125.653083722,3.66,1.1,1,300,1 +"160001616",1994-05-06,"160001617",5410,160,5,1994,1,1,2,"2252",NA,"data update and replacement 06/11/2020","E0196N6345","CNS",6344118.30916768,196855.250964443,3.66,1.1,1,300,8 +"160003920",1994-08-26,"160003954",6270,160,1,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5810","SNS",5811975.12146158,593216.279665651,2.78,0.83,1,300,1 +"160004764",1994-08-02,"160004764",800,160,1,1994,1,1,2,"2251",NA,"data update and replacement 06/11/2020","E0211N6325","CNS",6324372.69602067,212589.719268442,3.59,1.08,1,300,0 +"160005514",1994-11-03,"160005522",1680,160,2,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0416N6010","SNS",6012143.47582142,413887.665896915,4.62,1.39,2,300,1 +"160005549",1994-11-04,"160005549",1680,160,2,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5815","SNS",5815443.19183061,599830.569121426,4.07,1.22,1,300,1 +"160005549",1994-11-04,"160005551",1680,160,2,1994,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0591N5815","SNS",5813988.23729146,591819.03388206,4.07,1.22,1,300,23 +"170000001",1997-06-30,"170000030",5410,170,6,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0526N5930","SNS",5929659.63320118,525998.468892123,3.904681,1.171404,1,300,1 +"170000149",1997-07-02,"170000205",800,170,6,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6470","CNS",6470067.28709438,155934.087625165,3.318967,0.99569,2,300,3 +"170000149",1997-07-02,"170000206",800,170,6,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6465","CNS",6466960.73719222,154738.080548873,3.318967,0.99569,2,300,4 +"170000149",1997-07-02,"170000207",800,170,6,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6465","CNS",6464165.212077,153660.412762505,2.655173,0.796552,2,300,1 +"170000160",1997-07-02,"170000214",5410,170,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0181N6460","CNS",6460392.45926478,180423.256369354,3.64645,1.093935,1,300,1 +"170000309",1997-07-04,"170000324",5410,170,6,1997,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0291N6425","CNS",6425330.1258933,289298.371088858,5.387147,1.616144,1,300,1 +"170000454",1997-07-08,"170000508",5410,170,6,1997,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0321N6340","CNS",6340086.31407727,322197.647378478,4.102189,1.230657,1,300,1 +"170001807",1998-07-08,"170001844",5410,170,6,1998,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0161N6450","CNS",6451878.0124797,162511.538123062,0.9170326,0.2751098,1,300,1 +"170001807",1998-07-08,"170001884",5410,170,6,1998,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0211N6430","CNS",6430095.39879535,210223.591096647,1.863207,0.558962,1,300,1 +"170002601",1995-07-03,"170002604",1730,170,6,1995,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0576N5850","SNS",5850660.4502594,578655.317921988,4.43,1.33,1,300,4 +"170003411",1995-07-18,"170003419",4960,170,6,1995,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0206N6205","SNS",6207095.65025016,207324.981187221,4.03,1.21,1,300,17 +"170003491",1995-07-19,"170003538",6270,170,6,1995,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0221N6150","SNS",6150729.39027317,221290.906425054,1.9,0.57,1,300,1 +"170003741",1999-06-30,"170003761",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0231N6455","CNS",6456537.73436039,230454.882243183,3.648959,1.094688,1,300,1 +"170003741",1999-06-30,"170003783",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6455","CNS",6456740.78416464,156657.725986364,3.621811,1.086543,2,300,1 +"170003890",1999-07-02,"170003926",800,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0161N6470","CNS",6470078.08454151,161950.332932067,3.815818,1.144746,2,300,1 +"170004035",1999-07-06,"170004070",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0346N6365","CNS",6366906.19611729,347969.547682658,3.77032,1.131096,2,300,2 +"170004035",1999-07-06,"170004075",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0366N6365","CNS",6366338.08445146,366878.373528154,3.77032,1.131096,1,300,5 +"170004114",1999-07-07,"170004115",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0231N6345","CNS",6344659.68914467,230761.478870379,3.614535,1.084361,1,300,17 +"170004114",1999-07-07,"170004117",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0221N6345","CNS",6345236.92263692,223557.066121815,3.55353,1.066059,1,300,6 +"170004180",1999-07-08,"170004190",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0286N6285","CNS",6286908.61821983,285187.548184164,3.732589,1.119777,1,300,15 +"170004192",1999-07-08,"170004211",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0206N6290","CNS",6290571.95214974,207217.515494243,3.677676,1.103303,1,300,2 +"170004192",1999-07-08,"170004212",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0201N6290","CNS",6290797.58343801,203531.099584073,3.677676,1.103303,1,300,2 +"170004192",1999-07-08,"170004218",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0181N6290","CNS",6290182.15245159,183271.758513183,3.676777,1.103033,1,300,4 +"170004355",1999-07-12,"170004355",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0191N6210","CNS",6207844.18748984,189347.25309698,3.598584,1.079575,2,300,11 +"170004433",1999-07-13,"170004458",5410,170,6,1999,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0271N6145","SNS",6147142.92303421,270570.587576025,3.711166,1.11335,1,300,1 +"170004569",1999-07-15,"170004590",5410,170,6,1999,1,1,2,"2302",NA,"data update and replacement 06/11/2020","E0301N6060","SNS",6062261.03997722,300534.987779375,3.495831,1.048749,1,300,2 +"170007069",2001-06-25,"170007085",5410,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0551N5895","SNS",5892899.40671533,552588.263718705,4.835284,1.450585,1,300,5 +"170007143",2001-06-27,"170007167",800,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6500","CNS",6502550.88664219,157176.040892462,4.838478,1.451543,1,300,1 +"170007143",2001-06-27,"170007170",800,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6490","CNS",6487994.53467029,157314.22499336,4.838478,1.451543,2,300,1 +"170007319",2001-07-01,"170007319",800,170,6,2001,3,1,1,"90",NA,"data update and replacement 06/11/2020","E0196N6345","CNS",6346266.68488621,194250.585742902,0,0,1,0,5 +"170007321",2001-07-02,"170007346",5410,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0326N6365","CNS",6367292.41389112,325815.709934256,3.837639,1.151292,1,300,1 +"170007392",2001-07-03,"170007396",5410,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0361N6340","CNS",6339410.78777688,358877.603949579,3.976567,1.19297,1,300,1 +"170007392",2001-07-03,"170007417",800,170,6,2001,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0291N6340","CNS",6339945.84401846,291104.142412795,3.693751,1.108125,1,300,1 +"170007470",2001-07-04,"170007493",5410,170,6,2001,1,1,2,"2302",NA,"data update and replacement 06/11/2020","E0381N6310","CNS",6310450.83398043,382003.729403198,3.571603,1.071481,1,300,10 +"170008089",2002-06-25,"170008109",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0446N6055","SNS",6056650.30717292,448713.285797924,4.408082,1.322425,1,300,3 +"170008089",2002-06-25,"170008126",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0411N6115","SNS",6115190.34795265,411314.934810502,3.163567,0.9490701,1,300,1 +"170008174",2002-06-26,"170008188",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6515","CNS",6515189.39570263,152550.214603716,5.278074,1.583422,2,300,11 +"170008174",2002-06-26,"170008200",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6465","CNS",6466263.43150828,153780.257024454,3.123637,0.937091,1,300,2 +"170008174",2002-06-26,"170008201",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6465","CNS",6463132.14636401,153877.127122729,3.123637,0.937091,1,300,22 +"170008174",2002-06-26,"170008202",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6460","CNS",6460000.86889127,153974.603339337,3.123637,0.937091,2,300,4 +"170008174",2002-06-26,"170008205",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6450","CNS",6450728.69082927,154249.410729675,3.042923,0.9128769,2,300,1 +"170008277",2002-06-27,"170008277",5410,170,6,2002,3,1,2,"90",NA,"data update and replacement 06/11/2020","E0351N6420","CNS",6421276.67559869,353313.739341964,3.113635,0.9340904,1,300,4 +"170008277",2002-06-27,"170008284",5410,170,6,2002,3,1,2,"90",NA,"data update and replacement 06/11/2020","E0376N6420","CNS",6421491.91616904,375091.058779818,3.05523,0.9165691,1,300,2 +"170008290",2002-06-28,"170008319",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0221N6390","CNS",6389682.88898914,220836.161776538,3.293978,0.9881933,1,300,1 +"170008326",2002-06-29,"170008330",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0196N6345","CNS",6345366.75865097,195097.932003525,1.596919,0.4790757,1,300,16 +"170008331",2002-06-29,"170008332",800,170,6,2002,4,1,2,"90",NA,"data update and replacement 06/11/2020","E0176N6305","CNS",6307074.6817723,174406.058533187,0,0,1,0,2 +"170008331",2002-06-29,"170008333",800,170,6,2002,4,1,2,"90",NA,"data update and replacement 06/11/2020","E0171N6305","CNS",6305167.45849578,169755.546975705,0,0,1,0,4 +"170008331",2002-06-29,"170008334",800,170,6,2002,4,1,2,"90",NA,"data update and replacement 06/11/2020","E0166N6295","CNS",6297349.49460463,167291.595383045,0,0,1,0,3 +"170008331",2002-06-29,"170008335",800,170,6,2002,4,1,2,"90",NA,"data update and replacement 06/11/2020","E0186N6320","CNS",6319752.02611422,183958.182756142,0,0,1,0,34 +"170008336",2002-06-30,"170008340",800,170,6,2002,4,1,2,"90",NA,"data update and replacement 06/11/2020","E0196N6350","CNS",6350826.38553574,194053.859000711,0,0,1,0,3 +"170008567",2002-07-04,"170008630",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0466N6110","SNS",6110149.42829213,468024.970884446,3.590301,1.07709,2,300,8 +"170008643",2002-07-08,"170008649",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0586N5840","SNS",5839404.76077842,585416.88811684,4.939368,1.48181,1,300,1 +"170008682",2002-07-09,"170008694",4960,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0416N6280","CNS",6281643.7404255,417239.320534345,3.614681,1.084404,1,300,14 +"170008682",2002-07-09,"170008714",4960,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0351N6280","CNS",6282440.41837597,350316.380737296,3.611723,1.083517,1,300,10 +"170008887",2002-07-12,"170008912",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6245","CNS",6242701.0715842,157421.220923748,3.673757,1.102127,1,300,5 +"170008887",2002-07-12,"170008913",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6240","CNS",6242561.90151207,153733.291549159,3.673757,1.102127,1,300,26 +"170008887",2002-07-12,"170008914",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6240","CNS",6242473.31512193,151335.780322329,1.102127,0.3306381,1,300,34 +"170008887",2002-07-12,"170008915",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6240","CNS",6240934.3333213,150612.561850902,3.047206,0.9141617,1,300,44 +"170008887",2002-07-12,"170008918",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6240","CNS",6237881.87216474,144950.791044768,3.514232,1.054269,1,300,6 +"170008887",2002-07-12,"170008919",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6240","CNS",6238075.90190375,141425.296804745,3.514232,1.054269,1,300,8 +"170008887",2002-07-12,"170008920",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6240","CNS",6238273.96567501,137899.804431806,3.514232,1.054269,1,300,7 +"170008887",2002-07-12,"170008921",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6240","CNS",6238474.94248637,134374.34686916,3.514232,1.054269,2,300,3 +"170008887",2002-07-12,"170008925",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6235","CNS",6235456.7731254,135768.545289063,3.608542,1.082563,1,300,6 +"170008887",2002-07-12,"170008926",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6235","CNS",6235131.42462994,139379.721233254,3.608542,1.082563,2,300,6 +"170008887",2002-07-12,"170008927",800,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6235","CNS",6234808.03173005,142990.979180322,3.608542,1.082563,1,300,6 +"170008955",2002-07-13,"170008955",800,170,6,2002,1,4,1,"90",NA,"data update and replacement 06/11/2020","E0116N6225","CNS",6223992.58379757,117834.448428356,0,0,1,0,12 +"170008961",2002-07-15,"170008969",5410,170,6,2002,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0246N6205","SNS",6204593.29819188,247484.955650076,3.624357,1.087307,1,300,5 +"170009223",2002-07-23,"170009223",800,170,6,2002,1,4,1,"90",NA,"data update and replacement 06/11/2020","E0201N6180","SNS",6180797.03306735,199971.174543554,0,0,1,0,8 +"170009223",2002-07-23,"170009224",800,170,6,2002,1,4,1,"90",NA,"data update and replacement 06/11/2020","E0181N6210","CNS",6208716.01487507,179318.629918874,0,0,1,0,49 +"170009227",2003-06-07,"170009227",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0216N6380","CNS",6382110.7108722,214574.310452581,1.422242,0.4266725,2,300,32 +"170009529",2003-06-09,"170009594",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6260","CNS",6261383.70674228,156660.900976758,1.060194,0.3180582,1,300,1 +"170009728",2003-06-10,"170009744",5410,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0271N6225","CNS",6225499.81179244,273138.530817388,1.229056,0.3687169,1,300,7 +"170009795",2003-06-11,"170009849",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6245","CNS",6246226.32187951,155985.394760355,1.473389,0.4420168,2,300,12 +"170009795",2003-06-11,"170009850",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0156N6245","CNS",6245604.72631059,154643.684435282,1.473389,0.4420168,2,300,2 +"170009795",2003-06-11,"170009851",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6245","CNS",6244983.54379358,153301.495154191,1.473389,0.4420168,1,300,1 +"170009795",2003-06-11,"170009852",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6245","CNS",6244361.65341334,151958.860006883,1.473389,0.4420168,1,300,5 +"170009795",2003-06-11,"170009853",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6245","CNS",6243741.2822405,150615.89844902,1.473389,0.4420168,1,300,2 +"170009795",2003-06-11,"170009854",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6245","CNS",6243121.32450175,149272.458195226,1.473389,0.4420168,1,300,3 +"170009795",2003-06-11,"170009855",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6240","CNS",6242500.65921259,147928.571406077,1.473389,0.4420168,1,300,2 +"170009795",2003-06-11,"170009856",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6240","CNS",6241881.51340925,146584.359482659,1.473389,0.4420168,2,300,19 +"170009795",2003-06-11,"170009857",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6240","CNS",6241262.78142187,145239.669122592,1.473389,0.4420168,1,300,88 +"170009795",2003-06-11,"170009859",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6240","CNS",6240532.85460387,143342.634167485,1.331785,0.3995356,1,300,3 +"170009795",2003-06-11,"170009863",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6240","CNS",6238866.57920472,138260.086229583,1.331785,0.3995356,1,300,2 +"170009795",2003-06-11,"170009864",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6240","CNS",6238450.93127589,136988.745918266,1.331785,0.3995356,1,300,15 +"170009795",2003-06-11,"170009865",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0136N6240","CNS",6238035.6518671,135717.124413444,1.331785,0.3995356,1,300,1 +"170009795",2003-06-11,"170009878",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6235","CNS",6234421.94284316,138825.726835641,1.75626,0.5268779,1,300,2 +"170009795",2003-06-11,"170009879",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0141N6235","CNS",6234293.71090869,140585.666081863,1.75626,0.5268779,1,300,4 +"170009795",2003-06-11,"170009881",800,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6235","CNS",6234038.31249264,144105.565391278,1.75626,0.5268779,1,300,1 +"170011471",2003-06-27,"170011480",5410,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0441N6345","CNS",6345804.88209482,443599.724333837,1.663297,0.4989892,1,300,1 +"170012235",2003-07-07,"170012329",5410,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0481N6000","SNS",5999439.93608464,482139.495969205,2.430325,0.7290974,1,300,1 +"170013320",2003-07-17,"170013377",4960,170,6,2003,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0341N6070","SNS",6069323.23191059,340219.455876318,1.613914,0.4841743,1,300,3 +"170013388",2003-06-29,"170013388",800,170,6,2003,NA,NA,2,"90",NA,"data update and replacement 06/11/2020","E0186N6320","CNS",6319188.67252247,184017.156562246,0,0,1,0,2 +"170013388",2003-06-29,"170013389",800,170,6,2003,NA,NA,2,"90",NA,"data update and replacement 06/11/2020","E0186N6320","CNS",6321990.0291454,183924.89825151,0,0,1,0,20 +"170013637",2004-06-30,"170013653",800,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6515","CNS",6514078.29130792,152468.363832096,2.012757,0.6038272,2,300,2 +"170013637",2004-06-30,"170013659",5410,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0151N6500","CNS",6501005.81600034,151387.8779015,2.197713,0.6593138,1,300,2 +"170013637",2004-06-30,"170013670",800,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0146N6480","CNS",6479816.60602026,143873.273517806,1.860098,0.5580294,1,300,1 +"170014019",2004-07-03,"170014122",800,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0196N6345","CNS",6344224.2523169,195465.75360715,2.475647,0.742694,1,300,2 +"170014429",2004-07-07,"170014467",5410,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0406N6280","CNS",6281613.55895906,405377.113387808,1.595835,0.4787504,1,300,1 +"170015299",2004-07-19,"170015373",5410,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0441N6170","SNS",6169361.90538164,441650.963747062,1.849364,0.5548091,1,300,9 +"170015567",2004-07-21,"170015653",5410,170,6,2004,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0296N6065","SNS",6062695.43138514,295991.516121909,1.695577,0.508673,1,300,1 +"180006922",2006-06-26,"180006939",4960,180,6,2006,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5870796.24080946,618445.370926373,1.601302,0.4803906,1,300,3 +"19910814725",1991-08-14,"19910814235786606639625",800,140,1,1991,9,1,1,"PW",NA,"MWTL","E0236N6065","SNS",6066395.79201953,235785.963503772,NA,0.6661,1,97,1 +"19911007722",1991-10-07,"19911007587518610198322",800,140,2,1991,9,1,1,"HB",NA,"MWTL","E0586N6100","SNS",6101983.29053551,587517.967610991,NA,0.6406,1,71,1 +"19990305427",1999-03-05,"19990305605100581481827",800,140,4,1999,9,1,1,"SL",NA,"MWTL","E0606N5815","SNS",5814817.7645281,605100.007246348,NA,0.5281,1,89,1 +"200000346",2001-03-18,"200007261",800,200,4,2001,1,1,1,"148",NA,"ESAS v5","E0156N6510","CNS",6511616.32001205,156419.129044015,4.1173,1.2352,1,300,2 +"20000102",1993-02-05,"20000120",800,20,4,1993,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0216N6380","CNS",6379918.88642689,216955.457289384,4,1.2,2,300,36 +"20000102",1993-02-05,"20000121",800,20,4,1993,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0216N6375","CNS",6376525.0204416,214912.209218437,3.9,1.17,2,300,2 +"20000103",1993-02-05,"20000115",800,20,4,1993,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0226N6395","CNS",6397296.97436265,226853.826135306,3.9,1.18,1,300,1 +"20000251",1993-02-11,"20000254",1730,20,4,1993,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0571N5830","SNS",5832000.29809267,571060.362557745,3.7,1.12,1,300,2 +"20000387",1993-02-23,"20000408",800,20,4,1993,1,1,1,"90",NA,"data update and replacement 06/11/2020","E0431N5790","SNS",5791942.35033476,429494.943838622,3.9,1.18,2,300,1 +"20000414",1993-02-24,"20000432",5410,20,4,1993,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0536N5735","SNS",5737267.94357288,537823.293863931,3.7,1.11,1,300,1 +"20000517",1993-01-18,"20000544",5410,20,3,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0866N6010","SNS",6009397.04118351,864353.006845746,1.8,0.36,1,200,1 +"20000518",1993-01-18,"20000546",5410,20,3,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0866N6010","SNS",6010220.48820261,867681.959151862,1.8,0.35,1,200,2 +"20000553",1993-01-18,"20000583",5410,20,3,1992,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0871N6010","SNS",6011453.23856757,869116.087315513,1.8,0.35,1,200,1 +"20000553",1993-01-18,"20000587",5410,20,3,1992,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0876N6015","SNS",6013040.87068997,877197.825437626,2.1,0.43,1,200,1 +"20000589",1993-01-27,"20000591",5410,20,3,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0876N6015","SNS",6014502.89296527,874561.073128567,2.1,0.62,1,300,5 +"20000590",1993-01-27,"20000592",5410,20,3,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0871N6015","SNS",6015295.78996117,872855.458015885,2.1,0.62,2,300,3 +"20000668",1993-01-29,"20000707",1730,20,3,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0821N6165","SNS",6163569.05594661,819596.244186044,2.8,0.85,1,300,8 +"20000882",1993-02-04,"20000882",1680,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0636N5915","SNS",5916287.66792791,635364.626653979,2,0.61,1,300,1 +"20001297",1993-02-12,"20001318",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5867102.79605345,612892.898154699,2.2,0.45,1,200,15 +"20001320",1993-03-11,"20001329",1730,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5864285.66116614,611389.253584234,2.7,0.81,1,300,2 +"20001320",1993-03-11,"20001341",1680,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5834528.61295927,607788.19949596,2.9,0.88,1,300,5 +"20001320",1993-03-11,"20001341",1730,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5834528.61295927,607788.19949596,2.9,0.88,1,300,3 +"20001320",1993-03-11,"20001342",1680,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5831559.78810394,607741.015106221,3.5,1.05,1,300,22 +"20001320",1993-03-11,"20001345",1680,20,4,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5843114.18259244,610188.092804054,2.5,0.75,2,300,3 +"20001321",1993-03-11,"20001332",1730,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5860","SNS",5858360.58128467,611862.666993223,2.7,0.81,1,300,2 +"20001321",1993-03-11,"20001333",5410,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5855","SNS",5855752.72664598,611361.368967386,2.7,0.81,1,300,1 +"20001321",1993-03-11,"20001337",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5845320.88749757,609350.194668306,2.7,0.8,2,300,19 +"20001321",1993-03-11,"20001338",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5842712.26601014,608845.885585722,2.7,0.8,2,300,102 +"20001321",1993-03-11,"20001346",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5845","SNS",5845716.72092095,610466.71426242,2.5,0.75,1,300,52 +"20001321",1993-03-11,"20001354",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5867102.79605345,612892.898154699,2.9,0.88,1,300,49 +"20001358",1993-03-12,"20001360",1680,20,4,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5860","SNS",5859146.94318625,613752.432819827,1,0.31,1,300,12 +"20001374",1993-05-10,"20001396",1680,20,5,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0636N5910","SNS",5910065.36606964,638319.236893846,2.2,0.66,1,300,4 +"20001480",1993-05-12,"20001509",6270,20,5,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0771N5970","SNS",5972117.89588378,771223.520651566,2.4,0.71,2,300,1 +"20001617",1993-05-14,"20001645",1680,20,5,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0831N6120","SNS",6119908.94554632,833557.543229162,2,0.6,1,300,10 +"20001781",1993-05-15,"20001799",6270,20,5,1993,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0816N6100","SNS",6098200.43169269,816741.05930497,1.9,0.38,2,200,1 +"20001887",1993-05-16,"20001889",1680,20,5,1993,1,1,2,"2258",NA,"data update and replacement 06/11/2020","E0861N6025","SNS",6023535.21042963,861705.984093441,1.9,0.58,1,300,55 +"20001930",1993-05-17,"20001933",1730,20,5,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0811N5980","SNS",5978743.16837948,811722.825348309,2.3,0.69,1,300,3 +"20001979",1993-05-17,"20001979",1680,20,5,1993,1,1,2,"2258",NA,"data update and replacement 06/11/2020","E0816N5975","SNS",5973260.05265696,815944.218717774,2.3,0.69,1,300,6 +"20002014",1993-05-18,"20002017",1680,20,5,1993,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0741N5940","SNS",5941585.55970786,739580.136370924,2,0.59,1,300,5 +"20002015",1993-05-18,"20002018",1680,20,5,1993,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5942046.46473017,737676.79129911,1,0.29,1,300,15 +"20004179",1992-01-08,"20004199",1680,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0721N5940","SNS",5939820.47593084,722524.930687301,1.8,0.36,1,200,12 +"20004263",1992-01-09,"20004263",5410,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0766N5920","SNS",5919741.08289884,765476.081531628,2.566667,0.77,1,300,3 +"20004263",1992-01-09,"20004269",1530,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0766N5920","SNS",5919741.08289884,765476.081531628,2.566667,0.77,1,300,14 +"20004276",1992-01-10,"20004299",1730,20,3,1991,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0771N5965","SNS",5965697.15460803,769504.295530785,2.15,0.43,1,200,7 +"20004304",1992-01-10,"20004321",1730,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0791N5970","SNS",5968112.99537862,791167.273999211,2.166667,0.65,1,300,2 +"20004539",1992-01-14,"20004567",1680,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0841N6060","SNS",6059817.10370777,841843.209220016,1.7,0.34,1,200,6 +"20004797",1992-01-21,"20004799",1680,20,3,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0841N6000","SNS",6001793.85423025,842919.064514343,2.566667,0.77,1,300,2 +"20004797",1992-01-21,"20004799",1730,20,3,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0841N6000","SNS",6001793.85423025,842919.064514343,2.566667,0.77,1,300,2 +"20004821",1992-01-22,"20004825",5410,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0846N5990","SNS",5988606.18061077,845875.498985517,2.7,0.81,1,300,25 +"20004821",1992-01-22,"20004862",5410,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0841N5980","SNS",5979376.79658433,841938.832317663,1.966667,0.59,1,300,2 +"20004821",1992-01-22,"20004868",5410,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0836N5990","SNS",5990105.7454197,835878.070918125,2.6,0.78,1,300,13 +"20004822",1992-01-22,"20004865",1730,20,3,1991,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0841N5985","SNS",5984740.4138518,838905.454679437,1.9,0.57,1,300,1 +"20004830",1992-01-22,"20004831",5410,20,3,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0846N5975","SNS",5977348.35933945,844731.448481646,1.8,0.54,1,300,3 +"20004973",1992-01-24,"20005015",1730,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0751N5950","SNS",5950181.00764036,751177.726196447,2.4,0.72,1,300,5 +"20004986",1992-01-24,"20004988",800,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0766N5960","SNS",5957839.3850871,768633.561298626,2.05,0.41,1,200,1 +"20004986",1992-01-24,"20004989",800,20,3,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0766N5960","SNS",5957801.60062439,767973.859629369,1.2,0.24,1,200,1 +"20005206",1992-01-30,"20005212",1680,20,3,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0636N5910","SNS",5909866.72541624,637880.611976736,2.7,0.81,2,300,37 +"20005206",1992-01-30,"20005261",1680,20,3,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5872526.02616915,614780.57008416,2.633333,0.79,1,300,1 +"20005956",1991-02-04,"20005965",5410,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0516N5690","SNS",5692118.29620341,518790.601046311,1.333333,0.2,1,150,21 +"20005958",1991-02-04,"20005963",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0521N5695","SNS",5693241.41264622,521569.686671147,2.133333,0.32,1,150,43 +"20005972",1991-02-05,"20005982",1730,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0476N5670","SNS",5668791.40318511,474831.40229925,3.733333,1.12,1,300,2 +"20006061",1991-02-18,"20006061",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0621N5900","SNS",5899351.9112448,619482.722154989,1.8,0.36,1,200,1 +"20006062",1991-02-18,"20006066",800,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0626N5905","SNS",5907274.66246859,624618.480493217,2.1,0.42,1,200,1 +"20006168",1991-02-21,"20006194",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0536N5720","SNS",5718898.08761836,535307.345714916,2.4,0.36,1,150,40 +"20006177",1991-02-21,"20006196",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0531N5715","SNS",5714430.94683658,532566.755984273,2.266667,0.34,1,150,24 +"20006232",1991-02-26,"20006323",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0571N5760","SNS",5760417.20513968,570038.267193721,2.05,0.41,1,200,5 +"20006242",1991-02-27,"20006246",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0591N5780","SNS",5780792.84096416,591640.796316857,2.133333,0.32,1,150,19 +"20006242",1991-02-27,"20006247",1730,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0591N5780","SNS",5778543.38823123,590313.560850247,2.133333,0.32,1,150,4 +"20006242",1991-02-27,"20006247",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0591N5780","SNS",5778543.38823123,590313.560850247,2.133333,0.32,1,150,18 +"20006242",1991-02-27,"20006254",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0591N5785","SNS",5783042.66913803,592966.790709588,2.333333,0.35,1,150,2 +"20006242",1991-02-27,"20006273",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0591N5795","SNS",5796351.04384855,590671.138321501,2.6,0.39,1,150,2 +"20006282",1991-02-28,"20006289",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0606N5815","SNS",5814430.03303902,604607.832176964,1.266667,0.19,1,150,1 +"20006282",1991-02-28,"20006296",4960,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0606N5830","SNS",5830089.67042547,608337.949499621,2.2,0.33,2,150,12 +"20006282",1991-02-28,"20006307",4960,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0611N5850","SNS",5852426.2997029,611887.446843843,2.6,0.39,1,150,7 +"20006282",1991-02-28,"20006308",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0611N5855","SNS",5854666.41967184,612509.742821333,2.6,0.39,1,150,1 +"20006282",1991-02-28,"20006314",5410,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0611N5870","SNS",5869079.45274172,610158.585646914,1.4,0.21,1,150,1 +"20007864",1992-02-12,"20007866",5410,20,4,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0531N5695","SNS",5696636.94805909,532680.991930696,2.75,0.55,1,200,1 +"20007916",1992-02-13,"20007931",1730,20,4,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0546N5730","SNS",5727891.06544016,546993.525057778,2.8,0.56,1,200,3 +"20007971",1992-02-13,"20008004",1730,20,4,1992,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0571N5745","SNS",5745939.870264,568863.668364399,1.2,0.24,1,200,8 +"20008021",1992-02-14,"20008041",1680,20,4,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0611N5865","SNS",5863596.00544833,613647.74939647,2.85,0.57,1,200,2 +"20008023",1992-02-14,"20008035",1680,20,4,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0611N5850","SNS",5847946.3437339,610640.954198219,2.6,0.52,1,200,8 +"20008023",1992-02-14,"20008043",1680,20,4,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0616N5870","SNS",5869205.24708454,615531.743682129,3,0.6,1,200,3 +"20008044",1992-02-14,"20008044",1680,20,4,1992,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0606N5815","SNS",5815542.24817858,604584.11773197,2.2,0.44,1,200,13 +"20008044",1992-02-14,"20008047",1680,20,4,1992,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0606N5820","SNS",5822244.70786963,605798.063680004,2.25,0.45,1,200,7 +"20008044",1992-02-14,"20008055",1730,20,4,1992,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0606N5840","SNS",5840114.87530784,608791.337152829,2.35,0.47,1,200,1 +"20008045",1992-02-14,"20008053",1680,20,4,1992,1,1,2,"2258",NA,"data update and replacement 06/11/2020","E0606N5835","SNS",5835650.84450369,608214.489298137,2.35,0.47,1,200,2 +"20008045",1992-02-14,"20008063",1680,20,4,1992,1,1,2,"2258",NA,"data update and replacement 06/11/2020","E0611N5860","SNS",5860243.42261664,613053.383134239,2.6,0.52,1,200,6 +"20009014",1991-03-12,"20009014",1730,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0641N5915","SNS",5914493.27404174,643740.015069229,2.6,0.52,1,200,1 +"20009014",1991-03-12,"20009016",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0646N5920","SNS",5919003.12458361,645599.573475981,1.35,0.27,1,200,12 +"20009014",1991-03-12,"20009019",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0651N5920","SNS",5921393.85876428,650846.690122149,2.05,0.41,1,200,16 +"20009015",1991-03-12,"20009017",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0646N5920","SNS",5919044.12752496,646929.060459559,2.05,0.41,2,200,6 +"20009020",1991-03-12,"20009032",1730,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0651N5925","SNS",5924730.59752264,650740.455265022,2.166667,0.65,1,300,1 +"20009021",1991-03-12,"20009047",1730,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0681N5930","SNS",5931286.65255348,679078.430303208,2,0.6,1,300,1 +"20009021",1991-03-12,"20009049",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0686N5930","SNS",5931516.66699657,685046.354599226,2.566667,0.77,2,300,36 +"20009054",1991-03-12,"20009059",1680,20,4,1991,1,1,2,"2327",NA,"data update and replacement 06/11/2020","E0651N5920","SNS",5921393.85876428,650846.690122149,2.05,0.41,1,200,15 +"20009060",1991-03-12,"20009085",1730,20,4,1991,1,1,2,"2327",NA,"data update and replacement 06/11/2020","E0681N5930","SNS",5931286.65255348,679078.430303208,2,0.6,2,300,1 +"20009090",1991-03-13,"20009098",4960,20,4,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0726N5945","SNS",5945633.29154139,727552.34409586,2.366667,0.71,1,300,10 +"20009090",1991-03-13,"20009101",4960,20,4,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5945","SNS",5945957.92823849,734164.778162629,2.133333,0.64,1,300,1 +"20009110",1991-03-13,"20009134",1730,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0671N5930","SNS",5931088.53159193,673773.499993829,1.95,0.39,1,200,2 +"20009136",1991-03-13,"20009145",1730,20,4,1991,1,1,2,"2309",NA,"data update and replacement 06/11/2020","E0731N5945","SNS",5945729.70450881,729536.09621677,2.266667,0.68,2,300,1 +"20009150",1991-03-13,"20009163",1680,20,4,1991,1,1,2,"2309",NA,"data update and replacement 06/11/2020","E0711N5940","SNS",5938101.69338031,709332.867019481,2.5,0.5,1,200,25 +"20009171",1991-03-13,"20009171",1730,20,4,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0691N5935","SNS",5934005.29019646,691586.952623462,2.8,0.56,1,200,2 +"20009208",1991-03-18,"20009227",1730,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0621N5915","SNS",5912750.20420183,621145.020884143,2.2,0.66,1,300,3 +"20009208",1991-03-18,"20009283",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0631N5920","SNS",5919744.49521163,632938.012772384,2.033333,0.61,1,300,7 +"20009230",1991-03-18,"20009300",1680,20,4,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0656N5925","SNS",5923834.46272331,657416.904798446,2.533333,0.76,1,300,9 +"20009304",1991-03-19,"20009315",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0676N5935","SNS",5933411.17206716,676342.896085542,3,0.6,1,200,30 +"20009306",1991-03-19,"20009327",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5928973.05620479,704419.674184378,1.8,0.36,1,200,27 +"20009306",1991-03-19,"20009327",1730,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5928973.05620479,704419.674184378,1.8,0.36,1,200,1 +"20009328",1991-03-20,"20009328",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5925","SNS",5926777.72489416,705179.650061802,1.3,0.26,1,200,16 +"20009328",1991-03-20,"20009328",1730,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5925","SNS",5926777.72489416,705179.650061802,1.3,0.26,1,200,2 +"20009328",1991-03-20,"20009330",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5928973.05620479,704419.674184378,1.5,0.3,1,200,85 +"20009328",1991-03-20,"20009331",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5935","SNS",5933421.21732683,704227.298187334,4,0.8,1,200,37 +"20009328",1991-03-20,"20009332",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5935","SNS",5935674.02265589,704793.647530769,3.9,0.78,1,200,19 +"20009329",1991-03-20,"20009329",1680,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0706N5930","SNS",5927861.01926053,704467.752502277,1.3,0.26,1,200,2 +"20009329",1991-03-20,"20009335",4960,20,4,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0711N5935","SNS",5937078.32762213,711369.452390375,2.55,0.51,1,200,1 +"20009339",1991-03-20,"20009343",1680,20,4,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0706N5940","SNS",5937869.3997943,704034.821899471,2.2,0.66,1,300,3 +"20010292",1992-04-21,"20010302",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0621N5900","SNS",5898239.60479525,619510.56573588,2.333333,0.7,1,300,1 +"20010292",1992-04-21,"20010342",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0661N5985","SNS",5983008.65917392,662008.328952348,2.333333,0.7,1,300,2 +"20010295",1992-04-21,"20010298",6270,20,5,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0616N5890","SNS",5889258.76253568,616388.971502241,2.333333,0.7,1,300,2 +"20010295",1992-04-21,"20010309",5410,20,5,1992,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0626N5910","SNS",5911776.62316369,626499.130707898,2.266667,0.68,1,300,1 +"20010407",1992-04-23,"20010407",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0816N6215","CNS",6216757.28753256,818623.429064368,2.133333,0.64,1,300,8 +"20010411",1992-04-23,"20010412",5410,20,5,1992,1,1,1,"2328",NA,"data update and replacement 06/11/2020","E0821N6205","SNS",6205690.50449953,820071.244008102,2.133333,0.64,2,300,3 +"20010552",1992-04-27,"20010553",1730,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0851N6115","SNS",6112927.88923143,849377.030307901,0.9333333,0.28,1,300,1 +"20010558",1992-04-28,"20010560",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0836N6050","SNS",6049285.99278461,835479.732657593,2.433333,0.73,1,300,2 +"20010637",1992-04-30,"20010641",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0816N5995","SNS",5995525.78912723,817721.554330638,2.133333,0.64,2,300,15 +"20011694",1991-05-29,"20011694",1680,20,5,1991,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0791N5970","SNS",5968154.09682197,791825.626436142,2.6,0.52,1,200,20 +"20011695",1991-05-29,"20011695",1680,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0791N5970","SNS",5969389.50440228,793730.717348884,2.25,0.45,1,200,2 +"20011695",1991-05-29,"20011696",1680,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0796N5970","SNS",5970625.72285894,795634.831582546,1.75,0.35,1,200,105 +"20011695",1991-05-29,"20011696",6270,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0796N5970","SNS",5970625.72285894,795634.831582546,1.75,0.35,2,200,1 +"20011695",1991-05-29,"20011769",1680,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0781N5975","SNS",5975368.51384089,782137.319436899,1.85,0.37,1,200,29 +"20011705",1991-05-29,"20011705",1680,20,5,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0796N5975","SNS",5976183.39177311,795282.542123897,2.25,0.45,1,200,15 +"20011705",1991-05-29,"20011709",1680,20,5,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0786N5975","SNS",5975690.00063478,787395.537624105,1.75,0.35,1,200,220 +"20011706",1991-05-29,"20011706",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0791N5975","SNS",5976058.78827827,793310.827790607,1.6,0.32,1,200,45 +"20011706",1991-05-29,"20011708",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0791N5975","SNS",5975812.09274592,789367.325371664,2.1,0.42,1,200,50 +"20011706",1991-05-29,"20011713",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0781N5975","SNS",5975249.49075397,780165.444025515,1.55,0.31,1,200,50 +"20011706",1991-05-29,"20011714",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0781N5975","SNS",5976321.64723899,779441.437309829,2.05,0.41,1,200,60 +"20011706",1991-05-29,"20011717",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0781N5980","SNS",5981879.88422013,779107.760654988,2.6,0.52,1,200,450 +"20011706",1991-05-29,"20011765",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0791N5975","SNS",5975812.09274592,789367.325371664,2.1,0.42,1,200,18 +"20011724",1991-05-29,"20011759",1680,20,5,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0801N5975","SNS",5976519.76084837,800540.325261503,1.9,0.38,1,200,10 +"20011809",1991-05-30,"20011812",1680,20,5,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0816N6020","SNS",6019886.57585332,814738.305083568,2.45,0.49,1,200,18 +"20011821",1991-05-30,"20011867",1680,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0806N6020","SNS",6021580.44187154,806775.774790107,2.4,0.48,1,200,25 +"20011850",1991-05-30,"20011861",1680,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0816N6015","SNS",6017619.18661626,814239.223009507,2.4,0.48,1,200,30 +"20011850",1991-05-30,"20011887",6270,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0801N6030","SNS",6032264.95196026,799539.181599802,2.4,0.48,2,200,2 +"20011858",1991-05-30,"20011862",1680,20,5,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0816N6020","SNS",6019886.57585332,814738.305083568,2.45,0.49,1,200,100 +"20011909",1991-05-31,"20011934",6270,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0816N6005","SNS",6007616.64641812,814923.019499142,2.4,0.48,2,200,1 +"20011919",1991-05-31,"20011972",6270,20,5,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0811N6000","SNS",5999570.7359217,811530.342745194,2,0.4,1,200,1 +"20012017",1992-05-04,"20012017",5410,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0736N5940","SNS",5940530.96184427,737089.598911942,3.033333,0.91,1,300,30 +"20012108",1992-05-07,"20012115",1680,20,5,1992,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0656N5925","SNS",5924924.65929086,656715.986333913,1.6,0.48,1,300,25 +"20013027",1991-06-19,"20013043",5410,20,6,1991,1,1,1,"92",NA,"data update and replacement 06/11/2020","E0631N5915","SNS",5915258.21438162,631732.255360125,2.95,0.59,1,200,1 +"20015684",1991-07-12,"20015724",4960,20,6,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0241N6510","CNS",6510977.49567513,241470.15658083,3.69,1.11,1,300,1 +"20021022",1991-09-05,"20021022",4960,20,1,1991,1,1,2,"92",NA,"data update and replacement 06/11/2020","E0456N5995","SNS",5995949.74766118,458159.239993112,4.8,1.44,1,300,1 +"20021205",1991-09-12,"20021250",800,20,1,1991,2,17,2,"2320",NA,"data update and replacement 06/11/2020","E0356N5995","SNS",5993487.67769872,358006.782664893,2.5,0.75,1,300,0 +"20021606",1992-09-17,"20021617",1680,20,1,1992,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5890","SNS",5887797.24357075,601028.753561764,2.7,0.81,1,300,8 +"20021606",1992-09-17,"20021620",1680,20,1,1992,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0601N5880","SNS",5880053.72437265,603203.200912856,2.566667,0.77,1,300,12 +"20022390",1992-09-21,"20022393",5410,20,1,1992,1,1,1,"2258",NA,"data update and replacement 06/11/2020","E0276N6105","SNS",6106971.65364529,278407.136408245,1.7,0.34,1,200,1 +"20024014",1991-10-23,"20024022",1680,20,2,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0741N5940","SNS",5941844.42160572,741004.698437159,4.566667,1.37,1,300,19 +"20024014",1991-10-23,"20024025",1680,20,2,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0751N5935","SNS",5934580.42275472,751346.641433384,3.466667,1.04,1,300,38 +"20024014",1991-10-23,"20024026",1680,20,2,1991,1,1,1,"94",NA,"data update and replacement 06/11/2020","E0751N5930","SNS",5932463.17028477,753454.665476463,2.766667,0.83,1,300,79 +"20024015",1991-10-23,"20024015",1680,20,2,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0726N5955","SNS",5953289.73849841,724534.883622062,2.5,0.75,1,300,14 +"20024015",1991-10-23,"20024021",1680,20,2,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0736N5945","SNS",5945011.85522053,737527.068467247,4.566667,1.37,1,300,41 +"20024015",1991-10-23,"20024023",1680,20,2,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0746N5940","SNS",5939825.67470817,745091.370021698,5.5,1.65,1,300,72 +"20024015",1991-10-23,"20024024",1680,20,2,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0746N5935","SNS",5936663.59801727,748577.848710803,3.466667,1.04,1,300,5 +"20024027",1991-10-28,"20024030",5410,20,2,1991,1,1,2,"2328",NA,"data update and replacement 06/11/2020","E0586N5820","SNS",5821883.15016061,587487.747115113,5.066667,1.52,1,300,1 +"20024368",1992-10-27,"20024387",800,20,2,1992,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0331N6065","SNS",6066736.54530818,332544.727408717,3,1.2,1,300,1 +"20027998",1991-05-24,"20028032",6270,20,5,1991,1,1,2,"94",NA,"data update and replacement 06/11/2020","E0576N5770","SNS",5768282.56954391,575413.472001968,2,0.6,1,300,1 +"20028495",2008-12-15,"20028536",1730,20,3,2008,1,1,2,"90",NA,"data update and replacement 06/11/2020","E0621N5870","SNS",5871124.93886229,623382.124321602,0.889717,0.2669151,1,300,3 +"20060826424",2006-08-26,"20060826647648592011524",6270,140,1,2006,9,1,1,"MH",NA,"MWTL","E0646N5920","SNS",5920115.21305004,647647.988337563,NA,0.7644,1,138,2 +"20060826424",2006-08-26,"20060826674824592942824",6270,140,1,2006,9,1,1,"MH",NA,"MWTL","E0676N5930","SNS",5929427.80106987,674823.997399367,NA,0.3433,1,138,1 +"20060826424",2006-08-26,"20060826724455593795424",6270,140,1,2006,9,1,1,"MH",NA,"MWTL","E0726N5940","SNS",5937953.90746262,724455.016351273,NA,0.3368,1,138,1 +"20070424415",2007-04-24,"20070424525064569694315",6270,140,5,2007,9,1,1,"PW",NA,"MWTL","E0526N5695","SNS",5696943.00658597,525064.003038128,NA,0.3046,1,92,2 +"20140118415",2014-01-18,"20140118611152585166315",800,140,3,2013,9,1,1,"PW",NA,"MWTL","E0611N5850","SNS",5851662.65917756,611152.00490963,NA,0.257,1,176,1 +"220000122",2008-03-18,"220008417",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0196N6350","CNS",6349357.32016475,198094.770799488,0.2255,0.0677,1,300,2 +"220000124",2008-03-19,"220008467",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6460","CNS",6459551.51508436,112161.810137154,0.2928,0.0878,2,300,1 +"220000124",2008-03-19,"220008468",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6460","CNS",6459311.06645294,112328.085831479,0.2872,0.0862,1,300,2 +"220000124",2008-03-19,"220008469",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6460","CNS",6459071.20111981,112488.507026064,0.289,0.0867,1,300,1 +"220000124",2008-03-19,"220008470",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6460","CNS",6458811.44269035,112623.267772464,0.2847,0.0854,2,300,1 +"220000124",2008-03-19,"220008473",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6460","CNS",6458055.55002372,113018.088294691,0.2834,0.085,1,300,2 +"220000124",2008-03-19,"220008478",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6455","CNS",6456770.88135092,113664.185313831,0.2832,0.085,1,300,4 +"220000124",2008-03-19,"220008479",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6455","CNS",6456523.41641674,113788.449541413,0.2807,0.0842,2,300,1 +"220000124",2008-03-19,"220008486",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6455","CNS",6454701.33073398,114661.938664626,0.2855,0.0856,1,300,1 +"220000124",2008-03-19,"220008490",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6455","CNS",6453668.37683175,115143.624782108,0.2889,0.0867,2,300,1 +"220000124",2008-03-19,"220008491",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6455","CNS",6453399.33294157,115260.073171894,0.2848,0.0855,2,300,4 +"220000124",2008-03-19,"220008492",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6455","CNS",6453140.25250379,115389.388576603,0.2848,0.0855,2,300,1 +"220000124",2008-03-19,"220008497",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6450","CNS",6451827.2786983,115987.01565635,0.2865,0.086,1,300,1 +"220000124",2008-03-19,"220008500",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0116N6450","CNS",6451052.41043108,116351.818897189,0.2928,0.0878,1,300,2 +"220000126",2008-03-19,"220008618",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0131N6420","CNS",6420807.32538712,130460.865860184,0.295,0.0885,2,300,1 +"220000126",2008-03-19,"220008623",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0131N6420","CNS",6419463.10031274,131073.100025126,0.2952,0.0886,2,300,1 +"220000126",2008-03-19,"220008624",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0131N6420","CNS",6419184.03660463,131185.088720781,0.2978,0.0893,1,300,1 +"220000126",2008-03-19,"220008644",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0126N6420","CNS",6418802.62269981,127073.343789768,0.2373,0.0712,2,300,1 +"220000127",2008-03-19,"220008651",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0126N6420","CNS",6418981.33068191,125395.63757561,0.2413,0.0724,1,300,1 +"220000129",2008-03-19,"220008768",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0111N6445","CNS",6443341.04480211,109692.046733282,0.276,0.0828,2,300,1 +"220000129",2008-03-19,"220008791",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0106N6450","CNS",6449064.82661593,107112.686194749,0.2703,0.0811,2,300,1 +"220000129",2008-03-19,"220008809",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0106N6455","CNS",6453590.88380996,105106.107079047,0.2755,0.0827,1,300,1 +"220000129",2008-03-19,"220008816",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0106N6455","CNS",6454298.94508813,103964.30520674,0.2738,0.0822,2,300,1 +"220000129",2008-03-19,"220008818",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0101N6455","CNS",6453875.04376154,103606.926240408,0.2713,0.0814,2,300,5 +"220000129",2008-03-19,"220008819",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0101N6455","CNS",6453668.35930473,103431.708937263,0.2701,0.081,2,300,1 +"220000130",2008-03-19,"220008840",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6450","CNS",6449301.24179755,99584.8029142713,0.2739,0.0822,1,300,1 +"220000130",2008-03-19,"220008844",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6450","CNS",6448488.66658861,98859.9018656201,0.2698,0.0809,2,300,2 +"220000130",2008-03-19,"220008862",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6445","CNS",6443994.56692399,99174.882570267,0.2834,0.085,1,300,1 +"220000130",2008-03-19,"220008880",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6440","CNS",6439314.01394174,101239.013223838,0.2781,0.0834,2,300,1 +"220000130",2008-03-19,"220008881",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6440","CNS",6439056.82159351,101344.52827921,0.2838,0.0852,2,300,6 +"220000130",2008-03-19,"220008882",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6440","CNS",6438789.12390768,101443.042525295,0.2798,0.0839,2,300,1 +"220000131",2008-03-19,"220008887",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0101N6435","CNS",6437460.59806141,101948.793736196,0.2827,0.0848,2,300,1 +"220000131",2008-03-19,"220008907",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6432189.80155246,104225.256094057,0.2895,0.0868,2,300,1 +"220000131",2008-03-19,"220008912",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6430876.27760521,104812.344636822,0.2906,0.0872,2,300,2 +"220000131",2008-03-19,"220008914",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6430358.60600161,105060.027993635,0.2874,0.0862,2,300,2 +"220000131",2008-03-19,"220008915",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6430099.19195623,105189.818471332,0.289,0.0867,2,300,1 +"220000131",2008-03-19,"220008916",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6429840.36732639,105313.708250554,0.2858,0.0858,2,300,3 +"220000131",2008-03-19,"220008918",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6429332.66316929,105574.482852186,0.2807,0.0842,2,300,5 +"220000131",2008-03-19,"220008919",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0106N6430","CNS",6429073.26813305,105704.351863692,0.2843,0.0853,1,300,1 +"220000131",2008-03-19,"220008946",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0111N6420","CNS",6422068.7834549,108900.675206057,0.2886,0.0866,2,300,1 +"220000133",2008-03-19,"220009019",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6420","CNS",6418920.77295655,98450.313035488,0.275,0.0825,2,300,2 +"220000133",2008-03-19,"220009039",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6423953.22425449,96342.626012937,0.2708,0.0812,1,300,2 +"220000133",2008-03-19,"220009040",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6424189.54734693,96222.6896948501,0.2721,0.0816,1,300,1 +"220000133",2008-03-19,"220009046",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6424507.5950001,95404.2608542047,0.2552,0.0766,1,300,1 +"220000133",2008-03-19,"220009049",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6423785.54903631,95115.6515921263,0.2574,0.0772,2,300,1 +"220000133",2008-03-19,"220009050",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6423549.16970817,95013.8757053036,0.2619,0.0786,1,300,3 +"220000133",2008-03-19,"220009051",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6425","CNS",6423312.19275159,94918.0218791706,0.2601,0.078,1,300,4 +"220000133",2008-03-19,"220009056",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6420","CNS",6422109.93986931,94388.9248233479,0.2645,0.0794,1,300,1 +"220000133",2008-03-19,"220009057",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0096N6420","CNS",6421863.67746917,94274.0971404672,0.2642,0.0793,1,300,1 +"220000134",2008-03-22,"220009088",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0076N6415","CNS",6416129.94512607,77882.1861605723,0.2383,0.0715,1,300,1 +"220000134",2008-03-22,"220009110",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0081N6415","CNS",6415296.54594371,83069.9700214944,0.2458,0.0738,1,300,1 +"220000134",2008-03-22,"220009116",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0086N6415","CNS",6414901.57307299,84500.8782855595,0.2479,0.0744,2,300,1 +"220000134",2008-03-22,"220009121",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0086N6415","CNS",6414420.7192456,85790.9278745561,0.2431,0.0729,1,300,1 +"220000134",2008-03-22,"220009128",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0086N6415","CNS",6414412.93743253,87502.2302257743,0.2552,0.0766,1,300,1 +"220000136",2008-03-22,"220009194",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0106N6415","CNS",6416421.7508078,104199.505027879,0.2628,0.0788,1,300,1 +"220000137",2008-03-22,"220009261",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0121N6420","CNS",6418554.21313492,121948.314556599,0.2606,0.0782,2,300,1 +"220000137",2008-03-22,"220009276",800,220,4,2008,1,1,2,"118",NA,"ESAS v5","E0126N6415","CNS",6417595.70338873,125841.225003998,0.269,0.0807,2,300,1 +"220000138",2008-03-22,"220009338",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0141N6410","CNS",6412029.90108969,141316.702679443,0.2554,0.0766,1,300,1 +"220000138",2008-03-22,"220009340",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0141N6410","CNS",6411873.26391755,141818.077943065,0.2549,0.0765,1,300,1 +"220000138",2008-03-22,"220009352",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0146N6410","CNS",6411215.52212921,144689.996211511,0.2075,0.0622,1,300,2 +"220000138",2008-03-22,"220009355",800,220,4,2008,1,1,2,"180",NA,"ESAS v5","E0146N6410","CNS",6411816.26352585,144605.262742258,0.2443,0.0733,1,300,1 +"220000139",2008-03-22,"220009373",800,220,4,2008,1,1,2,"181",NA,"ESAS v5","E0141N6415","CNS",6415479.12229688,142684.128523299,0.2195,0.0659,1,300,1 +"220000619",2008-09-19,"220042798",6270,220,1,2008,1,1,2,"118",NA,"ESAS v5","E0216N6400","CNS",6399397.04588004,215843.644261149,0.4267,0.128,2,300,1 +"220000724",2008-09-28,"220048868",800,220,1,2008,1,1,2,"172",NA,"ESAS v5","E0156N6280","CNS",6279347.8829652,157221.085945333,0.3153,0.0946,1,300,1 +"220000727",2008-09-28,"220049080",6270,220,1,2008,1,1,2,"118",NA,"ESAS v5","E0156N6270","CNS",6268920.14998816,156316.455413393,0.2989,0.0897,2,300,1 +"220000734",2008-09-29,"220049453",800,220,1,2008,1,1,2,"118",NA,"ESAS v5","E0156N6250","CNS",6249812.04857235,156269.04842751,0.3281,0.0984,2,300,1 +"220000738",2008-09-30,"220049900",800,220,1,2008,1,1,2,"118",NA,"ESAS v5","E0296N5985","SNS",5985106.87648422,296057.853663196,0.3151,0.0945,2,300,1 +"220000738",2008-09-30,"220049907",800,220,1,2008,1,1,2,"118",NA,"ESAS v5","E0296N5985","SNS",5983974.39489606,295206.509760038,0.2918,0.0875,2,300,1 +"30002295",1992-01-22,"30029315",4960,30,3,1991,1,1,1,"999",NA,"ESAS v5","E0831N6125","SNS",6127315.92500114,830445.292649982,3.26,0.98,2,300,3 +"30002757",1993-03-13,"30030265",1680,30,4,1993,1,1,1,"999",NA,"ESAS v5","E0846N6035","SNS",6033312.7892948,846014.676904741,3.4,1.02,1,300,16 +"30002758",1993-03-14,"30030306",1680,30,4,1993,1,1,1,"999",NA,"ESAS v5","E0831N6080","SNS",6078129.68547358,830115.933893163,2.8,0.83,1,300,10 +"30002759",1993-03-15,"30030364",5410,30,4,1993,1,1,1,"999",NA,"ESAS v5","E0786N6120","SNS",6120755.44342385,784122.584259707,3.2,0.96,1,300,1 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v5","E0871N5990","SNS",5992079.51018021,871696.310745608,2.5396,0.7619,1,300,10 +"50000067",1991-06-20,"50000837",1730,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0856N6010","SNS",6008436.76073305,858134.592789679,3.4099,1.023,1,300,1 +"50000067",1991-06-20,"50000838",5410,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0856N6010","SNS",6007785.30552963,854768.877247672,3.4102,1.0231,1,300,5 +"50000072",1991-07-04,"50000887",5410,50,6,1991,1,1,2,"5164",NA,"ESAS v5","E0836N6000","SNS",6000033.60806461,833988.141041838,4.9839,1.4952,1,300,1 +"50000072",1991-07-04,"50000901",5410,50,6,1991,1,1,2,"5164",NA,"ESAS v5","E0861N5995","SNS",5993758.46216092,858964.41805181,2.2748,0.6824,1,300,1 +"50000072",1991-07-04,"50000903",1730,50,6,1991,1,1,2,"5164",NA,"ESAS v5","E0861N5995","SNS",5992678.65775266,863464.968013918,2.2754,0.6826,1,300,7 +"50000072",1991-07-04,"50000907",1730,50,6,1991,1,1,2,"5164",NA,"ESAS v5","E0871N5990","SNS",5989791.48675207,872024.979420642,2.3642,0.7093,1,300,1 +"50000074",1991-07-06,"50000949",6270,50,6,1991,1,1,1,"5174",NA,"ESAS v5","E0831N5970","SNS",5969553.90912305,833559.930106508,6.4903,1.9471,1,300,1 +"50000074",1991-07-06,"50000950",1680,50,6,1991,1,1,1,"5174",NA,"ESAS v5","E0831N5975","SNS",5973606.81103004,832913.231586504,1.7102,0.5131,1,300,1 +"50000077",1991-07-22,"50000999",1730,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0861N5995","SNS",5994482.97261767,860377.691289947,2.4067,0.722,1,300,1 +"50000077",1991-07-22,"50001002",1730,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0866N5995","SNS",5993524.28527461,867241.935099079,2.4213,0.7264,1,300,13 +"50000078",1991-07-23,"50001010",1730,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0871N5990","SNS",5990318.61604661,872741.554350762,1.2356,0.3707,2,300,6 +"50000078",1991-07-23,"50001016",6270,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0871N5995","SNS",5996789.67776,873124.015268371,1.173,0.3519,2,300,2 +"50000078",1991-07-23,"50001017",6270,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0871N6000","SNS",5998031.90212268,872444.087975345,1.6941,0.5082,1,300,8 +"50000078",1991-07-23,"50001021",1730,50,6,1991,1,1,2,"5174",NA,"ESAS v5","E0866N6000","SNS",5999374.05694873,865019.999191084,2.5838,0.7751,1,300,4 +"50000080",1991-07-31,"50001071",5410,50,6,1991,1,1,2,"5200",NA,"ESAS v5","E0856N5995","SNS",5994870.35267261,855557.807213609,2.7481,0.8244,1,300,5 +"50000080",1991-07-31,"50001073",1730,50,6,1991,1,1,2,"5200",NA,"ESAS v5","E0861N5995","SNS",5993993.62784899,863697.953735757,4.8886,1.4666,1,300,1 +"50000081",1991-08-01,"50001091",6270,50,1,1991,1,1,2,"5200",NA,"ESAS v5","E0846N6005","SNS",6007176.91137446,847366.16761665,3.0737,0.9221,1,300,1 +"50000081",1991-08-01,"50001094",1730,50,1,1991,1,1,2,"5200",NA,"ESAS v5","E0856N6010","SNS",6008355.70575287,855789.115209849,2.7268,0.818,2,300,20 +"50000085",1991-09-03,"50056613",5410,50,1,1991,1,1,2,"5200",NA,"ESAS v5","E0871N5995","SNS",5994130.92542545,870087.894564938,2.6237,0.7871,1,300,1 +"50000085",1991-09-03,"50056620",5410,50,1,1991,1,1,2,"5200",NA,"ESAS v5","E0851N5995","SNS",5995703.50811431,850067.142641911,2.6237,0.7871,1,300,1 +"50000087",1991-10-06,"50004851",1680,50,2,1991,1,1,1,"5174",NA,"ESAS v5","E0821N6000","SNS",6000962.34430741,823813.103730166,4.5725,1.3717,1,300,7 +"50000088",1991-10-11,"50001159",1680,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0821N6015","SNS",6013194.36979195,820025.873814846,0.8178,0.2453,1,300,30 +"50000089",1991-10-11,"50001164",1680,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0821N6010","SNS",6012278.44551262,822422.916436994,4.8554,1.4566,2,300,6 +"50000089",1991-10-11,"50001168",1680,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0841N6000","SNS",6002356.04264607,838880.777299367,4.6782,1.4035,1,300,10 +"50000089",1991-10-11,"50001171",1680,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0851N5995","SNS",5995941.46821026,850701.037907717,4.4111,1.3233,1,300,10 +"50000089",1991-10-11,"50001172",1680,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0856N5995","SNS",5995076.52892732,855186.255852162,4.7166,1.415,1,300,10 +"50000090",1991-10-15,"50001175",1680,50,2,1991,1,1,1,"5174",NA,"ESAS v5","E0871N5995","SNS",5992820.23026688,870614.687120667,5.6419,1.6926,1,300,1 +"50000092",1991-11-19,"50001223",5410,50,2,1991,1,1,2,"5174",NA,"ESAS v5","E0841N6000","SNS",6001875.13682634,839330.196514945,4.5224,1.3567,1,300,3 +"50000110",1991-12-28,"50001326",1730,50,3,1991,1,1,2,"5187",NA,"ESAS v5","E0861N5995","SNS",5996327.08499002,862236.670952387,2.0697,0.6209,1,300,1 +"50000137",1992-03-02,"50001567",1680,50,4,1992,1,1,2,"5156",NA,"ESAS v5","E0836N6000","SNS",6000250.70924576,834827.139198494,3.3142,0.9943,2,300,10 +"50000137",1992-03-02,"50001567",5410,50,4,1992,1,1,2,"5156",NA,"ESAS v5","E0836N6000","SNS",6000250.70924576,834827.139198494,3.3142,0.9943,1,300,1 +"50000138",1992-03-03,"50001585",1730,50,4,1992,1,1,2,"5156",NA,"ESAS v5","E0876N5985","SNS",5986893.72247593,874918.754473459,2.1241,0.6372,1,300,2 +"50000139",1992-04-05,"50001632",1680,50,5,1992,1,1,2,"5183",NA,"ESAS v5","E0871N5995","SNS",5992980.12002471,869256.64828561,2.3409,0.7023,1,300,4 +"50000143",1992-04-30,"50001709",1680,50,5,1992,1,1,2,"5164",NA,"ESAS v5","E0841N5970","SNS",5969562.69902264,842778.78291389,6.0727,1.8218,1,300,1 +"50000148",1992-05-05,"50001800",1680,50,5,1992,1,1,2,"5164",NA,"ESAS v5","E0836N5975","SNS",5972934.46551218,838622.067893628,5.7863,1.7359,1,300,14 +"50000148",1992-05-05,"50001801",1680,50,5,1992,1,1,2,"5164",NA,"ESAS v5","E0841N5970","SNS",5968968.00717747,841981.662662131,4.5694,1.3708,1,300,2 +"50000149",1992-05-11,"50001810",6270,50,5,1992,1,1,1,"5174",NA,"ESAS v5","E0856N6010","SNS",6009155.03919355,858459.74945449,5.5044,1.6513,2,300,1 +"50000151",1992-05-14,"50120563",6270,50,5,1992,1,1,2,"5156",NA,"ESAS v5","E0446N6020","SNS",6018019.45104417,445995.836102037,3.89,1.17,1,300,1 +"50000151",1992-05-14,"50120581",6270,50,5,1992,1,1,2,"5156",NA,"ESAS v5","E0391N6040","SNS",6042104.12767016,393401.800443771,3.8,1.14,1,300,1 +"50000173",1992-05-31,"50001848",1680,50,5,1992,1,1,2,"5187",NA,"ESAS v5","E0851N6010","SNS",6009531.70089599,849690.090594552,4.7924,1.4377,2,300,25 +"50000173",1992-05-31,"50001850",1680,50,5,1992,1,1,2,"5187",NA,"ESAS v5","E0841N6010","SNS",6010712.52075593,840091.238624913,4.7748,1.4324,1,300,21 +"50000174",1992-06-01,"50001869",5410,50,6,1992,1,1,1,"5201",NA,"ESAS v5","E0851N5960","SNS",5961606.85850895,853569.724326556,4.8971,1.4691,1,300,1 +"50000175",1992-06-02,"50065077",1680,50,6,1992,1,1,2,"5187",NA,"ESAS v5","E0841N6075","SNS",6072988.60782256,839573.415803737,2.9488,0.8846,2,300,68 +"50000176",1992-06-02,"50001875",1680,50,6,1992,1,1,2,"5187",NA,"ESAS v5","E0841N6075","SNS",6073096.27971246,839222.22269049,2.431,0.7293,1,300,12 +"50000178",1992-06-09,"50001919",1680,50,6,1992,1,1,2,"5174",NA,"ESAS v5","E0826N6005","SNS",6005407.26279722,826665.751037075,2.8532,0.856,1,300,9 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v5","E0866N5995","SNS",5995564.40284715,867310.821369411,0.41,0.12,1,300,2 +"50000497",1996-08-27,"50073068",4960,50,1,1996,1,1,2,"5187",NA,"ESAS v5","E0831N6070","SNS",6072124.86471746,831869.914795732,0.2747,0.0824,1,300,5 +"50000497",1996-08-27,"50073103",5410,50,1,1996,1,1,2,"5187",NA,"ESAS v5","E0826N6070","SNS",6068548.99753422,825720.612882196,0.2747,0.0824,1,300,7 +"50000500",1996-08-27,"50073703",4960,50,1,1996,1,1,3,"5112",NA,"ESAS v5","E0831N6070","SNS",6072124.86471746,831869.914795732,0.2747,0.0824,1,300,5 +"50000500",1996-08-27,"50073738",5410,50,1,1996,1,1,3,"5112",NA,"ESAS v5","E0826N6070","SNS",6068548.99753422,825720.612882196,0.2747,0.0824,1,300,7 +"50000501",1996-08-28,"50075000",4960,50,1,1996,1,1,3,"5112",NA,"ESAS v5","E0736N5975","SNS",5973666.6396509,737197.999558232,0.2963,0.0889,1,300,1 +"50000504",1996-09-23,"50069373",1730,50,1,1996,1,1,2,"5112",NA,"ESAS v5","E0861N5995","SNS",5996340.0847469,861115.80679224,1.2991,0.3897,1,300,7 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v5","E0846N6015","SNS",6016397.5007432,848716.4270824,0.3179,0.0954,1,300,12 +"50000524",1997-05-01,"50042719",1730,50,5,1997,1,1,2,"5187",NA,"ESAS v5","E0851N6025","SNS",6025382.66692493,849454.303049255,0.3179,0.0954,1,300,3 +"50000524",1997-05-01,"50042762",5410,50,5,1997,1,1,2,"5187",NA,"ESAS v5","E0841N6035","SNS",6036898.89015292,842741.356160233,0.3334,0.1,1,300,1 +"50000525",1997-05-02,"50043207",4960,50,5,1997,1,1,2,"5187",NA,"ESAS v5","E0841N6000","SNS",6001887.30151114,841616.991403042,0.3303,0.0991,1,300,6 +"50000527",1997-05-04,"50045139",5410,50,5,1997,1,1,2,"5187",NA,"ESAS v5","E0826N6070","SNS",6068588.26346274,825180.268156735,0.2994,0.0898,2,300,1 +"50000536",1997-05-26,"50048617",1680,50,5,1997,1,1,2,"5174",NA,"ESAS v5","E0746N5970","SNS",5967689.92510332,746213.720055903,0.3278,0.0983,1,300,65 +"50000536",1997-05-26,"50048650",1680,50,5,1997,1,1,2,"5174",NA,"ESAS v5","E0756N5970","SNS",5971025.82700493,756394.689943208,0.3278,0.0983,1,300,20 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v5","E0831N6005","SNS",6002913.01403162,830225.541973525,2.2257,0.6677,1,300,1 +"50000557",1997-06-30,"50065805",1730,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0861N5995","SNS",5994178.58895899,862635.496037627,2.0467,0.614,1,300,10 +"50000557",1997-06-30,"50065806",5410,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5993907.67769788,864311.110999464,1.323,0.3969,1,300,2 +"50000557",1997-06-30,"50065809",5410,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5992513.52698107,869293.835363277,1.6437,0.4931,1,300,9 +"50000557",1997-06-30,"50065810",5410,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5991472.97106453,870544.335500272,1.6163,0.4849,1,300,1 +"50000560",1997-07-03,"50049165",4960,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0776N6030","SNS",6030402.24287636,778545.568375816,3.6025,1.0808,1,300,3 +"50000561",1997-07-04,"50049199",5410,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0826N5990","SNS",5988911.61847855,826508.492841825,3.7846,1.1354,2,300,4 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v5","E0871N5990","SNS",5990230.23133524,872893.837407054,1.2397,0.3719,1,300,1 +"50000562",1997-07-01,"50065820",1730,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5991126.51149806,872359.922627332,0.8264,0.2479,1,300,22 +"50000562",1997-07-01,"50065827",5410,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5996136.40557474,863938.128107849,1.3469,0.4041,1,300,2 +"50000562",1997-07-01,"50065832",5410,50,6,1997,1,1,1,"5174",NA,"ESAS v5","E0851N5995","SNS",5997105.30544662,852633.971397351,3.1034,0.931,1,300,2 +"50000564",1997-07-08,"50052361",5410,50,6,1997,1,1,1,"5194",NA,"ESAS v5","E0816N6020","SNS",6019554.28837134,815428.428925234,0.8797,0.2639,1,300,1 +"50000566",1997-07-09,"50052491",1730,50,6,1997,1,1,1,"5194",NA,"ESAS v5","E0861N6075","SNS",6074594.0088899,862080.460829705,2.4146,0.7244,1,300,8 +"50000566",1997-07-09,"50052497",1730,50,6,1997,1,1,1,"5194",NA,"ESAS v5","E0861N6075","SNS",6076167.91836336,863436.957919512,1.7297,0.5189,1,300,26 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v5","E0831N6005","SNS",6006403.99461393,832641.227026801,1.1338,0.3401,1,300,2 +"50000574",1997-07-17,"50052791",6270,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0831N6005","SNS",6005970.23398351,833440.75518781,0.6803,0.2041,1,300,2 +"50000574",1997-07-17,"50052792",6270,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0836N6005","SNS",6005455.0332302,834193.812665837,1.1482,0.3445,2,300,3 +"50000574",1997-07-17,"50052808",6270,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0846N5995","SNS",5995774.134592,848040.30922258,1.101,0.3303,1,300,1 +"50000574",1997-07-17,"50052820",6270,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0861N5995","SNS",5994094.36319338,863129.811457603,1.0496,0.3149,1,300,1 +"50000577",1997-07-16,"50052953",4960,50,6,1997,1,1,1,"5194",NA,"ESAS v5","E0816N5995","SNS",5994397.33495302,815753.171669467,2.059,0.6177,1,300,11 +"50000578",1997-07-16,"50052969",6270,50,6,1997,1,1,1,"5194",NA,"ESAS v5","E0836N6010","SNS",6010811.42694866,836265.605442497,2.5919,0.7776,2,300,1 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v5","E0846N5965","SNS",5967594.22103804,844115.374016517,4.6918,1.4075,2,300,6 +"50000593",1997-07-29,"50053392",1730,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0861N5955","SNS",5955261.79140261,861655.77741549,5.7622,1.7287,2,300,2 +"50000595",1997-07-30,"50053394",1730,50,6,1997,1,1,2,"5194",NA,"ESAS v5","E0776N5950","SNS",5951865.61019964,774304.665258954,1.1031,0.3309,1,300,1 +"50000595",1997-07-30,"50053422",1730,50,6,1997,1,1,2,"5194",NA,"ESAS v5","E0806N5970","SNS",5969497.80832455,804476.739988388,0.6147,0.1844,1,300,2 +"50000599",1997-08-03,"50051225",6270,50,1,1997,1,1,2,"5194",NA,"ESAS v5","E0871N5990","SNS",5989293.56367847,872368.525742475,0.3868,0.116,2,300,2 +"50000599",1997-08-03,"50051230",6270,50,1,1997,1,1,2,"5194",NA,"ESAS v5","E0871N5990","SNS",5987726.38786752,873458.292827368,0.3726,0.1118,1,300,3 +"50000599",1997-08-03,"50051249",6270,50,1,1997,1,1,2,"5194",NA,"ESAS v5","E0876N5985","SNS",5982943.59565178,877504.995219335,0.3642,0.1093,2,300,1 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v5","E0866N6065","SNS",6066816.79154024,867130.38616627,0.5451,0.1635,1,300,20 +"50000611",1997-08-13,"50054292",1730,50,1,1997,1,1,1,"5112",NA,"ESAS v5","E0876N6015","SNS",6014204.76142391,874154.160814557,0.8323,0.2497,1,300,1 +"50000614",1997-08-13,"50054013",5410,50,1,1997,1,1,1,"5162",NA,"ESAS v5","E0791N5965","SNS",5962908.05030254,793053.290760705,2.9419,0.8826,1,300,2 +"50000623",1997-08-20,"50054562",4960,50,1,1997,1,1,1,"5194",NA,"ESAS v5","E0856N6070","SNS",6072165.38375553,858127.972587108,2.7205,0.5441,1,200,90 +"50000623",1997-08-20,"50054563",4960,50,1,1997,1,1,1,"5194",NA,"ESAS v5","E0856N6070","SNS",6071597.06429601,855464.319556737,2.7083,0.5417,1,200,25 +"50000627",1997-08-21,"50054674",1730,50,1,1997,1,1,1,"5194",NA,"ESAS v5","E0856N5960","SNS",5959187.80206594,856447.21089302,1.2385,0.3716,2,300,2 +"50000628",1997-08-23,"50054683",1730,50,1,1997,1,1,1,"5201",NA,"ESAS v5","E0876N6015","SNS",6013609.02516776,876310.626335146,1.4462,0.4339,1,300,6 +"50000628",1997-08-23,"50054685",5410,50,1,1997,1,1,1,"5201",NA,"ESAS v5","E0871N6015","SNS",6013536.00366044,873354.871901558,1.6869,0.5061,1,300,1 +"50000629",1997-08-26,"50054716",1730,50,1,1997,1,1,1,"5187",NA,"ESAS v5","E0876N6015","SNS",6013571.77132196,874895.159109257,1.3792,0.4138,1,300,9 +"50000630",1997-08-26,"50054760",5410,50,1,1997,1,1,1,"5112",NA,"ESAS v5","E0846N5965","SNS",5967427.36807709,844882.695362184,1.3631,0.4089,1,300,1 +"50000636",1997-08-28,"50055554",5410,50,1,1997,1,1,1,"5112",NA,"ESAS v5","E0856N5960","SNS",5957887.44463342,858299.06543517,3.301,0.9903,2,300,1 +"50000637",1997-09-06,"50055577",5410,50,1,1997,1,1,1,"5194",NA,"ESAS v5","E0836N5980","SNS",5981473.81360259,836225.820962165,1.5397,0.4619,1,300,1 +"50000657",1997-09-12,"50055679",1730,50,1,1997,1,1,1,"5194",NA,"ESAS v5","E0841N5975","SNS",5973716.37626795,838928.985262417,1.9139,0.5742,2,300,3 +"50000659",1997-09-13,"50065907",4960,50,1,1997,1,1,1,"5201",NA,"ESAS v5","E0876N6015","SNS",6013756.8506556,874696.213536316,1.1647,0.3494,1,300,2 +"50000671",1997-11-06,"50079792",5410,50,2,1997,1,1,3,"5174",NA,"ESAS v5","E0796N5970","SNS",5969883.71135685,794135.69403784,0.3471,0.1041,1,300,1 +"50000678",1997-11-21,"50066041",5410,50,2,1997,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5990516.57903857,871306.986228244,0.4142,0.1243,1,300,2 +"50000682",1998-01-24,"50066384",5410,50,3,1997,1,1,1,"5174",NA,"ESAS v5","E0841N6000","SNS",6000066.92912173,841929.191601679,2.4463,0.7339,1,300,2 +"50000685",1998-02-24,"50066450",1730,50,4,1998,1,1,1,"5174",NA,"ESAS v5","E0856N5995","SNS",5996234.78545847,856006.222664992,0.5174,0.1552,1,300,3 +"50000692",1998-03-20,"50069011",5410,50,4,1998,1,1,1,"5112",NA,"ESAS v5","E0866N5995","SNS",5995344.69139631,867077.824612165,0.3321,0.0996,1,300,1 +"50000695",1998-03-23,"50068042",5410,50,4,1998,1,1,1,"5112",NA,"ESAS v5","E0721N5960","SNS",5960213.94796571,722498.725063412,0.2985,0.0895,2,300,1 +"50000696",1998-03-24,"50067590",1680,50,4,1998,1,1,1,"5112",NA,"ESAS v5","E0756N6055","SNS",6053099.06676076,756606.303183045,0.3232,0.097,1,300,8 +"50000697",1998-03-25,"50067410",1680,50,4,1998,1,1,1,"5112",NA,"ESAS v5","E0836N6095","SNS",6094189.72795583,834103.602014923,0.3368,0.101,1,300,4 +"50000699",1998-05-04,"50155055",1730,50,5,1998,1,1,2,"5174",NA,"ESAS v5","E0846N6030","SNS",6028503.89965481,848176.625717384,0.3,0.09,1,300,1 +"50000700",1998-05-05,"50155622",1680,50,5,1998,1,1,2,"5174",NA,"ESAS v5","E0831N6045","SNS",6043824.4658769,829845.121203725,0.33,0.1,1,300,5 +"50000703",1998-05-08,"50157613",1730,50,5,1998,1,1,2,"5174",NA,"ESAS v5","E0796N6010","SNS",6007901.82388145,794761.98824025,0.35,0.11,1,300,1 +"50000704",1998-05-04,"50158026",1730,50,5,1998,1,1,2,"5182",NA,"ESAS v5","E0846N6030","SNS",6028503.89965481,848176.625717384,0.3,0.09,1,300,1 +"50000708",1998-05-08,"50160333",1730,50,5,1998,1,1,2,"5182",NA,"ESAS v5","E0796N6010","SNS",6007901.82388145,794761.98824025,0.36,0.11,1,300,1 +"50000709",1998-05-14,"50071863",1730,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0861N5955","SNS",5955777.77334836,861390.101087445,3.9587,1.1876,2,300,1 +"50000709",1998-05-14,"50071864",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0856N5960","SNS",5957945.02455312,858175.143703236,3.7707,1.1312,1,300,5 +"50000709",1998-05-14,"50071877",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0826N5975","SNS",5974343.90974013,828821.955488878,2.2601,0.678,2,300,1 +"50000709",1998-05-14,"50071882",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0816N5975","SNS",5977369.48507699,818200.88580895,2.831,0.8493,1,300,1 +"50000710",1998-05-15,"50071911",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0816N5970","SNS",5972555.75553954,817525.209329646,1.3112,0.3934,1,300,1 +"50000710",1998-05-15,"50071914",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0811N5975","SNS",5972969.81932017,813248.090329592,2.7936,0.8381,1,300,2 +"50000711",1998-05-16,"50071980",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0771N5960","SNS",5958267.1729343,773520.031178482,1.7038,0.5111,1,300,1 +"50000711",1998-05-16,"50071984",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0766N5965","SNS",5962862.57650146,765971.939040144,3.1851,0.9555,1,300,1 +"50000712",1998-05-17,"50072078",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0766N5975","SNS",5976185.50211368,768404.485905268,2.6036,0.7811,2,300,2 +"50000713",1998-05-18,"50072142",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0796N5975","SNS",5974774.73082843,797523.782221452,2.663,0.7989,2,300,100 +"50000713",1998-05-18,"50072144",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0801N5980","SNS",5978947.14098691,801313.693190266,2.8155,0.8447,1,300,16 +"50000714",1998-05-14,"50071886",1730,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0861N5955","SNS",5955777.77334836,861390.101087445,3.9587,1.1876,1,300,1 +"50000715",1998-05-15,"50071946",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0816N5970","SNS",5972555.75553954,817525.209329646,1.3112,0.3934,2,300,1 +"50000715",1998-05-15,"50071949",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0811N5975","SNS",5972969.81932017,813248.090329592,2.7936,0.8381,2,300,2 +"50000715",1998-05-15,"50071951",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0806N5970","SNS",5971476.87501277,807929.076992427,2.5486,0.7646,1,300,2 +"50000716",1998-05-16,"50072025",6270,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0771N5960","SNS",5958267.1729343,773520.031178482,1.7038,0.5111,2,300,4 +"50000716",1998-05-16,"50072068",1730,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0776N5955","SNS",5957538.38672772,774409.976656608,1.0379,0.3114,1,300,2 +"50000717",1998-05-17,"50072112",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0766N5975","SNS",5976185.50211368,768404.485905268,2.6036,0.7811,1,300,2 +"50000718",1998-05-18,"50085103",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0796N5960","SNS",5961799.9187419,795306.8732511,2.0988,0.6296,1,300,4 +"50000718",1998-05-18,"50085113",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0806N5985","SNS",5982754.36185691,808476.207220052,2.8884,0.8665,1,300,11 +"50000718",1998-05-18,"50085116",1680,50,5,1998,1,1,2,"5187",NA,"ESAS v5","E0811N5990","SNS",5989346.82720477,810072.738258481,3.1659,0.9498,1,300,75 +"50000719",1998-06-15,"50071733",1730,50,6,1998,1,1,1,"5102",NA,"ESAS v5","E0881N6050","SNS",6048539.76678072,881733.813183421,2.9719,0.8916,1,300,2 +"50000720",1998-06-16,"50071826",5410,50,6,1998,1,1,1,"5102",NA,"ESAS v5","E0871N6055","SNS",6056905.2078656,871063.508728289,4.2083,1.2625,1,300,1 +"50000720",1998-06-16,"50071862",5410,50,6,1998,1,1,1,"5102",NA,"ESAS v5","E0886N6055","SNS",6053379.6401882,887502.80636353,1.3697,0.4109,1,300,1 +"50000722",1998-06-24,"50069312",5410,50,6,1998,1,1,1,"5201",NA,"ESAS v5","E0836N5970","SNS",5969807.50876475,833826.336512026,2.0441,0.6132,2,300,2 +"50000722",1998-06-24,"50069324",5410,50,6,1998,1,1,1,"5201",NA,"ESAS v5","E0841N5950","SNS",5950353.40348126,842698.905797228,1.6566,0.497,2,300,9 +"50000754",1999-08-05,"50166695",5410,50,1,1999,1,1,3,"5174",NA,"ESAS v5","E0851N6015","SNS",6015612.30400409,853071.071863439,0.22,0.07,1,300,2 +"50000771",2000-06-20,"50090470",5410,50,6,2000,1,1,1,"5102",NA,"ESAS v5","E0881N5980","SNS",5982437.11415267,881301.636716702,0.3277,0.0983,1,300,6 +"50000771",2000-06-20,"50090531",5410,50,6,2000,1,1,1,"5102",NA,"ESAS v5","E0871N5995","SNS",5995761.37080722,872850.994886247,0.2115,0.0635,1,300,2 +"50000771",2000-06-20,"50090729",6270,50,6,2000,1,1,1,"5102",NA,"ESAS v5","E0861N6040","SNS",6038120.36519069,860060.749026978,0.291,0.0873,1,300,2 +"50000771",2000-06-20,"50090738",5410,50,6,2000,1,1,1,"5102",NA,"ESAS v5","E0856N6040","SNS",6039956.83437943,858538.978002435,0.2595,0.0778,1,300,1 +"50000772",2000-06-21,"50091663",5410,50,6,2000,1,1,1,"5102",NA,"ESAS v5","E0841N6075","SNS",6077568.6638165,840884.174113609,0.1767,0.053,1,300,1 +"50000773",2000-06-20,"50091013",1730,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0871N6000","SNS",5998004.15289939,871682.120438439,0.2185,0.0656,2,300,1 +"50000773",2000-06-20,"50091016",5410,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0871N6000","SNS",5998304.83425194,871249.542432582,0.1842,0.0552,1,300,1 +"50000773",2000-06-20,"50091200",6270,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0861N6040","SNS",6038120.36519069,860060.749026978,0.291,0.0873,2,300,2 +"50000773",2000-06-20,"50091209",5410,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0856N6040","SNS",6039956.83437943,858538.978002435,0.2595,0.0778,1,300,1 +"50000773",2000-06-20,"50091326",6270,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0846N6065","SNS",6062867.71619684,848582.560293968,0.2732,0.082,2,300,2 +"50000775",2000-06-22,"50092517",5410,50,6,2000,1,1,2,"5187",NA,"ESAS v5","E0876N6045","SNS",6046947.51301953,877934.85890725,0.3013,0.0904,1,300,3 +"50000788",2000-11-07,"50089539",5410,50,2,2000,1,1,2,"5170",NA,"ESAS v5","E0871N5995","SNS",5993897.74839224,869968.072387332,0.2612,0.0784,1,300,1 +"50000788",2000-11-07,"50089665",1530,50,2,2000,1,1,2,"5170",NA,"ESAS v5","E0851N6020","SNS",6021659.56202467,853372.851462795,0.2222,0.0667,1,300,2 +"50000821",2001-02-10,"50116726",5410,50,4,2001,1,1,2,"5148",NA,"ESAS v5","E0746N5940","SNS",5939752.18531093,744749.945657889,0.24,0.07,1,300,2 +"50000821",2001-02-10,"50116763",5410,50,4,2001,1,1,2,"5148",NA,"ESAS v5","E0751N5935","SNS",5934796.28335589,751840.359929097,0.23,0.07,1,300,1 +"50000821",2001-02-10,"50116770",5410,50,4,2001,1,1,2,"5148",NA,"ESAS v5","E0751N5935","SNS",5933756.94270326,753046.121279374,0.23,0.07,2,300,5 +"50000825",2001-02-28,"50094691",5410,50,4,2001,5,1,1,"5194",NA,"ESAS v5","E0851N5985","SNS",5986402.87464498,853706.620722014,0.46,0.14,1,300,1 +"50000825",2001-02-28,"50094823",5410,50,4,2001,5,1,1,"5194",NA,"ESAS v5","E0856N5965","SNS",5963771.63332375,855740.242963482,0.66,0.2,1,300,1 +"50000833",2001-03-14,"50098671",5410,50,4,2001,1,1,2,"5194",NA,"ESAS v5","E0666N6060","SNS",6061637.09678748,665088.046717143,0.29,0.09,1,300,1 +"50000834",2001-03-15,"50099280",1680,50,4,2001,1,1,2,"5194",NA,"ESAS v5","E0871N5990","SNS",5991669.37938872,870343.937283416,0.46,0.14,1,300,2 +"50000834",2001-03-15,"50099302",1730,50,4,2001,1,1,2,"5194",NA,"ESAS v5","E0876N5985","SNS",5984151.73092826,876270.185768213,0.42,0.13,1,300,1 +"50000835",2001-03-20,"50095087",1680,50,4,2001,5,1,1,"5194",NA,"ESAS v5","E0831N5980","SNS",5979886.23249745,833256.944424955,0.4,0.12,1,300,50 +"50000835",2001-03-20,"50095092",1680,50,4,2001,5,1,1,"5194",NA,"ESAS v5","E0831N5980","SNS",5978856.58433406,831508.004813804,0.4,0.12,1,300,13 +"50000850",2001-04-24,"50095493",5410,50,5,2001,5,1,1,"5194",NA,"ESAS v5","E0861N5955","SNS",5956305.10715028,860545.66127381,0.71,0.21,2,300,1 +"50000850",2001-04-24,"50095497",5410,50,5,2001,5,1,1,"5194",NA,"ESAS v5","E0856N5960","SNS",5958127.83181089,858373.643282359,0.71,0.21,1,300,1 +"50000851",2001-04-25,"50095826",1730,50,5,2001,5,1,1,"5194",NA,"ESAS v5","E0751N5955","SNS",5955781.24573929,751440.619784782,0.33,0.1,2,300,2 +"50000869",2001-12-02,"50122092",1730,50,3,2001,1,1,2,"5195",NA,"ESAS v5","E0781N5965","SNS",5966887.72092356,783034.099481103,0.29,0.09,1,300,5 +"50000872",2001-12-05,"50123120",800,50,3,2001,1,1,2,"5195",NA,"ESAS v5","E0831N6000","SNS",6001083.18698129,832433.347145086,0.28,0.08,2,300,1 +"50000881",2002-05-17,"50131684",1730,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0846N6065","SNS",6063824.30903185,848087.114711288,0.32,0.1,1,300,1 +"50000881",2002-05-17,"50131692",1730,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0846N6060","SNS",6061509.71131872,846908.326128068,0.33,0.1,1,300,2 +"50000881",2002-05-17,"50131698",1730,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0846N6060","SNS",6059922.12475346,845731.164752986,0.33,0.1,1,300,8 +"50000881",2002-05-17,"50131699",1730,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0846N6060","SNS",6059661.42764135,845536.754872289,0.33,0.1,1,300,26 +"50000882",2002-05-27,"50136957",4960,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5984037.85576364,877072.461011584,0.38,0.11,1,300,65 +"50000882",2002-05-27,"50137119",4960,50,5,2002,1,1,2,"5177",NA,"ESAS v5","E0831N6020","SNS",6020395.73393011,833496.723883271,0.31,0.09,2,300,95 +"50000886",2002-06-18,"50139300",4960,50,6,2002,1,1,3,"5171",NA,"ESAS v5","E0851N5995","SNS",5993754.27184105,849410.859444512,0.35,0.11,1,300,30 +"50000886",2002-06-18,"50139580",5410,50,6,2002,1,1,3,"5171",NA,"ESAS v5","E0776N5965","SNS",5963321.70135556,777708.019658725,0.22,0.07,1,300,4 +"50000889",2002-06-21,"50140944",5410,50,6,2002,1,1,2,"5194",NA,"ESAS v5","E0771N5960","SNS",5959892.0561345,773074.44650197,0.28,0.08,1,300,1 +"50000890",2002-06-21,"50141478",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0771N5960","SNS",5959892.0561345,773074.44650197,0.28,0.08,1,300,1 +"50000891",2002-06-25,"50141496",5410,50,6,2002,1,1,2,"5171",NA,"ESAS v5","E0771N5960","SNS",5957648.48671087,773622.358498591,0.16,0.05,1,300,3 +"50000891",2002-06-25,"50141506",5410,50,6,2002,1,1,2,"5171",NA,"ESAS v5","E0771N5960","SNS",5959274.8316903,772819.275386087,0.19,0.06,1,300,3 +"50000891",2002-06-25,"50141731",5410,50,6,2002,1,1,2,"5171",NA,"ESAS v5","E0831N5985","SNS",5985541.70327331,829079.768272149,0.26,0.08,1,300,1 +"50000891",2002-06-25,"50141921",5410,50,6,2002,1,1,2,"5171",NA,"ESAS v5","E0866N6010","SNS",6011818.19070585,864759.081452875,0.25,0.08,1,300,13 +"50000893",2002-07-09,"50133194",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0811N5980","SNS",5979503.52907925,809114.184590638,0.32,0.09,1,300,3 +"50000893",2002-07-09,"50133247",6270,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0826N5975","SNS",5974970.32316602,824535.455814248,0.29,0.09,1,300,2 +"50000893",2002-07-09,"50133299",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0831N5965","SNS",5965098.36773251,833103.285969214,0.25,0.08,1,300,2 +"50000893",2002-07-09,"50133312",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0836N5960","SNS",5962117.89777137,834535.128036463,0.25,0.08,1,300,8 +"50000893",2002-07-09,"50133316",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0836N5960","SNS",5961200.23159973,834978.596178655,0.25,0.08,1,300,1 +"50000893",2002-07-09,"50133318",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0836N5960","SNS",5960748.14881471,835216.47677803,0.28,0.08,2,300,1 +"50000893",2002-07-09,"50133319",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0836N5960","SNS",5960513.12517013,835365.911789246,0.28,0.08,1,300,1 +"50000893",2002-07-09,"50133325",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0836N5960","SNS",5959113.71027237,836255.402986131,0.28,0.08,2,300,1 +"50000893",2002-07-09,"50133360",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0841N5950","SNS",5951044.94870342,841764.629146186,0.29,0.09,1,300,1 +"50000898",2002-07-16,"50130219",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0896N5985","SNS",5986527.7317637,898328.305959603,0.36,0.11,1,300,2 +"50000898",2002-07-16,"50130308",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0871N5990","SNS",5992355.73544941,871515.859358053,0.34,0.1,1,300,2 +"50000898",2002-07-16,"50130317",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5993802.35774039,869052.659221029,0.3,0.09,1,300,5 +"50000898",2002-07-16,"50130320",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0866N5995","SNS",5993965.7909589,868156.228203585,0.3,0.09,1,300,4 +"50000898",2002-07-16,"50130321",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0866N5995","SNS",5994020.13313949,867855.252435023,0.3,0.09,2,300,7 +"50000898",2002-07-16,"50130700",1730,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0851N6065","SNS",6066707.22399117,848948.280196599,0.22,0.07,1,300,1 +"50000899",2002-07-17,"50131023",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0861N5990","SNS",5989479.62027296,862152.319844164,0.48,0.15,1,300,3 +"50000899",2002-07-17,"50131078",5410,50,6,2002,1,1,2,"5177",NA,"ESAS v5","E0871N5990","SNS",5990422.81748677,871532.240236474,0.25,0.07,2,300,1 +"50000903",2002-08-08,"50134042",5410,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0591N5970","SNS",5968969.96247064,593433.733734313,0.4,0.12,1,300,1 +"50000908",2002-08-14,"50135969",4960,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0676N6120","SNS",6120440.5605603,678616.768311078,0.32,0.1,1,300,1 +"50000909",2002-08-15,"50136389",4960,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0701N6020","SNS",6020495.20169792,702149.412220735,0.37,0.11,1,300,2 +"50000909",2002-08-15,"50136846",1730,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0846N5960","SNS",5959122.01004534,848594.259191192,0.67,0.2,1,300,4 +"50000909",2002-08-15,"50136865",1730,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0861N5955","SNS",5954646.56314202,860487.110986221,0.67,0.2,1,300,4 +"50000912",2002-08-29,"50130078",1730,50,1,2002,1,1,2,"5177",NA,"ESAS v5","E0866N5995","SNS",5993664.35293786,867316.534642309,0.3,0.09,1,300,2 +"50000913",2002-09-10,"50142942",6270,50,1,2002,1,1,2,"5171",NA,"ESAS v5","E0886N5980","SNS",5982127.17272021,886353.348370676,0.38,0.11,2,300,1 +"50000913",2002-09-10,"50142992",1730,50,1,2002,1,1,2,"5171",NA,"ESAS v5","E0871N5990","SNS",5990770.02157272,872236.798547356,0.34,0.1,2,300,2 +"50000913",2002-09-10,"50143178",5410,50,1,2002,1,1,2,"5171",NA,"ESAS v5","E0866N6025","SNS",6027196.34323869,864835.578395562,0.27,0.08,1,300,11 +"50000915",2002-09-12,"50144336",6270,50,1,2002,1,1,2,"5171",NA,"ESAS v5","E0856N6000","SNS",6001968.19512877,857163.458536803,0.31,0.09,1,300,1 +"50000923",2002-11-06,"50147946",1730,50,2,2002,1,1,2,"5171",NA,"ESAS v5","E0746N5940","SNS",5940071.31241293,745098.509399252,0.2,0.06,1,300,4 +"50000924",2002-11-07,"50148283",5410,50,2,2002,1,1,2,"5171",NA,"ESAS v5","E0766N5920","SNS",5921479.30738806,763851.659442678,0.33,0.1,1,300,5 +"50000928",2002-11-12,"50149954",1530,50,2,2002,1,1,2,"5171",NA,"ESAS v5","E0826N5975","SNS",5972951.07785799,827597.985717623,0.33,0.1,1,300,2 +"50000964",2001-04-07,"50178776",1680,50,5,2001,1,1,1,"5148",NA,"ESAS v5","E0741N6000","SNS",6000475.37096568,740378.901927874,0.24,0.07,1,300,2 +"50000971",2001-06-17,"50181935",5410,50,6,2001,1,1,2,"5202",NA,"ESAS v5","E0726N5990","SNS",5991415.15529102,728790.180456986,0.25,0.08,1,300,1 +"50000971",2001-06-17,"50182014",5410,50,6,2001,1,1,2,"5202",NA,"ESAS v5","E0721N5995","SNS",5995960.3892317,723260.950338013,0.22,0.07,1,300,10 +"50000980",2003-03-21,"50188924",1680,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0826N6025","SNS",6023438.97603589,825792.989055061,0.26,0.08,1,300,7 +"50000980",2003-03-21,"50188926",5410,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0826N6025","SNS",6022924.54162273,825783.754148968,0.26,0.08,1,300,2 +"50000980",2003-03-21,"50189030",1680,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0841N6010","SNS",6008722.95680514,841137.45817197,0.26,0.08,1,300,10 +"50000981",2003-02-25,"50189333",1730,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0901N5985","SNS",5987195.65906591,899460.990053729,0.31,0.09,2,300,1 +"50000981",2003-02-25,"50189679",1680,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0841N6035","SNS",6033503.65146298,840521.404611857,0.28,0.08,1,300,2 +"50000981",2003-02-25,"50189731",1680,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0856N6030","SNS",6031726.60892615,854054.587080487,0.27,0.08,1,300,7 +"50000982",2003-02-26,"50189798",1730,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0846N6065","SNS",6063645.30564592,847211.89991484,0.27,0.08,1,300,2 +"50000983",2003-02-27,"50190435",1730,50,4,2003,1,1,2,"5171",NA,"ESAS v5","E0876N5985","SNS",5987430.08346554,874360.367929288,0.18,0.05,2,300,5 +"50000984",2002-04-16,"50192085",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0881N5980","SNS",5982346.21458462,880191.607411326,0.36,0.11,1,300,3 +"50000984",2002-04-16,"50192135",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0871N5995","SNS",5994550.48840594,868900.760018553,0.35,0.1,1,300,14 +"50000984",2002-04-16,"50192196",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0856N6005","SNS",6003094.36745087,856694.931122305,0.21,0.06,1,300,36 +"50000984",2002-04-16,"50192263",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0856N6015","SNS",6017508.76455217,854911.761025493,0.27,0.08,1,300,2 +"50000984",2002-04-16,"50192321",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0861N6030","SNS",6028258.36500792,862441.255371656,0.26,0.08,1,300,15 +"50000984",2002-04-16,"50192336",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0861N6030","SNS",6031911.28228329,861276.041524494,0.27,0.08,1,300,8 +"50000984",2002-04-16,"50192352",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0861N6030","SNS",6032404.92143679,859596.401046598,0.23,0.07,1,300,11 +"50000984",2002-04-16,"50192355",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0861N6030","SNS",6031867.70330358,859161.451469454,0.23,0.07,1,300,2 +"50000984",2002-04-16,"50192403",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0851N6030","SNS",6030646.94724628,850046.252411368,0.24,0.07,1,300,40 +"50000984",2002-04-16,"50192403",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0851N6030","SNS",6030646.94724628,850046.252411368,0.24,0.07,1,300,12 +"50000984",2002-04-16,"50192421",1680,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0846N6035","SNS",6033404.96018478,846641.221037789,0.25,0.07,1,300,6 +"50000984",2002-04-16,"50192568",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0846N6065","SNS",6065076.43112184,848671.935970008,0.21,0.06,1,300,6 +"50000984",2002-04-16,"50192570",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0846N6065","SNS",6065475.75108534,848777.373334806,0.21,0.06,1,300,2 +"50000985",2002-04-17,"50192582",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0846N6065","SNS",6065356.8634462,848540.043318128,0.33,0.1,1,300,2 +"50000985",2002-04-17,"50192735",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0821N6065","SNS",6065915.71191058,823584.253868986,0.22,0.06,1,300,2 +"50000986",2002-04-18,"50193121",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0841N6055","SNS",6055750.61139397,842598.345715364,0.32,0.1,1,300,3 +"50000986",2002-04-18,"50193310",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0836N6010","SNS",6008195.57439483,837769.412118536,0.26,0.08,2,300,55 +"50000986",2002-04-18,"50193323",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0836N6005","SNS",6004880.46639853,837808.677269476,0.24,0.07,2,300,2 +"50000986",2002-04-18,"50193407",5410,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0856N5995","SNS",5994604.5223607,857620.612029321,0.34,0.1,1,300,2 +"50000986",2002-04-18,"50193417",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0861N5995","SNS",5994207.71753073,861005.397391458,0.34,0.1,1,300,2 +"50000986",2002-04-18,"50193550",1730,50,5,2002,1,1,2,"5173",NA,"ESAS v5","E0901N5985","SNS",5986812.3837477,900856.025062307,0.4,0.12,1,300,2 +"50001017",2002-04-11,"50204088",5410,50,5,2002,1,1,2,"5195",NA,"ESAS v5","E0821N5975","SNS",5976419.98961953,820636.662436856,0.12,0.04,2,300,1 +"50001018",2002-04-12,"50204532",5410,50,5,2002,1,1,2,"5195",NA,"ESAS v5","E0726N5980","SNS",5979922.4812558,724666.021573384,0.35,0.1,1,300,10 +"50001020",2002-04-15,"50205480",1730,50,5,2002,1,1,2,"5195",NA,"ESAS v5","E0816N6010","SNS",6007731.37954049,818542.630256445,0.18,0.05,1,300,3 +"50001020",2002-04-15,"50205599",1680,50,5,2002,1,1,2,"5195",NA,"ESAS v5","E0821N5975","SNS",5975306.85910484,822305.388877219,0.31,0.09,1,300,5 +"50001021",2002-05-13,"50206046",6270,50,5,2002,1,1,2,"5194",NA,"ESAS v5","E0751N6000","SNS",5998670.79440888,749868.665072066,0.36,0.11,1,300,2 +"50001023",2002-05-15,"50206915",1680,50,5,2002,1,1,2,"5194",NA,"ESAS v5","E0796N5975","SNS",5976494.57879601,793850.802736457,0.37,0.11,1,300,27 +"50001023",2002-05-15,"50206934",1680,50,5,2002,1,1,2,"5194",NA,"ESAS v5","E0786N5980","SNS",5978837.51162389,787590.283216896,0.33,0.1,1,300,25 +"50001024",2002-04-23,"50207609",5410,50,5,2002,1,1,2,"5172",NA,"ESAS v5","E0861N5995","SNS",5996164.72631193,862875.223031019,0.46,0.14,2,300,1 +"50001025",2002-04-26,"50207746",5410,50,5,2002,1,1,2,"5172",NA,"ESAS v5","E0846N5995","SNS",5995325.85727519,846085.421221699,0.33,0.1,1,300,1 +"50001025",2002-04-26,"50207779",5410,50,5,2002,1,1,2,"5172",NA,"ESAS v5","E0856N5995","SNS",5994692.89957766,857607.197547158,0.35,0.11,1,300,1 +"50001027",2002-04-28,"50208209",5410,50,5,2002,1,1,2,"5172",NA,"ESAS v5","E0786N6030","SNS",6031267.46675222,785161.145040312,0.17,0.05,1,300,6 +"50001028",2003-08-12,"50208417",1730,50,1,2003,1,1,3,"5170",NA,"ESAS v5","E0876N6010","SNS",6012295.6121617,878704.226709036,0.33,0.1,1,300,1 +"50001028",2003-08-12,"50208674",1730,50,1,2003,1,1,3,"5170",NA,"ESAS v5","E0871N6010","SNS",6012299.76681636,872726.088704455,0.44,0.13,1,300,5 +"50001029",2003-08-13,"50208711",1730,50,1,2003,1,1,3,"5170",NA,"ESAS v5","E0876N6010","SNS",6012265.95084756,878207.357629305,0.33,0.1,1,300,4 +"50001031",2003-08-15,"50209338",5410,50,1,2003,1,1,3,"5170",NA,"ESAS v5","E0821N6015","SNS",6014005.2016393,819962.920805818,0.42,0.13,1,300,10 +"50001034",2003-08-18,"50210023",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012610.26630842,881161.728947415,0.5,0.15,1,300,2 +"50001034",2003-08-18,"50210051",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6010975.87054239,869200.334041757,0.51,0.15,1,300,7 +"50001034",2003-08-18,"50210264",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012250.68880715,877203.454023071,0.46,0.14,1,300,7 +"50001035",2003-08-19,"50210278",5410,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012597.61009006,880873.690500459,0.43,0.13,1,300,2 +"50001035",2003-08-19,"50210279",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012530.83001669,880471.867148893,0.4,0.12,1,300,10 +"50001035",2003-08-19,"50210280",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012455.13228116,880097.055924318,0.39,0.12,1,300,4 +"50001035",2003-08-19,"50210282",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012335.00381901,879318.558937229,0.4,0.12,1,300,2 +"50001035",2003-08-19,"50210285",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012257.91054738,878109.471460361,0.41,0.12,1,300,7 +"50001035",2003-08-19,"50210285",5410,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012257.91054738,878109.471460361,0.41,0.12,2,300,1 +"50001035",2003-08-19,"50210297",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6012160.74726249,872113.36067356,0.54,0.16,1,300,5 +"50001037",2003-08-21,"50210773",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012466.03633329,879958.184865937,0.45,0.13,2,300,140 +"50001037",2003-08-21,"50210775",1730,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012337.87030464,879081.80375026,0.44,0.13,1,300,7 +"50001037",2003-08-21,"50210978",6270,50,1,2003,1,1,2,"5170",NA,"ESAS v5","E0866N6010","SNS",6009516.96652812,865204.162824681,0.53,0.16,1,300,2 +"50001039",2003-06-17,"50211477",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0771N5955","SNS",5954714.58192699,772084.704813913,0.24,0.07,1,300,1 +"50001039",2003-06-17,"50211487",1730,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0771N5955","SNS",5953210.02673292,773066.504251885,0.19,0.06,1,300,4 +"50001039",2003-06-17,"50211501",1730,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0771N5955","SNS",5953118.85807712,773416.375723732,0.17,0.05,1,300,2 +"50001039",2003-06-17,"50211784",1730,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0781N5955","SNS",5957378.02071896,781113.45248742,0.27,0.08,1,300,2 +"50001041",2003-06-19,"50212552",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0771N5955","SNS",5955178.80872321,772011.406859602,0.25,0.08,1,300,1 +"50001041",2003-06-19,"50212663",1730,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0766N5940","SNS",5942585.95669995,765961.833424807,0.37,0.11,1,300,1 +"50001041",2003-06-19,"50212691",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0761N5940","SNS",5939180.61540611,762712.613146736,0.29,0.09,1,300,2 +"50001042",2003-06-21,"50212774",1730,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0791N5960","SNS",5960008.96051973,791301.65475567,0.13,0.04,2,300,2 +"50001042",2003-06-21,"50212780",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0791N5960","SNS",5960158.31031484,792086.807891586,0.14,0.04,2,300,1 +"50001042",2003-06-21,"50212874",1730,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0801N5965","SNS",5963325.9400038,802939.935310196,0.14,0.04,1,300,2 +"50001042",2003-06-21,"50212888",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0806N5965","SNS",5963691.60514042,804617.123703887,0.13,0.04,2,300,2 +"50001043",2003-06-22,"50212967",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0806N5970","SNS",5971181.39309538,804789.804972234,0.36,0.11,1,300,8 +"50001043",2003-06-22,"50213055",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0831N5970","SNS",5967977.81442718,832289.107421354,0.41,0.12,1,300,1 +"50001043",2003-06-22,"50213057",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0831N5965","SNS",5967226.04667356,832554.439997867,0.4,0.12,1,300,1 +"50001043",2003-06-22,"50213104",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0831N5980","SNS",5977802.33737213,830189.435330878,0.3,0.09,1,300,2 +"50001043",2003-06-22,"50213194",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0846N6000","SNS",6000407.75583741,843962.431432252,0.33,0.1,1,300,2 +"50001043",2003-06-22,"50213275",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0866N6010","SNS",6010353.28508253,868126.821812361,0.32,0.1,1,300,1 +"50001044",2003-06-23,"50213335",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0876N6015","SNS",6013391.42973902,876794.728476425,0.36,0.11,1,300,2 +"50001044",2003-06-23,"50213342",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0876N6015","SNS",6014155.6190348,874374.834788271,0.35,0.11,1,300,8 +"50001044",2003-06-23,"50213405",5410,50,6,2003,1,1,3,"5151",NA,"ESAS v5","E0851N6015","SNS",6017021.91343474,852215.45689979,0.35,0.1,1,300,1 +"50001045",2003-06-26,"50214171",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0846N6065","SNS",6063381.49122168,845077.826939521,0.36,0.11,2,300,2 +"50001046",2003-06-27,"50214207",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0846N6060","SNS",6060792.66073151,846593.1685158,0.33,0.1,1,300,3 +"50001046",2003-06-27,"50214274",5410,50,6,2003,1,1,2,"5151",NA,"ESAS v5","E0841N6040","SNS",6042047.90242025,842413.363240729,0.33,0.1,1,300,1 +"50001063",2003-08-05,"50224915",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986207.63963292,885657.387184544,0.29,0.09,1,300,40 +"50001063",2003-08-05,"50224918",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986624.29538339,884882.723055878,0.3,0.09,1,300,900 +"50001063",2003-08-05,"50224921",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986944.60973229,884023.688806773,0.32,0.1,1,300,1400 +"50001063",2003-08-05,"50224922",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987055.11795615,883737.067323192,0.29,0.09,1,300,500 +"50001063",2003-08-05,"50224924",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987019.07386413,883165.271938087,0.3,0.09,1,300,550 +"50001063",2003-08-05,"50224925",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986929.14587825,882888.609922684,0.27,0.08,1,300,12 +"50001063",2003-08-05,"50224926",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986807.53264515,882634.371604733,0.29,0.09,1,300,1900 +"50001063",2003-08-05,"50225111",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0831N5995","SNS",5995076.8906665,831542.59127608,0.25,0.08,2,300,1 +"50001065",2003-08-07,"50226047",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0801N5970","SNS",5970398.93310616,798906.60980141,0.21,0.06,1,300,3 +"50001065",2003-08-07,"50226064",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0801N5975","SNS",5973074.2667178,801456.130258193,0.23,0.07,1,300,4 +"50001065",2003-08-07,"50226087",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0806N5975","SNS",5976501.72899086,805069.120192254,0.22,0.06,1,300,4 +"50001065",2003-08-07,"50226295",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0831N5970","SNS",5970745.38059253,830841.919337872,0.18,0.06,1,300,1 +"50001065",2003-08-07,"50226331",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0826N5970","SNS",5968776.08212694,824424.221054278,0.17,0.05,2,300,3 +"50001065",2003-08-07,"50226333",4960,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0826N5970","SNS",5968742.56185578,824102.363620406,0.19,0.06,1,300,6 +"50001065",2003-08-07,"50226335",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0821N5970","SNS",5968597.47487736,823781.626107112,0.18,0.05,2,300,7 +"50001065",2003-08-07,"50226336",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0821N5970","SNS",5968607.83297142,823608.889210366,0.17,0.05,1,300,2 +"50001065",2003-08-07,"50226337",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0821N5970","SNS",5968596.42699623,823444.277860932,0.16,0.05,1,300,3 +"50001066",2003-08-08,"50226666",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0851N5995","SNS",5994656.1882524,851140.937966484,0.22,0.07,1,300,4 +"50001066",2003-08-08,"50226686",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0856N5995","SNS",5994473.03975358,855186.439071921,0.18,0.05,1,300,16 +"50001066",2003-08-08,"50226815",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986226.89708317,875936.98919092,0.19,0.06,1,300,6 +"50001066",2003-08-08,"50226818",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986239.29683766,876504.065229745,0.19,0.06,2,300,100 +"50001066",2003-08-08,"50226819",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986288.05785081,876691.674608495,0.2,0.06,2,300,100 +"50001066",2003-08-08,"50226821",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986261.27584622,877050.557137933,0.19,0.06,2,300,100 +"50001066",2003-08-08,"50226825",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986321.01625399,877785.557271063,0.19,0.06,2,300,8 +"50001066",2003-08-08,"50226826",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986335.96961919,877969.306605933,0.19,0.06,1,300,100 +"50001066",2003-08-08,"50226827",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986373.14748319,878151.246313397,0.19,0.06,1,300,24 +"50001066",2003-08-08,"50226828",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0876N5985","SNS",5986421.97586737,878338.84122251,0.19,0.06,2,300,4 +"50001066",2003-08-08,"50226834",5410,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986679.10162675,879434.259838494,0.2,0.06,1,300,7 +"50001066",2003-08-08,"50226836",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986655.75257848,879832.527506098,0.2,0.06,2,300,3 +"50001066",2003-08-08,"50226837",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986639.07368078,880038.681177903,0.21,0.06,1,300,100 +"50001066",2003-08-08,"50226838",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986632.97484265,880237.365508057,0.2,0.06,1,300,200 +"50001066",2003-08-08,"50226839",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986649.10123454,880434.231027046,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226840",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986665.23601321,880631.09622281,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226841",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986681.37917871,880827.961095177,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226843",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986735.9072947,881219.865702051,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226845",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986868.22674112,881605.373959841,0.21,0.06,1,300,67 +"50001066",2003-08-08,"50226846",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986982.76710424,881774.324961769,0.21,0.06,1,300,100 +"50001066",2003-08-08,"50226847",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987086.74557565,881950.743506107,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226849",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987141.37030053,882342.612240617,0.2,0.06,1,300,200 +"50001066",2003-08-08,"50226850",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987125.34385609,882555.329369371,0.22,0.07,1,300,200 +"50001066",2003-08-08,"50226851",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987109.32707163,882768.049146647,0.2,0.06,1,300,46 +"50001066",2003-08-08,"50226855",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986969.76888277,883651.627603058,0.2,0.06,1,300,31 +"50001066",2003-08-08,"50226859",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986729.31711654,884530.4337864,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226860",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986678.43424656,884726.263309465,0.2,0.06,1,300,100 +"50001066",2003-08-08,"50226861",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986628.10346863,884928.660460135,0.22,0.07,1,300,153 +"50001066",2003-08-08,"50226864",1730,50,1,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986396.13816547,885502.962577087,0.2,0.06,1,300,1 +"50001071",2003-04-22,"50228427",1680,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6012348.95613814,873608.812722262,0.51,0.15,1,300,1 +"50001071",2003-04-22,"50228430",1680,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6012334.27158292,872046.79195463,0.52,0.15,1,300,30 +"50001071",2003-04-22,"50228453",5410,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008831.26620144,860041.830183932,0.56,0.17,1,300,5 +"50001072",2003-04-23,"50228654",6270,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012093.57092805,875692.200566177,0.49,0.15,2,300,2 +"50001072",2003-04-23,"50228668",1730,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0866N6010","SNS",6010627.58577254,868341.39149575,0.58,0.17,2,300,2 +"50001072",2003-04-23,"50228680",1730,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008874.55793143,861023.86849984,0.58,0.17,1,300,1 +"50001072",2003-04-23,"50228824",1680,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008512.02271433,859961.653023356,0.54,0.16,1,300,1 +"50001072",2003-04-23,"50228836",1680,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0866N6010","SNS",6009605.38387158,866458.582667343,0.56,0.17,2,300,2 +"50001073",2003-04-24,"50228883",1730,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012265.58207765,874646.848098577,0.54,0.16,1,300,1 +"50001074",2003-04-25,"50229152",5410,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0851N6010","SNS",6009376.09933349,850877.205154862,0.54,0.16,1,300,13 +"50001074",2003-04-25,"50229315",1730,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012237.55209882,878814.12237023,0.47,0.14,2,300,2 +"50001075",2003-04-26,"50229326",5410,50,5,2003,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012495.13355648,880310.564791128,0.44,0.13,2,300,1 +"50001080",2001-07-02,"50230539",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5989430.14589751,872119.96244905,0.39,0.12,1,300,19 +"50001081",2001-07-03,"50230582",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5992398.31841124,871070.542835655,0.26,0.08,1,300,13 +"50001081",2001-07-03,"50230594",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0871N5995","SNS",5994621.02099219,869785.047139802,0.26,0.08,1,300,1 +"50001081",2001-07-03,"50230664",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0851N5995","SNS",5996778.83795454,851868.760614232,0.28,0.08,1,300,1 +"50001081",2001-07-03,"50230675",5410,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0846N5995","SNS",5997495.28403771,848490.083461949,0.29,0.09,1,300,1 +"50001100",2004-03-09,"50236827",5410,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0871N5995","SNS",5995721.10365005,873183.80904221,0.24,0.07,1,300,1 +"50001100",2004-03-09,"50236829",5410,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0871N5995","SNS",5996151.73949595,872977.728948306,0.23,0.07,1,300,1 +"50001100",2004-03-09,"50237034",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6025","SNS",6027296.83103591,863145.608734799,0.26,0.08,2,300,2 +"50001100",2004-03-09,"50237045",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6030","SNS",6029305.03186901,863516.252057797,0.22,0.07,1,300,12 +"50001100",2004-03-09,"50237069",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6035","SNS",6035481.25159492,861888.92581588,0.28,0.08,1,300,13 +"50001100",2004-03-09,"50237072",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6035","SNS",6036299.46434259,861915.543978694,0.28,0.08,1,300,8 +"50001100",2004-03-09,"50237085",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6040","SNS",6039893.61773997,862407.300462709,0.29,0.09,2,300,6 +"50001100",2004-03-09,"50237112",1680,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6040","SNS",6041220.65499159,860814.304036249,0.24,0.07,1,300,2 +"50001101",2004-03-10,"50237433",5410,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0871N6025","SNS",6024285.6605613,872986.647828406,0.29,0.09,1,300,1 +"50001101",2004-03-10,"50237434",5410,50,4,2004,1,1,2,"5171",NA,"ESAS v5","E0871N6025","SNS",6024352.86829227,873262.889826183,0.28,0.08,1,300,17 +"50001103",2002-08-18,"50237858",6270,50,1,2002,1,1,1,"5174",NA,"ESAS v5","E0821N6000","SNS",5998878.79144983,823722.477176455,0.49,0.15,1,300,1 +"50001104",2002-08-22,"50238041",5410,50,1,2002,1,1,1,"5174",NA,"ESAS v5","E0836N5960","SNS",5961367.35912036,835284.832011695,0.4,0.12,1,300,7 +"50001104",2002-08-22,"50238079",6270,50,1,2002,1,1,1,"5174",NA,"ESAS v5","E0831N5975","SNS",5975158.82628745,832280.260819999,0.44,0.13,2,300,1 +"50001106",2001-04-10,"50238417",5410,50,5,2001,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5996588.45717415,864284.663359397,0.46,0.14,1,300,2 +"50001106",2001-04-10,"50238436",1680,50,5,2001,1,1,1,"5174",NA,"ESAS v5","E0856N5995","SNS",5996058.479497,856329.337259217,0.42,0.13,1,300,3 +"50001106",2001-04-10,"50238446",1680,50,5,2001,1,1,1,"5174",NA,"ESAS v5","E0851N5995","SNS",5996778.10289874,852593.069514299,0.43,0.13,1,300,3 +"50001106",2001-04-10,"50238556",1680,50,5,2001,1,1,1,"5174",NA,"ESAS v5","E0851N6020","SNS",6022078.5321746,853733.528778209,0.3,0.09,1,300,4 +"50001107",2001-06-06,"50238779",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0841N6000","SNS",6000828.30161657,841649.285492301,0.36,0.11,1,300,1 +"50001108",2001-06-07,"50238926",1730,50,6,2001,1,1,1,"5174",NA,"ESAS v5","E0861N5995","SNS",5996629.62111964,862674.089290574,0.34,0.1,1,300,6 +"50001116",2003-05-21,"50240699",1680,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6002723.66646845,876390.554124699,0.16,0.05,1,300,6 +"50001116",2003-05-21,"50240710",5410,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6003961.03883397,875381.564522817,0.15,0.04,1,300,2 +"50001116",2003-05-21,"50240714",5410,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6004137.00201188,874794.774901192,0.16,0.05,1,300,3 +"50001116",2003-05-21,"50240793",4960,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0866N6010","SNS",6008128.80707613,868271.704547016,0.2,0.06,1,300,80 +"50001116",2003-05-21,"50240794",4960,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0866N6010","SNS",6008331.90592411,868294.913796571,0.2,0.06,2,300,40 +"50001116",2003-05-21,"50240831",1680,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6005483.25209822,874573.631584595,0.24,0.07,1,300,9 +"50001116",2003-05-21,"50240838",1680,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6005568.54277535,876171.818789716,0.23,0.07,2,300,4 +"50001117",2003-07-02,"50240905",5410,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0896N5985","SNS",5984534.46794611,894636.685502867,0.36,0.11,1,300,1 +"50001117",2003-07-02,"50240921",5410,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0891N5985","SNS",5982893.37136931,888835.913782249,0.33,0.1,1,300,1 +"50001117",2003-07-02,"50240967",6270,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0871N5990","SNS",5988534.15548398,873492.315074173,0.34,0.1,2,300,1 +"50001118",2003-07-03,"50241362",1730,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0846N6050","SNS",6050120.68389805,848652.316230546,0.3,0.09,2,300,1 +"50001118",2003-07-03,"50241442",5410,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0866N6020","SNS",6017778.49781463,865342.594513452,0.34,0.1,1,300,1 +"50001118",2003-07-03,"50241510",5410,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0866N6005","SNS",6007050.79708086,867792.441726702,0.24,0.07,1,300,2 +"50001118",2003-07-03,"50241560",1730,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5996041.17722383,872993.229719653,0.32,0.1,1,300,5 +"50001118",2003-07-03,"50241561",1730,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5995749.70470915,872983.761469175,0.29,0.09,2,300,31 +"50001118",2003-07-03,"50241562",1730,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5995464.59273193,872914.450134776,0.29,0.09,1,300,1 +"50001118",2003-07-03,"50241563",1730,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5995261.49818436,872752.826093936,0.26,0.08,1,300,9 +"50001118",2003-07-03,"50241637",5410,50,6,2003,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5985207.53295195,887214.475002794,0.34,0.1,1,300,1 +"50001120",2003-05-07,"50242404",1680,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0871N6010","SNS",6009254.58251164,869206.877390125,0.2,0.06,1,300,6 +"50001120",2003-05-07,"50242437",1730,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0866N6005","SNS",6003532.64010126,866290.116863105,0.29,0.09,1,300,2 +"50001120",2003-05-07,"50242585",1730,50,5,2003,1,1,2,"5177",NA,"ESAS v5","E0896N5985","SNS",5986270.86159672,897550.286598725,0.22,0.07,1,300,2 +"50001123",2003-03-26,"50243245",5410,50,4,2003,1,1,2,"5142",NA,"ESAS v5","E0896N5985","SNS",5984512.3532136,894903.103244893,0.34,0.1,1,300,2 +"50001123",2003-03-26,"50243274",1730,50,4,2003,1,1,2,"5142",NA,"ESAS v5","E0886N5980","SNS",5981900.59666287,884969.920193708,0.37,0.11,1,300,1 +"50001124",2003-03-27,"50243713",5410,50,4,2003,1,1,2,"5142",NA,"ESAS v5","E0851N6020","SNS",6018019.26182346,852250.443201985,0.25,0.08,2,300,1 +"50001124",2003-03-27,"50243915",1730,50,4,2003,1,1,2,"5142",NA,"ESAS v5","E0881N5980","SNS",5981257.09158279,879863.998315493,0.26,0.08,1,300,1 +"50001124",2003-03-27,"50243971",5410,50,4,2003,1,1,2,"5142",NA,"ESAS v5","E0896N5985","SNS",5983906.04900559,895272.121733992,0.29,0.09,1,300,1 +"50001126",2000-06-25,"50244036",1730,50,6,2000,1,1,1,"5170",NA,"ESAS v5","E0881N6010","SNS",6012232.52499795,879432.125156239,4.35,1.3,1,300,9 +"50001127",2000-09-19,"50244064",1680,50,1,2000,1,1,1,"5194",NA,"ESAS v5","E0861N6000","SNS",5999609.84403265,861762.429721581,1.57,0.47,1,300,1 +"50001130",2003-12-04,"50244804",5410,50,3,2003,1,1,2,"5194",NA,"ESAS v5","E0886N5985","SNS",5985445.56042059,887386.322329894,0.22,0.07,1,300,1 +"50001130",2003-12-04,"50244815",5410,50,3,2003,1,1,2,"5194",NA,"ESAS v5","E0891N5985","SNS",5984562.80183694,889403.722696984,0.21,0.06,1,300,5 +"50001130",2003-12-04,"50244825",5410,50,3,2003,1,1,2,"5194",NA,"ESAS v5","E0891N5985","SNS",5984609.38978544,891555.349153225,0.21,0.06,1,300,1 +"50001131",1999-05-22,"50245091",1680,50,5,1999,1,1,1,"5162",NA,"ESAS v5","E0821N6010","SNS",6009371.82005822,821741.093396752,0.53,0.16,1,300,36 +"50001132",1997-07-28,"50245103",1730,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0876N6015","SNS",6013748.50110484,875005.529561514,1.79,0.54,1,300,9 +"50001132",1997-07-28,"50245104",1730,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0871N6015","SNS",6013643.91656868,873306.718029971,1.61,0.48,1,300,6 +"50001132",1997-07-28,"50245105",1730,50,6,1997,1,1,1,"5112",NA,"ESAS v5","E0871N6015","SNS",6012719.1017301,871273.592534978,2.94,0.88,1,300,11 +"50001139",2000-06-22,"50246303",5410,50,6,2000,1,1,1,"5170",NA,"ESAS v5","E0871N6010","SNS",6012175.00654939,869931.691295526,3.04,0.91,1,300,1 +"50001141",2002-10-03,"50246448",1730,50,2,2002,1,1,2,"5187",NA,"ESAS v5","E0876N6015","SNS",6013370.04633898,874340.247345697,0.49,0.15,1,300,5 +"50001141",2002-10-03,"50246494",1680,50,2,2002,1,1,2,"5187",NA,"ESAS v5","E0851N6010","SNS",6009096.34292637,852921.077372592,0.55,0.17,1,300,2 +"50001143",2001-07-04,"50246700",5410,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0861N5955","SNS",5957614.54575381,860278.943540873,0.53,0.16,1,300,3 +"50001143",2001-07-04,"50246711",5410,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0856N5960","SNS",5959191.52247213,856055.30361832,0.48,0.14,1,300,2 +"50001143",2001-07-04,"50246757",1730,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0836N5975","SNS",5974754.10766045,838298.184084153,0.5,0.15,1,300,6 +"50001145",2001-02-28,"50247030",1730,50,4,2001,1,1,2,"5188",NA,"ESAS v5","E0896N5985","SNS",5984942.07605129,895746.117996027,0.23,0.07,2,300,1 +"50001149",2001-06-25,"50247907",1730,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5961171.61496076,835046.937446241,0.42,0.13,1,300,2 +"50001149",2001-06-25,"50247921",5410,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0836N5955","SNS",5956396.13180084,838381.71407149,0.38,0.11,1,300,1 +"50001149",2001-06-25,"50247945",1730,50,6,2001,1,1,1,"5201",NA,"ESAS v5","E0841N5950","SNS",5948180.86417322,842471.706527352,0.38,0.11,1,300,1 +"50001150",1998-06-26,"50248006",5410,50,6,1998,1,1,1,"5201",NA,"ESAS v5","E0836N5965","SNS",5963857.50784068,834808.277593534,0.46,0.14,2,300,7 +"50001151",1999-07-15,"50248241",1730,50,6,1999,1,1,1,"5201",NA,"ESAS v5","E0831N5970","SNS",5970020.75007167,832745.822806866,0.38,0.12,2,300,2 +"50001151",1999-07-15,"50248267",5410,50,6,1999,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5960389.47177672,835354.877557044,0.42,0.13,1,300,2 +"50001152",1999-07-21,"50248369",6270,50,6,1999,1,1,1,"5201",NA,"ESAS v5","E0836N5965","SNS",5966549.48897631,833873.904879221,0.48,0.14,1,300,1 +"50001152",1999-07-21,"50248370",1730,50,6,1999,1,1,1,"5201",NA,"ESAS v5","E0831N5965","SNS",5967006.32941439,833702.222519423,0.48,0.14,1,300,3 +"50001152",1999-07-21,"50248394",4960,50,6,1999,1,1,1,"5201",NA,"ESAS v5","E0831N5980","SNS",5977859.9214918,829425.810701002,0.51,0.15,1,300,3 +"50001159",2000-03-20,"50250131",1680,50,4,2000,1,1,1,"5107",NA,"ESAS v5","E0871N5990","SNS",5989163.20137363,873111.645476797,0.41,0.12,1,300,53 +"50001160",2000-03-21,"50250388",1680,50,4,2000,1,1,2,"5107",NA,"ESAS v5","E0831N6005","SNS",6006953.07683595,831735.098216958,0.47,0.14,1,300,4 +"50001161",2000-03-16,"50250517",1680,50,4,2000,1,1,1,"5107",NA,"ESAS v5","E0866N5995","SNS",5993759.79502353,864988.51272947,0.55,0.17,1,300,2 +"50001162",1998-12-22,"50250571",4960,50,3,1998,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5995752.87847019,867572.636204504,0.44,0.13,1,300,6 +"50001163",1998-12-31,"50250724",1730,50,3,1998,1,1,1,"5182",NA,"ESAS v5","E0866N5995","SNS",5996054.07897124,864880.16898111,0.46,0.14,1,300,1 +"50001164",1999-03-06,"50250859",1680,50,4,1999,1,1,1,"5182",NA,"ESAS v5","E0856N5995","SNS",5996470.09685685,857753.119406324,0.46,0.14,2,300,2 +"50001169",2000-07-03,"50251948",1730,50,6,2000,1,1,2,"5999",NA,"ESAS v5","E0866N5995","SNS",5992919.24812733,868351.510697343,0.41,0.12,2,300,2 +"50001172",2001-04-11,"50252869",5410,50,5,2001,1,1,3,"5174",NA,"ESAS v5","E0796N6015","SNS",6016100.27176246,798490.293440494,0.34,0.1,1,300,1 +"50001173",2001-04-25,"50253174",1680,50,5,2001,1,1,2,"5174",NA,"ESAS v5","E0806N6030","SNS",6028873.20266043,804303.695966229,0.31,0.09,1,300,2 +"50001173",2001-04-25,"50253207",5410,50,5,2001,1,1,2,"5174",NA,"ESAS v5","E0811N6025","SNS",6026516.70582006,812088.710107973,0.35,0.1,1,300,64 +"50001180",1998-08-03,"50255351",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5993533.42415322,866792.902993109,0.24,0.07,1,300,2 +"50001180",1998-08-03,"50255370",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5991263.36450586,870719.418063875,0.24,0.07,2,300,3 +"50001180",1998-08-03,"50255371",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5991180.78191384,870805.187588008,0.24,0.07,1,300,2 +"50001181",1998-08-04,"50255411",6270,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0876N5985","SNS",5986361.04762056,875107.08266405,0.21,0.06,1,300,1 +"50001181",1998-08-04,"50255446",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0871N5995","SNS",5993196.17440105,870841.791820836,0.22,0.07,1,300,2 +"50001181",1998-08-04,"50255467",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5995588.39682887,866485.185992542,0.25,0.07,1,300,3 +"50001181",1998-08-04,"50255480",1730,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0861N5995","SNS",5996741.3615195,863528.250416306,0.25,0.08,2,300,19 +"50001181",1998-08-04,"50255501",6270,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0861N6000","SNS",6000536.88853679,859748.424618953,0.27,0.08,1,300,1 +"50001181",1998-08-04,"50255681",6270,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0831N6015","SNS",6017577.9253748,833151.213751342,0.3,0.09,2,300,1 +"50001181",1998-08-04,"50255717",6270,50,1,1998,1,1,1,"5174",NA,"ESAS v5","E0821N6015","SNS",6014936.25929056,822898.335573974,0.3,0.09,1,300,2 +"50001182",2000-04-03,"50255848",1680,50,5,2000,1,1,1,"5110",NA,"ESAS v5","E0841N6000","SNS",5998993.50384049,842370.414391056,0.32,0.09,1,300,1 +"50001185",2001-05-16,"50256559",1730,50,5,2001,1,1,2,"5107",NA,"ESAS v5","E0841N6075","SNS",6074147.06996911,839227.33259914,0.31,0.09,1,300,1 +"50001197",2004-04-27,"50261782",1680,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5986997.20641666,882493.218556406,0.33,0.1,1,300,2 +"50001198",2004-04-28,"50262127",1730,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0821N5970","SNS",5969538.98973456,818967.324128135,0.28,0.08,2,300,1 +"50001198",2004-04-28,"50262521",1730,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0871N6025","SNS",6027487.08406617,870408.724610941,0.13,0.04,1,300,3 +"50001198",2004-04-28,"50262522",1730,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0871N6025","SNS",6027421.54807022,870289.645237213,0.14,0.04,1,300,3 +"50001198",2004-04-28,"50262534",4960,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0871N6025","SNS",6026769.72546986,868862.72239884,0.14,0.04,1,300,360 +"50001199",2004-04-29,"50262836",5410,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5993833.50391747,870981.98251901,0.19,0.06,1,300,1 +"50001200",2004-05-05,"50263243",1730,50,5,2004,1,1,2,"5177",NA,"ESAS v5","E0881N5980","SNS",5981372.6315922,880046.352360328,0.31,0.09,1,300,5 +"50001201",2002-08-08,"50263396",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5975","SNS",5975167.66370795,831777.344611895,0.46,0.14,1,300,1 +"50001201",2002-08-08,"50263418",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5965","SNS",5966473.52078859,833435.686767492,0.4,0.12,1,300,9 +"50001202",2002-09-11,"50263533",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5975","SNS",5973132.89783524,833158.663166893,0.57,0.17,1,300,2 +"50001203",2002-09-24,"50263716",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5975","SNS",5976848.7679075,829814.454197531,0.43,0.13,1,300,1 +"50001203",2002-09-24,"50263719",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5975","SNS",5976103.56850514,830488.313131175,0.36,0.11,1,300,1 +"50001203",2002-09-24,"50263751",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5965","SNS",5964873.20619957,833861.193006659,0.37,0.11,1,300,1 +"50001203",2002-09-24,"50263753",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5965","SNS",5964088.92849179,833983.447339748,0.48,0.14,1,300,1 +"50001203",2002-09-24,"50263758",5410,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5962085.23674662,834703.161977026,0.3,0.09,1,300,11 +"50001203",2002-09-24,"50263759",5410,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5961802.44687009,834809.557648718,0.3,0.09,1,300,1 +"50001203",2002-09-24,"50263760",1680,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5961508.54656218,834916.758386018,0.3,0.09,2,300,7 +"50001203",2002-09-24,"50263762",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5960888.11040942,835299.271911123,0.39,0.12,1,300,3 +"50001203",2002-09-24,"50263762",5410,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5960888.11040942,835299.271911123,0.39,0.12,1,300,25 +"50001203",2002-09-24,"50263764",5410,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5960238.6216304,835743.636312181,0.39,0.12,1,300,4 +"50001203",2002-09-24,"50263766",5410,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5960","SNS",5959588.69677548,836181.512969817,0.39,0.12,2,300,7 +"50001204",2002-09-30,"50263792",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5955","SNS",5957490.83871009,838382.321464864,0.51,0.15,1,300,1 +"50001204",2002-09-30,"50263809",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0836N5965","SNS",5965057.70445729,834411.03061946,0.54,0.16,1,300,1 +"50001204",2002-09-30,"50263813",1680,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5965","SNS",5967006.32941439,833702.222519423,0.48,0.14,1,300,2 +"50001204",2002-09-30,"50263821",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5970","SNS",5971028.87432677,833256.069068091,0.52,0.16,2,300,2 +"50001204",2002-09-30,"50263825",1730,50,1,2002,1,1,1,"5201",NA,"ESAS v5","E0831N5975","SNS",5973132.89783524,833158.663166893,0.57,0.17,2,300,2 +"50001207",2000-05-09,"50264364",6270,50,5,2000,1,1,1,"5174",NA,"ESAS v5","E0851N5995","SNS",5996840.21341767,852087.947626359,0.43,0.13,1,300,1 +"50001216",2002-06-03,"50267037",5410,50,6,2002,1,1,2,"5174",NA,"ESAS v5","E0876N5985","SNS",5985543.15244623,874532.355795128,0.15,0.04,1,300,1 +"50001217",2002-06-04,"50267056",6270,50,6,2002,1,1,1,"5174",NA,"ESAS v5","E0876N5990","SNS",5988158.52907048,873826.250290395,0.24,0.07,1,300,2 +"50001217",2002-06-04,"50267096",6270,50,6,2002,1,1,1,"5174",NA,"ESAS v5","E0866N5995","SNS",5995979.60592826,866625.531603716,0.33,0.1,2,300,2 +"50001227",2004-06-10,"50271409",5410,50,6,2004,1,1,5,"5194",NA,"ESAS v5","E0866N6015","SNS",6017421.12299929,867870.06780442,0.31,0.09,2,300,1 +"50001232",2001-05-09,"50272197",6270,50,5,2001,1,1,1,"5174",NA,"ESAS v5","E0816N6010","SNS",6010759.4463496,815488.290988741,0.38,0.11,2,300,1 +"50001233",1997-07-02,"50272506",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0876N6015","SNS",6012985.54287556,876617.77254396,0.62,0.19,1,300,25 +"50001233",1997-07-02,"50272508",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0876N6015","SNS",6013220.72622545,875390.053587303,0.62,0.19,1,300,6 +"50001233",1997-07-02,"50272514",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0871N6015","SNS",6013995.69534944,873232.218026309,0.22,0.07,1,300,2 +"50001233",1997-07-02,"50272761",5410,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0836N6070","SNS",6071338.04230336,834077.198758971,0.4,0.12,2,300,1 +"50001233",1997-07-02,"50272876",5410,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0851N6025","SNS",6024178.06068985,848898.200434934,0.43,0.13,2,300,4 +"50001234",1997-07-03,"50272974",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0876N6015","SNS",6012907.05193426,876341.754710011,0.29,0.09,1,300,36 +"50001234",1997-07-03,"50272974",6270,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0876N6015","SNS",6012907.05193426,876341.754710011,0.29,0.09,1,300,2 +"50001234",1997-07-03,"50272984",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0871N6015","SNS",6013470.44443198,872545.9368127,0.29,0.09,1,300,1 +"50001234",1997-07-03,"50273015",5410,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0861N6010","SNS",6009132.56516773,861890.520661378,0.29,0.09,1,300,1 +"50001234",1997-07-03,"50273059",5410,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0861N6000","SNS",6000352.13170852,862105.826066073,0.34,0.1,1,300,2 +"50001234",1997-07-03,"50273272",5410,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0856N6045","SNS",6043145.5597553,857720.988830709,0.36,0.11,1,300,20 +"50001234",1997-07-03,"50273382",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0871N6015","SNS",6015239.93019093,872442.056774736,0.34,0.1,1,300,12 +"50001234",1997-07-03,"50273390",1730,50,6,1997,1,1,1,"5102",NA,"ESAS v5","E0876N6015","SNS",6013057.75584063,875311.396407964,0.34,0.1,1,300,9 +"50001235",1997-08-05,"50273420",1730,50,1,1997,1,1,1,"5174",NA,"ESAS v5","E0871N5990","SNS",5991197.90514665,872413.566188108,0.35,0.11,1,300,4 +"50001235",1997-08-05,"50273579",6270,50,1,1997,1,1,1,"5174",NA,"ESAS v5","E0846N6020","SNS",6022091.83476735,848650.976336735,0.35,0.11,1,300,2 +"50001236",1997-08-04,"50273709",6270,50,1,1997,1,1,1,"5174",NA,"ESAS v5","E0821N6010","SNS",6007676.94600267,822896.100332281,0.3,0.09,2,300,1 +"50001243",2004-06-22,"50275270",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0891N5985","SNS",5984325.05767026,890303.078860592,0.34,0.1,1,300,2 +"50001243",2004-06-22,"50275283",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5985810.04584754,886390.965733017,0.33,0.1,1,300,2 +"50001243",2004-06-22,"50275684",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0861N6000","SNS",6000493.49694699,858902.870506349,0.28,0.08,1,300,1 +"50001243",2004-06-22,"50275740",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5992686.38336402,871456.374321398,0.28,0.08,1,300,3 +"50001244",2004-06-23,"50275942",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986752.69569681,884812.613088982,0.18,0.05,1,300,4 +"50001244",2004-06-23,"50275945",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5986500.89527289,885282.812821077,0.18,0.05,1,300,1 +"50001244",2004-06-23,"50275960",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0886N5985","SNS",5985070.32704054,887715.089769646,0.2,0.06,1,300,3 +"50001244",2004-06-23,"50276004",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0881N5980","SNS",5982295.9378982,881352.889868105,0.24,0.07,1,300,1 +"50001244",2004-06-23,"50276030",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5987091.04251963,881322.822745257,0.32,0.09,1,300,1 +"50001244",2004-06-23,"50276064",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0891N5985","SNS",5984527.85822721,891251.434593041,0.34,0.1,1,300,1 +"50001244",2004-06-23,"50276070",5410,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0891N5985","SNS",5984974.59440729,893230.49983155,0.33,0.1,1,300,5 +"50001245",2004-10-05,"50276174",6270,50,2,2004,1,1,2,"5177",NA,"ESAS v5","E0946N5955","SNS",5956549.23842654,946960.11809972,0.27,0.08,1,300,1 +"50001245",2004-10-05,"50276195",1730,50,2,2004,1,1,2,"5177",NA,"ESAS v5","E0951N5955","SNS",5954296.28381866,949997.341996488,0.25,0.07,2,300,16 +"50001246",2004-09-07,"50276671",6270,50,1,2004,1,1,2,"5171",NA,"ESAS v5","E0861N6005","SNS",6006598.46110453,859217.234619148,0.23,0.07,2,300,3 +"50001248",2004-09-09,"50277984",1730,50,1,2004,1,1,2,"5171",NA,"ESAS v5","E0886N5985","SNS",5986248.39248638,885878.687193034,0.21,0.06,1,300,10 +"50001255",2004-07-21,"50282709",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0871N6010","SNS",6011108.53858184,869321.074945621,0.45,0.13,1,300,1 +"50001255",2004-07-21,"50282732",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012276.98956786,878613.779988646,0.36,0.11,1,300,5 +"50001255",2004-07-21,"50282734",1730,50,6,2004,1,1,2,"5177",NA,"ESAS v5","E0881N6010","SNS",6012335.5414945,879325.084648248,0.36,0.11,1,300,100 +"50001259",2004-08-16,"50283700",4960,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0791N5960","SNS",5962174.51293104,793456.616999049,0.29,0.09,1,300,3 +"50001259",2004-08-16,"50283877",6270,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0831N5990","SNS",5989272.16848254,831121.397207391,0.29,0.09,1,300,1 +"50001259",2004-08-16,"50283938",1730,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0846N6000","SNS",5999405.54977634,845655.325413954,0.3,0.09,1,300,2 +"50001259",2004-08-16,"50283964",1730,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0851N6005","SNS",6004805.39316237,850876.843394432,0.29,0.09,2,300,1 +"50001259",2004-08-16,"50284035",1730,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0866N6010","SNS",6010489.42922598,868431.272721512,0.24,0.07,1,300,1 +"50001259",2004-08-16,"50284036",6270,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0866N6010","SNS",6010597.10564954,868659.155754704,0.25,0.07,1,300,1 +"50001259",2004-08-16,"50284038",1730,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6010801.90743064,869122.310476811,0.26,0.08,2,300,20 +"50001259",2004-08-16,"50284064",6270,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012101.36726367,876335.362689334,0.31,0.09,2,300,5 +"50001259",2004-08-16,"50284066",6270,50,1,2004,1,1,2,"5170",NA,"ESAS v5","E0876N6010","SNS",6012152.08440473,876955.317974273,0.31,0.09,1,300,3 +"50001260",2004-08-17,"50284099",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013251.63191755,878770.089714875,0.22,0.07,1,300,2 +"50001260",2004-08-17,"50284108",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013437.78987108,876541.360535656,0.24,0.07,2,300,1 +"50001260",2004-08-17,"50284397",5410,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0846N6025","SNS",6023990.61574558,844200.470505854,0.29,0.09,1,300,1 +"50001260",2004-08-17,"50284424",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0851N6025","SNS",6024430.53789264,851909.257101633,0.27,0.08,2,300,1 +"50001260",2004-08-17,"50284488",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0866N6020","SNS",6017758.74260888,866498.468390851,0.25,0.07,2,300,5 +"50001260",2004-08-17,"50284490",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0866N6020","SNS",6017629.55094151,866981.017113002,0.25,0.08,2,300,2 +"50001260",2004-08-17,"50284507",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6016153.31459954,871154.031299172,0.26,0.08,2,300,1 +"50001260",2004-08-17,"50284509",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6015912.45459021,871632.86327267,0.27,0.08,1,300,2 +"50001260",2004-08-17,"50284510",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6015639.37073276,872127.486134006,0.25,0.08,2,300,1 +"50001260",2004-08-17,"50284512",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6015363.69587951,872589.565000765,0.27,0.08,1,300,1 +"50001260",2004-08-17,"50284516",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6014715.67656605,873561.143609888,0.3,0.09,1,300,1 +"50001260",2004-08-17,"50284519",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6014209.03975287,874344.224167555,0.31,0.09,1,300,4 +"50001260",2004-08-17,"50284528",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013370.28103542,876809.595659317,0.28,0.08,1,300,1 +"50001260",2004-08-17,"50284532",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013267.56023229,877875.47461223,0.27,0.08,2,300,4 +"50001260",2004-08-17,"50284533",1730,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013300.63928134,878142.057908376,0.27,0.08,2,300,3 +"50001261",2004-08-18,"50284552",5320,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013402.46825546,877884.102150354,0.33,0.1,1,300,1 +"50001261",2004-08-18,"50284570",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6015","SNS",6015309.72451803,872751.471293171,0.32,0.09,1,300,2 +"50001261",2004-08-18,"50284716",5320,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0836N6030","SNS",6031226.74284471,837085.231268488,0.27,0.08,1,300,1 +"50001261",2004-08-18,"50284784",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0851N6040","SNS",6038016.42066275,849958.551490897,0.27,0.08,1,300,9 +"50001261",2004-08-18,"50284838",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0836N6045","SNS",6042833.00496286,837962.437719311,0.2,0.06,1,300,9 +"50001261",2004-08-18,"50284884",5320,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0846N6045","SNS",6047012.4752518,847028.109188971,0.25,0.08,1,300,2 +"50001262",2004-08-23,"50285187",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0836N6060","SNS",6060613.9630725,835311.807890818,0.32,0.1,1,300,3 +"50001263",2004-08-24,"50285321",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0836N6065","SNS",6064155.15799951,838603.555541449,0.3,0.09,1,300,2 +"50001263",2004-08-24,"50285565",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0806N6065","SNS",6064331.13476493,806941.583826108,0.32,0.1,1,300,4 +"50001264",2004-08-25,"50285736",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0846N6040","SNS",6038404.85564137,847142.7122735,0.27,0.08,1,300,2 +"50001264",2004-08-25,"50285808",5410,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0861N6045","SNS",6047094.59338546,863542.269158895,0.26,0.08,1,300,1 +"50001264",2004-08-25,"50285846",4960,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0871N6050","SNS",6047943.03318585,873738.673858717,0.27,0.08,1,300,1 +"50001264",2004-08-25,"50285853",5410,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6050","SNS",6048010.93727134,875649.569681606,0.28,0.08,1,300,8 +"50001264",2004-08-25,"50285862",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0876N6045","SNS",6046876.56218708,877888.581237373,0.28,0.08,1,300,1 +"50001264",2004-08-25,"50285871",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0881N6045","SNS",6046889.76955571,880196.088296918,0.27,0.08,1,300,1 +"50001264",2004-08-25,"50285889",5410,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0881N6050","SNS",6050446.1550864,883385.687856703,0.26,0.08,1,300,1 +"50001264",2004-08-25,"50285900",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0886N6050","SNS",6052575.06967665,885277.917849455,0.27,0.08,1,300,1 +"50001264",2004-08-25,"50285901",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0886N6055","SNS",6052716.45671829,885493.959009652,0.26,0.08,1,300,1 +"50001264",2004-08-25,"50285902",6270,50,1,2004,1,1,3,"5170",NA,"ESAS v5","E0886N6055","SNS",6052836.73849024,885724.810516885,0.26,0.08,1,300,1 +"50001265",2004-11-03,"50285950",1680,50,2,2004,1,1,2,"5170",NA,"ESAS v5","E0886N5985","SNS",5985886.60053199,885697.265824753,0.3,0.09,1,300,1 +"50001265",2004-11-03,"50286006",5410,50,2,2004,1,1,2,"5170",NA,"ESAS v5","E0871N5990","SNS",5991760.49916784,871761.475366668,0.33,0.1,1,300,1 +"50001265",2004-11-03,"50286232",1680,50,2,2004,1,1,2,"5170",NA,"ESAS v5","E0846N6015","SNS",6014262.82937368,845454.938680148,0.27,0.08,2,300,2 +"50001266",2004-11-04,"50286382",5410,50,2,2004,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013400.58805779,877588.694040521,0.34,0.1,1,300,1 +"50001275",2004-08-24,"50290556",1730,50,1,2004,1,1,2,"5194",NA,"ESAS v5","E0876N6010","SNS",6012283.98950604,877745.997554499,0.44,0.13,1,300,3 +"50001275",2004-08-24,"50290580",1730,50,1,2004,1,1,2,"5194",NA,"ESAS v5","E0866N6010","SNS",6009311.30156555,865148.191776929,0.58,0.17,1,300,6 +"50001275",2004-08-24,"50290588",5410,50,1,2004,1,1,2,"5194",NA,"ESAS v5","E0861N6010","SNS",6008839.28584571,860573.329637164,0.57,0.17,1,300,1 +"50001275",2004-08-24,"50290669",5410,50,1,2004,1,1,2,"5194",NA,"ESAS v5","E0831N6010","SNS",6011257.04356339,832402.566430558,0.54,0.16,1,300,1 +"50001277",2005-03-31,"50291157",1680,50,4,2005,1,1,2,"5177",NA,"ESAS v5","E0851N6065","SNS",6066021.18126348,849182.839544255,0.3,0.09,1,300,4 +"50001277",2005-03-31,"50291157",1730,50,4,2005,1,1,2,"5177",NA,"ESAS v5","E0851N6065","SNS",6066021.18126348,849182.839544255,0.3,0.09,1,300,2 +"50001277",2005-03-31,"50291281",1680,50,4,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6035","SNS",6034416.12504202,854865.33602336,0.26,0.08,2,300,2 +"50001277",2005-03-31,"50291407",1730,50,4,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6005","SNS",6003248.97204601,858978.219595169,0.22,0.07,1,300,1 +"50001277",2005-03-31,"50291529",1730,50,4,2005,1,1,2,"5177",NA,"ESAS v5","E0881N5985","SNS",5983297.24410977,879102.40582549,0.29,0.09,1,300,1 +"50001278",2005-04-12,"50291626",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0886N5980","SNS",5982566.27618648,888267.981054797,0.37,0.11,1,300,1 +"50001278",2005-04-12,"50291754",1730,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0856N5995","SNS",5996709.22757263,855199.262654534,0.22,0.07,1,300,1 +"50001282",2005-05-28,"50292705",4960,50,5,2005,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6013307.49362198,876315.580409431,0.53,0.16,1,300,5 +"50001286",2005-05-26,"50295049",1680,50,5,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6025","SNS",6027226.32206866,872295.656968953,0.2,0.06,1,300,7 +"50001287",2005-04-13,"50295154",1730,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6062843.11267658,859276.456973603,0.2,0.06,1,300,4 +"50001287",2005-04-13,"50295155",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6062869.59018724,859469.154335195,0.2,0.06,1,300,1 +"50001287",2005-04-13,"50295158",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6062871.81730646,860059.876902247,0.19,0.06,1,300,2 +"50001287",2005-04-13,"50295208",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067419.53032383,869450.082732403,0.25,0.08,1,300,2 +"50001287",2005-04-13,"50295216",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067981.12198362,871526.176506008,0.31,0.09,1,300,2 +"50001287",2005-04-13,"50295216",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067981.12198362,871526.176506008,0.31,0.09,2,300,20 +"50001287",2005-04-13,"50295222",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067445.3955667,873439.517653191,0.32,0.1,2,300,13 +"50001287",2005-04-13,"50295224",1730,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066854.1474912,873728.557326365,0.32,0.1,1,300,2 +"50001287",2005-04-13,"50295225",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066530.40565897,873735.840776393,0.32,0.1,2,300,2 +"50001287",2005-04-13,"50295270",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6054918.54687679,870015.568482136,0.25,0.07,2,300,380 +"50001287",2005-04-13,"50295499",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6020","SNS",6020793.81160681,863470.914560696,0.25,0.07,1,300,25 +"50001287",2005-04-13,"50295564",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013143.41313974,876906.988608429,0.17,0.05,1,300,2 +"50001287",2005-04-13,"50295574",1680,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6012894.92592359,878648.357358432,0.17,0.05,1,300,6 +"50001288",2005-04-18,"50295782",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0776N5965","SNS",5965151.62489239,777526.620201868,0.3,0.09,1,300,90 +"50001289",2005-04-19,"50296032",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0781N5975","SNS",5972889.20831585,783568.001097946,0.18,0.05,1,300,1 +"50001289",2005-04-19,"50296301",5410,50,5,2005,1,1,2,"5177",NA,"ESAS v5","E0791N5995","SNS",5995001.23179456,791078.18067888,0.27,0.08,1,300,21 +"50001295",2005-06-23,"50300426",1730,50,6,2005,1,1,2,"5171",NA,"ESAS v5","E0871N6010","SNS",6011614.23629091,870883.194312529,0.42,0.13,1,300,1 +"50001297",2005-04-22,"50301078",1680,50,5,2005,1,1,2,"5170",NA,"ESAS v5","E0816N5995","SNS",5994504.93646726,816837.786084071,0.35,0.11,1,300,2 +"50001298",2005-06-16,"50301306",5410,50,6,2005,1,1,2,"5194",NA,"ESAS v5","E0856N6010","SNS",6008542.78279743,854881.359481955,0.56,0.17,1,300,2 +"50001298",2005-06-16,"50301470",5410,50,6,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013326.42942232,875177.841140911,0.56,0.17,1,300,1 +"50001300",2005-06-01,"50301646",4960,50,6,2005,1,1,1,"5201",NA,"ESAS v5","E0841N6010","SNS",6009942.78581532,842970.089916025,0.58,0.18,1,300,20 +"50001302",2005-06-27,"50301857",5410,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0826N6010","SNS",6012095.07856506,827889.984368217,0.58,0.17,1,300,1 +"50001303",2005-07-11,"50301967",1730,50,6,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013328.85328177,878757.166009475,0.53,0.16,2,300,3 +"50001303",2005-07-11,"50301977",1730,50,6,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6015","SNS",6013220.21040947,873597.205841897,0.48,0.14,1,300,3 +"50001303",2005-07-11,"50301984",5410,50,6,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6011547.05613786,870882.036681287,0.49,0.15,1,300,1 +"50001304",2005-08-16,"50302227",6270,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6011707.38534507,870790.302059365,0.52,0.16,1,300,1 +"50001305",2005-08-23,"50302455",5410,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013251.92365861,875361.238663781,0.53,0.16,1,300,1 +"50001305",2005-08-23,"50302675",1730,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013303.09577979,876808.528641865,0.48,0.15,2,300,11 +"50001306",2005-07-18,"50302716",1730,50,6,2005,1,1,1,"5171",NA,"ESAS v5","E0871N6010","SNS",6012589.6713197,872577.830322695,0.29,0.09,1,300,4 +"50001311",2005-05-14,"50304463",1730,50,5,2005,1,1,2,"5170",NA,"ESAS v5","E0866N6015","SNS",6017535.86847601,867906.798727284,0.35,0.1,1,300,5 +"50001311",2005-05-14,"50304474",1730,50,5,2005,1,1,2,"5170",NA,"ESAS v5","E0861N6020","SNS",6017961.11584027,863557.458788194,0.32,0.1,1,300,4 +"50001314",2005-07-09,"50305448",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6010","SNS",6011834.51579042,884610.422484913,0.18,0.05,1,300,2 +"50001314",2005-07-09,"50305451",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6010","SNS",6011896.0061716,885078.489305687,0.15,0.05,1,300,12 +"50001314",2005-07-09,"50305456",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6010","SNS",6011984.88049409,885872.880568717,0.12,0.04,1,300,20 +"50001314",2005-07-09,"50305496",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012073.56074213,875858.066905527,0.26,0.08,1,300,4 +"50001315",2005-07-10,"50305889",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0881N6030","SNS",6028884.62181453,880094.495472168,0.21,0.06,1,300,1 +"50001315",2005-07-10,"50305891",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0881N6030","SNS",6028839.52618226,879685.774112856,0.2,0.06,1,300,1 +"50001315",2005-07-10,"50305899",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6030","SNS",6028756.80137036,878010.088206982,0.22,0.07,1,300,8 +"50001315",2005-07-10,"50306123",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6050","SNS",6047695.0179038,871803.496104826,0.28,0.08,1,300,1 +"50001315",2005-07-10,"50306128",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6050","SNS",6047865.59381972,873204.002481599,0.28,0.08,1,300,2 +"50001315",2005-07-10,"50306189",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6055","SNS",6053455.33326111,887209.699216716,0.26,0.08,1,300,3 +"50001315",2005-07-10,"50306193",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6055","SNS",6053397.62678138,888237.465890368,0.26,0.08,1,300,1 +"50001316",2005-07-12,"50306247",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6045","SNS",6046682.43734456,878648.154490565,0.34,0.1,1,300,2 +"50001316",2005-07-12,"50306255",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6050","SNS",6047900.36027998,876206.307297627,0.35,0.1,1,300,2 +"50001316",2005-07-12,"50306284",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6045","SNS",6047361.44538112,865756.150919144,0.37,0.11,1,300,8 +"50001316",2005-07-12,"50306287",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6045","SNS",6047364.05072338,864674.156093227,0.35,0.11,1,300,1 +"50001316",2005-07-12,"50306292",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6045","SNS",6047171.82890981,862969.164414578,0.36,0.11,1,300,10 +"50001316",2005-07-12,"50306322",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6040","SNS",6038381.92145979,857272.434740359,0.37,0.11,1,300,1 +"50001316",2005-07-12,"50306546",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6075","SNS",6076345.14885643,863416.190657415,0.14,0.04,1,300,5 +"50001316",2005-07-12,"50306547",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6075","SNS",6076472.02102108,863464.237209573,0.14,0.04,1,300,3 +"50001317",2005-08-15,"50306584",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012270.55023125,878535.470952726,0.34,0.1,1,300,4 +"50001317",2005-08-15,"50306588",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012146.74180882,876890.059678086,0.34,0.1,1,300,7 +"50001317",2005-08-15,"50306589",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012120.04276866,876563.767650474,0.33,0.1,1,300,5 +"50001317",2005-08-15,"50306589",5320,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012120.04276866,876563.767650474,0.33,0.1,2,300,2 +"50001317",2005-08-15,"50306596",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012242.1930703,874221.785035875,0.34,0.1,2,300,8 +"50001317",2005-08-15,"50306708",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6000","SNS",6001230.85349735,867571.905643719,0.25,0.07,2,300,119 +"50001317",2005-08-15,"50306710",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6000","SNS",6000877.59951918,867909.394899067,0.24,0.07,2,300,14 +"50001317",2005-08-15,"50306713",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6000","SNS",6000431.5240977,868484.752900134,0.24,0.07,1,300,1350 +"50001317",2005-08-15,"50306721",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6000","SNS",5999412.01806168,869718.434214728,0.24,0.07,1,300,10 +"50001317",2005-08-15,"50306722",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6000","SNS",5999260.0347438,869914.983641702,0.25,0.08,1,300,750 +"50001317",2005-08-15,"50306730",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6000","SNS",5998588.07863276,871852.568850506,0.26,0.08,1,300,50 +"50001317",2005-08-15,"50306731",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6000","SNS",5998437.23452193,872062.317362098,0.26,0.08,1,300,85 +"50001317",2005-08-15,"50306738",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5996960.08939238,872741.253245043,0.23,0.07,1,300,6 +"50001317",2005-08-15,"50306743",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5995874.51763591,873145.075762046,0.24,0.07,1,300,8 +"50001317",2005-08-15,"50306747",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5994992.11804539,873433.760516619,0.22,0.07,1,300,53 +"50001317",2005-08-15,"50306751",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5994817.25932026,872373.262282918,0.29,0.09,1,300,1 +"50001318",2005-08-16,"50306980",1730,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013489.31556833,876077.418730691,0.28,0.08,1,300,6 +"50001318",2005-08-16,"50307238",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6065610.00945576,873831.3917792,0.28,0.08,1,300,10 +"50001318",2005-08-16,"50307239",4960,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6065876.07743459,873802.907856011,0.27,0.08,1,300,3 +"50001318",2005-08-16,"50307243",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066977.93857374,873737.799528775,0.27,0.08,1,300,6 +"50001318",2005-08-16,"50307244",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067211.13214,873582.204687748,0.28,0.08,1,300,7 +"50001318",2005-08-16,"50307245",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067467.61236519,873437.682246453,0.29,0.09,1,300,2 +"50001318",2005-08-16,"50307247",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067730.81298109,872968.098010956,0.29,0.09,2,300,1 +"50001318",2005-08-16,"50307250",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067925.68940705,872075.872521615,0.31,0.09,1,300,1 +"50001318",2005-08-16,"50307302",5410,50,1,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6065","SNS",6063917.73872342,855471.683374793,0.35,0.11,1,300,2 +"50001319",2005-08-17,"50307569",5410,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013452.66459658,877269.162512784,0.27,0.08,1,300,1 +"50001319",2005-08-17,"50307928",5410,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016913.70282575,869767.321378731,0.31,0.09,1,300,1 +"50001319",2005-08-17,"50307946",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013904.55427432,874999.378282966,0.32,0.09,1,300,12 +"50001319",2005-08-17,"50307969",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012518.56921383,882483.420315054,0.31,0.09,1,300,1 +"50001320",2005-08-18,"50307996",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013427.74894425,876555.317430118,0.23,0.07,1,300,9 +"50001320",2005-08-18,"50307999",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013596.43061579,875608.959217182,0.24,0.07,1,300,3 +"50001320",2005-08-18,"50308008",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6014719.38429248,873606.797500305,0.25,0.07,1,300,9 +"50001320",2005-08-18,"50308017",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6015899.23777314,871607.680374426,0.25,0.08,1,300,9 +"50001321",2005-08-19,"50308444",4960,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6015354.17193342,872610.028061323,0.24,0.07,1,300,1 +"50001321",2005-08-19,"50308457",5410,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016863.59967114,869561.404199575,0.24,0.07,2,300,2 +"50001321",2005-08-19,"50308493",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0861N6020","SNS",6017769.94294532,861133.188943082,0.25,0.08,1,300,23 +"50001321",2005-08-19,"50308577",4960,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0866N6020","SNS",6017887.93652301,864186.238057272,0.31,0.09,1,300,4 +"50001324",2005-06-08,"50309761",1730,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012572.77269491,881789.013351144,0.18,0.05,1,300,4 +"50001325",2005-06-09,"50310147",5410,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0856N6025","SNS",6024514.76930655,855175.592596533,0.25,0.07,1,300,2 +"50001326",2005-06-10,"50310395",1680,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6010","SNS",6007946.97353235,862877.318427184,0.22,0.07,1,300,5 +"50001326",2005-06-10,"50310504",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6003212.82230104,876080.830777376,0.21,0.06,1,300,2 +"50001326",2005-06-10,"50310505",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6003380.97707834,875948.66696973,0.21,0.06,1,300,1 +"50001327",2005-06-11,"50310808",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6005","SNS",6005777.24813398,873372.369708049,0.25,0.07,1,300,1 +"50001327",2005-06-11,"50310817",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6005","SNS",6005505.79645764,875538.81461647,0.25,0.08,1,300,1 +"50001328",2005-06-12,"50310942",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6015","SNS",6015252.59455652,872736.409582188,0.23,0.07,1,300,1 +"50001328",2005-06-12,"50311145",1730,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6010","SNS",6012397.57149828,878584.15432603,0.27,0.08,1,300,3 +"50001329",2005-06-14,"50311342",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013600.28584306,878793.939994281,0.3,0.09,2,300,1 +"50001329",2005-06-14,"50311542",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6050","SNS",6047690.44641032,861338.028614034,0.23,0.07,1,300,1 +"50001329",2005-06-14,"50311760",5410,50,6,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6025","SNS",6026935.83385832,857991.250534194,0.23,0.07,1,300,1 +"50001330",2005-06-15,"50312286",5410,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016758.79783204,869648.561984365,0.3,0.09,1,300,1 +"50001330",2005-06-15,"50312299",5410,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6014670.70020441,873420.365189084,0.3,0.09,1,300,1 +"50001330",2005-06-15,"50312307",5410,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013575.67087349,875354.534760124,0.28,0.08,1,300,4 +"50001331",2005-10-05,"50312378",1730,50,2,2005,1,1,2,"5177",NA,"ESAS v5","E0881N5980","SNS",5981722.48299558,882676.341853589,0.35,0.1,1,300,3 +"50001331",2005-10-05,"50312388",1680,50,2,2005,1,1,2,"5177",NA,"ESAS v5","E0881N5980","SNS",5982171.28300706,879419.047460554,0.33,0.1,1,300,2 +"50001331",2005-10-05,"50312446",1730,50,2,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5995","SNS",5994949.83209528,873186.653178756,0.33,0.1,1,300,5 +"50001331",2005-10-05,"50312657",1730,50,2,2005,1,1,2,"5177",NA,"ESAS v5","E0881N6010","SNS",6012508.54559998,882497.394522867,0.16,0.05,1,300,10 +"50001332",2005-10-06,"50312867",1730,50,2,2005,1,1,2,"5177",NA,"ESAS v5","E0871N5990","SNS",5988684.36334044,873691.468944327,0.33,0.1,1,300,1 +"50001337",2005-09-20,"50315197",1730,50,1,2005,1,1,3,"5177",NA,"ESAS v5","E0866N5995","SNS",5994967.94892326,868808.180434745,0.37,0.11,1,300,2 +"50001338",2005-09-21,"50316072",1730,50,1,2005,1,1,3,"5177",NA,"ESAS v5","E0866N6050","SNS",6051706.66542515,867366.611822013,0.21,0.06,1,300,2 +"50001338",2005-09-21,"50316304",1730,50,1,2005,1,1,3,"5177",NA,"ESAS v5","E0876N6015","SNS",6013295.30468377,876986.501385522,0.23,0.07,2,300,4 +"50001338",2005-09-21,"50316309",1730,50,1,2005,1,1,3,"5177",NA,"ESAS v5","E0876N6015","SNS",6013254.06155395,878119.611400733,0.23,0.07,1,300,1 +"50001338",2005-09-21,"50316312",1730,50,1,2005,1,1,3,"5177",NA,"ESAS v5","E0876N6015","SNS",6013186.05586536,878788.623846461,0.22,0.07,1,300,9 +"50001339",2005-09-22,"50316329",1730,50,1,2005,1,1,3,"5188",NA,"ESAS v5","E0881N6010","SNS",6012566.44534496,880902.54957083,0.35,0.1,1,300,6 +"50001339",2005-09-22,"50316347",1730,50,1,2005,1,1,3,"5188",NA,"ESAS v5","E0876N6010","SNS",6012164.05509607,874773.350435555,0.35,0.11,1,300,1 +"50001339",2005-09-22,"50316348",1730,50,1,2005,1,1,3,"5188",NA,"ESAS v5","E0876N6010","SNS",6012180.34898742,874423.875118864,0.35,0.11,1,300,180 +"50001339",2005-09-22,"50316476",5410,50,1,2005,1,1,3,"5188",NA,"ESAS v5","E0866N5995","SNS",5995908.22429762,867698.681751434,0.23,0.07,1,300,3 +"50001353",2006-01-04,"50319856",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6065","SNS",6063224.23292381,854896.472136787,0.2,0.06,1,300,1 +"50001353",2006-01-04,"50319887",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6063006.92369321,860633.534222065,0.2,0.06,1,300,2 +"50001353",2006-01-04,"50319933",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067485.61416781,869574.475565519,0.26,0.08,1,300,20 +"50001353",2006-01-04,"50319937",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067822.39451088,870546.304481692,0.26,0.08,1,300,30 +"50001353",2006-01-04,"50319941",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067963.16727345,871715.852212908,0.32,0.1,1,300,1 +"50001353",2006-01-04,"50319942",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067922.505481,872037.195655815,0.33,0.1,2,300,2 +"50001353",2006-01-04,"50319947",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067364.97386014,873413.706432645,0.31,0.09,1,300,2 +"50001353",2006-01-04,"50319949",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066912.27132911,873619.885085078,0.3,0.09,1,300,2 +"50001353",2006-01-04,"50319951",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066347.875174,873692.494588953,0.29,0.09,2,300,2 +"50001353",2006-01-04,"50319952",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066048.48174782,873723.728725446,0.3,0.09,1,300,1 +"50001353",2006-01-04,"50319953",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6065737.44703523,873749.43148136,0.31,0.09,1,300,2 +"50001353",2006-01-04,"50319959",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6063893.63506003,874174.614580959,0.28,0.08,2,300,1 +"50001353",2006-01-04,"50319967",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6060","SNS",6061839.07917679,873402.116763163,0.25,0.08,1,300,1 +"50001353",2006-01-04,"50319997",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6055574.38534079,870378.530384469,0.23,0.07,1,300,1 +"50001353",2006-01-04,"50319999",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6055221.6081291,870166.555734061,0.23,0.07,1,300,2 +"50001358",2005-09-07,"50321304",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013340.91559039,876450.770148673,0.51,0.15,1,300,2 +"50001358",2005-09-07,"50321379",4960,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0841N6010","SNS",6010998.9542035,840489.988888119,0.49,0.15,1,300,4 +"50001358",2005-09-07,"50321482",6270,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0846N6010","SNS",6008870.81519236,848637.102019021,0.49,0.15,1,300,1 +"50001358",2005-09-07,"50321543",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013284.30553881,876442.26506129,0.44,0.13,1,300,8 +"50001358",2005-09-07,"50321546",1730,50,1,2005,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013315.60374573,877779.575170069,0.44,0.13,2,300,5 +"50001369",2006-01-30,"50325682",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0886N6050","SNS",6051598.982951,884187.707162288,0.25,0.08,1,300,10 +"50001369",2006-01-30,"50325696",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0881N6050","SNS",6048800.98323926,881914.000954658,0.26,0.08,1,300,30 +"50001369",2006-01-30,"50325711",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0881N6045","SNS",6046575.77134802,879113.558885369,0.25,0.07,1,300,20 +"50001369",2006-01-30,"50325758",1730,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0866N6045","SNS",6045873.21091064,867961.961384967,0.27,0.08,1,300,1 +"50001370",2006-01-31,"50326162",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6070","SNS",6071899.1270618,858013.017632351,0.22,0.06,1,300,1 +"50001370",2006-01-31,"50326166",5410,50,3,2005,1,1,2,"5177",NA,"ESAS v5","E0856N6070","SNS",6071646.47614218,857223.005615577,0.2,0.06,1,300,1 +"50001371",2006-03-15,"50326734",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0846N6065","SNS",6066965.63493458,848240.975918797,0.17,0.05,1,300,6 +"50001371",2006-03-15,"50326737",1730,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0846N6065","SNS",6066916.31326839,847745.475515271,0.17,0.05,1,300,4 +"50001371",2006-03-15,"50326738",1730,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0846N6065","SNS",6066971.57579449,847592.086600693,0.17,0.05,1,300,9 +"50001372",2006-03-16,"50326787",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0856N6065","SNS",6062736.69565985,857083.726951379,0.24,0.07,1,300,30 +"50001372",2006-03-16,"50326788",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0856N6065","SNS",6062654.01622119,857311.010043765,0.24,0.07,1,300,150 +"50001372",2006-03-16,"50326789",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0856N6060","SNS",6062571.34784198,857538.302361735,0.24,0.07,1,300,300 +"50001372",2006-03-16,"50326790",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0856N6060","SNS",6062534.66030327,857781.451158475,0.25,0.07,1,300,30 +"50001372",2006-03-16,"50326791",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0856N6060","SNS",6062497.47533286,858018.152236113,0.24,0.07,1,300,130 +"50001372",2006-03-16,"50326801",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6062961.25967677,860481.328223587,0.25,0.08,1,300,15 +"50001372",2006-03-16,"50326805",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6063173.81660635,861464.371798145,0.26,0.08,1,300,5 +"50001372",2006-03-16,"50326817",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6065031.7235867,863581.391661568,0.26,0.08,1,300,3 +"50001372",2006-03-16,"50326819",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0866N6065","SNS",6065436.92946042,863892.841837324,0.25,0.08,1,300,3 +"50001372",2006-03-16,"50326831",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0866N6065","SNS",6066662.27730277,866604.255259627,0.27,0.08,1,300,30 +"50001372",2006-03-16,"50326832",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0866N6065","SNS",6066749.85091114,866856.75164442,0.26,0.08,1,300,1 +"50001372",2006-03-16,"50326838",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0866N6065","SNS",6067163.45659113,868367.714738518,0.27,0.08,1,300,40 +"50001372",2006-03-16,"50326840",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067304.9672232,868868.869346868,0.26,0.08,1,300,120 +"50001372",2006-03-16,"50326845",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067684.91626418,870096.809766497,0.25,0.08,1,300,1 +"50001372",2006-03-16,"50326848",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067887.44736585,870794.060554066,0.24,0.07,1,300,280 +"50001372",2006-03-16,"50326850",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067969.98292737,871254.53031969,0.23,0.07,1,300,20 +"50001372",2006-03-16,"50326852",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067929.84156836,871718.593149055,0.23,0.07,1,300,10 +"50001372",2006-03-16,"50326853",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067914.54712049,871940.503431681,0.22,0.07,1,300,5 +"50001372",2006-03-16,"50326853",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067914.54712049,871940.503431681,0.22,0.07,1,300,4 +"50001372",2006-03-16,"50326856",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067811.59196006,872591.491023775,0.22,0.07,1,300,1 +"50001372",2006-03-16,"50326857",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067751.37435129,872810.633756379,0.23,0.07,1,300,20 +"50001372",2006-03-16,"50326858",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067691.16710258,873029.783373202,0.23,0.07,1,300,11 +"50001372",2006-03-16,"50326859",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6070","SNS",6067653.18704797,873247.105285731,0.22,0.07,1,300,5 +"50001372",2006-03-16,"50326861",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6067320.08494552,873547.236459633,0.22,0.07,1,300,4 +"50001372",2006-03-16,"50326863",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066920.79774215,873723.047934868,0.22,0.06,1,300,9 +"50001372",2006-03-16,"50326864",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066711.33761685,873759.838131326,0.22,0.07,1,300,32 +"50001372",2006-03-16,"50326866",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6065","SNS",6066275.44597208,873763.40647265,0.21,0.06,2,300,1 +"50001372",2006-03-16,"50326871",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6065217.03869896,873948.294279245,0.22,0.06,1,300,1 +"50001372",2006-03-16,"50326873",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6064796.52045131,874002.539772328,0.21,0.06,1,300,1 +"50001372",2006-03-16,"50326878",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6063768.77602575,874152.456098652,0.2,0.06,1,300,5 +"50001372",2006-03-16,"50326882",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6062965.06308347,873900.523197333,0.21,0.06,1,300,2 +"50001372",2006-03-16,"50326883",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6065","SNS",6062759.24953699,873846.047560297,0.21,0.06,1,300,2 +"50001372",2006-03-16,"50326889",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6060","SNS",6061569.28313632,873385.366457708,0.22,0.07,1,300,11 +"50001372",2006-03-16,"50326906",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6060","SNS",6058318.12559565,871520.193816347,0.21,0.06,1,300,5 +"50001372",2006-03-16,"50326911",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6057369.12000178,871266.222355207,0.2,0.06,1,300,24 +"50001372",2006-03-16,"50326917",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6056353.00242344,870601.229131877,0.21,0.06,1,300,16 +"50001372",2006-03-16,"50326918",5410,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6056176.86998258,870498.509755004,0.21,0.06,1,300,25 +"50001372",2006-03-16,"50326975",1730,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0866N6045","SNS",6046915.60333358,863960.621649735,0.22,0.07,1,300,1 +"50001372",2006-03-16,"50326986",1730,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6045","SNS",6044997.9782946,862907.835920915,0.2,0.06,1,300,2 +"50001372",2006-03-16,"50326993",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6045","SNS",6043851.75641379,862131.887497609,0.2,0.06,1,300,2 +"50001372",2006-03-16,"50327171",1680,50,4,2006,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013394.58287027,876012.917107012,0.21,0.06,1,300,9 +"50001374",2006-05-03,"50327612",1730,50,5,2006,1,1,2,"5207",NA,"ESAS v5","E0841N6055","SNS",6055633.0135944,842964.665425541,0.28,0.08,1,300,30 +"50001374",2006-05-03,"50327657",5410,50,5,2006,1,1,2,"5207",NA,"ESAS v5","E0836N6050","SNS",6051773.45492541,834515.897571038,0.24,0.07,2,300,1 +"50001374",2006-05-03,"50327765",6270,50,5,2006,1,1,2,"5207",NA,"ESAS v5","E0841N6070","SNS",6068431.73764357,839158.187886399,0.21,0.06,2,300,4 +"50001376",2005-09-22,"50328101",1680,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013341.46148315,874263.766838962,0.5,0.15,1,300,1 +"50001376",2005-09-22,"50328107",5410,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6011918.61991128,871607.456718737,0.5,0.15,2,300,1 +"50001376",2005-09-22,"50328320",1730,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013245.2740043,874593.387314802,0.49,0.15,1,300,1 +"50001376",2005-09-22,"50328328",1730,50,1,2005,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013284.09352241,878484.973542526,0.48,0.15,1,300,3 +"50001377",2005-10-06,"50328569",1730,50,2,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6010990.61837616,869941.455955377,0.42,0.13,2,300,2 +"50001377",2005-10-06,"50328579",1730,50,2,2005,1,1,2,"5194",NA,"ESAS v5","E0871N6015","SNS",6013244.0055196,873477.067321224,0.37,0.11,1,300,1 +"50001378",2005-10-14,"50328703",1680,50,2,2005,1,1,2,"5171",NA,"ESAS v5","E0826N6010","SNS",6008610.29313965,828203.793653536,0.54,0.16,1,300,60 +"50001378",2005-10-14,"50328704",1680,50,2,2005,1,1,2,"5171",NA,"ESAS v5","E0826N6010","SNS",6008912.08404722,827736.169107484,0.56,0.17,1,300,4 +"50001378",2005-10-14,"50328750",1680,50,2,2005,1,1,2,"5171",NA,"ESAS v5","E0831N6005","SNS",6005980.11551866,832494.518785443,0.51,0.15,2,300,23 +"50001378",2005-10-14,"50328783",1680,50,2,2005,1,1,2,"5171",NA,"ESAS v5","E0846N5995","SNS",5995724.69867409,845291.876128107,0.55,0.16,1,300,11 +"50001380",2006-02-25,"50329018",5410,50,4,2006,1,1,2,"5188",NA,"ESAS v5","E0871N5990","SNS",5992341.81408963,870923.369699792,0.5,0.15,1,300,1 +"50001380",2006-02-25,"50329027",1730,50,4,2006,1,1,2,"5188",NA,"ESAS v5","E0866N5995","SNS",5995185.12031926,867321.145100905,0.52,0.16,2,300,1 +"50001382",2006-06-02,"50329448",1680,50,6,2006,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013304.92805582,878194.252052386,0.55,0.17,1,300,110 +"50001382",2006-06-02,"50329457",5410,50,6,2006,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6012622.9980731,872575.132894948,0.44,0.13,1,300,1 +"50001382",2006-06-02,"50329468",1680,50,6,2006,1,1,2,"5194",NA,"ESAS v5","E0866N6010","SNS",6009863.03792677,867587.824804843,0.55,0.17,1,300,110 +"50001383",2006-06-13,"50329685",6270,50,6,2006,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6013494.49159381,874770.113310851,0.49,0.15,1,300,3 +"50001384",2005-07-19,"50330039",4960,50,6,2005,1,1,2,"5170",NA,"ESAS v5","E0821N6020","SNS",6017708.28572941,823725.012225088,0.33,0.1,1,300,15 +"50001404",2006-03-29,"50335162",1680,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0871N5995","SNS",5994050.91225201,869645.974942325,0.44,0.13,1,300,3 +"50001404",2006-03-29,"50335188",5410,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0856N5995","SNS",5995766.82948443,858630.88542838,0.46,0.14,1,300,3 +"50001404",2006-03-29,"50335190",5410,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0856N5995","SNS",5995661.54670783,857703.645255338,0.47,0.14,1,300,4 +"50001405",2006-03-29,"50335248",1680,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0831N6005","SNS",6005791.26378516,831733.324663833,0.49,0.15,2,300,2 +"50001405",2006-03-29,"50335265",5410,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0841N6000","SNS",6001645.7627358,839097.242910667,0.49,0.15,1,300,1 +"50001405",2006-03-29,"50335299",1680,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0851N5995","SNS",5995148.59419408,853645.957266086,0.47,0.14,1,300,1 +"50001405",2006-03-29,"50335333",5410,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0871N5990","SNS",5992399.15925606,869401.875167195,0.46,0.14,1,300,7 +"50001405",2006-03-29,"50335342",5410,50,4,2006,1,1,2,"5171",NA,"ESAS v5","E0871N5990","SNS",5989574.69563802,871831.178613355,0.4,0.12,1,300,1 +"50001406",2006-04-10,"50335451",5410,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6017000.29101419,870009.663796938,0.23,0.07,1,300,1 +"50001406",2006-04-10,"50335473",1680,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6014334.42036373,874373.417063962,0.23,0.07,1,300,2 +"50001406",2006-04-10,"50335566",1680,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6017339.64897771,868945.84308818,0.22,0.06,1,300,9 +"50001406",2006-04-10,"50335574",5410,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6016650.78211742,870667.689039738,0.24,0.07,1,300,1 +"50001406",2006-04-10,"50335579",5410,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6016091.25225507,871631.537269428,0.22,0.07,1,300,1 +"50001406",2006-04-10,"50335639",5410,50,5,2006,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6015758.90380228,872222.823287118,0.26,0.08,1,300,1 +"50001411",2006-04-28,"50336621",5410,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0831N6005","SNS",6006350.71864186,832986.479359806,0.32,0.1,1,300,3 +"50001411",2006-04-28,"50336738",5410,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013235.3091574,877346.094310612,0.39,0.12,2,300,1 +"50001412",2006-05-02,"50337152",5410,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0816N6020","SNS",6020852.12682399,817085.70924433,0.26,0.08,1,300,11 +"50001415",2006-05-04,"50337752",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0781N6030","SNS",6029394.69211547,783217.120385441,0.3,0.09,2,300,2 +"50001416",2006-05-05,"50338117",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0826N6015","SNS",6017184.20343975,826420.586435206,0.31,0.09,2,300,1 +"50001416",2006-05-05,"50338419",1680,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0881N6010","SNS",6012377.96096273,882541.098726764,0.31,0.09,1,300,24 +"50001417",2006-05-06,"50338575",1730,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0861N6020","SNS",6017967.30125141,862645.483809754,0.27,0.08,1,300,3 +"50001417",2006-05-06,"50338606",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0866N6020","SNS",6018050.16649085,865806.289030773,0.1,0.03,2,300,2 +"50001417",2006-05-06,"50338700",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0866N6015","SNS",6017380.18837811,868057.014756637,0.11,0.03,2,300,1 +"50001417",2006-05-06,"50338720",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6014939.41820446,873149.096291032,0.13,0.04,2,300,4 +"50001417",2006-05-06,"50338733",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013507.11018175,876568.526480234,0.1,0.03,2,300,1 +"50001417",2006-05-06,"50338734",6270,50,5,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013485.63980431,876852.691731749,0.1,0.03,1,300,1 +"50001418",2006-06-26,"50338764",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0891N5985","SNS",5983696.15376242,892796.341581525,0.36,0.11,1,300,1 +"50001418",2006-06-26,"50338765",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0891N5985","SNS",5983600.66297378,892460.477691021,0.35,0.11,1,300,1 +"50001418",2006-06-26,"50338770",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0891N5985","SNS",5983310.75188804,890745.418964674,0.36,0.11,1,300,1 +"50001418",2006-06-26,"50338779",1730,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0886N5980","SNS",5982430.276287,887710.503659193,0.35,0.11,1,300,5 +"50001418",2006-06-26,"50338804",1730,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0881N5980","SNS",5982527.80773231,878854.415185925,0.34,0.1,1,300,4 +"50001418",2006-06-26,"50338807",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0876N5985","SNS",5983138.60747531,877978.324042053,0.35,0.11,1,300,2 +"50001418",2006-06-26,"50338841",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0871N5990","SNS",5992152.61825949,871492.567540116,0.32,0.09,1,300,2 +"50001418",2006-06-26,"50338935",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0856N6010","SNS",6007670.35622228,856177.081052314,0.24,0.07,1,300,35 +"50001418",2006-06-26,"50339152",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0846N6065","SNS",6063952.79061532,848304.334081782,0.25,0.07,1,300,1 +"50001419",2006-06-27,"50339325",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0841N6065","SNS",6067324.5106651,839740.379396044,0.26,0.08,1,300,17 +"50001421",2006-06-29,"50339906",1730,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0866N5995","SNS",5996564.57088231,868641.452947702,0.3,0.09,1,300,14 +"50001422",2006-07-17,"50340023",1680,50,6,2006,1,1,2,"5168",NA,"ESAS v5","E0856N5960","SNS",5958774.13587662,856591.538585631,0.37,0.11,1,300,1 +"50001422",2006-07-17,"50340051",1680,50,6,2006,1,1,2,"5168",NA,"ESAS v5","E0846N5965","SNS",5965834.69375117,847663.828373286,0.41,0.12,1,300,25 +"50001422",2006-07-17,"50340070",5410,50,6,2006,1,1,2,"5168",NA,"ESAS v5","E0841N5970","SNS",5970689.10742302,840922.493841208,0.46,0.14,1,300,1 +"50001422",2006-07-17,"50340082",1730,50,6,2006,1,1,2,"5168",NA,"ESAS v5","E0836N5975","SNS",5974729.42760001,837341.362494281,0.45,0.13,2,300,14 +"50001424",2006-07-21,"50340743",5410,50,6,2006,1,1,2,"5168",NA,"ESAS v5","E0611N6310","CNS",6311867.00379587,608956.052241178,0.34,0.1,1,300,2 +"50001437",2006-08-14,"50343917",800,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0851N5960","SNS",5961445.0334243,853794.231815745,0.26,0.08,1,300,1 +"50001437",2006-08-14,"50343918",800,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0856N5960","SNS",5961258.0037597,853980.856428425,0.26,0.08,2,300,1 +"50001438",2006-05-15,"50344142",5410,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0851N6015","SNS",6014848.80993853,850394.7276325,0.22,0.07,1,300,1 +"50001440",2006-05-17,"50344531",1730,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0851N6065","SNS",6065958.34684547,849382.275359555,0.31,0.09,1,300,1 +"50001440",2006-05-17,"50344553",5410,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0851N6065","SNS",6063423.03571702,852433.811722186,0.2,0.06,1,300,1 +"50001440",2006-05-17,"50344594",1680,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6065","SNS",6062998.18594257,860523.841643298,0.2,0.06,1,300,5 +"50001440",2006-05-17,"50344626",5410,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6053639.97588155,870106.76856603,0.2,0.06,1,300,1 +"50001440",2006-05-17,"50344630",5410,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0871N6055","SNS",6052918.56681756,869761.922427699,0.2,0.06,1,300,2 +"50001440",2006-05-17,"50344706",6270,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6040","SNS",6041152.99011266,861935.334319544,0.22,0.07,1,300,1 +"50001440",2006-05-17,"50344880",1680,50,5,2006,1,1,2,"5177",NA,"ESAS v5","E0861N6000","SNS",5999234.32655945,862818.836199966,0.24,0.07,1,300,5 +"50001442",2006-07-13,"50345463",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0856N6065","SNS",6062728.13065026,857116.866805911,0.28,0.08,1,300,1 +"50001443",2006-07-14,"50345580",5410,50,6,2006,1,1,2,"5113",NA,"ESAS v5","E0841N6065","SNS",6067609.9953527,839524.484170191,0.17,0.05,1,300,1 +"50001445",2006-10-03,"50346052",1680,50,2,2006,1,1,3,"5113",NA,"ESAS v5","E0836N6060","SNS",6060754.96705076,836910.865590763,0.19,0.06,1,300,16 +"50001445",2006-10-03,"50346067",1680,50,2,2006,1,1,3,"5113",NA,"ESAS v5","E0836N6060","SNS",6057910.93475479,836830.142096075,0.19,0.06,1,300,5 +"50001446",2006-09-11,"50346552",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6014586.60834529,873624.146519033,0.31,0.09,1,300,4 +"50001446",2006-09-11,"50346553",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6014756.92373213,873380.541321959,0.3,0.09,1,300,1 +"50001446",2006-09-11,"50346558",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6015617.15285748,872129.284058777,0.3,0.09,1,300,1 +"50001446",2006-09-11,"50346565",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6016597.56670604,870284.853135265,0.3,0.09,1,300,4 +"50001446",2006-09-11,"50346568",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6016955.72193433,869455.5873438,0.3,0.09,1,300,9 +"50001446",2006-09-11,"50346573",1730,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0866N6015","SNS",6017537.28463097,868064.111904846,0.3,0.09,1,300,1 +"50001448",2006-09-13,"50347210",5410,50,1,2006,1,1,2,"5113",NA,"ESAS v5","E0866N6050","SNS",6050763.69268324,867182.342403873,0.23,0.07,1,300,1 +"50001449",2006-08-04,"50347492",1730,50,1,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013325.95970802,875309.228070369,0.56,0.17,1,300,2 +"50001450",2006-09-14,"50347518",1730,50,1,2006,1,1,2,"5194",NA,"ESAS v5","E0871N6010","SNS",6010834.4720168,869389.037521971,0.53,0.16,1,300,1 +"50001451",2006-07-31,"50347773",1730,50,6,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016739.88473952,870247.144738499,0.24,0.07,1,300,1 +"50001451",2006-07-31,"50347827",5410,50,6,2006,1,1,2,"5170",NA,"ESAS v5","E0856N6015","SNS",6017391.00318409,856854.948151505,0.27,0.08,2,300,2 +"50001457",2006-08-11,"50349063",1730,50,1,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6015836.23296859,871796.533142776,0.36,0.11,2,300,1 +"50001478",2007-05-03,"50358530",5410,50,5,2007,1,1,2,"5113",NA,"ESAS v5","E0836N6035","SNS",6035007.80933074,837452.639695131,0.27,0.08,1,300,3 +"50001478",2007-05-03,"50358773",5410,50,5,2007,1,1,2,"5113",NA,"ESAS v5","E0826N6055","SNS",6053042.12572881,827396.601485789,0.26,0.08,1,300,13 +"50001479",2007-05-04,"50359267",4960,50,5,2007,1,1,2,"5113",NA,"ESAS v5","E0786N6075","SNS",6075987.56869566,783946.924213477,0.26,0.08,2,300,2 +"50001481",2007-06-18,"50360375",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0831N6155","SNS",6153764.4358732,829975.958126797,0.3,0.09,1,300,6 +"50001481",2007-06-18,"50360633",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0766N6120","SNS",6121444.01819537,766993.872687093,0.25,0.08,1,300,1 +"50001481",2007-06-18,"50360899",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0821N6110","SNS",6109907.91200725,820979.928218684,0.26,0.08,1,300,2 +"50001482",2007-06-19,"50361403",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0796N6085","SNS",6086863.80521592,798656.935156983,0.25,0.08,1,300,2 +"50001483",2007-06-20,"50361902",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0791N6055","SNS",6053034.07002322,789297.996819061,0.26,0.08,1,300,3 +"50001483",2007-06-20,"50361974",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0806N6050","SNS",6050263.04159939,805290.058741277,0.25,0.08,1,300,3 +"50001483",2007-06-20,"50362347",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0856N6020","SNS",6020528.39979731,854357.479646937,0.31,0.09,1,300,2 +"50001483",2007-06-20,"50362373",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0861N6020","SNS",6017728.22553987,861313.513548086,0.3,0.09,1,300,2 +"50001483",2007-06-20,"50362380",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0861N6020","SNS",6017794.75338673,863432.907965472,0.31,0.09,1,300,2 +"50001483",2007-06-20,"50362409",5410,50,6,2007,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6015854.72370106,871886.918715725,0.3,0.09,1,300,1 +"50001492",2006-06-28,"50365521",1730,50,6,2006,1,1,2,"5173",NA,"ESAS v5","E0871N6015","SNS",6013024.46344024,873251.892673486,0.43,0.13,1,300,7 +"50001493",2006-08-07,"50365783",4960,50,1,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013295.90837199,873978.516879038,0.46,0.14,2,300,12 +"50001494",2006-07-27,"50365927",1730,50,6,2006,1,1,2,"5194",NA,"ESAS v5","E0866N6010","SNS",6009671.4441933,866867.24367456,0.49,0.15,2,300,17 +"50001494",2006-07-27,"50366112",4960,50,6,2006,1,1,2,"5194",NA,"ESAS v5","E0861N6010","SNS",6009051.16302915,862987.493072339,0.44,0.13,1,300,10 +"50001495",2006-11-29,"50366191",1730,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0881N6045","SNS",6047267.12800497,880829.746838472,0.35,0.11,1,300,1 +"50001495",2006-11-29,"50366195",1730,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0881N6045","SNS",6046582.392048,879595.635739017,0.34,0.1,1,300,4 +"50001495",2006-11-29,"50366196",1730,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0881N6045","SNS",6046608.77818315,879241.248120683,0.36,0.11,2,300,1 +"50001495",2006-11-29,"50366200",5410,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6045","SNS",6047068.62685873,877911.759923592,0.36,0.11,2,300,1 +"50001495",2006-11-29,"50366208",1730,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0876N6050","SNS",6048215.35831335,875280.689060399,0.37,0.11,1,300,1 +"50001495",2006-11-29,"50366490",5410,50,2,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6012395.61782333,872114.080121956,0.32,0.1,2,300,1 +"50001497",2006-12-01,"50366761",5410,50,3,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016693.34814715,870224.647894036,0.36,0.11,1,300,20 +"50001497",2006-12-01,"50366762",5410,50,3,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016832.69656339,869872.252142951,0.37,0.11,1,300,1 +"50001497",2006-12-01,"50366790",5410,50,3,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6017159.80846119,868934.039077855,0.26,0.08,1,300,8 +"50001497",2006-12-01,"50366796",5410,50,3,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016526.10494753,870369.347573965,0.26,0.08,2,300,1 +"50001497",2006-12-01,"50366801",1730,50,3,2006,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6015963.78400822,871576.2132254,0.27,0.08,1,300,1 +"50001498",2007-06-05,"50366973",1680,50,6,2007,1,1,3,"5170",NA,"ESAS v5","E0846N6020","SNS",6021695.27865024,847548.292291703,0.26,0.08,1,300,1 +"50001498",2007-06-05,"50367253",6270,50,6,2007,1,1,3,"5170",NA,"ESAS v5","E0871N6025","SNS",6023503.21857294,870645.636461421,0.28,0.08,1,300,1 +"50001498",2007-06-05,"50367263",6270,50,6,2007,1,1,3,"5170",NA,"ESAS v5","E0871N6025","SNS",6026035.19051314,871397.058364332,0.27,0.08,2,300,1 +"50001498",2007-06-05,"50367391",1680,50,6,2007,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013418.69260597,875623.46654068,0.26,0.08,2,300,19 +"50001499",2007-06-06,"50367471",1680,50,6,2007,1,1,3,"5171",NA,"ESAS v5","E0866N6020","SNS",6018112.19584031,864194.693628892,0.29,0.09,1,300,9 +"50001500",2007-06-07,"50368498",5410,50,6,2007,1,1,3,"5171",NA,"ESAS v5","E0876N6045","SNS",6047363.22032017,877013.571550529,0.27,0.08,1,300,12 +"50001504",2007-05-16,"50369874",6270,50,5,2007,1,1,3,"5177",NA,"ESAS v5","E0861N6010","SNS",6008903.359207,861961.079061598,0.45,0.14,1,300,1 +"50001504",2007-05-16,"50370092",5410,50,5,2007,1,1,3,"5177",NA,"ESAS v5","E0881N6010","SNS",6012542.95612972,881968.889032046,0.51,0.15,1,300,2 +"50001505",2007-07-05,"50370103",5410,50,6,2007,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6013416.74406474,876694.139747204,0.54,0.16,1,300,1 +"50001505",2007-07-05,"50370136",1680,50,6,2007,1,1,2,"5171",NA,"ESAS v5","E0861N6010","SNS",6008734.58895179,860949.425959304,0.52,0.15,1,300,10 +"50001505",2007-07-05,"50370288",5410,50,6,2007,1,1,2,"5171",NA,"ESAS v5","E0856N6010","SNS",6008185.0697226,857484.041710614,0.52,0.16,1,300,7 +"50001505",2007-07-05,"50370333",5410,50,6,2007,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6013296.0652337,878766.432899555,0.49,0.15,1,300,2 +"50001507",2007-08-10,"50370645",5410,50,1,2007,1,1,2,"5113",NA,"ESAS v5","E0846N6010","SNS",6009178.96269365,848574.393993744,0.49,0.15,2,300,2 +"50001507",2007-08-10,"50370840",1730,50,1,2007,1,1,2,"5113",NA,"ESAS v5","E0881N6010","SNS",6012508.27156781,882090.037150056,0.51,0.15,1,300,1 +"50001508",2007-08-24,"50371063",1730,50,1,2007,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013298.0876054,874280.428557333,0.47,0.14,1,300,1 +"50001508",2007-08-24,"50371066",1730,50,1,2007,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013301.36826777,875830.070874831,0.52,0.16,1,300,21 +"50001509",2007-09-13,"50371309",1730,50,1,2007,1,1,2,"5177",NA,"ESAS v5","E0861N6010","SNS",6009120.44863836,863724.436160748,0.43,0.13,1,300,2 +"50001509",2007-09-13,"50371344",1730,50,1,2007,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013335.2853357,877337.897255686,0.41,0.12,1,300,2 +"50001509",2007-09-13,"50371356",1730,50,1,2007,1,1,2,"5177",NA,"ESAS v5","E0881N6010","SNS",6012461.39854896,882199.058216941,0.43,0.13,1,300,3 +"50001510",2006-10-13,"50371388",1730,50,2,2006,1,1,2,"5171",NA,"ESAS v5","E0861N5995","SNS",5995907.32783393,859864.991395193,0.48,0.15,2,300,2 +"50001511",2007-03-29,"50371485",1730,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0871N5990","SNS",5988882.87293248,872962.566643359,0.45,0.13,2,300,2 +"50001512",2006-07-11,"50371761",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0871N6015","SNS",6012827.67978326,873031.420158673,0.37,0.11,1,300,1 +"50001512",2006-07-11,"50371771",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0871N6010","SNS",6010739.84030727,869324.364136518,0.42,0.13,1,300,1 +"50001512",2006-07-11,"50371792",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0861N6010","SNS",6008619.63948439,860623.36366831,0.44,0.13,1,300,1 +"50001512",2006-07-11,"50371838",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0836N6010","SNS",6010104.84219996,838515.300838727,0.5,0.15,1,300,1 +"50001512",2006-07-11,"50371843",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0836N6010","SNS",6010494.72626508,836059.10950571,0.5,0.15,1,300,2 +"50001512",2006-07-11,"50371943",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0851N6010","SNS",6008563.76615321,852094.929888321,0.47,0.14,1,300,3 +"50001512",2006-07-11,"50372000",1730,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0876N6015","SNS",6013273.29708477,876581.090881997,0.45,0.14,1,300,3 +"50001512",2006-07-11,"50372001",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0876N6015","SNS",6013253.54291734,877022.762458686,0.44,0.13,1,300,1 +"50001512",2006-07-11,"50372003",5410,50,6,2006,1,1,3,"5171",NA,"ESAS v5","E0876N6015","SNS",6013271.84596391,877927.669002871,0.45,0.13,1,300,1 +"50001514",2006-08-31,"50372295",1730,50,1,2006,1,1,2,"5194",NA,"ESAS v5","E0876N6015","SNS",6013333.28182592,877994.87443908,0.57,0.17,1,300,4 +"50001514",2006-08-31,"50372331",1730,50,1,2006,1,1,2,"5194",NA,"ESAS v5","E0861N6010","SNS",6008594.90187333,859304.788860484,0.52,0.16,1,300,4 +"50001516",2007-04-20,"50372982",1680,50,5,2007,1,1,2,"5173",NA,"ESAS v5","E0866N6010","SNS",6010255.32990227,868719.359001625,0.45,0.14,1,300,30 +"50001516",2007-04-20,"50372983",1680,50,5,2007,1,1,2,"5173",NA,"ESAS v5","E0871N6010","SNS",6010477.12360957,869115.490637876,0.45,0.14,2,300,3 +"50001516",2007-04-20,"50372983",5410,50,5,2007,1,1,2,"5173",NA,"ESAS v5","E0871N6010","SNS",6010477.12360957,869115.490637876,0.45,0.14,2,300,3 +"50001516",2007-04-20,"50373016",1680,50,5,2007,1,1,2,"5173",NA,"ESAS v5","E0881N6010","SNS",6012464.64931517,882238.212454188,0.39,0.12,2,300,8 +"50001517",2007-04-25,"50373026",5410,50,5,2007,1,1,2,"5177",NA,"ESAS v5","E0876N6015","SNS",6013359.76969817,877500.090248814,0.56,0.17,1,300,2 +"50001517",2007-04-25,"50373210",5410,50,5,2007,1,1,2,"5177",NA,"ESAS v5","E0861N6010","SNS",6008754.02872694,861197.555484267,0.54,0.16,1,300,1 +"50001518",2007-09-26,"50373503",1680,50,1,2007,1,1,3,"5113",NA,"ESAS v5","E0871N6015","SNS",6013122.33784995,873493.507926593,0.39,0.12,1,300,2 +"50001522",2007-02-27,"50373860",1730,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0871N5990","SNS",5992185.1445556,871061.229673076,0.57,0.17,1,300,1 +"50001522",2007-02-27,"50373891",5410,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0856N5995","SNS",5996016.29793527,855344.659542581,0.58,0.17,1,300,1 +"50001524",2007-02-27,"50374048",1730,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0861N5995","SNS",5994399.26922076,861168.391467222,0.58,0.17,1,300,2 +"50001524",2007-02-27,"50374061",1730,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0866N5995","SNS",5992907.6177301,868345.841358227,0.58,0.17,1,300,2 +"50001525",2007-03-27,"50374113",1730,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0866N6010","SNS",6010810.13665017,868806.33212837,0.33,0.1,1,300,2 +"50001525",2007-03-27,"50374509",5410,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6016145.4209754,871056.234236251,0.3,0.09,2,300,2 +"50001525",2007-03-27,"50374550",5410,50,4,2007,1,1,2,"5171",NA,"ESAS v5","E0881N6010","SNS",6012240.32750693,882499.96617037,0.27,0.08,1,300,1 +"50001526",2007-03-28,"50374752",4960,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0831N6040","SNS",6038455.38043276,830827.127861864,0.33,0.1,1,300,2 +"50001526",2007-03-28,"50374777",4960,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0821N6040","SNS",6040675.13072599,822834.209898815,0.34,0.1,1,300,4 +"50001526",2007-03-28,"50374806",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0816N6045","SNS",6044671.13595639,814500.725087738,0.33,0.1,1,300,23 +"50001526",2007-03-28,"50374847",5410,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0806N6055","SNS",6056334.94178923,807905.722992384,0.33,0.1,1,300,1 +"50001526",2007-03-28,"50375165",5410,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0856N6070","SNS",6071446.13913956,855677.089466435,0.17,0.05,1,300,11 +"50001527",2007-03-29,"50375247",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0841N6065","SNS",6065352.90199547,841159.549305958,0.29,0.09,1,300,1 +"50001527",2007-03-29,"50375321",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0841N6085","SNS",6082927.75230009,838895.888571285,0.3,0.09,1,300,2 +"50001527",2007-03-29,"50375711",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0841N6050","SNS",6049883.32734432,839163.333313382,0.3,0.09,2,300,8 +"50001527",2007-03-29,"50375753",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0846N6040","SNS",6041505.96109581,848386.832038275,0.31,0.09,1,300,6 +"50001527",2007-03-29,"50375804",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0856N6035","SNS",6035418.26868467,857968.141448106,0.33,0.1,1,300,4 +"50001527",2007-03-29,"50375816",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0861N6040","SNS",6039212.02427476,859302.378493111,0.34,0.1,1,300,15 +"50001527",2007-03-29,"50375858",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0871N6045","SNS",6047121.16412106,870800.846447676,0.36,0.11,2,300,1 +"50001527",2007-03-29,"50375886",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0881N6045","SNS",6046976.18214693,880293.217416697,0.35,0.11,1,300,2 +"50001527",2007-03-29,"50375891",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0881N6050","SNS",6048244.69203547,881549.922813153,0.37,0.11,1,300,3 +"50001527",2007-03-29,"50375894",1680,50,4,2007,1,1,2,"5170",NA,"ESAS v5","E0881N6050","SNS",6049104.23346603,882194.944263019,0.36,0.11,1,300,10 +"50001530",2007-11-12,"50376274",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6053473.77011117,887032.231633163,0.21,0.06,1,300,1 +"50001530",2007-11-12,"50376275",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6053423.36726941,886834.571970554,0.2,0.06,1,300,1 +"50001530",2007-11-12,"50376290",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6050","SNS",6051936.5826478,884211.293530638,0.21,0.06,1,300,1 +"50001530",2007-11-12,"50376291",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6050","SNS",6051780.15299051,884081.165152836,0.21,0.06,1,300,5 +"50001530",2007-11-12,"50376294",1680,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6051290.31241888,883712.010339732,0.2,0.06,1,300,15 +"50001530",2007-11-12,"50376299",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6050479.32413487,883115.665220247,0.2,0.06,1,300,70 +"50001530",2007-11-12,"50376301",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6050156.52070657,882869.109186979,0.2,0.06,1,300,4 +"50001530",2007-11-12,"50376309",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6048895.51743527,881840.868532077,0.2,0.06,2,300,3 +"50001530",2007-11-12,"50376311",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6048541.0966052,881616.342590674,0.21,0.06,1,300,100 +"50001530",2007-11-12,"50376314",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6048009.21756649,881276.243386703,0.21,0.06,1,300,55 +"50001530",2007-11-12,"50376318",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6047390.12706453,880695.549500867,0.22,0.07,1,300,15 +"50001530",2007-11-12,"50376321",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6047019.27758765,880139.618650851,0.23,0.07,1,300,2 +"50001530",2007-11-12,"50376323",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6046881.46306577,879694.606678267,0.24,0.07,1,300,18 +"50001530",2007-11-12,"50376420",1680,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0866N6050","SNS",6050354.04172128,866401.305661245,0.29,0.09,1,300,3 +"50001530",2007-11-12,"50376467",1680,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0871N6060","SNS",6061440.17252947,873311.518897388,0.27,0.08,1,300,8 +"50001531",2007-11-13,"50376578",5410,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0856N6070","SNS",6072003.58611242,857920.491111965,0.3,0.09,1,300,1 +"50001531",2007-11-13,"50376615",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0851N6065","SNS",6065579.3977576,849106.681395475,0.3,0.09,1,300,1 +"50001532",2007-10-15,"50377088",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6053185.00362035,886268.533242432,0.22,0.07,1,300,1 +"50001532",2007-10-15,"50377090",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6052960.44644118,885864.150591036,0.23,0.07,1,300,2 +"50001532",2007-10-15,"50377094",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0886N6050","SNS",6052414.91105863,884972.238629353,0.26,0.08,1,300,1 +"50001532",2007-10-15,"50377115",1730,50,2,2007,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6048049.34449294,881488.058774895,0.26,0.08,1,300,8 +"50001534",2007-04-24,"50377701",5410,50,5,2007,1,1,2,"5173",NA,"ESAS v5","E0821N6060","SNS",6061440.23417766,822069.069872064,0.29,0.09,1,300,8 +"50001535",2007-04-25,"50378075",1680,50,5,2007,1,1,2,"5171",NA,"ESAS v5","E0866N6020","SNS",6017804.88189423,866796.487838214,0.31,0.09,1,300,6 +"50001535",2007-04-25,"50378643",5410,50,5,2007,1,1,2,"5171",NA,"ESAS v5","E0821N6065","SNS",6066585.38457855,822169.417931691,0.3,0.09,1,300,7 +"50001536",2007-04-26,"50379211",1680,50,5,2007,1,1,2,"5171",NA,"ESAS v5","E0861N6045","SNS",6046000.47125298,860892.171008097,0.28,0.08,1,300,2 +"50001536",2007-04-26,"50379282",1680,50,5,2007,1,1,2,"5171",NA,"ESAS v5","E0876N6060","SNS",6058146.68796941,876627.020540382,0.23,0.07,1,300,6 +"50001580",2008-05-25,"50390729",1680,50,5,2008,1,1,2,"5187",NA,"ESAS v5","E0851N6010","SNS",6008615.82576881,850284.957133075,0.5,0.15,2,300,1 +"50001581",2008-05-26,"50390819",5410,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0881N6050","SNS",6047665.67562677,881181.131979828,0.31,0.1,1,300,1 +"50001581",2008-05-26,"50390853",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0871N6050","SNS",6047774.95081551,871001.769906937,0.31,0.1,1,300,7 +"50001581",2008-05-26,"50390854",4960,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0871N6050","SNS",6047739.56542661,870704.832844215,0.3,0.1,1,300,3 +"50001582",2008-05-27,"50391778",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0836N6095","SNS",6093509.02362267,838581.188391912,0.22,0.07,1,300,1 +"50001583",2008-05-28,"50392004",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0876N6065","SNS",6065700.47564107,873843.395209624,0.26,0.09,1,300,10 +"50001583",2008-05-28,"50392088",1730,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0866N6050","SNS",6049307.83997622,865488.942809154,0.22,0.07,1,300,2 +"50001583",2008-05-28,"50392112",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0866N6045","SNS",6047544.15295106,866914.496970271,0.23,0.08,2,300,10 +"50001583",2008-05-28,"50392176",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0881N6045","SNS",6047368.99489493,880710.359933807,0.26,0.09,1,300,452 +"50001583",2008-05-28,"50392179",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0881N6050","SNS",6047890.28785681,881325.33418417,0.27,0.09,2,300,2 +"50001583",2008-05-28,"50392194",1680,50,5,2008,1,1,2,"5113",NA,"ESAS v5","E0886N6050","SNS",6050878.60873467,883870.596939902,0.27,0.09,2,300,14 +"50001584",2008-05-28,"50392332",1680,50,5,2008,1,1,2,"5187",NA,"ESAS v5","E0821N6015","SNS",6013875.92196073,820030.879517667,0.41,0.12,1,300,14 +"50001585",2008-06-03,"50392396",1680,50,6,2008,1,1,2,"5171",NA,"ESAS v5","E0856N6010","SNS",6008178.42365131,855953.89021812,0.48,0.14,2,300,11 +"50001585",2008-06-03,"50392471",1680,50,6,2008,1,1,2,"5171",NA,"ESAS v5","E0821N6015","SNS",6014412.3198638,819875.641437471,0.39,0.12,1,300,1 +"50001586",2008-04-17,"50392610",1680,50,5,2008,1,1,2,"5193",NA,"ESAS v5","E0826N6010","SNS",6009383.18057435,825242.774926758,0.46,0.14,1,300,4 +"50001586",2008-04-17,"50392714",1680,50,5,2008,1,1,2,"5193",NA,"ESAS v5","E0866N5995","SNS",5993476.7307291,867206.147910315,0.44,0.13,1,300,6 +"50001587",2008-04-29,"50392970",1730,50,5,2008,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013414.71509129,876805.957524485,0.56,0.17,1,300,3 +"50001595",2008-03-26,"50395896",1680,50,4,2008,1,1,2,"5159",NA,"ESAS v5","E0856N5995","SNS",5996345.33909243,854680.603248392,0.47,0.14,2,300,5 +"50001595",2008-03-26,"50395926",1680,50,4,2008,1,1,2,"5159",NA,"ESAS v5","E0841N6000","SNS",6000784.37045172,841961.592585389,0.47,0.14,1,300,19 +"50001595",2008-03-26,"50395957",1680,50,4,2008,1,1,2,"5159",NA,"ESAS v5","E0831N6010","SNS",6008671.82588052,829222.967437093,0.48,0.14,1,300,5 +"50001596",2008-05-07,"50396126",6270,50,5,2008,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013385.98399892,876591.57345218,0.49,0.15,1,300,1 +"50001596",2008-05-07,"50396130",6270,50,5,2008,1,1,3,"5170",NA,"ESAS v5","E0876N6015","SNS",6013415.62216513,874625.49184726,0.48,0.14,2,300,8 +"50001596",2008-05-07,"50396341",6270,50,5,2008,1,1,3,"5170",NA,"ESAS v5","E0861N6010","SNS",6008654.01485754,860062.24729992,0.43,0.13,1,300,1 +"50001596",2008-05-07,"50396345",6270,50,5,2008,1,1,3,"5170",NA,"ESAS v5","E0861N6010","SNS",6008851.57312636,861728.631090089,0.41,0.12,1,300,1 +"50001598",2008-06-24,"50396582",6270,50,6,2008,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6016613.32399392,870480.42655194,0.3,0.09,2,300,1 +"50001598",2008-06-24,"50396585",6270,50,6,2008,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6017031.18441671,869698.824756088,0.29,0.09,2,300,1 +"50001598",2008-06-24,"50396694",5410,50,6,2008,1,1,2,"5170",NA,"ESAS v5","E0871N6025","SNS",6026411.67686028,872316.022217229,0.18,0.05,1,300,1 +"50001598",2008-06-24,"50396711",5410,50,6,2008,1,1,2,"5170",NA,"ESAS v5","E0876N6030","SNS",6028016.29706987,875943.325006895,0.27,0.08,1,300,1 +"50001599",2008-06-25,"50396969",1730,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0876N6010","SNS",6012030.04112918,875598.844140516,0.31,0.09,1,300,2 +"50001599",2008-06-25,"50397010",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6010","SNS",6007747.91606489,872160.948654428,0.15,0.04,1,300,2 +"50001599",2008-06-25,"50397014",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6005","SNS",6007211.45425319,872440.955245818,0.15,0.05,1,300,2 +"50001599",2008-06-25,"50397024",1730,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6005","SNS",6005817.46022548,873178.371686571,0.17,0.05,1,300,25 +"50001599",2008-06-25,"50397029",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6005","SNS",6005321.79391673,873823.652206119,0.17,0.05,2,300,1 +"50001600",2008-06-26,"50397402",1730,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0876N6015","SNS",6013422.14904177,877987.575478928,0.36,0.11,2,300,3 +"50001600",2008-06-26,"50397426",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6015","SNS",6016686.35956233,870415.488475494,0.36,0.11,1,300,3 +"50001600",2008-06-26,"50397503",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0851N6030","SNS",6029627.58410352,849869.27823544,0.31,0.09,1,300,1 +"50001600",2008-06-26,"50397622",1730,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0866N6050","SNS",6051067.43988645,866096.351991623,0.28,0.08,1,300,1 +"50001600",2008-06-26,"50397653",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0871N6055","SNS",6057537.33723164,871271.96474343,0.27,0.08,1,300,1 +"50001600",2008-06-26,"50397830",5410,50,6,2008,1,1,3,"5171",NA,"ESAS v5","E0886N6055","SNS",6053429.97836834,886912.184611569,0.29,0.09,1,300,1 +"50001601",2008-02-25,"50397869",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6047158.2198389,880597.561203484,0.3,0.09,1,300,13 +"50001601",2008-02-25,"50397873",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6046707.04675056,879480.89028267,0.3,0.09,1,300,1 +"50001601",2008-02-25,"50397874",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6046716.08101979,879186.65565378,0.3,0.09,1,300,6 +"50001601",2008-02-25,"50397909",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6045","SNS",6046432.76892743,869761.648708279,0.27,0.08,1,300,2 +"50001601",2008-02-25,"50397939",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0861N6045","SNS",6043034.67721486,863103.412829411,0.27,0.08,1,300,30 +"50001601",2008-02-25,"50397995",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0861N6045","SNS",6047252.65958025,861033.975283258,0.25,0.08,1,300,3 +"50001601",2008-02-25,"50398026",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6050","SNS",6051703.36359313,868154.759273024,0.28,0.08,1,300,14 +"50001601",2008-02-25,"50398050",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6055","SNS",6057293.48141715,871571.626023696,0.29,0.09,1,300,6 +"50001601",2008-02-25,"50398065",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6060","SNS",6061410.57182508,873359.454227016,0.29,0.09,1,300,45 +"50001601",2008-02-25,"50398088",5410,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6070","SNS",6067638.95634879,873345.639327498,0.3,0.09,1,300,1 +"50001601",2008-02-25,"50398092",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6070","SNS",6067934.7534854,872321.740808243,0.27,0.08,1,300,3 +"50001601",2008-02-25,"50398100",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6070","SNS",6067821.89532499,870267.297369218,0.22,0.07,2,300,4 +"50001601",2008-02-25,"50398113",4960,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6065","SNS",6066597.67350719,868011.401178821,0.21,0.06,1,300,65 +"50001601",2008-02-25,"50398195",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6060","SNS",6061379.23557159,867506.824872705,0.21,0.06,1,300,78 +"50001601",2008-02-25,"50398197",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6060","SNS",6061792.78174211,867642.194848739,0.22,0.06,1,300,16 +"50001601",2008-02-25,"50398200",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6060","SNS",6062135.7225837,868147.088522898,0.2,0.06,1,300,45 +"50001601",2008-02-25,"50398201",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6060","SNS",6062217.07250583,868322.383602271,0.19,0.06,1,300,65 +"50001601",2008-02-25,"50398233",4960,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6060","SNS",6058947.08562339,871416.636129196,0.2,0.06,2,300,75 +"50001601",2008-02-25,"50398235",4960,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6060","SNS",6058558.27886623,871311.933275631,0.2,0.06,1,300,25 +"50001601",2008-02-25,"50398247",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6055","SNS",6056238.19748441,870701.689133227,0.19,0.06,2,300,2 +"50001601",2008-02-25,"50398250",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6055","SNS",6055687.55566446,870531.958500964,0.19,0.06,1,300,12 +"50001601",2008-02-25,"50398303",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0876N6050","SNS",6047826.78911413,875586.544217648,0.26,0.08,1,300,1 +"50001601",2008-02-25,"50398303",1730,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0876N6050","SNS",6047826.78911413,875586.544217648,0.26,0.08,1,300,3 +"50001601",2008-02-25,"50398364",1680,50,4,2008,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6053383.66182776,888466.690169655,0.24,0.07,1,300,15 +"50001602",2008-10-08,"50398374",5410,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0886N6055","SNS",6052816.29525804,885615.788985947,0.26,0.08,1,300,4 +"50001602",2008-10-08,"50398420",5410,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0876N6050","SNS",6048037.5528901,875836.419927937,0.3,0.09,1,300,1 +"50001602",2008-10-08,"50398464",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0861N6045","SNS",6046538.63467211,862459.186309357,0.32,0.09,1,300,6 +"50001602",2008-10-08,"50398939",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0876N6015","SNS",6013224.34131602,878030.09552252,0.28,0.08,1,300,4 +"50001603",2008-10-09,"50399025",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0866N6010","SNS",6009389.56380855,864583.542995214,0.29,0.09,1,300,1 +"50001603",2008-10-09,"50399068",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0851N6010","SNS",6011480.13394826,853401.420034679,0.28,0.08,1,300,1 +"50001603",2008-10-09,"50399105",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0841N6010","SNS",6011612.1704647,843331.289984966,0.27,0.08,2,300,34 +"50001603",2008-10-09,"50399125",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0836N6010","SNS",6012625.61551083,837851.271102349,0.28,0.08,1,300,58 +"50001603",2008-10-09,"50399333",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0856N6030","SNS",6029262.09406683,857005.01679537,0.23,0.07,1,300,27 +"50001603",2008-10-09,"50399354",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0856N6035","SNS",6032962.17738772,856004.118506277,0.26,0.08,1,300,15 +"50001603",2008-10-09,"50399475",1680,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6045","SNS",6047250.87420426,880902.843656645,0.27,0.08,1,300,23 +"50001603",2008-10-09,"50399482",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6048747.38549467,882074.975161213,0.27,0.08,1,300,2 +"50001603",2008-10-09,"50399487",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0881N6050","SNS",6049824.38268847,882910.114947804,0.27,0.08,1,300,2 +"50001603",2008-10-09,"50399494",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0886N6050","SNS",6051342.58717287,884066.013961019,0.27,0.08,1,300,1 +"50001603",2008-10-09,"50399496",1730,50,2,2008,1,1,2,"5188",NA,"ESAS v5","E0886N6050","SNS",6051773.6371362,884401.008410973,0.27,0.08,2,300,2 +"50001604",2008-12-16,"50399551",1730,50,3,2008,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008807.81364724,861883.173925075,0.32,0.09,1,300,1 +"50001605",2008-12-17,"50399906",5410,50,3,2008,1,1,2,"5170",NA,"ESAS v5","E0871N6015","SNS",6017105.18968773,869089.311118245,0.32,0.1,2,300,1 +"50001605",2008-12-17,"50399907",5410,50,3,2008,1,1,2,"5170",NA,"ESAS v5","E0866N6015","SNS",6017204.89212513,868799.217063974,0.31,0.09,1,300,1 +"50001608",2008-07-31,"50400603",1730,50,6,2008,1,1,1,"5187",NA,"ESAS v5","E0866N6010","SNS",6009582.76103967,866033.326287623,0.48,0.14,1,300,1 +"50001609",2008-08-05,"50400643",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013393.09504422,877497.356686454,0.52,0.16,1,300,11 +"50001609",2008-08-05,"50400648",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013359.11175725,874892.78185321,0.51,0.15,1,300,6 +"50001609",2008-08-05,"50400651",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6013063.61714901,873458.869458193,0.47,0.14,1,300,6 +"50001610",2008-06-21,"50400894",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0876N6015","SNS",6013426.19999308,878309.081632372,0.52,0.16,2,300,1 +"50001610",2008-06-21,"50400897",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0876N6015","SNS",6013403.07241113,876800.343017032,0.5,0.15,2,300,3 +"50001610",2008-06-21,"50400915",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0871N6010","SNS",6010553.07703236,869365.617939946,0.45,0.14,1,300,1 +"50001611",2008-06-22,"50401053",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0836N6010","SNS",6010274.41016781,835642.169676483,0.51,0.15,1,300,2 +"50001611",2008-06-22,"50401133",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0876N6015","SNS",6013362.46548425,878077.864048719,0.45,0.14,2,300,3 +"50001612",2008-07-01,"50401190",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0856N6010","SNS",6008217.80510262,856896.820959788,0.49,0.15,1,300,1 +"50001614",2008-06-25,"50401386",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0876N6015","SNS",6013367.18616664,876908.366738858,0.55,0.16,2,300,1 +"50001614",2008-06-25,"50401393",1730,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0871N6015","SNS",6013116.5090067,873283.830972869,0.49,0.15,1,300,1 +"50001614",2008-06-25,"50401393",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0871N6015","SNS",6013116.5090067,873283.830972869,0.49,0.15,1,300,1 +"50001615",2008-06-27,"50401517",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0831N6010","SNS",6011042.32856253,830194.703705722,0.56,0.17,1,300,4 +"50001615",2008-06-27,"50401597",5410,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0876N6015","SNS",6013153.59165919,873878.437636386,0.53,0.16,1,300,5 +"50001615",2008-06-27,"50401612",1680,50,6,2008,1,1,1,"5170",NA,"ESAS v5","E0881N6010","SNS",6012477.11405249,882118.90569844,0.55,0.17,2,300,4 +"50001616",2008-08-26,"50401635",4960,50,1,2008,1,1,2,"5188",NA,"ESAS v5","E0871N6010","SNS",6011657.62133049,870866.56283271,0.52,0.16,2,300,1 +"50001618",2008-09-25,"50402137",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0871N6010","SNS",6011228.96743928,870263.892094946,0.49,0.15,1,300,4 +"50001618",2008-09-25,"50402329",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0866N6010","SNS",6009913.03107945,867373.596890737,0.52,0.16,1,300,5 +"50001618",2008-09-25,"50402351",1730,50,1,2008,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013329.16459928,876990.295739233,0.5,0.15,1,300,6 +"50001619",2008-10-06,"50402442",1680,50,2,2008,1,1,2,"5170",NA,"ESAS v5","E0841N6010","SNS",6009861.78858858,839917.810796761,0.53,0.16,2,300,1 +"50001619",2008-10-06,"50402545",1680,50,2,2008,1,1,2,"5170",NA,"ESAS v5","E0851N6010","SNS",6008641.28817592,850913.459649386,0.52,0.16,2,300,14 +"50001619",2008-10-06,"50402561",1680,50,2,2008,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008326.64437166,859161.454530158,0.51,0.15,1,300,2 +"50001620",2009-04-23,"50402750",5410,50,5,2009,1,1,2,"5173",NA,"ESAS v5","E0821N6015","SNS",6013247.91662846,820474.285932177,0.42,0.13,1,300,4 +"50001621",2009-04-29,"50402925",1730,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6009039.4588246,862554.803801241,0.51,0.15,2,300,3 +"50001621",2009-04-29,"50402932",1680,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0861N6010","SNS",6008611.43862695,859086.707309355,0.49,0.15,1,300,3 +"50001621",2009-04-29,"50402997",1680,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0826N6010","SNS",6012041.4576592,825245.186001338,0.52,0.16,1,300,8 +"50001621",2009-04-29,"50403104",4960,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0871N6010","SNS",6010633.45895056,869392.019749089,0.47,0.14,2,300,1 +"50001622",2009-05-12,"50403144",4960,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0876N6015","SNS",6013461.45867213,875462.360025656,0.5,0.15,2,300,40 +"50001624",2009-06-02,"50403684",1680,50,6,2009,1,1,2,"5171",NA,"ESAS v5","E0861N6010","SNS",6008877.94833162,862633.210752236,0.51,0.15,2,300,70 +"50001624",2009-06-02,"50403832",1680,50,6,2009,1,1,2,"5171",NA,"ESAS v5","E0851N6010","SNS",6008397.97838984,852849.769613831,0.52,0.16,1,300,36 +"50001624",2009-06-02,"50403881",5410,50,6,2009,1,1,2,"5171",NA,"ESAS v5","E0876N6015","SNS",6013235.69142693,878712.284365174,0.58,0.17,1,300,1 +"50001630",2009-07-22,"50404558",5410,50,6,2009,1,1,2,"5113",NA,"ESAS v5","E0856N6010","SNS",6008658.20696029,857099.197212249,0.46,0.14,1,300,1 +"50001630",2009-07-22,"50404610",5410,50,6,2009,1,1,2,"5113",NA,"ESAS v5","E0831N6010","SNS",6011414.78274351,832266.569825817,0.49,0.15,1,300,2 +"50001631",2009-08-14,"50404997",1730,50,1,2009,1,1,2,"5113",NA,"ESAS v5","E0871N6015","SNS",6012652.12228923,872382.315982875,0.53,0.16,1,300,1 +"50001631",2009-08-14,"50405004",1730,50,1,2009,1,1,2,"5113",NA,"ESAS v5","E0876N6015","SNS",6013278.69990905,875963.275804481,0.54,0.16,2,300,7 +"50001635",2009-03-10,"50405821",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0841N6020","SNS",6021207.63916798,842399.270732119,0.19,0.06,1,300,15 +"50001636",2009-03-11,"50406003",1680,50,4,2009,1,1,2,"5232",NA,"ESAS v5","E0801N6015","SNS",6013380.83678222,802143.731775564,0.08,0.02,1,300,17 +"50001637",2009-03-11,"50406302",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0801N6015","SNS",6013390.66702158,802123.44173067,0.07,0.02,1,300,7 +"50001637",2009-03-11,"50406303",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0801N6015","SNS",6013396.64411023,802044.471847023,0.08,0.03,2,300,10 +"50001640",2009-03-13,"50407057",5410,50,4,2009,1,1,2,"5232",NA,"ESAS v5","E0801N6115","SNS",6113731.45067411,800389.906321884,0.26,0.08,1,300,1 +"50001644",2009-03-15,"50408417",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0806N6065","SNS",6067198.40494063,804928.033433252,0.25,0.08,1,300,2 +"50001648",2009-03-17,"50409780",1680,50,4,2009,1,1,2,"5232",NA,"ESAS v5","E0831N5995","SNS",5993334.40021707,830291.468406777,0.36,0.11,1,300,6 +"50001648",2009-03-17,"50409875",1730,50,4,2009,1,1,2,"5232",NA,"ESAS v5","E0836N5975","SNS",5974940.87012577,836248.534312124,0.31,0.09,1,300,4 +"50001649",2009-03-17,"50410123",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0816N5985","SNS",5983043.17361357,813955.485643078,0.39,0.12,1,300,3 +"50001649",2009-03-17,"50410173",1680,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0831N5995","SNS",5993129.14116993,830075.686406883,0.36,0.11,1,300,6 +"50001649",2009-03-17,"50410214",1730,50,4,2009,1,1,2,"5113",NA,"ESAS v5","E0836N5975","SNS",5975305.20198224,836189.193899775,0.33,0.1,1,300,4 +"50001650",2009-07-28,"50410246",5410,50,6,2009,1,1,2,"5170",NA,"ESAS v5","E0856N5960","SNS",5961510.56065901,853921.955447818,0.27,0.08,2,300,1 +"50001650",2009-07-28,"50410275",5410,50,6,2009,1,1,2,"5170",NA,"ESAS v5","E0846N5965","SNS",5965866.39980929,847939.76701943,0.25,0.08,2,300,2 +"50001651",2009-07-29,"50410643",5410,50,6,2009,1,1,2,"5170",NA,"ESAS v5","E0736N5980","SNS",5978484.39617877,736577.414888163,0.3,0.09,1,300,2 +"50001651",2009-07-29,"50410865",6270,50,6,2009,1,1,2,"5170",NA,"ESAS v5","E0791N5970","SNS",5972290.39770591,792656.617521228,0.35,0.1,1,300,1 +"50001654",2009-08-01,"50412005",5410,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0831N6070","SNS",6071726.33992037,829633.671289473,0.22,0.07,1,300,1 +"50001654",2009-08-01,"50412109",5410,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0831N6095","SNS",6093147.28722728,832557.614531067,0.21,0.06,1,300,1 +"50001656",2009-08-03,"50413548",6270,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0696N5965","SNS",5966448.72262522,696788.391718668,0.21,0.06,2,300,1 +"50001657",2009-08-04,"50413785",6270,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0651N5930","SNS",5931552.18140176,653742.028329935,0.25,0.08,2,300,2 +"50001658",2009-08-05,"50414672",6270,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0851N6010","SNS",6008165.33606267,849944.842877738,0.18,0.05,2,300,1 +"50001659",2009-09-14,"50414974",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0861N5955","SNS",5955467.06961017,861998.748166232,0.4,0.12,1,300,2 +"50001659",2009-09-14,"50414975",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0861N5955","SNS",5955699.16495807,861675.206822149,0.4,0.12,1,300,12 +"50001659",2009-09-14,"50414984",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0856N5955","SNS",5957448.57508882,858551.761385741,0.4,0.12,1,300,2 +"50001659",2009-09-14,"50415025",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0846N5965","SNS",5966604.15243667,846440.612154661,0.36,0.11,1,300,1 +"50001665",2009-09-20,"50418074",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0866N5995","SNS",5992890.60884857,868413.134048698,0.13,0.04,1,300,2 +"50001665",2009-09-20,"50418075",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0866N5995","SNS",5992833.33264935,868536.383192757,0.14,0.04,2,300,2 +"50001665",2009-09-20,"50418084",1730,50,1,2009,1,1,2,"5170",NA,"ESAS v5","E0871N5990","SNS",5992313.80659975,869593.343917201,0.12,0.04,1,300,3 +"50001666",2009-05-15,"50418320",4960,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0821N5995","SNS",5994914.36330434,823723.700141236,0.35,0.11,1,300,6 +"50001667",2009-05-16,"50418622",5410,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0761N6065","SNS",6066797.86583421,762268.737802874,0.23,0.07,1,300,1 +"50001669",2009-05-18,"50419694",1680,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0796N6015","SNS",6016582.99356119,798360.800401958,0.26,0.08,2,300,2 +"50001670",2009-05-19,"50419856",1680,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0806N5995","SNS",5995365.69057014,806238.071567165,0.31,0.09,1,300,5 +"50001670",2009-05-19,"50419936",1680,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0796N5975","SNS",5976433.65482563,796586.385201649,0.34,0.1,1,300,31 +"50001676",2009-05-25,"50423500",1730,50,5,2009,1,1,2,"5170",NA,"ESAS v5","E0861N5955","SNS",5954309.26124009,862925.523877457,0.26,0.08,1,300,2 +"50001678",2010-02-14,"50423745",1680,50,4,2010,1,1,3,"5171",NA,"ESAS v5","E0741N5985","SNS",5986000.89786076,741821.874332736,0.26,0.08,1,300,30 +"50001679",2010-02-15,"50424408",1730,50,4,2010,1,1,3,"5171",NA,"ESAS v5","E0731N5990","SNS",5990537.38585249,733317.852733001,0.28,0.08,2,300,1 +"50001680",2010-03-04,"50424572",5410,50,4,2010,1,1,2,"5232",NA,"ESAS v5","E0861N5955","SNS",5955590.60947854,861716.799213599,0.27,0.08,1,300,3 +"50001680",2010-03-04,"50424573",5410,50,4,2010,1,1,2,"5232",NA,"ESAS v5","E0861N5955","SNS",5955729.34818085,861486.847782267,0.27,0.08,2,300,2 +"50001687",2010-04-01,"50427117",5410,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0846N5965","SNS",5967369.94043708,845012.777127143,0.29,0.09,1,300,2 +"50001687",2010-04-01,"50427217",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0826N5985","SNS",5986550.01982518,827228.432623422,0.29,0.09,1,300,5 +"50001687",2010-04-01,"50427480",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0816N6070","SNS",6067883.85644804,814921.663025816,0.29,0.09,1,300,2 +"50001688",2010-04-01,"50427616",1730,50,5,2010,1,1,3,"5173",NA,"ESAS v5","E0836N5980","SNS",5978043.27636934,836672.022602892,0.28,0.08,1,300,2 +"50001688",2010-04-01,"50427656",1680,50,5,2010,1,1,3,"5173",NA,"ESAS v5","E0831N5985","SNS",5985156.4291703,829001.563891499,0.28,0.08,1,300,2 +"50001688",2010-04-01,"50427925",1680,50,5,2010,1,1,3,"5173",NA,"ESAS v5","E0816N6070","SNS",6067937.51995329,814730.167861245,0.29,0.09,2,300,2 +"50001689",2010-04-02,"50428178",5410,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0776N6085","SNS",6085024.6947851,777650.115903513,0.32,0.1,1,300,1 +"50001691",2010-04-03,"50428870",5410,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0781N6095","SNS",6095473.26054636,779137.523843414,0.32,0.1,1,300,1 +"50001692",2010-04-03,"50429210",5410,50,5,2010,1,1,3,"5173",NA,"ESAS v5","E0781N6095","SNS",6095508.5477976,778987.506286768,0.32,0.1,1,300,1 +"50001699",2010-04-07,"50432296",5410,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0861N5955","SNS",5956166.08987083,860190.983287582,0.34,0.1,1,300,3 +"50001710",2010-05-28,"50437405",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0786N6005","SNS",6004211.91844356,787580.241976148,0.3,0.09,1,300,20 +"50001710",2010-05-28,"50437484",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0786N5990","SNS",5991930.03978127,784393.842571113,0.29,0.09,1,300,24 +"50001710",2010-05-28,"50437533",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0781N6005","SNS",6002774.77678269,779812.270002779,0.31,0.09,1,300,3 +"50001710",2010-05-28,"50437595",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0776N5995","SNS",5996835.89163955,776232.54589964,0.3,0.09,1,300,2 +"50001710",2010-05-28,"50437801",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0766N5985","SNS",5985614.34860148,763992.103391834,0.29,0.09,1,300,9 +"50001710",2010-05-28,"50437806",1680,50,5,2010,1,1,3,"5171",NA,"ESAS v5","E0761N5985","SNS",5986938.38564891,763739.218265948,0.31,0.09,1,300,29 +"50001713",2010-05-30,"50439320",1680,50,5,2010,1,1,2,"5232",NA,"ESAS v5","E0841N6010","SNS",6008431.43191566,843680.059951734,0.28,0.09,2,300,2 +"50001715",2010-07-14,"50439911",5410,50,6,2010,1,1,2,"5232",NA,"ESAS v5","E0716N6045","SNS",6046229.20177084,717695.305663157,0.24,0.07,1,300,1 +"50001715",2010-07-14,"50440360",5410,50,6,2010,1,1,2,"5232",NA,"ESAS v5","E0781N6120","SNS",6120164.06802511,782025.285053041,0.29,0.09,1,300,1 +"50001716",2010-07-15,"50440755",1730,50,6,2010,1,1,2,"5232",NA,"ESAS v5","E0836N6040","SNS",6042162.01535383,836897.890108212,0.23,0.07,1,300,10 +"50001716",2010-07-15,"50440851",5410,50,6,2010,1,1,2,"5232",NA,"ESAS v5","E0831N6065","SNS",6062630.84096986,831750.558664954,0.2,0.06,1,300,1 +"50001716",2010-07-15,"50440999",5410,50,6,2010,1,1,2,"5232",NA,"ESAS v5","E0796N6070","SNS",6068990.41422247,798324.572667052,0.2,0.06,1,300,1 +"50001722",2010-07-29,"50444547",5410,50,6,2010,1,1,2,"5171",NA,"ESAS v5","E0871N6015","SNS",6016967.35506392,869461.212958022,0.39,0.12,2,300,1 +"50001722",2010-07-29,"50444623",1730,50,6,2010,1,1,2,"5171",NA,"ESAS v5","E0871N6005","SNS",6005588.39694457,873387.653005061,0.31,0.09,1,300,17 +"50001723",2010-07-20,"50444799",5410,50,6,2010,1,1,2,"5194",NA,"ESAS v5","E0821N6010","SNS",6010061.41514482,822499.519466197,0.21,0.06,2,300,4 +"50001746",2010-08-17,"50448628",5410,50,1,2010,1,1,2,"5194",NA,"ESAS v5","E0846N5965","SNS",5965790.47721751,846918.36906535,0.39,0.12,1,300,7 +"50001746",2010-08-17,"50448658",1730,50,1,2010,1,1,2,"5194",NA,"ESAS v5","E0856N5960","SNS",5959229.02389365,855813.501073182,0.37,0.11,1,300,8 +"50001749",2010-08-05,"50450759",6270,50,1,2010,1,1,3,"5171",NA,"ESAS v5","E0776N5995","SNS",5997195.92621301,776283.257492757,0.28,0.09,2,300,1 +"50001749",2010-08-05,"50450879",5410,50,1,2010,1,1,3,"5171",NA,"ESAS v5","E0786N5995","SNS",5992854.69193126,784369.939248847,0.27,0.08,1,300,1 +"50001845",2011-07-27,"50480831",5410,55,6,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0676N6145","SNS",6145716.84764688,678225.238132579,0.23,0.07,1,300,1 +"50001856",2011-08-12,"50482898",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0856N5960","SNS",5960103.03615628,855256.029525125,0.38,0.12,1,300,17 +"50001856",2011-08-12,"50482905",5410,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0851N5960","SNS",5962082.7866008,853361.316917629,0.4,0.12,1,300,5 +"50001856",2011-08-12,"50482934",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0841N5970","SNS",5968571.8468737,843129.621272759,0.43,0.13,1,300,8 +"50001856",2011-08-12,"50482949",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0836N5975","SNS",5973332.11120824,838560.123479652,0.46,0.14,1,300,58 +"50001856",2011-08-12,"50482954",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0836N5975","SNS",5975045.18485822,837073.915226229,0.46,0.14,1,300,66 +"50001856",2011-08-12,"50482956",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0836N5975","SNS",5975714.54126057,836443.85910196,0.46,0.14,2,300,11 +"50001856",2011-08-12,"50482957",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0836N5975","SNS",5976037.66212722,836123.122291037,0.45,0.14,1,300,45 +"50001856",2011-08-12,"50482960",1730,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0836N5975","SNS",5976668.80821504,834960.855594126,0.44,0.13,1,300,2 +"50001857",2011-08-13,"50483287",5410,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0586N5970","SNS",5969507.59707871,584266.734292339,0.38,0.12,1,300,4 +"50001861",2011-08-18,"50483843",4960,55,1,2011,1,1,2,"5250",5244,"data update 06/11/2020","E0511N6190","SNS",6191993.53686322,511463.127988216,0.39,0.12,1,300,7 +"50001892",2012-04-29,"50492043",4960,55,5,2012,1,1,2,"5249",5245,"data update 06/11/2020","E0781N5970","SNS",5972624.48929469,780112.094598249,0.32,0.1,1,300,8 +"50001895",2012-05-02,"50493096",1730,55,5,2012,1,1,2,"5249",5245,"data update 06/11/2020","E0791N5975","SNS",5975437.83240271,789449.964919599,0.24,0.07,2,300,2 +"50001896",2012-05-03,"50493226",4960,55,5,2012,1,1,2,"5249",5245,"data update 06/11/2020","E0776N5975","SNS",5973249.96104039,777137.86590552,0.27,0.08,1,300,14 +"50001896",2012-05-03,"50493563",1680,55,5,2012,1,1,2,"5249",5245,"data update 06/11/2020","E0856N5960","SNS",5959501.36564344,855573.789710324,0.38,0.11,1,300,1 +"50001983",2013-04-21,"50514964",5410,55,5,2013,1,1,2,"5249",5207,"data update 06/11/2020","E0831N6045","SNS",6044087.04878844,831154.538392014,0.3,0.09,1,300,4 +"50001983",2013-04-21,"50515030",5410,55,5,2013,1,1,2,"5249",5207,"data update 06/11/2020","E0841N6030","SNS",6028260.59431877,843624.881659259,0.31,0.09,1,300,4 +"50002042",2013-10-04,"50531587",1730,55,2,2013,1,1,2,"5249",5232,"data update 06/11/2020","E0831N5980","SNS",5977640.38971999,833246.053689891,0.25,0.08,2,300,2 +"50002044",2013-10-06,"50532133",1680,55,2,2013,1,1,2,"5249",5232,"data update 06/11/2020","E0801N6070","SNS",6069420.80046987,800773.576691938,0.23,0.07,1,300,6 +"50002069",2014-04-17,"50539496",5410,55,5,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0851N5960","SNS",5962336.00636697,853163.119522768,0.34,0.1,1,300,1 +"50002069",2014-04-17,"50539580",5410,55,5,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0836N5980","SNS",5977700.22385613,836716.65343137,0.3,0.09,1,300,1 +"50002070",2014-04-19,"50540103",1680,55,5,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0806N5980","SNS",5982559.84062203,806379.473002038,0.34,0.1,1,300,5 +"50002073",2014-04-22,"50541687",5410,55,5,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0761N5975","SNS",5975647.16269617,762086.397456939,0.33,0.1,1,300,25 +"50002073",2014-04-22,"50541803",6270,55,5,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0776N5970","SNS",5969831.46252988,776304.320592959,0.36,0.11,1,300,1 +"50002107",2014-07-08,"50549765",6270,55,6,2014,1,1,2,"5249",5265,"data update 06/11/2020","E0806N5990","SNS",5988765.12169054,807708.365169469,0.32,0.1,1,300,1 +"50002108",2014-07-09,"50549971",5410,55,6,2014,1,1,3,"5249",5173,"data update 06/11/2020","E0811N5980","SNS",5980656.986324,810007.119744642,0.37,0.11,1,300,1 +"50002108",2014-07-09,"50550005",6270,55,6,2014,1,1,3,"5249",5173,"data update 06/11/2020","E0821N5985","SNS",5987438.59143671,820437.615086665,0.39,0.12,1,300,2 +"50002108",2014-07-09,"50550117",1730,55,6,2014,1,1,3,"5249",5173,"data update 06/11/2020","E0866N5995","SNS",5993100.99049369,868106.281521685,0.31,0.09,1,300,9 +"50002108",2014-07-09,"50550178",5410,55,6,2014,1,1,3,"5249",5173,"data update 06/11/2020","E0871N5995","SNS",5993090.67739722,869801.740143339,0.42,0.13,1,300,1 +"50002114",2014-07-15,"50553265",5410,55,6,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0731N6100","SNS",6100803.82221122,730570.270793962,0.28,0.08,1,300,1 +"50002115",2014-07-16,"50554025",5410,55,6,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0706N6065","SNS",6066835.44645214,704006.1030059,0.26,0.08,2,300,9 +"50002116",2014-07-17,"50554349",5410,55,6,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0856N5995","SNS",5994694.9061639,857488.449771342,0.3,0.09,2,300,1 +"50002116",2014-07-17,"50554354",1730,55,6,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0861N5995","SNS",5994553.38420587,858981.874158141,0.3,0.09,1,300,28 +"50002116",2014-07-17,"50554357",1730,55,6,2014,1,1,2,"5249",5173,"data update 06/11/2020","E0861N5995","SNS",5994454.85872029,859872.484893552,0.3,0.09,1,300,2 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/ESAS_opwerking_new_met_zeeeend.R b/script_py_r/kec4_pyconversion-master/ESAS_opwerking_new_met_zeeeend.R new file mode 100644 index 0000000000000000000000000000000000000000..48a0a9e3883f098afb82f866077e16008ee651f8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/ESAS_opwerking_new_met_zeeeend.R @@ -0,0 +1,1283 @@ +### ESAS dataset opwerking ------------------------- +#--------------------------------------------------- +# Marinka van Puijenbroek & Susanne van Donk ------- +# v1 - 2021-02-17 +# Information about terminology: https://ices-tools-dev.github.io/esas/tables/ +# Careful! This website is updated and can have differences with the dataset. +# For correct information check file send by VanErmen. https://docs.google.com/document/d/15tNRx-AjP3IhhPzLK-hlf5VcF7iSy3i8/edit + +# Disclaimer +# Wageningen Marine Research (WMR) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (software) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + +# Load packages and functions ------------ + +# Remove all data +rm(list= ls()) + +# Load packages +library(tidyverse) +library(sf) +library(readxl) +library(lubridate) + +# Functies +`%notin%` <- function(x,y) !(x %in% y) + +# Ggplot theme Marinka + +theme_marinka <- function () { + theme_classic(base_size=12)%+replace% + theme( + legend.title = element_text(face = "bold"), + strip.background = element_rect(fill = "grey", colour = "white"), + strip.text = element_text(face = "bold", margin = margin(0.2,0.2,0.2,0.2, "cm")) + ) +} +# Step 1: Combine data: SKIP if dataset already saved, go to step 2 ----------------------------- +#* Load ESAS data ------------------------ + +setwd("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/ESAS_dataset/ESAS v6") + +temp = list.files(pattern="*v6.0.csv") +names <-str_sub(temp,1,-5) + +###Load all files +for(i in names){ + filepath <- file.path(paste(i,".csv",sep="")) + assign(i, read.csv(filepath)) +} + +ESAS_all <- full_join(TRP_ESAS_v6.0, POS_ESAS_v6.0, by = "tripkey") %>% + full_join(OBS_ESAS_v6.0, by ="poskey") %>% + mutate(across(.cols = c("tripkey", "poskey", "campaign_key", "observer1"), as.character)) + + +#* Load MWTL data -------------------------- + +# Load MWTL 1991 - 2014 +temp <- Sys.glob("W:/IMARES/DATA/KEC4-0/4. Data/1. Ruwe data/MWTL/buwa-62b54e/datalevering_feb2021_MardikLeopold/*/*.xlsx") + +names <-str_sub(temp,140,-6) + +mwtl <- map2(temp, names, function(i,t) { + assign(t, read_excel(i)) +}) + +names(mwtl) <- names +list2env(mwtl, .GlobalEnv) + +# Fix date +NL_2014_TRP_ESAS_aerial <- NL_2014_TRP_ESAS_aerial %>% + unite(date, day:year, sep = "/") + +# Fix time +NL_2014_POS_ESAS_aerial <- NL_2014_POS_ESAS_aerial %>% + unite(time, hours:seconds, sep = ":") + +mwtl_1991_2014 <- full_join(NL_2014_TRP_ESAS_aerial, NL_2014_POS_ESAS_aerial, by = "tripkey") %>% + full_join(NL_2014_SPC_ESAS_aerial, by ="poskey") + +# Load MWTL > 2014 +#temp <- Sys.glob("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/MWTL/buwa-62b54e/datalevering_feb2021_MardikLeopold/*/*.txt") +# nieuwe dataset in maart 2021 omdat in eerdere strip 0 miste +temp <- Sys.glob("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/MWTL/buwa-62b54e/datalevering_feb2021_MardikLeopold/exports_esas_2014-2020_nieuw/*.txt") + + +names <-str_sub(temp,146,-5) + +mwtl <- map2(temp, names, function(i,t) { + assign(t, read.table(i, sep = ";", na.strings = c("", "NULL"), header = T)) +}) + +names(mwtl) <- names +list2env(mwtl, .GlobalEnv) + +MWTL_2014 <- full_join(esas_export_trip, esas_export_position, by = "tripkey") %>% + full_join(esas_export_observations, by ="poskey") + +MWTL <- bind_rows(MWTL_2014,mwtl_1991_2014) %>% + mutate(across(.cols = c("distance"), as.character)) %>% + add_column(origin = "MWTL") +unique(MWTL$date) + +# Combine ESAS + MWTL + +All_data <- bind_rows(ESAS_all, MWTL) + +rm(list=setdiff(ls(), "All_data")) + + +write.csv(All_data,"W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/Combined_dataset/ESAS_MWTL_raw.csv", row.names = F) + +# Step 2: Load dataset with all data ------------------------------ + +All_data <- read.csv("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/Combined_dataset/ESAS_MWTL_raw.csv") +str(All_data) + +unique(All_data$transect_width) +unique(All_data$distance) # check 18 voor MWTL + +# Noordzee grid +GRID_shape <- st_read("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/GIS bestand noordzee/GRID_north_sea.shp") + + + +# Step 3: Select counts in Northsea subareas ------------------------------------------ + +All_data <- All_data[complete.cases(All_data[ , 27:28]),] + +DT_sf = st_as_sf(All_data, coords = c("longitude","latitude"), # First x (longitude), than y (latitude) + crs = 4326, agr = "constant") + +DT_sf_utm31 <- DT_sf %>% st_transform(32631) + +data_join <- st_join(DT_sf_utm31, GRID_shape, join = st_intersects) + +data_northsea <- data_join %>% + filter(SubArea %in% c("SNS", "CNS")) + +rm(All_data,data_join,DT_sf,DT_sf_utm31) + +#### Explore dataset! ------------------------------------------ + +# data_count_method2 <- data_northsea %>% +# st_drop_geometry() %>% +# filter(count_method == 2) %>% +# group_by(transect, distance) %>% +# summarise(count_method = first(count_method), n = n()) +# +# write.csv(data_count_method2, "ESAS_count_method_check.csv") +# +# EURING_codes <- read.csv("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/ESAS_dataset/ESAS v6/EURING_codes.csv") +# +# species <- data_northsea %>% +# st_drop_geometry() %>% +# group_by(euring_species_code) %>% +# summarise(n()) +# +# EURING_codes_ESAS <- right_join(species, EURING_codes, by = c("euring_species_code" = "code")) +# +# +# write.csv(EURING_codes_ESAS, "ESAS_species_check.csv") +# +# +# data_trip <- data_northsea %>% +# st_drop_geometry() %>% +# unite("trip_profile", count_method:species_counted) %>% +# group_by(trip_profile) %>% +# summarise(observatie = n()) +# +# write.csv(data_trip, "ESAS_tripprofile_check.csv") + +# Add seasons and select years ------------- + +# change date, add seasons & select data after 1990 + +data_northsea <- data_northsea %>% # 2078889 + mutate( + date = parse_date_time(date, c('%Y-%m-%d', '%d/%m/%Y')),# Verschillende dataformats + maand = month(date), + season = case_when( + maand %in% c(8,9) ~ 1, + maand %in% c(10,11) ~ 2, + maand %in% c(12,1) ~ 3, + maand %in% c(2,3) ~ 4, + maand %in% c(4,5) ~ 5, + maand %in% c(6,7) ~ 6 + ), year = ifelse( maand == 1, year(date) - 1, year(date)) + ) %>% + filter(year > 1990) + +# Set unique poskeys apart +Uni_poskeys <- data_northsea %>% + #mutate(latitude = unlist(map(.$geometry,1)), + # longitude = unlist(map(.$geometry,2))) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + group_by(tripkey, date, poskey) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed)) + +zero_poskey <- Uni_poskeys %>% filter(area_surveyed %in% c(0,NA)) + +dim(Uni_poskeys) +length(unique(Uni_poskeys$poskey)) +length(unique(data_northsea$poskey)) +# Data range per origin +data_northsea %>% dplyr::group_by(origin) %>% dplyr::summarize(max = max(date), + min = min(date)) + +# Korte check +shapefile <- readOGR("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/Shapefiles/Mainland.shp") +shapefile2 <- spTransform(shapefile, CRS = "+proj=utm +zone=31 +datum=WGS84 +units=m +no_defs") +extent(shapefile2) + +Uni_poskeys %>% filter(year==1995) %>% + ggplot() + + theme_marinka()+ + geom_point(aes(y=latitude, x=longitude), col="darkblue",alpha=0.5) + + geom_polygon(data = shapefile2, aes(y = lat, x = long, group = group), fill="lightgrey",color = "lightgrey")+ + coord_equal() + + +#Step 4: Combine or splits birds species ------------------------ + +# Create grid of 50x50 km to determine fraction between two species +GRID_shape_northsea <- GRID_shape %>% + filter(SubArea %in% c("SNS", "CNS")) + +GRID_50km <- st_make_grid(GRID_shape_northsea, cellsize = c(50000,50000)) %>% + st_sf() %>% + add_column(grid_50id = 1:399) + +data_northsea2 <- st_join(data_northsea, GRID_50km, join = st_intersects) + +# for computers with a small memory +rm(GRID_shape_northsea,GRID_50km,GRID_shape) + +#* Split zeekoet (6340) en alk (6360) op locatie en seizoen ---- +# 6345 --> zeekoet/alk observaties Uria aalge / Alca torda + +data_6345_fraction<- data_northsea2 %>% + st_drop_geometry() %>% + filter( euring_species_code %in% c(6340, 6360)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number))%>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_6340 = '6340',sp_6360 = '6360') %>% + mutate(sp_6340 = replace_na(sp_6340,0), + sp_6360 = replace_na(sp_6360,0), + fraction_6340 = sp_6340/(sp_6360+sp_6340)) %>% # fraction zeekoet per grid + dplyr::select(-sp_6340, -sp_6360) + +data_6345 <- data_northsea2 %>% + filter(euring_species_code == 6345) %>% + left_join(data_6345_fraction, by = c("season", "grid_50id" )) %>% + mutate("6340" = number*fraction_6340, "6360" = number* (1- fraction_6340)) %>% + dplyr::select(-euring_species_code, -number, -fraction_6340) %>% + #mutate(latitude = unlist(map(.$geometry,1)), + # longitude = unlist(map(.$geometry,2))) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("6340":"6360"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code))%>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") +# for check: run above lines without st_as_sf +#data_6345 %>% filter(year==1995) %>% +# ggplot() + +# theme_marinka()+ +# geom_point(aes(y=latitude, x=longitude), col="darkblue",alpha=0.5) + +# geom_polygon(data = shapefile2, aes(y = lat, x = long, group = group), fill="lightgrey",color = "lightgrey")+ +# coord_equal() + + +# Split 6549 --> Alcidae; zeekoet (6340), alk (6360), Papegaaiduiker (6540) + +data_6549_fraction <- data_northsea2 %>% + st_drop_geometry() %>% + filter(euring_species_code %in% c(6340, 6360,6540)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_6340 = '6340',sp_6360 = '6360',sp_6540 = '6540') %>% + mutate(sp_6340 = replace_na(sp_6340,0), + sp_6360 = replace_na(sp_6360,0), + sp_6540 = replace_na(sp_6540,0), + fraction_6340 = sp_6340/(sp_6360+sp_6340+sp_6540), + fraction_6360 = sp_6360/(sp_6360+sp_6340+sp_6540), + fraction_6540 = sp_6540/(sp_6360+sp_6340+sp_6540)) %>% + dplyr::select(-sp_6340, -sp_6360,-sp_6540) + +hist(data_6549_fraction$fraction_6340) +hist(data_6549_fraction$fraction_6360) +hist(data_6549_fraction$fraction_6540) + +data_6549 <- data_northsea2 %>% + filter(euring_species_code == 6549) %>% + left_join(data_6549_fraction, by = c("season", "grid_50id" )) %>% + mutate("6340" = number*fraction_6340, + "6360" = number*fraction_6360, + "6540" = number*fraction_6540) %>% + dplyr::select(-euring_species_code, -number, -fraction_6340,-fraction_6360,-fraction_6540) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("6340":"6540"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code)) %>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + + +# Combine data +data_northsea3 <- data_northsea2 %>% + filter(euring_species_code %notin% c(6345,6549)) %>% + bind_rows(data_6345,data_6549) + +rm(data_6345_fraction,data_6345,data_6549,data_6549_fraction,data_northsea2) +rm(data_northsea) + +#* Split Gulls ---- + +# Combine lesser black backed gull subspecies (fuscus, graelsii) + +data_northsea4 <- data_northsea3 %>% + mutate(euring_species_code = if_else(euring_species_code %in% c(5911,5912), 5910, euring_species_code)) + +# combine herring gull subspecies (argentatus argentatus) + +data_northsea4 <- data_northsea4 %>% + mutate(euring_species_code = if_else(euring_species_code %in% c(5921), 5920, euring_species_code)) + +# Split lesser black backed gull (5910) / herring gull (5920) + +data_northsea_5919_fraction<- data_northsea4 %>% + st_drop_geometry() %>% + filter( euring_species_code %in% c(5910, 5920)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_5910 = '5910',sp_5920 = '5920') %>% + mutate(sp_5910 = replace_na(sp_5910,0), + sp_5920 = replace_na(sp_5920,0), + fraction_5910 = sp_5910/(sp_5920+sp_5910)) %>% + dplyr::select(-sp_5910, -sp_5920) + +hist(data_northsea_5919_fraction$fraction_5910) + +data_herring_5919 <- data_northsea4 %>% + filter(euring_species_code == 5919) %>% + left_join(data_northsea_5919_fraction, by = c("season", "grid_50id" )) %>% + mutate("5910" = number*fraction_5910, "5920" = number* (1- fraction_5910)) %>% + dplyr::select(-euring_species_code, -number, -fraction_5910) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("5910":"5920"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code))%>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + + + +# Combine data +data_northsea5 <- data_northsea4 %>% + filter(euring_species_code %notin% c(5919)) %>% + bind_rows(data_herring_5919) + +# tussendoorse check. +check <- data_northsea4 %>% + filter(euring_species_code == 5919) %>% + full_join(data_northsea_5919_fraction, by = c("season", "grid_50id" )) %>% + mutate("5910" = number*fraction_5910, "5920" = number* (1- fraction_5910)) %>% dplyr::select(tripkey,euring_species_code,fraction_5910,number,"5910","5920") + +rm(data_northsea3,data_northsea4,data_herring_5919,data_northsea_5919_fraction,check) + + +# Split Euring 6009 - Unidentified larus gull in meest voorkomende meeuwen +# Kleine mantelmeeuw (5910), Zilvermeeuw (5920), Pontische meeuw (5927), geelpootmeeuw (5926), Grote Mantelmeeuw (6000), kokmeeuw (5820) en stormmeeuw (5900) + +data_6009_fraction <- data_northsea5 %>% + st_drop_geometry() %>% + filter(euring_species_code %in% c(5910,5920,5927,5926,6000,5820,5900)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_5910 = '5910',sp_5920 = '5920',sp_5927 = '5927',sp_5926 = '5926',sp_6000 = '6000',sp_5820 = '5820',sp_5900 = '5900') %>% + mutate(sp_5910 = replace_na(sp_5910,0), + sp_5920 = replace_na(sp_5920,0), + sp_5927 = replace_na(sp_5927,0), + sp_5926 = replace_na(sp_5926,0), + sp_6000 = replace_na(sp_6000,0), + sp_5820 = replace_na(sp_5820,0), + sp_5900 = replace_na(sp_5900,0), + fraction_5910 = sp_5910/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_5920 = sp_5920/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_5927 = sp_5927/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_5926 = sp_5926/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_6000 = sp_6000/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_5820 = sp_5820/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900), + fraction_5900 = sp_5900/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900)) %>% + dplyr::select(-sp_5910,-sp_5920,-sp_5927,-sp_5926,-sp_6000,-sp_5820,-sp_5900) + +# Grootste fracties gaan naar Zilvermeeuw 5920 en Kleine 5910, Grote Mantelmeeuw 6000 +hist(data_6009_fraction$fraction_5910) +hist(data_6009_fraction$fraction_5920) +hist(data_6009_fraction$fraction_5927) +hist(data_6009_fraction$fraction_5926) +hist(data_6009_fraction$fraction_6000) +hist(data_6009_fraction$fraction_5820) +hist(data_6009_fraction$fraction_5900) + +data_6009 <- data_northsea5 %>% + filter(euring_species_code == 6009) %>% + left_join(data_6009_fraction, by = c("season", "grid_50id" )) %>% + mutate("5910" = number*fraction_5910, + "5920" = number*fraction_5920, + "5927" = number*fraction_5927, + "5926" = number*fraction_5926, + "6000" = number*fraction_6000, + "5820" = number*fraction_5820, + "5900" = number*fraction_5900) %>% + dplyr::select(-euring_species_code, -number, -fraction_5910,-fraction_5920,-fraction_5927,-fraction_5926,-fraction_6000,-fraction_5820,-fraction_5900) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("5910":"5920","5927","5926","6000","5820","5900"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code))%>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + +Nul <- data_6009 %>% filter(number==0) +rm(Nul) + + +# Split 6049 unidentified gull in meest voorkomende meeuwen +# Kleine mantelmeeuw (5910), Zilvermeeuw (5920), Pontische meeuw (5927), geelpootmeeuw (5926), Grote Mantelmeeuw (6000),kokmeeuw (5820) en stormmeeuw (5900), drieteenmeeuw (6020) +data_6049_fraction <- data_northsea5 %>% + st_drop_geometry() %>% + filter(euring_species_code %in% c(5910,5920,5927,5926,6000,5820,5900,6020)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_5910 = '5910',sp_5920 = '5920',sp_5927 = '5927',sp_5926 = '5926',sp_6000 = '6000',sp_5820 = '5820',sp_5900 = '5900',sp_6020 = '6020') %>% + mutate(sp_5910 = replace_na(sp_5910,0), + sp_5920 = replace_na(sp_5920,0), + sp_5927 = replace_na(sp_5927,0), + sp_5926 = replace_na(sp_5926,0), + sp_6000 = replace_na(sp_6000,0), + sp_5820 = replace_na(sp_5820,0), + sp_5900 = replace_na(sp_5900,0), + sp_6020 = replace_na(sp_6020,0), + fraction_5910 = sp_5910/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_5920 = sp_5920/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_5927 = sp_5927/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_5926 = sp_5926/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_6000 = sp_6000/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_5820 = sp_5820/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_5900 = sp_5900/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020), + fraction_6020 = sp_6020/(sp_5910+sp_5920+sp_5927+sp_5926+sp_6000+sp_5820+sp_5900+sp_6020)) %>% + dplyr::select(-sp_5910,-sp_5920,-sp_5927,-sp_5926,-sp_6000,-sp_5820,-sp_5900,-sp_6020) + +hist(data_6049_fraction$fraction_6000) +hist(data_6049_fraction$fraction_5910) +hist(data_6049_fraction$fraction_5920) + +data_6049 <- data_northsea5 %>% + filter(euring_species_code == 6049) %>% + left_join(data_6049_fraction, by = c("season", "grid_50id" )) %>% + mutate("5910" = number*fraction_5910, + "5920" = number*fraction_5920, + "5927" = number*fraction_5927, + "5926" = number*fraction_5926, + "6000" = number*fraction_6000, + "5820" = number*fraction_5820, + "5900" = number*fraction_5900, + "6020" = number*fraction_6020) %>% + dplyr::select(-euring_species_code, -number, -fraction_5910,-fraction_5920,-fraction_5927,-fraction_5926,-fraction_6000,-fraction_5820,-fraction_5900,-fraction_6020) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("5910":"5920","5927","5926","6000","5820","5900","6020"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code))%>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + + + +# Combine data + +data_northsea6 <- data_northsea5 %>% + filter(euring_species_code %notin% c(6009,6049)) %>% + bind_rows(data_6009,data_6049) + + +rm(data_northsea5,data_6009,data_6049,data_6009_fraction,data_6049_fraction) + + + + +#* Samenvoegen Visdief/Noordse stern/Grote stern ---- + + +# Visdief en noordse stern samenvoegen, Visdief (6150), Noordse stern (6160) als 6169 (visdief/Noordse stern) +# 6259 (Stern sp ) en 6285 (unidentified tern) omzetten naar code 6169 (visdief/Noordse stern) + +data_northsea7 <- data_northsea6 %>% + mutate(euring_species_code = if_else(euring_species_code %in% c(6150,6160,6259,6285), 6169, euring_species_code)) + +data_northsea7 %>% filter(euring_species_code==6150) +rm(data_northsea6) + + +#* Splitsen 849 Aalschover sp. in Kuifaalschover (800)/Aalschover (720) --------- + +data_849_fraction<- data_northsea7 %>% + st_drop_geometry() %>% + filter( euring_species_code %in% c(800, 720)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_800 = '800',sp_720 = '720') %>% + mutate(sp_800 = replace_na(sp_800,0), + sp_720 = replace_na(sp_720,0), + fraction_800 = sp_800/(sp_800+sp_720), + fraction_720 = sp_720/(sp_800+sp_720)) %>% + dplyr::select(-sp_800, -sp_720) + +hist(data_849_fraction$fraction_800) +hist(data_849_fraction$fraction_720) + +data_849 <- data_northsea7 %>% + filter(euring_species_code == 849) %>% + left_join(data_849_fraction, by = c("season", "grid_50id" )) %>% + mutate("800" = number*fraction_800, "720" = number* fraction_720) %>% + dplyr::select(-euring_species_code, -number, -fraction_800, -fraction_720) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("800":"720"), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code)) %>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + + +data_northsea8 <- data_northsea7 %>% + filter(euring_species_code %notin% c(849)) %>% + bind_rows(data_849) + +rm(data_849,data_northsea7,data_849_fraction) + + +#* Samenvoegen duikers Roodkeelduiker (20), parelduiker (30) als Gavia sp. (59) -------- + +data_northsea9 <- data_northsea8 %>% + mutate(euring_species_code = if_else(euring_species_code %in% c(20,30), 59, euring_species_code)) + +rm(data_northsea8) + +#* Split 5709 unidentified skua in middelste jager (5660), kleine jager (5670), kleinste jager (5680)---------- + +data_5709_fraction<- data_northsea9 %>% + st_drop_geometry() %>% + filter( euring_species_code %in% c(5660, 5670,5680)) %>% + group_by(season, grid_50id, euring_species_code) %>% + summarise(sum_number = sum(number)) %>% + pivot_wider(names_from = euring_species_code, values_from = sum_number ) %>% + rename(sp_5660 = '5660',sp_5670 = '5670',sp_5680 = '5680') %>% + mutate(sp_5660 = replace_na(sp_5660,0), + sp_5670 = replace_na(sp_5670,0), + sp_5680 = replace_na(sp_5680,0), + fraction_5660 = sp_5660/(sp_5660+sp_5670+sp_5680), + fraction_5670 = sp_5670/(sp_5660+sp_5670+sp_5680), + fraction_5680 = sp_5680/(sp_5660+sp_5670+sp_5680)) %>% + dplyr::select(-sp_5660,-sp_5670,-sp_5680) + +hist(data_5709_fraction$fraction_5680) +hist(data_5709_fraction$fraction_5670) + +data_5709 <- data_northsea9 %>% + filter(euring_species_code == 5709) %>% + left_join(data_5709_fraction, by = c("season", "grid_50id" )) %>% + mutate("5660" = number*fraction_5660, "5670" = number* fraction_5670, "5680" = number* fraction_5680) %>% + dplyr::select(-euring_species_code, -number, -fraction_5660, -fraction_5670, -fraction_5680) %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() %>% + pivot_longer(cols = c("5660","5670","5680" ), + names_to = "euring_species_code", values_to = "number") %>% + mutate(euring_species_code = as.numeric(euring_species_code))%>% + st_as_sf(., coords = c("longitude", "latitude"), + crs = 4326, agr = "constant") + +data_northsea10 <- data_northsea9 %>% + filter(euring_species_code %notin% c(5709)) %>% + bind_rows(data_5709) + +rm(data_5709,data_northsea9,data_5709_fraction) + +# Even opzij zetten +data_northsea_speciesmerged <- data_northsea10 %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() + +# Select species for dot distribution maps +# Per poskey summed, both in and out transect +Selection_dots <- data_northsea_speciesmerged %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% +filter(euring_species_code %in% c(800L, 1530L, 1680L, 1730L, 4960L,5340L, 5410L, 6270L,15820L)) + +str(Selection_dots) + +table(Selection_dots$origin,Selection_dots$euring_species_code) +table(Selection_dots$origin,Selection_dots$year) + +# Remove some unnecessary things as in final dataset +Selection_dots <- Selection_dots %>% ungroup() %>% dplyr::select(euring_species_code,poskey,tripkey,date,year,season, area_surveyed,latitude,longitude,transect,count_method,species_counted,GRID_ID,SubArea,origin,number) %>% + mutate(origin = case_when( + origin %in% "MWTL" ~ "MWTL", + origin %notin% "MWTL" ~ "ESAS")) %>% + mutate(vehicle = ifelse(count_method %in% c(8,9),"airplane", "ship")) + +unique(Selection_dots$euring_species_code) +table(Selection_dots$origin,Selection_dots$euring_species_code) +table(Selection_dots$origin,Selection_dots$year) + +# Some species have one or two observations of very high numbers. These might be mistakes. +Selection_dots %>% ggplot(aes(x = number)) + geom_histogram(binwidth = 3)+ facet_wrap(~euring_species_code, scales = "free") + +# Save dataset for species with dotmaps +write.csv(Selection_dots,"W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/2. Final data/ESAS_dot_distribution_maps_06_2021.csv", row.names = F) + + +rm(Selection_dots) + +#write.csv(Selection_dots,"C:/Users/donk017/OneDrive - Wageningen University & Research/Den Helder/KEC/ESAS_dot_distribution_maps_05_2021.csv", row.names = F) + + +#Step 5:---------- Correctiefactor stroken ---------------- + +# For step 5 & 6 we do not need geometry, so we remove it from the dataset to speed things up +data_northsea10 <- data_northsea10 %>% + mutate(longitude = unlist(map(.$geometry,1)), + latitude = unlist(map(.$geometry,2))) %>% + st_drop_geometry() + +# Probleem 21-06. Veel lagere effort voor bepaalde soorten dan eerder +table(data_northsea10$count_method,data_northsea10$origin) + +table(data_northsea10$transect,data_northsea10$origin) + +#*MWTL-------------- + +# MWTL data 1991 - 2013 geen distance estimation, so no correction factors, transect 1 = within transect (we switched this to 2, to be similar to ESAS ) +# After 2014, transect 2 is correct in the dataset (right?) +# Dataset with method 9 is without distance estimation, and will be used uncorrected +# zeros in the dataset come from splitting data +# !!!Correction of MWTL is done later, because of changing the dataframe and adding zeros problem!!! +# Here we do change the code for common scoter. With counting method 9, code 2131 (Melanitta nigra nigra) is used, after that code 2130 (Melanitta nigra). We change 2131 in 2130 + +MWTL_northsea_1991_2013_1 <- data_northsea10 %>% # 238483 + filter(origin == c("MWTL")) %>% + filter(count_method == 9) %>% + mutate(transect = if_else(transect == 1, 2,1)) %>% + mutate(euring_species_code = if_else(euring_species_code == 2131,2130,euring_species_code)) %>% + filter(transect %in% c(2)) %>% + dplyr::select(tripkey,data_provider,date,season,year,count_method,species_counted,number_of_observers,observer1,observer2,origin,poskey, km_travelled, area_surveyed,obskey, transect, transect_width,euring_species_code, number,GRID_ID, SubArea,latitude,longitude) + +unique(MWTL_northsea_1991_2013_1$year) +table(MWTL_northsea_1991_2013_1$euring_species_code) + + +# MWTL data >= 2014 distance estimation used, for correction effective strip width is used from MWTL report below +MWTL_northsea_2014 <- data_northsea10 %>% + filter(origin == c("MWTL")) %>% + filter(count_method == 8) %>% # count_method 8 --> Visual aerial survey line transect method with distance sampling + filter(transect %in% c(2)) # Positions were zero birds are observed have transect NA - but we add empty poskeys later + +table(MWTL_northsea_2014$euring_species_code) +unique(MWTL_northsea_2014$year) +table(MWTL_northsea_2014$distance_bins,MWTL_northsea_2014$distance) +unique(MWTL_northsea_2014$distance) + +MWTL_northsea_2014_1 <- MWTL_northsea_2014 %>% + dplyr::select(tripkey,data_provider,date,season, year, count_method,species_counted,number_of_observers,observer1,observer2,origin,poskey, km_travelled, area_surveyed,obskey, transect,transect_width, euring_species_code, number,GRID_ID, SubArea,latitude,longitude) + +head(MWTL_northsea_2014_1) + +# We checked whether the species for which dot maps will be created, have sightings outside the transect - this was not the case +# Combine the number of birds per position; poskey (over the different distance bins) +MWTL_northsea_correct <- bind_rows(MWTL_northsea_1991_2013_1,MWTL_northsea_2014_1) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L,5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) + +table(MWTL_northsea_correct$year,MWTL_northsea_correct$euring_species_code) + +MWTL_northsea_correct %>% ggplot(aes(x = number)) + geom_histogram(binwidth = 3)+ facet_wrap(~euring_species_code, scales = "free") + +rm(MWTL_northsea_1991_2013_1,MWTL_northsea_2014,MWTL_northsea_2014_1) + + + + + + + +#*ESAS-------------- + +# Split dataset on origin +# For the correction, we only use birds IN the transect. This is 2, we got confirmation from Nicolas Vanermen + +data_northsea10 %>% + group_by(transect, origin) %>% + summarise(n()) + +unique(data_northsea10$origin) + +# Selection ESAS transect 2 & transect NA (Positions were no birds are observed have transect NA ) +ESAS_northsea <- data_northsea10 %>% + filter(origin %notin% c("MWTL")) %>% + filter(transect %in% c(2)) # NA is poskeys without birds seen, but zeros are later added to dataset + +unique(ESAS_northsea$year) +unique(ESAS_northsea$transect) +hist(ESAS_northsea$number,xlim=c(0,50),ylim=c(0,50000),breaks=100000) +dim(ESAS_northsea) + +# For ship-based surveys applying the standard 300 m wide transect: 0|50|100|200|300. Sometimes wider or less wide +# Other distance_bins in ESAS data are count method 8: Visual aerial survey line transect method with distance sampling +table(ESAS_northsea$distance_bins, ESAS_northsea$count_method) + + +##** Stap a: Correction factor airplane counts ESAS-------------------------------- + +# Vliegtuigtellingen ESAS +ESAS_fly <- ESAS_northsea %>% + filter(count_method == 8) + +# ESAS flight counts: the first distance bin is missing as they cant see what is directly under the airplane +# first bin from 44-91 (distance = 67.5) +# The transect_width is the furthest distance_bin - 44 +# U is a bird not assigned to a distance bin +table(ESAS_fly$distance_bins,ESAS_fly$distance) + +# check how bin 67.5 relates to bin 127 for flight counts, per meter +# Seems quite the same +check <- ESAS_fly %>% + filter(distance %in% c(67.5,127,297.5)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(number = sum(number, na.rm =T)) %>% + mutate(number = case_when( + distance==67.5 ~ number/47, + distance==127 ~ number/72, + distance==297.5 ~number/269)) + +check$distance <- factor(check$distance,levels=c("67.5","127","297.5")) +check %>% ggplot(aes(x=distance,y=number)) + + geom_bar(stat='identity')+ + facet_wrap(~euring_species_code,scales="free") +rm(check) + +# correct number of birds in 3rd bin +correction_fly <- ESAS_fly %>% + filter(distance_bins %in% "44|91|163|432") %>% + filter(distance %in% c(67.5,127,297.5)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + mutate(distance = case_when( + distance==67.5 ~ "BinsClose", + distance==127 ~ "BinsClose", + distance==297.5 ~"BinFar")) %>% + group_by(euring_species_code,distance) %>% + summarise(Total = sum(number, na.rm =T)) %>% + pivot_wider(names_from = distance, values_from = Total) %>% + mutate(Total_mon = BinsClose+BinFar, + Total_pos = (269/119)*BinsClose, # width of furthest bin/width of two closest bins * number in closest bins + corr_factor = Total_pos/Total_mon) %>% + mutate(corr_factor = if_else(euring_species_code %in% c(2060), 1, corr_factor)) %>% # Eider has correctionfactor 1, source: Mardik Leopold + mutate(corr_factor = if_else(corr_factor < 1, 1, corr_factor)) %>% # als <1 dan veranderen in 1 + dplyr::select(-BinsClose,-BinFar,-Total_mon, -Total_pos) + +# Apply correction to distance_bins %in% "44|91|163|432" +ESAS_fly_AD <- ESAS_fly %>% + filter(distance_bins %in% "44|91|163|432") %>% + filter(distance %in% c(67.5,127,297.5)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(correction_fly, by = "euring_species_code") %>% + mutate(number = number*corr_factor) %>% dplyr::select(-corr_factor) + +# Select U (no distance bin) +ESAS_fly_U<- ESAS_fly %>% + filter(distance_bins %in% "44|91|163|432") %>% + filter(distance %in% c("U")) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + + +# The other distance_bins we leave uncorrected (only close by bins) +ESAS_fly_uncorrected<- ESAS_fly %>% + filter(distance_bins %in% "44|91|163") %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + +# Combine and calculate total +# Here we have to remove correctionfactor, otherwise the rows do not add +ESAS_fly_correct <- bind_rows(ESAS_fly_AD,ESAS_fly_U,ESAS_fly_uncorrected) + +rm(ESAS_fly_AD,ESAS_fly_U,ESAS_fly_uncorrected,correction_fly) + +ESAS_fly_correct <- ESAS_fly_correct %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) + +table(ESAS_fly_correct$year,ESAS_fly_correct$count_method) + + + + +##** Stap b: Calculate correction factor boottellingen per species, per transect_width----------------- + +ESAS_ship <- ESAS_northsea %>% + filter(count_method %notin% c(8)) + +unique(ESAS_ship$count_method) + +#* checks data -------------------- +#check of onze aanname A = B klopt +check <- ESAS_ship %>% + filter(distance %in% c("A","B")) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(number = sum(number, na.rm =T)) + +check %>% ggplot(aes(x=distance,y=number)) + + geom_bar(stat='identity')+ + facet_wrap(~euring_species_code,scales="free") +# Andere checks +check <- ESAS_ship %>% + filter(distance %in% c("A","B","C","D")) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + +table(check$distance_bins, check$count_method) +table(check$count_method) # t?ch nr 13... + +# Most surveys have the standard 300m wide transect. 500m is special count with band E. 600m is possibly a mistake and are left out +table(check$transect_width,check$distance) +# check area_surveyed or km_travelled. No NA values +check %>% filter(area_surveyed %in% c(NA)) +check %>% filter(km_travelled %in% NA ) +rm(check) + + + + +#* Calculation per transect_width -------------------- +# We ignore transect_width 92 & 180 & 250 --> mistakes or very low nr of observations +# For the most common dtransect_width of 300: +# We assume that 100% of birds were seen in distance bins A & B +# And that A + B = C +# And that A + B = D +# So the total birds that potentially could be seen is A+B*3 +# The correction factor is potential birds seen divided by actual birds seen: (A+B*3)/(A+B+C+D) +# The seaduck species 2060 and 2130 get 1 since distance estimation is less important +# We only do corrections for the birds of interest +# Transect width 600 => measured at both sides of the ship, correction factor the same as transect width 300 + +# transect_width == 300 of transect_width == 600 +# transect_width 600 is aan beide kanten van het schip geteld, dus 2*300 +# Calculate correction over cases with transect with 300 +list_correction_standard <- ESAS_ship %>% + filter(distance %in% c("A","B","C","D")) %>% + filter(transect_width == c(300,600)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(Total = sum(number, na.rm =T)) %>% + pivot_wider(names_from = distance, values_from = Total) %>% + mutate(A = replace_na(A,0), + B = replace_na(B,0), + C = replace_na(C,0), + D = replace_na(D,0), + Total_mon = A+B+C+D, + Total_pos = (A+B)*3, + corr_factor = Total_pos/Total_mon) %>% + mutate(corr_factor = if_else(euring_species_code %in% c(2060,15820), 1, corr_factor)) %>% # Eider has correctionfactor 1, source: Mardik Leopold + mutate(corr_factor = if_else(corr_factor < 1, 1, corr_factor)) %>% # als <1 dan veranderen in 1 + dplyr::select(-A,-B,-C,-D,-Total_mon, -Total_pos) + +# Apply correction to cases with transect_width 300 +# we have to check, when transect_width is smaller than 300, how and if to correct C & D +# Select only complete cases of A, B, C and D --> transect_width == 300 +ESAS_northsea_AD_300 <- ESAS_ship %>% + filter(distance %in% c("A","B","C","D")) %>% + filter(transect_width %in% c(300,600)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L,5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(list_correction_standard, by = "euring_species_code") %>% + mutate(number = number*corr_factor)%>% + dplyr::select(-corr_factor) + + +# transect_width 100 --> no correction +ESAS_northsea_AD_100 <- ESAS_ship %>% + filter(distance %in% c("A","B")) %>% + filter(transect_width == 100) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L,5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(list_correction_standard, by = "euring_species_code") %>% + mutate(number = number)%>% + dplyr::select(-corr_factor) %>% + filter(!is.na(tripkey)) + +rm(list_correction_standard) + +# transect_width 150, half a C (only 50 meter instead of 100) +# correctionfactor calculated as (A+B) *1.5 +list_correction_150 <- ESAS_ship %>% + filter(distance %in% c("A","B","C")) %>% + filter(transect_width == 150) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(Total = sum(number, na.rm =T)) %>% + pivot_wider(names_from = distance, values_from = Total) %>% + mutate(A = replace_na(A,0), + B = replace_na(B,0), + C = replace_na(C,0), + Total_mon = A+B+C, + Total_pos = (A+B)*1.5, + corr_factor = Total_pos/Total_mon) %>% + mutate(corr_factor = if_else(euring_species_code %in% c(2060,15820), 1, corr_factor)) %>% # Eider has correctionfactor 1, source: Mardik Leopold + mutate(corr_factor = if_else(corr_factor < 1, 1, corr_factor)) %>% # als <1 dan veranderen in 1 + dplyr::select(-A,-B,-C,-Total_mon, -Total_pos) + +ESAS_northsea_AD_150 <- ESAS_ship %>% + filter(distance %in% c("A","B","C")) %>% + filter(transect_width == 150) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L,3236L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(list_correction_150, by = "euring_species_code") %>% + mutate(number = number*corr_factor)%>% + dplyr::select(-corr_factor) + +rm(list_correction_150) + +# transect_width 200 +# correctionfactor calculated as (A+B) *2 +list_correction_200 <- ESAS_ship %>% + filter(distance %in% c("A","B","C")) %>% + filter(transect_width == 200) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(Total = sum(number, na.rm =T)) %>% + pivot_wider(names_from = distance, values_from = Total) %>% + mutate(A = replace_na(A,0), + B = replace_na(B,0), + C = replace_na(C,0), + Total_mon = A+B+C, + Total_pos = (A+B)*2, + corr_factor = Total_pos/Total_mon) %>% + mutate(corr_factor = if_else(euring_species_code %in% c(2060,15820), 1, corr_factor)) %>% # Eider has correctionfactor 1, source: Mardik Leopold + mutate(corr_factor = if_else(corr_factor < 1, 1, corr_factor)) %>% # als <1 dan veranderen in 1 + dplyr::select(-A,-B,-C,-Total_mon, -Total_pos) + +# Apply correction to cases with transect_width 200 +ESAS_northsea_AD_200 <- ESAS_ship %>% + filter(distance %in% c("A","B","C")) %>% + filter(transect_width == 200) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(list_correction_200, by = "euring_species_code") %>% + mutate(number = number*corr_factor)%>% + dplyr::select(-corr_factor) + +rm(list_correction_200) + +# transect_width 500 +# These counts have an extra bin which is 200 meters wide --> E +# correctionfactor calculated as (A+B) * 5 +# Only 6 species out of selection. +list_correction_500 <- ESAS_ship %>% + filter(distance %in% c("A","B","C","D","E")) %>% + filter(transect_width == 500) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(euring_species_code,distance) %>% + summarise(Total = sum(number, na.rm =T)) %>% + pivot_wider(names_from = distance, values_from = Total) %>% + mutate(A = replace_na(A,0), + B = replace_na(B,0), + C = replace_na(C,0), + D = replace_na(D,0), + E = replace_na(E,0), + Total_mon = A+B+C+D+E, + Total_pos = (A+B)*5, + corr_factor = Total_pos/Total_mon) %>% + mutate(corr_factor = if_else(euring_species_code %in% c(2060,15820), 1, corr_factor)) %>% # Eider has correctionfactor 1, source: Mardik Leopold + mutate(corr_factor = if_else(corr_factor < 1, 1, corr_factor)) %>% # als 0 dan veranderen in 1 + dplyr::select(-A,-B,-C,-D,-E,-Total_mon, -Total_pos) + +# Apply correction to cases with transect_width 200 +ESAS_northsea_AD_500 <- ESAS_ship %>% + filter(distance %in% c("A","B","C","D","E")) %>% + filter(transect_width == 500) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) %>% + full_join(list_correction_500, by = "euring_species_code") %>% + mutate(number = number*corr_factor)%>% + dplyr::select(-corr_factor) + +rm(list_correction_500) + + +# Selecteer F voor de hierboven gecorrigeerde zwemmende vogels, voor count_methods waarbij er met snapshot geteld wordt +ESAS_northsea_F <- ESAS_ship %>% + filter(distance %in% c("F")) %>% + filter(transect_width %in% c(100,150,200,300,500,600)) %>% + filter(count_method %in% c(1,5,7)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + + +# Select W (no distance bin) for above corrected birds in bins +ESAS_northsea_W <- ESAS_ship %>% + filter(distance %in% c("W")) %>% + filter(transect_width %in% c(100,150,200,300,500,600)) %>% + filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + +unique(ESAS_northsea_W$euring_species_code) + + + +# Combine and calculate total +# Here we have to remove correctionfactor, otherwise the rows do not add +ESAS_ship_correct <- bind_rows(ESAS_northsea_AD_100,ESAS_northsea_AD_150,ESAS_northsea_AD_200,ESAS_northsea_AD_300,ESAS_northsea_AD_500,ESAS_northsea_W,ESAS_northsea_F) + +rm(ESAS_northsea_AD_100,ESAS_northsea_AD_150,ESAS_northsea_AD_200,ESAS_northsea_AD_300,ESAS_northsea_AD_500,ESAS_northsea_W,ESAS_northsea_F,ESAS_ship) + +ESAS_ship_correct <- ESAS_ship_correct %>% + group_by(tripkey, date, poskey, euring_species_code) %>% + summarise(data_provider = first(data_provider), season = first(season), year = first(year), count_method = first(count_method), species_counted = first(species_counted), number_of_observers = first(number_of_observers), observer1 = first(observer1), observer2 = first(observer2), origin = first(origin), GRID_ID = first(GRID_ID), SubArea = first(SubArea),latitude = first(latitude),longitude = first(longitude),km_travelled = first(km_travelled), area_surveyed = first(area_surveyed), transect = first(transect), transect_width = first(transect_width), number = sum(number, na.rm = T)) + +table(ESAS_ship_correct$year,ESAS_ship_correct$transect_width) + +# Combine ship and airplane +ESAS_northsea_correct <- bind_rows(ESAS_ship_correct,ESAS_fly_correct) + +table(ESAS_northsea_correct$year,ESAS_northsea_correct$origin) +table(ESAS_northsea_correct$year,ESAS_northsea_correct$euring_species_code) + +#* Correction of area_surveyed based on distance_bin & km_travelled +hist(ESAS_northsea_correct$area_surveyed) + +# Zeros in area_surveyed...? +zero <- ESAS_northsea_correct %>% filter(area_surveyed %in% c(0,NA)) +zero2 <- ESAS_northsea_correct %>% filter(km_travelled %in% c(0,NA)) + +ESAS_northsea_correct <- ESAS_northsea_correct %>% mutate(area_surveyed = km_travelled * (transect_width/1000)) + +zero <- ESAS_northsea_correct %>% filter(area_surveyed %in% c(0,NA)) +zero2 <- ESAS_northsea_correct %>% filter(km_travelled %in% c(0,NA)) + +hist(ESAS_northsea_correct$area_surveyed) + + +# ESAS & MWTL together +Northsea_corrected <- bind_rows(ESAS_northsea_correct,MWTL_northsea_correct) + +table(Northsea_corrected$year,Northsea_corrected$origin) +unique(Northsea_corrected$euring_species_code) + +rm(ESAS_fly,ESAS_fly_correct,ESAS_ship_correct,ESAS_northsea,ESAS_northsea_correct,MWTL_northsea_correct) + + + +# check if starling has enough datapoints +Northsea_corrected %>% filter(euring_species_code==15820) %>% + ggplot() + + theme_marinka()+ + geom_polygon(data = shapefile2, aes(x = long, y = lat, group = group), fill="lightgrey",color = "lightgrey")+ + geom_point(aes(y=latitude, x=longitude,size=number), col="darkblue",alpha=0.5) + + coord_sf(xlim=c(0,1000000),ylim=c(5500000,6500000)) + +Northsea_corrected %>% filter(euring_species_code==2130) %>% + ggplot() + + theme_marinka()+ + geom_polygon(data = shapefile2, aes(x = long, y = lat, group = group), fill="lightgrey",color = "lightgrey")+ + geom_point(aes(y=latitude, x=longitude,size=number), col="darkblue",alpha=0.5) + + coord_sf(xlim=c(0,1000000),ylim=c(5500000,6500000)) + + + + +#Step 6:---------- Add zeros ---------------- + +# temporary (to test) +# Later --> change Test into Northsea_corrected & don't filter +table(Northsea_corrected$euring_species_code,Northsea_corrected$origin) + + +# Add euring_species_code & number to uni_poskeys & merge Northsea_corrected with all unique uni_poskeys +Uni_poskeys <- Uni_poskeys %>% mutate(euring_species_code = NA,number = NA) #1140093 +list <- unique(Northsea_corrected$poskey) +# remove poskeys from Uni_poskeys that are already in database +Uni_poskeys_adj <- Uni_poskeys %>% filter(poskey %notin% list) + +Northsea_corrected2 <- bind_rows(Northsea_corrected,Uni_poskeys_adj) +length(unique(Northsea_corrected$poskey)) +length(unique(Northsea_corrected2$poskey)) +length(unique(Uni_poskeys$poskey)) + +# Important to run this line! +Northsea_corrected2 <- Northsea_corrected2 %>% ungroup() + +Northsea_corrected_zero <- Northsea_corrected2 %>% complete(euring_species_code, nesting(poskey, tripkey,date,latitude,longitude,data_provider, season, year, count_method, species_counted, number_of_observers, observer1, observer2, origin, GRID_ID, SubArea,km_travelled, area_surveyed, transect), fill = list(number = 0)) %>% + filter(!is.na(euring_species_code)) + +table(Northsea_corrected_zero$year,Northsea_corrected_zero$origin) + + +check <- Northsea_corrected_zero[duplicated(Northsea_corrected_zero$poskey) | duplicated(Northsea_corrected_zero$poskey, fromLast = TRUE), ] + + +dim(Northsea_corrected_zero) # Correct number of rows is the number of species (19) * number of poskeys +length(unique(Northsea_corrected_zero$poskey)) *19 +length(unique(Northsea_corrected$poskey)) +length(unique(Northsea_corrected_zero$euring_species_code)) + +rm(Uni_poskeys,Uni_poskeys_adj) + +#* Fix effective strip width MWTL -------- + +# Zie MWTL rapportage - Effective strip width for some species. We will use that! +effc_strip_width <- data.frame( + euring_species_code = c(59L, 220L, 710L, 720L, 2060L,2130L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L), + eff_strip_width = c(114L, 114L, 135L, 116L, NA,NA, NA, 114L, 94L, 108L, 113L, 111L, 103L, 109L, 108L, 98L, 98L, 88L,NA), + origin = "MWTL", + count_method = 8 +) + +Northsea_corrected_zero_2 <- Northsea_corrected_zero %>% +full_join(effc_strip_width, by = c("euring_species_code", "origin", "count_method")) %>% + mutate(eff_strip_width = eff_strip_width/1000, # strip with in km + area_surveyed = if_else(!is.na(eff_strip_width),eff_strip_width*km_travelled, area_surveyed)) %>% + dplyr::select(-eff_strip_width) + +check <- Northsea_corrected_zero_2 %>% filter(origin == "MWTL") %>% filter(year <2014) +check <- check %>% group_by(euring_species_code,year) %>% summarize(som = sum(number)) +rm(check) + +#*count method & species counted--------------- +# & add scientific & english species names +# read xlsx + +library(readxl) +count_species_method <- read_excel("W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/1. Ruwe data/ESAS_dataset/ESAS v6/ESAS_species_check.xlsx") + +count_species_method <- count_species_method %>% dplyr::select(euring_species_code,count_method,`species counted`) + +countmethod <- count_species_method %>% dplyr::select(euring_species_code,count_method) +countmethod <- countmethod %>% separate(col = count_method, into=toupper(letters[1:7])) +countmethod <- as.data.frame(sapply(countmethod, as.numeric )) +countmethod <- countmethod %>% pivot_longer(cols = c("A","B","C","D","E","F","G")) %>% dplyr::select(euring_species_code,value)%>% drop_na() %>% rename(count_method=value) %>% filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 3236L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + + +# check of dit alle soorten van interesse zijn. +spec_count <- count_species_method %>% dplyr::select(euring_species_code,`species counted`) +spec_count <- spec_count %>% separate(col = `species counted`, into=toupper(letters[1:12])) +spec_count <- as.data.frame(sapply(spec_count , as.numeric )) +spec_count <- spec_count %>% pivot_longer(cols = c("A","B","C","D","E","F","G","H","I","J","K","L")) %>% dplyr::select(euring_species_code,value)%>% drop_na() %>% rename(species_counted=value)%>% filter(euring_species_code %in% c(59L, 220L, 710L, 720L, 2060L,2130L, 3236L, 5670L, 5690L, 5780L, 5910L, 5920L, 6000L, 6020L, 6110L, 6169L, 6340L, 6360L, 6540L,15820L)) + + +# Couple dataset to corrected dataset, on euring. +# Select right species per count method +Northsea_corrected_method <- Northsea_corrected_zero_2 %>% inner_join(countmethod, by=c("count_method","euring_species_code")) +dim(Northsea_corrected_zero_2) +dim(Northsea_corrected_method) + +# 6360 kan in alle count methods zitten +# 6000 niet in 2 +# 15820 niet in 2 en 9 +b6000 <- Northsea_corrected_method %>% filter(euring_species_code==6000) +unique(b6000$count_method) +b6360 <- Northsea_corrected_method %>% filter(euring_species_code==6360) +unique(b6360$count_method) +b15820 <- Northsea_corrected_method %>% filter(euring_species_code==15820) +unique(b15820$count_method) +rm(b6000,b6360,b15820) + +# select right species per species counted +Northsea_corrected_methodspecies <- Northsea_corrected_method %>% inner_join(spec_count,by=c("species_counted","euring_species_code")) +dim(Northsea_corrected_methodspecies) + +# 6360 mag in ongeveer alle specied_counted zitten +# 6000 mag niet in 2,4,5,6 zitten +b6000 <- Northsea_corrected_methodspecies %>% filter(euring_species_code==6000) +unique(b6000$species_counted) +b6360 <- Northsea_corrected_methodspecies %>% filter(euring_species_code==6360) +unique(b6360$species_counted) + +check <- Northsea_corrected_methodspecies %>% filter(origin == "MWTL") %>% filter(year <2014) +check <- check %>% group_by(euring_species_code,year) %>% summarize(som = sum(number)) +check$som <- round(check$som, digits = 0) +rm(check) + + +unique(Northsea_corrected_methodspecies$count_method) +unique(Northsea_corrected_methodspecies$species_counted) + +# Step 7: FINAL database save ------------------------- + +# Select columns that are of interest +# Lat, long, species, area_surveyed + +# In earlier database, these are the column-names (Jan-Tjalling): TRIP_PROFILE,"NUMBER_","EURING","DENSITY","TRANSECT","XUTM","YUTM","AREA_SURVE","SEASON","SPECIES_OB","COUNT_TYPE","YEAR_","GRID_ID_KEC3","SOURCE_DB" + + +# And rename origin +Northsea_FINAL <- Northsea_corrected_methodspecies %>% + dplyr::select(euring_species_code,poskey,tripkey,date,year,season, area_surveyed,latitude,longitude,transect,count_method,species_counted,GRID_ID,SubArea,origin,number) %>% + mutate(origin = case_when( + origin %in% "MWTL" ~ "MWTL", + origin %notin% "MWTL" ~ "ESAS")) %>% + mutate(vehicle = ifelse(count_method %in% c(8,9),"airplane", "ship")) %>% + mutate(density_ind_1km2 = number/area_surveyed) + +ggplot() + geom_histogram(data = sample_n(Northsea_FINAL,20000), aes(x = number), bins = 1000) + +Northsea_FINAL %>% filter(number>0) %>% filter(density_ind_1km2>0) %>% ggplot(aes(x=number,y=density_ind_1km2)) + + geom_point()+ + coord_cartesian(xlim = c(0,100),ylim = c(0,100)) + +# Meeste area_surveyed kleiner dan 1km2, dus daarom is de density_km2 meestal hoger dan number +Northsea_FINAL %>% filter(number>0) %>% ggplot(aes(x=area_surveyed)) + + geom_histogram(binwidth=0.1) + +Northsea_FINAL %>% filter(number>0) %>% ggplot(aes(x=number)) + + geom_histogram(binwidth=0.5)+ + coord_cartesian(xlim = c(0,5)) + +Northsea_FINAL %>% filter(density_ind_1km2>0) %>% ggplot(aes(x=density_ind_1km2)) + + geom_histogram(binwidth=0.5)+ + coord_cartesian(xlim = c(0,5)) + +write.csv(Northsea_FINAL,"W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/2. Final data/ESAS_Final_Corrected_05_2021.csv", row.names = F) + + +# Save zee-eend even apart +#Northsea_scoter <- Northsea_FINAL %>% filter(euring_species_code==2130) +#write.csv(Northsea_scoter,"W:/IMARES/IJmuiden/Afdeling/Projecten/KEC4/4. Data/2. Final data/ESAS_Final_common_scoter_05_2021.csv", row.names = F) + +#write.csv(Northsea_scoter,"C:/Users/donk017/OneDrive - Wageningen University & Research/Den Helder/KEC/ESAS_Final_common_scoter_05_2021.csv", row.names = F) + +#write.csv(Northsea_FINAL,"C:/Users/donk017/OneDrive - Wageningen University & Research/Den Helder/KEC/ESAS_Final_Corrected_05_2021.csv", row.names = F) + +check <- Northsea_FINAL %>% filter(origin == "MWTL") %>% filter(euring_species_code %in% c(15820,2130)) +check <- check %>% group_by(euring_species_code,year) %>% summarize(som = sum(number)) + +# Zeros in area_surveyed...? +zero <- Northsea_FINAL %>% filter(area_surveyed %in% c(0,NA)) +zero2 <- ESAS_northsea_correct %>% filter(km_travelled %in% c(0,NA)) diff --git a/script_py_r/kec4_pyconversion-master/HelloWorld.py b/script_py_r/kec4_pyconversion-master/HelloWorld.py new file mode 100644 index 0000000000000000000000000000000000000000..5596b44786f04e4810aefe9f8d712f08ed310f71 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/HelloWorld.py @@ -0,0 +1,16 @@ +# This is a sample Python script. + +# Press Shift+F10 to execute it or replace it with your code. +# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. + + +def print_hi(name): + # Use a breakpoint in the code line below to debug your script. + print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint. + + +# Press the green button in the gutter to run the script. +if __name__ == '__main__': + print_hi('PyCharm') + +# See PyCharm help at https://www.jetbrains.com/help/pycharm/ diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/README.html b/script_py_r/kec4_pyconversion-master/IBM_maps/README.html new file mode 100644 index 0000000000000000000000000000000000000000..d7ee9cbbd9bcfca9ea0a000645fae7199b86bc7a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/README.html @@ -0,0 +1,533 @@ + + + + + + + + + + + + + +KEC4: Preprocessing maps of Northern Gannet abundance for use in KEC4 IBM + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +

This folder contains the necessary files to create the maps required for the Individual Based Model (IBM) of the Northern Gannet used in the KEC4 project. This IBM is used to estimate the additional mortality from habitat loss due to avoidance of Offshore Wind Farms (OWFs). This IBM was originally created for a separate Wind-op-Zee project (WOZEP) and described in:

+

Van Kooten, T., Soudijn, F., Tulp, T., Chen, C., Benden D., Leopold, M. “The consequences of seabird habitat loss from offshore wind turbines; Displacement and population level effects in 5 selected species” Wageningen Marine Research (University & Research centre), Wageningen Marine Research report C063/19. 116 pp.

+

R-script that process the files in this folder are stored in the KEC4ibm repository at: [https://git.wur.nl/ecodyn/KEC4ibm/-/tree/master/src/R] and copied here for convenience.

+
+

Content of R-Files

+
    +
  • The file prep_map.R reads input csv files of the NS grid layout (5kmX5km) and Northern Gannet abundances per grid cell and creates one .mat file per season and batch, which are stored in /matfiles.
    • +
  • The file plot_map.R reads the worked grids from prep_map.R and makes plot for checking.
    • +
  • The file prep_OWF_scenario.R reads input csv files with OWF data and creates maps for OWF scenarios
    • +
  • The file combine_OWF_predicted.R reads the maps with OWF scenarios created in prep_OWF_scenario.R and the maps with predicted northern gannet abundances created in prep_map.R to create habitat quality maps for the IBM.
    • +
  • The file plot_matfiles.R make a plot of each map in /matfiles subfolder
    • +
+
+
+

Content of Folders

+
    +
  • /csv contains input csv files (provided by Jan Tjalling vd Wal)
    • +
  • /rds contains R data objects files (.rds) with the worked grid and merged data.frames.
    • +
  • /matfiles contains the matrices (maps) used in the IBM. There are two matrices per season and batch and scenario. +
      +
    • "MB_predicted_Grid_5km_….": the matrix with predicted abundances.
      • +
    • "MB_presence_Grid_5km_…" : 0/1 matrix indicating whether an abundance measurement exists for that grid cell, with 0 indicating abundance was NOT predicted (NA). A 1 indicates abundance was predicted (note that this prediction can be equal to zero).
      • +
    • +
  • /figures contains all the figures produced by plot_map.R
    • +
+
+
+

Layout of the matrices in .matfiles

+

Matrix lay-out

+
    +
  • Easting in columns
    • +
  • Northing in rows
    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
EASTING:12..219
N284E1N284E2N284…E219N284
O...….
R...….
T...….
H...….
I...….
N2E1N2E2N2…E219N2
G1E1N1E2N1…E219N1
+
+
+

Dimensions of the habitat map

+

The dimension of the KEC4 North Sea grid (in csv/windsp5c_vectorgrid.csv) are

+
    +
  • 219 columns (Easting)
    • +
  • 284 rows (Northing)
    • +
  • 219 x 284 = 62196 grid cells
    • +
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/README.md b/script_py_r/kec4_pyconversion-master/IBM_maps/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c9b071a2787464d88476fa8e863abb6f75a3878c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/README.md @@ -0,0 +1,52 @@ +--- +title: "KEC4: Preprocessing maps of Northern Gannet abundance for use in KEC4 IBM" +--- + +This folder contains the necessary files to create the maps required for the Individual Based Model (IBM) of the Northern Gannet used in the KEC4 project. This IBM is used to estimate the additional mortality from habitat loss due to avoidance of Offshore Wind Farms (OWFs). This IBM was originally created for a separate Wind-op-Zee project (WOZEP) and described in: + + Van Kooten, T., Soudijn, F., Tulp, T., Chen, C., Benden D., Leopold, M. + *"The consequences of seabird habitat loss from offshore wind turbines; Displacement and population level effects in 5 selected species"* + Wageningen Marine Research (University & Research centre), Wageningen Marine Research report C063/19. 116 pp. + +R-script that process the files in this folder are stored in the KEC4ibm repository at: [https://git.wur.nl/ecodyn/KEC4ibm/-/tree/master/src/R] and copied here for convenience. + +### Content of R-Files + - The file **prep_map.R** reads input csv files of the NS grid layout (5kmX5km) and Northern Gannet abundances per grid cell and creates one .mat file per season and batch, which are stored in /matfiles. + - The file **plot_map.R** reads the worked grids from prep_map.R and makes plot for checking. + - The file **prep_OWF_scenario.R** reads input csv files with OWF data and creates maps for OWF scenarios + - The file **combine_OWF_predicted.R** reads the maps with OWF scenarios created in **prep_OWF_scenario.R** and the maps with predicted northern gannet abundances created in **prep_map.R** to create habitat quality maps for the IBM. + - The file **plot_matfiles.R** make a plot of each map in /matfiles subfolder + +### Content of Folders + - **/csv** contains input csv files (provided by Jan Tjalling vd Wal) + - **/rds** contains R data objects files (.rds) with the worked grid and merged data.frames. + - **/matfiles** contains the matrices (maps) used in the IBM. There are two matrices per season and batch and scenario. + - **\"MB_predicted_Grid_5km_....\"**: the matrix with predicted abundances. + - **\"MB_presence_Grid_5km_...\"** : 0/1 matrix indicating whether an abundance measurement exists for that grid cell, with 0 indicating abundance was NOT predicted (NA). A 1 indicates abundance was predicted (note that this prediction can be equal to zero). + - **/figures** contains all the figures produced by plot_map.R + +### Layout of the matrices in .matfiles + +Matrix lay-out + + - Easting in columns + - Northing in rows + +| |EASTING:| 1 | 2 | .. | 219 | +|:---:| ---:|:---:|:---:|:---:|:---:| +|**N**| 284| E1N284 | E2N284 | ... | E219N284 +|**O**| . | . | . | ... | . +|**R**| . | . | . | ... | . +|**T**| . | . | . | ... | . +|**H**| . | . | . | ... | . +|**I**| . | . | . | ... | . +|**N**| 2 | E1N2 | E2N2 | ... | E219N2 +|**G**| 1 | E1N1 | E2N1 | ... | E219N1 + + +### Dimensions of the habitat map +The dimension of the KEC4 North Sea grid (in **csv/windsp5c_vectorgrid.csv**) are + + - 219 columns (Easting) + - 284 rows (Northing) + - 219 x 284 = 62196 grid cells diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/combine_OWF_predicted.R b/script_py_r/kec4_pyconversion-master/IBM_maps/combine_OWF_predicted.R new file mode 100644 index 0000000000000000000000000000000000000000..46b78c0adc2716e5a558cf244fd8699575ad5a00 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/combine_OWF_predicted.R @@ -0,0 +1,89 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# File : combine_OWF_predicted.R +# +# Project : KEC4 +# +# Description : - Load the OWP maps +# - Load the predicted densities +# - Multiple OWP maps with predicted densities +# - Save output to .mat files +# +# Dependencies : - rds: "rds/OWP_ScaleDensity_Grid_5km_(INT30 / NAT30fav)_(a1.0 / a0.8)" +# : - mat: "" +# +# Author : Vincent Hin +# Institute : Wageningen Marine Research +# Date created : 16 Aug 2021 +# Date modified : 16 Aug 2021 +##################################################################### + +## Read the OWPs grids from .rds +MAT_INT30_a1.0 <- readRDS(file = "rds/OWP_ScaleDensity_Grid_5km_INT30_a1.0.rds") +MAT_INT30_a0.8 <- readRDS(file = "rds/OWP_ScaleDensity_Grid_5km_INT30_a0.8.rds") +MAT_NAT30val_a1.0 <- readRDS(file = "rds/OWP_ScaleDensity_Grid_5km_NAT30fav_a1.0.rds") +MAT_NAT30val_a0.8 <- readRDS(file = "rds/OWP_ScaleDensity_Grid_5km_NAT30fav_a0.8.rds") +MAT_INT30_OWP <- readRDS(file = "rds/OWP_Presence_Grid_5km_INT30.rds") + +## Get names of density maps +fld <- "matfiles" +fls <- list.files(path = fld, pattern = "MB_predicted_Grid_5km_INT.[1-6].mat") + +## Read the density maps, multiple with all OWP maps and save to .mat file +for(i in fls) { + mat_predicted <- as.matrix(read.table(file.path(fld, i)), sep = "") + nm <- sub(pattern = "MB_predicted_Grid_5km_INT.", replacement = "", i) + nm <- sub(".mat", "", nm) + + ## Multiple OWP matrices with Abundance matrix + mat_predicted_INT30_a1.0 <- mat_predicted * MAT_INT30_a1.0 + mat_predicted_INT30_a0.8 <- mat_predicted * MAT_INT30_a0.8 + mat_predicted_NAT30fav_a1.0 <- mat_predicted * MAT_NAT30val_a1.0 + mat_predicted_NAT30fav_a0.8 <- mat_predicted * MAT_NAT30val_a0.8 + + ## Write the OWP adjusted habitat maps + write.table(formatC(mat_predicted_INT30_a1.0, digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_INT30_a1.0_INT.", nm, ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + write.table(formatC(mat_predicted_INT30_a0.8, digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_INT30_a0.8_INT.", nm, ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + write.table(formatC(mat_predicted_NAT30fav_a1.0, digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_NAT30fav_a1.0_INT.", nm, ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + write.table(formatC(mat_predicted_NAT30fav_a0.8, digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_NAT30fav_a0.8_INT.", nm, ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + ## + + +} + +# Extra. Create density map for INT30 scenario with binary OWP map +mat_predicted <- as.matrix(read.table(file.path(fld, fls[1])), sep = "") +mat_predicted_INT30_OWP <- mat_predicted * (1.0 - MAT_INT30_OWP) + +write.table(formatC(mat_predicted_INT30_OWP, digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_INT30_OWP_INT.1.mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + +## ---- EOF ---- ## \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPattributes.csv b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPattributes.csv new file mode 100644 index 0000000000000000000000000000000000000000..534974f926ebf6853b0944d76178cb8bc94f6a70 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPattributes.csv @@ -0,0 +1,179 @@ +fid,UniekID,Wind Farm,Country Na,Wind Far_1,Other Name,Windfarm S,Round/Cate,Verwachte,Analyst Ce,Capacity M,Turbine MW,Num Turbin,Total Turb,Hub Height,Rotor Diam,Foundation,Water Dept,Water De_1,Area (SqKm,ingeschatt,Lat,Lon,ScaleFactor +"1",BE01,BE01,Belgium,Thornton Bank phase I,"Zone 1, C-Power",Fully Commissioned,Concession identified through decree of May 2004,"2008",High,30,5.075,"6",157,94,"126",Grounded: Gravity-Base,20,20,4.09801766598395,,51.54389659,2.93845124,1 +"2",BE02,BE02,Belgium,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",Fully Commissioned,Concession identified through decree of May 2004,"2013",High,216,3,"72",127,71,"112",Grounded: Monopile,15.3,23,23.1412703583795,,51.61941118,2.90184956,1 +"3",BE03,BE03,Belgium,Belwind,Zone 3 and Bligh Bank,Fully Commissioned,Concession identified through decree of May 2004,"2009",High,165,3,"55",117,72,"90",Grounded: Monopile,11.6,20,23.5853382156077,,51.67021111,2.8028375,1 +"4",BE04,BE04,Belgium,Norther,"North Sea Power, Norther",Fully Commissioned,Concession identified through decree of May 2004,"2018",High,369.6,8.4,"44",183,101,"164",Grounded: Monopile,13,26,53.6995040258063,,51.52694081,3.01316418,1 +"5",BE05,BE05,Belgium,Rentel,,Fully Commissioned,Concession identified through decree of May 2004,"2017",High,309,7,"42",183,106,"154",Grounded: Monopile,23.5,34,34.4043899275409,,51.59134824,2.94456284,1 +"6",BE06,BE06,Belgium,Seamade (SeaStar),Zone 6,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,252,8.4,"30",192.5,109,"167",Grounded: Monopile,20,26.5,28.5648839669269,,51.63418002,2.8616514,1 +"7",BE07,BE07,Belgium,Seamade (Mermaid),Zone 7,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,235,8.4,"28",192.5,109,"167",Grounded: Monopile,26,26,25.368742853723,,51.71861135,2.73948958,1 +"8",BE08,BE08,Belgium,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",Fully Commissioned,Concession identified through decree of May 2004,"2016",High,165,3.3,"50",135,79,"112",Grounded: Monopile,20,33,41.9892144324826,,51.66439838,2.81648834,1 +"9",BE09,BE09,Belgium,Thornton Bank phase II,"Zone 1, C-Power 2",Fully Commissioned,Concession identified through decree of May 2004,"2010",High,184.5,6.15,"30",158,95,"126",Grounded: Jacket (Piled),5.9,20,26.5927646792896,,51.5564308,2.96915138,1 +"10",BE10,BE10,Belgium,Thornton Bank phase III,"Zone 1, C-Power 2",Fully Commissioned,Concession identified through decree of May 2004,"2011",High,110.7,6.15,"18",158,95,"126",Grounded: Jacket (Piled),10.2,21.5,16.6876085150946,,51.54027173,2.92176148,1 +"11",BE12,BE12,Belgium,Northwester 2,Zone 7,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,219,9.5,"23",187,105,"164",Grounded: Monopile,24.5,37,21.815423556101,,51.68675369,2.75288457,1 +"12",BE14,BE14,Belgium,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",Development Zone,2023 Tender,"2025",Medium,700,12,"59",245,135,"220",Grounded: Not Specified,8.1,33,62.102782485355,70,51.62110382,2.55674602,1.12716366640267 +"13",BE15,BE15,Belgium,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",Development Zone,2025 Tender,"2027",Medium,1400,15,"94",261,143,"236",Grounded: Not Specified,4.9,35,271.782754436073,140,51.47308888,2.43931753,0.515117304961048 +"14",DE01,DE01,Germany,Alpha Ventus,Borkum West I,Fully Commissioned,,"2008",High,60,5,"12",151,92,"120",Various,28,30,8.73610717264621,,54.01089678,6.60654332,1 +"15",DE02,DE02,Germany,DanTysk,"Dan Tysk, Dantysk",Fully Commissioned,,"2013",High,288,3.6,"80",148,88,"120",Grounded: Monopile,21,29,88.8172063358872,,55.13772274,7.20076354,1 +"16",DE03,DE03,Germany,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",Consent Authorised,WindSeeG: 1st and 2nd Tender,"2023",High,900,11,"81",225,125,"200",Grounded: Not Specified,29,31,91.8298599417256,,54.04622646,6.19331525,1 +"17",DE04,DE04,Germany,Borkum Riffgrund 1,,Fully Commissioned,,"2013",High,312,4,"78",147,87,"120",Various,23,29,48.2847375393535,,53.96734348,6.55413942,1 +"18",DE05,DE05,Germany,Amrumbank West,,Fully Commissioned,,"2013",High,302,3.775,"80",150,90,"120",Grounded: Monopile,20,25,43.4191769691324,,54.52297276,7.70565387,1 +"19",DE06,DE06,Germany,Nordsee Ost,Innogy Nordsee Ost,Fully Commissioned,,"2012",High,295.2,6.15,"48",155,92,"126",Grounded: Jacket (Piled),22,25,48.7213168587201,,54.44455387,7.68215399,1 +"20",DE07,DE07,Germany,Meerwind Süd/Ost,Meerwind,Fully Commissioned,,"2012",High,288,3.6,"80",149,89,"120",Grounded: Monopile,24,27,54.8039338907529,,54.39216259,7.70413509,1 +"21",DE08,DE08,Germany,Butendiek,Offshore-Bürgerpark Butendiek,Fully Commissioned,,"2014",High,288,3.6,"80",150,90,"120",Grounded: Monopile,18.4,21,44.660141589676,,55.0174822,7.77401776,1 +"22",DE09,DE09,Germany,Global Tech I,GT1,Fully Commissioned,,"2012",High,400,5,"80",140,82,"116",Grounded: Tripod,38,41,54.483795206958,,54.50891743,6.36543494,1 +"23",DE0H,DE0H,Germany,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",Consent Authorised,WindSeeG: 1st and 2nd Tender,"2023",High,241.75,11,"22",225,125,"200",Grounded: Not Specified,30,34,31.3867870820095,,54.03746874,7.10462029,1 +"24",DE0K,DE0K,Germany,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",Fully Commissioned,,"2018",High,203,6.344,"32",180.5,104.5,"152",Grounded: Monopile,28,33,48.7855502076397,,54.05034154,6.46366893,1 +"25",DE11,DE11,Germany,Hohe See,Hochsee Windpark 'Nordsee',Fully Commissioned,,"2018",High,497,7,"71",182,105,"154",Grounded: Monopile,39,40,55.1726071810185,,54.44334719,6.32994366,1 +"26",DE12,DE12,Germany,Sandbank,Sandbank24,Fully Commissioned,,"2015",High,288,4,"72",159.75,94.75,"130",Grounded: Monopile,25.5,29,73.2423494876009,,55.19962637,6.85239725,1 +"27",DE13,DE13,Germany,Gode Wind 1 and 2,Gode Wind I and II,Fully Commissioned,,"2015",High,582,6.264,"97",188,111,"154",Grounded: Monopile,28,34,96.5321161487852,,54.0412232,6.99450814,1 +"28",DE19,DE19,Germany,EnBW He Dreiht,Hochsee Windpark He dreiht,Consent Authorised,WindSeeG: 1st Tender,"2024",High,900,10,"70",218,121.5,"193",Grounded: Monopile,37,40,82.3177185612138,,54.36529145,6.18647301,1 +"29",DE20,DE20,Germany,Nordergründe,"OWP Nordergründe, Nordergrunde",Fully Commissioned,,"2016",High,110.7,6.15,"18",147,84,"126",Grounded: Monopile,3,11,7.86280046325181,,53.83495012,8.16918216,1 +"30",DE21,DE21,Germany,Riffgat,"OWP Riffgat, Borkum Riffgat",Fully Commissioned,,"2012",High,108,3.6,"30",150,90,"120",Grounded: Monopile,18,23,12.9300481517701,,53.69242975,6.47544661,1 +"31",DE23,DE23,Germany,BARD Offshore 1,BARD,Fully Commissioned,,"2010",High,400,5,"80",151,90,"122",Grounded: Tripile,39,41,76.8533771850899,,54.35537441,5.98085192,1 +"32",DE24,DE24,Germany,Deutsche Bucht,"DBU, Debu",Fully Commissioned,,"2018",High,252,8.4,"31",190,108,"164",Grounded: Monopile,38,40,27.4123599580331,,54.30539739,5.79302719,1 +"33",DE26,DE26,Germany,Merkur,MEG Offshore I,Fully Commissioned,,"2017",High,396,6,"66",177.6,102.6,"150",Grounded: Monopile,27,33,54.5600187813524,,54.04306092,6.55370567,1 +"34",DE27,DE27,Germany,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",Fully Commissioned,,"2011",High,200,5,"40",148,90,"116",Grounded: Tripod,28,33,37.8189198765473,,54.04635684,6.45749314,1 +"35",DE28,DE28,Germany,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",Fully Commissioned,,"2015",High,332.1,6.15,"54",153,90,"126",Grounded: Monopile,28,29,43.6622276002561,,53.98161225,6.81734194,1 +"36",DE2Q,DE2Q,Germany,N-3.5,,Development Zone,German Central Tender 2023,"2027",Medium,420,15,"28",261,143,"236",Grounded: Not Specified,29,33,44.7708371818327,42,54.0256931,6.84904222,0.938110668545705 +"37",DE2R,DE2R,Germany,N-3.6,,Development Zone,German Central Tender 2023,"2027",Medium,480,15,"32",261,143,"236",Grounded: Not Specified,30,34,46.7608366857453,48,54.04565357,6.77231105,1.02650002442391 +"38",DE2S,DE2S,Germany,N-3.7,,Development Zone,German Central Tender 2021,"2025",Medium,225,15,"15",261,143,"236",Grounded: Not Specified,30,32,27.0263032542515,22.5,54.04490205,7.062126,0.83252229460796 +"39",DE2T,DE2T,Germany,N-3.8,,Development Zone,German Central Tender 2021,"2025",Medium,433,15,"29",261,143,"236",Grounded: Not Specified,33,33,42.6105049248221,43.3,54.06796156,6.89332102,1.01618134017408 +"40",DE2V,DE2V,Germany,N-6.6,,Development Zone,German Central Tender 2024,"2028",Medium,630,15,"42",261,143,"236",Grounded: Not Specified,38,39,61.3119297220673,63,54.26591893,5.95355963,1.02753249303333 +"41",DE2W,DE2W,Germany,N-6.7,,Development Zone,German Central Tender 2024,"2028",Medium,270,15,"18",261,143,"236",Grounded: Not Specified,40,40,40.9442832662726,27,54.36063549,5.84539799,0.659432717979483 +"42",DE2X,DE2X,Germany,N-7.2,,Development Zone,German Central Tender 2022,"2026",Medium,930,15,"62",261,143,"236",Grounded: Not Specified,37,39,96.2171463264876,93,54.28834022,6.22358211,0.966563690056126 +"43",DE2Z,DE2Z,Germany,N-8.4,,Development Zone,German Central Tender,"2026",Medium,425,15,"28",261,143,"236",Grounded: Not Specified,40,40,49.3677304236695,42.5,54.53463794,6.31355851,0.860886243610325 +"44",DE30,DE30,Germany,Borkum Riffgrund 2,,Fully Commissioned,,"2018",High,450,8.3,"56",187,105,"164",Various,25,29,56.6031857121368,,53.95177099,6.48745918,1 +"45",DE33,DE33,Germany,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",Pre-Construction,WindSeeG: 2nd Tender,"2021",High,342,9,"38",191,107.5,"167",Grounded: Monopile,22,26,27.4162253268616,,54.48914974,7.69545631,1 +"46",DE36,DE36,Germany,Veja Mate,,Fully Commissioned,,"2016",High,402,6,"67",180.28,103.28,"154",Grounded: Monopile,39,41,68.8073232177383,,54.31808719,5.87282159,1 +"47",DE39,DE39,Germany,Albatros,"Hohe Extension, Albatros 1",Fully Commissioned,,"2019",High,112,7,"16",182,105,"154",Grounded: Monopile,40,40,18.7958298442165,,54.48600809,6.24913654,1 +"48",DE3A,DE3A,Germany,N-9.1,,Development Zone,German Central Tender 2024,"2028",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,42,126.574980829048,100,54.43751005,5.76512543,0.790045547271775 +"49",DE3B,DE3B,Germany,N-9.2,,Development Zone,German Central Tender 2024,"2028",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,41,127.145180387325,100,54.52131578,5.89977262,0.786502482401362 +"50",DE3C,DE3C,Germany,N-10.1,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"57",261,143,"236",Grounded: Not Specified,41,42,116.801604170185,100,54.60193314,6.14335151,0.856152624875733 +"51",DE3D,DE3D,Germany,N-11-1,,Development Zone,German Central Tender 2026+,"2031",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,39,42,395.617774789928,100,54.78220476,6.37218683,0.252769229221563 +"52",DE3E,DE3E,Germany,N-12.1,,Development Zone,German Central Tender 2026+,"2034",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914841,100,54.84468775,6.14651684,0.183574489386104 +"53",DE3I,DE3I,Germany,N-10.2,,Development Zone,German Central Tender 2025,"2029",Medium,700,15,"47",261,143,"236",Grounded: Not Specified,41,41,116.356321492184,70,54.67504415,5.98511505,0.60160031790539 +"54",DE3J,DE3J,Germany,N-12.2,,Development Zone,German Central Tender 2026+,"2034",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914841,100,54.84468775,6.14651684,0.183574489386104 +"55",DE3K,DE3K,Germany,N-12.3,,Development Zone,German Central Tender 2026+,"2033",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914845,100,54.84468775,6.14651684,0.183574489386103 +"56",DE3M,DE3M,Germany,N-12.4,,Development Zone,German Central Tender 2026+,"2033",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914845,100,54.84468775,6.14651684,0.183574489386103 +"57",DE3O,DE3O,Germany,N-11-2,,Development Zone,German Central Tender 2026+,"2031",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,39,42,395.617774789928,100,54.78220476,6.37218683,0.252769229221563 +"58",DE3Q,DE3Q,Germany,N-13-2,,Development Zone,German Central Tender 2026+,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,40,48,301.898512939832,100,55.06058697,6.35809751,0.331237140011782 +"59",DE3R,DE3R,Germany,N-13-3,,Development Zone,German Central Tender 2026+,"2030",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,40,48,301.898512939832,100,55.06058697,6.35809751,0.331237140011782 +"60",DE3S,DE3S,Germany,N-9.3,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,40,43,127.595385492944,100,54.43628044,5.63087554,0.783727402160089 +"61",DE3T,DE3T,Germany,N-9.4,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,42,123.131784626929,100,54.55953326,5.75514749,0.812137989415042 +"62",DK03,DK03,Denmark,Horns Rev 1,,Fully Commissioned,,"2002",High,160,2,"80",110,70,"80",Grounded: Monopile,6,11,30.6443531449027,,55.48610077,7.84038064,1 +"63",DK0V,DK0V,Denmark,Nordsøen - Tender 1,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,27.5,43,416.515741865753,100,56.549218,6.2643305,0.240086964185452 +"64",DK0W,DK0W,Denmark,Nordsøen - Tender 3,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,400.963310746687,100,56.30297162,6.41826499,0.249399377249197 +"65",DK0X,DK0X,Denmark,Nordsøen - Tender 2,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,35,42,438.789688729757,100,56.63111021,6.67237267,0.227899612430474 +"66",DK0Y,DK0Y,Denmark,Nordsøen - Tender 4,,Development Zone,,"2035",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,32,48,343.30791832147,100,56.7921246,6.32260056,0.291283697996039 +"67",DK0Z,DK0Z,Denmark,Nordsøen - Tender 5,,Development Zone,,"2035",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,34,43,362.352413339133,100,56.29716697,6.08264947,0.275974427984306 +"68",DK10,DK10,Denmark,Horns Rev 2,,Fully Commissioned,Tender (DK),"2008",High,209.3,2.3,"91",114.5,68,"93",Grounded: Monopile,9,17,47.2499961623854,,55.60042043,7.58252166,1 +"69",DK19,DK19,Denmark,Horns Rev 3,Horns Rev A HR3-HR4,Fully Commissioned,Tender (DK),"2017",High,406.7,8.3,"49",187,102,"164",Grounded: Monopile,10.6,19.1,140.32545500473,,55.69409292,7.65486583,1 +"70",DK1A,DK1A,Denmark,Nordsøen - Tender 6,,Development Zone,,"2036",,1000,20,"50",305,165,"280",Grounded: Not Specified,30.5,40,372.436596776018,100,56.53221977,7.06288008,0.268502077576817 +"71",DK1B,DK1B,Denmark,Nordsøen - Tender 7,,Development Zone,,"2037",,1000,20,"50",305,165,"280",Grounded: Not Specified,34,40,357.128848779714,100,56.32274482,6.84800141,0.280010982987494 +"72",DK1C,DK1C,Denmark,Nordsøen - Tender 8,,Development Zone,,"2038",,1000,20,"50",305,165,"280",Grounded: Not Specified,36,40,347.303012493645,100,56.08000108,6.55868117,0.28793300490542 +"73",DK1D,DK1D,Denmark,Nordsøen - Tender 9,,Development Zone,,"2039",,1000,20,"50",305,165,"280",Grounded: Not Specified,37,42,339.036488523039,100,56.035332,6.06920374,0.294953503192635 +"74",DK1E,DK1E,Denmark,Nordsøen - Tender 10,,Development Zone,,"2032",,1000,20,"50",305,165,"280",Grounded: Not Specified,42,45,324.045207650715,100,55.82309559,6.29156517,0.308598916567805 +"75",DK22,DK22,Denmark,Thor - 2020 Tender,Horns Rev 4,Development Zone,2019 Tender (DK),"2025",Medium,1000,16,"75",261,143,"236",Grounded: Not Specified,27,34,490.795015669501,100,56.32540117,7.65243573,0.203751050453494 +"76",DK48,DK48,Denmark,Vesterhav Nord/Syd,North Sea (south),Consent Authorised,Nearshore Tender (DK),"2022",Medium,344,8.4,"41",193,109,"167",Grounded: Monopile,16,24,146.87543752293,34.4,56.37756229,8.02229099,0.234212068267912 +"77",UK04,UK04,United Kingdom,Dudgeon,,Fully Commissioned,Round 2,"2016",High,402,6,"67",187,110,"154",Grounded: Monopile,11.5,23.5,72.2836989959603,,53.26535507,1.38071537,1 +"78",UK05,UK05,United Kingdom,Greater Gabbard,GGOWL,Fully Commissioned,Round 2,"2009",High,504,3.6,"140",131,77.5,"107",Grounded: Monopile,3.7,37,180.487713677604,,51.88398379,1.93583846,1 +"79",UK07,UK07,United Kingdom,Gunfleet Sands,GFS,Fully Commissioned,Round 1,"2008",High,172.8,3.6,"48",128.5,75,"107",Grounded: Monopile,0,12.9,26.8600264044145,,51.73015694,1.22922906,1 +"80",UK0V,UK0V,United Kingdom,Dogger Bank B,"Creyke Beck B, Zone 3",Pre-Construction,Round 3,"2023",High,1200,13,"95",260,150,"220",Grounded: Monopile,20,33,649.041485906262,,54.97737666,1.67972877,1 +"81",UK10,UK10,United Kingdom,Humber Gateway,,Fully Commissioned,Round 2,"2013",High,219,3,"73",136,80,"112",Grounded: Monopile,10.6,16.2,38.776871919882,,53.6442876,0.29312324,1 +"82",UK11,UK11,United Kingdom,Inner Dowsing,,Fully Commissioned,Round 1,"2007",High,97.2,3.6,"27",133.5,80,"107",Grounded: Monopile,6.4,7.9,16.5219908613015,,53.18907175,0.44525315,1 +"83",UK12,UK12,United Kingdom,Kentish Flats,,Fully Commissioned,Round 1,"2004",High,90,3,"30",115,70,"90",Grounded: Monopile,3,4.5,17.5088015412025,,51.4604165,1.0939165,1 +"84",UK13,UK13,United Kingdom,Lincs,,Fully Commissioned,Round 2,"2011",High,270,3.6,"75",145.25,85.25,"120",Grounded: Monopile,7.6,16.4,57.6548427538279,,53.19047417,0.49160338,1 +"85",UK14,UK14,United Kingdom,London Array,,Fully Commissioned,Round 2,"2011",High,630,3.6,"175",147,87,"120",Grounded: Monopile,0,23,128.468897937706,,51.62680883,1.49516009,1 +"86",UK15,UK15,United Kingdom,Lynn,,Fully Commissioned,Round 1,"2007",High,97.2,3.6,"27",133.5,80,"107",Grounded: Monopile,7,11,14.4793360621024,,53.13666862,0.4521628,1 +"87",UK18,UK18,United Kingdom,Race Bank,ROW01,Fully Commissioned,Round 2,"2016",High,573.3,6.3,"91",187,110,"154",Grounded: Monopile,6,23,82.3752465649017,,53.27657921,0.84137697,1 +"88",UK1F,UK1F,United Kingdom,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",Consent Authorised,Round 3,"2024",High,1200,14,"95",248,138,"220",Grounded: Monopile,21,30,610.388995188024,,55.03972759,2.8226577,1 +"89",UK1G,UK1G,United Kingdom,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",Consent Authorised,Round 3,"2024",High,1400,14,"100",252,141,"222",Grounded: Monopile,21,33,642.918801505068,140,54.98993661,2.2285549,0.217756891962501 +"90",UK1J,UK1J,United Kingdom,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",Concept/Early Planning,Round 3,"2031",Medium,1000,20,"50",305,165,"280",Grounded: Not Specified,24.5,55,534.545090118118,100,54.03860767,1.2710722,0.187074957470665 +"91",UK1K,UK1K,United Kingdom,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",Consent Authorised,Round 3,"2026",Medium,2400,11,"231",205,123,"164",Grounded: Not Specified,26,73,758.101979826106,240,53.87299926,2.53716073,0.316580099230253 +"92",UK1U,UK1U,United Kingdom,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",Under Construction,Round 3,"2020",High,1386,8.4,"165",204.4,120.9,"167",Grounded: Monopile,25,67,528.863231533713,,53.94023544,1.6876632,1 +"93",UK23,UK23,United Kingdom,Scroby Sands,,Fully Commissioned,Round 1,"2003",High,60,2,"30",100,60,"80",Grounded: Monopile,0,10.1,9.77479075043837,,52.64572523,1.78781929,1 +"94",UK27,UK27,United Kingdom,Sheringham Shoal,,Fully Commissioned,Round 2,"2009",High,316.8,3.6,"88",135.25,81.75,"107",Grounded: Monopile,14,23,49.6562543312242,,53.13557362,1.14747958,1 +"95",UK28,UK28,United Kingdom,Teesside,Redcar,Fully Commissioned,Round 1,"2012",High,62.1,2.3,"27",126.5,80,"93",Grounded: Monopile,6,18,9.81006268476869,,54.64531751,-1.09408429,1 +"96",UK29,UK29,United Kingdom,Thanet,,Fully Commissioned,Round 2,"2009",High,300,3,"100",115,70,"90",Grounded: Monopile,14.4,23,47.5555281705209,,51.4304064,1.63370997,1 +"97",UK2Q,UK2Q,United Kingdom,East Anglia Hub - ONE North,"EA1N, East Anglia Hub",Consent Application Submitted,Round 3,"2024",Medium,800,14,"58",300,140,"222",Grounded: Monopile,32,59,238.527559732585,80,52.38105836,2.43113765,0.335391013473196 +"98",UK2Y,UK2Y,United Kingdom,ForthWind Offshore Wind Demonstration Project Phase 2,2B Energy Powerblock,Concept/Early Planning,,,,53,12,"7",225,125,"200",Various,10,24,15.7504030795213,,56.13760438,-3.02423266,1 +"99",UK30,UK30,United Kingdom,Triton Knoll,,Partial Generation/Under Construction,Round 2,"2020",High,857,9.5,"90",189,107,"164",Grounded: Monopile,12.3,30,177.385780666715,,53.47846028,0.83858297,1 +"100",UK34,UK34,United Kingdom,Westermost Rough,,Fully Commissioned,Round 2,"2014",High,210,6,"35",177,102,"154",Grounded: Monopile,12,22,47.5170331104598,,53.80588225,0.1498239,1 +"101",UK39,UK39,United Kingdom,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",Consent Application Submitted,Round 3,"2025",Medium,900,14,"65",251,140,"222",Grounded: Monopile,30,73,251.054341394847,90,52.13269299,2.20666027,0.358488124523018 +"102",UK3F,UK3F,United Kingdom,Scottish Sectoral Marine Plan - E3,,Development Zone,ScotWind,"2030",Low,1000,20,"50",305,165,"280",Floating: Not Specified,54,125,519.726845258834,100,56.99776921,-1.48641529,0.192408764165719 +"103",UK3G,UK3G,United Kingdom,Scottish Sectoral Marine Plan - E2,,Development Zone,ScotWind,"2035",,2000,20,"100",305,165,"280",Floating: Not Specified,63,93,1390.9806175828,200,57.34306553,-0.08353952,0.14378345569441 +"104",UK3I,UK3I,United Kingdom,Scottish Sectoral Marine Plan - E1,,Development Zone,ScotWind,"2035",,3000,20,"150",305,165,"280",Floating: Not Specified,62,148,3871.86458599749,300,56.71435246,-0.42248326,0.0774820485935751 +"105",UK3L,UK3L,United Kingdom,Scottish Sectoral Marine Plan - NE6,,Development Zone,ScotWind,"2034",Low,2000,20,"100",305,165,"280",Floating: Not Specified,58,125,755.472167218755,200,58.05810167,-1.65680883,0.264735100349617 +"106",UK3N,UK3N,United Kingdom,Scottish Sectoral Marine Plan - NE7,,Development Zone,ScotWind,"2032",Low,3000,20,"150",305,165,"280",Floating: Not Specified,91,119,740.45486193662,300,58.16211001,-0.62257398,0.405156364582935 +"107",UK3O,UK3O,United Kingdom,Scottish Sectoral Marine Plan - NE8,,Development Zone,ScotWind,"2037",,1000,20,"50",305,165,"280",Floating: Not Specified,74,102,382.721944887829,100,58.41112453,-1.2334218,0.261286297626097 +"108",UK3P,UK3P,United Kingdom,Scottish Sectoral Marine Plan - NE3,,Development Zone,ScotWind,"2036",,1000,20,"50",305,165,"280",Floating: Not Specified,69,90,300.194859592604,100,58.51271028,-2.1427869,0.333116963214196 +"109",UK3Q,UK3Q,United Kingdom,Scottish Sectoral Marine Plan - NE4,,Development Zone,ScotWind,"2037",,1000,20,"50",305,165,"280",Grounded: Not Specified,43,82,492.21662335689,100,58.2005869,-2.49270311,0.20316258178768 +"110",UK40,UK40,United Kingdom,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",Under Construction,Round 3,"2019",High,950,9.5,"100",280,105,"164",Grounded: Jacket (Piled),39,50,336.350852052243,,58.1882694,-2.72036686,1 +"111",UK44,UK44,United Kingdom,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",Pre-Construction,Round 3,"2021",High,1140,10,"114",205,123,"164",Grounded: Jacket (Suction Bucket),39,61,403.267713739498,,56.59691754,-1.71957856,1 +"112",UK47,UK47,United Kingdom,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",Fully Commissioned,,"2018",High,93.2,8.8,"11",191,109,"164",Grounded: Jacket (Suction Bucket),20,30,30.5718540080577,,57.22570891,-1.99650044,1 +"113",UK4F,UK4F,United Kingdom,Race Bank Extension,,Concept/Early Planning,2017 Extension Opportunity,"2029",Medium,573,15,"38",261,143,"236",Grounded: Not Specified,3.3,21.5,178.26729188464,57.3,53.29354357,0.91555526,0.32142744411622 +"114",UK4G,UK4G,United Kingdom,Dudgeon Extension,,Concept/Early Planning,2017 Extension Opportunity,"2028",Medium,402,20,"115",305,165,"280",Grounded: Not Specified,10.6,31,146.614960129056,40.2,53.27882195,1.37380062,0.274187572431998 +"115",UK4H,UK4H,United Kingdom,Sheringham Shoal Extension,,Concept/Early Planning,2017 Extension Opportunity,"2028",Medium,317,20,"16",305,165,"280",Grounded: Not Specified,13.6,21,119.751080038085,31.7,53.16698375,1.16306147,0.264715775339298 +"116",UK4I,UK4I,United Kingdom,Five Estuaries,Galloper Extension,Concept/Early Planning,2017 Extension Opportunity,"2030",Medium,353,20,"18",305,165,"280",Grounded: Not Specified,31,57,207.206788243746,35.3,51.90139244,2.16543274,0.170361214027772 +"117",UK4J,UK4J,United Kingdom,North Falls,Greater Gabbard Extension,Concept/Early Planning,2017 Extension Opportunity,"2029",Medium,504,15,"34",261,143,"236",Grounded: Not Specified,9.5,59,213.009465825662,50.4,51.75373073,1.89921265,0.236609203279492 +"118",UK4N,UK4N,United Kingdom,Kincardine - Phase 2,KOWL,Under Construction,Scottish Floating Demonstration,"2020",High,48,9.6,"5",180,104,"164",Floating: Semi-Submersible Platform - Steel,61,100,36.5205208383717,,57.00674209,-1.85390541,1 +"119",UK4P,UK4P,United Kingdom,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",Consent Authorised,Round 3,"2024",Medium,360,10,"36",205,123,"164",Grounded: Jacket (Piled),44,54,74.5673233992684,36,56.56191878,-1.86222668,0.482785198112035 +"120",UK4X,UK4X,United Kingdom,Round 4 - Area 1 (RWE Renewables),,Concept/Early Planning,Leasing Round 4,"2035",Medium,1500,20,"75",305,165,"280",Grounded: Not Specified,14,36,538.167469692955,150,54.62499758,1.47870583,0.2787236472795 +"121",UK4Y,UK4Y,United Kingdom,Round 4 - Area 2 (RWE Renewables),,Concept/Early Planning,Leasing Round 4,"2035",,1500,20,"75",305,165,"280",Grounded: Not Specified,14,33,542.209354015866,150,54.50075208,1.90376825,0.276645909719239 +"122",UK4Z,UK4Z,United Kingdom,Round 4 - Area 3 (GIG & Total),,Concept/Early Planning,Leasing Round 4,"2034",Medium,1500,20,"75",305,165,"280",Grounded: Not Specified,7,37,547.613942487847,150,53.5683674,1.28480603,0.273915597032719 +"123",UK53,UK53,United Kingdom,Beatrice,BOWL,Fully Commissioned,Scottish Territorial Waters 1,"2017",High,588,7,"84",198.4,110,"154",Grounded: Jacket (Piled),35,50,156.977052985059,,58.25402325,-2.88454523,1 +"124",UK54,UK54,United Kingdom,Inch Cape,ICOL,Consent Authorised,Scottish Territorial Waters 1,"2026",Medium,1000,15,"72",291,,"250",Grounded: Monopile,36,54,179.93149120997,100,56.49461093,-2.19559965,0.555767082946618 +"125",UK56,UK56,United Kingdom,Neart na Gaoithe,NnG,Under Construction,Scottish Territorial Waters 1,"2020",High,448,8,"54",208,125,"167",Grounded: Jacket (Piled),44,56,126.294724989167,,56.26767895,-2.25005051,1 +"126",UK60,UK60,United Kingdom,Kentish Flats Extension,Kentish Flats 2,Fully Commissioned,Round 2.5,"2015",High,49.5,3.3,"15",139.6,83.6,"112",Grounded: Monopile,1,4,18.2587656002391,,51.45071428,1.07998646,1 +"127",UK62,UK62,United Kingdom,Galloper,,Fully Commissioned,Round 2.5,"2016",High,353,6,"56",180.5,103.5,"154",Grounded: Monopile,4,50,150.765237754745,,51.89314988,2.03450963,1 +"128",UK64,UK64,United Kingdom,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",Fully Commissioned,Round 3,"2018",High,714,7,"102",167,90,"154",Grounded: Jacket (Piled),29.5,41,190.525909758042,,52.23270284,2.49853336,1 +"129",UK66,UK66,United Kingdom,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",Consent Authorised,Round 3,"2023",Medium,1400,14,"100",251,140,"222",Grounded: Monopile,24.5,48,347.52477375532,140,52.66515209,2.85566697,0.402848978181247 +"130",UK67,UK67,United Kingdom,Norfolk Vanguard,East Anglia Array,Consent Application Submitted,Round 3,"2025",Medium,1800,20,"158",305,198.5,"303",Grounded: Not Specified,21.5,40,665.369908154925,180,52.87021588,2.69987804,0.270526210749658 +"131",UK69,UK69,United Kingdom,Norfolk Boreas,East Anglia Six,Consent Application Submitted,Round 3,"2027",Medium,1800,20,"158",350,,"303",Grounded: Not Specified,22.5,41,782.568852318447,180,53.03851213,2.94162843,0.230011710109251 +"132",UK70,UK70,United Kingdom,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",Fully Commissioned,Demonstration,"2017",High,41.5,8.3,"5",191.5,109.5,"164",Grounded: Gravity-Base,29,39,9.75287302016675,,55.13097973,-1.40106756,1 +"133",UK74,UK74,United Kingdom,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",Concept/Early Planning,Round 3,"2028",Medium,2300,20,"115",305,165,"280",Grounded: Not Specified,39,64,717.976920511343,,56.29191816,-1.46712425,1 +"134",UK75,UK75,United Kingdom,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",Concept/Early Planning,Round 3,"2031",Medium,1850,20,"93",305,165,"280",Grounded: Not Specified,32,64,852.098582558707,,56.34493328,-1.79275443,1 +"135",UK76,UK76,United Kingdom,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",Fully Commissioned,Scottish Floating Demonstration,"2017",High,30,6,"5",178,101,"154",Floating: Spar Floater - Steel,96,110,23.7028961583754,,57.48425352,-1.36262218,1 +"136",UK77,UK77,United Kingdom,Moray West,"Zone 1, Moray Firth Western Development Area",Consent Authorised,Round 3,"2024",Medium,950,15,"85",265,,"230",Grounded: Not Specified,35,52,261.978624301432,95,58.09724802,-3.00714744,0.362625005201544 +"137",UK79,UK79,United Kingdom,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",Consent Authorised,Demonstration,"2024",Medium,58.4,14,"5",258,140,"236",Floating: Semi-Submersible Platform,43,54,30.3366065021439,,55.18510691,-1.35296017,1 +"138",UK80,UK80,United Kingdom,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",Pre-Construction,Round 3,"2022",High,1200,13,"95",260,150,"220",Grounded: Monopile,18.3,25,564.618967790395,,54.76947456,1.90895964,1 +"139",UK81,UK81,United Kingdom,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",Fully Commissioned,Round 3,"2018",High,1218,7,"174",200,113,"154",Grounded: Monopile,23.5,37,462.931508996161,,53.88330242,1.92187445,1 +"140","1","1",Netherlands,Borssele 2,Orsted,Fully Commissioned,2016 Tender,"2020",High,376,8,"47",192,108,"167",Grounded: Monopile,15.6,38,81.2314890278253,,51.68301223,3.06622564,1 +"141","2","2",Netherlands,Borssele 3,Blauwwind,Fully Commissioned,2016 Tender,"2019",High,366,9.5,"39",190,108,"164",Grounded: Monopile,14.3,38,91.7480853589124,,51.69975608,2.92823038,1 +"142","3","3",Netherlands,Borssele 4 - Blauwwind,Blauwwind,Fully Commissioned,2016 Tender,"2019",High,366,9.5,"39",190,108,"164",Grounded: Monopile,14.3,38,97.0265440084513,,51.69975608,2.92823038,1 +"143","4","4",Netherlands,Borssele Site V -Two towers,Two Towers,Fully Commissioned,2016 Tender - Demonstrator,"2020",High,19,9.5,"2",190,108,"164",Grounded: Monopile,22,24,3.06602636784179,,51.70915697,3.00330596,1 +"144","5","5",Netherlands,Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",Fully Commissioned,Round 1 (NL),"2006",High,108,3,"36",115,70,"90",Grounded: Monopile,15,18,38.3221488638661,,52.60621656,4.41925086,1 +"145","6","6",Netherlands,Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",Fully Commissioned,Round 1 (NL),"2006",High,120,2,"60",99,59,"80",Grounded: Monopile,19,24,31.0515035941986,,52.58788938,4.22381982,1 +"146","7","7",Netherlands,Eneco Luchterduinen,Luchterduinen,Fully Commissioned,Round 2 (NL),"2014",High,129,3,"43",137,81,"112",Grounded: Monopile,18,22,25.5882253902796,,52.40509242,4.16285737,1 +"147","8","8",Netherlands,Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",Fully Commissioned,Round 2 (NL),"2015",High,300,4,"75",154,89,"130",Grounded: Monopile,32,34,46.389100525219,,54.03650301,5.96493437,1 +"148","9","9",Netherlands,Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",Fully Commissioned,Round 2 (NL),"2015",High,300,4,"75",154,89,"130",Grounded: Monopile,32,34,47.5115496229591,,54.03650301,5.96493437,1 +"149","10","10",Netherlands,Hollandse Kust Zuid Holland IV,Vattenfall,Pre-Construction,2018 Tender,"2022",High,385,11,"70",251,121,"193",Grounded: Monopile,17,22,116.58357444553,,52.27960249,4.08390741,1 +"150","11","11",Netherlands,Hollandse Kust Zuid Holland III,Vattenfall,Development Zone,2018 Tender,"2022",High,385,11,"70",251,121,"193",Grounded: Monopile,17,22,69.8275750029334,,52.27960249,4.08390741,1 +"151","12","12",Netherlands,Hollandse Kust Zuid Holland II,Vattenfall,Pre-Construction,2017 Tender,"2021",High,385,11,"70",251,121,"193",Grounded: Monopile,17.9,22,62.4433020185146,,52.33407403,4.01082547,1 +"152","13","13",Netherlands,Hollandse Kust Zuid Holland I,Vattenfall,Pre-Construction,2017 Tender,"2021",High,385,11,"70",251,121,"193",Grounded: Monopile,17.9,22,89.627387342407,,52.33407403,4.01082547,1 +"153","14","14",Netherlands,Borssele 1,Orsted,Fully Commissioned,2016 Tender,"2020",High,376,8,"47",192,108,"167",Grounded: Monopile,15.6,38,82.8536979641582,,51.68301223,3.06622564,1 +"154","15","15",Netherlands,Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",Pre-Construction,2019 Tender,"2022",High,700,11,"69",225,121,"200",Grounded: Monopile,19,24,153.883793981307,,52.71410564,4.26460906,1 +"155",bw4,bw4,Netherlands,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,Development Zone,2030 Roadmap,"2026",Medium,700,15,"47",,,,Grounded: Not Specified,34,37,155.60756600162,70,54.02257343,5.65156705,0.449849591499113 +"156",bw6,bw6,Netherlands,IJmuiden Ver,,Development Zone,2030 Roadmap,"2027",Medium,4000,15,"267",261,143,"236",Grounded: Not Specified,23,41,461.233617921657,400,52.865641,3.53421658,0.867239473571812 +"157",bw7,bw7,Netherlands,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",Development Zone,2030 Roadmap,"2024",Medium,1400,12,"117",245,135,"220",Grounded: Not Specified,19.1,31,219.226094600169,140,52.62929154,3.71622035,0.638610108232491 +"158",bw8,bw8,Netherlands,Hollandse Kust West zuidelijke punt,,Development Zone,,"2028",Medium,700,15,"47",261,143,"236",Grounded: Monopile,,,158.335030973185,70,52.5522,3.6218,0.442100522984425 +"159","258","258",Netherlands,Zoekgebied 1 Noord,,Development Zone,,"2030",Medium,4000,20,"200",305,165,"280",Grounded: Monopile,,,644.723429440967,400,53.431,3.2233,0.620421070080912 +"160","259","259",Netherlands,zoekgebied 3,,Development Zone,,"2034",Low,2000,20,"100",305,165,"280",Grounded: Monopile,,,319.505487063697,200,53.7831,3.473,0.625967340461128 +"161","261","261",Netherlands,Zoekgebied 8,,Development Zone,,"2036",Low,2000,20,"100",305,165,"280",Grounded: Monopile,,,209.21013121624,200,53.3739,4.0859,0.955976648154192 +"162","262","262",Netherlands,Zoekgebied 5 middenberm,,Development Zone,,"2030",Medium,2000,20,"100",305,165,"280",Grounded: Monopile,,,399.743058191578,200,54.3947,5.0373,0.500321383702802 +"163",263k,"263",Netherlands,Zoekgebied 5 Oost klein,,Development Zone,,"2029",Medium,2000,15,"134",261,143,"236",Grounded: Monopile,,,725.409010174026,200,54.254,5.6158,0.27570652858588 +"164",263o,"263",Netherlands,Zoekgebied 5 Oost origineel,,Development Zone,,"2029",Medium,4000,15,"267",261,143,"236",Grounded: Monopile,,,725.409010174026,400,54.1496,5.6547,0.55141305717176 +"165","264","264",Netherlands,Zoekgebied 7,,Development Zone,,"2038",Low,8000,20,"400",305,165,"280",Grounded: Monopile,,,1116.05386919912,800,54.3616,3.516,0.716811277733466 +"166","265","265",Netherlands,Zoekgebied 6,,Development Zone,,"2039",Low,10000,20,"500",305,165,"280",Grounded: Monopile,,,1438.99986915346,1000,54.5704,4.6037,0.694927095850457 +"167","268","268",Netherlands,Hollandse Kust West Noord,,Development Zone,,"2028",Medium,1400,15,"94",261,143,"236",Grounded: Monopile,,,229.985447950972,140,52.8988,4.1257,0.608734166649731 +"168","269","269",Netherlands,Hollandse Kust West Zuid,,Development Zone,,"2028",Medium,1400,15,"94",261,143,"236",Grounded: Monopile,,,249.108224664114,140,52.274,3.6329,0.562004727819683 +"169","270","270",Netherlands,IJmuiden Ver Noord,,Development Zone,,"2028",Medium,2000,15,"134",261,143,"236",Grounded: Monopile,,,279.980362175595,200,52.9858,3.6619,0.71433581429031 +"170","272","272",Netherlands,Zoekgebied 1 Zuid,,Development Zone,,"2030",Medium,2000,20,"100",305,165,"280",Grounded: Monopile,,,241.727315769368,200,53.1474,3.1475,0.827378566478684 +"171","274","274",Netherlands,Zoekgebied 2 Noord,,Development Zone,,"2030",Medium,4000,20,"200",305,165,"280",Grounded: Monopile,,,704.701767722724,400,53.2176,3.9425,0.567616002004108 +"172","278","278",Netherlands,Zoekgebied 2 Zuid,,Development Zone,,"2029",Medium,4000,15,"267",261,143,"236",Grounded: Monopile,,,565.593094671214,400,52.9858,3.7936,0.707222212874655 +"173","279","279",Netherlands,zoekgebied 4,,Development Zone,,"2034",Medium,4000,20,"200",305,165,"280",Grounded: Monopile,,,607.020287523306,400,53.8823,5.8689,0.658956559149009 diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPdata2VGgrid_id.csv b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPdata2VGgrid_id.csv new file mode 100644 index 0000000000000000000000000000000000000000..0cad3b70b6f74052347644c3ab5b8f4e94dfb8a6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/IBM_710_OWPdata2VGgrid_id.csv @@ -0,0 +1,3368 @@ +fid,UniekID,Wind Farm,Wind Far_1,Other Name,ScaleFactor,GRID_ID +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6205 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0716N6225 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6225 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6225 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0731N6225 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0711N6210 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0716N6210 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6210 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6210 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0711N6215 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0716N6215 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6215 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6215 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0711N6220 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0716N6220 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6220 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6220 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0731N6220 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0716N6230 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6230 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6230 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0731N6230 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0721N6235 +"72",DK1C,DK1C,Nordsøen - Tender 8,,0.28793300490542,E0726N6235 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0571N5790 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0566N5790 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0561N5790 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0561N5795 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0566N5795 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0571N5795 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0561N5800 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0566N5800 +"151","12","12",Hollandse Kust Zuid Holland II,Vattenfall,1,E0571N5800 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0471N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0476N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0481N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0486N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0491N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0496N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0501N6090 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0471N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0476N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0481N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0486N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0491N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0496N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0501N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0506N6095 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0471N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0476N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0481N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0486N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0491N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0496N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0501N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0506N6100 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0471N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0476N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0481N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0486N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0491N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0496N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0501N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0506N6105 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0471N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0476N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0481N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0486N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0491N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0496N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0501N6110 +"88",UK1F,UK1F,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",1,E0506N6110 +"5",BE05,BE05,Rentel,,1,E0496N5710 +"5",BE05,BE05,Rentel,,1,E0491N5715 +"5",BE05,BE05,Rentel,,1,E0496N5715 +"5",BE05,BE05,Rentel,,1,E0501N5715 +"5",BE05,BE05,Rentel,,1,E0496N5720 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5765 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5765 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0451N5765 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0456N5765 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0456N5770 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0451N5770 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5770 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5770 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5790 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5790 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0451N5775 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5775 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5775 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0451N5785 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5785 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5785 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0451N5780 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0446N5780 +"101",UK39,UK39,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",0.358488124523018,E0441N5780 +"132",UK70,UK70,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",1,E0216N6115 +"132",UK70,UK70,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",1,E0221N6115 +"132",UK70,UK70,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",1,E0216N6120 +"132",UK70,UK70,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",1,E0221N6120 +"135",UK76,UK76,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",1,E0241N6375 +"135",UK76,UK76,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",1,E0236N6375 +"135",UK76,UK76,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",1,E0241N6380 +"135",UK76,UK76,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",1,E0236N6380 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0676N6055 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0681N6055 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0686N6055 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0676N6060 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0681N6060 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0671N6045 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0676N6045 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0681N6045 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0671N6050 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0676N6050 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0681N6050 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0686N6050 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0676N6040 +"61",DE3T,DE3T,N-9.4,,0.812137989415042,E0681N6040 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0486N5715 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0491N5715 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0486N5720 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0491N5720 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0496N5720 +"6",BE06,BE06,Seamade (SeaStar),Zone 6,1,E0491N5725 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0471N5790 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0471N5780 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5780 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5775 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5790 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0461N5790 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0461N5775 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0471N5785 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5785 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0461N5785 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0456N5785 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0461N5780 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0461N5795 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5795 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0471N5795 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0466N5800 +"128",UK64,UK64,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",1,E0471N5800 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0616N5995 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0621N5995 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0616N6000 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0621N6000 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0626N6000 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6025 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6025 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6025 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0621N6005 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0626N6005 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6005 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0621N6010 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0626N6010 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6010 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6010 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0626N6015 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6015 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6015 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0626N6020 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6020 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6020 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6020 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0631N6030 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6030 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6030 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6035 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6035 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6040 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6040 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0636N6045 +"162","262","262",Zoekgebied 5 middenberm,,0.500321383702802,E0641N6045 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6225 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6230 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6230 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6235 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0686N6235 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6235 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6235 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6250 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0686N6250 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6250 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6250 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6240 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0686N6240 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6240 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6240 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6245 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0686N6245 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6245 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6245 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6255 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0686N6255 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0691N6255 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0696N6255 +"67",DK0Z,DK0Z,Nordsøen - Tender 5,,0.275974427984306,E0681N6260 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0396N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0386N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0396N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0386N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0396N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0401N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0406N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0411N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0416N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0421N6055 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0386N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0396N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0401N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0406N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0411N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0416N6060 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0411N6040 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0416N6040 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0401N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0406N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0411N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0416N6045 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0401N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0406N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0411N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0416N6050 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0386N6065 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6065 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0396N6065 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0401N6065 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0406N6065 +"120",UK4X,UK4X,Round 4 - Area 1 (RWE Renewables),,0.2787236472795,E0391N6070 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6260 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6260 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6260 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6265 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6270 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6275 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6280 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6285 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6290 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0241N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6295 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0241N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6300 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0236N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0241N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6305 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0301N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0306N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0311N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0316N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0321N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0326N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0331N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0336N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0266N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0241N6310 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0276N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0281N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0286N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0291N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0296N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0271N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0261N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0256N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0251N6315 +"104",UK3I,UK3I,Scottish Sectoral Marine Plan - E1,,0.0774820485935751,E0246N6315 +"160","259","259",zoekgebied 3,,0.625967340461128,E0531N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0536N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0541N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0546N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0551N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0556N5965 +"160","259","259",zoekgebied 3,,0.625967340461128,E0536N5955 +"160","259","259",zoekgebied 3,,0.625967340461128,E0541N5955 +"160","259","259",zoekgebied 3,,0.625967340461128,E0546N5955 +"160","259","259",zoekgebied 3,,0.625967340461128,E0531N5960 +"160","259","259",zoekgebied 3,,0.625967340461128,E0536N5960 +"160","259","259",zoekgebied 3,,0.625967340461128,E0541N5960 +"160","259","259",zoekgebied 3,,0.625967340461128,E0546N5960 +"160","259","259",zoekgebied 3,,0.625967340461128,E0551N5960 +"160","259","259",zoekgebied 3,,0.625967340461128,E0531N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0536N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0541N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0546N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0551N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0556N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0551N5975 +"160","259","259",zoekgebied 3,,0.625967340461128,E0556N5975 +"160","259","259",zoekgebied 3,,0.625967340461128,E0561N5975 +"160","259","259",zoekgebied 3,,0.625967340461128,E0566N5975 +"160","259","259",zoekgebied 3,,0.625967340461128,E0561N5970 +"160","259","259",zoekgebied 3,,0.625967340461128,E0566N5970 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6060 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0691N6080 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6080 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6080 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6080 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6080 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0686N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0691N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6075 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6065 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6065 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6065 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0691N6070 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6070 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6070 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6070 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6070 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6100 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6100 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0691N6085 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6085 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6085 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6085 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6085 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6090 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6090 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6090 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0711N6090 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0696N6095 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6095 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6095 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0701N6105 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6105 +"56",DE3M,DE3M,N-12.4,,0.183574489386103,E0706N6110 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0691N6285 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0696N6285 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0691N6290 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0696N6290 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0701N6290 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0706N6290 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0691N6295 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0696N6295 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0701N6295 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0706N6295 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0711N6295 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0696N6300 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0701N6300 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0706N6300 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0711N6300 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0696N6305 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0701N6305 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0706N6305 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0711N6305 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0716N6305 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0701N6310 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0706N6310 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0711N6310 +"66",DK0Y,DK0Y,Nordsøen - Tender 4,,0.291283697996039,E0716N6310 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6225 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6225 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6220 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6220 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0816N6220 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6230 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6230 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6235 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6275 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6275 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6280 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6280 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6285 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6285 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6290 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6290 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0806N6295 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0811N6295 +"76",DK48,DK48,Vesterhav Nord/Syd,North Sea (south),0.234212068267912,E0816N6295 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0551N5865 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0556N5865 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0541N5870 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0546N5870 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0551N5870 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0556N5870 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0561N5870 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0536N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0541N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0546N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0551N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0556N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0561N5875 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0556N5880 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0551N5880 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0546N5880 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0541N5880 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0536N5880 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0536N5885 +"169","270","270",IJmuiden Ver Noord,,0.71433581429031,E0541N5885 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0131N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0131N6445 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0136N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0136N6445 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0141N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0141N6445 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0146N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0151N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0156N6450 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0136N6455 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0141N6455 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0146N6455 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0151N6455 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0156N6455 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0141N6460 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0146N6460 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0151N6460 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0156N6460 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0151N6465 +"136",UK77,UK77,Moray West,"Zone 1, Moray Firth Western Development Area",0.362625005201544,E0156N6465 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0541N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0546N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0551N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0556N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0561N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0566N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0571N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0576N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0581N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0586N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0591N5900 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0546N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0551N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0556N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0561N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0566N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0571N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0576N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0581N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0586N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0591N5905 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0546N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0551N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0556N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0561N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0566N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0571N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0576N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0581N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0586N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0591N5910 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0551N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0556N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0561N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0566N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0571N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0576N5915 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0551N5920 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0556N5920 +"171","274","274",Zoekgebied 2 Noord,,0.567616002004108,E0561N5920 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0171N6240 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0176N6240 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0181N6240 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0171N6245 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0176N6245 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0181N6245 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0181N6250 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0171N6250 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0176N6250 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0171N6255 +"125",UK56,UK56,Neart na Gaoithe,NnG,1,E0176N6255 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0481N5720 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0486N5720 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0491N5720 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0481N5725 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0486N5725 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0491N5725 +"8",BE08,BE08,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",1,E0486N5730 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0706N6100 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6100 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6100 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0721N6100 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0726N6100 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0706N6095 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6095 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6095 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0706N6105 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6105 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6105 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0721N6105 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0726N6105 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0706N6110 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6110 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6110 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0721N6110 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0706N6115 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6115 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6115 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0721N6115 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0711N6120 +"58",DE3Q,DE3Q,N-13-2,,0.331237140011782,E0716N6120 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5870 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0556N5875 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5875 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0566N5875 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0571N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0566N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0556N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0551N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0546N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0541N5880 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0541N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0546N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0551N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0556N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0566N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0571N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0576N5885 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0541N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0546N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0551N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0556N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0566N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0571N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0576N5890 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0541N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0546N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0551N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0556N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0561N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0566N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0571N5895 +"172","278","278",Zoekgebied 2 Zuid,,0.707222212874655,E0576N5895 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5890 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0401N5890 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5895 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0401N5895 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5900 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0401N5900 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0386N5905 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0391N5905 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5905 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0376N5910 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0381N5910 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0386N5910 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0391N5910 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5910 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0376N5915 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0381N5915 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0386N5915 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0391N5915 +"114",UK4G,UK4G,Dudgeon Extension,,0.274187572431998,E0396N5915 +"10",BE10,BE10,Thornton Bank phase III,"Zone 1, C-Power 2",1,E0496N5705 +"10",BE10,BE10,Thornton Bank phase III,"Zone 1, C-Power 2",1,E0491N5710 +"10",BE10,BE10,Thornton Bank phase III,"Zone 1, C-Power 2",1,E0496N5710 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0806N6045 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0801N6040 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0806N6040 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0811N6040 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0801N6030 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0801N6035 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0806N6035 +"20",DE07,DE07,Meerwind Süd/Ost,Meerwind,1,E0811N6035 +"94",UK27,UK27,Sheringham Shoal,,1,E0371N5885 +"94",UK27,UK27,Sheringham Shoal,,1,E0376N5885 +"94",UK27,UK27,Sheringham Shoal,,1,E0381N5885 +"94",UK27,UK27,Sheringham Shoal,,1,E0371N5890 +"94",UK27,UK27,Sheringham Shoal,,1,E0376N5890 +"94",UK27,UK27,Sheringham Shoal,,1,E0381N5890 +"94",UK27,UK27,Sheringham Shoal,,1,E0371N5895 +"94",UK27,UK27,Sheringham Shoal,,1,E0376N5895 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0396N5965 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0401N5965 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0406N5965 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0411N5965 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0426N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0431N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0436N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0441N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0396N5970 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0401N5970 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0406N5970 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0411N5970 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0396N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0401N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0406N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0411N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0416N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0421N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0426N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0431N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0436N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0441N5975 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0396N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0401N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0406N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0411N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0416N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0421N5980 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0396N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0401N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0406N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0411N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0416N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0421N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0426N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0431N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0436N5985 +"92",UK1U,UK1U,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",1,E0441N5985 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0481N5845 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0486N5845 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0491N5845 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0496N5845 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0501N5845 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0451N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0456N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0501N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0506N5865 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0456N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0486N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0491N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0496N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0501N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0506N5860 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0456N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0481N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0486N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0491N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0496N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0501N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0506N5855 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0456N5870 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5870 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5870 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5870 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0481N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0486N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0491N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0496N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0501N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0506N5850 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0461N5875 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0466N5875 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5875 +"130",UK67,UK67,Norfolk Vanguard,East Anglia Array,0.270526210749658,E0471N5880 +"96",UK29,UK29,Thanet,,1,E0401N5695 +"96",UK29,UK29,Thanet,,1,E0406N5695 +"96",UK29,UK29,Thanet,,1,E0411N5695 +"96",UK29,UK29,Thanet,,1,E0401N5700 +"96",UK29,UK29,Thanet,,1,E0406N5700 +"96",UK29,UK29,Thanet,,1,E0411N5700 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0151N6450 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0156N6450 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0151N6455 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0156N6455 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6455 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6455 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0171N6455 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0156N6460 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6460 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6460 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0171N6460 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0156N6465 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6465 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6465 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0171N6465 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0171N6470 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6470 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6470 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0156N6470 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0171N6475 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6475 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6475 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0166N6480 +"110",UK40,UK40,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",1,E0161N6480 +"38",DE2S,DE2S,N-3.7,,0.83252229460796,E0766N5990 +"38",DE2S,DE2S,N-3.7,,0.83252229460796,E0761N5995 +"38",DE2S,DE2S,N-3.7,,0.83252229460796,E0766N5995 +"38",DE2S,DE2S,N-3.7,,0.83252229460796,E0766N6000 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0776N6250 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0781N6250 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6250 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6250 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6250 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0771N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0776N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0781N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6240 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0771N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0776N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0781N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6245 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0776N6255 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0781N6255 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6255 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6255 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6255 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0781N6260 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6260 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6260 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6260 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0786N6265 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6265 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6265 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6270 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6270 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0791N6275 +"75",DK22,DK22,Thor - 2020 Tender,Horns Rev 4,0.203751050453494,E0796N6275 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0731N5980 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0736N5980 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0726N5985 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0731N5985 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0736N5985 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0726N5990 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0731N5990 +"17",DE04,DE04,Borkum Riffgrund 1,,1,E0736N5990 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0681N6015 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0676N6020 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0681N6020 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0686N6020 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0681N6025 +"32",DE24,DE24,Deutsche Bucht,"DBU, Debu",1,E0686N6025 +"45",DE33,DE33,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",1,E0801N6045 +"45",DE33,DE33,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",1,E0806N6045 +"45",DE33,DE33,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",1,E0801N6050 +"45",DE33,DE33,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",1,E0806N6050 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0576N5790 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0571N5790 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0566N5790 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0566N5780 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0571N5780 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0566N5785 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0571N5785 +"150","11","11",Hollandse Kust Zuid Holland III,Vattenfall,1,E0576N5785 +"39",DE2T,DE2T,N-3.8,,1.01618134017408,E0756N5990 +"39",DE2T,DE2T,N-3.8,,1.01618134017408,E0751N5995 +"39",DE2T,DE2T,N-3.8,,1.01618134017408,E0756N5995 +"39",DE2T,DE2T,N-3.8,,1.01618134017408,E0751N6000 +"39",DE2T,DE2T,N-3.8,,1.01618134017408,E0756N6000 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6230 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6230 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6230 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6235 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6235 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6235 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6235 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6240 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6240 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6240 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6240 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6245 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6245 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6245 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6245 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6245 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6255 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6250 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0216N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6265 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6265 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6260 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6265 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6265 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6265 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6270 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6270 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6270 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6270 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6270 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0191N6275 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0196N6275 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0201N6275 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0206N6275 +"134",UK75,UK75,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",1,E0211N6275 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0226N6115 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0226N6120 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0216N6125 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0221N6125 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0226N6125 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0221N6130 +"137",UK79,UK79,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",1,E0216N6130 +"143","4","4",Borssele Site V -Two towers,Two Towers,1,E0501N5730 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0411N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0416N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0421N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0426N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0431N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0436N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0441N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0446N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0451N5965 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0426N5980 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0436N5980 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0441N5980 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0411N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0416N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0421N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0426N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0431N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0436N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0441N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0446N5970 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0411N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0416N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0421N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0426N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0431N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0436N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0441N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0446N5975 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0416N5980 +"139",UK81,UK81,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",1,E0421N5980 +"93",UK23,UK23,Scroby Sands,,1,E0421N5830 +"93",UK23,UK23,Scroby Sands,,1,E0416N5830 +"93",UK23,UK23,Scroby Sands,,1,E0421N5835 +"93",UK23,UK23,Scroby Sands,,1,E0416N5835 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0756N5990 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0761N5990 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0766N5990 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0756N5995 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0761N5995 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0766N5995 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0756N6000 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0761N6000 +"27",DE13,DE13,Gode Wind 1 and 2,Gode Wind I and II,1,E0766N6000 +"18",DE05,DE05,Amrumbank West,,1,E0801N6055 +"18",DE05,DE05,Amrumbank West,,1,E0806N6055 +"18",DE05,DE05,Amrumbank West,,1,E0811N6055 +"18",DE05,DE05,Amrumbank West,,1,E0801N6050 +"18",DE05,DE05,Amrumbank West,,1,E0806N6050 +"18",DE05,DE05,Amrumbank West,,1,E0811N6050 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0646N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0651N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0656N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0661N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0666N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0671N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0676N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0681N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0686N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0691N5985 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0651N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0656N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0661N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0666N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0671N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0676N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0681N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0686N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0691N5990 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0681N5995 +"155",bw4,bw4,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,0.449849591499113,E0686N5995 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0456N5690 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0456N5695 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5690 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5695 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0466N5695 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0451N5700 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0456N5700 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5700 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0466N5700 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0451N5705 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0456N5705 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5705 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0466N5705 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0471N5705 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0456N5710 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5710 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0466N5710 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0471N5710 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0461N5715 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0466N5715 +"13",BE15,BE15,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",0.515117304961048,E0471N5715 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0731N5990 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0736N5990 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0731N5995 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0736N5995 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0731N6000 +"33",DE26,DE26,Merkur,MEG Offshore I,1,E0736N6000 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0551N5790 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0546N5790 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0541N5790 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0536N5790 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0546N5785 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0541N5785 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0536N5785 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0536N5795 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0541N5795 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0546N5795 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0551N5795 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0536N5800 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0541N5800 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0546N5800 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0551N5800 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0541N5805 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0546N5805 +"168","269","269",Hollandse Kust West Zuid,,0.562004727819683,E0551N5805 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5865 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5865 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5860 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5870 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5870 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5875 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5875 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5875 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0521N5880 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5880 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5880 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5880 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5885 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5885 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5885 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5890 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5890 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0516N5890 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5895 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0511N5895 +"170","272","272",Zoekgebied 1 Zuid,,0.827378566478684,E0506N5900 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6260 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6260 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6260 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0761N6260 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0741N6265 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6265 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6265 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6265 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0761N6265 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0741N6270 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6270 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6270 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6270 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0741N6275 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6275 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6275 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6275 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0741N6280 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6280 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6280 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6280 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0741N6285 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0746N6285 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0751N6285 +"70",DK1A,DK1A,Nordsøen - Tender 6,,0.268502077576817,E0756N6285 +"146","7","7",Eneco Luchterduinen,Luchterduinen,1,E0576N5805 +"146","7","7",Eneco Luchterduinen,Luchterduinen,1,E0581N5805 +"146","7","7",Eneco Luchterduinen,Luchterduinen,1,E0576N5810 +"146","7","7",Eneco Luchterduinen,Luchterduinen,1,E0581N5810 +"30",DE21,DE21,Riffgat,"OWP Riffgat, Borkum Riffgat",1,E0726N5955 +"30",DE21,DE21,Riffgat,"OWP Riffgat, Borkum Riffgat",1,E0731N5955 +"95",UK28,UK28,Teesside,Redcar,1,E0231N6065 +"95",UK28,UK28,Teesside,Redcar,1,E0236N6065 +"153","14","14",Borssele 1,Orsted,1,E0506N5725 +"153","14","14",Borssele 1,Orsted,1,E0511N5725 +"153","14","14",Borssele 1,Orsted,1,E0496N5730 +"153","14","14",Borssele 1,Orsted,1,E0501N5730 +"153","14","14",Borssele 1,Orsted,1,E0506N5730 +"153","14","14",Borssele 1,Orsted,1,E0511N5730 +"153","14","14",Borssele 1,Orsted,1,E0506N5735 +"153","14","14",Borssele 1,Orsted,1,E0501N5735 +"153","14","14",Borssele 1,Orsted,1,E0496N5735 +"153","14","14",Borssele 1,Orsted,1,E0501N5740 +"153","14","14",Borssele 1,Orsted,1,E0506N5740 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0536N5985 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0536N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0536N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6000 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N5985 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N5985 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N5985 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N5990 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N5995 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6025 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6025 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6025 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6025 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6025 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6005 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6010 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0541N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6015 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6020 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6020 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6020 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6020 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6020 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6030 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6030 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6030 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6030 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6030 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6035 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6035 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6035 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6035 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6035 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0546N6040 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0551N6040 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0556N6040 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0561N6040 +"165","264","264",Zoekgebied 7,,0.716811277733466,E0566N6040 +"19",DE06,DE06,Nordsee Ost,Innogy Nordsee Ost,1,E0801N6045 +"19",DE06,DE06,Nordsee Ost,Innogy Nordsee Ost,1,E0806N6045 +"19",DE06,DE06,Nordsee Ost,Innogy Nordsee Ost,1,E0801N6040 +"19",DE06,DE06,Nordsee Ost,Innogy Nordsee Ost,1,E0806N6040 +"19",DE06,DE06,Nordsee Ost,Innogy Nordsee Ost,1,E0801N6035 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0701N6015 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0706N6015 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0711N6015 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0701N6020 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0706N6020 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0711N6020 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0716N6020 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0721N6020 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0701N6025 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0706N6025 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0711N6025 +"42",DE2X,DE2X,N-7.2,,0.966563690056126,E0716N6025 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0541N5815 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0536N5815 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0536N5820 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0541N5820 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0546N5820 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0551N5820 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0536N5825 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0541N5825 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0546N5825 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0551N5825 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0536N5830 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0541N5830 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0546N5830 +"158",bw8,bw8,Hollandse Kust West zuidelijke punt,,0.442100522984425,E0551N5830 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0421N6060 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0426N6060 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6055 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6055 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6060 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6060 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0441N6060 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0411N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0416N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0421N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0426N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0441N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0446N6080 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0411N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0416N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0421N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0426N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0441N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0446N6065 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0411N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0416N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0421N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0426N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0441N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0446N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0451N6070 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0411N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0416N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0421N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0426N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0431N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0436N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0441N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0446N6075 +"138",UK80,UK80,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",1,E0451N6075 +"83",UK12,UK12,Kentish Flats,,1,E0371N5700 +"83",UK12,UK12,Kentish Flats,,1,E0366N5700 +"83",UK12,UK12,Kentish Flats,,1,E0371N5705 +"83",UK12,UK12,Kentish Flats,,1,E0366N5705 +"86",UK15,UK15,Lynn,,1,E0331N5890 +"86",UK15,UK15,Lynn,,1,E0326N5890 +"140","1","1",Borssele 2,Orsted,1,E0501N5715 +"140","1","1",Borssele 2,Orsted,1,E0506N5715 +"140","1","1",Borssele 2,Orsted,1,E0501N5720 +"140","1","1",Borssele 2,Orsted,1,E0506N5720 +"140","1","1",Borssele 2,Orsted,1,E0501N5725 +"140","1","1",Borssele 2,Orsted,1,E0506N5725 +"140","1","1",Borssele 2,Orsted,1,E0511N5725 +"140","1","1",Borssele 2,Orsted,1,E0501N5730 +"140","1","1",Borssele 2,Orsted,1,E0506N5730 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6200 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6200 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6205 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6205 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6205 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0701N6205 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0681N6225 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6225 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6225 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6225 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6210 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6210 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6210 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6215 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6215 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6215 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0681N6220 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6220 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0691N6220 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0696N6220 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0681N6230 +"73",DK1D,DK1D,Nordsøen - Tender 9,,0.294953503192635,E0686N6230 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0576N6000 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0581N6000 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N6000 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6000 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0576N5990 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0581N5990 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N5990 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0576N5995 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0581N5995 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N5995 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N5995 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6000 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0581N6005 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N6005 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6005 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0581N6010 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N6010 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6010 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6010 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6010 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N6015 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6015 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6015 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6015 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6015 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0586N6020 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6020 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6020 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6020 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6020 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6025 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6025 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6025 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6025 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6025 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6005 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0591N6030 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6030 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6030 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6030 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6030 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6035 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6035 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6035 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6035 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6035 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0596N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6040 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6045 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6045 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6045 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6045 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6045 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0601N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6050 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0631N6055 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0606N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0631N6060 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6065 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6065 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6065 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6065 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0631N6065 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0611N6070 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0616N6070 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6070 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6070 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0631N6070 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0621N6075 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0626N6075 +"166","265","265",Zoekgebied 6,,0.694927095850457,E0631N6075 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0751N5990 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0756N5990 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0746N5995 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0751N5995 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0756N5995 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0746N6000 +"36",DE2Q,DE2Q,N-3.5,,0.938110668545705,E0751N6000 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0746N5985 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0751N5985 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0756N5985 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0746N5990 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0751N5990 +"35",DE28,DE28,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",1,E0756N5990 +"7",BE07,BE07,Seamade (Mermaid),Zone 7,1,E0481N5725 +"7",BE07,BE07,Seamade (Mermaid),Zone 7,1,E0481N5730 +"7",BE07,BE07,Seamade (Mermaid),Zone 7,1,E0486N5730 +"7",BE07,BE07,Seamade (Mermaid),Zone 7,1,E0481N5735 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0561N5800 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0566N5800 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0571N5800 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0576N5800 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0581N5800 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0561N5805 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0566N5805 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0571N5805 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0576N5805 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0581N5805 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0571N5810 +"152","13","13",Hollandse Kust Zuid Holland I,Vattenfall,1,E0576N5810 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0746N5990 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0751N5990 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0746N5995 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0751N5995 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0746N6000 +"37",DE2R,DE2R,N-3.6,,1.02650002442391,E0751N6000 +"145","6","6",Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",1,E0581N5825 +"145","6","6",Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",1,E0586N5825 +"145","6","6",Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",1,E0581N5830 +"145","6","6",Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",1,E0586N5830 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0241N6470 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6470 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6470 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6470 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6475 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6475 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6475 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6480 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6480 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6480 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6485 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6485 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6485 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6465 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6465 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6495 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6495 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0261N6495 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0266N6495 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0246N6490 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6490 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6490 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0261N6490 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0251N6500 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0256N6500 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0261N6500 +"107",UK3O,UK3O,Scottish Sectoral Marine Plan - NE8,,0.261286297626097,E0261N6485 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0586N5915 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0591N5915 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0596N5915 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0581N5920 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0586N5920 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0591N5920 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0596N5920 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0601N5920 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0586N5925 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0591N5925 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0596N5925 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0601N5925 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0591N5930 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0596N5930 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0601N5930 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0596N5935 +"161","261","261",Zoekgebied 8,,0.955976648154192,E0601N5935 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0596N5825 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0601N5825 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0591N5830 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0596N5830 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0601N5830 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0591N5835 +"144","5","5",Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",1,E0596N5835 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6235 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6235 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6235 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6250 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6250 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6250 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6250 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6240 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6240 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6240 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6240 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6245 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6245 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6245 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6245 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6255 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6255 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6255 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6255 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0731N6260 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0736N6260 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6260 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6260 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0741N6265 +"71",DK1B,DK1B,Nordsøen - Tender 7,,0.280010982987494,E0746N6265 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0726N5990 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0731N5990 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0721N5995 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0726N5995 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0731N5995 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0721N6000 +"34",DE27,DE27,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",1,E0726N6000 +"99",UK30,UK30,Triton Knoll,,1,E0346N5925 +"99",UK30,UK30,Triton Knoll,,1,E0351N5925 +"99",UK30,UK30,Triton Knoll,,1,E0356N5925 +"99",UK30,UK30,Triton Knoll,,1,E0356N5920 +"99",UK30,UK30,Triton Knoll,,1,E0361N5925 +"99",UK30,UK30,Triton Knoll,,1,E0361N5920 +"99",UK30,UK30,Triton Knoll,,1,E0366N5925 +"99",UK30,UK30,Triton Knoll,,1,E0366N5920 +"99",UK30,UK30,Triton Knoll,,1,E0346N5930 +"99",UK30,UK30,Triton Knoll,,1,E0351N5930 +"99",UK30,UK30,Triton Knoll,,1,E0356N5930 +"99",UK30,UK30,Triton Knoll,,1,E0361N5930 +"99",UK30,UK30,Triton Knoll,,1,E0346N5935 +"99",UK30,UK30,Triton Knoll,,1,E0351N5935 +"99",UK30,UK30,Triton Knoll,,1,E0356N5935 +"99",UK30,UK30,Triton Knoll,,1,E0361N5935 +"173","279","279",zoekgebied 4,,0.658956559149009,E0671N5970 +"173","279","279",zoekgebied 4,,0.658956559149009,E0676N5970 +"173","279","279",zoekgebied 4,,0.658956559149009,E0681N5970 +"173","279","279",zoekgebied 4,,0.658956559149009,E0686N5970 +"173","279","279",zoekgebied 4,,0.658956559149009,E0691N5970 +"173","279","279",zoekgebied 4,,0.658956559149009,E0646N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0651N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0646N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0651N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0656N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0661N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0666N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0671N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0676N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0681N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0686N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0691N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0696N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0701N5980 +"173","279","279",zoekgebied 4,,0.658956559149009,E0646N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0651N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0656N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0661N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0666N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0671N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0676N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0681N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0686N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0691N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0696N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0701N5985 +"173","279","279",zoekgebied 4,,0.658956559149009,E0656N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0661N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0666N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0671N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0676N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0681N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0686N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0691N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0696N5975 +"173","279","279",zoekgebied 4,,0.658956559149009,E0701N5975 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0576N5830 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0581N5835 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0576N5835 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0586N5835 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0576N5840 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0581N5840 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0586N5840 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0591N5840 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0581N5845 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0586N5845 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0591N5845 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0586N5850 +"154","15","15",Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",1,E0591N5850 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6060 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0691N6080 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6080 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6080 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6080 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6080 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0686N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0691N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6075 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6065 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6065 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6065 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0691N6070 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6070 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6070 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6070 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6070 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6100 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6100 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0691N6085 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6085 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6085 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6085 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6085 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6090 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6090 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6090 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0711N6090 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0696N6095 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6095 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6095 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0701N6105 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6105 +"55",DE3K,DE3K,N-12.3,,0.183574489386103,E0706N6110 +"112",UK47,UK47,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",1,E0201N6350 +"112",UK47,UK47,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",1,E0196N6350 +"112",UK47,UK47,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",1,E0201N6355 +"112",UK47,UK47,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",1,E0196N6355 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0461N5715 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0466N5715 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0471N5715 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0466N5720 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0471N5720 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0466N5725 +"12",BE14,BE14,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",1.12716366640267,E0471N5725 +"123",UK53,UK53,Beatrice,BOWL,1,E0151N6460 +"123",UK53,UK53,Beatrice,BOWL,1,E0146N6465 +"123",UK53,UK53,Beatrice,BOWL,1,E0151N6465 +"123",UK53,UK53,Beatrice,BOWL,1,E0156N6465 +"123",UK53,UK53,Beatrice,BOWL,1,E0161N6470 +"123",UK53,UK53,Beatrice,BOWL,1,E0156N6470 +"123",UK53,UK53,Beatrice,BOWL,1,E0151N6470 +"123",UK53,UK53,Beatrice,BOWL,1,E0146N6470 +"123",UK53,UK53,Beatrice,BOWL,1,E0161N6475 +"123",UK53,UK53,Beatrice,BOWL,1,E0156N6475 +"123",UK53,UK53,Beatrice,BOWL,1,E0151N6475 +"123",UK53,UK53,Beatrice,BOWL,1,E0151N6480 +"123",UK53,UK53,Beatrice,BOWL,1,E0156N6480 +"123",UK53,UK53,Beatrice,BOWL,1,E0161N6480 +"3",BE03,BE03,Belwind,Zone 3 and Bligh Bank,1,E0486N5720 +"3",BE03,BE03,Belwind,Zone 3 and Bligh Bank,1,E0486N5725 +"3",BE03,BE03,Belwind,Zone 3 and Bligh Bank,1,E0491N5725 +"3",BE03,BE03,Belwind,Zone 3 and Bligh Bank,1,E0486N5730 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6060 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6060 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6060 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6080 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6080 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6080 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0726N6080 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6075 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6075 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6075 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0726N6075 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6065 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6065 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6065 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0726N6065 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6070 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6070 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6070 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0726N6070 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6085 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6085 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0721N6085 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0706N6090 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6090 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0716N6090 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0706N6095 +"57",DE3O,DE3O,N-11-2,,0.252769229221563,E0711N6095 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0576N5790 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0571N5790 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0571N5795 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0576N5795 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0581N5795 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0571N5800 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0576N5800 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0581N5800 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0581N5805 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0586N5805 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0581N5810 +"149","10","10",Hollandse Kust Zuid Holland IV,Vattenfall,1,E0586N5810 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0366N5895 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0361N5895 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0356N5895 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0366N5900 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0361N5900 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0356N5900 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0371N5905 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0366N5905 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0361N5905 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0356N5905 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0346N5910 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0351N5910 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0351N5915 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0356N5910 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0356N5915 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0361N5910 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0361N5915 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0366N5910 +"113",UK4F,UK4F,Race Bank Extension,,0.32142744411622,E0371N5910 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6025 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6025 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6025 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0441N6025 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0416N6055 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0421N6055 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6030 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6030 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6030 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0441N6030 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0446N6030 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0421N6035 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6035 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6035 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6035 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0441N6035 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0411N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0416N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0421N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0441N6040 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0416N6045 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0421N6045 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6045 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6045 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6045 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0416N6050 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0421N6050 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6050 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0431N6050 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0436N6050 +"121",UK4Y,UK4Y,Round 4 - Area 2 (RWE Renewables),,0.276645909719239,E0426N6055 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0791N6175 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0796N6175 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0801N6175 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0786N6180 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0791N6180 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0796N6180 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0801N6180 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0786N6185 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0791N6185 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0796N6185 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0786N6190 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0791N6190 +"69",DK19,DK19,Horns Rev 3,Horns Rev A HR3-HR4,1,E0796N6190 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0646N6000 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0651N6000 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6000 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6025 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6025 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0646N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0651N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0671N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0676N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0646N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0651N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0671N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0676N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0681N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0686N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0691N6010 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0651N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0671N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0676N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0681N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0686N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0691N6015 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0651N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0671N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0676N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0681N6020 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0681N6005 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6025 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0671N6025 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0676N6025 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0656N6030 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0661N6030 +"163",263k,"263",Zoekgebied 5 Oost klein,,0.27570652858588,E0666N6030 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0691N6020 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0696N6020 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0691N6025 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0696N6025 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0691N6030 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0696N6030 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0691N6035 +"31",DE23,DE23,BARD Offshore 1,BARD,1,E0696N6035 +"84",UK13,UK13,Lincs,,1,E0336N5890 +"84",UK13,UK13,Lincs,,1,E0331N5890 +"84",UK13,UK13,Lincs,,1,E0336N5895 +"84",UK13,UK13,Lincs,,1,E0331N5895 +"84",UK13,UK13,Lincs,,1,E0336N5900 +"84",UK13,UK13,Lincs,,1,E0331N5900 +"84",UK13,UK13,Lincs,,1,E0336N5905 +"84",UK13,UK13,Lincs,,1,E0331N5905 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0726N5980 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0731N5980 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0736N5980 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0726N5985 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0731N5985 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0736N5985 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0726N5990 +"44",DE30,DE30,Borkum Riffgrund 2,,1,E0736N5990 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0566N5845 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5845 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5865 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0576N5865 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0581N5865 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5860 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0576N5860 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0581N5860 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0566N5855 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5855 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0576N5855 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5870 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0576N5870 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0581N5870 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0586N5870 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0566N5850 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0571N5850 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0576N5850 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0581N5875 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0586N5875 +"167","268","268",Hollandse Kust West Noord,,0.608734166649731,E0586N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0511N5865 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5860 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5860 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5860 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5860 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5860 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5855 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5855 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0511N5870 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0511N5875 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0481N5880 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5885 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5885 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5885 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5885 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5885 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0486N5890 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5890 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5890 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5890 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5890 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0491N5895 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0496N5895 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5895 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5895 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0501N5900 +"131",UK69,UK69,Norfolk Boreas,East Anglia Six,0.230011710109251,E0506N5900 +"40",DE2V,DE2V,N-6.6,,1.02753249303333,E0686N6015 +"40",DE2V,DE2V,N-6.6,,1.02753249303333,E0691N6015 +"40",DE2V,DE2V,N-6.6,,1.02753249303333,E0696N6015 +"40",DE2V,DE2V,N-6.6,,1.02753249303333,E0691N6020 +"40",DE2V,DE2V,N-6.6,,1.02753249303333,E0696N6020 +"2",BE02,BE02,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",1,E0491N5715 +"2",BE02,BE02,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",1,E0496N5715 +"2",BE02,BE02,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",1,E0491N5720 +"2",BE02,BE02,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",1,E0496N5720 +"81",UK10,UK10,Humber Gateway,,1,E0321N5940 +"81",UK10,UK10,Humber Gateway,,1,E0316N5945 +"81",UK10,UK10,Humber Gateway,,1,E0321N5945 +"81",UK10,UK10,Humber Gateway,,1,E0326N5945 +"81",UK10,UK10,Humber Gateway,,1,E0316N5950 +"81",UK10,UK10,Humber Gateway,,1,E0321N5950 +"81",UK10,UK10,Humber Gateway,,1,E0326N5950 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0241N6315 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0236N6315 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0231N6315 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0226N6315 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0221N6315 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0246N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0241N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0236N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0231N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0226N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0221N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0216N6320 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0246N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0241N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0236N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0231N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0226N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0221N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0216N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0211N6325 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0241N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0236N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0231N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0226N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0221N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0216N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0211N6330 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0236N6335 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0231N6335 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0226N6335 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0221N6335 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0216N6335 +"102",UK3F,UK3F,Scottish Sectoral Marine Plan - E3,,0.192408764165719,E0211N6335 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0706N6100 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6100 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6100 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0721N6100 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0726N6100 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0706N6095 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6095 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6095 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0706N6105 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6105 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6105 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0721N6105 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0726N6105 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0706N6110 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6110 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6110 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0721N6110 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0706N6115 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6115 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6115 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0721N6115 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0711N6120 +"59",DE3R,DE3R,N-13-3,,0.331237140011782,E0716N6120 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0456N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0461N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0466N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5950 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5950 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0486N5950 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5955 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5955 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5955 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0486N5955 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0461N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0466N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0486N5960 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0486N5965 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0446N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0451N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0456N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0461N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0466N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5980 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0451N5985 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0456N5985 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5985 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5985 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5985 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0481N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0451N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0456N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0461N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0466N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5970 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0451N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0456N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0461N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0466N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0471N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0476N5975 +"91",UK1K,UK1K,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",0.316580099230253,E0446N5985 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0766N6110 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0771N6110 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0766N6115 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0771N6115 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0766N6120 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0771N6120 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0766N6125 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0771N6125 +"15",DE02,DE02,DanTysk,"Dan Tysk, Dantysk",1,E0766N6130 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5930 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5890 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5890 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5895 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5895 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5895 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5900 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5900 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5900 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0526N5900 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5905 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5905 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5905 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5905 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0526N5905 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0501N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0526N5910 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0501N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0526N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0531N5915 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0501N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0526N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0531N5920 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0501N5925 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0506N5925 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0511N5925 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0516N5925 +"159","258","258",Zoekgebied 1 Noord,,0.620421070080912,E0521N5925 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0546N5825 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0551N5825 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0541N5830 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0546N5830 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0551N5830 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0556N5830 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0556N5835 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0551N5835 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0546N5835 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0541N5835 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0546N5840 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0551N5840 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0556N5840 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0551N5845 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0556N5845 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0561N5845 +"157",bw7,bw7,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",0.638610108232491,E0556N5850 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0691N6260 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6260 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6260 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6260 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0691N6265 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6265 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6265 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6265 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0711N6265 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0691N6270 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6270 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6270 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6270 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0711N6270 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0691N6275 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6275 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6275 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6275 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0711N6275 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0691N6280 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6280 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6280 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6280 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0711N6280 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0696N6285 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0701N6285 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0706N6285 +"63",DK0V,DK0V,Nordsøen - Tender 1,,0.240086964185452,E0711N6285 +"14",DE01,DE01,Alpha Ventus,Borkum West I,1,E0736N5990 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0686N6055 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0681N6045 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0686N6045 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0691N6045 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0696N6045 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0681N6050 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0686N6050 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0691N6050 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0681N6040 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0686N6040 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0691N6040 +"49",DE3B,DE3B,N-9.2,,0.786502482401362,E0696N6040 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5735 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5735 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0431N5735 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5740 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5740 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0446N5740 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5745 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5745 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0446N5745 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5750 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5750 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5755 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5755 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0446N5755 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0451N5755 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0436N5760 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0441N5760 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0446N5760 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0451N5760 +"116",UK4I,UK4I,Five Estuaries,Galloper Extension,0.170361214027772,E0456N5760 +"46",DE36,DE36,Veja Mate,,1,E0681N6015 +"46",DE36,DE36,Veja Mate,,1,E0686N6015 +"46",DE36,DE36,Veja Mate,,1,E0691N6015 +"46",DE36,DE36,Veja Mate,,1,E0681N6020 +"46",DE36,DE36,Veja Mate,,1,E0686N6020 +"46",DE36,DE36,Veja Mate,,1,E0691N6020 +"46",DE36,DE36,Veja Mate,,1,E0681N6025 +"46",DE36,DE36,Veja Mate,,1,E0686N6025 +"46",DE36,DE36,Veja Mate,,1,E0691N6025 +"46",DE36,DE36,Veja Mate,,1,E0686N6030 +"46",DE36,DE36,Veja Mate,,1,E0691N6030 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6225 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6220 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6225 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6230 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6230 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6230 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6230 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0236N6230 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6235 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6235 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6235 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6235 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0236N6235 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6240 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6240 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6240 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6240 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0236N6240 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6245 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6245 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6245 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6245 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0236N6245 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0211N6255 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6255 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6250 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6255 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6250 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6255 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6250 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6255 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6250 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0211N6260 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6260 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6260 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6260 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0231N6260 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0211N6265 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6265 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6265 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6265 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0216N6270 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0221N6270 +"133",UK74,UK74,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",1,E0226N6270 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0196N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0201N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0206N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0211N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0216N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0221N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0226N6275 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0196N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0196N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0196N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0201N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0201N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0201N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0206N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0206N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0206N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0211N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0211N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0211N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0216N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0216N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0216N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0221N6290 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0221N6285 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0221N6280 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0196N6295 +"111",UK44,UK44,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",1,E0201N6295 +"22",DE09,DE09,Global Tech I,GT1,1,E0716N6045 +"22",DE09,DE09,Global Tech I,GT1,1,E0721N6045 +"22",DE09,DE09,Global Tech I,GT1,1,E0716N6050 +"22",DE09,DE09,Global Tech I,GT1,1,E0721N6050 +"22",DE09,DE09,Global Tech I,GT1,1,E0716N6040 +"22",DE09,DE09,Global Tech I,GT1,1,E0721N6040 +"79",UK07,UK07,Gunfleet Sands,GFS,1,E0381N5730 +"79",UK07,UK07,Gunfleet Sands,GFS,1,E0376N5730 +"79",UK07,UK07,Gunfleet Sands,GFS,1,E0381N5735 +"79",UK07,UK07,Gunfleet Sands,GFS,1,E0376N5735 +"127",UK62,UK62,Galloper,,1,E0426N5730 +"127",UK62,UK62,Galloper,,1,E0431N5730 +"127",UK62,UK62,Galloper,,1,E0436N5735 +"127",UK62,UK62,Galloper,,1,E0431N5735 +"127",UK62,UK62,Galloper,,1,E0426N5735 +"127",UK62,UK62,Galloper,,1,E0431N5740 +"127",UK62,UK62,Galloper,,1,E0436N5740 +"127",UK62,UK62,Galloper,,1,E0431N5750 +"127",UK62,UK62,Galloper,,1,E0436N5750 +"127",UK62,UK62,Galloper,,1,E0431N5755 +"127",UK62,UK62,Galloper,,1,E0436N5755 +"127",UK62,UK62,Galloper,,1,E0441N5755 +"127",UK62,UK62,Galloper,,1,E0426N5760 +"127",UK62,UK62,Galloper,,1,E0431N5760 +"127",UK62,UK62,Galloper,,1,E0436N5760 +"127",UK62,UK62,Galloper,,1,E0441N5760 +"98",UK2Y,UK2Y,ForthWind Offshore Wind Demonstration Project Phase 2,2B Energy Powerblock,1,E0131N6235 +"98",UK2Y,UK2Y,ForthWind Offshore Wind Demonstration Project Phase 2,2B Energy Powerblock,1,E0126N6235 +"98",UK2Y,UK2Y,ForthWind Offshore Wind Demonstration Project Phase 2,2B Energy Powerblock,1,E0126N6240 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0671N6025 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0676N6025 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0681N6045 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0676N6040 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0681N6040 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0686N6040 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0671N6030 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0676N6030 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0681N6030 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0671N6035 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0676N6035 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0681N6035 +"48",DE3A,DE3A,N-9.1,,0.790045547271775,E0686N6035 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0676N6020 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0681N6020 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0676N6025 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0681N6025 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0686N6025 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0681N6030 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0686N6030 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0691N6030 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0691N6035 +"41",DE2W,DE2W,N-6.7,,0.659432717979483,E0696N6035 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6080 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6080 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6080 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0461N6080 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0461N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0436N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0461N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0431N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0436N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0461N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0431N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0436N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0461N6100 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0466N6085 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0466N6090 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0466N6095 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6075 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6075 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0436N6105 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6105 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6105 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6105 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0456N6105 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0441N6110 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0446N6110 +"89",UK1G,UK1G,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",0.217756891962501,E0451N6110 +"9",BE09,BE09,Thornton Bank phase II,"Zone 1, C-Power 2",1,E0491N5710 +"9",BE09,BE09,Thornton Bank phase II,"Zone 1, C-Power 2",1,E0496N5710 +"9",BE09,BE09,Thornton Bank phase II,"Zone 1, C-Power 2",1,E0501N5710 +"9",BE09,BE09,Thornton Bank phase II,"Zone 1, C-Power 2",1,E0496N5715 +"9",BE09,BE09,Thornton Bank phase II,"Zone 1, C-Power 2",1,E0501N5715 +"11",BE12,BE12,Northwester 2,Zone 7,1,E0481N5720 +"11",BE12,BE12,Northwester 2,Zone 7,1,E0481N5725 +"11",BE12,BE12,Northwester 2,Zone 7,1,E0486N5725 +"11",BE12,BE12,Northwester 2,Zone 7,1,E0481N5730 +"11",BE12,BE12,Northwester 2,Zone 7,1,E0486N5730 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6440 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6440 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6455 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6455 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6455 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0186N6455 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6460 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6460 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6460 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0186N6460 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0191N6460 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6465 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6465 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6465 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0186N6465 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6470 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0186N6470 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6470 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6470 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0166N6470 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6475 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6475 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6475 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0166N6475 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0166N6480 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6480 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6480 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0161N6480 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6485 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0166N6485 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6445 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6445 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0171N6450 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0176N6450 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0181N6450 +"109",UK3Q,UK3Q,Scottish Sectoral Marine Plan - NE4,,0.20316258178768,E0186N6450 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0716N6055 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0721N6055 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0706N6045 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0711N6045 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0716N6045 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0711N6050 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0716N6050 +"43",DE2Z,DE2Z,N-8.4,,0.860886243610325,E0721N6050 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6225 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6225 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6230 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6230 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6235 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6235 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6235 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0701N6250 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6250 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6250 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6250 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0721N6250 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6240 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6240 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6240 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0701N6245 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6245 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6245 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6245 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0721N6245 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0701N6255 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0706N6255 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0711N6255 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6255 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0721N6255 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0716N6260 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0721N6260 +"64",DK0W,DK0W,Nordsøen - Tender 3,,0.249399377249197,E0726N6260 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6060 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0691N6080 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6080 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6080 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6080 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6080 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0686N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0691N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6075 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6065 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6065 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6065 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0691N6070 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6070 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6070 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6070 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6070 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6100 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6100 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0691N6085 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6085 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6085 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6085 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6085 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6090 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6090 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6090 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0711N6090 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0696N6095 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6095 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6095 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0701N6105 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6105 +"54",DE3J,DE3J,N-12.2,,0.183574489386104,E0706N6110 +"87",UK18,UK18,Race Bank,ROW01,1,E0361N5900 +"87",UK18,UK18,Race Bank,ROW01,1,E0356N5900 +"87",UK18,UK18,Race Bank,ROW01,1,E0351N5900 +"87",UK18,UK18,Race Bank,ROW01,1,E0361N5905 +"87",UK18,UK18,Race Bank,ROW01,1,E0356N5905 +"87",UK18,UK18,Race Bank,ROW01,1,E0351N5905 +"87",UK18,UK18,Race Bank,ROW01,1,E0351N5910 +"87",UK18,UK18,Race Bank,ROW01,1,E0356N5910 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6060 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0691N6080 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6080 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6080 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6080 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6080 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0686N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0691N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6075 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6065 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6065 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6065 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0691N6070 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6070 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6070 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6070 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6070 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6100 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6100 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0691N6085 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6085 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6085 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6085 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6085 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6090 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6090 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6090 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0711N6090 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0696N6095 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6095 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6095 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0701N6105 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6105 +"52",DE3E,DE3E,N-12.1,,0.183574489386104,E0706N6110 +"85",UK14,UK14,London Array,,1,E0396N5715 +"85",UK14,UK14,London Array,,1,E0401N5715 +"85",UK14,UK14,London Array,,1,E0391N5715 +"85",UK14,UK14,London Array,,1,E0391N5720 +"85",UK14,UK14,London Array,,1,E0396N5720 +"85",UK14,UK14,London Array,,1,E0401N5720 +"85",UK14,UK14,London Array,,1,E0396N5725 +"85",UK14,UK14,London Array,,1,E0391N5725 +"85",UK14,UK14,London Array,,1,E0401N5725 +"85",UK14,UK14,London Array,,1,E0396N5730 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0701N5990 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0706N5990 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0711N5990 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0716N5990 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0701N5995 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0706N5995 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0711N5995 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0716N5995 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0711N6000 +"16",DE03,DE03,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",1,E0716N6000 +"47",DE39,DE39,Albatros,"Hohe Extension, Albatros 1",1,E0706N6045 +"47",DE39,DE39,Albatros,"Hohe Extension, Albatros 1",1,E0711N6045 +"47",DE39,DE39,Albatros,"Hohe Extension, Albatros 1",1,E0716N6045 +"47",DE39,DE39,Albatros,"Hohe Extension, Albatros 1",1,E0706N6040 +"47",DE39,DE39,Albatros,"Hohe Extension, Albatros 1",1,E0711N6040 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6465 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6465 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6465 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6465 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6465 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0271N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0301N6455 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6460 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6460 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6460 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6460 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6460 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6445 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6445 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6445 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6445 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6445 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0271N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6450 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6440 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6440 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6440 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6440 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6440 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0276N6435 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6435 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6435 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6435 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6435 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0281N6470 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6470 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6470 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0296N6470 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0286N6475 +"106",UK3N,UK3N,Scottish Sectoral Marine Plan - NE7,,0.405156364582935,E0291N6475 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6330 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6330 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6330 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6330 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6335 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6335 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6335 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6335 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6340 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6340 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6340 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6340 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6345 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6345 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6345 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6345 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6350 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6350 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6350 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6350 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6355 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6355 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6355 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6355 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0291N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0296N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0301N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0306N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0311N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0316N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0271N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0276N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0276N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0281N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0281N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0281N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0286N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0286N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0286N6360 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0291N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0296N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0301N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0306N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0311N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0316N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0341N6365 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0271N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0276N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0281N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0286N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0291N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0296N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0301N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0306N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0311N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0316N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6375 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0271N6380 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0276N6380 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0281N6380 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0291N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0296N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0301N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0306N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0311N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0316N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0321N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0326N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0331N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0336N6370 +"103",UK3G,UK3G,Scottish Sectoral Marine Plan - E2,,0.14378345569441,E0341N6370 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0746N6115 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0751N6115 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0741N6135 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0746N6135 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0741N6120 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0746N6120 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0751N6120 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0741N6125 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0746N6125 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0741N6130 +"26",DE12,DE12,Sandbank,Sandbank24,1,E0746N6130 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0766N5990 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0771N5990 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0766N5995 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0771N5995 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0766N6000 +"23",DE0H,DE0H,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",1,E0771N6000 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6425 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6425 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0241N6440 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0241N6435 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6430 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6430 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6430 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6430 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0241N6430 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0241N6445 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6450 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6420 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6465 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6465 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6465 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6455 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6455 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6455 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6455 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0231N6455 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0211N6460 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0216N6460 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0221N6460 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0226N6460 +"105",UK3L,UK3L,Scottish Sectoral Marine Plan - NE6,,0.264735100349617,E0236N6425 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0381N5885 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0386N5885 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0371N5890 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0376N5890 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0381N5890 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0386N5890 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0366N5895 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0371N5895 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0376N5895 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0381N5895 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0371N5900 +"115",UK4H,UK4H,Sheringham Shoal Extension,,0.264715775339298,E0376N5900 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6485 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6485 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6485 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0191N6500 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6500 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6495 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6490 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6500 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6495 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6490 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6500 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6495 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6490 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0191N6505 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6505 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6505 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6505 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0196N6510 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0201N6510 +"108",UK3P,UK3P,Scottish Sectoral Marine Plan - NE3,,0.333116963214196,E0206N6510 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0711N6045 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0716N6045 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0711N6040 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0716N6040 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0721N6040 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0716N6030 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0721N6030 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0711N6035 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0716N6035 +"25",DE11,DE11,Hohe See,Hochsee Windpark 'Nordsee',1,E0721N6035 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5720 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5720 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5725 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5725 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0431N5725 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0416N5730 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5730 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5730 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0431N5730 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0436N5730 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0436N5735 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0431N5735 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5735 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5735 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0416N5735 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5740 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5740 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0431N5740 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5755 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5755 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5760 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5760 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0421N5765 +"117",UK4J,UK4J,North Falls,Greater Gabbard Extension,0.236609203279492,E0426N5765 +"148","9","9",Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0696N5990 +"148","9","9",Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0701N5990 +"148","9","9",Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0696N5995 +"148","9","9",Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0701N5995 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0696N6055 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0701N6055 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0706N6055 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0711N6055 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0696N6060 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0701N6060 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0706N6060 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0711N6060 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0696N6050 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0701N6050 +"50",DE3C,DE3C,N-10.1,,0.856152624875733,E0706N6050 +"1",BE01,BE01,Thornton Bank phase I,"Zone 1, C-Power",1,E0496N5710 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0686N5985 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0691N5985 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0686N5990 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0691N5990 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0681N5995 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0686N5995 +"147","8","8",Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",1,E0691N5995 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0206N6325 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0201N6325 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0211N6330 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0206N6330 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0201N6330 +"118",UK4N,UK4N,Kincardine - Phase 2,KOWL,1,E0206N6335 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0496N5705 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0501N5705 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0506N5705 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0496N5710 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0501N5710 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0506N5710 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0501N5715 +"4",BE04,BE04,Norther,"North Sea Power, Norther",1,E0506N5715 +"141","2","2",Borssele 3,Blauwwind,1,E0496N5715 +"141","2","2",Borssele 3,Blauwwind,1,E0501N5715 +"141","2","2",Borssele 3,Blauwwind,1,E0491N5720 +"141","2","2",Borssele 3,Blauwwind,1,E0496N5720 +"141","2","2",Borssele 3,Blauwwind,1,E0501N5720 +"141","2","2",Borssele 3,Blauwwind,1,E0491N5725 +"141","2","2",Borssele 3,Blauwwind,1,E0496N5725 +"141","2","2",Borssele 3,Blauwwind,1,E0501N5725 +"141","2","2",Borssele 3,Blauwwind,1,E0496N5730 +"141","2","2",Borssele 3,Blauwwind,1,E0501N5730 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0376N5925 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0381N5925 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0386N5925 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0366N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0371N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0376N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0381N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0386N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0391N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0396N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0401N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0406N5930 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0366N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0371N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0376N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0381N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0386N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0391N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0396N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0401N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0406N5935 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0371N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0376N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0381N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0386N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0391N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0396N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0401N5940 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0376N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0381N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0386N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0391N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0396N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0401N5945 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0391N5950 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0396N5950 +"122",UK4Z,UK4Z,Round 4 - Area 3 (GIG & Total),,0.273915597032719,E0401N5950 +"82",UK11,UK11,Inner Dowsing,,1,E0331N5895 +"82",UK11,UK11,Inner Dowsing,,1,E0326N5895 +"82",UK11,UK11,Inner Dowsing,,1,E0331N5900 +"82",UK11,UK11,Inner Dowsing,,1,E0326N5900 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5840 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0521N5845 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0526N5845 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5845 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5845 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5845 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0526N5865 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5865 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5865 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5865 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0526N5860 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5860 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5860 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5860 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0546N5860 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0521N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0526N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0546N5855 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5870 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5870 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5870 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0521N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0526N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0541N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0546N5850 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0531N5875 +"156",bw6,bw6,IJmuiden Ver,,0.867239473571812,E0536N5875 +"77",UK04,UK04,Dudgeon,,1,E0391N5895 +"77",UK04,UK04,Dudgeon,,1,E0396N5895 +"77",UK04,UK04,Dudgeon,,1,E0386N5900 +"77",UK04,UK04,Dudgeon,,1,E0391N5900 +"77",UK04,UK04,Dudgeon,,1,E0396N5900 +"77",UK04,UK04,Dudgeon,,1,E0386N5905 +"77",UK04,UK04,Dudgeon,,1,E0391N5905 +"77",UK04,UK04,Dudgeon,,1,E0396N5905 +"77",UK04,UK04,Dudgeon,,1,E0386N5910 +"77",UK04,UK04,Dudgeon,,1,E0391N5910 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N5985 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N5985 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0391N5985 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0396N5985 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0376N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0391N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0396N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0401N5990 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0371N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0376N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0391N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0396N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0401N5995 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0366N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0371N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0376N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0391N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0396N6000 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0366N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0371N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0376N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0391N6005 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0361N6010 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0366N6010 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0371N6010 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0376N6010 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0381N6010 +"90",UK1J,UK1J,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",0.187074957470665,E0386N6010 +"126",UK60,UK60,Kentish Flats Extension,Kentish Flats 2,1,E0371N5700 +"126",UK60,UK60,Kentish Flats Extension,Kentish Flats 2,1,E0366N5700 +"126",UK60,UK60,Kentish Flats Extension,Kentish Flats 2,1,E0361N5700 +"126",UK60,UK60,Kentish Flats Extension,Kentish Flats 2,1,E0366N5705 +"126",UK60,UK60,Kentish Flats Extension,Kentish Flats 2,1,E0361N5705 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0691N6055 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0696N6055 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0686N6060 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0691N6060 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0696N6060 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0701N6060 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0681N6065 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0686N6065 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0691N6065 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0696N6065 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0701N6065 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0681N6070 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0686N6070 +"53",DE3I,DE3I,N-10.2,,0.60160031790539,E0691N6070 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0401N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6080 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0401N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6085 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0401N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6090 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0401N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6095 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0401N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6100 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0406N6105 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0411N6105 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0416N6105 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0421N6105 +"80",UK0V,UK0V,Dogger Bank B,"Creyke Beck B, Zone 3",1,E0426N6105 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0486N5725 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0491N5725 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0496N5725 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0481N5730 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0486N5730 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0491N5730 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0496N5730 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0501N5735 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0496N5735 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0491N5735 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0486N5735 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0481N5735 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0496N5740 +"142","3","3",Borssele 4 - Blauwwind,Blauwwind,1,E0501N5740 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0721N5990 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0726N5990 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0721N5995 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0726N5995 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0731N5995 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0726N6000 +"24",DE0K,DE0K,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",1,E0731N6000 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0691N6175 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0696N6175 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6175 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0696N6180 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6180 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6180 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0696N6185 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6185 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6185 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0711N6185 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0696N6190 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6190 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6190 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0711N6190 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6195 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6195 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0711N6195 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0716N6195 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0701N6200 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6200 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0711N6200 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0716N6200 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0706N6205 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0711N6205 +"74",DK1E,DK1E,Nordsøen - Tender 10,,0.308598916567805,E0716N6205 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0196N6275 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0201N6275 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0206N6275 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0196N6285 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0196N6280 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0201N6285 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0201N6280 +"119",UK4P,UK4P,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",0.482785198112035,E0206N6280 +"21",DE08,DE08,Butendiek,Offshore-Bürgerpark Butendiek,1,E0806N6100 +"21",DE08,DE08,Butendiek,Offshore-Bürgerpark Butendiek,1,E0801N6105 +"21",DE08,DE08,Butendiek,Offshore-Bürgerpark Butendiek,1,E0806N6105 +"21",DE08,DE08,Butendiek,Offshore-Bürgerpark Butendiek,1,E0801N6110 +"21",DE08,DE08,Butendiek,Offshore-Bürgerpark Butendiek,1,E0806N6110 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0176N6265 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0181N6265 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0186N6265 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0191N6265 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0181N6270 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0186N6270 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0191N6270 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0176N6270 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0181N6275 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0176N6275 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0176N6280 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0181N6280 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0176N6285 +"124",UK54,UK54,Inch Cape,ICOL,0.555767082946618,E0181N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6270 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6270 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6270 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6270 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6275 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6275 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6275 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6275 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6280 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6280 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6280 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6280 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0736N6280 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0736N6285 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6290 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6290 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6290 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6290 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0736N6290 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0716N6295 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0721N6295 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0726N6295 +"65",DK0X,DK0X,Nordsøen - Tender 2,,0.227899612430474,E0731N6295 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6060 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6060 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6060 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6080 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6080 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6080 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0726N6080 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6075 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6075 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6075 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0726N6075 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6065 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6065 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6065 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0726N6065 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6070 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6070 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6070 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0726N6070 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6085 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6085 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0721N6085 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0706N6090 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6090 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0716N6090 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0706N6095 +"51",DE3D,DE3D,N-11-1,,0.252769229221563,E0711N6095 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0646N6000 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0651N6000 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6000 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6025 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6025 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0646N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0651N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0671N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0676N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0646N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0651N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0671N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0676N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0681N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0686N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0691N6010 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0651N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0671N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0676N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0681N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0686N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0691N6015 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0651N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0671N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0676N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0681N6020 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0681N6005 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6025 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0671N6025 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0676N6025 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0656N6030 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0661N6030 +"164",263o,"263",Zoekgebied 5 Oost origineel,,0.55141305717176,E0666N6030 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5815 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5820 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5820 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5820 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5825 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5825 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5825 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5830 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5830 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5830 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0496N5830 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0496N5835 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5835 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5835 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5835 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5840 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5840 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5840 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0496N5840 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0501N5840 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0481N5845 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0486N5845 +"129",UK66,UK66,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",0.402848978181247,E0491N5845 +"129",UK66,UK66,East Anglia Hub - 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+nat_30_favoured;2;NAT +nat_30_favoured;3;NAT +nat_30_favoured;4;NAT +nat_30_favoured;5;NAT +nat_30_favoured;7;NAT +nat_30_favoured;8;NAT +nat_30_favoured;9;NAT +nat_30_favoured;15;NAT +nat_30_favoured;bw7;NAT +nat_30_favoured;13;NAT +nat_30_favoured;12;NAT +nat_30_favoured;11;NAT +nat_30_favoured;10;NAT +nat_30_favoured;bw6;NAT +nat_30_favoured;6;NAT +nat_30_favoured;bw4;NAT +nat_30_favoured;bw8;NAT +nat_30_favoured;258;NAT +nat_30_favoured;272;NAT +nat_30_favoured;274;NAT +nat_30_favoured;263o;NAT +nat_30_favoured;270;NAT +nat_30_fallback;14;NAT +nat_30_fallback;1;NAT +nat_30_fallback;2;NAT +nat_30_fallback;3;NAT +nat_30_fallback;4;NAT +nat_30_fallback;5;NAT +nat_30_fallback;7;NAT +nat_30_fallback;8;NAT +nat_30_fallback;9;NAT +nat_30_fallback;15;NAT +nat_30_fallback;bw7;NAT +nat_30_fallback;13;NAT +nat_30_fallback;12;NAT +nat_30_fallback;11;NAT +nat_30_fallback;10;NAT +nat_30_fallback;bw6;NAT +nat_30_fallback;6;NAT +nat_30_fallback;bw4;NAT +nat_30_fallback;bw8;NAT +nat_30_fallback;272;NAT +nat_30_fallback;274;NAT +nat_30_fallback;263o;NAT +nat_30_fallback;270;NAT +int_30;14;INT +int_30;1;INT +int_30;2;INT +int_30;3;INT +int_30;4;INT +int_30;5;INT +int_30;7;INT +int_30;8;INT +int_30;9;INT +int_30;15;INT +int_30;bw7;INT +int_30;13;INT +int_30;12;INT +int_30;11;INT +int_30;10;INT +int_30;bw6;INT +int_30;6;INT +int_30;bw4;INT +int_30;bw8;INT +int_30;258;INT +int_30;272;INT +int_30;274;INT +int_30;263o;INT +int_30;270;INT +int_30;BE01;INT +int_30;BE02;INT +int_30;BE03;INT +int_30;BE04;INT +int_30;BE05;INT +int_30;BE06;INT +int_30;BE07;INT +int_30;BE08;INT +int_30;BE09;INT +int_30;BE10;INT +int_30;BE12;INT +int_30;BE14;INT +int_30;BE15;INT +int_30;DE39;INT +int_30;DE01;INT +int_30;DE05;INT +int_30;DE23;INT +int_30;DE04;INT +int_30;DE30;INT +int_30;DE03;INT +int_30;DE08;INT +int_30;DE02;INT +int_30;DE24;INT +int_30;DE19;INT +int_30;DE09;INT +int_30;DE13;INT +int_30;DE0H;INT +int_30;DE11;INT +int_30;DE33;INT +int_30;DE07;INT +int_30;DE26;INT +int_30;DE3C;INT +int_30;DE3I;INT +int_30;DE3R;INT +int_30;DE2Q;INT +int_30;DE2R;INT +int_30;DE2S;INT +int_30;DE2T;INT +int_30;DE2V;INT +int_30;DE2W;INT +int_30;DE2X;INT +int_30;DE2Z;INT +int_30;DE3A;INT +int_30;DE3B;INT +int_30;DE3S;INT +int_30;DE3T;INT +int_30;DE20;INT +int_30;DE28;INT +int_30;DE06;INT +int_30;DE21;INT +int_30;DE12;INT +int_30;DE27;INT +int_30;DE0K;INT +int_30;DE36;INT +int_30;DK03;INT +int_30;DK10;INT +int_30;DK19;INT +int_30;DK22;INT +int_30;DK48;INT +int_30;UK04;INT +int_30;UK05;INT +int_30;UK07;INT +int_30;UK0V;INT +int_30;UK10;INT +int_30;UK11;INT +int_30;UK12;INT +int_30;UK13;INT +int_30;UK14;INT +int_30;UK15;INT +int_30;UK18;INT +int_30;UK1F;INT +int_30;UK1G;INT +int_30;UK1K;INT +int_30;UK1U;INT +int_30;UK23;INT +int_30;UK27;INT +int_30;UK28;INT +int_30;UK29;INT +int_30;UK2Q;INT +int_30;UK30;INT +int_30;UK34;INT +int_30;UK39;INT +int_30;UK3F;INT +int_30;UK40;INT +int_30;UK44;INT +int_30;UK47;INT +int_30;UK4F;INT +int_30;UK4G;INT +int_30;UK4H;INT +int_30;UK4I;INT +int_30;UK4J;INT +int_30;UK4N;INT +int_30;UK4P;INT +int_30;UK53;INT +int_30;UK54;INT +int_30;UK56;INT +int_30;UK60;INT +int_30;UK62;INT +int_30;UK64;INT +int_30;UK66;INT +int_30;UK67;INT +int_30;UK69;INT +int_30;UK70;INT +int_30;UK74;INT +int_30;UK76;INT +int_30;UK77;INT +int_30;UK79;INT +int_30;UK80;INT +int_30;UK81;INT +nat_30_restricted;14;NAT +nat_30_restricted;1;NAT +nat_30_restricted;2;NAT +nat_30_restricted;3;NAT +nat_30_restricted;4;NAT +nat_30_restricted;5;NAT +nat_30_restricted;7;NAT +nat_30_restricted;8;NAT +nat_30_restricted;9;NAT +nat_30_restricted;15;NAT +nat_30_restricted;bw7;NAT +nat_30_restricted;13;NAT +nat_30_restricted;12;NAT +nat_30_restricted;11;NAT +nat_30_restricted;10;NAT +nat_30_restricted;bw6;NAT +nat_30_restricted;6;NAT +nat_30_restricted;bw4;NAT +nat_30_restricted;bw8;NAT +nat_30_restricted;274;NAT +nat_30_restricted;263o;NAT +nat_30_restricted;270;NAT diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/csv/gridid710_selection_kec4.csv b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/gridid710_selection_kec4.csv new file mode 100644 index 0000000000000000000000000000000000000000..3afce9cb11a29eb502ba4a1df360d83fc21b8c5b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/gridid710_selection_kec4.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03da9547d34457f4554ece4c22fb799b52acacbf7deaf38147c75361f8aa26f4 +size 4190017 diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/csv/windsp5c_vectorgrid.csv b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/windsp5c_vectorgrid.csv new file mode 100644 index 0000000000000000000000000000000000000000..ddbf5cd94ac7c7fa408a0574e52b0d98d1a4ad73 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/csv/windsp5c_vectorgrid.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b8912b6f0dd3117995a84dd537fbcf91365ac466186fc339610ab4970c73216 +size 4124119 diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/initialize.R b/script_py_r/kec4_pyconversion-master/IBM_maps/initialize.R new file mode 100644 index 0000000000000000000000000000000000000000..0bf8fdc8bbf61e30eba9af51deef3747c8ed586c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/initialize.R @@ -0,0 +1,56 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 + + +## This script is sourced and loads some packages +## and defines some plotting options +library(raster) +library(rgdal) +library(OpenStreetMap) +library(maptools) +library(sp) +library(plot.matrix) +library(tidyverse) + +season_lbs <- c('1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul") + +theme_WMR <- theme_classic() + + theme(text = element_text(size = 8), + title = element_text(size = 9), + rect = element_rect(size = 0.5), + line = element_line(size = 0.5)) + +ggopts <- ggplot2::theme_bw() + + ggplot2::theme( + panel.grid.minor = element_blank(), + panel.grid.major = element_blank(), + axis.title = element_text(size = 8), + axis.text = element_text(size = 8), + legend.text = element_text(size = 8), + legend.title = element_text(size = 8), + legend.key.height = unit(8, "pt"), + legend.position = 'bottom', + legend.box = "horizontal", + legend.margin = margin(0,0,0,0), + legend.box.margin = margin(0,0,0,0), + strip.text.x = element_text(size = 8), + strip.text.y = element_text(size = 8), + strip.background = element_rect(fill = "white"), + plot.title = element_text(size = 9, hjust = 0.5)) +## \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/plot_map.R b/script_py_r/kec4_pyconversion-master/IBM_maps/plot_map.R new file mode 100644 index 0000000000000000000000000000000000000000..55f2f5278e59bdd107bf634295c17e9c4ff9d3c0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/plot_map.R @@ -0,0 +1,237 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# File : plot_map.R +# +# Project : KEC4 +# +# Description : - Reads and plot the North Sea grid used for KEC4 +# - Reads the data frames created by prep_map.R +# - Plot the surveyed area coloured by country +# - Plot the presence of Northern Gannets +# +# Dependencies : (Created in prep_map.R and transform_grid.R): +# - The fine grid : NS_KEC4_grid_25km_IBM.rds +# - The coarse grid : NS_KEC4_grid_5km_IBM.rds +# - The MB abundance in fine grid : ID710_Grid_5km_Batch_INT_NA_Seasons_6.rds +# - The MB abundance in coarse grid : ID710_Grid_25km_Batch_INT_NA_Seasons_6.rds +# +# Author : Vincent Hin +# Institure : Wageningen Marine Research +# Date created : 9 May 2021 +# Date modified : 30 June 2021 +##################################################################### +source("initialize.R") + +## +NSgrid <- readRDS("rds/NS_KEC4_grid_5km_IBM.rds") +MRG <- readRDS(file = "rds/ID710_Grid_5km_Batch_INT_NA_Seasons_6.rds") + +outfld <- "figures" +if(!file.exists(outfld)) dir.create(outfld, recursive = TRUE) + +####################################################### +## : 1 - SETUP CRS AND PLOT THE GRID +####################################################### + +## Get the coordinate reference system UTM31 / EPSG 32631 +UTM31 <- CRS("+init=epsg:32631") + +## Convert coordinates to SpatialPoints +pts <- data.frame('Easting' = NSgrid$Easting, 'Northing' = NSgrid$Northing) +pts_utm <- pts +coordinates(pts_utm) <- ~Easting+Northing +proj4string(pts_utm) <- UTM31 + +## Convert to LONLAT and OSM projections +pts_lonlat <- spTransform(pts_utm, longlat()) +pts_osm <- spTransform(pts_utm, osm()) + +## Get the boundaries of the NSgrid +upperLeft <- c(raster::ymax(pts_lonlat) + 0.2, raster::xmin(pts_lonlat) - 0.2) +lowerRight <- c(raster::ymin(pts_lonlat) - 0.2, raster::xmax(pts_lonlat) + 0.2) + +## Get a background map from OpenStreetMaps +osm <- openmap(upperLeft, lowerRight, type="osm") + +## Convert OpenStreetMap to UTM31 projection +osm_utm <- openproj(osm, projection = UTM31) +saveRDS(osm_utm, file = "rds/osm_utm.rds") + +## Regular plot of backgroundmap +plot(osm_utm) +axis(side = 1); axis(2) +## Add grid to backgroundmap +plot(pts_utm, col = 'black', pch = 19, cex = 0.1, add = T, axis = FALSE) + +## ggplot NSgrid - Coloured by VGshow +gg1 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 North Sea grid, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = NSgrid, aes(x = Easting, y = Northing, colour = as.factor(VGshow)), + size = 0.1, shape = 15, alpha = 0.5) + + scale_colour_manual(values = c('0' = 'gray', + '1' = 'white')) + + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "NSgrid_VGshow_EPSG32631.pdf"), width = 12, height = 12) +gg1 +dev.off() + +## ggplot NSgrid - Coloured by Land +gg2 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 North Sea grid, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = NSgrid, aes(x = Easting, y = Northing, colour = as.factor(Land)), + size = 0.1, shape = 15) + + scale_colour_manual(values = c('0' = 'gray', + '1' = 'white')) + + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "NSgrid_Land_EPSG32631.pdf"), width = 12, height = 12) +gg2 +dev.off() + +## ggplot NSgrid VGshow == 0 & Land == 0 +gg3 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 North Sea grid, VGshow == 0 & Land == 0. EPSG = 32631", + x = "Easting", y = "Northing") + + geom_point(data = subset(NSgrid, Land == 0 & VGshow == 0), + aes(x = Easting, y = Northing), colour = 'red', size = 0.1, shape = 15) + + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "NSgrid_VGshow0_Land0_EPSG32631.pdf"), width = 12, height = 12) +gg3 +dev.off() + +## ggplot NSgrid VGshow == 1, coloured by country +gg4 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 North Sea grid, VGshow = 1, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = subset(NSgrid, VGshow == 1), aes(x = Easting, y = Northing, colour = CountryCode), + size = 0.2, shape = 19, alpha = 0.5) + + scale_colour_manual(values = c('XX' = 'black', + 'UK' = 'blue', + 'BE' = 'red', + 'NL' = 'orange', + 'DK' = 'magenta', + 'DE' = 'yellow', + 'NO' = 'green')) + + guides(colour = guide_legend(override.aes = list(size = 5))) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "NSgrid_VGshow1_CountryColours_EPSG32631.pdf"), width = 12, height = 12) +gg4 +dev.off() + + +####################################################### +## : 2 - PLOT NORTHERN GANNET ABUNDANCES +####################################################### + +## ggplot Morus bassanus presence ~ INT Batch, VGshow = 1 +gg5 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 Morus bassanus presence, Batch = INT, VGshow = 1, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = subset(MRG, VGshow == 1 & !is.na(Predicted) & Batch == "INT"), + aes(x = Easting, y = Northing, colour = Predicted > 0.), size = 0.2, shape = 19) + + # scale_colour_discrete(colors = c("blue", "red", "orange", "yellow")) + + facet_wrap(Season ~ ., ncol = 3, labeller = labeller(Season = season_lbs)) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "MB_presence_INT_VGshow1_Grid_5km_EPSG32631.pdf"), width = 12, height = 12) +gg5 +dev.off() +## + +## ggplot Morus bassanus presence ~ NAT Batch, VGshow = 1 +gg6 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 Morus bassanus presence, Batch = NAT, VGshow = 1, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = subset(MRG, VGshow == 1 & !is.na(Predicted) & Batch == "NAT"), + aes(x = Easting, y = Northing, colour = Predicted > 0.), size = 0.2, shape = 19) + + # scale_colour_discrete(colors = c("blue", "red", "orange", "yellow")) + + facet_wrap(Season ~ ., ncol = 3, labeller = labeller(Season = season_lbs)) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "MB_presence_NAT_VGshow1_Grid_5km_EPSG32631.pdf"), width = 12, height = 12) +gg6 +dev.off() +## + +## ggplot Morus bassanus predicted density ~ INT Batch, VGshow = 1 +gg7 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 Morus bassanus predicted density, Batch = INT, VGshow = 1, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = subset(MRG, VGshow == 1 & !is.na(Predicted) & Batch == "INT"), + aes(x = Easting, y = Northing, colour = Predicted), size = 0.1, shape = 15) + + scale_colour_gradientn(colors = c("blue", "red", "orange", "yellow")) + + facet_wrap(Season ~ ., ncol = 3, labeller = labeller(Season = season_lbs)) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "MB_predicted_INT_VGshow1_Grid_5km_EPSG32631.pdf"), width = 12, height = 12) +gg7 +dev.off() +## + +## ggplot Morus bassanus is NA ~ coloured by VGshow +## All VGshow = 0 are predicted NA +gg8 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(title = "KEC4 Morus bassanus predicted density == NA, EPSG = 32631", x = "Easting", y = "Northing") + + geom_point(data = subset(MRG, is.na(Predicted)), + aes(x = Easting, y = Northing, colour = as.factor(VGshow)), + alpha = 0.5, size = 0.2, shape = 15) + + facet_grid(Season ~ Batch, labeller = labeller(Season = season_lbs)) + + ggopts +## +cairo_pdf(filename = file.path(outfld, "MB_NA_predicted_Grid_5km_EPSG32631.pdf"), width = 6, height = 15) +gg8 +dev.off() +## + +####################################################### +## : 3 - PLOT SCALED DENSITY MAPS +####################################################### +MRG_split_scaled <- readRDS(file = "rds/ScaledPredicted_Season_Batch.rds") +MRG_scaled <- bind_rows(MRG_split_scaled) +osm_utm <- readRDS(file = "rds/osm_utm.rds") + +## ggplot Morus bassanus predicted density ~ INT Batch, VGshow = 1 +gg9 <- + OpenStreetMap::autoplot.OpenStreetMap(osm_utm) + + labs(x = "Easting", y = "Northing") + + geom_point(data = subset(MRG_scaled, VGshow == 1 & !is.na(Predicted) & Batch == "INT"), + aes(x = Easting, y = Northing, colour = Predicted), size = 0.1, shape = 15) + + scale_colour_gradientn(colors = c("blue", "red", "orange", "yellow")) + + facet_wrap(Season ~ ., ncol = 3, labeller = labeller(Season = season_lbs)) + + theme_classic() + theme(text = element_text(size = 10), title = element_text(size = 10), + rect = element_rect(size = 0.5), line = element_line(size = 0.5)) +## +cairo_pdf(filename = file.path(outfld, "MB_scaled_INT_VGshow1_Grid_5km_EPSG32631.pdf"), width = 12, height = 12) +gg9 +dev.off() + +## ---- EOF ----- ## diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/plot_matfiles.R b/script_py_r/kec4_pyconversion-master/IBM_maps/plot_matfiles.R new file mode 100644 index 0000000000000000000000000000000000000000..125edee5cd1985d83128c08573cb34e9e9fc7549 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/plot_matfiles.R @@ -0,0 +1,115 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# File : plot_matfiles.R +# +# Project : KEC4 +# +# Description : - Reads all matrices in /figures/matrices +# - Makes a plot of every matrix in /figures/matrices +# +# Dependencies : - .mat files with matrices for the KEC4ibm +# +# Author : Vincent Hin +# Institure : Wageningen Marine Research +# Date created : 27 May 2021 +# Date modified : 6 June 2021 +###################################################################### +source('read_IBMmap.R') + +## list all files in matfiles/ folder +fls <- list.files(pattern = "*.mat", path = "matfiles/") + +outfld <- "figures/matrices" +if(!file.exists(outfld)) dir.create(outfld, recursive = TRUE) + +for(i in 1:length(fls)){ + + mat <- read_IBMmap(fln = fls[i], fld = "matfiles/", type = "table") + mat_rast <- rasterFromXYZ(mat) + + ## Create name of plotfile + pltnm <- gsub(".mat", ".pdf", fls[i]) + pltnm <- file.path(outfld, pltnm) + + pdf(file = pltnm, width = 12, height = 12) + plot(mat_rast, "value", main = fls[i], xlab = "Easting", ylab = "Northing") + dev.off() +} +## + +## Get all files with predicted density +pred.fls <- list.files(pattern = "MB_predicted.*.mat", path = "matfiles/") + +for(i in 1:length(pred.fls)){ + mat <- as.matrix(read.table(file.path("matfiles", pred.fls[i]), sep = "")) + mat <- mat[nrow(mat):1,] + colnames(mat) <- 1:ncol(mat) + mat <- t(mat) + mat <- reshape2::melt(mat) + names(mat)[1:2] <- c('x', 'y') + mat$value <- log(mat$value) + + mat_rast <- rasterFromXYZ(mat) + + ## Create name of file + pltnm <- gsub(".mat", ".pdf", pred.fls[i]) + pltnm <- gsub("predicted", "log_predicted", pltnm) + pltnm <- file.path(outfld, pltnm) + + pdf(file = pltnm, width = 12, height = 12) + par(mar = c(5,5,4,4)) + plot(mat_rast, "value", main = pred.fls[i], xlab = "Easting", ylab = "Northing") + dev.off() +} +## + +## ---- MAKE A FIGURE FOR THE REPORT ---- +fl1 <- "OWP_ScaleDensity_Grid_5km_INT30_a1.0.mat" +mat1 <- read_IBMmap(fln = fl1, fld = "matfiles/", type = "table") +mat1_rast <- rasterFromXYZ(mat1) +fl2 <- "MB_predicted_Grid_5km_INT30_a1.0_INT.3.mat" +mat2 <- read_IBMmap(fln = fl2, fld = "matfiles/", type = "table") +mat2_rast <- rasterFromXYZ(mat2) +fl3 <- "MB_predicted_Grid_5km_INT.3.mat" +mat3 <- read_IBMmap(fln = fl3, fld = "matfiles/", type = "table") +mat3_rast <- rasterFromXYZ(mat3) + +## Set ranges +EW_range <- c(38,212) +NS_range <- c(9,189) + +png("figures/Maps_season3.png", width = 7.5, height = 2.5, units = 'in', res = 600) +par(mfrow = c(1,3), mar = c(4,5,3,3), oma = c(0,0,0,1)) +plot(mat1_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range, ylim = NS_range, main = "OWF scale") +plot(mat3_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range, ylim = NS_range, main = "Habitat quality") +plot(mat2_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range, ylim = NS_range, main = "OWF-adjusted\nhabitat quality") +dev.off() + +## +EW_range2 <- c(100,150) +NS_range2 <- c(9,100) + +par(mfrow = c(1,3)) +plot(mat1_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range2, ylim = NS_range2, main = "OWF scale") +plot(mat3_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range2, ylim = NS_range2, main = "Habitat quality") +plot(mat2_rast, "value", xlab = "Easting", ylab = "Northing", xlim = EW_range2, ylim = NS_range2, main = "OWF-adjusted\nhabitat quality") +## diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/prep_OWF_scenario.R b/script_py_r/kec4_pyconversion-master/IBM_maps/prep_OWF_scenario.R new file mode 100644 index 0000000000000000000000000000000000000000..103ed9f088612120d1905f6b2bebb8db1cd33f2b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/prep_OWF_scenario.R @@ -0,0 +1,228 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# File : prep_OWF_scenario.R +# +# Project : KEC4 +# +# Description : - Read csv data of Offshore Wind Farms +# - Match OWFs to KEC4 grid +# - Outputs to MatLab file for input in IBM simulation +# +# Dependencies : - csv: "IBM_710_OWPattributes.csv" +# : - csv: "IBM_710_OWPdata2VGgrid_id.csv" +# : - csv: "OWP_SB_Scenarios.csv" +# +# Author : Vincent Hin +# Institure : Wageningen Marine Research +# Date created : 17 May 2021 +# Date modified : 17 May 2021 +##################################################################### +rm(list = ls()) + +## Read csv data + +## Wind Farms attributes +OWP_atts <- read.csv("csv/IBM_710_OWPattributes.csv") + +## Wind Farm Location (gridcells) +OWP_grid <- read.csv("csv/IBM_710_OWPdata2VGgrid_id.csv") + +## Wind Farm ID per scenario +OWP_scns <- read.csv("csv/OWP_SB_Scenarios.csv", sep = ";") +OWP_scns <- OWP_scns %>% rename("scenario" = matches("SCENARIO")) + +## Read the KEC4 grid +grid <- readRDS("rds/NS_KEC4_grid_5km_IBM.rds") +gridKMSquare <- unique(grid$Shape_Area / 1000^2) + +## Read list with predicted abundances of Morus bassanus per batch and season +Pred_list <- readRDS("rds/ScaledPredicted_Season_Batch.rds") +## + +## Add the OWP area to OWP_grid df +OWP_idx <- match(OWP_grid$UniekID, OWP_atts$UniekID) +OWP_grid$Area..SqKm <- OWP_atts$Area..SqKm[OWP_idx] +OWP_grid$ingeschatt <- OWP_atts$ingeschatt[OWP_idx] + +## Get the unique IDs for Scenario INT and nat_30_favoured +INT30_OWPs <- OWP_scns$UniekID[OWP_scns$scenario == "int_30"] +NAT30fav_OWPs <- OWP_scns$UniekID[OWP_scns$scenario == "nat_30_favoured"] + +## Split the OWP_grid into a list +OWP_gridlist <- lapply(split(OWP_grid, list(OWP_grid$UniekID)), as.list) +OWP_gridlist <- lapply(OWP_gridlist, function(x) lapply(x, unique)) + +## Calculate the size of each OWP relative to the number of gridcells it covers +for(i in 1:length(OWP_gridlist)) { + OWP_gridlist[[i]]$OWP_fraction <- + OWP_gridlist[[i]]$Area..SqKm * OWP_gridlist[[i]]$ScaleFactor / (gridKMSquare * length(OWP_gridlist[[i]]$GRID_ID)) +} + +## Select all OWPs for Scenarios int_30 & nat_30_favoured +OWP_gridlist_INT <- OWP_gridlist[match(INT30_OWPs, names(OWP_gridlist))] +OWP_gridlist_NAT30fav <- OWP_gridlist[match(NAT30fav_OWPs, names(OWP_gridlist))] +## + +## Add columns for INT30 and NAT30fav scenarios +grid$INT30_scale_a1.0 <- 1 +grid$INT30_scale_a0.8 <- 1 +grid$INT30_number <- 0 +grid$INT30_name <- "" +grid$NAT30fav_scale_a1.0 <- 1 +grid$NAT30fav_scale_a0.8 <- 1 +grid$NAT30fav_number <- 0 +grid$NAT30fav_name <- "" + +## Fill grid OWP variables for INT scenario +for(i in 1:length(OWP_gridlist_INT)) { + grid_idx <- match(OWP_gridlist_INT[[i]]$GRID_ID, grid$GRID_ID) + grid$INT30_scale_a1.0[grid_idx] <- grid$INT30_scale_a1.0[grid_idx] * (1 - OWP_gridlist_INT[[i]]$OWP_fraction) + grid$INT30_scale_a0.8[grid_idx] <- grid$INT30_scale_a0.8[grid_idx] * (1 - 0.8 * OWP_gridlist_INT[[i]]$OWP_fraction) + grid$INT30_name[grid_idx] <- paste(grid$INT30_name[grid_idx], OWP_gridlist_INT[[i]]$UniekID) + grid$INT30_number[grid_idx] <- grid$INT30_number[grid_idx] + 1 +} + +## Fill grid OWP variables for NAT30_favoured scenario +for(i in 1:length(OWP_gridlist_NAT30fav)) { + grid_idx <- match(OWP_gridlist_NAT30fav[[i]]$GRID_ID, grid$GRID_ID) + grid$NAT30fav_scale_a1.0[grid_idx] <- grid$NAT30fav_scale_a1.0[grid_idx] * (1 - OWP_gridlist_NAT30fav[[i]]$OWP_fraction) + grid$NAT30fav_scale_a0.8[grid_idx] <- grid$NAT30fav_scale_a0.8[grid_idx] * (1 - 0.8 * OWP_gridlist_NAT30fav[[i]]$OWP_fraction) + grid$NAT30fav_name[grid_idx] <- paste(grid$NAT30fav_name[grid_idx], OWP_gridlist_NAT30fav[[i]]$UniekID) + grid$NAT30fav_number[grid_idx] <- grid$NAT30fav_number[grid_idx] + 1 +} + +## Add columns +grid$INT30_hasOWP <- ifelse(grid$INT30_number > 0, 1, 0) +grid$NAT30fav_hasOWP <- ifelse(grid$NAT30fav_number > 0, 1, 0) + +## Trim white spaces from names +grid$INT30_name <- trimws(grid$INT30_name) +grid$NAT30fav_name <- trimws(grid$NAT30fav_name) +## + +## -------------------------------------------- ## +## : 3 - Convert scenarios to matrix and save ---- +## -------------------------------------------- ## + +## Number of rows must equal number of Northings +## Number of cols must equal number of Eastings +nrows <- length(unique(grid$Northing_idx)) +ncols <- length(unique(grid$Easting_idx)) + +## Create matrices with OWP scalars +MAT_INT30_a1.0 <- matrix(data = 1.0, + nrow = nrows, + ncol = ncols, + dimnames = NULL) +MAT_NAT30fav_a1.0 <- MAT_NAT30fav_a0.8 <- MAT_INT30_a0.8 <- MAT_INT30_a1.0 +## + +## +for(i in 1:nrow(grid)){ + ridx <- grid[i,"Northing_idx"] + cidx <- grid[i,"Easting_idx"] + MAT_INT30_a1.0[nrows+1-ridx,cidx] <- grid[i,"INT30_scale_a1.0"] + MAT_INT30_a0.8[nrows+1-ridx,cidx] <- grid[i,"INT30_scale_a0.8"] + MAT_NAT30fav_a1.0[nrows+1-ridx,cidx] <- grid[i,"NAT30fav_scale_a1.0"] + MAT_NAT30fav_a0.8[nrows+1-ridx,cidx] <- grid[i,"NAT30fav_scale_a0.8"] +} +## + +# Create binary OWP maps +MAT_INT30_OWP <- ifelse(MAT_INT30_a1.0 < 1., 1, 0) +MAT_NAT30fav_OWP <- ifelse(MAT_NAT30fav_a1.0 < 1., 1, 0) + +## Save the OWPs grids to .rds +saveRDS(MAT_INT30_a1.0, file = "rds/OWP_ScaleDensity_Grid_5km_INT30_a1.0.rds") +saveRDS(MAT_INT30_a0.8, file = "rds/OWP_ScaleDensity_Grid_5km_INT30_a0.8.rds") +saveRDS(MAT_NAT30fav_a1.0, file = "rds/OWP_ScaleDensity_Grid_5km_NAT30fav_a1.0.rds") +saveRDS(MAT_NAT30fav_a0.8, file = "rds/OWP_ScaleDensity_Grid_5km_NAT30fav_a0.8.rds") +saveRDS(MAT_INT30_OWP, file = "rds/OWP_Presence_Grid_5km_INT30.rds") +saveRDS(MAT_NAT30fav_OWP, file = "rds/OWP_Presence_Grid_5km_NAT30fav.rds") + +## Save to .mat files (for plotting) +write.table(formatC(MAT_INT30_a1.0, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_ScaleDensity_Grid_5km_INT30_a1.0.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +write.table(formatC(MAT_INT30_a0.8, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_ScaleDensity_Grid_5km_INT30_a0.8.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +write.table(formatC(MAT_NAT30fav_a1.0, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_ScaleDensity_Grid_5km_NAT30fav_a1.0.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +write.table(formatC(MAT_NAT30fav_a0.8, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_ScaleDensity_Grid_5km_NAT30fav_a0.8.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +write.table(formatC(MAT_INT30_OWP, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_Presence_Grid_5km_INT30.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +write.table(formatC(MAT_NAT30fav_OWP, digits = 18, format = "e", mode = "double"), + file = "matfiles/OWP_Presence_Grid_5km_NAT30fav.mat", + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +## + +## --------------------- ## +## --- MAKE SOME PLOTS ---- +## --------------------- ## +head(grid) + +grid %>% + filter(NAT30fav_number > 0) %>% + dplyr::select(NAT30fav_scale_a0.8, NAT30fav_scale_a1.0) %>% + pivot_longer(cols = 1:2) %>% + ggplot() + + geom_histogram(aes(x = value, y = ..density.., fill = name), + colour = 'black', bins = 20, position = "dodge") + + theme_classic() +## + +## +grid %>% + filter(NAT30fav_number > 0) %>% + ggplot(aes(x = NAT30fav_scale_a0.8, NAT30fav_scale_a1.0)) + + geom_point() + + geom_abline(slope = 1, intercept = 0) + + theme_classic() +## + +## --- Make a table of NAT OWPs +NAT30fav_Table <- + OWP_atts %>% + filter(UniekID %in% NAT30fav_OWPs) %>% + dplyr::select(UniekID, Wind.Far_1, Windfarm.S, Verwachte, Capacity.M) %>% + rename("Name" = "Wind.Far_1", "ID" = "UniekID", "Status" = "Windfarm.S", "Capacity" = "Capacity.M") %>% + knitr::kable(format = "markdown") +cat(NAT30fav_Table, file = "Nat30favoured_OWFs.Rmd", sep = "\n") +rmarkdown::render("Nat30favoured_OWFs.Rmd", output_format = "word_document") +## + +## --- Make a table of NAT OWPs +INT30_Table <- + OWP_atts %>% + filter(UniekID %in% INT30_OWPs) %>% + dplyr::select(UniekID, Wind.Far_1, Country.Na, Windfarm.S, Verwachte, Capacity.M) %>% + rename("Name" = "Wind.Far_1", "ID" = "UniekID", "Country" = "Country.Na", "Status" = "Windfarm.S", "Capacity" = "Capacity.M") %>% + knitr::kable(format = "markdown") +cat(INT30_Table, file = "INT30_OWFs.Rmd", sep = "\n") +rmarkdown::render("INT30_OWFs.Rmd", output_format = "word_document") +## + diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/prep_map.R b/script_py_r/kec4_pyconversion-master/IBM_maps/prep_map.R new file mode 100644 index 0000000000000000000000000000000000000000..b10b65cee8c03c2fede47b2d7ac48f284caa8870 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/prep_map.R @@ -0,0 +1,242 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# File : prep_map.R +# +# Project : KEC4 +# +# Description : - Reads the KEC4 North Sea grid (NSgrid) +# - Reads the predicted Northern Gannet abundance per grid cell (ID710). +# - Merges the two data.frames +# - Creates matrices with predicted abundance per grid cell +# - Outputs to MatLab file for input in IBM simulation +# +# Dependencies : provided by Jan Tjalling van der Wal at W:\IMARES\Data\GIS_applicaties\KEC4_2021\IBM710 +# - windsp5c_vectorgrid.csv +# - gridid710-selection_kec4.csv +# +# Author : Vincent Hin +# Institure : Wageningen Marine Research +# Date created : 8 May 2021 +# Date modified : 30 June 2021 +##################################################################### +source("initialize.R") + +## Read the North Sea grid +NSgrid <- read.csv("csv/windsp5c_vectorgrid.csv", stringsAsFactors=F) +## Read the Northern Gannet (ID710) predicted abundances +ID710 <- read.csv("csv/gridid710_selection_kec4.csv", stringsAsFactors=F) + +##################################################################### +## : 1 - Build Easting and Northing locations from the GRID_ID column +##################################################################### + +## Extract Easting from GRID_ID +NSgrid$Easting <- substr(NSgrid$GRID_ID, 2, 5) + +## Remove leading zeros +NSgrid$Easting <- gsub(pattern = "0.*-", replacement = "-", NSgrid$Easting) + +## Convert to numeric and add 3 zeros +NSgrid$Easting <- as.numeric(paste0(NSgrid$Easting, '000')) + +## Same for Northing (no minus here) +NSgrid$Northing <- substr(NSgrid$GRID_ID, 7, 10) +NSgrid$Northing <- as.numeric(paste0(NSgrid$Northing, '000')) + +## Specify grid length in meters: +grid.len <- 5000 + +## Create a sequence for Easting with interval of length grid.len +Eseq <- seq(min(NSgrid$Easting), max(NSgrid$Easting), by = grid.len) + +## Get the Easting index of the grid +NSgrid$Easting_idx <- findInterval(NSgrid$Easting, Eseq) + +## Do the same for the Northing +Nseq <- seq(min(NSgrid$Northing), max(NSgrid$Northing), by = grid.len) +NSgrid$Northing_idx <- findInterval(NSgrid$Northing, Nseq) + +## Save the worked 5 km grid to .rds for use in plot_map.R +# saveRDS(object = NSgrid, file = "rds/NS_KEC4_grid_5km_IBM.rds") + +##################################################################### +## : 2 - Merge the grid with the Northern gannet abundance data +##################################################################### + +## For the abundance data: check out number of GRID_IDs per Season and Batch +ID710 %>% + group_by(Season, Batch) %>% + summarize(n = n(), + NAs = sum(is.na(Predicted))) +## + +## Merge the grid with the predicted abundances. +## Set ALL = TRUE to retain unsurveyed grid cells +## which are filled with NA +MRG <- merge(NSgrid, ID710, all = TRUE) +# saveRDS(object = MRG, file = "rds/ID710_Grid_5km_Batch_INT_NA_Seasons_6.rds") + +## Make a histogram plot of Predicted density +MRG %>% + filter(VGshow == 1 & !is.na(Predicted)) %>% + filter(Predicted > 0) %>% + ggplot(aes(x = Predicted, y = ..density..)) + + geom_histogram(bins = 100) + + facet_grid(Season ~ Batch, scales = "free", labeller = labeller(Season = season_lbs)) + + ggopts +## + +## Histogram plot of log(Predicted) density +MRG %>% + filter(VGshow == 1 & !is.na(Predicted)) %>% + filter(Predicted > 0) %>% + ggplot(aes(x = log(Predicted), y = ..density..)) + + geom_histogram(bins = 100) + + facet_grid(Season ~ Batch, scales = "free", labeller = labeller(Season = season_lbs)) + + ggopts +## + +## Boxplot of log(Predicted) density +MRG %>% + filter(!is.na(Predicted)) %>% + filter(Predicted > 0) %>% + ggplot(aes(x = as.factor(Season), y = log(Predicted), fill = as.factor(Season))) + + geom_boxplot() + + facet_wrap(. ~ Batch, nrow = 2, scales = "free", ncol = 3, labeller = labeller(Season = season_lbs)) + + ggopts +## + +## Calculate summary of Predicted density +MRG %>% + group_by(Season, Batch, VGshow) %>% + summarize(mean = mean(Predicted, na.rm = TRUE), + median = median(Predicted, na.rm = TRUE), + max = max(Predicted, na.rm = TRUE), + min = min(Predicted, na.rm = TRUE), + q95 = quantile(Predicted, 0.95, na.rm = TRUE), + q99 = quantile(Predicted, 0.99, na.rm = TRUE), + q95pos = quantile(Predicted[Predicted > 0.], 0.95, na.rm = TRUE), + q99pos = quantile(Predicted[Predicted > 0.], 0.99, na.rm = TRUE), + N = n(), + Nq95 = floor(0.05 * n()), + Nq99 = floor(0.01 * n()), + Npos = sum(Predicted > 0, na.rm = TRUE), + Nzeros = sum(Predicted == 0, na.rm = TRUE), + NAs = sum(is.na(Predicted))) + + +##################################################################### +## : 3 - Convert grid to matrix and output to .mat file +##################################################################### + +## Split based on Batch (INT/NAT) and Season +## NOTE that NAs in Batch / Season are not in MRG_split +MRG_split <- split(MRG, list(MRG$Batch, MRG$Season)) +names(MRG_split) + +## Copy MRG_split prevent rerunning mistakes +MRG_split_scaled <- MRG_split + +## Remove values larger than 99th quantile of positive values!! +## Rescale with maximum +for(i in 1:length(MRG_split_scaled)){ + + ## Calculate quantile based on unscaled `MRG_split` + q <- quantile(MRG_split[[i]]$Predicted[MRG_split[[i]]$Predicted > 0], probs = 0.99, na.rm = TRUE) + idx <- which(MRG_split_scaled[[i]]$Predicted > q) + + ## Remove large values + MRG_split_scaled[[i]]$Predicted[idx] <- q + + ## Rescale to max abundance + MRG_split_scaled[[i]]$Predicted <- MRG_split_scaled[[i]]$Predicted / max(MRG_split_scaled[[i]]$Predicted, na.rm = TRUE) + + hist(MRG_split_scaled[[i]]$Predicted, main = names(MRG_split_scaled)[i]) +} +## + +## Export the split to RDS +saveRDS(MRG_split_scaled, file = "rds/ScaledPredicted_Season_Batch.rds") + +## Sum the rows of all splits +map_df(MRG_split_scaled, nrow) %>% sum() + +## Equals the rows of MRG minus the NAs in MRG +nrow(MRG) - length(which(is.na(MRG$Season))) + +## Get the names of all split +spl.names <- names(MRG_split_scaled) + +## Matrix lay-out +## Easting in columns +## Northing in rows +# +# Easting: 1 2 ... 219 +# ------------------------------------- +# N 284 | E1N284 E2N284 ... E219N284 +# O . | . . ... . +# R . | . . ... . +# T . | . . ... . +# H . | . . ... . +# I . | . . ... . +# N 2 | E1N2 E2N2 ... E219N2 +# G 1 | E1N1 E2N1 ... E219N1 + +## Create matrices for every Batch and Season +## Output to .mat style file +for(j in 1:length(MRG_split_scaled)){ + + ## Create two empty matrices for the grid + ## Number of rows must equal number of Northings + ## Number of cols must equal number of Eastings + MAT1 <- matrix(data = 0.0, + nrow = length(unique(MRG$Northing_idx)), + ncol = length(unique(MRG$Easting_idx)), + dimnames = NULL) + ## + MAT2 <- MAT1 + + ## + for(i in 1:nrow(MRG_split_scaled[[j]])){ + + ridx <- MRG_split_scaled[[j]][i,"Northing_idx"] + cidx <- MRG_split_scaled[[j]][i,"Easting_idx"] + val <- MRG_split_scaled[[j]][i,"Predicted"] + if(is.na(val)) next + if(MAT2[ridx,cidx] > 0.) { + break("Error: overwriting an existing cell") + } else { + MAT1[ridx,cidx] <- val + MAT2[ridx,cidx] <- 1. + } + } + ## + + write.table(formatC(MAT1[nrow(MAT1):1,], digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_predicted_Grid_5km_", spl.names[j], ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) + write.table(formatC(MAT2[nrow(MAT2):1,], digits = 18, format = "e", mode = "double"), + file = paste0("matfiles/MB_presence_Grid_5km_", spl.names[j], ".mat"), + row.names = FALSE, col.names = FALSE, sep = " ", quote = FALSE) +} + +## --------- EOF ------- ## \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBM_maps/read_IBMmap.R b/script_py_r/kec4_pyconversion-master/IBM_maps/read_IBMmap.R new file mode 100644 index 0000000000000000000000000000000000000000..076c67350d2dd23d5b8919661f5806632a1b1730 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBM_maps/read_IBMmap.R @@ -0,0 +1,38 @@ +# Function to read a single mat file and convert to long df +read_IBMmap <- function(fln = NULL, fld = ".", type = "csv", confine.x = NULL, confine.y = NULL) { + + if(type == "csv"){ + x <- as.matrix(read.csv(file = file.path(fld, fln), header = FALSE)) + } else if (type == "table") { + x <- as.matrix(read.table(file = file.path(fld, fln), sep = "", header = FALSE)) + } else { + stop("Unknown type") + } + + ## Confine the matrix + if(!is.null(confine.x)) x <- x[,confine.x[1]:confine.x[2]] + if(!is.null(confine.y)) x <- x[confine.y[1]:confine.y[2],] + + ## Flip the matrix in vertical direction + x <- x[nrow(x):1,] + colnames(x) <- 1:ncol(x) + x <- t(x) + valnm <- str_split(fln, pattern = "_", simplify = TRUE)[,2] + valnm <- gsub(".mat", "", valnm) + x <- reshape2::melt(x, value.name = paste0("time_", valnm)) + names(x)[1:2] <- c('x', 'y') + return(x) +} + +read_IBMmaps <- function(flns = NULL, fld = ".", type = "csv") { + + ## Read all maps with read_IBMmap + maps <- lapply(as.list(flns), read_IBMmap, fld = fld, type = type) + ## Extract third column + maps_df <- as.data.frame(sapply(maps, "[[", 3)) + ## Extract names of third columns + maps_nms <- sapply(lapply(maps, names), "[[", 3) + names(maps_df) <- maps_nms + ## cbind col and row idx with values + cbind(maps[[1]][,1:2], maps_df) +} diff --git 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a/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz4.cfg b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz4.cfg new file mode 100644 index 0000000000000000000000000000000000000000..b156813babfacdc95a1cb9834b27a95fb82fc088 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz4.cfg @@ -0,0 +1,20 @@ +{ + "habitat_maps": [ + [ + "MB_predicted_Grid_5km_INT.1.mat", + "" + ] + ], + "initial_energy": "gannet_season1/gannet-season1-sz4-energy.mat", + "initial_position": "gannet_season1/gannet-season1-sz4-pos.mat", + "land_map": "MB_presence_Grid_5km_INT.1.mat", + "max_iters": 360, + "metabolism": 0.37438583187577024, + "nb_months": 2, + "nb_runs": 100, + "nbthreads": 4, + "nothreads": 0, + "stepsize": 4, + "survival": 0.9, + "xtol": 0.0001 +} \ No newline at end of file diff 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a/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz7.cfg b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz7.cfg new file mode 100644 index 0000000000000000000000000000000000000000..6f6304f508d5121ecc0d485cd86d5df5c29ef17e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season1/gannet-season1-sz7.cfg @@ -0,0 +1,20 @@ +{ + "habitat_maps": [ + [ + "MB_predicted_Grid_5km_INT.1.mat", + "" + ] + ], + "initial_energy": "gannet_season1/gannet-season1-sz7-energy.mat", + "initial_position": "gannet_season1/gannet-season1-sz7-pos.mat", + "land_map": "MB_presence_Grid_5km_INT.1.mat", + "max_iters": 360, + "metabolism": 0.3868847677968006, + "nb_months": 2, + "nb_runs": 100, + "nbthreads": 4, + "nothreads": 0, + "stepsize": 7, + "survival": 0.9, + "xtol": 0.0001 +} \ No newline at end of file diff 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a/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season5/gannet-season5-sz5.cfg b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season5/gannet-season5-sz5.cfg new file mode 100644 index 0000000000000000000000000000000000000000..e4246287eaf07db3009b3fcfa6fb3159347e02a0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season5/gannet-season5-sz5.cfg @@ -0,0 +1,20 @@ +{ + "habitat_maps": [ + [ + "MB_predicted_Grid_5km_INT.5.mat", + "" + ] + ], + "initial_energy": "gannet_season5/gannet-season5-sz5-energy.mat", + "initial_position": "gannet_season5/gannet-season5-sz5-pos.mat", + "land_map": "MB_presence_Grid_5km_INT.5.mat", + "max_iters": 360, + "metabolism": 0.25678565017187793, + "nb_months": 2, + "nb_runs": 100, + "nbthreads": 4, + "nothreads": 0, + "stepsize": 5, + "survival": 0.9, + "xtol": 0.0001 +} \ No newline at end of file diff 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b/script_py_r/kec4_pyconversion-master/IBMresults/IBM_cfgfile_definite/seasons_trained/gannet_season6/gannet-season6-sz8.cfg @@ -0,0 +1,20 @@ +{ + "habitat_maps": [ + [ + "MB_predicted_Grid_5km_INT.6.mat", + "" + ] + ], + "initial_energy": "gannet_season6/gannet-season6-sz8-energy.mat", + "initial_position": "gannet_season6/gannet-season6-sz8-pos.mat", + "land_map": "MB_presence_Grid_5km_INT.6.mat", + "max_iters": 360, + "metabolism": 0.3426013368534768, + "nb_months": 2, + "nb_runs": 100, + "nbthreads": 4, + "nothreads": 0, + "stepsize": 8, + "survival": 0.9, + "xtol": 0.0001 +} \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/plot_IBMoutput.R b/script_py_r/kec4_pyconversion-master/IBMresults/plot_IBMoutput.R new file mode 100644 index 0000000000000000000000000000000000000000..04fa6942e25dbcc85fb7b5ce88b8be58c05884de --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/plot_IBMoutput.R @@ -0,0 +1,247 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +# Purpose of this script: +# +# Current script reads data.frames with Northern Gannet IBM output generated by 'prep_IBMoutput.R' +# and makes several plots + +# Initialize: +source("src/initialize.R") + +## Load rds data objects (created in prep_IBMoutput): +seasons_trained <- readRDS("rds/seasons_trained.rds") +seasons_annual <- readRDS("rds/seasons_annual.rds") + +## Plot survival per season, stepsize, and scenario +gg.Survival_stepsize_seasons <- + ggplot(seasons_trained, + aes(x = stepsize, y = S50_period)) + + geom_pointrange(aes(ymax = S95_period, ymin = S5_period, colour = avoidance), + position = position_dodge2(width = 0.5), size = 0.5, fatten = 1) + + geom_hline(aes(yintercept = 0.9^(2/12)), col = "red") + + facet_grid(rows = vars(season), cols = vars(windfarms), + labeller = labeller(season = season_lbs, + windfarms = c('INT30' = 'International', 'NAT30fav' = 'Nat Rekenvariant III'))) + + scale_colour_discrete(type = cbp2[c(3,6)]) + + labs(x = 'Max. travel length', y = 'Bimonthly survival', colour = 'Avoidance') + + ggopts + + theme(legend.box="vertical") +## +ggsave(filename = "Survival_stepsize_seasons.pdf", plot = gg.Survival_stepsize_seasons, path = fig.path, width = A4width_in, height = 7.5) +ggsave(filename = "Survival_stepsize_seasons.png", plot = gg.Survival_stepsize_seasons, path = fig.path, width = A4width_in, height = 7.5, units = 'in', dpi = 300) + +## Plot metabolism ~ stepsize | season +gg.Metabolism_stepsize_seasons <- + seasons_trained %>% + filter(windfarms == "INT30" & avoidance == "1.0") %>% + ggplot(aes(x = stepsize, y = metabolism)) + + geom_line(colour = cbp2[6], size = 0.5) + + geom_point(colour = cbp2[6], size = 1.0) + + facet_wrap(season ~ ., ncol = 6, labeller = labeller(season = season_lbs)) + + labs(x = "Max. travel length", y = "Calibrated metabolism") + + ggopts +ggsave(filename = "Metabolism_stepsize_seasons.pdf", gg.Metabolism_stepsize_seasons, path = fig.path, width = A4width_in, height = 2) +ggsave(filename = "Metabolism_stepsize_seasons.png", gg.Metabolism_stepsize_seasons, path = fig.path, width = A4width_in, height = 2, units = 'in', dpi = 300) +## + +## Plot survival ~ metabolism | scenario X avoidance +gg.Survival_metabolism_snc <- + seasons_trained %>% + filter(season == 3) %>% + ggplot(aes(x = metabolism, y = S50_period, colour = avoidance)) + + geom_pointrange(aes(ymax = S95_period, ymin = S5_period), position = position_dodge2(width = 0.01), size = 0.5, fatten = 1) + + geom_text(data = seasons_trained %>% filter(season == 3 & avoidance == "1.0"), + mapping = aes(label = stepsize), size = 2, colour = 'black', nudge_y = -0.005) + + facet_grid(windfarms ~ season, labeller = labeller(windfarms = c('INT30' = 'International', 'NAT30fav' = 'Nat Rekenvariant III'), season = season_lbs)) + + scale_colour_discrete(type = cbp2[c(3,6)]) + + labs(x = "Metabolism", y = "Bimonthly survival", colour = "Avoidance") + + ggopts +## +ggsave(filename = "Survival_metabolism_snc.pdf", gg.Survival_metabolism_snc, path = fig.path, width = 0.5*A4width_in, height = 3) +ggsave(filename = "Survival_metabolism_snc.png", gg.Survival_metabolism_snc, path = fig.path, width = 0.5*A4width_in, height = 3, units = 'in', dpi = 300) +## + +## Plot Annual survival per stepsize and scenario +gg.Survival_stepsize_annual <- + ggplot(seasons_annual, + aes(x = stepsize, y = S50_annual, colour = factor(avoidance))) + + geom_pointrange(aes(ymax = S95_annual, ymin = S5_annual), position = position_dodge2(width = 0.5), size = 0.5, fatten = 1) + + geom_hline(aes(yintercept = 0.9), col = "red") + + facet_grid(cols = vars(windfarms), labeller = labeller(windfarms = c('INT30' = 'International', 'NAT30fav' = 'Nat Rekenvariant III'))) + + scale_colour_discrete(type = cbp2[c(3,6)]) + + labs(x = 'Max. travel length', y = 'Annual survival', colour = 'Avoidance') + + ggopts + + theme(legend.box="vertical") +ggsave(filename = "Survival_stepsize_annual.pdf", gg.Survival_stepsize_annual, path = fig.path, width = A4width_in, height = 2.5) +ggsave(filename = "Survival_stepsize_annual.png", gg.Survival_stepsize_annual, path = fig.path, width = A4width_in, height = 2.5, units = 'in', dpi = 300) +## + +## Plot additional OWP mortality per stepsize and scenario +gg.Mortality_stepsize_annual <- + ggplot(seasons_annual, + aes(x = stepsize, y = m50_annual, colour = factor(avoidance))) + + geom_pointrange(aes(ymax = m95_annual, ymin = m5_annual), position = position_dodge2(width = 0.5), size = 0.5, fatten = 1) + + geom_hline(aes(yintercept = 0.0), col = "red") + + facet_grid(cols = vars(windfarms), labeller = labeller(windfarms = c('INT30' = 'International', 'NAT30fav' = 'Nat Rekenvariant III'))) + + scale_colour_discrete(type = cbp2[c(3,6)]) + + labs(x = 'Max. travel length', y = 'Additional annual OWF mortality', colour = 'Avoidance') + + ggopts + + theme(legend.box="vertical") +ggsave(filename = "Mortality_stepsize_annual.pdf", gg.Mortality_stepsize_annual, path = fig.path, width = A4width_in, height = 2.5) +ggsave(filename = "Mortality_stepsize_annual.png", gg.Mortality_stepsize_annual, path = fig.path, width = A4width_in, height = 2.5, units = 'in', dpi = 300) +## + +## +seasons_trained %>% + group_by(stepsize) %>% + summarize(max = max(survival_period - S50_period)) +## + +## +Table.Mortality_annual_sz18 <- + seasons_annual %>% + filter(stepsize %in% c('1', '8')) %>% + select(stepsize, avoidance, windfarms, matches("m[0-9]+_annual")) %>% + mutate(m95_annual = pmax(m95_annual, 0)) %>% + rowwise() %>% + mutate(across(where(is.numeric), signif, 3)) %>% + mutate(m = paste0(m50_annual, " +/- (", m95_annual, "; ", m5_annual, ")")) %>% + mutate(m5_annual = NULL, m50_annual = NULL, m95_annual = NULL) %>% + pivot_wider(values_from = m, names_from = avoidance) %>% + arrange(stepsize, desc(windfarms)) %>% + mutate(windfarms, windfarms = recode(windfarms, 'INT30' = 'International', 'NAT30fav' = 'Rekenvariant III')) %>% + relocate(windfarms) %>% + rename('Realistic' = '0.8', 'Worst case' = '1.0', 'Max. travel length' = 'stepsize', 'Scenario' = 'windfarms') %>% + knitr::kable(format = 'markdown') +## +fln <- file.path(tab.path, "Table.Mortality_annual18.Rmd") +cat(Table.Mortality_annual_sz18, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +## + +## +Table.Mortality_annual <- + seasons_annual %>% + select(stepsize, avoidance, windfarms, matches("m[0-9]+_annual")) %>% + mutate(m95_annual = pmax(m95_annual, 0)) %>% + rowwise() %>% + mutate(across(where(is.numeric), signif, 3)) %>% + mutate(m = paste0(m50_annual, " +/- (", m95_annual, "; ", m5_annual, ")")) %>% + mutate(m5_annual = NULL, m50_annual = NULL, m95_annual = NULL) %>% + pivot_wider(values_from = m, names_from = c(avoidance, windfarms)) %>% + rename('International realistic' = '0.8_INT30', + 'International worst case' = '1.0_INT30', + 'Nat. rekenvariant III realistic' = '0.8_NAT30fav', + 'Nat. rekenvariant III worst case' = '1.0_NAT30fav', + 'Max. travel length' = 'stepsize') %>% + knitr::kable(format = 'markdown') +## +fln <- file.path(tab.path, "Table.Mortality_annual.Rmd") +cat(Table.Mortality_annual, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## +Table.Survival_annual <- + seasons_annual %>% + select(stepsize, avoidance, windfarms, matches("S[0-9]+_annual")) %>% + rowwise() %>% + mutate(across(where(is.numeric), signif, 3)) %>% + mutate(S = paste0(S50_annual, " +/- (", S5_annual, "; ", S95_annual, ")")) %>% + mutate(S5_annual = NULL, S50_annual = NULL, S95_annual = NULL) %>% + pivot_wider(values_from = S, names_from = c(avoidance, windfarms)) %>% + rename('International realistic' = '0.8_INT30', + 'International worst case' = '1.0_INT30', + 'Nat. rekenvariant III realistic' = '0.8_NAT30fav', + 'Nat. rekenvariant III worst case' = '1.0_NAT30fav', + 'Max. travel length' = 'stepsize') %>% + knitr::kable(format = 'markdown') +## +fln <- file.path(tab.path, "Table.Survival_annual.Rmd") +cat(Table.Survival_annual, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## +Table.Mortality_period <- + seasons_trained %>% + filter(stepsize %in% c('1','8')) %>% + mutate(m50_period = pmax(survival_period - S50_period, 0)) %>% + select(windfarms, avoidance, stepsize, season, m50_period) %>% + mutate(season, season = recode(season, "1" = "Aug + Sep", "2" = "Okt + Nov", "3" = "Dec + Jan", "4" = "Feb + Mar", "5" = "Apr + May", "6" = "Jun + Jul")) %>% + mutate(windfarms, windfarms = recode(windfarms, 'INT30' = 'International', 'NAT30fav' = 'Rekenvariant III')) %>% + mutate(avoidance, avoidance = recode(avoidance, '0.8' = 'REAL', '1.0' = 'WC')) %>% + rowwise() %>% + mutate(across(where(is.numeric), signif, 3)) %>% + pivot_wider(values_from = m50_period, names_from = season) %>% + arrange(stepsize, desc(windfarms), avoidance) %>% + rename('scenario' = 'windfarms', 'max. travel length' = 'stepsize') %>% + knitr::kable(format = 'markdown') +## +fln <- file.path(tab.path, "Table_Mortality_period.Rmd") +cat(Table.Mortality_period, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +## + +## +gg.Mortality_period <- + seasons_trained %>% + filter(stepsize %in% c('1','8')) %>% + mutate(m50_period = pmax(survival_period - S50_period, 0), + m5_period = pmax(survival_period - S5_period, 0), + m95_period = pmax(survival_period - S95_period, 0)) %>% + mutate(season, season = recode(season, '1' = "Aug + Sep", "2" = "Okt + Nov", "3" = "Dec + Jan", "4" = "Feb + Mar", "5" = "Apr + May", "6" = "Jun + Jul")) %>% + ggplot(aes(x = season, y = m50_period, colour = factor(avoidance))) + + geom_pointrange(aes(ymin = m5_period, ymax = m95_period), position = position_dodge2(width = 0.5), size = 0.5, fatten = 1) + + facet_grid(stepsize ~ windfarms, + labeller = labeller(stepsize = c('1' = 'Max. 1 grid cell', '8' = 'Max. 8 grid cells'), + windfarms = c('INT30' = 'International', 'NAT30fav' = 'Nat Rekenvariant III'))) + + scale_colour_discrete(type = cbp2[c(3,6)]) + + labs(x = 'Season', y = 'Additional OWF mortality', colour = 'Avoidance') + + ggopts + + theme(axis.text.x = element_text(angle = 45, vjust = 0.5)) +## +ggsave(filename = "seasons_trained/Mortality_period.pdf", gg.Mortality_period, path = fig.path, width = 5, height = 4) +ggsave(filename = "seasons_trained/Mortality_period.png", gg.Mortality_period, path = fig.path, width = 5, height = 4, units = 'in', dpi = 300) +## + +## Output additional OWF mortality rates for use in KEC4popmodels +mortality_table <- + seasons_annual %>% + filter(stepsize %in% c(1,8)) %>% + dplyr::select(stepsize, avoidance, windfarms, m5_annual, m50_annual, m95_annual) %>% + rename(scenario = windfarms) %>% + pivot_longer(cols = contains("_annual"), names_to = "stat", values_to = "m") %>% + mutate(m, m = pmax(m, 0)) %>% + separate(stat, into = c("stat", "annual")) %>% + mutate(avoidance, avoidance = recode(avoidance, '0.8' = 'REAL', '1.0' = 'WC')) %>% + mutate(scenario, scenario = recode(scenario, 'INT30' = 'International', 'NAT30fav' = 'Rekenvariant III')) %>% + mutate(scenario = paste0(scenario, " ", avoidance)) %>% + mutate(stat = paste0(stat, "_sz", stepsize)) %>% + arrange(stepsize, scenario) %>% + mutate(avoidance = NULL, annual = NULL, stepsize = NULL, n = 6, batch = "INT", species = "Morus bassanus") %>% + relocate(species, scenario, stat, batch, n) +## +fln <- file.path(tab.path, "KEC4_MB_IBM_mortality.txt") +write.table(mortality_table, file = fln, sep = "\t", row.names = FALSE, col.names = TRUE, quote = T) +## diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/prep_IBMoutput.R b/script_py_r/kec4_pyconversion-master/IBMresults/prep_IBMoutput.R new file mode 100644 index 0000000000000000000000000000000000000000..76c3be5a21ebe669ca6d43c596d47822347080bd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/prep_IBMoutput.R @@ -0,0 +1,75 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + + +# Purpose of this script: +# +# Current script reads output data generated by the Northern Gannet IBM +# All data is stored in a data.frame and data.frame object is saved to .rds file + +# Initialize: +source("src/initialize.R") + +## Top-level folder with IBM output +outfld <- "IBM_cfgfile_definite/seasons_trained/" + +## Names of different scenarios +scns.flds <- c("INT30-realistic", "INT30-worstcase", "NAT30-realistic", "NAT30-worstcase") + +## Vector with different seasons +seasons <- 1:6 + +## Read all data (6 seasons, 4 scenarios) +seasons_trained <- + map_dfr(seasons, function(season) { + + out <- map_dfr(scns.flds, function(scn) cfg_to_df(path = file.path(outfld, paste0("gannet_season", season), scn))) + out$S5_period <- out$S5^(out$nb_months / 12.) + out$S50_period <- out$S50^(out$nb_months / 12.) + out$S95_period <- out$S95^(out$nb_months / 12.) + out$survival_period <- out$survival^(out$nb_months / 12.) + out$avoidance <- factor(str_replace_all(str_extract(out$habitat_map, "_a[:digit:].[:digit:]_"), "_|a", "")) + out$windfarms <- str_extract(out$habitat_map, "INT30|NAT30fav") + out$season <- factor(season, levels = seasons) + out + }) +## + +## Save data.frame with output to .rds +saveRDS(seasons_trained, "rds/seasons_trained.rds") + +## Convert to wide df and calculate annual survival from bimonthly survival rates +seasons_annual <- + seasons_trained %>% + dplyr::select(stepsize, avoidance, windfarms, season, S5_period, S50_period, S95_period) %>% + pivot_wider(id_cols = c(stepsize, avoidance, windfarms), + values_from = c(S5_period, S50_period, S95_period), + names_from = season) %>% + rowwise() %>% + mutate(S5_annual = prod(c_across(S5_period_1:S5_period_6))) %>% + mutate(S50_annual = prod(c_across(S50_period_1:S50_period_6))) %>% + mutate(S95_annual = prod(c_across(S95_period_1:S95_period_6))) %>% + mutate(m5_annual = 0.9 - S5_annual) %>% + mutate(m50_annual = 0.9 - S50_annual) %>% + mutate(m95_annual = 0.9 - S95_annual) +## + +## Save data.frame with output to .rds +saveRDS(seasons_annual, "rds/seasons_annual.rds") diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/convert_initpos.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/convert_initpos.R new file mode 100644 index 0000000000000000000000000000000000000000..6e82b2fcc5c68dd2ff0c966943be1a5318825b34 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/convert_initpos.R @@ -0,0 +1,49 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +## Function to convert initial position vector to a density map +convert_initpos <- function(init, nb_rows = 284, nb_cols = 219) { + + # Make the grid + row_idx <- seq(1,nb_rows) + col_idx <- seq(1,nb_cols) + grid <- expand.grid('x' = col_idx, 'y' = row_idx) + + # Assign unique IDs to each gridcell + # -1 to begin at grid ID zero + grid$ID <- (grid$y - 1) * nb_cols + (grid$x - 1) + + ## Count density per position + map_agg <- + data.frame('position' = init[,1]) %>% + group_by(position) %>% + dplyr::summarise(n = n()) + + grid$abundance <- 0 + + idx <- match(map_agg$position, grid$ID) + grid$abundance[idx] <- map_agg$n + grid$ID <- NULL + + # Flip the rows + grid$y <- nb_rows - grid$y + 1 + + return(grid) +} diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/extract_names.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/extract_names.R new file mode 100644 index 0000000000000000000000000000000000000000..1bc77a78aa1e99d3b320363d368eecb85248f8ff --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/extract_names.R @@ -0,0 +1,49 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +extract_name <- function(nm) { + + season <- str_extract(nm, "season[0-9]") + season <- as.numeric(str_extract(season, "[0-9]")) + + sz <- str_extract(nm, "(sz|stepsize)[0-9]") + sz <- as.numeric(str_extract(sz, "[0-9]")) + + timestep <- str_extract(nm, "\\[[0-9]+]") + timestep <- as.numeric(str_extract(timestep, "[0-9]+")) + + scn <- str_extract(nm, "INT30|NAT30fav") + if(is.na(scn)) scn <- "null" + + avoidance <- str_extract(nm, "a1.0|a0.8") + if(is.na(avoidance)) avoidance <- "null" + + list('name' = nm, 'timestep' = timestep, 'season' = season, 'stepsize' = sz, 'scenario' = scn, 'avoidance' = avoidance) +} + +set_habmap_name <- function(x) { + + scn.str <- paste0(x$scenario,"_") + av.str <- paste0(x$avoidance,"_") + if(x$scenario == "null") scn.str <- av.str <- "" + + paste0("MB_predicted_Grid_5km_", scn.str, av.str, "INT.", x$season, ".mat") + +} diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/initialize.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/initialize.R new file mode 100644 index 0000000000000000000000000000000000000000..d87599f70ff8fea6e76c134347fcb6b2ec8227b8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/initialize.R @@ -0,0 +1,41 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +library(tidyverse) +library(jsonlite) +library(raster) +source("src/read_JSON.R") +source("src/read_IBMmap.R") +source("src/read_IBMoutput.R") +source("src/plot_opts.R") +source("src/convert_initpos.R") +source("src/extract_names.R") +source("src/map_to_posener.R") +source("src/sort_folder.R") + +## Set output directories +fig.path <- "figs" +tab.path <- "tables" +rds.path <- "rds" + +## Create output directories +if(!dir.exists(fig.path)) dir.create(fig.path, recursive = TRUE) +if(!dir.exists(tab.path)) dir.create(tab.path, recursive = TRUE) +if(!dir.exists(rds.path)) dir.create(rds.path, recursive = TRUE) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/map_to_posener.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/map_to_posener.R new file mode 100644 index 0000000000000000000000000000000000000000..1bd971d8cee74d6d7ceeff56a1d1ed7c1f2f0776 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/map_to_posener.R @@ -0,0 +1,52 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +## Convert density map to bird positions +map_to_posener <- function(density_map, habitat_map, max_iters) { + + ## Get number of columns + nb_cols <- length(unique(density_map$x)) + if(nb_cols != length(unique(habitat_map$x))) stop("Wrong x dimensions") + if(length(unique(density_map$y)) != length(unique(habitat_map$y))) stop("Wrong y dimensions") + + ## Assign ID column to maps (ID starts at zero) + density_map$ID <- (density_map$y - 1) * nb_cols + density_map$x - 1 + habitat_map$ID <- (habitat_map$y - 1) * nb_cols + habitat_map$x - 1 + + ## Get the position of each bird from inverse rle + positions <- inverse.rle(list('lengths' = density_map[,3], 'values' = density_map$ID)) + + ## Check for correct length + if(length(positions) != sum(density_map[,3])) stop("Inverse RLE does not match number birds") + + ## Get the positions from matching the ID column + idx <- match(positions, density_map$ID) + xpos <- density_map[idx,'x'] + ypos <- density_map[idx,'y'] + + ## Get the habitat quality of each bird position + idx <- match(positions, habitat_map$ID) + energy <- habitat_map[idx,3] / max_iters + + return(data.frame('x' = xpos, + 'y' = ypos, + 'position' = positions, + 'energy' = energy)) +} diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/plot_opts.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/plot_opts.R new file mode 100644 index 0000000000000000000000000000000000000000..83607e7243f1b556be66adf245afa903d77f86f2 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/plot_opts.R @@ -0,0 +1,51 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +season_lbs <- c('1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul") + +theme_WMR <- theme_classic() + + theme(text = element_text(size = 8), + title = element_text(size = 9), + rect = element_rect(size = 0.5), + line = element_line(size = 0.5)) + +ggopts <- ggplot2::theme_bw() + + ggplot2::theme( + panel.grid.minor = element_blank(), + panel.grid.major = element_blank(), + axis.title = element_text(size = 8), + axis.text = element_text(size = 8), + legend.text = element_text(size = 8), + legend.title = element_text(size = 8), + legend.key.height = unit(8, "pt"), + legend.position = 'bottom', + legend.box = "horizontal", + legend.margin = margin(0,0,0,0), + legend.box.margin = margin(0,0,0,0), + strip.text.x = element_text(size = 8), + strip.text.y = element_text(size = 8), + strip.background = element_rect(fill = "white"), + plot.title = element_text(size = 9, hjust = 0.5)) +## + +## Define colours (from: https://www.datanovia.com/en/blog/ggplot-colors-best-tricks-you-will-love/) +cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73", + "#F0E442", "#0072B2", "#D55E00", "#CC79A7") +A4width_in <- 6.3 \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMmap.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMmap.R new file mode 100644 index 0000000000000000000000000000000000000000..11bee338f372e76b403207d5e0ba877bc214fb36 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMmap.R @@ -0,0 +1,58 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +# Function to read a single mat file and convert to long df +read_IBMmap <- function(fln = NULL, fld = ".", type = "csv", confine.x = NULL, confine.y = NULL) { + + if(type == "csv"){ + x <- as.matrix(read.csv(file = file.path(fld, fln), header = FALSE)) + } else if (type == "table") { + x <- as.matrix(read.table(file = file.path(fld, fln), sep = "", header = FALSE)) + } else { + stop("Unknown type") + } + + ## Confine the matrix + if(!is.null(confine.x)) x <- x[,confine.x[1]:confine.x[2]] + if(!is.null(confine.y)) x <- x[confine.y[1]:confine.y[2],] + + ## Flip the matrix in vertical direction + x <- x[nrow(x):1,] + colnames(x) <- 1:ncol(x) + x <- t(x) + valnm <- str_split(fln, pattern = "_", simplify = TRUE)[,2] + valnm <- gsub(".mat", "", valnm) + x <- reshape2::melt(x, value.name = paste0("time_", valnm)) + names(x)[1:2] <- c('x', 'y') + return(x) +} + +read_IBMmaps <- function(flns = NULL, fld = ".", type = "csv") { + + ## Read all maps with read_IBMmap + maps <- lapply(as.list(flns), read_IBMmap, fld = fld, type = type) + ## Extract third column + maps_df <- as.data.frame(sapply(maps, "[[", 3)) + ## Extract names of third columns + maps_nms <- sapply(lapply(maps, names), "[[", 3) + names(maps_df) <- maps_nms + ## cbind col and row idx with values + cbind(maps[[1]][,1:2], maps_df) +} diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMoutput.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMoutput.R new file mode 100644 index 0000000000000000000000000000000000000000..f19512bb302e9ca6f5af8519f780892bd6d09d43 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_IBMoutput.R @@ -0,0 +1,58 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +# Function read all IBM config files within folder and assembles output in data.frame +cfg_to_df <- function(path = NULL) { + + path <- file.path(path) + fls <- list.files(path, pattern = ".cfg") + cfg <- map(fls, read_IBMcfg, fld = path) %>% set_names(nm = fls) + + sz <- sapply(cfg, "[[", "stepsize") + m <- sapply(cfg, "[[", 'metabolism') + S50 <- sapply(cfg, "[[", "S50") + S5 <- sapply(cfg, "[[", "S5") + S95 <- sapply(cfg, "[[", "S95") + avoid <- sapply(cfg, "[[", 'wf_avoidance') + land_map <- sapply(cfg, "[[", "land_map") + hab_map <- sapply(cfg, "[[", "habitat_maps") + hab_map <- as.character(hab_map[1,]) + windfarms <- sapply(cfg, "[[", "windfarms") + max_iters <- sapply(cfg, "[[", "max_iters") + nb_months <- sapply(cfg, "[[", "nb_months") + nb_runs <- sapply(cfg, "[[", "nb_runs") + surv <- sapply(cfg, "[[", 'survival') + + cfg.df <- data.frame("stepsize" = sz, + "metabolism" = m, + "S50" = S50, + "S5" = S5, + "S95" = S95, + "avoidance" = ifelse(is.null(avoid[[1]]), 'none', avoid), + "habitat_map" = hab_map, + "land_map" = land_map, + "windfarms" = ifelse(is.null(windfarms[[1]]), 'none', windfarms), + "max_iters" = max_iters, + "nb_months" = nb_months, + "nb_runs" = nb_runs, + "survival" = surv, row.names = NULL) + + return(cfg.df) +} diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/read_JSON.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_JSON.R new file mode 100644 index 0000000000000000000000000000000000000000..837ec883113906f620b44fc1f5e32a946c74b686 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/read_JSON.R @@ -0,0 +1,43 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +## Define a function to read CFG file and add info from name +read_IBMcfg <- function(fln = NULL, fld = ".", convert.name = FALSE){ + + fl <- file.path(fld, fln) + + cfg <- jsonlite::fromJSON(fl) + + if(convert.name){ + nm_info <- + fln %>% + str_split(pattern = "-") %>% + unlist() %>% as.list() %>% + set_names(nm = c("OWF", "survival_corrected", "avoidance")) + + nm_info$survival_corrected <- as.numeric(str_replace(nm_info$survival_corrected, pattern = "S", replacement = "")) + nm_info$avoidance <- str_replace_all(nm_info$avoidance, pattern =".cfg$|a", replacement = "") + + cfg <- c(cfg, nm_info) + } + + return(cfg) +} +## diff --git a/script_py_r/kec4_pyconversion-master/IBMresults/src/sort_folder.R b/script_py_r/kec4_pyconversion-master/IBMresults/src/sort_folder.R new file mode 100644 index 0000000000000000000000000000000000000000..ae2db84689bcea9f2b6161182556b62e17019247 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/IBMresults/src/sort_folder.R @@ -0,0 +1,37 @@ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# + +## Function sorts files into subfolders based on `sort` string +sort_folder <- function(fld, sorter = 'stepsize', levels = 1:8, flpat = ".png") { + + oldwd <- getwd() + on.exit(setwd(oldwd)) + + setwd(fld) + fls <- list.files(pattern = flpat) + sort.str <- paste0(sorter, levels) + + for(i in sort.str) { + if(!dir.exists(i)) dir.create(i) + idx <- grep(pattern = i, x = fls) + file.rename(from = fls[idx], to = file.path(i, fls[idx])) + } +} +## diff --git a/script_py_r/kec4_pyconversion-master/K4_CSV2SQL3SplitSpreadIter.py b/script_py_r/kec4_pyconversion-master/K4_CSV2SQL3SplitSpreadIter.py new file mode 100644 index 0000000000000000000000000000000000000000..712011a2e6f14356e36420d27a4a1e9dd1ba9709 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/K4_CSV2SQL3SplitSpreadIter.py @@ -0,0 +1,310 @@ +# 20210516, Jan Tjalling van der Wal +# Different approach to on-boarding the prepared data from the CSV into +# manageable data(sub)sets in preparation for seabird-density maps +# +# By opting for a database-based approach (SQLite3.db) instead of geopandas +# the data can mostly reside on disk instead of loading it all into memory. +# GeoPandas skilfully manages to overflow the data to elsewhere (balancing is 31.9 of 32 GB available RAM), +# but the loading and manipulations are slow. +# +# 20210517: Nice idea, but this is not quicker that pandas/geopandas. +# Processes about 130 lines per second, it works but takes roughly 2 days to complete (estimate). +# ca.21.500.000 lines @ 130 per second = 46 hours +# +# Step 0.0: Ensure that for each Iter(ation) in [0,1,2,] a SQL3db is ready and waiting +# Step 0.1: Ensure that in each of those files a table is defined to accept the processed records. +# Step 1.0: With CSV_Infile start a iteration using DictReader. And process line/row by row +# Step 1.1: Discard unneeded fields/key from the Dict +# a.o. poskey, tripkey, date, area_surveyed, easting, northing, transect, +# count_method, species counted, subarea, number, geom, fid. +# Step 1.2: Use (re-use) the SB_iter_dict to determine iter0, iter1, iter2 +# Add key íter: to the dictionary +# If iter==0 then +# Add key spread: and give the value '==' +# Make dict a list_of_dict(with len == 1)! +# else +# add key spread: and give the value '00' (indicating not spread yet)! +# Step 1.3: Check whether density_ind_1km2 > 10 +# if so and iter1, or iter2 the spread ( 5 x 5 or 11 x 11) aka for [-n,n,1] where n=2 or n=5. +# Call function to spread the data for that row. +# Step 1.4 write to table list_of_dict +# The approach will frequently connect to each of the three sql3db's but from +# using the same approach with salt marsh vegetation data this is known to be quick. +# Step 2.0 For each of newly filled tables in the three sql3db create indices +# idx_grid_id on grid_id (to be used at later stage as an effective join-field/index to vg_clean_labels (XY)) +# idx_multi on origin, year, season, euring_species_code (for effective aggregating in a next script). +# +# +# Function +# write2table(target_sql3db, target_table, [list_of_dict_holding_table_rows]): +# returns nothing (or false=not written or true=written)" +# """ +# Functions takes a list_of_dictionaries, holding data matching the target table +# with specified target for the sql3db and table the data should be written to. +# """ +# +# spreaddata(list_of_dict): returns list_of_dict +# """ +# Function uses data held in the list_of_dict (lenght==1) to spread +# the received data over a wider area. +# Check that list_of_dict has length==1, if not warn and pass input back as output. +# if dict['iter'] = 1 then spread_range = [-2,2,1] else spread_range = [-5,5,1] +# if dict['iter'] = 1 then divisor = 25 else 121 +# xrange, yrange = spread_range, spread_range +# base_easting = int(dict['grid_id'][1:5]) (E1234N6789) +# base_northing = int(dict['grid_id'][6,10]) +# for i,j in xrange, yrange: +# new_dict = dict # copy +# new_dict['grid_id'] = "E" + str(i*5 + base_easting).zfill(4) + "N" + str(j*5 + base_northing).zfill(4) +# new_dict['density_ind_1km2'] = new_dict['density_ind_1km2']/divisor +# add new_dict to a list of dict +# return [new_list_of_spread_dict] +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import sqlite3 +from sqlite3 import Error +import csv +import win32timezone + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +def prepare_sql3databasetables(): + sqlStr_CreateTable = \ + """ + CREATE TABLE IF NOT EXISTS SB_data ( + euring INTEGER, + year INTEGER, + season INTEGER, + grid_id TEXT, + origin TEXT, + vehicle TEXT, + density_ind_1km2 REAL, + iter INTEGER, + spread TEXT + ); + """ + sqlStr_DeleteAllTable = \ + """ + DELETE from SB_DATA; + """ + for iteration in sb_iter_list: + sb_sql3_current = sb_sql3db_base.replace('_data_', '_data_iter_'+str(iteration)) + sb_conn = create_connection(sb_sql3_current) + if sb_conn is not None: + # create SB_data table (if not exists) + execute_sql(sb_conn, sqlStr_CreateTable, None) # Ensuring the table exists + execute_sql(sb_conn, sqlStr_DeleteAllTable, None) # And if it existed that it is empty. + else: + print("Error! Cannot create the database connection") + sb_conn.commit() + + +def spreaddata(in_row_dict) -> list: + """ + Function spreads the data from in_row_dict according to the specifications as per KEC3, w + which is deduced from fields in the dictionary. + in_row_dict['iter'] should be either 1 or 2 + For iter = 1 new grid-id's are generated based on a 5 x 5 matrix (xrange=yrange=[-2,2,1]) + and in_row_dict['density_ind_1km2'] is replaced with its value divided by 5-squared. + For iter =2 similar but a larger 11 x 11 matrix + :param in_row_dict: + :return: list_out_row_dict + """ + list_out_row_dict = [] + if in_row_dict['iter'] == 1: + xrange, yrange = range(-2, 3, 1), range(-2, 3, 1) + divisor = len(xrange)**2 + spread_value = '--' + else: + xrange, yrange = range(-5, 6, 1), range(-5, 6, 1) + divisor = len(xrange)**2 + spread_value = '__' + for i in xrange: + for j in yrange: + out_dict = in_row_dict.copy() # make a copy + # grid_id is like "E1234N6789" + grid_e = int(out_dict['grid_id'][1:5]) + grid_n = int(out_dict['grid_id'][6:10]) + out_dict['grid_id'] = "E" + str(i*5 + grid_e).zfill(4) + "N" + str(j*5 + grid_n).zfill(4) + out_dict['density_ind_1km2'] = out_dict['density_ind_1km2']/divisor + out_dict['spread'] = spread_value + # add new_dict to a list of dict + list_out_row_dict.append(out_dict) + return list_out_row_dict + + +def iter_reader_writer(): + keys_to_drop = ['poskey', 'tripkey', 'date', 'area_surveyed', 'latitude', 'longitude', 'transect', 'count_method', + 'species_counted', 'SubArea', 'number', 'GRID_ID', 'euring_species_code'] + keys_to_cast_to_int = ['year', 'season', 'euring'] + keys_to_cast_to_float = ['density_ind_1km2'] + + # set-up three connections for each of the targets (iter=0,2,3) + sb_conn_list = [] + for i in range(0, 3, 1): + sb_conn_list.append(create_connection(sb_sql3db_base.replace('_data_', '_data_iter_' + str(i)))) + + start_time = win32timezone.now() + with open(sb_csv, 'r') as sb_data_in: + sb_data_reader = csv.DictReader(sb_data_in) + i = 0 + for row in sb_data_reader: + row_list = [] + row['grid_id'] = row['GRID_ID'] + row['euring'] = row['euring_species_code'] + for key in keys_to_drop: + del row[key] + for key in keys_to_cast_to_int: + row[key] = int(row[key]) + for key in keys_to_cast_to_float: + try: + row[key] = float(row[key]) + except ValueError as e: + if row[key] == 'NA': + row[key] = 0.0 + else: + print(e) + if row['euring'] in sb_species_iter0_dict: + row['iter'] = 0 + row['spread'] = '==' + elif row['euring'] in sb_species_iter1_dict: + row['iter'] = 1 + if row['density_ind_1km2'] > 10: + row['spread'] = '00' + else: + row['spread'] = '==' + elif row['euring'] in sb_species_iter2_dict: + row['iter'] = 2 + if row['density_ind_1km2'] > 10: + row['spread'] = '00' + else: + row['spread'] = '==' + else: + row['iter'] = 9 # error value should not occur + row['spread'] = 'xx' # error value should not occur + # if row['spread'] == '==': # iter0, no spreading so everything stays the same or == + if row['iter'] == 0: + row_list.append(row) + else: # iter= 1 or iter 2 and spread == '00' (or not spread yet!) + # after spreading + # iter=1 will have spread =='--' (double minus) + # iter=2 will have spread =='__' (double underscore) + # row_list.append(row) + row_list = spreaddata(row) + pass + columns = ", ".join([col for col in row.keys()]) + value_list = [val for _, val in row.items()] + values_tuple = tuple(value_list) + # 9 columns in the row and so 9 ?-placeholders for data from a typle + # sqlStr_InsertRow = f"INSERT INTO SB_DATA ({columns}) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);" + sqlStr_InsertRow = "INSERT INTO SB_DATA (" + columns + ") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);" + # Switched to having three open connections, to speed up the script. + # sb_conn = create_connection(sb_sql3db_base.replace('_data_', '_data_iter_' + str(row['iter']))) + for row_out in row_list: + # execute_sql(sb_conn+"_"+str(row['iter']), sqlStr_InsertRow, values_tuple) + execute_sql(sb_conn_list[row_out['iter']], sqlStr_InsertRow, values_tuple) + # sb_conn.commit() # Not committing changes every row! + # print(row_list) + # print(sqlStr_InsertRow, '\t' , values_tuple) + i += 1 + if i % 10_000 == 0: + # print(f"Lines read+processed: {i} in {win32timezone.now() - start_time}") + # numba does not like f-strings + elapsed = win32timezone.now() - start_time + print("Lines read+processed: " + str(i) + " in " + str(elapsed)) + # sb_conn.commit() # commit happens once every so many (e.g. 10.000 lines) + for sb_conn in sb_conn_list: + sb_conn.commit() + # sb_conn.commit() # Ensuring there is a final commit! 10000 + for sb_conn in sb_conn_list: + sb_conn.commit() + + +if __name__ == '__main__': + sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somoteria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' + } + sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' + } + sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' + } + + sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} + sb_scenario_list = ['NAT', 'INT'] + sb_iter_list = [0, 1, 2] + + sb_csv = r"D:\GIS_data\KEC4_2021\PyConversion\ESAS_MWTL_in\ESAS_Final_Corrected_05_2021.csv" + sb_sql3db_base = r"D:\GIS_data\KEC4_2021\PyConversion\K4_data_.sql3db" + + print(f"Preparing SQL3-dbs like {sb_sql3db_base} and tables") + prepare_sql3databasetables() + print(f"Starting iterating over the lines in: \n {sb_csv}") + iter_reader_writer() diff --git a/script_py_r/kec4_pyconversion-master/KEC3extractDevelopmentData_for_KEC4.py b/script_py_r/kec4_pyconversion-master/KEC3extractDevelopmentData_for_KEC4.py new file mode 100644 index 0000000000000000000000000000000000000000..f9d4ea3cdc0942593b82433354cf50e0131ab94f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC3extractDevelopmentData_for_KEC4.py @@ -0,0 +1,120 @@ +# Python to +# extract development dataset for KEC4 (2021) from KEC3 (2018) +# KEC3 had ESRI storage (fGDB) and ArcPy-libray +# KEC4 will have GeoPackage and non-ESRI geospatial libraries (QGIS or other) +# +# Target KEC3 dataset is the 'raw' first data from ESAS+MWTL (external databases) as prepared with R-script +# by Susanne van Donk (fresh), and Marinka van Puijenbroek (experienced from KEC3) +# +# Initial intent with the created development dataset is to have a start for recoding the \ +# KEC3-processing for use in the KEC4-project (repeat the method, but adding more data). +# KEC4 is expected to have more data in two respects: +# 1) More recent data, that has been surveyed and made available since KEC3 in 2018 (so 2017/18/19/..) +# 2) Additional data from foreign surveys that have been contributed to ESAS, now it is being maintained (again) at INBO (BE). +# +# Jan Tjalling van der Wal, WMR, 20210305 + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import fiona +import geopandas +import pandas + +# import matplotlib.pyplot as plt + +# Open ESRI source fGDB +KEC3fGDB = "D:\GIS_data\KEC2018\BirdDBs\KEC_2018_4AGP\KEC_2018.gdb" +# KEC3fGDB = "D:\GIS_data\KEC2018\BirdDBs\5_WMR_fullDataSet(2,3,4)\RAW_DATA2GIS_ESAS_MWTL.gdb\RAW_DATA2GIS_ESAS_MWTL.gdb" +# KEC3layers = fiona.listlayers(KEC3fGDB) +# print(KEC3layers) +## +dest_gpkg = "D:\GIS_data\KEC4_2021\PyConversion\KEC3__AllData2GIS.gpkg" +retrieveWindsp5c = False # Switch to skip or not retrieving the base datasets WindSp5c (_label and _vgCeltan) +if retrieveWindsp5c == True: + for lyr in ['windsp5c', 'windsp5c_label','windsp5c_vgClean']: + gdf = geopandas.read_file(KEC3fGDB, layer = lyr) + print(f"Writing to GPKG: {lyr}") + gdf.to_file(filename = dest_gpkg, layer = lyr, driver = "GPKG") +## + +# KEC3source is a csv-file exported from +# "D:\GIS_data\KEC2018\BirdDBs\5_WMR_fullDataSet(2,3,4)\RAW_DATA2GIS_ESAS_MWTL_Update2019June.accdb" +# Expected/required fields included: +# "TRIP_PROFILE","NUMBER_","EURING","DENSITY","TRANSECT","XUTM","YUTM","AREA_SURVE","SEASON","SPECIES_OB", +# "COUNT_TYPE","YEAR_","GRID_ID_KEC3","SOURCE_DB" +## +retrieve3DB_SBdata = True # Swith to skip or not retrieving (importing) raw data from 3 databases and all Seabirds +# 3 database = ESASship, ESASfly and MWTL (which a 'fly'). +if retrieve3DB_SBdata == True: + KEC3source = "D:\GIS_data\KEC4_2021\PyConversion\KEC3_ESAS_MWTL_AllData2GIS.csv" + df = pandas.read_csv(KEC3source) + ## + # Renaming XUTM and YUTM to Lon resp Lat (that's what they are rather than Easting,Northing) + # And X and Y appear to have been swapped around so Y->Lon, X->Lat. + ## + df = df.rename(columns={"YUTM": "Lon", "XUTM": "Lat"}) + print(df.head()) + ## + gdf = geopandas.GeoDataFrame(df, geometry = geopandas.points_from_xy(df.Lon, df.Lat), crs="EPSG:4326") + print(gdf.head()) + ## + # gdf.plot() + # plt.show() + # Plotting using MatPlotLib won't work at this time Python 3.9.1 is not supported yet (too new). + ## + print(gdf.crs) + ## + lyr_name = "SB_data_raw_WGS84" + print(f"Writing to GPKG: {lyr_name}") + gdf.to_file(filename = dest_gpkg, layer = lyr_name, driver = "GPKG") + ## + # (Re-)projecting to UTM31WGS84 (EPSG:32631) + ## + gdf = gdf.to_crs("EPSG:32631") + lyr_name = "SB_data_raw_UTM31WGS84" + print(f"Writing to GPKG: {lyr_name}") + gdf.to_file (filename = dest_gpkg, layer = lyr_name, driver = "GPKG") + + # Still within if retrieve3DB_SBdata, and continuing + gdf_vg_name = 'windsp5c_vgClean' + gdf_sbutm31_name = "SB_data_raw_UTM31WGS84" + gdf_vg = geopandas.read_file(filename= dest_gpkg, layer = gdf_vg_name) + gdf_sbutm31 = geopandas.read_file(filename= dest_gpkg, layer = gdf_sbutm31_name) + print(f"Starting overlay (intersection of) {gdf_sbutm31_name} with {gdf_vg_name}") + gdf_new = geopandas.overlay(gdf_sbutm31, gdf_vg, how = 'intersection', keep_geom_type = True) + lyr_name = 'SB_data_vg' + print(gdf_new.head()) + print(f"Writing to GPKG: {lyr_name}") + gdf_new.to_file(filename= dest_gpkg, layer = lyr_name, driver = "GPKG") + +#gdf_2condens = geopandas.read_file(filename= dest_gpkg, layer =lyr_name) +# Condensing won't be succesfull with GeoPandas/Pandas.groupby() it only takes one argument. +# This will have to an SQL-expression executed against the layer/table +# SELECT add relevant list of relevant fields here +# adding the aggregation as well avg(number) as avg_num, avg(density) as avg_density, count(grid_id) as n_obs +# FROM layer/table here +# GROUP BY trip_profile, euring, season, grid_id, year +# WHERE check whether to include e.g. Active4Birds=1 and/or VGshow=1 and/or Estuarine=0 +# SQLstring below build and tested using SQLite Studio. +SQlstring = """ + SELECT TRIP_PROFILE, avg(NUMBER_) as avg_number, EURING, avg(DENSITY) as avg_density, + SEASON, YEAR_, SOURCE_DB, GRID_ID, count(grid_id) as nObservations, + Active4Birds, VGshow, Estuarine + FROM SB_data_vg + WHERE Active4Birds=1 and VGSHow=1 and Estuarine=0 + Group by TRIP_PROFILE, EURING, SEASON, YEAR_, SOURCE_DB, GRID_ID, Active4Birds, VGshow,Estuarine; + """ diff --git a/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4_IDW.py b/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4_IDW.py new file mode 100644 index 0000000000000000000000000000000000000000..dd01dbd8975704c3f198e0b08211c3f1baa0d5aa --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4_IDW.py @@ -0,0 +1,74 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) check for existence of +# a) CSV input file, containing the ESAS+MWTL-seabird-records to be processed further to IDWmaps +# b) GPKG outputfile, to hold the same data for further processing in QGIS +# 2) if 1 a not found abort +# 3) if 1b not found create +# 4) import the CSV and save in the GPKG. +# 5) perform any other steps that are useful at this point (to be decided). + +# 20210527: Script renamed from _KD to _IDW, Jan Tjaling van der Wal +# variables are still named KD, but output will go to a GPKG with IDW in its name. +# This is better as the interpolation methods being used is IDW (Inverse Distance Weighting). + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +import pandas as pd +import geopandas + + +def kd_csv_to_gpkg(): + kd_csv = r"D:\GIS_data\KEC4_2021\PyConversion\ESAS_MWTL_in\ESAS_Final_Corrected_05_2021.csv" + kd_gpkg = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWdata_0.gpkg" + kd_csv_file = Path(kd_csv) + kd_gpkg_file = Path(kd_gpkg) + if kd_csv_file.is_file(): + # dot_csv is an existing file + if kd_gpkg_file.is_file(): + dot_gpkg_exists = True + # Just to know whether to overwrite or not. + else: + kd_gpkg_exists = False + df = pd.read_csv(kd_csv) + print(df.head(10)) + print(df.shape) + df = df.rename(columns={"longitude": "easting", "latitude": "northing"}) + # gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.longitude, df.latitude), crs="EPSG:4326") + gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.easting, df.northing), crs="EPSG:32631") + print(gdf.head()) + print(gdf.shape) + # gdf =gdf.to_crs("EPSG:32631") + # Not needed data in dot_csv is already UTM31/WGS84 + # print(gdf.head()) + lyr_name = "SB_kd_data_UTM31WGS84" + print(f"Writing to GeoPackage: \t {kd_gpkg} for \n\r {lyr_name}") + gdf.to_file(filename=kd_gpkg_file, layer=lyr_name, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'dot_csv' as given below:") + print(f"{kd_csv}") + + #Break_point here! + print("Waiting for you to inspect the results: kd_csv_to_gpkg() ") + + +if __name__=='__main__': + kd_csv_to_gpkg() + #Break_point here! + print("Waiting for you to inspect the results: main() ") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4dotMaps.py b/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4dotMaps.py new file mode 100644 index 0000000000000000000000000000000000000000..6b70962e49ed1e35d36b478cccf20f85f1d94949 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_CSV2GPKG4dotMaps.py @@ -0,0 +1,74 @@ +# 20200503: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) check for existence of +# a) CSV input file, containing the ESAS+MWTL-seabird-records to be processed as DotMaps +# b) GPKG outputfile, to hold the same data for further processing in QGIS +# 2) if 1 a not found abort +# 3) if 1b not found create +# 4) import the CSV and save in the GPKG. +# 5) perform any other steps that are useful at this point (to be decided). + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +import pandas as pd +import geopandas + + +def dot_csv_to_gpkg(): + #dot_csv = r"D:\GIS_data\KEC4_2021\PyConversion\ESAS_MWTL_in\ESAS_dot_distribution_maps_04_2021.csv" + # 20120507, Jan Tjalling van der Wal + # _05_2011.csv is a replacement adding Starling data (Sturnus vulgaris, 15820) + # which has sufficient data for full cover maps, but the occasional massive count may + # be 'disruptive' for a good results. Expectation handed down from Mardik, Marinka, Susanne. + # Therefore included here as well for dot-maps. + dot_csv = r"D:\GIS_data\KEC4_2021\PyConversion\ESAS_MWTL_in\ESAS_dot_distribution_maps_05_2021.csv" + dot_gpkg = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotData2GIS.gpkg" + dot_csv_file = Path(dot_csv) + dot_gpkg_file = Path(dot_gpkg) + if dot_csv_file.is_file(): + # dot_csv is an existing file + if dot_gpkg_file.is_file(): + dot_gpkg_exists = True + # Just to know whether to overwrite or not. + else: + dot_gpkg_exists = False + df = pd.read_csv(dot_csv) + print(df.head(10)) + df = df.rename(columns={"longitude": "easting", "latitude": "northing"}) + # gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.longitude, df.latitude), crs="EPSG:4326") + gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df.easting, df.northing), crs="EPSG:32631") + print(gdf.head()) + # gdf =gdf.to_crs("EPSG:32631") + # Not needed data in dot_csv is already UTM31/WGS84 + # print(gdf.head()) + lyr_name = "SB_dot_data_UTM31WGS84" + print(f"Writing to GeoPackage: \t {dot_gpkg} for \n\r {lyr_name}") + gdf.to_file(filename=dot_gpkg_file, layer=lyr_name, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'dot_csv' as given below:") + print(f"{dot_csv}") + + #Break_point here! + print("Waiting for you to inspect the results: dot_csv_to_gpkg() ") + + +if __name__=='__main__': + dot_csv_to_gpkg() + #Break_point here! + print("Waiting for you to inspect the results: main() ") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_DotMaps2PNG.py b/script_py_r/kec4_pyconversion-master/KEC4_DotMaps2PNG.py new file mode 100644 index 0000000000000000000000000000000000000000..036e97c49ccbd99e9ef2617f766877d6fdbac144 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_DotMaps2PNG.py @@ -0,0 +1,154 @@ +# 20210503: Jan Tjalling van der Wal (Wageningen Marine Research) +# Done: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 0 - RUN from QGIS PYTHON CONSOLE! +# 1 - Open the prepared QGS/QGS project file ideally generated from KEC4qgz_blank and KEC4qgz_out +# In effect pointing to "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_manually.qgz" +# 2 - Loop over the species layer and deactivate all (= do not show). Use sb_species_dict +# 3 - Activate the prepared layout with N-arrow, scale-bar, legend and basemap, title. +# 4 - Reset the title to "Title is scientific name" +# 5 - Restart a loop over the species (sb_species_dict) +# Activate the layer for the current species +# Set the scientific name as the title on the layout +# Generate a PNG file in set location .\DotMaps\ +# with a generated name like .png +# Deactivate that specific layer +# 6 - Restart a loop and set to show all species layers +# 7 - Revisit the layout and reset the title to "Title is scientific name" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# Imports +import qgis, os, time + +# Setting some global variables +# KEC4qgz_blank = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_blank.qgz" +# KEC4qgz_out = KEC4qgz_blank.replace('_blank', '') +KEC4qgz_sb8 = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_manually.qgz" +KEC4dot_gpkg = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotData2GIS.gpkg" +KEC4dot_qml = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_Dot_count_by_size_colour_by_season.qml" +sb_dot_layer = 'SB_dot_data_UMT31WGS84' +sb_species_dict = {800: 'Phalacrocorax aristotelis', + 1530: 'Cygnus columbianus', + 1680: 'Branta bernicla', + 1730: 'Tadorna tadorna', + 4960: 'Calidris canutus', + 5320: 'Limosa limosa', + 5410: 'Numenius arquata', + 6270: 'Chlidonias niger'} +# 20210507, Jan Tjalling van der Wal. Starling/15820/Sturnus vulgaris added. +# 20210608, Jan Tjalling van der Wal, Bar-tailed Godwit/5340/Limosa lapponica added. +# L.lapponica was mentioned in the tender call, but somehow L.limosa got (pre-)processed initially +sb_species_dict[15820] = 'Sturnus vulgaris' +sb_species_dict[5340] = 'Limosa lapponica' +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +# project = Qqs.Project.fileName() +# KEC4qgz_sb8 = os.path.join("'"+"', '".join(KEC4qgz_sb8.split('\\'))+"'") +# project is reported back using / instead of \ +# manipulating KEC4qgz_sb4 as above yields an 'escaped' string with \\ +KEC4qgz_sb8 = KEC4qgz_sb8.replace('\\', '/') +if projectname != KEC4qgz_sb8: + print(f"Current project {projectname} is NOT the correct project {KEC4qgz_sb8}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {KEC4qgz_sb8}!") +interface = qgis.utils.iface +root = QgsProject.instance().layerTreeRoot() + +# Step 2: Unchecking the layers +print('Unchecking the layers') +for child in root.children(): + child_name = child.name() + if child_name[:7] == 'SB-dot-': + try: + euring = int(child_name.split('-')[-1]) + except: + euring = 0 + pass + if euring in sb_species_dict: # key is an integer! + new_state = Qt.Unchecked + child.setItemVisibilityChecked(new_state) + print(f"Layer {child_name} is no longer showing. {euring}") + else: + print(f"Not bothering with {child_name}.") + pass + +# Step 3 Get to the layout +print("Accessing layouts") +#prjct = QgsProject() +#layout = QgsLayout(prjct) +manager = QgsProject.instance().layoutManager() +for layout in manager.printLayouts(): + print(layout.name()) +kec4_layout = manager.layoutByName("KEC4_DotMap") +# Step 4 Re-set the title +print("Resetting title on layout") +kec4_title = kec4_layout.itemById('Title') +kec4_title.setText('Titel is de wetenschappelijke naam') + +# Step 5 Sequentially check a layer, generate PNG and uncheck again. +print('Looping over species and generating PNG-output') +dm_dir = 'DotMaps' +base_dir = os.path.dirname(KEC4qgz_sb8.replace('/', '\\')) +if os.path.exists(os.path.join(base_dir, dm_dir)): + pass +else: + os.mkdir(os.path.join(base_dir, dm_dir)) +for child in root.children(): + child_name = child.name() + if child_name[:7] == 'SB-dot-': + # euring = int(child_name.split('-')[-1]) + # above fails on other non-target layers, + # therefore a the try/except below. + try: + euring = int(child_name.split('-')[-1]) + except: + euring = 0 + pass + if euring in sb_species_dict: + new_state = Qt.Checked + child.setItemVisibilityChecked(new_state) + # Do the Layout-stuff + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText(sb_species_dict[euring]) + # preparing the filename + genu, spec = sb_species_dict[euring].split(' ') + png_name = str(euring)+genu[:4].upper()+spec[:4].upper()+'.png' + output_file = os.path.join(base_dir, dm_dir, png_name) + export_image_settings = QgsLayoutExporter.ImageExportSettings() + export_image_settings.cropToContents = True + export_image_settings.dpi = 300 + print(f"Outputting to {output_file}") + exporter = QgsLayoutExporter(kec4_layout) + exporter.exportToImage(output_file, export_image_settings) + # And reset to unckecked + new_state = Qt.Unchecked + child.setItemVisibilityChecked(new_state) + +# Restore order and have a sensible Title +kec4_title = kec4_layout.itemById('Title') +kec4_title.setText('Titel is de wetenschappelijke naam') +# And to finish of save the current state. +QgsProject.write(QgsProject.instance()) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_DotMaps_SB8.py b/script_py_r/kec4_pyconversion-master/KEC4_DotMaps_SB8.py new file mode 100644 index 0000000000000000000000000000000000000000..611f3bbebbbc17214173ca2b0e55b68672774070 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_DotMaps_SB8.py @@ -0,0 +1,144 @@ +# 20200503: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open a QGIS-project: "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_blank.qgz" +# prepared for this purpose with a.o. +# OSM monochrome basemap; study_area_extend; study_area; study_area_eez +# Layout: KEC4DotMap (which has a legend, north_arrow, scale-bar) +# 2) Open the GeoPackage with the data with the 8 seabird that have +# insufficient data for full cover maps (interpolated with Kernel Density) +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotData2GIS.gpkg" +# 3) Loop over these 8 species and +# create a layer in the QGIS-project for each and +# apply the designed layer symbology to it as store in: +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_Dot_count_by_size_colour_by_season.qml" +# 4) Save the resulting QGIS-project as "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8.qgz" + +# TODO: getting this to work Python/QGIS is refusing to add the initial layer. +# Therefore follow-on steps like setting a filter to a specific EU-ring is moot. +# Opted to do that manually -for just 8 species and no repeats for seasons that is feasible. +# QGIS-project: KEC4_DotMap_SB8_manaully.qgz is the base for a fresh script that +# should iterate over the 8 filtered layers activate each, set a title and create .PNG-output of the 'layout'. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +def quantum_mechanics(): + """ + Function intended to handle all the crap required to get access to QGIS-gui/core/processing. + For an OpenSource project this appears to be made intentionally hard to achieve. :-( + + See also pyQgis_test.py, where this code has been piloted. + If that code does not work e.g. fails on the unavailable dataset, you'll need to figure out + how to get to that point. + """ + import sys + import os + # The use these to extend and set some properties correctly for Python+QGis + sys.path.extend([r'C:\OSGeo4W64\apps\qgis-ltr\python', r'C:\OSGeo4W64\apps\Python37\lib\site-packages']) + os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = r'C:\OSGeo4W64\apps\Qt5\plugins' + os.environ['PATH'] += r';C:\OSGeo4W64\apps\qgis\bin;C:\OSGeo4W64\apps\Qt5\bin' + # And this as well. + sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python') + sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins') + sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins\processing') + # Now continue with what needs to be accomplished + + from qgis.core import QgsApplication, QgsProcessingFeedback + from qgis.analysis import QgsNativeAlgorithms + + # QgsApplication.setPrefixPath(r'C:\OSGeo4W64\apps\qgis-dev', True) + QgsApplication.setPrefixPath(r'C:\OSGeo4W64\apps\qgis-ltr', True) + qgs = QgsApplication([], False) + qgs.initQgis() + + # Add the path to processing so we can import it next + sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins') + + QgsApplication.processingRegistry().addProvider(QgsNativeAlgorithms()) + feedback = QgsProcessingFeedback() + + # from qgis.core import QgsProject, QgsVectorLayer # , QqsPathResolver + # from qgis.gui import QgsLayerTreeMapCanvasBridge + import qgis.core + import qgis.utils + + project = qgis.core.QgsProject.instance() + project.read(KEC4qgz_blank) + # canvas = qgis.gui.QgsMapCanvas() + # canvas.show() + # print(QgsProject.instance().fileName()) + print(project.fileName()) + + # vlayer = qgis.core.QgsVectorLayer(KEC4dot_gpkg+"|layername="+sb_dot_layer, 'sb_dot_blankbase', 'ogr') + vlayer = qgis.core.QgsVectorLayer(KEC4dot_gpkg + "|layername=" + sb_dot_layer, 'sb_dot_blankbase') + # + ## + ## + ## + ## print(vlayer.name(), '.isValid()=', vlayer.isValid()) + ## print(KEC4dot_gpkg+"|layername="+sb_dot_layer) + ## fieldname = "euring_species_code" + ## attrIndex = vlayer.fields().indexOf(fieldname) + ## print(fieldname, 'has attrIndex positon', attrIndex) + ## uniqValues = vlayer.uniqueValues(attrIndex) + ## i = 1 + ## fieldValueList = [] + ## for sEn in uniqValues: + ## print(f"{i} \t {sEn}") + ## fieldValueList.append(sEn) + ## i += 1 + ## + # + if not vlayer.isValid(): + print("Layer failed to load") + print(KEC4dot_gpkg+"|layername="+sb_dot_layer) + else: + project.addMapLayer(vlayer) + # qgis.utils.iface.addLayer(vlayer) + ## + ## +if __name__=='__main__': + KEC4qgz_blank = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_blank.qgz" + KEC4qgz_out = KEC4qgz_blank.replace('_blank','') + KEC4dot_gpkg = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotData2GIS.gpkg" + KEC4dot_qml = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_Dot_count_by_size_colour_by_season.qml" + sb_dot_layer = 'SB_dot_data_UMT31WGS84' + sb_species_dict = {800: 'Phalacrocorax aristotelis', + 1530: 'Cygnus columbianus', + 1680: 'Branta bernicla', + 1730: 'Tadorna tadorna', + 4960: 'Calidris canutus', + 5320: 'Limosa limosa', + 5410: 'Numenius arquata', + 6270: 'Chlidonias niger' } + # Next two appends added here 20210610, Jan Tjalling van der Wal + # Addition mainly due to the absence of 5340 in the previous iteration + # mentioned in the tender/call but not present in a previous pre-processed .csv. + sb_species_dict[15820] = 'Sturnus vulgaris' + sb_species_dict[5340] = 'Limosa lapponica', + sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar',5: 'Apr+May', 6: 'Jun+Jul'} + quantum_mechanics() diff --git a/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter0_condensor.py b/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter0_condensor.py new file mode 100644 index 0000000000000000000000000000000000000000..f00f27a76f0160812e91ea0876fda6da5200f6cf --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter0_condensor.py @@ -0,0 +1,85 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +# 2) Select/Filter for Iter0-seabird-species, based on sb_species_iter0_dict +# 3) Write GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg" +# +# Script to be run from PyCharm (connected to QGIS/Python in C:\OSGeo4W64\apps\Python37 ) +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +# import pandas as pd +import geopandas + + +def idw_gpkg_condens2iter0(): + sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' + } + # 220: 'Fulmarus glacialis', removed from iter0_dict, this is a iter1-species (FishyTail)! + # It may be present in iter0.gpkg, but should NOT be processed.! + kd_gpkg_orig = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + kd_gpkg_in = kd_gpkg_orig.replace('data_0', 'iter0') + idw_gpkg_out = kd_gpkg_in.replace('KDiter','IDWiter') + kd_gpkg_in_file = Path(kd_gpkg_in) + idw_gpkg_out_file = Path(idw_gpkg_out) + lyr_name_orig = "SB_kd_data_UTM31WGS84" + lyr_name_in = lyr_name_orig.replace('_UTM31WGS84', 'iter0_raw') + lyr_name_out = lyr_name_in.replace('iter0_raw', 'iter0_cdn').replace('_kd_', '_idw_') + if kd_gpkg_in_file.is_file(): + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + print(gdf.head()) + print(gdf.shape) + #Perform the selection, this is basically Pandas-syntax, inherited by GeoPandas + euring_list = [k for k in sb_species_iter0_dict.keys()] + #euring_string = ", ".join([str(e) for e in euring_list]) + iter_selector = gdf['euring_species_code'].isin(euring_list) + gdf = gdf[iter_selector] + df_groupy1 = gdf.groupby(['GRID_ID', 'season', 'year', 'origin', 'euring_species_code'])['area_surveyed', 'density_ind_1km2', 'easting', 'northing'].mean().reset_index() + gdf_groupy1 = geopandas.GeoDataFrame(df_groupy1, geometry=geopandas.points_from_xy(df_groupy1.easting, df_groupy1.northing), crs="EPSG:32631") + print(gdf_groupy1.head()) + print(gdf_groupy1.shape) + print(f"Writing to GeoPackage: \t {idw_gpkg_out_file} for \n\r {lyr_name_out}") + + gdf_groupy1.to_file(filename=idw_gpkg_out_file, layer=lyr_name_out, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'KDdata_0' as given below:") + print(f"{kd_gpkg_in}") + + #Break_point here! + print("Waiting for you to inspect the results: idw_gpkg_condens2iter0()") + + +if __name__=='__main__': + idw_gpkg_condens2iter0() + #Break_point here! + print("Waiting for you to inspect the results: main()") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter1_condensor.py b/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter1_condensor.py new file mode 100644 index 0000000000000000000000000000000000000000..f9696549d55b14fbd17f9114c38f248ad5f6e81f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_GPGK_iter1_condensor.py @@ -0,0 +1,121 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +# 2) Select/Filter for Iter0-seabird-species, based on sb_species_iter0_dict +# 3) Write GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg" +# +# Script to be run from PyCharm (connected to QGIS/Python in C:\OSGeo4W64\apps\Python37 ) +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +# import pandas as pd +import geopandas + + +def idw_gpkg_condens2iter1(): + # sb_species_iter0_dict = { + # 59: 'Gavia sp', + # 720: 'Phalacrocorax carbo', + # 2060: 'Somateria mollissima', + # 2130: 'Melanitta nigra', + # 15820: 'Sturnus vulgaris', + # 5670: 'Stercorarius parasiticus', + # 5690: 'Stercorarius skua', + # 5780: 'Hydrocoloeus minutus', + # 6110: 'Thalasseus sandvicensis', + # 6169: 'Sterna paradisaea+hirundo', + # 6340: 'Uria aalge', + # 6360: 'Alca torda', + # 6540: 'Fratercula arctica' + # } + # 220: 'Fulmarus glacialis', removed from iter0_dict, this is a iter1-species (FishyTail)! + # It may be present in iter0.gpkg, but should NOT be processed.! + sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' + } + # sb_species_iter2_dict = { + # 5910: 'Larus fuscus', + # 5920: 'Larus argentatus', + # 6000: 'Larus marinus' + # } + kd_gpkg_orig = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + kd_gpkg_in = kd_gpkg_orig.replace('data_0', 'iter1') + idw_gpkg_out = kd_gpkg_in.replace('KDiter','IDWiter') + kd_gpkg_in_file = Path(kd_gpkg_in) + idw_gpkg_out_file = Path(idw_gpkg_out) + lyr_name_orig = "SB_kd_data_UTM31WGS84" + lyr_name_in = lyr_name_orig.replace('_UTM31WGS84', 'iter1_raw') + lyr_name_out_intermediate = lyr_name_in.replace('_kd_data', '_idw_data') + lyr_name_out_a = lyr_name_out_intermediate.replace('iter1_raw', 'iter1_cdn_a') + lyr_name_out_b = lyr_name_out_a.replace('_cdn_a', '_cdn_b') + # lyr_name_out_a resp b are intended to hold either the + # densities that need not be 'spread out' (fishy_tail or 5 x5 ) for iter1 + # or '(flatten, 11 x11 )for iter 2 in a + # And b holding the ones to be 'spread_out' + if kd_gpkg_in_file.is_file(): + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + print(gdf.head()) + print(gdf.shape) + #Perform the selection, this is basically Pandas-syntax, inherited by GeoPandas + euring_list = [k for k in sb_species_iter1_dict.keys()] + #euring_string = ", ".join([str(e) for e in euring_list]) + # select for lyr_name_out_a: the right species and a density lte 10 birds/km2. + iter_selector = gdf['euring_species_code'].isin(euring_list) & gdf['density_ind_1km2']<=10 + gdf1 = gdf[iter_selector] + df_groupy1 = gdf1.groupby(['GRID_ID', 'season', 'year', 'origin', 'euring_species_code'])['area_surveyed', 'density_ind_1km2', 'easting', 'northing'].mean().reset_index() + gdf_groupy1 = geopandas.GeoDataFrame(df_groupy1, + geometry=geopandas.points_from_xy(df_groupy1.easting, df_groupy1.northing), + crs="EPSG:32631") + print(gdf_groupy1.head()) + print(gdf_groupy1.shape) + print(f"Writing to GeoPackage: \t {idw_gpkg_out_file} for \n\r {lyr_name_out_a}") + gdf_groupy1.to_file(filename=idw_gpkg_out_file, layer=lyr_name_out_a, driver="GPKG" ) + + # select for lyr_name_out_b: the right species and a density gt 10 birds/km2. + iter_selector = gdf['euring_species_code'].isin(euring_list) & gdf['density_ind_1km2']>10 + gdf2 = gdf[iter_selector] + # TODO: Insert the spreading bit here! + # for i in [-2,2,1]: + # for j in [-2,2,1]: + + # Spreading done! + # Continue with GroupBy + df_groupy2 = gdf2.groupby(['GRID_ID', 'season', 'year', 'origin', 'euring_species_code'])['area_surveyed', 'density_ind_1km2', 'easting', 'northing'].mean().reset_index() + gdf_groupy2 = geopandas.GeoDataFrame(df_groupy1, + geometry=geopandas.points_from_xy(df_groupy2.easting, df_groupy2.northing), + crs="EPSG:32631") + print(gdf_groupy2.head()) + print(gdf_groupy2.shape) + print(f"Writing to GeoPackage: \t {idw_gpkg_out_file} for \n\r {lyr_name_out_b}") + gdf_groupy2.to_file(filename=idw_gpkg_out_file, layer=lyr_name_out_b, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'KDdata_0' as given below:") + print(f"{kd_gpkg_in}") + + #Break_point here! + print("Waiting for you to inspect the results: idw_gpkg_condens2iter1()") + + +if __name__=='__main__': + idw_gpkg_condens2iter1() + #Break_point here! + print("Waiting for you to inspect the results: main()") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter0.py b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter0.py new file mode 100644 index 0000000000000000000000000000000000000000..8e8f5f2c64ecc00d337260bce0cd3420c0391b9c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter0.py @@ -0,0 +1,82 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +# 2) Select/Filter for Iter0-seabird-species, based on sb_species_iter0_dict +# 3) Write GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg" +# +# Script to be run from PyCharm (connected to QGIS/Python in C:\OSGeo4W64\apps\Python37 ) +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +# import pandas as pd +import geopandas + + +def kd_gpkg_data2iter0(): + sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' + } + # 220: 'Fulmarus glacialis', removed from iter0_dict, this is a iter1-species (FishyTail)! + # It may be present in iter0.gpkg, but should NOT be processed.! + kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + kd_gpkg_out = kd_gpkg_in.replace('data_0', 'iter0') + kd_gpkg_in_file = Path(kd_gpkg_in) + kd_gpkg_out_file = Path(kd_gpkg_out) + if kd_gpkg_in_file.is_file(): + lyr_name_in = "SB_kd_data_UTM31WGS84" + #lyr_name_in = "SB_kd_data_small" + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + print(gdf.head()) + print(gdf.shape) + # Perform the selection, this is basically Pandas-syntax, inherited by GeoPandas + euring_list = [k for k in sb_species_iter0_dict.keys()] + # euring_string = ", ".join([str(e) for e in euring_list]) + iter_selector = gdf['euring_species_code'].isin(euring_list) + gdf = gdf[iter_selector] + print(gdf.head()) + print(gdf.shape) + lyr_name_out = lyr_name_in.replace('_UTM31WGS84', 'iter0_raw') + print(f"Writing to GeoPackage: \t {kd_gpkg_out} for \n\r {lyr_name_out}") + + gdf.to_file(filename=kd_gpkg_out_file, layer=lyr_name_out, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'KDdata_0' as given below:") + print(f"{kd_gpkg_in}") + + #Break_point here! + print("Waiting for you to inspect the results: kd_gpkg_data2iter0()") + + +if __name__=='__main__': + kd_gpkg_data2iter0() + #Break_point here! + print("Waiting for you to inspect the results: main()") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter1.py b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter1.py new file mode 100644 index 0000000000000000000000000000000000000000..ecfb3d20a51003a7baab541471a05088e5b14819 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter1.py @@ -0,0 +1,85 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DOINE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +# 2) Select/Filter for Iter0-seabird-species, based on sb_species_iter1_dict +# 3) Write GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter1.gpkg" +# +# Script to be run from PyCharm (connected to QGIS/Python in C:\OSGeo4W64\apps\Python37 ) +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +# import pandas as pd +import geopandas + + +def kd_gpkg_data2iter1(): + sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' + } + # sb_species_iter0_dict = { + # 59: 'Gavia sp', + # 720: 'Phalacrocorax carbo', + # 2060: 'Somateria mollissima', + # 2130: 'Melanitta nigra', + # 15820: 'Sturnus vulgaris', + # 5670: 'Stercorarius parasiticus', + # 5690: 'Stercorarius skua', + # 5780: 'Hydrocoloeus minutus', + # 6110: 'Thalasseus sandvicensis', + # 6169: 'Sterna paradisaea+hirundo', + # 6340: 'Uria aalge', + # 6360: 'Alca torda', + # 6540: 'Fratercula arctica' + # } + kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + kd_gpkg_out = kd_gpkg_in.replace('data_0', 'iter1') + kd_gpkg_in_file = Path(kd_gpkg_in) + kd_gpkg_out_file = Path(kd_gpkg_out) + if kd_gpkg_in_file.is_file(): + lyr_name_in = "SB_kd_data_UTM31WGS84" + #lyr_name_in = "SB_kd_data_small" + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + print(gdf.head()) + print(gdf.shape) + # Perform the selection, this is basically Pandas-syntax, inherited by GeoPandas + euring_list = [k for k in sb_species_iter1_dict.keys()] + # euring_string = ", ".join([str(e) for e in euring_list]) + iter_selector = gdf['euring_species_code'].isin(euring_list) + gdf = gdf[iter_selector] + print(gdf.head()) + print(gdf.shape) + lyr_name_out = lyr_name_in.replace('_UTM31WGS84', 'iter1_raw') + print(f"Writing to GeoPackage: \t {kd_gpkg_out} for \n\r {lyr_name_out}") + + gdf.to_file(filename=kd_gpkg_out_file, layer=lyr_name_out, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'KDdata_0' as given below:") + print(f"{kd_gpkg_in}") + + #Break_point here! + print("Waiting for you to inspect the results: kd_gpkg_data2iter1()") + + +if __name__=='__main__': + kd_gpkg_data2iter1() + #Break_point here! + print("Waiting for you to inspect the results: main()") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter2.py b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter2.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5363d9bc9506d7569dd448075582f152edc0c1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_GPKG_KD_iter2.py @@ -0,0 +1,90 @@ +# 20210507: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 1) Open GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +# 2) Select/Filter for Iter1-seabird-species, based on sb_species_iter2_dict +# 3) Write GPKG "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter2.gpkg" +# +# Script to be run from PyCharm (connected to QGIS/Python in C:\OSGeo4W64\apps\Python37 ) +# + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +from pathlib import Path +# import pandas as pd +import geopandas + + +def kd_gpkg_data2iter2(): + sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' + } + # sb_species_iter1_dict = { + # 710: 'Morus bassanus', + # 6020: 'Rissa tridactyla' + # } + # sb_species_iter0_dict = { + # 59: 'Gavia sp', + # 220: 'Fulmarus glacialis', + # 720: 'Phalacrocorax carbo', + # 2060: 'Somateria mollissima', + # 2130: 'Melanitta nigra', + # 15820: 'Sturnus vulgaris', + # 5670: 'Stercorarius parasiticus', + # 5690: 'Stercorarius skua', + # 5780: 'Hydrocoloeus minutus', + # 6110: 'Thalasseus sandvicensis', + # 6169: 'Sterna paradisaea+hirundo', + # 6340: 'Uria aalge', + # 6360: 'Alca torda', + # 6540: 'Fratercula arctica' + # } + kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + kd_gpkg_out = kd_gpkg_in.replace('data_0', 'iter2') + kd_gpkg_in_file = Path(kd_gpkg_in) + kd_gpkg_out_file = Path(kd_gpkg_out) + if kd_gpkg_in_file.is_file(): + lyr_name_in = "SB_kd_data_UTM31WGS84" + #lyr_name_in = "SB_kd_data_small" + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + print(gdf.head()) + print(gdf.shape) + # Perform the selection, this is basically Pandas-syntax, inherited by GeoPandas + euring_list = [k for k in sb_species_iter2_dict.keys()] + # euring_string = ", ".join([str(e) for e in euring_list]) + iter_selector = gdf['euring_species_code'].isin(euring_list) + gdf = gdf[iter_selector] + print(gdf.head()) + print(gdf.shape) + lyr_name_out = lyr_name_in.replace('_UTM31WGS84', 'iter2_raw') + print(f"Writing to GeoPackage: \t {kd_gpkg_out} for \n\r {lyr_name_out}") + + gdf.to_file(filename=kd_gpkg_out_file, layer=lyr_name_out, driver="GPKG" ) + else: + print("Error> missing file. Check the name and location for 'KDdata_0' as given below:") + print(f"{kd_gpkg_in}") + + #Break_point here! + print("Waiting for you to inspect the results: kd_gpkg_data2iter2()") + + +if __name__=='__main__': + kd_gpkg_data2iter2() + #Break_point here! + print("Waiting for you to inspect the results: main()") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_IDW_Season2Yr2VG.py b/script_py_r/kec4_pyconversion-master/KEC4_IDW_Season2Yr2VG.py new file mode 100644 index 0000000000000000000000000000000000000000..0d4b5571e30d1ae6cf0d2b9ec79764c38d065550 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_IDW_Season2Yr2VG.py @@ -0,0 +1,336 @@ +# 20210604; Jan Tjalling van der Wal +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following tasks: +# A = Calculate the yearly average density per Batch, Euring and Grid_ID (across all seasons, max=6) +# Will be based on Statistics by Categories, bonus for 'Predicted'are other statistics like: +# min, max, sum, median (50-percentile), stddev, +# minority, majority, +# q1 (25-percentile), q3 (75-percentile) and iqr (interquartile range= 25 to 75) +# N.B. does not include data on effort! +# B = Join those results to the 'fishnet'-polygons of vg_clean! +# C = Similar join for the Seasonal data +# + +# Once done the stage has been set for preparing maps and output thereof for use in reports etc. +# + +# Script to be run from QGIS PYTHON CONSOLE! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +from pathlib import Path +import qgis.utils +import qgis.core +import qgis.processing +# from qgis.PyQt.QtGui import QColor +import sqlite3 +import win32timezone +from osgeo import ogr + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWsmpl_dev.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +idw_gpkg_path = Path(idw_gpkg_) +idw_gpkg_out = idw_gpkg_.replace('seasonal', 'YrAvg') +idw_gpkg_out_path = Path(idw_gpkg_out) +vg_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\vectorgrid_clean.gpkg" +vg_gpkg_path = Path(vg_gpkg_) + +lyr_name_vg = 'vectorgrid_clean' # vg_clean_labels_int resp. _nat +lyr_name_in_s = 'ptsSeaBirdDensitySeasonal' +lyr_name_out_s = 'vgSeaBirdDensitySeasonal' +lyr_name_out_y = 'vgSeaBirdDensityYrAvg' + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +sql_str_updateEffort = """ +UPDATE ptsSeaBirdDensitySeasonal + SET nobsv = 'nobsv'+ 0 + WHERE nobsv is NULL; + """ + + +start_time = win32timezone.now() +print(f"{start_time}") + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname != idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +interface = qgis.utils.iface +root = qgis.core.QgsProject.instance().layerTreeRoot() + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + + +print("Updating Effort (nobsv) from NULL to zero") +# 20210625: Jan Tjalling van der Wal, no data records have nobsv is NULL not ==0. +# This is awkward when visualising in GIS, so introduced an update-statement to fix that. +# Could/should have been part of KEC4_IDWplus_dev.py, but that takes several hours to complete this a half. +# create an SQLite3-connection +idw_conn_tmp = ogr.Open(idw_gpkg_,1) # OGR-connection! +# execute the already defined statement sql_str_pts_sb_idx_besg +idw_conn_tmp.ExecuteSQL(sql_str_updateEffort) +# discard +del idw_conn_tmp + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +# And then after not complaining for over a day, suddenly the fact that +# the destination file already exists has become a problem. +# So introducing a check for existence and if exists removing the offending file. +if idw_gpkg_out_path.is_file(): + idw_gpkg_out_path.unlink() + +# A = Calculate the yearly average density per Batch, Euring and Grid_ID (across all seasons, max=6) +# Will be based on Statistics by Categories, bonus for 'Predicted'are other statistics like: +# min, max, sum, median (50-percentile), stddev, +# minority, majority, +# q1 (25-percentile), q3 (75-percentile) and iqr (??) +# N.B. does not include data on effort! +algorithm_id = 'qgis:statisticsbycategories' +#param_dict = {'CATEGORIES_FIELD_NAME': ['Batch', 'EUring', 'GRID_ID'], +# 'INPUT': 'KEC4_IDW_SB_seasonal ptsSeaBirdDensitySeasonal', +# 'OUTPUT': idw_gpkg_out, 'VALUES_FIELD_NAME': 'Predicted'} +# param_dict now taking data straight from 'disk' 20210610, Jan Tjalling van der Wal +param_dict = {'CATEGORIES_FIELD_NAME': ['Batch', 'EUring', 'GRID_ID'], + 'INPUT': idw_gpkg_+ "|layername=" + lyr_name_in_s, + 'OUTPUT': idw_gpkg_out, 'VALUES_FIELD_NAME': 'Predicted'} +print(f"Starting processing of {algorithm_id}") +tbl_yr_avg = qgis.processing.run(algorithm_id, param_dict) +# This is a string, when successful and equals idw_gpkg_out +# GPKG-filename is the table-name. +print(f"Finishing processing of {algorithm_id}") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +# B = Join those results to the 'fishnet'-polygons of vg_clean! +# The join-type that works is Join_Attributes_By_Location thus to work +# the data from tbl_yr_avg needs to made 'geo'. +# Other join-algorithms do not have an option for 'one-to-many', +# creating a polygon (grid-cell) for each matching row. +tablename = idw_gpkg_out.split('\\')[-1].replace('.gpkg', '') +idw_conn_ = ogr.Open(idw_gpkg_out) # OGR-connection! +# idw_conn_in = ogr.Open(idw_gpkg_in) +# for i in idw_conn_: +# print(f"Checking tables/layers in {idw_gpkg_out}") +# lyr = qgis.core.QgsVectorLayer(idw_gpkg_out+"|layername="+i.GetName(), i.GetName(), 'ogr') +# lyr = qgis.core.QgsLay +# print(f"{lyr} .isValid()= {lyr.isValid()}") +# +# for i in idw_conn_: +# # lyr = qgis.core.QgsVectorLayer(idw_gpkg_out+"|layername="+i.GetName(), i.GetName(), 'ogr') +# lyr = qgis.core.QgsVectorLayer(idw_gpkg_out+"|layername="+tablename, tablename, 'ogr') +# if lyr.name() ==tablename: +# pass +# TODO: Load the table from GPKG into a 'layer' or similar. +lyr = qgis.core.QgsVectorLayer(idw_gpkg_out + "|layername=" + tablename, tablename, 'ogr') +print(f"{lyr.name()}.isValid()= {lyr.isValid()}") + +fields2add = {'Easting': QVariant.Double, 'Northing': QVariant.Double, 'Predicted': QVariant.Double} +for field in fields2add: + try: + lyr.startEditing() + layer_provider = lyr.dataProvider() + layer_provider.addAttributes([QgsField(field, fields2add[field])]) + lyr.updateFields() + lyr.commitChanges() + except: + print(f"{field} already present, field_type = {fields2add[field]}") +# Retrieve the current set of fields, and print it for reference. +lyrFields = [] +for field in lyr.fields(): + lyrFields.append(field.name()) +print(lyrFields) +# Next determine the field_indices of the fields to be updated. +# And store for later use when looping thru the new data to add. +field2update_index_list = [] +for i, field in enumerate(lyrFields): + if field in fields2add: + field2update_index_list.append(i) + print('Adding: ', i, field) +print(field2update_index_list) +f_count = 0 +lyr.startEditing() +for f in lyr.getFeatures(): + f_count += 1 + a = f.attributes() + # print(f_count, a) + # Split the GRID_ID into an Easting and Northing, assing those values to variables + # then build the attributes-dictionary to store the Easting and Northing + # in preparation for algorithmid = native:createpointslayerfromtable + grid_e = int(a[3][1:5]) * 1000 + grid_n = int(a[3][6:10]) * 1000 + predi = a[9]/6 # sum == ninth attribute, dividing by totalSeasons 6 + # Calculating Predicted as a mean over 6 six season irrespective of the number + # of seasons with a value or NULL. This is the better, more honest estimator. + # NULL values are not used by mean, thus the yearly average remains to high. + # Zero is a better assumption, which is what this calculation implements. + attributes = {field2update_index_list[0]: grid_e, + field2update_index_list[1]: grid_n, + field2update_index_list[2]: predi} + f2update = f.id() + f2upd_result = lyr.dataProvider().changeAttributeValues({f2update: attributes}) + if not f2upd_result: # so if False instead of True + print(f"Ouch: fid={f2update} has not been updated with these values {attributes}") + progressor = 5000 + if f_count % progressor == 0: + elapsed = win32timezone.now() - start_time + print(f_count, "\t Time taken upto this point " + str(elapsed)) +lyr.commitChanges() + +# TODO: Add another iteration / loop to +# Collect the total nobsv from +# lyr_name_out_s = 'vgSeaBirdDensitySeasonal' +# and use that to update +# lyr_name_out_y = 'vgSeaBirdDensityYrAvg' +# Thus ensuring that the yearly average seabird densities also have their own effort data attached +# N.B. This will require adding an nobsv-field (type=int) to lyr_name_out_y to begin with. + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +algorithm_id = 'native:createpointslayerfromtable' +param_dict = {'INPUT': idw_gpkg_out + '|layername=' + lyr.name(), + 'OUTPUT': 'TEMPORARY_OUTPUT', + 'MFIELD': '', 'TARGET_CRS': QgsCoordinateReferenceSystem('EPSG:32631'), + 'XFIELD': list(fields2add.keys())[0], + 'YFIELD': list(fields2add.keys())[1], + 'ZFIELD': ''} +print(f"Starting processing of {algorithm_id}") +xy_yravg_result = qgis.processing.run(algorithm_id, param_dict) +print(f"Finishing processing of {algorithm_id}") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +algorithm_id = 'native:joinattributesbylocation' +param_dict = {'DISCARD_NONMATCHING': False, + 'INPUT': vg_gpkg_ + '|layername=vectorgrid_clean', + 'JOIN': xy_yravg_result['OUTPUT'], + 'JOIN_FIELDS': ['Batch', 'EUring', 'Predicted', 'count', 'unique', 'min', 'max', 'range', 'sum', 'mean', 'median', 'stddev', 'minority', 'majority', 'q1', 'q3', 'iqr', ], + 'METHOD': 0, + 'OUTPUT': 'ogr:dbname=\'' + idw_gpkg_out + '\' table=\"' + lyr_name_out_y + '\" (geom)', + 'PREDICATE': [0], 'PREFIX': ''} + +print(f"Starting processing of {algorithm_id}") +qgis.processing.run(algorithm_id, param_dict) +print(f"Finishing processing of {algorithm_id}") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +# C = Similar join for the Seasonal data +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +algorithm_id = 'native:joinattributesbylocation' +param_dict = {'DISCARD_NONMATCHING': False, + 'INPUT': vg_gpkg_ + '|layername=' + lyr_name_vg, + 'JOIN': idw_gpkg_ + '|layername=' + lyr_name_in_s, + 'JOIN_FIELDS': ['Batch', 'Predicted', 'EUring', 'Season', 'nobsv', 'firstyear', 'lastyear'], + 'METHOD': 0, + 'OUTPUT': 'ogr:dbname=\'' + idw_gpkg_ + '\' table=\"' + lyr_name_out_s + '\" (geom)', + 'PREDICATE': [0], 'PREFIX': ''} +print(f"Starting processing of {algorithm_id}") +qgis.processing.run(algorithm_id, param_dict) +print(f"Finishing processing of {algorithm_id}") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +print("Script completed!") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_IDWcalc_dev.py b/script_py_r/kec4_pyconversion-master/KEC4_IDWcalc_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..a1d2341918676c6ad7cc2eda5438af494318d79c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_IDWcalc_dev.py @@ -0,0 +1,338 @@ +# 20210508; Jan Tjalling van der Wal +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# KEC4 Inverse Distance Weighting IWD calculation - development +# Using KEC4_IterAll4IDW.gpkg as the data-source for Iter in [0,1,2] +# Subsetting already pioneered in precursor-script: KEC4_KDcalc_dev.py +# Subset using +# date TEXT, Unused +# season INTEGER, Used +# year INTEGER Used +# area_surveyed REAL, Unused? Yes +# northing REAL, Unused? Yes +# easting REAL, Unused? Yes +# transect REAL, Unused +# count_method INTEGER, Unused +# species_counted INTEGER, Unused +# GRID_ID TEXT, Used +# SubArea TEXT, Unused +# origin TEXT, Used +# number REAL, Not Used not a Target +# vehicle TEXT, Not Used (change from earlier) +# density_ind_1km2 REAL Target +# +# +# 0 - Acces QGIS, get set for adding vectorlayer +# 1 - Add Field ssn_yr and fill fill with new value +# 2 - Subset base-dataset on origin, year, season +# This will involve setting up a QgsExpression to select the required data +# NAT-KD = origin = MWTL + year >=2000 +# INT-KD = origin unfiltered (ESAS+MWTL) + year unfiltered (>=1990) +# For both include euring_species_code = ; season = s +# + +# Script to be run from QGIS PYTHON CONSOLE! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +from pathlib import Path +import qgis.utils +import qgis.core +import qgis.processing +from qgis.PyQt.QtGui import QColor +import win32timezone +from time import sleep + +# Based on https://docs.qgis.org/3.16/en/docs/pyqgis_developer_cookbook/composer.html +# 10.1 Simple Rendering. +def finished(): + png_image = render.renderedImage() + png_image_save_result = png_image.save(png_out_str, "png") + return png_image_save_result + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWcalc_dev.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IterAll4IDW.gpkg" +# idw_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_5910S5_4IDW.gpkg" +idw_gpkg_in_file = Path(idw_gpkg_in) +idw_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_idw_check_points.qml" +# kd_stats_out = kd_gpkg_start.replace('data_0', current_iter).replace('.gpkg', '.xlsx') +idw_checks_path = r"D:\GIS_data\KEC4_2021\PyConversion\Checks4IDW" +# idw_checks_path = r"D:\GIS_data\KEC4_2021\PyConversion\Checks4IDW5910s5" +sleepy = False # Keep at False! With or without yields same results. +if sleepy: + sleeptime = 5 + idw_checks_path = idw_checks_path + "slp" + str(sleeptime) +else: + sleeptime = 0 +# 20210630, Jan Tjalling van der Wal +# sleepy and sleeptime introduced to check a hunch +# It has been noticed that for S=5 or S=6 the final results have flaws. +# Flaws that originate in the calculated TIFF, which have gaps. +# Gaps with inf.0 or NULL where these are not expected. +# Without sleep-timing the script fires of all script using upp all resources. +# A small leak of somekind might cause those gaps. +# Introducing a sleep timer should reduced strain on system resources +# and could result in better results. +print("Checking for existing destination path for TIFs") +dest_path_exists = Path(idw_checks_path).exists() +print(f"{idw_checks_path} exists? {dest_path_exists}") +if not dest_path_exists: + Path(idw_checks_path).mkdir() + + +# Layer naming +lyr_name_start = "sb_vb_xy_data" +# lyr_name_in = lyr_name_start.replace('_UTM31WGS84', current_iter+'_raw') +lyr_name_in = lyr_name_start +lyr_name_mem = lyr_name_start.replace('_data', '_data_mem') + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# sb_species_dict = {5910: 'Larus fuscus'} +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +# sb_seasons_dict = {5: 'Apr+May'} +sb_scenario_list = ['NAT', 'INT'] + +start_time = win32timezone.now() +print(f"{start_time}") +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname !=idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +# qgis.utils.iface +interface = qgis.utils.iface +root = QgsProject.instance().layerTreeRoot() + +# settings = QgsMapSettings() +# settings.setBackgroundColor(Qcolor(255,255,255)) # white +# settings.setOutPutSize(Qsize(1024,1024)) + +# sample subset_string below +# subset_string = "\"origin\" in ('MWTL', 'ESAS') and \"year\">1999 and \"season\"=1 and \"euring_species_code\"=710" + +# Extent KEC4_StudyAreaExtend +# 95000.0000000000000000,5600000.0000000000000000 : 1000000.0000000000000000,6750000.5000000000000000 +# 75000.0000000000000000,5600000.0000000000000000 : 1000000.0000000000000000,6750000.5000000000000000 +# bbox =QgsRectangle(93823.8106000004045200, 5637627.4166000001132488, 968823.8105999999679625, 6667627.4166000001132488) # default = INT +bbox =QgsRectangle(75000.0,5600000.0 , 1000000.0,6750000.5) # default = wider than StudyAreaExtent +crs = '[EPSG:32631]' +for area in ['NAT', 'INT']: + if area == 'NAT': + origins = "'MWTL'" + # # origins = "'MWTL', 'ESAS'" + # # above line should ideally have been used. It would have resulted in a better reproduction of KEC3. + # # KEC3 did include both origins. This mistake was noticed too late during the project to allow for + # # yet another recalculation. It has been discussed with the contractor, who agreed on proceeding as is. + start_year = 1999 # Selecting > start_year later on so this includes 2000 and later + bbox =QgsRectangle(468823.8106000004336238, 5692627.4166000001132488, 738823.8106000004336238, 6177627.4166000001132488) + else: + origins = "'MWTL', 'ESAS'" + start_year = 1990 # Selecting > start_year later on so this includes 1991 and later + # bbox =QgsRectangle(93823.8106000004045200, 5637627.4166000001132488, 968823.8105999999679625, 6667627.4166000001132488) # default = INT + bbox = QgsRectangle(75000.0, 5600000.0, 1000000.0, 6750000.5) # default = wider than StudyAreaExtent + xmin, xmax, ymin, ymax = bbox.xMinimum(), bbox.xMaximum(), bbox.yMinimum(), bbox.yMaximum() + bbox_str = ", ".join([str(xmin), str(xmax), str(ymin), str(ymax)]) + " " + crs + # print(bbox_str) + for season in sb_seasons_dict: + for euring in sb_species_dict: + # subset_string = "\"origin\" in ('MWTL', 'ESAS') and \"year\">1999 and \"season\"=1 and \"euring_species_code\"=710" + # 20210721: Jan Tjalling van der Wal: KEC3-recipe has currently not been followed to the letter as it turns out. + # For EUring in sb_species_iter0_dict the origins should have included ESAS as well (like bat=INT). + # Not implemented at this time. Discussed with FLoor (and she with Abel (BuWa)), not useful to change. + # The additional ESAS-data is likely to be 'flooded'/washed out by the MWTL-data so we are looking for + # agreement from RWS (contractor) to leave this as is. + # for euring in sb_species_iter0_dict: + # origins = = "'MWTL', 'ESAS'" + # Above two lines should suffice to implement the outlined improvement. + # Without further changes this script and all following scripts need to be fully repeated. + # Alternatively with more modifications a smaller redo is feasible that be used to + # splice updated result into the final results (delete from where euring in itero and batch=NAT) + # then insert/append the renewed results. + subset_string = "\"origin\" in (" + origins+ ") and \"year\">" + str(start_year) + \ + " and \"season\"="+ str(season) + " and \"euring\"=" + str(euring)+ "" + print(subset_string) + xy_layer_name =area + "_E" + str(euring) + "S" + str(season) + "XY" + current_layer = interface.addVectorLayer(idw_gpkg_in + "|layername=" + lyr_name_in + "|subset=" + subset_string, xy_layer_name, 'ogr') + # Applying a preset style from a QML-file to the current layer + current_layer.loadNamedStyle(idw_style) + #settings.setLayers([current_layer]) + #settings.setExtent(current_layer.extent()) + #render = QgsMapRendererParallelJob(settings) + #png_name = 'XY_' + area + str(euring) + 'S' + str(season) + '.png' + # png_out_path = Path(kd_checks_path+"\\"+ png_name) + #png_out_str = idw_checks_path + "\\" + png_name + + #png_image_save_result = render.finished.connect(finished) + # start the renderingComplete + #render.start + + #print(str(png_image_save_result) + " > " + png_out_str) + + # Calculating a IDW/nearestNeighbor Map from the current layers with points. + idw_out_ext = '.tiff' + idw_name = 'IDW_' + area + str(euring) + 'S' + str(season) + idw_out_ext + idw_out_str = idw_checks_path+"\\"+idw_name + # algorithm_id = 'qgis:heatmapkerneldensityestimation' + algorithm_id = 'gdal:gridinversedistancenearestneighbor' + # qgis.processing.algorithmHelp('gdal:gridinversedistancenearestneighbor') + # Line above will give Help on how to define and fill the param_dict. + # KD variety of param_dict = {'INPUT' : current_layer, 'RADIUS': 5000, 'PIXEL_SIZE': 5000, 'WEIGHT_FIELD': 'density_ind_1km2', 'KERNEL': 0, 'OUTPUT_VALUE': 0, 'OUTPUT': kd_out_str} + # param_dict = { 'DATA_TYPE' : 6, 'EXTRA' : '', + # 'INPUT' : xy_layer_name + " " + lyr_name_start, + # 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, + # 'OPTIONS' : '', 'OUTPUT' : idw_out_str, + # 'POWER' : 2, 'RADIUS' : 12500, 'SMOOTHING' : 0, + # 'Z_FIELD' : 'mean_density_ind_1km2' } + # 20210624: Jan Tjalling van der Wal + # param_dict below has a potentially faulty setting + # 'NODATA' : 0 appears to 'filter' zeroes from the input as being 'no data', + # not what I had expected from the help and the GUI where is says: + # "NODATA marker to fill empty points" as such a text that appears aimed at the output. + # Output generated with this setting has been store in Checks4IDW_SoftZero + # param_dict_original = { 'DATA_TYPE' : 5, 'EXTRA' : '', + # 'INPUT' : xy_layer_name + " " + lyr_name_start, + # 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, + # 'OPTIONS' : 'COMPRESS=NONE|BIGTIFF=IF_NEEDED', 'OUTPUT' : idw_out_str.replace(idw_out_ext, "_NN" + idw_out_ext), + # 'POWER' : 2, 'RADIUS' : 317217, 'SMOOTHING' : 0, 'Z_FIELD' : 'mean_densi' } + # 20210624: Jan Tjalling van der Wal + # param_dict below has been adjusted have a non-zero and non-occurring NODATA of -9.9 + # aiming to now keep hard observed zeroes in the interpolation. + # -9.9 would be invalid as input there is no negative density, it does indicate that data is float. + param_dict_original = { 'DATA_TYPE' : 5, 'EXTRA' : '', + 'INPUT' : xy_layer_name + " " + lyr_name_start, + 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : -9.9, + 'OPTIONS' : 'COMPRESS=NONE|BIGTIFF=IF_NEEDED', 'OUTPUT' : idw_out_str.replace(idw_out_ext, "_NN" + idw_out_ext), + 'POWER' : 2, 'RADIUS' : 317217, 'SMOOTHING' : 0, 'Z_FIELD' : 'mean_densi' } + # param_dict_original = filled with values and choices closely resembling the settings use with ESRI + # ArcMap/ArcGIS Pro. Visually the QGIS results with 'gdal:gridinversedistancenearestneighbor' + # are less spread out. So checking parameters and varying some settings to seek a better resemblance. + # param_dict = { 'DATA_TYPE' : 5, 'EXTRA' : '', + # 'INPUT' : xy_layer_name + " " + lyr_name_start, + # 'MAX_POINTS' : 12, 'MIN_POINTS' : 5, 'NODATA' : 0, + # 'OPTIONS' : 'COMPRESS=NONE|BIGTIFF=IF_NEEDED', 'OUTPUT' : idw_out_str.replace(idw_out_ext, "_NN" + idw_out_ext), + # 'POWER' : 2, 'RADIUS' : 25000, 'SMOOTHING' : 0, 'Z_FIELD' : 'mean_densi' } + param_dict = param_dict_original + # 'gdal:gridinversedistancenearestneighbor' also has the benefit that the + # generated .tif/.tiff-files can be previewed. + # 'Z_FIELD' : 'mean_density_ind_1km2' changed to 'mean_densi' (data is saved as a ShapeFile in the background + # and the truncates the field name to this. + qgis.processing.run(algorithm_id, param_dict) + + # Sleep timer added: 20210630, JAn Tjalling van der Wal + sleep(sleeptime) + # gdal_grid invdistnn NOT to be used, no valid output generated. + # gdal_grid invdistnn ,sample QGIS dictionary specifying input and output parameters. + # { 'DATA_TYPE' : 6, 'EXTRA' : '', + # 'INPUT' : 'D:\\GIS_data\\KEC4_2021\\PyConversion\\KEC4_KDiter1.gpkg|layername=SB_kd_dataiter1_raw|subset=\"origin\" in (\'MWTL\', \'ESAS\') and \"year\">1990 and \"season\"=4 and \"euring_species_code\"=710', + # 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, + # 'OPTIONS' : '', 'OUTPUT' : 'TEMPORARY_OUTPUT', 'POWER' : 2, 'RADIUS' : 12500, 'SMOOTHING' : 0, + # 'Z_FIELD' : '' } +# # NOT to be used 'gdal:gridinversedistance', does not give useful results +# # algorith_id = 'gdal:gridinversedistance' +# # param_dict = { 'INPUT': xy_layer_name + " " + lyr_name_start, +# # 'POWER' : 2, 'SMOOTHING' : 0, 'RADIUS_1': 12500 , 'RADIUS_2': 12500, 'ANGLE': 45, +# # 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, +# # 'Z_FIELD' : 'mean_densi', +# # 'OPTIONS' : 'COMPRESS=NONE|BIGTIFF=IF_NEEDED', +# # 'OUTPUT' : idw_out_str.replace(idw_out_ext, "_PW" + idw_out_ext) +# # } +# # qgis.processing.run(algorithm_id, param_dict) +# # # + # +# # NOT to be used 'qgis:idwinterpolation', no results for some seasons (mostly 6) otherwise not bad. +# # algorithm_id = 'qgis:idwinterpolation' +# # param_dict = { 'DISTANCE_COEFFICIENT' : 2, +# # 'EXTENT' : bbox_str, +# # 'INTERPOLATION_DATA' : xy_layer_name + " " + lyr_name_start + '::~::0::~::11::~::0', +# # 'OUTPUT' : idw_out_str, 'PIXEL_SIZE' : 5000, +# # 'Z_FIELD': 'mean_density_ind_1km2'} +# # '::~::0::~::11::~::0' This bit of code tells idwinterpolation which field to target. +# # Cut_and_Paste from the dialog-box inthe QGIS gui. +# # The 11 is most likely the 0-based index of the target field. +# # So switch to 12 for an interpolation of the effort to match the density-maps. +# # qgis.processing.run(algorithm_id, param_dict) +# # NOT: Calculate the effort-map. +# # No additional benefits from this. Better to propagate effort-count (nObservations0 thru to end-result +# # param_dict['INTERPOLATION_DATA'] = param_dict['INTERPOLATION_DATA'].replace('~::11::~','~::12::~') +# # param_dict['OUTPUT'] = param_dict['OUTPUT'].replace('IDW_', 'EFF_') +# # qgis.processing.run(algorithm_id, param_dict) + # Discarding the currentlayer + # current_layer.remove() + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +print("Done") +# 20210531: Jan Tjalling van der Wal +# Script stops here. The calls to qgis.processing.run are spawning into separate processes +# as a result the calculated IDW-rasters are not (yet) available when the code below starts to add them. +# Therefore that needs to be done in a separate script. +# # Add the calculated TIFF with the IDW raster +# path_to_tif = idw_out_str +# rlayer = qgis.core.QgsRasterLayer(path_to_tif,area + "_E" + str(euring) + "+S" + str(season)) +# if not rlayer.isValid(): +# print("Raster layer failed to load!") +# # obtain the layer tree of the top-level group in the project +# layerTree = iface.layerTreeCanvasBridge().rootGroup() +# # the position is a number starting from 0, with -1 an alias for the end +# layerTree.insertChildNode(1, QgsLayerTreeLayer(rlayer)) +# +# # Sample back to Points: algorithm_id = 'native:rastersampling' +# # vgclean_labels to be used as the inputs?? I think so. Keep CountryCode, so you can have int or select==NL +# # After sampling each will have a new 'temp/memory'-result +# # That needs to be saved/appended to the final results. +# \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_IDWplusYR_dev.py b/script_py_r/kec4_pyconversion-master/KEC4_IDWplusYR_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..056e74f49882d66006034294ec088c8d4166c075 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_IDWplusYR_dev.py @@ -0,0 +1,201 @@ +# 20210603; Jan Tjalling van der Wal +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# KEC4 Inverse Distance Weight adding information on Effort, +# effort is recorded for each Batch (INT, NAT), EUring, Season and GRID_ID +# as the total number of observation) nObvs available for processing count zero and non-zero observations. +# Also including first_year and last_year with data. +# + +# 20210706: Jan Tjalling van der Wal +# Adapting KEC4_IDW_Season2Yr2VG.py to add effort information to the +# geodata in +# KEC4_IDW_SB_YrAvg.gpkg layer = vgSeaBirdDensityYrAvg +# 0 - Acces QGIS, get set for adding vectorlayer +# 1 - Access the existing Geopackage +# 2 - Add a field nobsv (int) to the layer/table to hold the effort count +# 3 - Retrieve the per season sum for each Batch, EUring, Grid_ID from +# KEC4_IDW_SB_seasonal.gpkg layer = vgSeabirdDensitySeasonal +# 4 - Use that information to run an Update-loop. +# 5 - commit +# 6 - final commit and done! close! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# import qgis.utils +# import qgis.core +# import qgis.processing +# from qgis.PyQt.QtGui import QColor +import sqlite3 +# IMPORTS +from pathlib import Path +from sqlite3 import Error + +import pandas +import win32timezone + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + +def df_updater(df_data_collector): + # Ensuring that geo-capabilities are active! + idw_conn_y.enable_load_extension(True) + idw_conn_y.execute('SELECT load_extension("mod_spatialite")') + str_sql_upd_target = "Update " + lyr_yravg + " set nobsv = ? where fid = ? ;" + current_batch = None + for row in df_data_collector.itertuples(): + if not current_batch: # is None + current_batch = row[2] + # print(f"nobsv = {row[5]}, FID = {row[1]}") + execute_sql(idw_conn_y, str_sql_upd_target, (row[5], row[1])) + elapsed = win32timezone.now() - start_time + print(f"{lyr_yravg} for Batch = {current_batch} has been updated.") + print("Time taken upto this point " + str(elapsed)) + + +# File naming +gpkg_s = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_Seasonal.gpkg" +gpkg_s_path = Path(gpkg_s) +gpkg_y = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_YrAvg.gpkg" +gpkg_y_path = Path(gpkg_y) +# vg_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\vectorgrid_clean.gpkg" +# vg_gpkg_path = Path(vg_gpkg_) +# Layer naming +lyr_seasonal = 'vgSeaBirdDensitySeasonal' +lyr_yravg = 'vgSeaBirdDensityYrAvg' + +# Dictionaries and related to control the flow +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_batch_list = ['NAT', 'INT'] + +sql_str_effort_batch = "SELECT \ + Batch, EUring, GRID_ID, sum(nobsv) as nObsv \ + FROM " + lyr_seasonal + " where Batch = :batch \ + group by Batch, EUring, GRID_ID;" + +sql_str_fid_beg = "SELECT " \ + "FID, Batch, EUring, GRID_ID \ + from " + lyr_yravg + " where Batch = :batch ;" + +sql_str_add_column_nobsv = "alter table " + lyr_yravg + \ + " add column nobsv integer default 0;" + +start_time = win32timezone.now() +print(f"{start_time}") + +idw_conn_y = create_connection(gpkg_y) # SQLite3-connection! +idw_conn_s = create_connection(gpkg_s) + +# Altering table lyr_yravg, adding a column for nobsv +print(f"Attempting to alter table {lyr_yravg}, if an error is reported.") +print('No worries! This is most likely not the first time the script is run.') +print('And the modification is already in place. ') +execute_sql(idw_conn_y, sql_str_add_column_nobsv) +# commit +idw_conn_y.commit() +# close + +print(f'Preparing the data from {gpkg_s} and {gpkg_y} to work with ...') +for batch in sb_batch_list: + print(f"currently working on Batch = {batch}") + df_nobsv4upd = pandas.read_sql_query(sql_str_effort_batch, idw_conn_s, params={"batch": batch}) + df_fid2match = pandas.read_sql_query(sql_str_fid_beg, idw_conn_y, params={"batch": batch}) + print(df_nobsv4upd.head()) + print(df_nobsv4upd.tail()) + print(df_nobsv4upd.shape) + print(df_fid2match.head()) + print(df_fid2match.tail()) + print(df_fid2match.shape) + df_fid_nobsv = pandas.merge(left=df_fid2match, right=df_nobsv4upd, how="inner", on=['Batch', 'EUring', 'GRID_ID']) + print(df_fid_nobsv.head()) + print(df_fid_nobsv.tail()) + print(df_fid_nobsv.shape) + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + df_updater(df_fid_nobsv) + + +# commit and close +idw_conn_y.commit() +idw_conn_y.close() +idw_conn_s.close() # only read/selected from so nohting to commit! + +print("Script completed!") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_IDWplus_dev.py b/script_py_r/kec4_pyconversion-master/KEC4_IDWplus_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccba69e9f0048df73f3c286ae871b9db20eb4d7 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_IDWplus_dev.py @@ -0,0 +1,312 @@ +# 20210603; Jan Tjalling van der Wal +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# KEC4 Inverse Distance Weight adding information on Effort, +# effort is recorded for each Batch (INT, NAT), EUring, Season and GRID_ID +# as the total number of observation) nObvs available for processing count zero and non-zero observations. +# Also including first_year and last_year with data. +# + +# 0 - Acces QGIS, get set for adding vectorlayer +# 1 - Create a fresh Geopackage (if not exists) +# 2 - Add the layer/table to write the XY-Sample-from_Raster to +# 3 - re-use the looping-construct that calculated the IDW-rasters +# 4 - add one, resample one, write the results to the final gpkg-table, remove that one +# 5 - commit +# 6 - final commit and done! close! + +# Script to be run from QGIS PYTHON CONSOLE! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +from pathlib import Path +import qgis.utils +import qgis.core +import qgis.processing +from qgis.PyQt.QtGui import QColor +import sqlite3 +import win32timezone +from osgeo import ogr + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWsmpl_dev.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IterAll4IDW.gpkg" +idw_gpkg_in_path = Path(idw_gpkg_in) +idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +idw_gpkg_path = Path(idw_gpkg_) +vg_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\vectorgrid_clean.gpkg" +vg_gpkg_path = Path(vg_gpkg_) + +lyr_name_vg = {"INT": 'vgclean_labels_int', "NAT": 'vgclean_labels_nat'} # vg_clean_labels_int resp. _nat +lyr_name_out = 'ptsSeaBirdDensitySeasonal' + +# Dictionaries and related to control the flow +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +sql_str_effort_union = """ +SELECT + 'NAT' as Batch, + euring, + season, + GRID_ID, + min(year) as FirstYear, + max(year) as LastYear, + sum(n_observations) as nObsv +FROM sb_vb_xy_data +where origin = 'MWTL' and year>1999 +group by + Batch, + euring, + season, + GRID_ID +Union +SELECT + 'INT' as Batch, + euring, + season, + GRID_ID, + min(year) as FirstYear, + max(year) as LastYear, + sum(n_observations) as nObsv +FROM sb_vb_xy_data +where origin in ('MWTL', 'ESAS') and year>1990 +group by + Batch, + euring, + season, + GRID_ID +; +""" + +sql_str_pts_sb_idx_besg = """ +CREATE INDEX if not exists idx_besg +ON ptsSeaBirdDensitySeasonal ( + Batch, + EUring, + Season, + GRID_ID +); +""" +# N.B. Documented here, not used within the script. +# Index create manually using SQLite Studio. +# Implemented after the fact (see further down), search for a Done: + +start_time = win32timezone.now() +print(f"{start_time}") + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname !=idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +interface = qgis.utils.iface +root = qgis.core.QgsProject.instance().layerTreeRoot() + +settings = QgsMapSettings() +# settings.setBackgroundColor(Qcolor(255,255,255)) # white +# settings.setOutPutSize(Qsize(1024,1024)) + +#idw_conn_ = create_connection(idw_gpkg_) +idw_conn_in = create_connection(idw_gpkg_in) # SQLite3-connection! +idw_conn_ = ogr.Open(idw_gpkg_) # OGR-connection! +# idw_conn_in = ogr.Open(idw_gpkg_in) + +# Done: Implement create index idw_gpgk_ +# Regretting not having implemented this on re-run. Jan Tjalling van der Wal 20210608 +# Previous run with index in-place (manually defined) took ca. 6 hours +# Re-run is now wel past 12 hours. +# create an SQLite3-connection +idw_conn_tmp = create_connection(idw_gpkg_) # SQLite3-connection! +# execute the already defined statement sql_str_pts_sb_idx_besg +execute_sql(idw_conn_tmp,sql_str_pts_sb_idx_besg) +# commit +idw_conn_tmp.commit() +# close +idw_conn_tmp.close() +# discard +del idw_conn_tmp + +for i in idw_conn_: + print(f"Checking tables/layers in {idw_gpkg_}") + lyr = qgis.core.QgsVectorLayer(idw_gpkg_+"|layername="+i.GetName(), i.GetName(), 'ogr') + print(f"{lyr} .isValid()= {lyr.isValid()}") +#for i in idw_conn_in: +# print(f"Checking tables/layers in {idw_gpkg_in}") +# lyr = qgis.core.QgsVectorLayer(idw_gpkg_in+"|layername="+i.GetName(), i.GetName(), 'ogr') +# print(f"{lyr} .isValid()=' {lyr.isValid()}") + +for i in idw_conn_: + lyr = qgis.core.QgsVectorLayer(idw_gpkg_+"|layername="+i.GetName(), i.GetName(), 'ogr') + if lyr.name() == 'ptsSeaBirdDensitySeasonal': + # Add new fields to the existing table (layer) + fields2add = {'nobsv': QVariant.Int, 'firstyear': QVariant.Int, 'lastyear': QVariant.Int} + for field in fields2add: + try: + lyr.startEditing() + layer_provider = lyr.dataProvider() + layer_provider.addAttributes([QgsField(field, fields2add[field])]) + lyr.updateFields() + lyr.commitChanges() + except: + print(f"{field} already present, field_type = {fields2add[field]}") + # Retrieve the current set of fields, and print it for reference. + lyrFields = [] + for field in lyr.fields(): + lyrFields.append(field.name()) + print(lyrFields) + # Next determine the field_indices of the fields to be updated. + # And store for later use when looping thru the new data to add. + field2update_index_list = [] + for i, field in enumerate(lyrFields): + if field in fields2add: + field2update_index_list.append(i) + print('Adding: ', i, field) + print(field2update_index_list) + + +idw_curr_in = idw_conn_in.cursor() +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +print(f'Preparing the data from {idw_gpkg_in} to iterate over ...') +idw_curr_in.execute(sql_str_effort_union) +in_field_names = [f[0] for f in idw_curr_in.description] +print(f"Field names idw_curr_in {in_field_names}") +print(f'Done preparing from {idw_gpkg_in} ...') +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) # This prep takes roughlly 30 seconds +i=0 +for row in idw_curr_in: + # print(i, row) + i+=1 + # if i>100: + # break + # Prepare a 'feature request' for the current row + # that should yield exactly one row/feature. + # Then use the data from row to update the feature attributes. + request = QgsFeatureRequest() + # request.setSubsetOfAttributes(['GRID_ID', 'Batch', 'Predicted', 'EUring', 'Season', 'firstyear', 'lastyear', 'nobsv']) + # filter = QgsExpression.createFieldEqualityExpression(in_field_names[3],row[3]) + str4filter = in_field_names[0] + "='" + row[0] + "' and " + \ + in_field_names[1] + "=" + str(row[1]) + " and " + \ + in_field_names[2] + "=" + str(row[2]) + " and " + \ + in_field_names[3] + "='" + row[3] + "'" + # filter = QgsExpression(str4filter) # Not required it would seem. + # print(str4filter) + request.setSubsetOfAttributes([0, 5, 8, 9, 10, 11 ]) # 11, 12, 13, 14]) + request.setFilterExpression(str4filter) + f_count=0 + lyr.startEditing() + for f in lyr.getFeatures(request): + f_count+=1 + #print(lyr.getFeatures(request).reccount) + #a = f.attributes() + # print(f_count, a) + attributes={} + attributes[11] = row[6] + attributes[12] = row[4] + attributes[13] = row[5] + f2update = f.id() + lyr.dataProvider().changeAttributeValues({f2update: attributes}) + progressor = 50000 + if i % progressor == 0: + elapsed = win32timezone.now() - start_time + print(i, "\t Time taken upto this point " + str(elapsed)) + lyr.commitChanges() + if f_count>1: + print("Warning! More than one matching features updated") + # Checked while developing and breaking at 100. No aberrations noticed. + +print("Script completed!") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_IDWsmpl_dev.py b/script_py_r/kec4_pyconversion-master/KEC4_IDWsmpl_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..03eabd8abf0ed3a0bfb2522540743526cdb9f7d5 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_IDWsmpl_dev.py @@ -0,0 +1,254 @@ +# 20210601; Jan Tjalling van der Wal +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# KEC4 Inverse Distance Weight resampling to vg_clean_labels - development +# Using either iter1 or iter2 GPKG to (iter1 selected smallest dataset,so quickest for prototyping) +# get the subsetting right for iter0 +# Subset using +# date TEXT, Unused +# season INTEGER, Used +# area_surveyed REAL, Unused? +# northing REAL, Unused? +# easting REAL, Unused? +# transect REAL, Unused +# count_method INTEGER, Unused +# species_counted INTEGER, Unused +# GRID_ID TEXT, Used +# SubArea TEXT, Unused +# origin TEXT, Used +# number REAL, Target1 +# vehicle TEXT, Used +# density_ind_1km2 REAL Target2 +# ssn_yr calculate year(date) if month(date)<12 else year(date)+1 +# +# 0 - Acces QGIS, get set for adding vectorlayer +# 1 - Create a fresh Geopackage (if not exists) +# 2 - Add the layer/table to write the XY-Sample-from_Raster to +# 3 - re-use the looping-construct that calculated the IDW-rasters +# 4 - add one, resample one, write the results to the final gpkg-table, remove that one +# 5 - commit +# 6 - final commit and done! close! + +# Script to be run from QGIS PYTHON CONSOLE! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +from pathlib import Path +import qgis.utils +import qgis.core +import qgis.processing +from qgis.PyQt.QtGui import QColor +import win32timezone + + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWsmpl_dev.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_out = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +idw_gpkg_out_path = Path(idw_gpkg_out) +idw_checks_path = r"D:\GIS_data\KEC4_2021\PyConversion\Checks4IDW" + +# Layer naming +lyr_name_rstr = "SB_IDW_raster" +lyr_name_vg = {"INT": 'vgclean_labels_int', "NAT": 'vgclean_labels_nat'} # vg_clean_labels_int resp. _nat +lyr_name_out = 'ptsSeaBirdDensitySeasonal' + +# Dictionaries and related to control the flow +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +start_time = win32timezone.now() +print(f"{start_time}") + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname !=idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +interface = qgis.utils.iface +root = qgis.core.QgsProject.instance().layerTreeRoot() + +settings = QgsMapSettings() +# settings.setBackgroundColor(Qcolor(255,255,255)) # white +# settings.setOutPutSize(Qsize(1024,1024)) + +# Checking for existing target/output GeoPackage +# If existing remove! +if idw_gpkg_out_path.is_file(): + idw_gpkg_out_path.unlink() + +blank_layer = QgsVectorLayer(path='', baseName='blank', providerLib='memory') +# sample subset_string below +# subset_string = "\"origin\" in ('MWTL', 'ESAS') and \"year\">1999 and \"season\"=1 and \"euring_species_code\"=710" +for area in ['NAT', 'INT']: + if area == 'NAT': + origins = "'MWTL'" + start_year = 2000 + else: + origins = "'MWTL', 'ESAS'" + start_year = 1990 + for season in sb_seasons_dict: + for euring in sb_species_dict: + # Retrieving the IDW/nearestNeighbor Map + idw_name = 'IDW_' + area + str(euring) + 'S' + str(season) + '_NN.tiff' + idw_in_str = idw_checks_path+"\\"+idw_name + # Add the calculated TIFF with the IDW raster + path_to_tif = idw_in_str + print(f"Path_to_TIFF: {idw_in_str} ") +# rlayer = qgis.core.QgsRasterLayer(path_to_tif,area + "_E" + str(euring) + "+S" + str(season)) +# if not rlayer.isValid(): +# print("Raster layer failed to load!") +# # obtain the layer tree of the top-level group in the project +# layerTree = iface.layerTreeCanvasBridge().rootGroup() +# # the position is a number starting from 0, with -1 an alias for the end +# layerTree.insertChildNode(-1, QgsLayerTreeLayer(rlayer)) + + + + # Sample back to Points: algorithm_id = 'native:rastersampling' + # vgclean_labels to be used as the inputs?? I think so. Keep CountryCode, so you can have int or select==NL + # After sampling each will have a new 'temp/memory'-result + # That needs to be saved/appended to the final results. + # Sample param_dict for 'native:rastersampling' + # { 'COLUMN_PREFIX' : 'SAMPLE_', + # 'INPUT' : 'D:/GIS_data/KEC4_2021/PyConversion/vectorgrid_clean.gpkg|layername=vectorgrid_clean_labels|subset=\"CountryCode\" = \'NL\'', + # 'OUTPUT' : 'TEMPORARY_OUTPUT', + # 'RASTERCOPY' : 'D:/GIS_data/KEC4_2021/PyConversion/Checks4IDW/IDW_NAT220S1_NN.tiff' } + # And returns a dict is would seem with the name of the 'temporary output' + algorithm_id = 'native:rastersampling' + param_dict = { 'COLUMN_PREFIX' : 'SAMPLE_', + 'INPUT' : lyr_name_vg[area], + 'OUTPUT' : 'TEMPORARY_OUTPUT', + 'RASTERCOPY' : idw_in_str } + vg_clean_xy_temp = qgis.processing.run(algorithm_id, param_dict) + # Adding a field to hold the Area (INT or NAT) + # And filling that new field with the current value. + vg_clean_xy_temp['OUTPUT'].startEditing() + vg_clean_xy_temp['OUTPUT'].addAttribute(QgsField('Batch', QVariant.String)) + vg_clean_xy_temp['OUTPUT'].addAttribute(QgsField('Predicted', QVariant.Double)) + vg_clean_xy_temp['OUTPUT'].addAttribute(QgsField('EUring', QVariant.String)) + vg_clean_xy_temp['OUTPUT'].addAttribute(QgsField('Season', QVariant.Int)) + vg_clean_xy_temp['OUTPUT'].updateFields() + field_index_area = vg_clean_xy_temp['OUTPUT'].fields().indexFromName('Batch') + field_index_pred = vg_clean_xy_temp['OUTPUT'].fields().indexFromName('Predicted') + field_index_sample_1 = vg_clean_xy_temp['OUTPUT'].fields().indexFromName('SAMPLE_1') + field_index_euring = vg_clean_xy_temp['OUTPUT'].fields().indexFromName('EUring') + field_index_season = vg_clean_xy_temp['OUTPUT'].fields().indexFromName('Season') + for f in vg_clean_xy_temp['OUTPUT'].getFeatures(): + vg_clean_xy_temp['OUTPUT'].changeAttributeValue(f.id(), field_index_area, area) + vg_clean_xy_temp['OUTPUT'].changeAttributeValue(f.id(), field_index_pred, f["SAMPLE_1"]) + vg_clean_xy_temp['OUTPUT'].changeAttributeValue(f.id(), field_index_euring, euring) + vg_clean_xy_temp['OUTPUT'].changeAttributeValue(f.id(), field_index_season, season) + vg_clean_xy_temp['OUTPUT'].commitChanges() + # vg_clean_xy_temp['OUTPUT'].stopEditing() # And suddenly the method is not valid anymore. + vg_clean_xy_temp['OUTPUT'].startEditing() + vg_clean_xy_temp['OUTPUT'].deleteAttributes([field_index_sample_1]) + vg_clean_xy_temp['OUTPUT'].updateFields() + vg_clean_xy_temp['OUTPUT'].commitChanges() + # print(vg_clean_xy_temp) + # print(vg_clean_xy_temp['OUTPUT'].name()) # 'Sampled' + # print(vg_clean_xy_temp['OUTPUT'].attributeList()) + # for field in vg_clean_xy_temp['OUTPUT'].fields(): + # print(field.name(), field.typeName()) + # Added field 'Area' is listed + + # Using Merge-layer to merge each result with a blank in-memory QgsVectorLayer. + # Once everything has been collected there, it can be saved in one go. + # Sample param_dict for ..... + # { 'CRS' : None, 'LAYERS' : + # ['D:/GIS_data/KEC4_2021/PyConversion/K4_data_IDWsmpl_test_packageLayers.gpkg|layername=Sampled', + # 'Point?crs=EPSG:32631&field=fid:long(0,0)&field=Id:long(0,0)&field=X:double(0,0)&field=Y:double(0,0)&field=GRID_ID:string(0,0)&field=SubArea:string(0,0)&field=CountryCode:string(0,0)&field=SAMPLE_1:double(0,0)&uid={42bcd99e-80f7-4ce7-a996-3bc5722abb62}'], + # 'OUTPUT' : 'TEMPORARY_OUTPUT' } + algorithm_id = 'native:mergevectorlayers' + param_dict = { 'CRS' : 'EPSG:32631', 'LAYERS' : [blank_layer, vg_clean_xy_temp['OUTPUT']], + 'OUTPUT' : 'TEMPORARY_OUTPUT'} + tussen_resultaat = qgis.processing.run(algorithm_id, param_dict) + blank_layer = tussen_resultaat['OUTPUT'] + # print(blank_layer['OUTPUT'].DataProvider().featureCount()) + print(blank_layer.name(), blank_layer.featureCount() ) + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +# The merged layers have duplicate FID, causing errors +# So need to loop over blank_layer.getFeatures() and update the FID. +field_index_fid = blank_layer.fields().indexFromName('fid') +field_index_layer = blank_layer.fields().indexFromName('layer') +field_index_path = blank_layer.fields().indexFromName('path') +delete_fields_list = [field_index_fid, field_index_layer, field_index_path] +blank_layer.startEditing() +blank_layer.deleteAttributes(delete_fields_list) +blank_layer.updateFields() +# i_for_fid = 0 +# for f in blank_layer.getFeatures(): +# i_for_fid += 1 +# blank_layer.changeAttributeValue(f.id(), field_index_fid, i_for_fid) +blank_layer.commitChanges() + +context = QgsProject.instance().transformContext() +options = QgsVectorFileWriter.SaveVectorOptions() +options.driverName = "GPKG" +options.layerName = lyr_name_out +status = QgsVectorFileWriter.writeAsVectorFormatV2(blank_layer , idw_gpkg_out, context, options) + +if status[0] == QgsVectorFileWriter.NoError: + print("succes!") +else: + print("fail", status) + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) +print("Done") \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_ItersAll2GPKG.py b/script_py_r/kec4_pyconversion-master/KEC4_ItersAll2GPKG.py new file mode 100644 index 0000000000000000000000000000000000000000..e6bdb2cd10cb74806fd1152b8b2c245abf8db4a2 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_ItersAll2GPKG.py @@ -0,0 +1,169 @@ +# 20210527, Jan Tjalling van der Wal +# Previous splitting/spreading/combining where database actions. +# Performing these as database actions, reduces the amount of data and eases processing. +# Now the data needs to be made geodata once more. +# +# Steps: +# 0: Read the data from K4_data_CombinedItersAll_.sql3db into a Pandas-dataframe df_sb_data +# 1: Read the data from VGclean (vectorgrid 'fishnet' =polygons and vg_labels = centroids=pointXY). +# In this case the labels, those positions are needed/used for IDW-processing later on. +# Geopandas-dataframe gdf_vg_labels +# 2: Drop non-essential columns from gdf_vg_labels +# 3: Merge/join gdf_vg_labels & df_sb_data to gdf_sb_vg_xy_data +# 'inner' join, non-matching rows from db_sb_data will be dropped! +# Those are outside the study-area and/or on land. +# 4: Write data to output GPKG, destination table sb_vb_xy_data +# 5: Create an index like idx_oyse +# +# Remark: Step 3 is different from KEC3, due to different capabilities between QGIS en ArcGIS Pro (or ArcMap). +# QGIS does not do 'life' joins of tabular data with geodata as ESRI-products do. +# So opting to add the geo-part more permanently. +# If QGIS does, I have not found that capability (yet). + +# Previous script: KEC4_SQL3IterCombinor.py + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# Imports +import pandas +import geopandas +import sqlite3 +from sqlite3 import Error +from pathlib import Path +import win32timezone + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +if __name__ == '__main__': + start_time = win32timezone.now() + print (f"Started at {start_time}") + # Preparing file paths + # Path D:\GIS_data\KEC4_2021\PyConversion\KEC3__AllData2GIS_Qgis.gpkg + # Source D:/GIS_data/KEC4_2021/PyConversion/KEC3__AllData2GIS_Qgis.gpkg|layername=windsp5c_vgClean + # windsp5c_label is the correct dataset to start with, but needs 'limiting' it has all centroids + # not just the 'clean' ones. + # "SubArea" in ('CNS', 'SNS') and "CoastalZero" is NULL and "Estuarine" = 0 + gdf_vg_labels_gpkg = r"D:\GIS_data\KEC4_2021\PyConversion\KEC3__AllData2GIS_Qgis.gpkg" + gdf_vg_labels_layer = 'windsp5c_label' + gdf_vg_labels_subset = '"SubArea" in (\'CNS\', \'SNS\') and "CoastalZero" is NULL and "Estuarine" = 0' + # + #df_sb_data_sql3db = r"D:\GIS_data\KEC4_2021\PyConversion\K4_data_CombinedItersAll_.sql3db" + df_sb_data_sql3db = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_CombinedItersAll_.sql3db" + # df_sb_data_sql3db = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWdata_0E5910S5_Combined.sql3db" + df_sb_data_layer = 'sb_data' + # preparing the output file/destination + gdf_out_dir = "\\".join(df_sb_data_sql3db.split("\\")[:-1]) + gdf_out_basename = 'KEC4_IterAll4IDW.gpkg' + # gdf_out_basename = 'KEC4_5910S5_4IDW.gpkg' + gdf_out_path = "\\".join([gdf_out_dir, gdf_out_basename]) + # Destination table name and SQL-statements to be used a bit later. + destination_table = 'sb_vb_xy_data' + + str_sql_create_idx1 = f"create index if not exists \ + idx_sb_oyse on \ + {destination_table}(origin, year, season, euring);" + + str_sql_select_sb_data = "select * from SB_Data;" + + +# Read the data into dataframes = Steps 0 and 1 +# df_sb_data = geopandas.read_file(df_sb_data_sql3db, layer=df_sb_data_layer) +# Geopandas does not do SQLite3, pandas does however +sql3_conn = create_connection(df_sb_data_sql3db) +df_sb_data = pandas.read_sql(str_sql_select_sb_data, sql3_conn) +print('df_sb_data head\n', df_sb_data.head()) +print('df_sb_data tail\n', df_sb_data.tail()) +print('df_sb_data shape', df_sb_data.shape) +# +gdf_vg_labels = geopandas.read_file(gdf_vg_labels_gpkg, layer=gdf_vg_labels_layer) +# Drop non-essential columns from gdf_vg_labels = Step 2 +# Columns to drop at first: +# fid, Id, temp1, temp2, POINT_X, POINT_Y, Land, Active4Birds, CountryCode, Depth, Dist2SNSshore, +# Dist2UKshore, Dist2EASTshore, SNS_Dist2BENLDEDKSENOShore, SNS_Dist2Shore, SNS_Dist2UKShore, +# Greater_North_Sea_aug2013, Celtic_Seas_aug2013 +gdf_columns2drop = ['Id', 'temp1', 'temp2', 'POINT_X', 'POINT_Y', 'Land', 'Active4Birds', 'CountryCode', + 'Depth', 'Dist2SNSshore', 'Dist2UKshore', 'Dist2EASTshore', 'SNS_Dist2BENLDEDKSENOShore', + 'SNS_Dist2Shore', 'SNS_Dist2UKShore', 'Greater_North_Sea_aug2013', 'Celtic_Seas_aug2013'] +print('gdf_vg_labels shape as read', gdf_vg_labels.shape) +gdf_vg_labels.drop(labels=gdf_columns2drop, axis='columns', inplace=True) +# Select only the required rows +filter_subarea = gdf_vg_labels['SubArea'].isin(['CNS', 'SNS']) +filter_coastalzero = gdf_vg_labels['CoastalZero'].isnull() +filter_estuarine = gdf_vg_labels['Estuarine'] == 0 +gdf_vg_filters = filter_subarea & filter_coastalzero & filter_estuarine +gdf_vg_labels = gdf_vg_labels[gdf_vg_filters] +print('gdf_vg_labels head\n', gdf_vg_labels.head()) +print('gdf_vg_labels tail\n', gdf_vg_labels.tail()) +print('gdf_vg_labels shape selected', gdf_vg_labels.shape) + +# 3: Merge/join gdf_vg_labels & df_sb_data to gdf_sb_vg_xy_data +gdf_sb_vb_xy = gdf_vg_labels.merge(df_sb_data, on='GRID_ID') +print('gdf_sb_vb_xy head\n', gdf_sb_vb_xy.head()) +print('gdf_sb_vb_xy tail\n', gdf_sb_vb_xy.tail()) +print('gdf_sb_vb_xy shape selected', gdf_sb_vb_xy.shape) + +# clean-up +del gdf_vg_labels +del df_sb_data + +# 4: Write data to output GPKG, destination table sb_vb_xy_data +print(f"Writing to GeoPackage: \t {Path(gdf_out_path)} for \n\t {destination_table}") +gdf_sb_vb_xy.to_file(filename=Path(gdf_out_path), layer=destination_table, driver="GPKG") + +# 5: Create an index like idx_oyse +gpkg_conn = create_connection(gdf_out_path) +print(f"Indexing on-going for {destination_table}") +execute_sql(gpkg_conn, str_sql_create_idx1) +gpkg_conn.commit() +gpkg_conn.close() +print(f"Completed. Started at {start_time}") +elapsed = win32timezone.now() - start_time +print(f"Elapsed time: {elapsed}") diff --git a/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012.py b/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012.py new file mode 100644 index 0000000000000000000000000000000000000000..9554a54360b0343536b5f674ddd12b017762923e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012.py @@ -0,0 +1,91 @@ +# 20210510, Jan Tjalling van der Wal +# Datasets as loaded from previous script have a date, but not yet a field SsnYr +# SeasonYear or SsnYr for short is to be added to each of the GPKG +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter1.gpkg" +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter2.gpkg" +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg" +# and to be filled with a value matching +# if month(date)==12, year(date)+1 else year(date) +# such that for grouping and selecting the December observation end-up in the same 'Winter' as the January ones. +# And yet another abondonned script, GeoPandas is about as reluctant to comply with +# my plans a QGIS is. +# Issues with the data as prepared from ESAS+MWTL using R (or even before) were detected. +# Currently the plan is for the R-data-preparations to include a 'season_year'. +# Therefore no current need to develop this any further. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# import qgis.core +import geopandas +from pathlib import Path + +gpkg_list = sorted(["D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter1.gpkg", +"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter2.gpkg", +"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg"]) + +for i, in_gpkg in enumerate(gpkg_list[::-1]): + lyr_name_start = "SB_kd_data_UTM31WGS84" + lyr_name_in = lyr_name_start.replace('_UTM31WGS84', 'iter'+ str(2-i) + '_raw') + lyr_name_out = lyr_name_in+'_SsnYr' + + kd_gpkg_in_file = Path(in_gpkg) + gdf = geopandas.read_file(kd_gpkg_in_file, layer=lyr_name_in) + # date is a string like 'yyyy-mm-dd' + gdf['month']=gdf['date'].str.split('-').str[1].astype(int) + gdf['year']=gdf['date'].str.split('-').str[0].astype(int) + year_selector = gdf['month']<12 + plusone_selector = gdf['month']=12 + gdf = gdf.loc(year_selector) + gdf['SsnYr'] = gdf['year'] + gdf = gdf.loc(plusone_selector) + gdf['SsnYr'] = gdf['year']+1 + geopandas.write_file(kd_gpkg_in_file, layer=lyr_name_out) + # feat = next(iter(target)) + # try: + # layer_provider = lyr.dataProvider() + # # QgsField(name, type {0: int, 1: float, 2: string}, length, precision) + # layer_provider.addAttributes([QgsField('SsnYr'),QVariant.Int]) + # lyr.updateFields() + # except: + # print(f"'SsnYr' already present in {i}: {in_gpgk}.") + # + # # Loop over all features and calculate SsnYr. + # # Update the fields QGIS-style + # i=0 + # for f in lyr.getFeatures(): + # # for field in f_fields: + # # print(field.name(), f['date']) + # i+=1 + # if i>1000: + # break + # f_datum = f['date'] + # f_jaar, f_maand, f_dag = [int(x) for x in f_datum.split('-')] + # #f_year = year(f_datum) + # #f_month = mont(f_datum) + # if f_maand==12: + # ssn_yr = f_jaar+1 + # else: + # ssn_yr = f_jaar + # print(f"{f_datum} = {f_maand} + {f_jaar} >> SeasonYear = {ssn_yr}") + # # print(f_datum) + # f_fid = int(f.id()) + # lyr.startEditing() + # lyr.DataProvider.ChangeAttributeValues(f_fid, ssn_yr_index, ssn_yr) + # lyr.commitChanges() + # + # else: + # print(lyr.name(), 'does not allow updates: no ChangeAttributeVAlues included in ', caps) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012_notLikeThis.py b/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012_notLikeThis.py new file mode 100644 index 0000000000000000000000000000000000000000..c1f95a52b62f00c9e7cf70196266136a16b482d2 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_KD_SsnYr_Add2Iter012_notLikeThis.py @@ -0,0 +1,79 @@ +# 20210510, Jan Tjalling van der Wal +# Datasets as loaded from previous script have a date, but not yet a field SsnYr +# SeasonYear or SsnYr for short is to be added to each of the GPKG +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter1.gpkg" +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter2.gpkg" +# "D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg" +# and to be filled with a value matching +# if month(date)==12, year(date)+1 else year(date) +# such that for grouping and selecting the December observation end-up in the same 'Winter' as the January ones. +# 20210510: abondoned for the time being: PyQGIS is not having it with respect to ChangingAttributeValues +# Attempting with Fiona. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import qgis.core + +gpkg_list = sorted(["D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter1.gpkg", +"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter2.gpkg", +"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDiter0.gpkg"]) +for i, in_gpgk in enumerate(gpkg_list): + lyr_name_start = "SB_kd_data_UTM31WGS84" + lyr_name_in = lyr_name_start.replace('_UTM31WGS84', 'iter'+ str(i) + '_raw') + + # Check for presence of SsnYr + # If not present, add it. + lyr = qgis.core.QgsVectorLayer(in_gpgk+"|layername="+lyr_name_in, lyr_name_in, 'ogr') + try: + layer_provider = lyr.dataProvider() + # QgsField(name, type {0: int, 1: float, 2: string}, length, precision) + layer_provider.addAttributes([QgsField('SsnYr'),QVariant.Int]) + lyr.updateFields() + except: + print(f"'SsnYr' already present in {i}: {in_gpgk}.") + + # Loop over all features and calculate SsnYr. + # Update the fields QGIS-style + caps = lyr.dataProvider().capabilities() + if caps & QgsVectorDataProvider.ChangeAttributeValues: + # Not attempting to do the updates unless the VectorDataProvider will allow it. + f_fields = lyr.fields() + ssn_yr_index = f_fields.indexFromName('SsnYr') + i=0 + for f in lyr.getFeatures(): + # for field in f_fields: + # print(field.name(), f['date']) + i+=1 + if i>1000: + break + f_datum = f['date'] + f_jaar, f_maand, f_dag = [int(x) for x in f_datum.split('-')] + #f_year = year(f_datum) + #f_month = mont(f_datum) + if f_maand==12: + ssn_yr = f_jaar+1 + else: + ssn_yr = f_jaar + print(f"{f_datum} = {f_maand} + {f_jaar} >> SeasonYear = {ssn_yr}") + # print(f_datum) + f_fid = int(f.id()) + lyr.startEditing() + lyr.DataProvider.ChangeAttributeValues(f_fid, ssn_yr_index, ssn_yr) + lyr.commitChanges() + + else: + print(lyr.name(), 'does not allow updates: no ChangeAttributeVAlues included in ', caps) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_KDcalc_dev.py b/script_py_r/kec4_pyconversion-master/KEC4_KDcalc_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..f43212f5cb87cd5c84f384b3f3aa8639e3e3dfb1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_KDcalc_dev.py @@ -0,0 +1,145 @@ +# 20210508; Jan Tjalling van der Wal +# KEC4 Inverse Distance Weight calculation - development +# Using either iter1 or iter2 GPKG to (iter1 selected smallest dataset,so quickest for prototyping) +# get the subsetting right for iter0 +# Subset using +# date TEXT, Unused +# season INTEGER, Used +# area_surveyed REAL, Unused? +# northing REAL, Unused? +# easting REAL, Unused? +# transect REAL, Unused +# count_method INTEGER, Unused +# species_counted INTEGER, Unused +# GRID_ID TEXT, Used +# SubArea TEXT, Unused +# origin TEXT, Used +# number REAL, Target1 +# vehicle TEXT, Used +# density_ind_1km2 REAL Target2 +# ssn_yr calculate year(date) if month(date)<12 else year(date)+1 +# +# 0 - Acces QGIS, get set for adding vectorlayer +# 1 - Add Field ssn_yr and fill fill with new value +# 2 - Subset base-dataset on origin, year, season +# This will involve setting up a QgsExpression to select the required data +# NAT-KD = origin = MWTL + year >=2000 +# INT-KD = origin unfiltered (ESAS+MWTL) + year unfiltered (>=1990) +# For both include euring_species_code = ; season = s +# + +# Script to be run from QGIS PYTHON CONSOLE! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +from pathlib import Path +import qgis.utils +import qgis.core +import qgis.processing +from qgis.PyQt.QtGui import QColor + + +# Based on https://docs.qgis.org/3.16/en/docs/pyqgis_developer_cookbook/composer.html +# 10.1 Simple Rendering. +def finished(): + png_image = render.renderedImage() + png_image_save_result = png_image.save(png_out_str, "png") + return png_image_save_result + +# File naming +current_iter = 'iter1' +kd_gpkg_start = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +kd_gpkg_in = kd_gpkg_start.replace('data_0', current_iter) +kd_gpkg_in_file = Path(kd_gpkg_in) +kd_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_kd_check_points.qml" +kd_stats_out = kd_gpkg_start.replace('data_0', current_iter).replace('.gpkg', '.xlsx') +kd_checks_path = r"D:\GIS_data\KEC4_2021\PyConversion\Checks4KD" + +# Layer naming +lyr_name_start = "SB_kd_data_UTM31WGS84" +lyr_name_in = lyr_name_start.replace('_UTM31WGS84', current_iter+'_raw') +lyr_name_mem = lyr_name_in.replace('_raw', '_mem') + +# Dictionaries and related to control the flow +sb_species_iter1_dict = { + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' + } +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +interface = qgis.utils.iface +root = qgis.core.QgsProject.instance().layerTreeRoot() + +settings = QgsMapSettings() +# settings.setBackgroundColor(Qcolor(255,255,255)) # white +# settings.setOutPutSize(Qsize(1024,1024)) + +# sample subset_string below +# subset_string = "\"origin\" in ('MWTL', 'ESAS') and \"year\">1999 and \"season\"=1 and \"euring_species_code\"=710" +for area in ['NAT', 'INT']: + if area == 'NAT': + origins = "'MWTL'" + start_year = 2000 + else: + origins = "'MWTL', 'ESAS'" + start_year = 1990 + for season in sb_seasons_dict: + for euring in sb_species_iter1_dict: + # subset_string = "\"origin\" in ('MWTL', 'ESAS') and \"year\">1999 and \"season\"=1 and \"euring_species_code\"=710" + subset_string = "\"origin\" in (" + origins+ ") and \"year\">" + str(start_year) + \ + " and \"season\"="+ str(season) + " and \"euring_species_code\"=" + str(euring)+ "" + print(subset_string) + current_layer = interface.addVectorLayer(kd_gpkg_in + "|layername=" + lyr_name_in + "|subset=" + subset_string, lyr_name_mem, 'ogr') + # Applying a preset style from a QML-file to the current layer + current_layer.loadNamedStyle(kd_style) + settings.setLayers([current_layer]) + settings.setExtent(current_layer.extent()) + render = QgsMapRendererParallelJob(settings) + png_name = 'XY_' + area + str(euring) + 'S' + str(season) + '.png' + # png_out_path = Path(kd_checks_path+"\\"+ png_name) + png_out_str = kd_checks_path+"\\"+ png_name + + png_image_save_result = render.finished.connect(finished) + # start the renderingComplete + render.start + + print(str(png_image_save_result) + " > " + png_out_str) + + # Calculating a IDW/nearestNeighbor Map from the current layers with points. + idw_name = 'IDW_' + area + str(euring) + 'S' + str(season) + '.tif' + idw_out_str = idw_checks_path+"\\"+idw_name + # algorithm_id = 'qgis:heatmapkerneldensityestimation' + algorithm_id = 'gdal:gridinversedistancenearestneighbor' + # qgis.processing.algorithmHelp('gdal:gridinversedistancenearestneighbor') + # Line above will give Help on how to define and fill the param_dict. + # KD variety of param_dict = {'INPUT' : current_layer, 'RADIUS': 5000, 'PIXEL_SIZE': 5000, 'WEIGHT_FIELD': 'density_ind_1km2', 'KERNEL': 0, 'OUTPUT_VALUE': 0, 'OUTPUT': kd_out_str} + param_dict = { 'DATA_TYPE' : 6, 'EXTRA' : '', + 'INPUT' : idw_input_layer_name, + 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, + 'OPTIONS' : '', 'OUTPUT' : idw_output_tif_name, + 'POWER' : 2, 'RADIUS' : 12500, 'SMOOTHING' : 0, + 'Z_FIELD' : 'density_ind_1km2' } + ## gdal_grid invdistnn ,sample QGIS dictionary specifying input and output parameters. + # { 'DATA_TYPE' : 6, 'EXTRA' : '', + # 'INPUT' : 'D:\\GIS_data\\KEC4_2021\\PyConversion\\KEC4_KDiter1.gpkg|layername=SB_kd_dataiter1_raw|subset=\"origin\" in (\'MWTL\', \'ESAS\') and \"year\">1990 and \"season\"=4 and \"euring_species_code\"=710', + # 'MAX_POINTS' : 15, 'MIN_POINTS' : 5, 'NODATA' : 0, + # 'OPTIONS' : '', 'OUTPUT' : 'TEMPORARY_OUTPUT', 'POWER' : 2, 'RADIUS' : 12500, 'SMOOTHING' : 0, + # 'Z_FIELD' : '' } + qgis.processing.run(algorithm_id, param_dict) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4_PandasSplitSpreadIter.py b/script_py_r/kec4_pyconversion-master/KEC4_PandasSplitSpreadIter.py new file mode 100644 index 0000000000000000000000000000000000000000..0f1aabfa2147afc713f4df54cb26f02de7fe2996 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_PandasSplitSpreadIter.py @@ -0,0 +1,296 @@ +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import pandas as pd +import numpy as np +# import geopandas as gpd +from pathlib import Path +import sqlite3 +from sqlite3 import Error +import win32timezone + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +def prepare_sql3databasetables(): + sqlStr_CreateTable = \ + """ + CREATE TABLE IF NOT EXISTS SB_data ( + euring INTEGER, + year INTEGER, + season INTEGER, + grid_id TEXT, + origin TEXT, + vehicle TEXT, + density_ind_1km2 REAL, + iter INTEGER, + spread TEXT + ); + """ + sqlStr_DeleteAllTable = \ + """ + DELETE from SB_DATA; + """ + for iteration in sb_iter_list: + sb_sql3_current = sb_sql3db_base.replace('Iter', 'Iter_'+str(iteration)) + sb_conn = create_connection(sb_sql3_current) + if sb_conn is not None: + # create SB_data table (if not exists) + execute_sql(sb_conn, sqlStr_CreateTable, None) # Ensuring the table exists + execute_sql(sb_conn, sqlStr_DeleteAllTable, None) # And if it existed that it is empty. + else: + print("Error! Cannot create the database connection") + sb_conn.commit() + + +def determine_iter(pd_series): + euring_ = pd_series['euring'] + if euring_ in sb_species_iter0_dict: + iter_ = 0 + elif euring_ in sb_species_iter1_dict: + iter_ = 1 + elif euring_ in sb_species_iter2_dict: + iter_ = 2 + else: + iter = 9 # error value should not occur + return iter_ + + +def determine_spread(pd_series): + iter_ = pd_series['iter'] + test_ = pd_series['density_ind_1km2'] + if iter_ == 0: + spread_ = '==' + else: + if test_ <= 10: # threshold for spreading + spread_ = '==' # signaling no spreading required. + else: + spread_ = '00' # signaling to be spread to new rows (concat) later on. + return spread_ + + +def spreader(pd_series): + iter_ = pd_series['iter'] + if np.isnan(pd_series['density_ind_1km2']): + density_ = 0 + else: + density_ = pd_series['density_ind_1km2'] + if iter_ == 0: + spreader_result =[] + return spreader_result + # spreader_result = [] + if iter_ == 1: + xrange, yrange = range(-2, 3, 1), range(-2, 3, 1) + divisor = len(xrange)**2 + spread_value = '--' + else: + xrange, yrange = range(-5, 6, 1), range(-5, 6, 1) + divisor = len(xrange)**2 + spread_value = '__' + spreader_result = [] + for i in xrange: + for j in yrange: + out_dict = pd_series.copy(deep=True) # make a copy + # grid_id is like "E1234N6789" + grid_e = int(out_dict['GRID_ID'][1:5]) + grid_n = int(out_dict['GRID_ID'][6:10]) + out_dict['GRID_ID'] = "E" + str(i*5 + grid_e).zfill(4) + "N" + str(j*5 + grid_n).zfill(4) + out_dict['density_ind_1km2'] = density_/divisor + out_dict['spread'] = spread_value + # + spreader_result.append(out_dict.to_dict()) + return spreader_result + + +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} + + +str_sql4pandasread_sql = """SELECT origin, year, season, + vehicle, euring_species_code as euring, GRID_ID, + density_ind_1km2 + FROM SB_kd_data_UTM31WGS84 + where density_ind_1km2 >= 0 and density_ind_1km2 < 22000;""" + +# kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" +kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWdata_0.gpkg" +# lyr_name_in = "SB_kd_data_UTM31WGS84" +# lyr_name_in = "SB_idw_data_E5910S5" +# lyr_name_in = "SB_KD_data_small" +# # SQL-statement use to create SB_KD_data_small with SQLite Studio +# # create table SB_KD_data_small as +# # SELECT fid, geom, euring_species_code, poskey, tripkey, +# # date, year, season, area_surveyed, +# # northing, easting, transect, count_method, species_counted, +# # GRID_ID, SubArea, origin, number, vehicle, +# # density_ind_1km2 +# # FROM SB_kd_data_UTM31WGS84 +# # where FID%10000=0; + +start_time = win32timezone.now() +print(start_time) +kd_gpkg_in_file = Path(kd_gpkg_in) +if kd_gpkg_in_file.is_file(): + sql3_conn = create_connection(kd_gpkg_in) +else: + print(f"File: {kd_gpkg_in} not found! Exiting.") + exit(2) + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + +# Prepare the output SQLite3-databases +sb_sql3db_base_name = kd_gpkg_in.split("\\")[-1].replace('.gpkg', '.sql3db').replace('IDWdata_0','Iter') +sb_sql3db_path = "\\".join([part for part in kd_gpkg_in.split("\\")[:-1]]) +sb_sql3db_base = "\\".join([sb_sql3db_path, sb_sql3db_base_name]) +sb_iter_list = [0, 1, 2] +# prepare_sql3databasetables() # Do not use df.to_sql wants the table to not exist (despite if_exists='replace'). +sb_conn_list = [] +for i in range(0, 3, 1): + sb_conn_list.append(create_connection(sb_sql3db_base.replace('Iter', 'Iter_' + str(i)))) +destination_table = 'SB_DATA' +for iter in sb_iter_list: + # df_name = 'df_iter'+str(iter) # results in string not the reference to that dataframe. + sb_conn_current = sb_conn_list[iter] + execute_sql(sb_conn_current, f"DROP TABLE {destination_table};") + +blokgrootte = 200_000 +klein = False +if klein: + str_sql4pandasread_sql = str_sql4pandasread_sql.replace('SB_kd_data_UTM31WGS84', 'SB_KD_data_small') + # For testing purposes a small (klein) dataset will do nicely + # Replacing the table_name in the prepared SQL-statement retargets. + blokgrootte = 200 +else: + blokgrootte = 200_000 + +data_type_dict = {'origin': 'text', 'year': 'integer', 'season': 'integer', + 'vehicle': 'text', 'euring': 'integer', 'GRID_ID': 'text', 'density_ind_1km2': 'real'} +# key = column_name, value = data_type +# added because the read_sql was erroring with 'str' cannot be interpreted as an integer. + +i=0 +for df in pd.read_sql_query(str_sql4pandasread_sql,sql3_conn, chunksize=blokgrootte): + if df.shape[0]>0: + df['iter'] = df.apply(determine_iter, axis='columns', result_type='expand') + df['spread'] = df.apply(determine_spread, axis='columns', result_type='expand') + iter_selector = df['spread']=='00' + df_selected = df[iter_selector] + print(f'{i}: Starting the spreading proces for Iter in [1,2] and density>10 birds/km2') + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + print(df_selected.shape) + for index, toppel in df_selected.iterrows(): + # rows_to_add_as_dict = df_selected.apply(spreader, axis = 'columns') + toppels2add = spreader(toppel) + df = df.append(toppels2add, ignore_index=False) + print(df.head()) + print(df.tail()) + print(df.shape) + print("Preparing the Pandas-subset for Iter in [0,1,2] and excluding rows that have been 'spread'.") + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + + filter_iter0 = df['iter']==0 + filter_spread0 = df['spread']=="==" + split_selector0 = filter_iter0 & filter_spread0 + df_iter0 = df[split_selector0] + filter_iter1 = df['iter']==1 + filter_spread1 = df['spread'].isin(["==","--"]) + split_selector1 = filter_iter1 & filter_spread1 + df_iter1 = df[split_selector1] + filter_iter2 = df['iter']==2 + filter_spread2 = df['spread'].isin(["==", "__"]) + split_selector2 = filter_iter2 & filter_spread2 + df_iter2 = df[split_selector2] + df_unspread = df[df['spread']=="00"] + print("Ready to output the subsets Iter0, Iter1, Iter2>>") + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + + for iter in sb_iter_list: + # df_name = 'df_iter'+str(iter) # results in string not the reference to that dataframe. + sb_conn_current = sb_conn_list[iter] + if iter==0: + df_iter0.to_sql(destination_table, sb_conn_current, if_exists='append', index=False, chunksize=1000) + elif iter==1: + df_iter1.to_sql(destination_table, sb_conn_current, if_exists='append', index=False, chunksize=1000) + elif iter==2: + df_iter2.to_sql(destination_table, sb_conn_current, if_exists='append', index=False, chunksize=1000) + sb_conn_current.commit() + i+=1 + else: + break + +print("Script completed!") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4_S5S6WestwardExtender.py b/script_py_r/kec4_pyconversion-master/KEC4_S5S6WestwardExtender.py new file mode 100644 index 0000000000000000000000000000000000000000..c7481df0a0aadf93113246ec58c30b489504341b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_S5S6WestwardExtender.py @@ -0,0 +1,232 @@ +# 20210706: Jan Tjalling van der Wal +# 20210706: Jan Tjalling van der Wal +# The calculated IDW-grids are lacking some westward extension for seasons 5 and 6. +# This is most likely caused by a lakc of data points near there for those season. +# N.B. Something similar happens in the northeast, towards Oslo in the Skagerrak. +# However as there are no OWP (existing or planned) over there, no problem to be fixed. + +# This script aims to achieve three improvements to the results. +# And if succesfull can be run independently and/or after KEC4_IDW_Season2Yr2VG.py. +# Approach taken: +# SQL-statement has been prepared that collects all the relevant data from: +# idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +# lyr_name_in_s = 'ptsSeaBirdDensitySeasonal' +# +# select FID, X, Y, GRID_ID, Batch, Euring, Season, MatchRowA, Pred2UPD, Pred4UPD from +# ( +# SELECT FID, X, Y, GRID_ID, Batch, +# Predicted as Pred2UPD, +# EUring, Season, +# substr(GRID_ID,-5) as MatchRowA +# FROM ptsSeaBirdDensitySeasonal +# where Batch = 'INT' and +# X < 135320 and X > 116320 +# and Y > 6230125 and Y < 6475130 +# and Pred2UPD is NULL +# ) as 'a' +# join +# ( +# SELECT Batch, +# Predicted as Pred4UPD, +# EUring, +# Season, +# substr(GRID_ID,-5) as MatchRowB +# FROM ptsSeaBirdDensitySeasonal +# where +# Batch = 'INT' and +# X < 136325 and X > 135320 +# and Y > 6230125 and Y <6475130 +# and Predicted >=0 +# ) as 'b' +# on a.Batch = b.Batch and a.EUring = b.EUring and a.Season = b.Season and a.MatchRowA = b.MatchRowB +# +# With this data (e.g. read into a Pandas-dataframe +# Layers +# lyr_name_in_s = 'ptsSeaBirdDensitySeasonal' +# lyr_name_out_s = 'vgSeaBirdDensitySeasonal' +# can be updated. +# ptsSeaBirdDensitySeasonal can be updated based on FID as a single key. +# vbSeaBirdDensitySeasona can be updated based on Batch, EUring, Season, GRID_ID. +# lyr_name_out_y = 'vgSeaBirdDensityYrAvg' +# vgSeaBirdDensityYrAvg can also be updated, by adding 1/6 of the density to the current_value. +# That is what is contributes to the annual mean seabird density at that location and for that batch and EUring. + +# N.B. This script needs to be run from the QGIS PYTHON CONSOLE +# Therefore it checks for the presence of a specific QGIS-project (.QGZ) to ensure that is the case! +# The reason this needs to be like this that outside QGIS the script encounters an error: +# "no such function: ST_IsEmpty" for each row/record. +# Need for QGIS PYTHON CONSOLE has been negated, the same error occurs there as well. +# Opting to activate the 'spatialite' extension on the sqlite3-connectiion and see if that works instead. +# This approach has been tried for KWveg-processing, and is prone to 'locking' the geopackage. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# IMPORTS +import pandas as pd +import numpy as np +# import geopandas as gpd +from pathlib import Path +import sqlite3 +from sqlite3 import Error +import win32timezone + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +# SQL-statements +str_sql_update_data_collector = """ +select FID, X, Y, GRID_ID, BatchA, EuringA, SeasonA, MatchRowA, Pred2UPD, Pred4UPD from +( +SELECT FID, X, Y, GRID_ID, Batch as BatchA, + Predicted as Pred2UPD, + EUring as EUringA, Season as SeasonA, + substr(GRID_ID,-5) as MatchRowA + FROM ptsSeaBirdDensitySeasonal + where BatchA = 'INT' and + X < 135320 and X > 116320 + and Y > 6230125 and Y < 6475130 + and Pred2UPD is NULL + ) as 'a' +join +( + SELECT Batch as BatchB, + Predicted as Pred4UPD, + EUring as EUringB, + Season as SeasonB, + substr(GRID_ID,-5) as MatchRowB + FROM ptsSeaBirdDensitySeasonal + where + BatchB = 'INT' and + X < 136325 and X > 135320 + and Y > 6230125 and Y <6475130 + and Predicted >=0 + ) as 'b' +on a.BatchA = b.BatchB and a.EUringA = b.EUringB and a.SeasonA = b.SeasonB and a.MatchRowA = b.MatchRowB +""" + +# File names and layer names +target_gpkg_1 = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +target_gpkg_2 = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_YrAvg.gpkg" +lyr_name_1 = 'ptsSeaBirdDensitySeasonal' +lyr_name_2 = 'vgSeaBirdDensitySeasonal' +lyr_name_3 = 'vgSeaBirdDensityYrAvg' +# idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\INT220YrAvg_MissedGridID_ManualRepair.qgz" + + +start_time = win32timezone.now() +print(f"Started at {start_time}") + +# # Running inside QGIS +# # Assuming correct QGS already open. +# projectname = qgis.core.QgsProject.instance().fileName() +# idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +# if projectname != idw_projectname2check: +# print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") +# print("Please correct this and retry.") +# # Need something to stop the script, but not shutdown QGIS +# # exit() and quit(() both shutdown QGIS == too much by far. +# # break() is for inside loops, so raises an error +# # TODO: fix above nicety. It is OKAY so continuing. +# else: +# print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +sql3_conn_target_1 = create_connection(target_gpkg_1) +sql3_conn_target_1.enable_load_extension(True) +sql3_conn_target_1.execute('SELECT load_extension("mod_spatialite")') +print("Collecting data for updating 3 'layers'") +df_data_collector = pd.read_sql_query(str_sql_update_data_collector, sql3_conn_target_1) +elapsed = win32timezone.now() - start_time +print("Data has been collected.") +print("Time taken upto this point " + str(elapsed)) + +print(f"Updating rows in {lyr_name_1}") +str_sql_upd_target1_base = "Update " + lyr_name_1 + " set Predicted = ? where FID = ? ;" +str_test_1 = "Update " + lyr_name_1 + " set Predicted = repl1 where FID = repl2 ;" +for row in df_data_collector.itertuples(): + # print(row) + # Pandas(Index=0, FID=1669965, X=116323.8106, Y=6230127.4166, GRID_ID='E0116N6230', BatchA='INT', EUringA='15820', + # SeasonA=5, MatchRowA='N6230', Pred2UPD=None, Pred4UPD=0.0) + # Pandas(Index=1, FID=1674974, X=116323.8106, Y=6415127.4166, GRID_ID='E0116N6415', BatchA='INT', EUringA='15820', + # SeasonA=5, MatchRowA='N6415', Pred2UPD=None, Pred4UPD=0.0) + # Pandas(Index=2, FID=1675144, X=116323.8106, Y=6420127.4166, GRID_ID='E0116N6420', BatchA='INT', EUringA='15820', + # SeasonA=5, MatchRowA='N6420', Pred2UPD=None, Pred4UPD=0.0) + # str_test_1.replace('repl1', str(row[10])).replace('repl2', str(row[1])) + # print(str_test_1) + execute_sql(sql3_conn_target_1, str_sql_upd_target1_base, (row[10], row[1])) +elapsed = win32timezone.now() - start_time +print(f"{lyr_name_1} has been updated.") +print("Time taken upto this point " + str(elapsed)) + +print(f"Updating rows in {lyr_name_2}") +str_sql_upd_target2_base = "Update " + lyr_name_2 + " set Predicted = ? where Batch = ? and EUring = ? and Season = ? and GRID_ID = ? ;" +# str_test_1 = "Update " + lyr_name_2 + " set Predicted = repl1 where Batch = repl1 and EUring = repl2 and Season = repl3 and GRID_ID = repl4 ;" +for row in df_data_collector.itertuples(): + execute_sql(sql3_conn_target_1, str_sql_upd_target2_base, (row[10], row[5], row[6], row[7], row[4])) +elapsed = win32timezone.now() - start_time +print(f"{lyr_name_2} has been updated.") +print("Time taken upto this point " + str(elapsed)) + +print(f"Updating rows in {lyr_name_3}") +sql3_conn_target_2 = create_connection(target_gpkg_2) +sql3_conn_target_2.enable_load_extension(True) +sql3_conn_target_2.execute('SELECT load_extension("mod_spatialite")') +str_sql_upd_target3_base = "Update " + lyr_name_3 + " set Predicted = Predicted+? where Batch = ? and EUring = ? and Season = ? and GRID_ID = ? ;" +for row in df_data_collector.itertuples(): + execute_sql(sql3_conn_target_1, str_sql_upd_target2_base, (row[10]/6, row[5], row[6], row[7], row[4])) +elapsed = win32timezone.now() - start_time +print(f"{lyr_name_3} has been updated.") +print("Time taken upto this point " + str(elapsed)) + +# commit and then close connections +sql3_conn_target_1.commit() +sql3_conn_target_1.close() +# +sql3_conn_target_2.commit() +sql3_conn_target_2.close() +# diff --git a/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCombinor.py b/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCombinor.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ac7489ab107a515b884508662a3ef703b2b2d4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCombinor.py @@ -0,0 +1,180 @@ +# 20210527, Jan Tjalling van der wal +# Script will combine three .SQL3DB into one single file +# And add two pertinent indices to the table to speed up subsequent queries and joins +# +# This combinor-script is made since after the previous 'condensing' of the trhee iterations +# the amount of data has shrunk sufficiently to become manageable as one single dataset. +# This should stream-line further processing. + +# SQL-statements to create (and drop) an index as intended for use in this script. +# If queries in later scripts use the fields in the same order, this index should be used +# and yield considerable speed gains. +# +## create +## index if not exists +## idx_sb_condensor +## on +## SB_DATA(origin, year, season, vehicle, euring, GRID_ID); +# +## drop +## index +## idx_sb_condensor; + +# 20210527: Succesfull precursor script = KEC4_SQL3IterCondensor.py + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import sqlite3 +from sqlite3 import Error +import win32timezone + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +if __name__ == '__main__': + # Destination table name and SQL-statements to be used a bit later. + destination_table = 'SB_DATA' + + sql_str_selector = """ + select + origin, year, season, euring, GRID_ID, mean_density_ind_1km2, n_observations + from SB_DATA; + """ + + sql_str_creator = """ + CREATE TABLE IF NOT EXISTS SB_DATA ( + origin text, + year integer, + season integer, + euring integer, + GRID_ID text, + mean_density_ind_1km2 real, + n_observations integer); + """ + + columns = ", ".join(['origin', 'year', 'season', 'euring', 'GRID_ID', 'mean_density_ind_1km2', 'n_observations']) + sql_str_insertor = "INSERT INTO SB_DATA (" + columns + ") VALUES (?, ?, ?, ?, ?, ?, ?);" + + str_sql_create_idx1= """ + create + index if not exists + idx_sb_oyseg + on + SB_DATA(origin, year, season, euring, GRID_ID); + """ + + str_sql_create_idx2= """ + create + index if not exists + idx_sb_g + on + SB_DATA(GRID_ID); + """ + + # kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + # sb_sql3db_base_name = kd_gpkg_in.split("\\")[-1].replace('.gpkg', '.sql3db').replace('KDdata_0', 'Iter') + #input_name = r"D:\GIS_data\KEC4_2021\PyConversion\K4_data_CondensedIter_x.sql3db" + input_name = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_CondensedIter_x.sql3db" + # input_name = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWdata_0E5910S5_Condensed.sql3db" + sb_sql3db_path = "\\".join([part for part in input_name.split("\\")[:-1]]) + sb_sql3db_basename = input_name.split("\\")[-1] + sb_sql3db_base = "\\".join([sb_sql3db_path, sb_sql3db_basename]) + sb_iter_list = [0, 1, 2] # , 1, 2] #truncated for development purposes. + # Manipulate this list to [1,2] to only target iterations 1 and 2 or to whatever needs doing + # Please make sure to also set the range accordlingly about 4 lines down at: for i in range(): + # prepare_sql3databasetables() # Do not use df.to_sql wants the table to not exist (despite if_exists='replace'). + start_time = win32timezone.now() + + sb_conn_list = [] + sql3db_filename_list = [] + for i in sb_iter_list: # reset to (0,3,1,) once development/testing complete + sql3db_filename = sb_sql3db_base.replace('_CondensedIter_x', '_CondensedIter_' + str(i)) + sb_conn_list.append(create_connection(sql3db_filename)) + sql3db_filename_list.append(sql3db_filename) + print(f"{sql3db_filename_list[i]} is {sb_conn_list[i]} and should not be 'None'!") + sb_conn_list.append(create_connection(sb_sql3db_base)) + sb_conn_outer_name = sb_sql3db_base.replace('_CondensedIter_x', '_CombinedItersAll_') + # sb_conn_outer_name = sb_sql3db_base.replace('_Condensed', '_Combined') + sb_conn_outer = create_connection(sb_conn_outer_name) + execute_sql(sb_conn_outer, sql_str_creator) + sb_conn_outer.commit() + + for iter in sb_iter_list: + elapsed = win32timezone.now() - start_time + print("Combining on-going for Iteration " + str(iter) + " began after " + str(elapsed)) + df_name = 'df_iter'+str(iter) # results in string not the reference to that dataframe. + sb_conn_current = sb_conn_list[iter] + # sb_conn_current = sb_conn_list[iter-2] + sb_curr_current = sb_conn_current.cursor() + # sb_curr_outer = sb_conn_outer.cursor() + + sb_curr_current.execute(sql_str_selector) + for row in sb_curr_current: + # Collect the data + # value_list = [val for _, val in row.items()] + # values_tuple = tuple(value_list) + # row is already a tuple when iterating over a cursor + values_tuple = row + # then inject (safely) into an Insert-statement + execute_sql(sb_conn_outer, sql_str_insertor, values_tuple) + # print(values_tuple) + sb_conn_outer.commit() # Commit the changes after writing has finished + # Add the indices! + execute_sql(sb_conn_outer, str_sql_create_idx1) + execute_sql(sb_conn_outer, str_sql_create_idx2) + sb_conn_outer.commit() # Commit the new indices after writing has finished + sb_conn_outer.close() # Then close the connection + for iter in sb_iter_list: + # And now the openened connection for the input-databases can be closed to finish of the processing. + #sb_conn_current = sb_conn_list[iter] + sb_conn_current = sb_conn_list[iter-2] + sb_conn_current.close() + elapsed = win32timezone.now() - start_time + print("Combining on-going for Iteration " + str(iter) + " completed " + str(elapsed)) diff --git a/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCondersor.py b/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCondersor.py new file mode 100644 index 0000000000000000000000000000000000000000..0de12cf53600b06e1703ba6909b06e0f527d768b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_SQL3IterCondersor.py @@ -0,0 +1,184 @@ +# 20210519, Jan Tjalling van der wal +# Script to condense an 'iter'(ation) held in a SQLite3-database to +# a single mean_density_ind_1km2 for each occuring combination of +# origin, year, season, vehicle, euring, GRID_ID. +# A count of the number of records included in the mean is also kept. +# +# Script will use SQL-statements fired at the database from Python. +# Query and performance tested against the smallest (iter0) +# Without adding an index gave a result in 41 seconds. +# With an index the results was available in 0.009 seconds. +# However creating the index takes 89 seconds and nearly doubles the files size (for obvious reasons). +# So for a result that is created only once, there is no net gain in creating the index. +# Documented here, that index is not beneficial al this point. No index created. +# select +# origin, year, season, vehicle, euring, GRID_ID, avg(density_ind_1km2) as mean_density_ind_1km2, count( +# density_ind_1km2) as n_observations +# from SB_DATA +# group by +# origin, year, season, vehicle, euring, GRID_ID +# order by +# origin, year, season, vehicle, euring, GRID_ID; +# +# !! In the code below 'vehicle' is not included in the group by and order by clauses +# !! there is no need for it in the downstream-processing. And it will condense the results a bit more. +# +## create +## index if not exists +## idx_sb_condensor +## on +## SB_DATA(origin, year, season, vehicle, euring, GRID_ID); +# +## drop +## index +## idx_sb_condensor; + +# 20210525: Succesfull precursor script = K4_CSV2SQL2SplitSpreadIter.py +# 20210525: unsucccesfull KEC4_PandasSplitSpreadIter.py +# 20210525: input-database (.sql3db) are those name K4_ not KEC4! + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import sqlite3 +from sqlite3 import Error +import win32timezone + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + +if __name__ == '__main__': + # Destination table name and SQL-statements to be used a bit later. + destination_table = 'SB_DATA' + sql_str_condensor = """ + select + origin, year, season, euring, GRID_ID, avg(density_ind_1km2) as mean_density_ind_1km2, count( + density_ind_1km2) as n_observations + from SB_DATA + group by + origin, year, season, euring, GRID_ID + order by + origin, year, season, euring, GRID_ID; + """ + + sql_str_creator = """ + CREATE TABLE IF NOT EXISTS SB_DATA ( + origin text, + year integer, + season integer, + euring integer, + GRID_ID text, + mean_density_ind_1km2 real, + n_observations integer); + """ + + columns = ", ".join(['origin', 'year', 'season', 'euring', 'GRID_ID', 'mean_density_ind_1km2', 'n_observations']) + sql_str_insertor = "\ + INSERT INTO SB_DATA (" + columns + ") VALUES (?, ?, ?, ?, ?, ?, ?);\ + " + + # kd_gpkg_in = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_KDdata_0.gpkg" + # sb_sql3db_base_name = kd_gpkg_in.split("\\")[-1].replace('.gpkg', '.sql3db').replace('KDdata_0', 'Iter') + # input_name = r"D:\GIS_data\KEC4_2021\PyConversion\K4_data_iter_x.sql3db" + input_name = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_iter_x.sql3db" + # input_name = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDWdata_0E5910S5.sql3db" + sb_sql3db_path = "\\".join([part for part in input_name.split("\\")[:-1]]) + sb_sql3db_basename = input_name.split("\\")[-1] + sb_sql3db_base = "\\".join([sb_sql3db_path, sb_sql3db_basename]) + sb_iter_list = [0, 1, 2] # , 1, 2] #truncated for development purposes. + # Manipulate this list to [1,2] to only target iterations 1 and 2 or to whatever needs doing + # Please make sure to also set the range accordlingly about 4 lines down at: for i in range(): + # prepare_sql3databasetables() # Do not use df.to_sql wants the table to not exist (despite if_exists='replace'). + start_time = win32timezone.now() + print(start_time) + + sb_conn_list = [] + sql3db_filename_list = [] + for i in sb_iter_list: # reset to (0,3,1,) once development/testing complete + sql3db_filename = sb_sql3db_base.replace('_iter_x', '_iter_' + str(i)) + sb_conn_list.append(create_connection(sql3db_filename)) + sql3db_filename_list.append(sql3db_filename) + print(f"{sql3db_filename_list[i]} is {sb_conn_list[i]} and should not be 'None'!") + + for iter in sb_iter_list: + elapsed = win32timezone.now() - start_time + print("Condensing on-going for Iteration " + str(iter) + " began after " + str(elapsed)) + sb_conn_current = create_connection(input_name) + df_name = 'df_iter'+str(iter) # results in string not the reference to that dataframe. + sb_conn_current = sb_conn_list[iter] + sb_conn_outer_name = sql3db_filename_list[iter].replace('_iter_', '_CondensedIter_') + # sb_conn_outer_name = sb_sql3db_basename.replace('.sql3db', '_Condensed.sql3db') + sb_conn_outer = create_connection(sb_conn_outer_name) + # sb_conn_outer = create_connection("\\".join([sb_sql3db_path,sb_conn_outer_name])) + execute_sql(sb_conn_outer, sql_str_creator) + # DONE: adjust this to create a cursor here so that the results can be read and written to a next set of sql3db's + # execute_sql(sb_conn_current, sql_str_condensor) + sb_curr_current = sb_conn_current.cursor() + # sb_curr_outer = sb_conn_outer.cursor() + execute_sql(sb_conn_outer, sql_str_creator) + sb_conn_outer.commit() + sb_curr_current.execute(sql_str_condensor) + for row in sb_curr_current: + # Collect the data + # value_list = [val for _, val in row.items()] + # values_tuple = tuple(value_list) + # row is already a tuple when iterating over a cursor + values_tuple = row + # then inject (safely) into an Insert-statement + execute_sql(sb_conn_outer, sql_str_insertor, values_tuple) + # print(values_tuple) + sb_conn_outer.commit() # Commit the changes after writing has finished + sb_conn_outer.close() # Then close the connection + for iter in sb_iter_list: + # And now the openened connection for the input-databases can be closed to finish of the processing. + # sb_conn_current = sb_conn_list[iter] + sb_conn_current.close() + elapsed = win32timezone.now() - start_time + print("Condensing on-going for Iteration " + str(iter) + " completed " + str(elapsed)) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4_VG_S_2PNG.py b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_2PNG.py new file mode 100644 index 0000000000000000000000000000000000000000..3e45f973d573e55c00f618de84c848f2a92c2c42 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_2PNG.py @@ -0,0 +1,241 @@ +# 20210608: Jan Tjalling van der Wal (Wageningen Marine Research) +# KEC4_VG_S_2PNG has been based on KEC4_DotMaps2PNG from +# 20210503: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 0 - RUN from QGIS PYTHON CONSOLE! +# 1 - Open the prepared QGS/QGS project file ideally generated from KEC4qgz_blank and KEC4qgz_out +# In effect pointing to "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_manually.qgz" +# 2 - Loop over the species layer and deactivate all (= do not show). Use sb_species_dict +# 3 - Activate the prepared layout with N-arrow, scale-bar, legend and basemap, title. +# 4 - Reset the title to "Title is scientific name" +# 5 - Restart a loop over the species (sb_species_dict) +# Activate the layer for the current species +# Set the scientific name as the title on the layout +# Generate a PNG file in set location .\DotMaps\ +# with a generated name like .png +# Deactivate that specific layer +# 6 - Restart a loop and set to show all species layers +# 7 - Revisit the layout and reset the title to "Title is scientific name" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# Imports +import qgis, os, time +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path + +# Setting some global variables +# KEC4qgz_blank = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_blank.qgz" +# KEC4qgz_out = KEC4qgz_blank.replace('_blank', '') +idw_qgz_vg_s = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_S.qgz" +# 20210720: Adding three more valid QGIS-projects for this script to work with +# Each of these has is based on KEC4_VG_S.qgz, but has a different symbology applied to the +# existing layers. +# KEC4_VG_S_Effort.qgz (effort (0 included), based on count of observations within vectorgrid-cell (nobs)) +# KEC4_VG_S_YrLst.qgz (last year of effort encountered, shows where old data underlies the result) +# KEC4_VG_S_YrRng.qgz (range in years of effort (last-first), higher values are usually better_ +idw_qgz_vg_s_effort = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_S_Effort.qgz" +idw_qgz_vg_s_yrlst = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_S_YrLst.qgz" +idw_qgz_vg_s_yrrng = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_S_YrRng.qgz" +valid_qgz_list = [idw_qgz_vg_s, idw_qgz_vg_s_effort, idw_qgz_vg_s_yrlst, idw_qgz_vg_s_yrrng] +idw_qgs_name = idw_qgz_vg_s.split("\\")[-1] + +# vg_sb_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_spectral.qml" +# 20210720 not used in this script. Is used in the 'filler'-script! + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +start_time = win32timezone.now() +print(f"{start_time}") + + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +# project = Qqs.Project.fileName() +# KEC4qgz_sb8 = os.path.join("'"+"', '".join(KEC4qgz_sb8.split('\\'))+"'") +# project is reported back using / instead of \ +# manipulating KEC4qgz_sb4 as above yields an 'escaped' string with \\ +# idw_qgz_vg_s = idw_qgz_vg_s.replace('\\', '/') +valid_qgz_vg_list = [qgz_vg.replace('\\', '/') for qgz_vg in valid_qgz_list] +# if projectname != idw_qgz_vg_s: +if projectname not in valid_qgz_vg_list: + print(f"Current project {projectname} is NOT a valid project for this script!") + print("These names are:") + for qgz in valid_qgz_list: + print(qgz) + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is a correct project for this script!") + print("It is part of this list:") + for i,qgz in enumerate(valid_qgz_list): + print(f"{i}: \t {qgz}") +interface = qgis.utils.iface +root = QgsProject.instance().layerTreeRoot() + +# Step 2: Unchecking the layers +print('Unchecking the layers') +for child in root.children(): + child_name = child.name() + if child_name[:3] in sb_scenario_list : + new_state = Qt.Unchecked + #child.setItemVisibilityChecked(new_state) + child.setItemVisibilityCheckedRecursive(new_state) + print(f"Layer {child_name} is no longer showing. Now including all children as well.") + else: + print(f"Not bothering with {child_name}.") + pass +#for group in sb_scenario_list: +# layer_group = root.findGroup(group) +# for child in layer_group.children(): +# child_name = child.name() +# if child_name[:3] in sb_scenario_list : +# new_state = Qt.Unchecked +# child.setItemVisibilityChecked(new_state) +# print(f"Layer {child_name} is no longer showing.") +# else: +# print(f"Not bothering with {child_name}.") +# pass + +# Step 3 Get to the layout +print("Accessing layouts") +#prjct = QgsProject() +#layout = QgsLayout(prjct) +manager = QgsProject.instance().layoutManager() +for layout in manager.printLayouts(): + print(layout.name()) +for layout in sb_scenario_list: + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{layout}") + # Step 4 Re-set the title + print("Resetting title on layout " + kec4_layout.name()) + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText('Tijdelijke titel is de wetenschappelijke naam') + +# Step 5 Sequentially check a layer, generate PNG and uncheck again. +print('Looping over species and generating PNG-output') +# vg_dir = 'VGmaps' +# 202107020: Adjusted to have vg_dir change according to the QGZ-project that is encountered +# thus sending the output to a different directory for each valid option. +# KEC4_VG_S.qgz +# KEC4_VG_S_Effort.qgz +# KEC4_VG_S_YrLst.qgz +# KEC4_VG_S_YrRng.qgz +if "YrRng " in projectname: + vg_dir = 'VGrange' +elif "YrLst" in projectname: + vg_dir = 'VGlast' +elif "Effort" in projectname: + vg_dir = 'VGeffort' +else: + vg_dir = 'VGmaps' +base_dir = os.path.dirname(idw_qgz_vg_s.replace('/', '\\')) +if os.path.exists(os.path.join(base_dir, vg_dir)): + pass +else: + os.mkdir(os.path.join(base_dir, vg_dir)) +for group in sb_scenario_list: + layer_group = root.findGroup(group) + # new_state = Qt.Checked + # layer_group.setItemVisibilityChecked(new_state) + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{group}") + for child in layer_group.children(): + child_name = child.name() + if child_name[:3] in sb_scenario_list : + new_state = Qt.Checked + # child.setItemVisibilityChecked(new_state) + try: + euring = int(child_name[3:-2]) + season = int(child_name[-1]) + except: + euring = 0 + season = 0 + pass + if euring in sb_species_dict: + new_state = Qt.Checked + # child.setItemVisibilityChecked(new_state) + child.setItemVisibilityCheckedParentRecursive(new_state) + # This should set the visibility for one layer and the parent group! + + # Do the Layout-stuff + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText(f"{sb_species_dict[euring]} ({euring}), {season} {sb_seasons_dict[season]}") + # preparing the filename + genu, spec = sb_species_dict[euring].split(' ') + png_name = group + str(euring)+genu[:4].upper()+spec[:4].upper()+ 'S' + str(season) + '.png' + output_file = os.path.join(base_dir, vg_dir, png_name) + export_image_settings = QgsLayoutExporter.ImageExportSettings() + export_image_settings.cropToContents = True + export_image_settings.dpi = 300 + print(f"Outputting to {output_file}") + exporter = QgsLayoutExporter(kec4_layout) + exporter.exportToImage(output_file, export_image_settings) + # And reset to unchecked + new_state = Qt.Unchecked + child.setItemVisibilityCheckedParentRecursive(new_state) + new_state = Qt.Unchecked + layer_group.setItemVisibilityCheckedRecursive(new_state) + +# Restore order and have a sensible Title +for layout in sb_scenario_list: + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{layout}") + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText('Tijdelijke titel is de wetenschappelijke naam') +# And to finish of save the current state. +QgsProject.write(QgsProject.instance()) + +print("Completed script: KEC4_VG_S_2PNG") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_VG_S_OWPHabLoss.py b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_OWPHabLoss.py new file mode 100644 index 0000000000000000000000000000000000000000..8bc1265883f6d389467e51fc127acf1317883f94 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_OWPHabLoss.py @@ -0,0 +1,198 @@ +# 20210721, Jan Tjalling van der Wal +# Script intended to perform the following: +# A) Load two datasets (from separate geopackages) into two geopandas.dataframes +# 1) OWP-geodata (just once,the full dataset) +# 2) SeaBirdDensity-geodata (in subsets: Batch, EUring, Season +# This should keep the amount of memory required within reasonable amounts and the script progressing nicely. +# B) When looping over Batch, EUring, Season +# 1) Iterate over the OWP and calculate the mean seabird denstity for the intersecting vg-grid-polygons. +# 2) Store the results in for each a third geopandasdataframe: +# OWP.UniekID, OWP.'Area (SqKm', OWP.ScaleFactor, SB.Batch, SB.EUring, SB.Season, Mean.Density +# C) Save the results (two ways): as gpkg, as a csv +# +# Revision of the processing logic. +# GeoPandas does not have an option (?) to selectively read rows from a geopackage based on attributes. +# It does allow passing in a bbox. So by taking that info from each OWP, a small enough sample can be read. +# And then processed. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import os +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path +import pandas as pd +import geopandas as gpd +import matplotlib as plt + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + +# File naming +sb_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +sb_gpkg_path = Path(sb_gpkg_) +owp_gpkg_ = r"D:\GIS_data\KEC4_2021\OWPdata\OWPdata_HabLoss500mBuffer.gpkg" +owp_gpkg_path = Path(owp_gpkg_) +lyr_name_owp = 'OWPdata_HabLoss500mBuffer' +lyr_name_sb = 'vgSeaBirdDensitySeasonal' +lyr_name_habloss = 'OWP_SB_HabLoss' +csv_savedir = owp_gpkg_path.parents[0] +owp_sb_habloss_csv = csv_savedir.joinpath(lyr_name_habloss +'.csv') + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +str_sql_vg_s_idx_bes = "CREATE INDEX if not exists idx_bes ON " + lyr_name_sb + " (Batch, EUring, Season); " + +start_time = win32timezone.now() +print(f"{start_time}") + +print('Building attribute index') +sql3conn = create_connection(sb_gpkg_) +execute_sql(sql3conn, str_sql_vg_s_idx_bes) +sql3conn.commit() +sql3conn.close() +del sql3conn +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +# Cleaning up existing results in the CSV-file +if owp_sb_habloss_csv.exists(): + owp_sb_habloss_csv.unlink() + +owp_gdf = gpd.read_file(owp_gpkg_, layer=lyr_name_owp) +# print(owp_gdf.head(10)) +# print(owp_gdf.shape) +# columns2keep = ['UniekID', 'Wind Farm', 'Area (SqKm', 'ingeschatt', 'ScaleFactor', 'geometry'] +owp_columns2drop = ['Country Na', 'Wind Far_1', 'Other Name', 'Windfarm S', 'Round/Cate', 'Verwachte', + 'Analyst Ce', 'Capacity M', 'Turbine MW', 'Num Turbin', 'Total Turb', 'Hub Height', + 'Rotor Diam','Foundation', 'Water Dept', 'Water De_1', 'Lat', 'Lon'] +owp_gdf.drop(labels=owp_columns2drop, axis='columns', inplace=True) +# print(owp_gdf.head(10)) +# print(owp_gdf.shape) +for index, row in owp_gdf.iterrows(): + print(index, row[['UniekID']], row[['geometry']]) + sb_gdf = gpd.read_file(sb_gpkg_, layer = lyr_name_sb, mask = row[['geometry']].all()) + # print(sb_gdf.head(10)) + # print(sb_gdf.shape) + # sb_columns2keep = ['Batch', 'Predicted', 'EUring', 'Season'] + sb_columns2drop = ['Id', 'GRID_ID', 'Land', 'SubArea', 'Active4Birds', 'CountryCode', + 'VGshow', 'Estuarine', 'Shape_Length', 'Shape_Area', 'nobsv', 'firstyear', 'lastyear', + 'geometry'] + sb_gdf.drop(labels=sb_columns2drop, axis='columns', inplace=True) + # print(sb_gdf.head(10)) + # print(sb_gdf.shape) + sb_df_grouped = sb_gdf.groupby(['Batch', 'EUring', 'Season'], as_index=False) + sb_df_grouped_meanpredicted = sb_df_grouped.aggregate(["mean"], as_index=False) + # sb_df_grouped_meanpredicted['UniekID'] = row['UniekID'] + # sb_df_grouped_meanpredicted['Wind_Farm'] = row['Wind Farm'] + # sb_df_grouped_meanpredicted['Area_KM2_GIS'] = row['Area (SqKm'] + # sb_df_grouped_meanpredicted['Area_KM2_ESTIMATED'] = row['ingeschatt'] + # sb_df_grouped_meanpredicted['ScaleFactor'] = row['ScaleFactor'] + # sb_df_grouped_meanpredicted['geometry'] = row['geometry'] + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(Enumerator = index) + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(UniekID = row['UniekID']) + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(Wind_Farm = row['Wind Farm']) + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(Area_KM2_GIS = row['Area (SqKm']) + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(Area_KM2_ESTIMATED = row['ingeschatt']) + sb_df_grouped_meanpredicted = sb_df_grouped_meanpredicted.assign(ScaleFactor = row['ScaleFactor']) + sb_df_grouped_meanpredicted['countSBHabLoss'] = (sb_df_grouped_meanpredicted['Area_KM2_GIS'] * + sb_df_grouped_meanpredicted['ScaleFactor'] * + sb_df_grouped_meanpredicted[('Predicted', 'mean')]) + sb_df_grouped_meanpredicted.reset_index() + # print(sb_df_grouped_meanpredicted.head(10)) + # print(sb_df_grouped_meanpredicted.shape) + #sb_gdf_grouped_meanpredicted.plot() + # plt.show() + # Intended as a test to see whether the data is there and can be visualised. But errors about MatPlotLib are given. + # sb_gdf_grouped_meanpredicted.to_file(owp_gpkg_, layer=lyr_name_habloss, driver='GPKG') + if owp_sb_habloss_csv.exists(): + # If the file already exist, write the current data to it, but append without a header. + sb_df_grouped_meanpredicted.to_csv(owp_sb_habloss_csv, sep = ';', header=False, index=True, mode='a') + else: + # If the file does not exist, create it, write the data, including a header (at the top). + sb_df_grouped_meanpredicted.to_csv(owp_sb_habloss_csv, sep=';', header=True, index=True, mode='w') + elapsed = win32timezone.now() - start_time + print("Time taken upto this point " + str(elapsed)) + +print(f"Output as .csv has been written to {owp_sb_habloss_csv}") +print("Completed script: KEC4_VG_S_OWPHabLoss") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_VG_S_filler.py b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_filler.py new file mode 100644 index 0000000000000000000000000000000000000000..ec474b4cfa5b6e15b23c46789d88fcd666806434 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_VG_S_filler.py @@ -0,0 +1,211 @@ +# 20210606, Jan Tjalling van der Wal +# Script intended to perform the following: +# Fill a QGIS-project with a long series of layer +# One layer +# for Batch in INT/NAT +# for EUring in [species_dict] +# for Season in +# While adding an harmonized symbology will be applied such that +# all layers are shown with the same colours and units. +# +# In order to optimize performance the following will be done early in te script: +# - Ensure that an 'attribute' index exists to quickly acces Batch, EUring, Season +# - Ensure that a spatial index exists for the same. +# This need only be done once as all the data comes from a single GPKG-table/layer. + +# PyQGIS generate layers for each unique value of a field +# Here for "species English Name" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import qgis, os +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_S.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +idw_gpkg_path = Path(idw_gpkg_) + +lyr_name_vg = 'vectorgrid_clean' # vg_clean_labels_int resp. _nat +lyr_name_in_s = 'vgSeaBirdDensitySeasonal' +lyr_name_in_y = 'vgSeaBirdDensityYrAvg' + +vg_sb_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_spectral.qml" + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +str_sql_vg_s_idx_bes = "CREATE INDEX if not exists idx_bes ON " + lyr_name_in_s + " (Batch, EUring, Season); " + +start_time = win32timezone.now() +print(f"{start_time}") + +print('Building attribute index') +sql3conn = create_connection(idw_gpkg_) +execute_sql(sql3conn, str_sql_vg_s_idx_bes) +sql3conn.commit() +sql3conn.close() +del sql3conn +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +# path_to_gpkg = os.path.join("D:\\GIS_data\\", "Temp", "Bonaire", "Seabirds_Caribbean.gpkg") +# fieldname = "species English name" +# vlayer = qgis.core.QgsVectorLayer(idw_gpkg_ + "|layername=data_v3", "seabird_data_v3") +vlayer = qgis.core.QgsVectorLayer(idw_gpkg_ + "|layername=" + lyr_name_in_s, lyr_name_in_s) +print(vlayer.name(), '.isValid()=', vlayer.isValid()) + +print('Checking for spatial index') +print(vlayer.name(), '.hasSpatialIndex=', vlayer.hasSpatialIndex()) +if vlayer.hasSpatialIndex() == QgsFeatureSource.SpatialIndexPresent: + print('Already there') +else: + print('Building spatial index') + index = QgsSpatialIndex(layer.getFeatures()) + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname !=idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +interface = qgis.utils.iface + +root = QgsProject.instance().layerTreeRoot() +print("Ensuring there is a Layer Group for each of the batches.") +for batch in sb_scenario_list: + group_checked = False + if root.findGroup(batch) == None: + print(f"{batch} not yet present as a Group") + inGroup = root.insertGroup(1, batch) # 0 is at the top, KEC4_StudyArea is there, so 1 is next. + else: + print(f"{batch} already present as a Group") + inGroup = root.findGroup(batch) + print(f"{inGroup.name()} ") + +print('Unchecking the group layers') +for child in root.children(): + child_name = child.name() + if child_name in sb_scenario_list: + new_state = Qt.Unchecked + child.setItemVisibilityChecked(new_state) + print(f"LayerGroup {child_name} is no longer showing.") + else: + print(f"Not bothering with {child_name}.") + pass + +i = 0 +for batch in sb_scenario_list: + for euring in sb_species_dict: + for season in sb_seasons_dict: + subSetStr = f'"Batch"=\'{batch}\' and "EUring"={euring} and "Season"={season}' + subSetName = f'{batch}{euring}S{season}' + print(f"Subset = {subSetStr}") + layerdef = idw_gpkg_ + "|layername=" + lyr_name_in_s + "|subset=" + subSetStr + "" + print(str(i), '\t', layerdef) + aLyr = QgsVectorLayer(layerdef, subSetName, "ogr") + if not aLyr.isValid(): + print("Layer failed to load") + else: + mLyr = QgsProject.instance().addMapLayer(aLyr, False) # false for Unchecked = not showing + mLyr.loadNamedStyle(vg_sb_style) + ingroup = root.findGroup(batch) + ingroup.addLayer(mLyr) + +print("Completed script: KEC4_VG_S_filler") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_2PNG.py b/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_2PNG.py new file mode 100644 index 0000000000000000000000000000000000000000..f9a9ea0a8e1a9caa8f4146e6af7fba99b62d6025 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_2PNG.py @@ -0,0 +1,208 @@ +# 20210608: Jan Tjalling van der Wal (Wageningen Marine Research) +# KEC4_VG_S_2PNG has been based on KEC4_DotMaps2PNG from +# 20210503: Jan Tjalling van der Wal (Wageningen Marine Research) +# DONE: Add copyright disclaimer, copyright notice, license notice GPLv3 +# Script is to do the following; +# 0 - RUN from QGIS PYTHON CONSOLE! +# 1 - Open the prepared QGS/QGS project file ideally generated from KEC4qgz_blank and KEC4qgz_out +# In effect pointing to "D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_manually.qgz" +# 2 - Loop over the species layer and deactivate all (= do not show). Use sb_species_dict +# 3 - Activate the prepared layout with N-arrow, scale-bar, legend and basemap, title. +# 4 - Reset the title to "Title is scientific name" +# 5 - Restart a loop over the species (sb_species_dict) +# Activate the layer for the current species +# Set the scientific name as the title on the layout +# Generate a PNG file in set location .\DotMaps\ +# with a generated name like .png +# Deactivate that specific layer +# 6 - Restart a loop and set to show all species layers +# 7 - Revisit the layout and reset the title to "Title is scientific name" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +# Imports +import qgis, os, time +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path + +# Setting some global variables +# KEC4qgz_blank = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_DotMap_SB8_blank.qgz" +# KEC4qgz_out = KEC4qgz_blank.replace('_blank', '') +idw_qgz_vg_y = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_Y.qgz" +idw_qgs_name = idw_qgz_vg_s.split("\\")[-1] + +vg_sb_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_spectral.qml" + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +start_time = win32timezone.now() +print(f"{start_time}") + + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +# project = Qqs.Project.fileName() +# KEC4qgz_sb8 = os.path.join("'"+"', '".join(KEC4qgz_sb8.split('\\'))+"'") +# project is reported back using / instead of \ +# manipulating KEC4qgz_sb4 as above yields an 'escaped' string with \\ +idw_qgz_vg_s = idw_qgz_vg_y.replace('\\', '/') +if projectname != idw_qgz_vg_s: + print(f"Current project {projectname} is NOT the correct project {idw_qgz_vg_y}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_qgz_vg_y}!") +interface = qgis.utils.iface +root = QgsProject.instance().layerTreeRoot() + +# Step 2: Unchecking the layers +print('Unchecking the layers') +for child in root.children(): + child_name = child.name() + if child_name[:3] in sb_scenario_list : + new_state = Qt.Unchecked + # child.setItemVisibilityChecked(new_state) + child.setItemVisibilityCheckedRecursive(new_state) + print(f"Layer {child_name} is no longer showing. Now including all children as well.") + else: + print(f"Not bothering with {child_name}.") + pass +#for group in sb_scenario_list: +# layer_group = root.findGroup(group) +# for child in layer_group.children(): +# child_name = child.name() +# if child_name[:3] in sb_scenario_list : +# new_state = Qt.Unchecked +# child.setItemVisibilityChecked(new_state) +# print(f"Layer {child_name} is no longer showing.") +# else: +# print(f"Not bothering with {child_name}.") +# pass + + +# Step 3 Get to the layout +print("Accessing layouts") +#prjct = QgsProject() +#layout = QgsLayout(prjct) +manager = QgsProject.instance().layoutManager() +for layout in manager.printLayouts(): + print(layout.name()) +for layout in sb_scenario_list: + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{layout}") + # Step 4 Re-set the title + print("Resetting title on layout " + kec4_layout.name()) + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText('Tijdelijke titel is de wetenschappelijke naam') + +# Step 5 Sequentially check a layer, generate PNG and uncheck again. +print('Looping over species and generating PNG-output') +vg_dir = 'VGmaps' +base_dir = os.path.dirname(idw_qgz_vg_s.replace('/', '\\')) +if os.path.exists(os.path.join(base_dir, vg_dir)): + pass +else: + os.mkdir(os.path.join(base_dir, vg_dir)) + +for group in sb_scenario_list: + layer_group = root.findGroup(group) + # new_state = Qt.Checked + # layer_group.setItemVisibilityChecked(new_state) + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{group}") + for child in layer_group.children(): + child_name = child.name() + if child_name[:3] in sb_scenario_list : + new_state = Qt.Checked + # child.setItemVisibilityChecked(new_state) + try: + euring = int(child_name[3:]) + except: + euring = 0 + pass + if euring in sb_species_dict: + new_state = Qt.Checked + # child.setItemVisibilityChecked(new_state) + child.setItemVisibilityCheckedParentRecursive(new_state) + # This should set the visibility for one layer and the parent group! + # Do the Layout-stuff + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText(f"{sb_species_dict[euring]} ({euring}, Yearly average)") + kec4_layout.refresh() + # preparing the filename + genu, spec = sb_species_dict[euring].split(' ') + png_name = group + str(euring)+genu[:4].upper()+spec[:4].upper() +'yravg' + '.png' + output_file = os.path.join(base_dir, vg_dir, png_name) + export_image_settings = QgsLayoutExporter.ImageExportSettings() + export_image_settings.cropToContents = True + export_image_settings.dpi = 300 + print(f"Outputting to {output_file}") + exporter = QgsLayoutExporter(kec4_layout) + exporter.exportToImage(output_file, export_image_settings) + # And reset to unckecked + new_state = Qt.Unchecked + child.setItemVisibilityChecked(new_state) + new_state = Qt.Unchecked + layer_group.setItemVisibilityChecked(new_state) + +# Restore order and have a sensible Title +for layout in sb_scenario_list: + kec4_layout = manager.layoutByName(f"KEC4_VGMap_{layout}") + kec4_title = kec4_layout.itemById('Title') + kec4_title.setText('Tijdelike titel is de wetenschappelijke naam') +# And to finish of save the current state. +QgsProject.write(QgsProject.instance()) + +print("Completed script: KEC4_VG_Y2PNG") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_filler.py b/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_filler.py new file mode 100644 index 0000000000000000000000000000000000000000..6f500d4b6b41b5cedeb30d0d96d4f56d5cbbed22 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_VG_Y_filler.py @@ -0,0 +1,210 @@ +# 20210606, Jan Tjalling van der Wal +# Script intended to perform the following: +# Fill a QGIS-project with a long series of layer +# One layer +# for Batch in INT/NAT +# for EUring in [species_dict] +# Skipping Season as this is intended for YearlyAverage results +# While adding an harmonized symbology will be applied such that +# all layers are shown with the same colours and units. +# +# In order to optimize performance the following will be done early in te script: +# - Ensure that an 'attribute' index exists to quickly acces Batch, EUring, Season +# - Ensure that a spatial index exists for the same. +# This need only be done once as all the data comes from a single GPKG-table/layer. + +# PyQGIS generate layers for each unique value of a field +# Here for "species English Name" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import qgis, os +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path + + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + + + +# File naming +idw_qgz_full_path = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_Y.qgz" +idw_qgs_name = idw_qgz_full_path.split("\\")[-1] +idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_YrAvg.gpkg" +idw_gpkg_path = Path(idw_gpkg_) + +lyr_name_vg = 'vectorgrid_clean' # vg_clean_labels_int resp. _nat +lyr_name_in_s = 'vgSeaBirdDensitySeasonal' +lyr_name_in_y = 'vgSeaBirdDensityYrAvg' + +vg_sb_style = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_VG_spectral.qml" + +# Dictionaries and related to control the flow +sb_species_iter0_dict = { + 59: 'Gavia sp', + 720: 'Phalacrocorax carbo', + 2060: 'Somateria mollissima', + 2130: 'Melanitta nigra', + 15820: 'Sturnus vulgaris', + 5670: 'Stercorarius parasiticus', + 5690: 'Stercorarius skua', + 5780: 'Hydrocoloeus minutus', + 6110: 'Thalasseus sandvicensis', + 6169: 'Sterna paradisaea+hirundo', + 6340: 'Uria aalge', + 6360: 'Alca torda', + 6540: 'Fratercula arctica' +} +sb_species_iter1_dict = { + 220: 'Fulmarus glacialis', + 710: 'Morus bassanus', + 6020: 'Rissa tridactyla' +} +sb_species_iter2_dict = { + 5910: 'Larus fuscus', + 5920: 'Larus argentatus', + 6000: 'Larus marinus' +} +# Combining the previous three into a single dictionary +sb_species_dict = {**sb_species_iter0_dict, **sb_species_iter1_dict, **sb_species_iter2_dict} +# While developing quicker to use just a single iter e.g. iter1 +# sb_species_dict = sb_species_iter1_dict +# +sb_seasons_dict = {1: 'Aug+Sep', 2: 'Oct+nov', 3: 'Dec+Jan', 4: 'Feb+Mar', 5: 'Apr+May', 6: 'Jun+Jul'} +sb_scenario_list = ['NAT', 'INT'] + +str_sql_vg_s_idx_be = "CREATE INDEX if not exists idx_be ON " + lyr_name_in_s + " (Batch, EUring);" + +start_time = win32timezone.now() +print(f"{start_time}") + +print('Building attribute index') +sql3conn = create_connection(idw_gpkg_) +execute_sql(sql3conn, str_sql_vg_s_idx_be) +sql3conn.commit() +sql3conn.close() +del sql3conn +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) + +# path_to_gpkg = os.path.join("D:\\GIS_data\\", "Temp", "Bonaire", "Seabirds_Caribbean.gpkg") +# fieldname = "species English name" +# vlayer = qgis.core.QgsVectorLayer(idw_gpkg_ + "|layername=data_v3", "seabird_data_v3") +vlayer = qgis.core.QgsVectorLayer(idw_gpkg_ + "|layername=" + lyr_name_in_y, lyr_name_in_y) +print(vlayer.name(), '.isValid()=', vlayer.isValid()) + +print('Checking for spatial index') +print(vlayer.name(), '.hasSpatialIndex=', vlayer.hasSpatialIndex()) +if vlayer.hasSpatialIndex() == QgsFeatureSource.SpatialIndexPresent: + print('Already there') +else: + print('Building spatial index') + index = QgsSpatialIndex(layer.getFeatures()) + +# Running inside QGIS +# Assuming correct QGS already open. +projectname = qgis.core.QgsProject.instance().fileName() +idw_projectname2check = idw_qgz_full_path.replace('\\', '/') +if projectname !=idw_projectname2check: + print(f"Current project {projectname} is NOT the correct project {idw_projectname2check}!") + print("Please correct this and retry.") + # Need something to stop the script, but not shutdown QGIS + # exit() and quit(() both shutdown QGIS == too much by far. + # break() is for inside loops, so raises an error + # TODO: fix above nicety. It is OKAY so continuing. +else: + print(f"Current project {projectname} is the correct project {idw_projectname2check}!") + +interface = qgis.utils.iface + +root = QgsProject.instance().layerTreeRoot() +print("Ensuring there is a Layer Group for each of the batches.") +for batch in sb_scenario_list: + group_checked = False + if root.findGroup(batch) == None: + print(f"{batch} not yet present as Group") + inGroup = root.insertGroup(1, batch) # 0 is at the top, KEC4_StudyArea is there, so 1 is next. + else: + print(f"{batch} already present as Group") + inGroup = root.findGroup(batch) + print(f"{inGroup.name()} ") + +print('Unchecking the group layers') +for child in root.children(): + child_name = child.name() + if child_name in sb_scenario_list: + new_state = Qt.Unchecked + child.setItemVisibilityChecked(new_state) + print(f"LayerGroup {child_name} is no longer showing.") + else: + print(f"Not bothering with {child_name}.") + pass + +i = 0 +for batch in sb_scenario_list: + for euring in sb_species_dict: + subSetStr = f'"Batch"=\'{batch}\' and "EUring"={euring}' + subSetName = f'{batch}{euring}' + print(f"Subset = {subSetStr}") + layerdef = idw_gpkg_ + "|layername=" + lyr_name_in_y + "|subset=" + subSetStr + "" + print(str(i), '\t', layerdef) + aLyr = QgsVectorLayer(layerdef, subSetName, "ogr") + if not aLyr.isValid(): + print("Layer failed to load") + else: + mLyr = QgsProject.instance().addMapLayer(aLyr, False) # false for Unchecked = not showing + mLyr.loadNamedStyle(vg_sb_style) + ingroup = root.findGroup(batch) + ingroup.addLayer(mLyr) + +print("Completed script: KEC4_VG_Y_filler") +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_YRavgEffort_GeoPandassy.py b/script_py_r/kec4_pyconversion-master/KEC4_YRavgEffort_GeoPandassy.py new file mode 100644 index 0000000000000000000000000000000000000000..ddae1f2e64b0833c425c31dbe12658c999873a6a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_YRavgEffort_GeoPandassy.py @@ -0,0 +1,111 @@ +# Aborted 20210706, Jan Tjalling van der Wal +# #@$%#@$%#@$%!@ Now the PC/Python/QGIS starts complaining about packages all of a sudden. +# ???? SQLite3, Pandas, GeoPandas and others have been used extensively in many of the related scripts +# for weeks if not months. GRRRRRRR + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import pandas as pd +import numpy as np +# from pathlib import Path +import sqlite3 +from sqlite3 import Error +import win32timezone + +def create_connection(db_file): + # https://www.sqlitetutorial.net/sqlite-python/create-tables/ + # create_connection will automatically create database is if does not exist. + """ create a database connection to the SQLite database + specified by db_file + :param db_file: database file + :return: Connection object or None + """ + conn = None + try: + conn = sqlite3.connect(db_file) + return conn + except Error as e: + print(e) + + return conn + + +def execute_sql(conn, sql_statement, data_tuple=None): + """ execute a sql statement on specified connection + :param conn: Connection object + :param sql_statement: a SQL statement + :param data_tuple: additional data_tuple to be used when inserting with variables. Defaults to none if omitted. + :return: + """ + try: + c = conn.cursor() + if data_tuple is not None: + c.execute(sql_statement, data_tuple) + else: + c.execute(sql_statement) + except Error as e: + print(e) + +if __name__ == '__main__': + + idw_gpkg_eff = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IterAll4IDW.gpkg" + idw_gpkg_eff_path = Path(idw_gpkg_eff) + + sql_str_effort_union = """ + SELECT + 'NAT' as Batch, + euring, + GRID_ID, + sum(n_observations) as nObsv + FROM sb_vb_xy_data + where origin = 'MWTL' and year>1999 + group by + Batch, + euring, + GRID_ID + Union + SELECT + 'INT' as Batch, + euring, + GRID_ID, + sum(n_observations) as nObsv + FROM sb_vb_xy_data + where origin in ('MWTL', 'ESAS') and year>1990 + group by + Batch, + euring, + GRID_ID + ; + """ + + sql_str_pts_sb_idx_beg = """ + CREATE INDEX if not exists idx_beg + ON ptsSeaBirdDensitySeasonal ( + Batch, + EUring, + GRID_ID + ); + """ + sql3_conn_eff = create_connection(idw_gpkg_eff) + execute_sql(sql3_conn_eff,sql_str_pts_sb_idx_beg) + + df_effort = pd.read.sql_query(sql_str_effort_union, sql3_conn_eff) + print(df_effort.head()) + print(df_effort.tail()) + print(df_effort.size()) + + # for df in pd.read_sql_query(str_sql4pandasread_sql,sql3_conn, chunksize=blokgrootte): \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4_ptsSBDS_updateInfzero.py b/script_py_r/kec4_pyconversion-master/KEC4_ptsSBDS_updateInfzero.py new file mode 100644 index 0000000000000000000000000000000000000000..5f29b0cd4dd9590c19840ddd5edd6e4137e15e2c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4_ptsSBDS_updateInfzero.py @@ -0,0 +1,192 @@ +# 20210625, Jan Tjalling van der Wal +# Set of SQL-update statements to be executed against: +# KEC4_IDW_SB_seasonal.gpkg +# to update 125 or so rows that have an invalid result stemming from an undiagnosed mishap from the IDW-interpolation result in erroneous inf.0-value +# in places where a result is clearly expected. N.B. gaps in the IDW are 'steady' same location has been noticed (after the fact) for earlier calculations/script-versions. +# +# SQL-statement will not run from SQLite Studio +# [18:38:28] Error while executing SQL query on database 'KEC4_IDW_SB_seasonal': no such function: ST_IsEmpty +# Therefore opting to wrap these statements in a Python-script. +# When executing these statement thru an OGR-connection that missing function will be available. +# Used earlier today to update nobsv 'vgSeaBirdDensitySeasonal' where that was left as NULL instead of 0 (zero). +# +# 20210706: Obsolete, by filtering erroneous NULL and Inf.0-values from the input the gaps do no longer appear. +# See also "..\KEC4_2021\PyConversion\GapFix=obsolete\GapCheck_StillThere_20210706_or_not.txt" + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import qgis, os +import win32timezone +import sqlite3 +from sqlite3 import Error +from pathlib import Path +import win32timezone +from osgeo import ogr + +list_sql_str_update4ptsSBDS_inf = [ + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0246N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0251N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0251N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0251N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0256N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0256N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0256N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0261N6120';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0261N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0261N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.848358913240107 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0266N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0426N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0426N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0431N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0431N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0431N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0436N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0436N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0436N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0436N6320';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0441N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0441N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0441N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.956232656165957 WHERE Batch = 'INT' AND Euring = 220 AND Season = 6 AND GRID_ID = 'E0441N6320';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0486N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0486N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0486N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0491N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0491N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0491N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0496N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0496N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0496N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0496N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0501N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0501N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0501N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0501N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0506N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 710 AND Season = 6 AND GRID_ID = 'E0506N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.103147642686963 WHERE Batch = 'INT' AND Euring = 720 AND Season = 6 AND GRID_ID = 'E0606N5830';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 3.92653277764718 WHERE Batch = 'INT' AND Euring = 5780 AND Season = 5 AND GRID_ID = 'E0606N5845';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 11.2074082493782 WHERE Batch = 'INT' AND Euring = 5910 AND Season = 5 AND GRID_ID = 'E0571N5820';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 2.27017306288083 WHERE Batch = 'INT' AND Euring = 5910 AND Season = 5 AND GRID_ID = 'E0606N5845';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 9.09069888045391 WHERE Batch = 'INT' AND Euring = 5910 AND Season = 6 AND GRID_ID = 'E0606N5845';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 15.5982380867004 WHERE Batch = 'INT' AND Euring = 5920 AND Season = 6 AND GRID_ID = 'E0606N5830';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 30.211533849438 WHERE Batch = 'INT' AND Euring = 5920 AND Season = 6 AND GRID_ID = 'E0606N5845';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0481N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0486N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0486N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0491N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0491N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0491N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0496N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0496N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0496N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0496N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0501N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0501N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0501N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0501N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0506N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0506N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0506N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0506N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0511N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0511N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.708479197839132 WHERE Batch = 'INT' AND Euring = 6020 AND Season = 6 AND GRID_ID = 'E0511N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.254365561716259 WHERE Batch = 'INT' AND Euring = 6169 AND Season = 6 AND GRID_ID = 'E0606N5830';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 1.97794778645038 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 2 AND GRID_ID = 'E0591N5825';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0431N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0431N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0431N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0431N6320';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0436N6305';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0436N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0436N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0436N6320';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0441N6310';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0441N6315';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.476247786233822 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0441N6320';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0481N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0481N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0486N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0486N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0486N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0486N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0486N6150';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6150';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0491N6155';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6125';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0496N6150';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0501N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0501N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0501N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0501N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0506N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0506N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0506N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0.558680571367343 WHERE Batch = 'INT' AND Euring = 6340 AND Season = 6 AND GRID_ID = 'E0506N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0486N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0486N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0486N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0491N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0491N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0491N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0496N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0496N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0496N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0496N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0501N6130';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0501N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0501N6140';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0501N6145';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0506N6135';", + "Update ptsSeaBirdDensitySeasonal SET Predicted = 0 WHERE Batch = 'INT' AND Euring = 6540 AND Season = 6 AND GRID_ID = 'E0506N6140';" ] + +# File naming +idw_gpkg_ = r"D:\GIS_data\KEC4_2021\PyConversion\KEC4_IDW_SB_seasonal.gpkg" +idw_gpkg_path = Path(idw_gpkg_) + +lyr_name_in_s = 'ptsSeaBirdDensitySeasonal' + +start_time = win32timezone.now() +print(f"{start_time}") + +print("Updating 'inf.0' values ... for {len(list_sql_str_update4ptsSBDS_inf) rows ") +print("in layer/dataset {lyr_name_in_s} within ") +print(f"{idw_gpkg_}") +# 20210625: Jan Tjalling van der Wal, no data records have nobsv is NULL not ==0. +# This is awkward when visualising in GIS, so introduced an update-statement to fix that. +# Could/should have been part of KEC4_IDWplus_dev.py, but that takes several hours to complete this a half. +# create an SQLite3-connection +idw_conn_tmp = ogr.Open(idw_gpkg_,1) # OGR-connection! +# execute the already defined statement sql_str_pts_sb_idx_besg +for i, sql_statement in enumerate(list_sql_str_update4ptsSBDS_inf): + idw_conn_tmp.ExecuteSQL(sql_statement) + print(i, '\t', sql_statement) +# discard +del idw_conn_tmp + +elapsed = win32timezone.now() - start_time +print("Time taken upto this point " + str(elapsed)) diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/.gitignore b/script_py_r/kec4_pyconversion-master/KEC4ibm/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..927c38fb799b58b1edeb553cbbee62eed480010d --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/.gitignore @@ -0,0 +1,3 @@ +build/* +rundir*/ +tmp/ \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/CMakeLists.txt b/script_py_r/kec4_pyconversion-master/KEC4ibm/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b45dca110bc378cf69de86c42fe8d108e977e03 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/CMakeLists.txt @@ -0,0 +1,29 @@ +PROJECT(wozep) +cmake_minimum_required(VERSION 2.6) +FIND_PACKAGE(SWIG REQUIRED) +FIND_PACKAGE(PythonLibs) +INCLUDE(${SWIG_USE_FILE}) + + +INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_PATH}) +INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR}) +SET(CMAKE_SWIG_FLAGS "") + +project(wozep-survival) +add_definitions(-std=gnu++11) +set(CMAKE_CXX_FLAGS_DEBUG "-pg") +include_directories(include/) +OPTION(BUILD_SHARED_LIBS "Build package with shared libraries." ON) +add_library(wozep src/lib/index.cpp src/lib/habitat.cpp src/lib/birds.cpp) +target_link_libraries(wozep armadillo pthread) +add_executable(wozeprun src/wozep.cpp) +target_link_libraries(wozeprun wozep armadillo pthread) +#SET_SOURCE_FILES_PROPERTIES(swig/habitat.i PROPERTIES SWIG_FLAGS "-includeall") +SET_SOURCE_FILES_PROPERTIES(swig/wozep.i PROPERTIES CPLUSPLUS ON) +SWIG_ADD_LIBRARY(wozep LANGUAGE python SOURCES swig/wozep.i) +SWIG_LINK_LIBRARIES(wozep ${PYTHON_LIBRARIES} wozep) +execute_process(COMMAND python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())" OUTPUT_VARIABLE PYTHON_SITE_PACKAGES OUTPUT_STRIP_TRAILING_WHITESPACE) +install(TARGETS wozep + LIBRARY DESTINATION ${PYTHON_SITE_PACKAGES}) +install(TARGETS _wozep DESTINATION ${PYTHON_SITE_PACKAGES}) +install(FILES ${CMAKE_BINARY_DIR}/wozep.py DESTINATION ${PYTHON_SITE_PACKAGES}) diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/gpl-2.0.txt b/script_py_r/kec4_pyconversion-master/KEC4ibm/gpl-2.0.txt new file mode 100644 index 0000000000000000000000000000000000000000..d159169d1050894d3ea3b98e1c965c4058208fe1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/gpl-2.0.txt @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/birds.h b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/birds.h new file mode 100644 index 0000000000000000000000000000000000000000..eb89395c712c97e6079768b7493ec0106a6b5dbc --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/birds.h @@ -0,0 +1,172 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + + +#ifndef __WOZEP_BIRDS__ +#define __WOZEP_BIRDS__ + +#include +#include +#include + +#include +#include "lib/habitat.h" + +namespace wozep { + +class WBirds { + private: + arma::vec m_energy; + arma::uvec m_position; + double m_metabolism; + WHabitat * m_habitat; + unsigned long m_nb_birds; + unsigned int m_max_iterations; + void remove_dead_birds(); + void get_intake(arma::vec &intake); + void shuffle(); + public: + + /** + * A Constructor + * Creates an WBirds object of size nb_birds. + * @param nb_birds, integer indicating the total number of birds + * @param metabolism, double indicating the energy use of each bird. + * @param habitat, WHabitat object giving the habitat suitability. + * @param max_iterations, unsigned integer gives the maximum number of iterations + **/ + WBirds(unsigned long nb_birds, double metabolism, WHabitat * habitat, unsigned int max_iterations); + + /** + * A Constructor + * Creates an WBirds object from an energy and a position file. + * @param energy_filename, filename of the file containing the initial energy of each bird. (vector, matlab style) + * @param position_filename, filename of the file containing the initial position of each bird. (vector, matlab style) + * @param metabolism, double indicating the energy use of each bird + * @param habitat, WHabitat object giving the habitat suitability. + * @param max_iterations, unsigned integer gives the maximum number of iterations + * + * The energy file contains on each line the energy level at t0 of each bird as a floating point number. + * The position file contains an integer indicating the cell number of the habitat grid the bird is + * foraging at t0. The position p is calculated as follows p = row * nb_cols + col. where nb_cols the + * number of collumns is of the habitat grid. + * + **/ + WBirds(std::string energy_filename, + std::string positions_filename, + double metabolism, + WHabitat * habitat, + unsigned int max_iterations); + + /** + * Stores the current energy level vector in a matlab style file + * @param filename, filepath of the file to write energy data to. + **/ + void save_energy(std::string filename); + + /** + * Stores the current position vector in a matlab style file + * @param filename, filepath of the file to write energy data to. + **/ + void save_positions(std::string filename); + + /** + * Stores the current density map + * @param filename, file path of the density destination file. + **/ + void save_density(std::string filename); + + /** + * loads a new energy vector from a matlab style file + * @param filename, filepath of the file to write energy data to. + **/ + void load_energy(std::string filename); + + /** + * loads a new position vector from a matlab style file + * @param filename, filepath of the file to write energy data to. + **/ + void load_positions(std::string filename); + + /** + * Places each bird randomly on the grid where the probability of + * each grid cell is defined by w_i/S where w_i is the value of + * grid cell i and S the sum of all grid cell values. + **/ + void random_positions(); + + /** + * Sets new habitat map. Next iteration wil use this map + **/ + void set_new_habitat_map(WHabitat * habitat); + + /** + * Performs one iteration. + * A single iteration consists of the following steps + * - calculate new energy level : E_t+1 = E_t + I_t - metabolism + * I_t is the intake given by the current grid cell of the bird. + * - remove any bird that died (E_t+1 <= 0) + * - shuffle remaining birds on map + **/ + void iterstep(); + + /** + * perform n itererations (calls to iterstep) + **/ + void do_n_iters(int n); + + /** + * Returns number of surviving birds. + **/ + unsigned long survival() const; + + +}; + + + +} +#endif //__WOZEP_BIRDS__ + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/habitat.h b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/habitat.h new file mode 100644 index 0000000000000000000000000000000000000000..48c4b8ccf5522710ea714cf7817951c732f7447e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/habitat.h @@ -0,0 +1,220 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + +#ifndef __WOZEP_HABITAT__ +#define __WOZEP_HABITAT__ + +#include +#include +#include +#include +#include + +namespace wozep { +/** + * A class representing a habitat suitability map +*/ + +class WBirdShuffle { + private: + std::discrete_distribution* m_distribution; + std::vector m_positions; + std::mt19937 *m_gen; + public: + WBirdShuffle(const std::vector &weights, const std::vector &positions, std::mt19937 *gen); + ~WBirdShuffle(); + int relocate(); +}; + +class WHabitat { + private: + arma::mat m_habitat; /** matrix to store suitability map **/ + arma::Mat m_map; /** matrix to store suitability map **/ + arma::Mat m_windfarm_map; /** matrix to store suitability map **/ + bool m_has_map; + bool m_has_windfarm_map; + bool m_normalize; /** The habitat map is normalized if set to True **/ + mutable std::mutex m_lock; + std::mt19937 m_gen; + std::vector< WBirdShuffle* > m_distributions; + double m_mistake; // change a bird flies into to a cell (or stays there) where there is no food. + double m_windfarm_avoidance; // change a bird flies into to a cell (or stays there) where is a windfarm. + int m_stepsize; // Nb of cells a bird can max move in a single timestep + bool __is_windfarm(int r, int c); + bool __is_water(int r, int c); + void init_random(); + public: + /** + * A constructor + * creates an WHabitat object from file + * @param filename, string containing the path to a data file + * containing the habitat suitability map data. + * @param normalize, bool value if set to true the data is normailized after being read. + **/ + WHabitat(std::string filename, bool normalize=true, int stepsize=1); + + /** + * A constructor + * creates an WHabitat object filled with random values. + * @param nb_rows, integer giving the number of rows of the habitat map + * @param nb_cols, integer giving the number of columns of the habitat map + * @param nb_zeros, integer giving the number of zero elements of the habitat map + * @param normalize, bool value if set to true the data is normailized after being read. + **/ + WHabitat(int nb_rows, + int nb_cols, + int nb_zeros, + bool normalize=true, + float zero_value=1E-10, + int stepsize=1); + + /** + * Move constructor + **/ + WHabitat(WHabitat&& other); + + + /** + * Destructor + **/ + + ~WHabitat(); + + /** + * Adds a map giving land/water (1/0) values for each cell. + * @param filename, string containing the path and filename of the land/water map. + */ + + void set_land_map(std::string filename); + + + /** + * Returns the maximum step a bird can mopve on the map + */ + int stepsize(); + + /** + * Adds a map indicating if a cell is part of a windfarm or not 1/0) values for each cell. + * @param filename, string containing the path and filename of the land/water map. + */ + + void set_windfarm_map(std::string filename, double avoidance); + + + /** + * Saves the habitat map data to file. + * @param filename, string containing the path and filename of the destination file + **/ + void save(std::string filename); + + /** + * returns the value of an element of the habitat map + * @param r, row index of element + * @param c, column index of element + **/ + double element(int r, int c); + + /* + * returns wether the element of the habitat map is land or water. + * if no land/water map is loaded this method always returns true. + * @param r, row index of element + * @param c, column index of element + */ + bool is_water(int r, int c); + + /* + * returns wether the element of the habitat map is part of a windfarm or not. + * if no windfarm map is loaded this method always returns false. + * @param r, row index of element + * @param c, column index of element + */ + bool is_windfarm(int r, int c); + + /* + * returns the attraction for cell (r,c) + * if a cell is in a windfarm habitat_value(r,c) * (1 - wf_avoidance) is returned + * otherwise the habitat_value(r,c) is returned. + */ + + double attraction(int r, int c); + + /** + * returns the value number of rows of the habitat map. + **/ + int n_row(); + + /** + * returns the value number of columns of the habitat map. + **/ + int n_col(); + + /** + * returns the mean of the habitat map + **/ + double mean(); + + /** + * returns the mean of the windfarm part of the habitat map + **/ + double mean_windfarm(); + + /** + * returns the mean of the the habitat map excluding the windfarms + * + **/ + double mean_external_windfarm(); + + /** + * returns one of the (max nine) neigbours of cell p (including itself). + **/ + + int shuffle_one(int p); + + /** + * sets the chance a birds goes to an area without food + * @param chance, double value + **/ + void set_mistake(double value); + +}; +} +#endif //__WOZEP_HABITAT__ diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/index.h b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/index.h new file mode 100644 index 0000000000000000000000000000000000000000..b0a0fb973a189915f7bbad9b0d44f2d764c9335a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/include/lib/index.h @@ -0,0 +1,50 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + +#ifndef __WOZEP_INDEX__ +#define __WOZEP_INDEX__ + +int calc_row(int p, int nb_cols); +int calc_col(int p, int nb_cols); +int calc_index(int row, int col, int nb_cols); + +#endif //__WOZEP_INDEX__ diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/readme.md b/script_py_r/kec4_pyconversion-master/KEC4ibm/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..8256ed03ca78842a50db5e80beb2d26b91700fc8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/readme.md @@ -0,0 +1,17 @@ + + +Build +----- +To compile the ibm run the following commands: + +$ create directory build +$ cd build +$ cmake .. +$ make +$ cd .. +$ mkdir rundir +$ cd rundir +$ cp ../build/libwozep.so ../build/_wozep.so ../build/*.py . +$ cp ../src/pywozep/*.py . + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/requirements.txt b/script_py_r/kec4_pyconversion-master/KEC4ibm/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..20a7ec229ed8e351d94252d665b77a7fade634c3 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/requirements.txt @@ -0,0 +1,14 @@ +argcomplete==1.9.4 +cycler==0.10.0 +kiwisolver==1.0.1 +matplotlib==3.0.0 +numpy==1.15.2 +pandas==0.23.4 +Pillow==5.3.0 +pyparsing==2.2.2 +python-dateutil==2.7.3 +pytz==2018.5 +scipy==1.1.0 +seaborn==0.9.0 +six==1.11.0 + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b.sh b/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b.sh new file mode 100644 index 0000000000000000000000000000000000000000..a43f13612397f93ce6c1ed7a696bbdd6119843d8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b.sh @@ -0,0 +1,37 @@ +#!/bin/sh + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 + +set -x +MAP1="MB_predicted_Grid_5km_INT.4.mat" +LANDMAP="MB_presence_Grid_5km_INT.4.mat" +DST="gannet_breeding" +BASE="gannet-b-" +#S=(0.9 0.8 0.4 0.2) +S=0.9 +sz=(1 2 3 4 5 6 7 8) +if [ ! -d "$DST" ]; then + mkdir -p "$DST" +fi + +rm -rf "$DST/*" + +for s in "${sz[@]}"; do + ./wozep_setup.py -d "$MAP1" -l "$LANDMAP" -N 10000 -o "$DST" -E 1 -I 1260 -R 100 -b "$BASE$s" -S $S -M 7 -sz $s +done diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b_scn.sh b/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b_scn.sh new file mode 100644 index 0000000000000000000000000000000000000000..464f71758d0d72c153e63a56db4c2e3df8806b28 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/scripts/mk_gannet_b_scn.sh @@ -0,0 +1,43 @@ +#!/bin/sh + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 + + +BASE="gannet_breeding" +REALISTIC="realistic" +WORSTCASE="worstcase" +OWF1="" +OWF2="" + +if [ ! -d "$BASE/$REALISTIC" ]; then + mkdir -p "$BASE/$REALISTIC" +fi +rm -rf "$BASE/$REALISTIC/*" + +if [ ! -d "$BASE/$WORSTCASE" ]; then + mkdir -p "$BASE/$WORSTCASE" +fi +rm -rf "$BASE/$WORSTCASE/*" +#S=(0.9 0.8 0.4 0.2) +sz=(1 2 3 4 5 6 7 8) +for s in "${sz[@]}"; do + ./wozep_OWF_convert.py --owf $OWF1 -o "$BASE/$WORSTCASE" --cfg "$BASE/gannet-b-$s.cfg" --avoidance 1.0 + ./wozep_OWF_convert.py --owf $OWF2 -o "$BASE/$REALISTIC" --cfg "$BASE/gannet-b-$s.cfg" --avoidance 1.0 +done + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/birds.cpp b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/birds.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6ef758cd5ee55152b795e23bb03af1228de5e0c0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/birds.cpp @@ -0,0 +1,198 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + +#include +#include "lib/birds.h" +#include "lib/index.h" + +using namespace wozep; + +WBirds::WBirds(unsigned long nb_birds, double metabolism, WHabitat * habitat, unsigned int max_iterations): + m_metabolism(metabolism), + m_habitat(habitat), + m_nb_birds(nb_birds), + m_max_iterations(max_iterations) { + m_energy = arma::ones(nb_birds); + m_position = arma::zeros(nb_birds); +} + +WBirds::WBirds(std::string energy_filename, + std::string positions_filename, + double metabolism, + WHabitat *habitat, + unsigned int max_iterations): + m_metabolism(metabolism), + m_habitat(habitat), + m_max_iterations(max_iterations) { + m_energy.load(energy_filename); + m_position.load(positions_filename); + m_nb_birds = m_energy.n_elem; +} + + + +void +WBirds::get_intake(arma::vec &intake) { + for(int i=0; i < m_nb_birds; i++) { + int p = m_position(i); + int r = calc_row(p, m_habitat->n_col()); + int c = calc_col(p, m_habitat->n_col()); + intake(i) = m_habitat->element(r,c) / m_max_iterations; + } +} + + + +void +WBirds::shuffle() { + for(int i=0; i < m_nb_birds; i++) { + int new_pos = m_habitat->shuffle_one(m_position(i)); + int nb_cols = m_habitat->n_col(); + int new_row = calc_row(new_pos, nb_cols); + int new_col = calc_col(new_pos, nb_cols); + int old_pos = m_position(i); + int old_row = calc_row(old_pos, nb_cols); + int old_col = calc_col(old_pos, nb_cols); + assert (std::abs(new_row - old_row) <= m_habitat->stepsize()); + assert (std::abs(new_col - old_col) <= m_habitat->stepsize()); + m_position(i) = new_pos; + } +} + +void +WBirds::remove_dead_birds() { + arma::uvec dead = arma::flipud( find(m_energy < 0)); + for(auto it = dead.begin(); + it != dead.end(); + it++ ) { + m_energy.shed_row(*it); + m_position.shed_row(*it); + } + + m_nb_birds = m_energy.n_elem; +} + + +void +WBirds::iterstep() { + arma::vec intake(m_nb_birds); + get_intake(intake); // get_intake scales the intake to the number of iterations + m_energy = m_energy + intake - m_metabolism/m_max_iterations; +// std::cout << "mean INTAKE: " << arma::mean(intake); + remove_dead_birds(); + shuffle(); +} + +void +WBirds::do_n_iters(int N) { + for (int i =0 ; i < N; i++) { + iterstep(); + } +} + + +void +WBirds::save_energy(std::string filename) { + m_energy.save(filename, arma::raw_ascii); +} + + +void +WBirds::save_positions(std::string filename) { + m_position.save(filename, arma::raw_ascii); +} + +void +WBirds::save_density(std::string filename) { + int nb_cols = m_habitat->n_col(); + int nb_rows = m_habitat->n_row(); + arma::umat density = arma::umat(nb_rows, nb_cols, arma::fill::zeros); + for(int i = 0; i < m_nb_birds; i ++) { + int p = m_position(i); + int row = calc_row(p, nb_cols); + int col = calc_col(p, nb_cols); + density(row, col) += 1; + } + density.save(filename, arma::csv_ascii); +} + + +void +WBirds::load_energy(std::string filename) { + m_energy.load(filename, arma::raw_ascii); +} + + +void +WBirds::load_positions(std::string filename) { + m_position.load(filename, arma::raw_ascii); +} + +void +WBirds::random_positions() { + std::mt19937 gen; + gen.seed(time(0)); + std::vector weights; + printf("hab size = (%d, %d)\n", m_habitat->n_row(), m_habitat->n_col()); + std::cout << std::endl; + for (int r=0; r < m_habitat->n_row(); r++) { + for (int c=0; c < m_habitat->n_col(); c++) { + double w = m_habitat->attraction(r,c); + weights.push_back(m_habitat->attraction(r,c)); + } + } + std::discrete_distribution distribution(std::begin(weights), std::end(weights)); + for(int i = 0; i < m_nb_birds; i ++) { + int v = distribution(gen); + m_position(i) = v; + } + +} + +void +WBirds::set_new_habitat_map(WHabitat *habitat) { + m_habitat = habitat; +} + +unsigned long +WBirds::survival() const { + return m_nb_birds; +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/habitat.cpp b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/habitat.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fbc9d382c2f44bb220ec51bf00c13081502dbc17 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/habitat.cpp @@ -0,0 +1,394 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + +#include +#include +#include "lib/habitat.h" +#include "lib/index.h" + + +using namespace wozep; + + +WHabitat::WHabitat(std::string filename, bool normalize, int stepsize): + m_has_map(false), + m_has_windfarm_map(false), + m_normalize(normalize), + m_mistake(0.0001), + m_windfarm_avoidance(0.0), + m_stepsize(stepsize) +{ + std::unique_lock lck{m_lock}; + m_habitat.load(filename, arma::raw_ascii); + int r = m_habitat.n_rows; + int c = m_habitat.n_cols; + + if (m_normalize) { + float maxval = arma::max(arma::max(m_habitat)); + m_habitat = m_habitat / maxval; + } + m_gen.seed(time(0)); + init_random(); +} + +WHabitat::WHabitat(int n, int m, int nb_zeros, bool normalize, float zero_value, int stepsize): + m_has_map(false), + m_has_windfarm_map(false), + m_normalize(normalize), + m_stepsize(stepsize) { + std::unique_lock lck{m_lock}; + m_habitat = arma::randu(m,n); + for(int i =0; i < nb_zeros; i++) { + int k = rand() % m; + int l = rand() % n; + while (m_habitat(k,l) < 10E-10) { + k = rand() % m; + l = rand() % n; + } + m_habitat(k,l) = zero_value; + } + + if (m_normalize) { + m_habitat = m_habitat / m_habitat.max(); + } + m_gen.seed(time(0)); + init_random(); +} + +WHabitat::WHabitat(WHabitat&& other) { + std::lock_guard lck(other.m_lock); + m_habitat = std::move(other.m_habitat); + m_map = std::move(other.m_map); + m_has_map = std::move(other.m_has_map); + m_normalize = std::move(other.m_normalize); + m_distributions = std::move(other.m_distributions); + m_mistake = std::move(other.m_mistake); + m_windfarm_avoidance = std::move(other.m_windfarm_avoidance); + m_stepsize = std::move(other.m_stepsize); +} + +WHabitat::~WHabitat() { + for (auto it = m_distributions.begin(); it != m_distributions.end(); it++) { + delete (*it); + } + + m_distributions.clear(); +} + +void +WHabitat::set_land_map(std::string filename) { + std::unique_lock lck{m_lock}; + m_map.load(filename, arma::raw_ascii); + m_has_map = true; + init_random(); +} + +void +WHabitat::set_windfarm_map(std::string filename, double avoidance) { + std::unique_lock lck{m_lock}; + m_windfarm_map.load(filename, arma::raw_ascii); + m_has_windfarm_map = true; + m_windfarm_avoidance = avoidance; + init_random(); +} + +int +WHabitat::stepsize() { + return m_stepsize; +} + + +void +WHabitat::save(std::string filename) { + std::unique_lock lck{m_lock}; + m_habitat.save(filename, arma::raw_ascii); +} + + +double +WHabitat::element(int r, int c) { + std::unique_lock lck{m_lock}; + return m_habitat(r,c); +} + +bool +WHabitat::is_water(int r, int c) { + std::unique_lock lck{m_lock}; + return __is_water(r, c); +} + +bool +WHabitat::__is_water(int r, int c) { + if (!m_has_map) { + return true; + } + if (m_map(r,c) > 0) { + return true; + } + return false; +} + +bool +WHabitat::is_windfarm(int r, int c) { + std::unique_lock lck{m_lock}; + return __is_windfarm(r,c); +} + +bool +WHabitat::__is_windfarm(int r, int c) { + if (!m_has_windfarm_map) { + return false; + } + if (m_windfarm_map(r,c) > 0) { + return true; + } + return false; +} + +int +WHabitat::n_col() { + std::unique_lock lck{m_lock}; + return m_habitat.n_cols; +} + +int +WHabitat::n_row() { + std::unique_lock lck{m_lock}; + return m_habitat.n_rows; +} + +double +WHabitat::mean() { + std::unique_lock lck{m_lock}; + double sum = 0.0; + int nb_cells = 0; + for(int r = 0; r < m_habitat.n_rows; r++) { + for(int c = 0; c < m_habitat.n_cols; c++) { + if (__is_water(r,c)) { + sum += m_habitat(r,c); + nb_cells += 1; + } + } + } + if (nb_cells > 0) { + return sum/nb_cells; + } + return 0.0; +} + +double +WHabitat::mean_windfarm() { + std::unique_lock lck{m_lock}; + double sum = 0.0; + int nb_cells = 0; + for(int r = 0; r < m_habitat.n_rows; r++) { + for(int c = 0; c < m_habitat.n_cols; c++) { + if (__is_water(r,c)) { + if (__is_windfarm(r,c)) { + sum += m_habitat(r,c); + nb_cells += 1; + } + } + } + } + if (nb_cells > 0) { + return sum/nb_cells; + } + return 0.0; +} + + +double +WHabitat::mean_external_windfarm() { + std::unique_lock lck{m_lock}; + double sum = 0.0; + int nb_cells = 0; + for(int r = 0; r < m_habitat.n_rows; r++) { + for(int c = 0; c < m_habitat.n_cols; c++) { + if (__is_water(r,c)) { + if (!__is_windfarm(r,c)) { + sum += m_habitat(r,c); + nb_cells += 1; + } + } + } + } + if (nb_cells > 0) { + return sum/nb_cells; + } + return 0.0; +} + +void +WHabitat::init_random() { + if (!m_distributions.empty()) { // free the memory of the old list if there + for (auto it = m_distributions.begin(); it != m_distributions.end(); it++) { + delete (*it); + } + m_distributions.clear(); + } + int nb_cols = m_habitat.n_cols; + int nb_rows = m_habitat.n_rows; + int nb_elem = nb_cols * nb_rows; + for (int p=0; p < nb_elem; p++) { + std::vector weights; + std::vector positions; + double wf_redistribute_weight = 0.0; + double weight_sum = 0.0; + int c_r = calc_row(p, nb_cols); + int c_c = calc_col(p, nb_cols); + int begin_r = std::max(c_r - m_stepsize, 0); + int end_r = std::min(c_r + m_stepsize, nb_rows-1); + int begin_c = std::max(c_c - m_stepsize, 0); + int end_c = std::min(c_c + m_stepsize, nb_cols-1); + int number_of_wf_cells = 0; +// std::cout<< p << ": row in [" << begin_r << ", " << end_r << "]" << std::endl; +// std::cout<< p << ": col in [" << begin_c << ", " << end_c << "]" << std::endl; + + bool has_windfarm = false; + + for (int r = begin_r; r <= end_r; r++) { + for (int c = begin_c; c <= end_c; c++) { + if (__is_water(r,c)) { + double weight = m_habitat(r,c); + if (__is_windfarm(r,c)) { + wf_redistribute_weight += weight * m_windfarm_avoidance; + has_windfarm = true; + } else { + weight_sum += weight; + } + } + } + } + + for (int r = begin_r; r <= end_r; r++) { + for (int c = begin_c; c <= end_c; c++) { + double weight = 0.0; + if (__is_water(r,c)) { + weight = m_habitat(r,c); + if (has_windfarm) { + if (__is_windfarm(r,c)) { + weight = weight * (1 - m_windfarm_avoidance); + } else { + weight += (weight * wf_redistribute_weight) / weight_sum; + } + } + + if (std::abs(weight) <= 10e-6 * std::abs(weight)) { + weight = m_mistake; /* if the cell in the habitat is unsuitable for a bird we make sure that there + is a small change it makes the mistake of going there. The value is determined by the wet thumb method, + maybe make this a configurable option ? + */ + } + } + weights.push_back(weight); +// int pp = calc_index(r, c, nb_cols); +// std::cout << p << ": weight at (" << r << ", " << c << ") [p=" << pp << "] = " << weight << std::endl; + positions.push_back(calc_index(r, c, nb_cols)); + } + } + + m_distributions.push_back(new WBirdShuffle(weights, positions, &m_gen)); + } +} + + + +void +WHabitat::set_mistake(double value) { + m_mistake = value; +} + +int +WHabitat::shuffle_one(int p) { + return (m_distributions[p])->relocate(); +} + +double +WHabitat::attraction(int r, int c) { + std::unique_lock lck{m_lock}; + + unsigned short map_val = 1; + if (__is_water(r,c)) { + if (m_has_map) { + map_val = m_map(r,c); + } + + double wf = 1.0; + if (m_has_windfarm_map) { + if (__is_windfarm(r,c)) { + wf = (1 - m_windfarm_avoidance); + } + } + + return map_val * m_habitat(r,c) * wf; + } + return 0; +} + + + +WBirdShuffle::WBirdShuffle(const std::vector &weights, const std::vector &positions, std::mt19937 *gen): + m_gen(gen) { + + //printf("len weights: %d", weights.size()); + //printf("len possitions: %d", positions.size()); + + m_distribution = new std::discrete_distribution(std::begin(weights), std::end(weights)); + for (auto it = positions.begin(); + it != positions.end(); + it++) { + m_positions.push_back(*it); + } +} + +WBirdShuffle::~WBirdShuffle() { + delete (m_distribution); + m_distribution = NULL; +} + +int +WBirdShuffle::relocate() { + //printf("*m_gen = 0x%x", m_gen); + //printf("*m_distributuin = 0x%x", m_distribution); + int pos = m_positions[(*m_distribution)(*m_gen)]; + return pos; +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/index.cpp b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/index.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cd6bf86c1ab31287ff61d6e5d4b01ca97d477a48 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/lib/index.cpp @@ -0,0 +1,55 @@ +/* +* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +* +* This file is part of wozep-survival, http://www.wur.nl +* +* Copyright (C) 2011-2013 Wageningen Marine Research +* Haringkade 1, 1976CP IJmuiden, The Netherlands. +* +* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +*/ + +#include "lib/index.h" + +int calc_row(int p, int nb_cols) { + return int(p / nb_cols); +} + +int calc_col(int p, int nb_cols) { + return int(p % nb_cols); +} + +int calc_index(int row, int col, int nb_cols) { + return int(row * nb_cols + col); +} + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/deviance_maps.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/deviance_maps.py new file mode 100644 index 0000000000000000000000000000000000000000..d81972ff3c43534807d2fcee2ddee6cfc6b77d4c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/deviance_maps.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# +import argparse +import numpy as np +import csv +import os + +def read_csv(filename): + lat = [] + lon = [] + val = [] + with open(filename, 'r') as f: + csvreader = csv.reader(f) + next(csvreader, None) + for row in csvreader: + lat.append(row[0]) + lon.append(row[1]) + val.append(float(row[2])) + return lat, lon, val + + +def write_csv(filename, lat, lon, val): + header = ["Latitude","Longitude","pred"] + with open(filename, 'w') as f: + writer = csv.writer(f) + writer.writerow(header) + for row in zip(lat, lon, val): + writer.writerow(row) + + +def deviance(low, mean, high): + low_diff = np.absolute(np.subtract(mean, low)) + high_diff = np.absolute(np.subtract(mean, high)) + mean_diff = np.true_divide(np.add(low_diff, high_diff), 2) + return low_diff, mean_diff, high_diff + + +def map_sum(low, mean, high): + low_sum = np.sum(low) + high_sum = np.sum(high) + mean_sum = np.sum(mean) + return low_sum, mean_sum, high_sum + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--low", dest="low", help="filename of low prediction map", required = True) + parser.add_argument("--mean", dest="mean", help="filename of mean prediction map", required=True) + parser.add_argument("--high", dest="high", help="filename of low prediction map", required=True) + parser.add_argument("--output", dest="output", help="output directory", default=".") + + args = parser.parse_args() + + lat, lon, low = read_csv(args.low) + _, _, mean = read_csv(args.mean) + _, _, high = read_csv(args.high) + + + nplow = np.array(low) + npmean = np.array(mean) + nphigh = np.array(high) + + print(nplow) + low_diff, mean_diff, high_diff = deviance(nplow, npmean, nphigh) + write_csv(os.path.join(args.output, "low.csv"), lat, lon, low_diff.tolist()) + write_csv(os.path.join(args.output, "mean.csv"),lat, lon, mean_diff.tolist()) + write_csv(os.path.join(args.output, "high.csv"),lat, lon, high_diff.tolist()) + + + S = map_sum(nplow, npmean, nphigh) + with open(os.path.join(args.output, "sum.csv"), 'w') as f: + f.write("low_sum, median_sum, high_sum\n") + f.write(", ".join(map(str, S))) + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep.py new file mode 100644 index 0000000000000000000000000000000000000000..9c14b3e8a2e78723220eabcedb009fd1fd5d74ee --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep.py @@ -0,0 +1,229 @@ +# This file was automatically generated by SWIG (http://www.swig.org). +# Version 3.0.12 +# +# Do not make changes to this file unless you know what you are doing--modify +# the SWIG interface file instead. + +from sys import version_info as _swig_python_version_info +if _swig_python_version_info >= (2, 7, 0): + def swig_import_helper(): + import importlib + pkg = __name__.rpartition('.')[0] + mname = '.'.join((pkg, '_wozep')).lstrip('.') + try: + return importlib.import_module(mname) + except ImportError: + return importlib.import_module('_wozep') + _wozep = swig_import_helper() + del swig_import_helper +elif _swig_python_version_info >= (2, 6, 0): + def swig_import_helper(): + from os.path import dirname + import imp + fp = None + try: + fp, pathname, description = imp.find_module('_wozep', [dirname(__file__)]) + except ImportError: + import _wozep + return _wozep + try: + _mod = imp.load_module('_wozep', fp, pathname, description) + finally: + if fp is not None: + fp.close() + return _mod + _wozep = swig_import_helper() + del swig_import_helper +else: + import _wozep +del _swig_python_version_info + +try: + _swig_property = property +except NameError: + pass # Python < 2.2 doesn't have 'property'. + +try: + import builtins as __builtin__ +except ImportError: + import __builtin__ + +def _swig_setattr_nondynamic(self, class_type, name, value, static=1): + if (name == "thisown"): + return self.this.own(value) + if (name == "this"): + if type(value).__name__ == 'SwigPyObject': + self.__dict__[name] = value + return + method = class_type.__swig_setmethods__.get(name, None) + if method: + return method(self, value) + if (not static): + if _newclass: + object.__setattr__(self, name, value) + else: + self.__dict__[name] = value + else: + raise AttributeError("You cannot add attributes to %s" % self) + + +def _swig_setattr(self, class_type, name, value): + return _swig_setattr_nondynamic(self, class_type, name, value, 0) + + +def _swig_getattr(self, class_type, name): + if (name == "thisown"): + return self.this.own() + method = class_type.__swig_getmethods__.get(name, None) + if method: + return method(self) + raise AttributeError("'%s' object has no attribute '%s'" % (class_type.__name__, name)) + + +def _swig_repr(self): + try: + strthis = "proxy of " + self.this.__repr__() + except __builtin__.Exception: + strthis = "" + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) + +try: + _object = object + _newclass = 1 +except __builtin__.Exception: + class _object: + pass + _newclass = 0 + +class WBirdShuffle(_object): + __swig_setmethods__ = {} + __setattr__ = lambda self, name, value: _swig_setattr(self, WBirdShuffle, name, value) + __swig_getmethods__ = {} + __getattr__ = lambda self, name: _swig_getattr(self, WBirdShuffle, name) + __repr__ = _swig_repr + + def __init__(self, weights, positions, gen): + this = _wozep.new_WBirdShuffle(weights, positions, gen) + try: + self.this.append(this) + except __builtin__.Exception: + self.this = this + __swig_destroy__ = _wozep.delete_WBirdShuffle + __del__ = lambda self: None + + def relocate(self): + return _wozep.WBirdShuffle_relocate(self) +WBirdShuffle_swigregister = _wozep.WBirdShuffle_swigregister +WBirdShuffle_swigregister(WBirdShuffle) + +class WHabitat(_object): + __swig_setmethods__ = {} + __setattr__ = lambda self, name, value: _swig_setattr(self, WHabitat, name, value) + __swig_getmethods__ = {} + __getattr__ = lambda self, name: _swig_getattr(self, WHabitat, name) + __repr__ = _swig_repr + + def __init__(self, *args): + this = _wozep.new_WHabitat(*args) + try: + self.this.append(this) + except __builtin__.Exception: + self.this = this + __swig_destroy__ = _wozep.delete_WHabitat + __del__ = lambda self: None + + def set_land_map(self, filename): + return _wozep.WHabitat_set_land_map(self, filename) + + def set_windfarm_map(self, filename, avoidance): + return _wozep.WHabitat_set_windfarm_map(self, filename, avoidance) + + def save(self, filename): + return _wozep.WHabitat_save(self, filename) + + def element(self, r, c): + return _wozep.WHabitat_element(self, r, c) + + def is_water(self, r, c): + return _wozep.WHabitat_is_water(self, r, c) + + def is_windfarm(self, r, c): + return _wozep.WHabitat_is_windfarm(self, r, c) + + def attraction(self, r, c): + return _wozep.WHabitat_attraction(self, r, c) + + def n_row(self): + return _wozep.WHabitat_n_row(self) + + def n_col(self): + return _wozep.WHabitat_n_col(self) + + def mean(self): + return _wozep.WHabitat_mean(self) + + def mean_windfarm(self): + return _wozep.WHabitat_mean_windfarm(self) + + def mean_external_windfarm(self): + return _wozep.WHabitat_mean_external_windfarm(self) + + def shuffle_one(self, p): + return _wozep.WHabitat_shuffle_one(self, p) + + def set_mistake(self, value): + return _wozep.WHabitat_set_mistake(self, value) +WHabitat_swigregister = _wozep.WHabitat_swigregister +WHabitat_swigregister(WHabitat) + +class WBirds(_object): + __swig_setmethods__ = {} + __setattr__ = lambda self, name, value: _swig_setattr(self, WBirds, name, value) + __swig_getmethods__ = {} + __getattr__ = lambda self, name: _swig_getattr(self, WBirds, name) + __repr__ = _swig_repr + + def __init__(self, *args): + this = _wozep.new_WBirds(*args) + try: + self.this.append(this) + except __builtin__.Exception: + self.this = this + + def save_energy(self, filename): + return _wozep.WBirds_save_energy(self, filename) + + def save_positions(self, filename): + return _wozep.WBirds_save_positions(self, filename) + + def save_density(self, filename): + return _wozep.WBirds_save_density(self, filename) + + def load_energy(self, filename): + return _wozep.WBirds_load_energy(self, filename) + + def load_positions(self, filename): + return _wozep.WBirds_load_positions(self, filename) + + def random_positions(self): + return _wozep.WBirds_random_positions(self) + + def set_new_habitat_map(self, habitat): + return _wozep.WBirds_set_new_habitat_map(self, habitat) + + def iterstep(self): + return _wozep.WBirds_iterstep(self) + + def do_n_iters(self, n): + return _wozep.WBirds_do_n_iters(self, n) + + def survival(self): + return _wozep.WBirds_survival(self) + __swig_destroy__ = _wozep.delete_WBirds + __del__ = lambda self: None +WBirds_swigregister = _wozep.WBirds_swigregister +WBirds_swigregister(WBirds) + +# This file is compatible with both classic and new-style classes. + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_convert.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..a158c2769fa10a42aec320e032cdb3165c3ea59b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_convert.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# + + +import os +import sys +import argparse +import pandas as pd +import numpy as np +from scipy.misc import imsave +import wozep_config + + +if __name__ == "__main__": + optparser = argparse.ArgumentParser() + optparser.add_argument("--owf", dest="owf", help="Filename of habitat data file with owf corrections", required=True) + optparser.add_argument("--cfg", dest="cfg", help="Base config file, writes a config file for each owf map that is created", required=True) + optparser.add_argument("--avoidance", type=float, dest="avoidance", help="windfarm displacement [0,1]", default = 1.0) + optparser.add_argument("-o", dest="output", help="Output directory", required=True) + + + args = optparser.parse_args() + cfg = wozep_config.WozepJSONConfig(args.cfg) + cfg.clear_habitat() + cfg.add_habitatmap(args.owf, "") + S = cfg.survival + sz = cfg.stepsize + + print(f"{args.cfg}: Habitat map {args.owf} : Survival {S} : Stepsize {sz}") + cfg.wf_avoidance = args.avoidance + cfg_fn = os.path.join(args.output,f'Map-{args.owf}-S{S}-stepsize{sz}-a{args.avoidance}.cfg') + cfg.save(cfg_fn) + + + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_debug.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_debug.py new file mode 100644 index 0000000000000000000000000000000000000000..6fb892332b8b85a8179cc8e4a99d9d56e0593e1d --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_OWF_debug.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# + + +import os +import sys +import json +import argparse +import pandas as pd +import numpy as np +import wozep +from scipy.misc import imsave +import wozep_config + + +def init_positions(hab_name, land_name, owf, avoidance, nb_birds, pos_name): + habitat = wozep.WHabitat(hab_name, True) + habitat.set_land_map(land_name) + habitat.set_windfarm_map(owf, avoidance) + bird = wozep.WBirds(int(nb_birds), 0, habitat, 0) + bird.random_positions() + bird.save_positions(pos_name) + return pos_name + +if __name__ == "__main__": + optparser = argparse.ArgumentParser() + optparser.add_argument("--percentile", dest="p", type=str, help="percentile or range [start:step:end] above the habitat should be closed", required=True) + #optparser.add_argument("--hab", dest="hab", help="Filename of habitat map", required=True) + #optparser.add_argument("--map", dest="map", help="Filename of water/land map", required=True) + optparser.add_argument("--cfg", dest="cfg", help="Base config file, writes a config file for each owf map that is created", required=True) + optparser.add_argument("-o", dest="output", help="image output format jpg or png", default = "") + optparser.add_argument("--avoidance", type=float, dest="avoidance", help="windfarm displacement [0,1]", default = 1.0) + + + args = optparser.parse_args() + img_exts = ['jpg', 'png'] + write_img = args.output in img_exts + p = json.loads(args.p) + if not os.path.exists(args.cfg): + optparser.error("Config file %s does not exist" % args.cfg) + sys.exit(1) + + cfg = wozep_config.WozepJSONConfig(args.cfg) + if isinstance(p, float): + perct = [p] + elif isinstance(p, list): + start, step, stop = p + p = range(int(start), int(stop), int(step)) + hab_fn = cfg.habitat_fnames[0] + fn, ext = os.path.splitext(hab_fn) + hab = np.loadtxt(hab_fn, delimiter=" ") + lnd_fn = cfg.land_map + if not lnd_fn: + optparser.error("Config file has no land/water map") + lnd = np.loadtxt(lnd_fn, delimiter=" ") + + for v in p: + if not args.output: + _dir = os.path.dirname(hab_fn) + else: + _dir = args.output + + owfmap = np.zeros(hab.shape[:2], dtype=np.uint8) + percentile = np.percentile(hab[lnd > 0], v) + owfmap[hab > percentile] = 1 + + output = os.path.join(_dir, f'owf-{v}{ext}') + pos_name = os.path.join(_dir, f'pos-{v}{ext}') + print(f'Creating map {output} for percentile {v} ({percentile})') + cfg_output = os.path.join(_dir, f'cfg-owf-{v}.cfg') + np.savetxt(output, owfmap, delimiter = " ", newline="\n") + oldpos = np.loadtxt(cfg.position) + nb_birds = oldpos.shape[0] + cfg.windfarms = output + cfg.wf_avoidance = args.avoidance + cfg.position = init_positions(hab_fn, lnd_fn, output, args.avoidance, nb_birds, pos_name) + cfg.save(cfg_output) + if write_img: + output = os.path.join(_dir, f'owf-{v}.{args.output}') + imsave(output, owfmap*255) + + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_config.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_config.py new file mode 100644 index 0000000000000000000000000000000000000000..c6e38049681dfbc6393dd1f20b242967727b0d47 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_config.py @@ -0,0 +1,326 @@ +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# +import json +import wozep +import math + +def read_habitat_conf(filename, land, windfarm=None): + map_names = [] + map_changes = [] + fnames = [] + + with open(filename, 'r') as f: + fn = f.readline() + max_iter = int(fn.strip()) + fn = f.readline() + initial_map = wozep.WHabitat(fn.strip(), True) + if land: + initial_map.set_land_map(land) + fnames.append(fn.strip()) + for l in f.readlines(): + l = l.strip() + try: + m, c = l.split(' ') + fnames.append(m.strip()) + map_names.append(wozep.WHabitat(m, True)) + if land: + map_names[-1].set_land_map(land) + if windfarm: + map_names[-1].set_windfarm_map(windfarm) + map_changes.append(int(c)) + except ValueError: + print("'%s' not a valid configuration line. skipping it" % l) + return (initial_map, map_names, map_changes, max_iter, fnames) + + + + + +class JSONConfig(object): + def __init__(self): + super().__init__() + self.__dict__["cfg"]={} + + def load(self, filename): + with open(filename, 'r') as cfg: + self.__dict__["cfg"] = json.load(cfg) + + def __getattr__(self, name): + if name in ["load", "save"]: + return self.__dict__[name] + try: + return self.__dict__["cfg"][name] + except KeyError: + return None + + def __setattr__(self, name, value): + self.__dict__["cfg"][name] = value + + def save(self, filename): + with open(filename, 'w') as cfg: + json.dump(self.__dict__["cfg"], cfg, sort_keys=True, indent=4) + +class WozepJSONConfig(object): + def __init__(self, cfg_filename=None): + self.__cfg = JSONConfig() + if cfg_filename: + self.__cfg.load(cfg_filename) + self.__filename = cfg_filename + else: + self.__filename = "" + self.__cfg.nbthreads = 4 + self.__cfg.nothreads = 0 + self.__cfg.xtol = 0.0001 + + + + def save(self, filename=None): + if filename: + self.__cfg.save(filename) + if not self.__filename: + self.__filename = filename + elif self.__filename: + self.__cfg.save(self.__filename) + else: + raise RuntimeError("No filename given") + + def __mk_habitat(self, map_data): + land = self.__cfg.land_map + m = wozep.WHabitat(map_data[0], True, self.__cfg.stepsize) + if land: + m.set_land_map(land) + return m + + @property + def maps(self): + map_names = [] + hab_maps = self.__cfg.habitat_maps + for m in hab_maps: + map_names.append(self.__mk_habitat(m)) + return map_names + + + def add_habitatmap(self, habitat_map, change_point): + if not isinstance(self.__cfg.habitat_maps, list): + self.__cfg.habitat_maps = [[habitat_map, change_point]] + else: + self.__cfg.habitat_maps.append([habitat_map, change_point]) + + def clear_habitat(self): + self.__cfg.habitat_maps = [] + + @property + def land_map(self): + return self.__cfg.land_map + + @land_map.setter + def land_map(self, val): + self.__cfg.land_map = val + + @property + def stepsize(self): + return int(self.__cfg.stepsize) + + @stepsize.setter + def stepsize(self, sz): + self.__cfg.stepsize = sz + + @property + def change_points(self): + change_points = [] + hab_maps = self.__cfg.habitat_maps + if len(hab_maps) > 1: + for m in hab_maps[1:]: + change_points.append(int(m[1])) + return change_points + + @property + def max_iters(self): + return int(self.__cfg.max_iters) + + @max_iters.setter + def max_iters(self, val): + self.__cfg.max_iters = val + + @property + def habitat_fnames(self): + fnames = [] + for m in self.__cfg.habitat_maps: + fnames.append(m[0]) + return fnames + + @property + def nb_runs(self): + return self.__cfg.nb_runs + + @nb_runs.setter + def nb_runs(self, val): + self.__cfg.nb_runs = val + + @property + def survival(self): + S = float(self.__cfg.survival) + if self.__cfg.nb_months: + S = self.__from_yearly(S) + return S + + @survival.setter + def survival(self, S): + """ + S is the yearly survival + """ + self.__cfg.survival = S + + @property + def nb_months(self): + return self.__cfg.nb_months + + @nb_months.setter + def nb_months(self, n): + self.__cfg.nb_months = n + + @property + def energy(self): + return self.__cfg.initial_energy + + @energy.setter + def energy(self, val): + self.__cfg.initial_energy = val + + @property + def position(self): + return self.__cfg.initial_position + + @position.setter + def position(self, val): + self.__cfg.initial_position = val + + @property + def nothreads(self): + if int(self.__cfg.nothreads): + return True + return False + + @property + def nbthreads(self): + return int(self.__cfg.nbthreads) + + @nbthreads.setter + def nbthreads(self, val): + self.__cfg.nbthreads = int + + @property + def metabolism(self): + return float(self.__cfg.metabolism) + + @metabolism.setter + def metabolism(self, value): + self.__cfg.metabolism = value + + def __to_yearly(self, S): + if self.__cfg.nb_months: + m = int(self.__cfg.nb_months) + S = math.exp(12 * math.log(S)/m) + return S + + def __from_yearly(self, S): + if self.__cfg.nb_months: + m = int(self.__cfg.nb_months) + S = math.exp(m * math.log(S)/12) + return S + + @property + def S5(self): + S = float(self.__cfg.S5) + return self.__from_yearly(S) + + @S5.setter + def S5(self, value): + S = float(value) + self.__cfg.S5 = self.__to_yearly(S) + + @property + def S50(self): + S = float(self.__cfg.S50) + return self.__from_yearly(S) + + @S50.setter + def S50(self, value): + S = float(value) + self.__cfg.S50 = self.__to_yearly(S) + + @property + def S95(self): + S = float(self.__cfg.S95) + return self.__from_yearly(S) + + @S95.setter + def S95(self, value): + S = float(value) + self.__cfg.S95 = self.__to_yearly(S) + + @property + def wf_avoidance(self): + return self.__cfg.wf_avoidance + + @wf_avoidance.setter + def wf_avoidance(self, val): + self.__cfg.wf_avoidance = val + + @property + def windfarms(self): + return self.__cfg.windfarms + + @windfarms.setter + def windfarms(self, val): + self.__cfg.windfarms = val + + @property + def Sraw(self): + return self.__cfg.Sraw + + @Sraw.setter + def Sraw(self, value_list): + if isinstance(value_list, list): + self.__cfg.Sraw = [self.__to_yearly(s) for s in value_list] + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_convert.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..d1c259c7eb67f88542517666022ce71aca5a18bc --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_convert.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# + + +import os +import sys +import argparse +import pandas as pd +import numpy as np +from scipy.misc import imsave + + +img_exts = ['.jpg', 'jpeg', '.png'] + +def mean(df): + d = df.groupby(['Latitude', 'Longitude'], as_index=False)['pred'].mean() + return pd.pivot_table(d, values='pred', index="Latitude", columns="Longitude").values + +def mwtl(df): + return df[df['SOURCE_DB'] == 'MWTL'] + +def convert_habitat(presence, density, max_percentile, aggregation, min_percentile=-1): + if aggregation == 'mean': + presence = mean(presence) + density = mean(density) + else: + presence = mean(mwtl(presence)) + density = mean(mwtl(density)) + + data = np.multiply(presence , density) + data = np.flipud(data) + numdata = np.nan_to_num(data) + if max_percentile > 0: + perc = np.percentile(numdata, max_percentile) + print("Removing %d data points (> %f)" % (data[numdata > perc].shape[0], perc)) + data[numdata > perc] = perc # remove cruft + + if min_percentile > 0: + perc = np.max(data) * (1 - (min_percentile/100)) + print("Removing %d data points (< %f)" % (data[numdata > perc].shape[0], perc)) + data[numdata < perc] = 0.0 # remove very low values + + return data + + +def mk_land_map(data): + land_water = np.ones(data.shape) + land_water[np.isnan(data)] = 0 + return land_water + +def save_map(data, filename): + data = np.nan_to_num(data) + ext = os.path.splitext(filename)[-1] + if ext in img_exts: + amax = np.amax(data) + img = data/amax + imsave(filename, img) + else: + np.savetxt(filename, data, delimiter = " ", newline="\n") + + + +if __name__ == "__main__": + aggregation = "mean;nwtl".split(';') + optparser = argparse.ArgumentParser() + optparser.add_argument("-p", dest="presence", help="Filename of presence absence data as csv", required=True) + optparser.add_argument("-d", dest="density", help="Filename of presence absence data as csv", required=True) + optparser.add_argument("-o", dest="output", help="Filename of output matlab file", required=True) + optparser.add_argument("-l", dest="land", help="create landwater/windfarm file and store as given file", required=False) + optparser.add_argument("-P", dest="percentile", type=float, help="Percentile for data cap", required=False, default=99) + optparser.add_argument("-a", dest="aggregation", help="Aggregation type: %s" % ", ".join(aggregation)) + + args = optparser.parse_args() + + presence = pd.read_csv(args.presence) + density = pd.read_csv(args.density) + + data = convert_habitat(presence, density, args.percentile, args.aggregation) + + if args.land: + land_water = mk_land_map(data) + save_map(land_water, args.land) + save_map(data, args.output) + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_display.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_display.py new file mode 100644 index 0000000000000000000000000000000000000000..5cf4413f267d9bd65fa295e434bfc65bb52a7eed --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_display.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# +import os +import sys +import argparse +import numpy as np +from scipy.misc import imsave + + + +if __name__ == "__main__": + optparser = argparse.ArgumentParser() + optparser.add_argument("-i", dest="input", help="Filename of input map data", required=True) + optparser.add_argument("-o", dest="output", help="Filename of output image", required=True) + + + args = optparser.parse_args() + mapdata = np.loadtxt(args.input, delimiter=",") + imsave(args.output, mapdata) diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_evaluate.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..496aeb20fa447ea76275e47d8489f40fa00d8f04 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_evaluate.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# +import os +import argparse +import numpy as np +import wozep +from wozep_run import run, Runner +from wozep_config import WozepJSONConfig + + + +def wozep_eval(metabolism, habitat, energy, position, max_iter, nb_runs, runner=run): + s = [] + bird = [] + for i in range(nb_runs): + bird.append(wozep.WBirds(energy, position, metabolism, habitat, max_iter)) + +# for i in range(nb_runs): +# bird[i].save_density("start_density-%d.mat" % i) + + s = list(map(runner, bird)) + +# for i in range(nb_runs): +# bird[i].save_density("final_density-%d.mat" % i) + + return s + +if __name__ == "__main__": + optparser = argparse.ArgumentParser() + optparser.add_argument("-C", dest="cfg", help="config file", required=True) + optparser.add_argument("-o", dest="out", help="file to write output to", required=False) + optparser.add_argument("-d", dest="debug", default=0, type=int, help="timestep of output generation") + args = optparser.parse_args() + + cfg = WozepJSONConfig(args.cfg) + hmaps = cfg.maps + habitat = hmaps[0] + others = hmaps[1:] + changepoints = cfg.change_points + max_iter = cfg.max_iters + fnames = cfg.habitat_fnames + + print("Map changes at iteration: ") + last = 0 + i = 0 + for cp in changepoints + [max_iter]: + print("\t%s %d --> %d\n" % (fnames[i], last, cp)) + last = cp + i += 1 + + if args.debug: + iterstep = args.debug + else: + iterstep = None + + runner = Runner(others, changepoints, iterstep=iterstep, max_iter=max_iter, density_save=args.debug, + prefix=os.path.splitext(os.path.basename(args.cfg))[0]) + + + target_s = float(cfg.survival) + metabolism=float(cfg.metabolism) + if cfg.windfarms: + habitat.set_windfarm_map(cfg.windfarms, cfg.wf_avoidance) + print("Avg value of habitat map: %s" % habitat.mean()) + print("Avg value of habitat map outside windfarms: %s" % habitat.mean_external_windfarm()) + print("Avg value of habitat map inside windfarms: %s" % habitat.mean_windfarm()) + print("\nNumber of runs: %d" % cfg.nb_runs) + print("\nm = %f" % metabolism) + + r = wozep_eval(metabolism, habitat, cfg.energy, cfg.position, max_iter, cfg.nb_runs, runner=runner) + Sraw = [i[3] for i in r] + S = np.array(Sraw) + cfg.S50 = np.percentile(S, 50) + cfg.S5 = np.percentile(S, 5) + cfg.S95 = np.percentile(S, 95) + cfg.Sraw = Sraw + cfg.save() + print("S[5%%] = %f" % cfg.S5) + print("S[50%%] = %f" % cfg.S50) + print("S[95%%] = %f" % cfg.S95) + print("done.") diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_run.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_run.py new file mode 100644 index 0000000000000000000000000000000000000000..2014af9ccec2c921805065ef730684b8abe0c5f5 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_run.py @@ -0,0 +1,147 @@ +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# + +import time + +def run(bird, stepsize=100, error=1, max_iter=5000): + init_birds = bird.survival() + S = init_birds + iters = 0 + stop = False + while iters < max_iter : + next_step = min(stepsize, max_iter - iters) + bird.do_n_iters(stepsize) + iters += stepsize + nb_birds = bird.survival() + diff = S - nb_birds + S = nb_birds + + return (S, diff, iters, S/init_birds) + + +class Runner(object): + """ + Runner object: excutes iterations with changing maps + + Keyword arguments: + habitats -- list of habitat maps + habitat_changes -- list of (iteration) points where the next habitat map is loaded + bird -- instance of wozep.WBirds + iterstep -- number of iterations between each check for convergence. default:100 + max_iter -- maximum iterations + """ + + def __init__(self, habitats, habitat_changes, iterstep=None, max_iter=5000, density_save=False, prefix=""): + self.__habitats = habitats # list of habitats + self.__habitat_changes = habitat_changes # list of numbers (int) indicating the iteration where the map should change. + self.__max_iter = max_iter + self.__stepsize = iterstep + self.__density_save = density_save + self.__error = 2 + self.__prefix = prefix + + def __call__(self, bird): + init_birds = bird.survival() + S = init_birds + iters = 0 + stop = False + hmap = 0 + if len(self.__habitat_changes) > 0: + N = self.__habitat_changes[hmap] + else: + hmap = -1 + N = self.__max_iter + + onego = False + if self.__stepsize is None: + iterstep = N + else: + iterstep = self.__stepsize + + print("Bird simulation 0x%x: starting simulation" % id(bird)) + print("Bird simulation 0x%x: Using map: %d" % (id(bird), hmap+1) ) + print("Bird simulation 0x%x: Doing iterations %d -> %d" % (id(bird), iters, N+iters)) + t0 = time.time() + while iters < self.__max_iter: + if hmap > 0: + print("Bird simulation 0x%x: Switching to next map: %d" % (id(bird), hmap+1) ) + print("Bird simulation 0x%x: Doing iterations %d -> %d" % (id(bird), iters, N+iters)) + bird.set_new_habitat_map(self.__habitats[hmap]) + N = self.__habitat_changes[hmap] - iters + if self.__stepsize is None: + iterstep = N + + n = 0 + while n < N: + bird.do_n_iters(iterstep) + n += iterstep + if self.__density_save: + if self.__prefix: + fn = f"{self.__prefix}-density-{id(bird)}_[{iters+n}].mat" + else: + fn = f"density-{id(bird)}_[{iters+n}].mat" + bird.save_density(fn) + + if self.__stepsize is not None: + n -= iterstep + if N - n > 0: + print (N - n); + bird.do_n_iters(N-n) + n += N - n + if self.__density_save: + if self.__prefix: + fn = f"{self.__prefix}-density-{id(bird)}_[{iters+n}].mat" + else: + fn = f"density-{id(bird)}_[{iters+n}].mat" + bird.save_density(fn) + + iters += N + hmap += 1 + + if hmap >= len(self.__habitat_changes): + hmap = - 1 + N = self.__max_iter - iters + nb_birds = bird.survival() + diff = S - nb_birds + S = nb_birds + print("Bird simulation 0x%x: end of simulation. Duration %fs" % (id(bird), time.time() - t0)) + print(f'Difference in Nb Birds: {diff}') + return (S, diff, iters, S/init_birds, init_birds) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_setup.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_setup.py new file mode 100644 index 0000000000000000000000000000000000000000..cc79a329b548218cee7f2745c675ea3d32085b25 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_setup.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survivag, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. +# + + +import os +import sys +import argparse +import wozep +import numpy as np +import pandas as pd + + +from wozep_config import WozepJSONConfig +from wozep_convert import convert_habitat, mk_land_map, save_map + +def mk_energy_distribution(factor, habitat, nb_birds): + energy = np.ones((int(nb_birds),)) + return energy * factor * habitat.mean() + +if __name__ == "__main__": + aggregation = "mean;nwtl".split(';') + optparser = argparse.ArgumentParser() + optparser.add_argument("-d", dest="density", help="Filename of bird density data in matlab .mat format", required=True) + optparser.add_argument("-l", dest="land_map", help="Filename of land/water dataq in matlab .mat format", required=True) + optparser.add_argument("-N", dest="nbbirds", type=int, help="number of birds", required=True) + optparser.add_argument("-o", dest="output", type=str, help="output directory", default=os.getcwd()) + optparser.add_argument("-b", dest="base", type=str, help="output basename", default="wozep") + optparser.add_argument("-E", dest="energy", type=float, help="Value to multiply the mean value of the habitat with as initial energy, default is 2*mean of habitat map", default=2) + optparser.add_argument("-I", dest="maxiters", type=int, help="max number of iteration", required=True) + optparser.add_argument("-R", dest="nbruns", type=int, help="max number of iteration", required=True) + optparser.add_argument("-S", dest="survival", type=float, help="Bird survival", required=True) + optparser.add_argument("-m", dest="metabolism", type=float, help="Bird metabolism", required=False) + optparser.add_argument("-M", dest="nb_months", type=int, help="Number of months the habitat map is valid", required=True) + optparser.add_argument("-sz", dest="stepsize", type=int, help="Maximum number of steps on the grid a bird can move in a singele timestep", required=True) + + args = optparser.parse_args() + + cfg = WozepJSONConfig() + base = args.base + cfg_name = os.path.join(args.output, f'{base}.cfg') + hab_name = os.path.join(args.output, f'{base}-habitat.mat') + cap_hab_name = os.path.join(args.output, f'{base}-habitat.mat') + cap_hab_img = os.path.join(args.output, f'{base}-habitat.png') + hab_img = os.path.join(args.output, f'{base}-habitat.png') + energy_name = os.path.join(args.output, f'{base}-energy.mat') + land_name = os.path.join(args.output, f'{base}-land.mat') + pos_name = os.path.join(args.output, f'{base}-pos.mat') + land_img = os.path.join(args.output, f'{base}-land.png') + + cfg.add_habitatmap(args.density, "") + cfg.land_map = args.land_map + + habitat = wozep.WHabitat(args.density, True) + habitat.set_land_map(args.land_map) + + energy = mk_energy_distribution(args.energy, habitat, args.nbbirds) + save_map(energy, energy_name) + cfg.energy = energy_name + bird = wozep.WBirds(int(args.nbbirds), 0, habitat, 0) + bird.random_positions() + bird.save_positions(pos_name) + cfg.position = pos_name + + cfg.max_iters = args.maxiters + cfg.nb_runs = args.nbruns + cfg.nb_months = args.nb_months + cfg.survival = args.survival + if args.metabolism: + cfg.metabolism = args.metabolism + cfg.stepsize = args.stepsize + cfg.save(cfg_name) diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_train.py b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_train.py new file mode 100644 index 0000000000000000000000000000000000000000..43df3311297738ce014a4a762bddd776cdcd04e2 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/pywozep/wozep_train.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python + +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +# +# +# This file is part of wozep-survival, http://www.wur.nl +# +# Copyright (C) 2011-2013 Wageningen Marine Research +# Haringkade 1, 1976CP IJmuiden, The Netherlands. +# +# 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + + +#from multiprocessing import Pool +import argparse +import time +import concurrent.futures +from scipy.optimize import bisect, newton +import pandas as pd +import seaborn as sns +import matplotlib as plt +import numpy as np +import wozep +from wozep_run import Runner +from wozep_config import WozepJSONConfig + +sns.set_style("darkgrid") + +def train_threaded(target_survival, habitat, bird_energy_filename, bird_positions_filename, max_iter, runner, nb_runs=10, nb_threads=5): + def f(m): + print("m = %f" % m) + s = [] + bird = [] + for i in range(nb_runs): + bird.append(wozep.WBirds(bird_energy_filename, bird_positions_filename, m, habitat, max_iter)) + S_target = target_survival * bird[0].survival() + with concurrent.futures.ThreadPoolExecutor(max_workers=nb_threads) as exc: + s = exc.map(runner, bird) + s = list(s) + S = sum([x[0] for x in s]) / nb_runs + return S - S_target + return bisect(f, 0, 1) + + +def train(target_survival, habitat, bird_energy_filename, bird_positions_filename, max_iter, runner, nb_runs=10, ): + def f(m): + print("m = %f" % m) + bird = [] + for i in range(nb_runs): + bird.append(wozep.WBirds(bird_energy_filename, bird_positions_filename, m, habitat, max_iter)) + S_target = target_survival * bird[0].survival() + s = list(map(runner, bird)) + print([i[0]-S_target for i in s]) + S = sum([x[0] for x in s]) / nb_runs + return S - S_target + return bisect(f, 0, 1) + + +def mk_plt(habitat, bird_energy_filename, bird_positions_filename, max_iter, stepsize=0.01): + data = {'m':[], 'S': []} + nb_steps = int(1/stepsize) + for i in range(nb_steps): + m = stepsize*i + bird = wozep.WBirds(bird_energy_filename, bird_positions_filename, m, habitati, max_iter) + r = run(bird) + data['m'].append(m) + data['S'].append(r[0]) + + df = pd.DataFrame.from_dict(data) + ax = sns.pointplot(x='m', y='S', data=df) + +# ax.xaxis.set_major_locator(plt.ticker.MultipleLocator(0.25)) +# ax.xaxis.set_major_formatter(plt.ticker.ScalarFormatter()) + plt.pyplot.show() + + + + +if __name__ == "__main__": + optparser = argparse.ArgumentParser() + optparser.add_argument("-C", dest="cfg", help="Filename of json config file", required=True) + optparser.add_argument("-o", dest="out", help="file to write output to") + optparser.add_argument("-E", dest="error", action="store_true", help="outputs the error") + optparser.add_argument("-d", dest="debug", action="store_true", help="outputs debug informations") + + args = optparser.parse_args() + + + cfg = WozepJSONConfig(args.cfg) + + poly = False + nb_runs = cfg.nb_runs + + + hmaps = cfg.maps + habitat = hmaps[0] + others = hmaps[1:] + changepoints = cfg.change_points + max_iter = cfg.max_iters + fnames = cfg.habitat_fnames + + print("Map changes at iteration: ") + last = 0 + i = 0 + for cp in changepoints + [max_iter]: + print("\t%s %d --> %d\n" % (fnames[i], last, cp)) + last = cp + i += 1 + + runner = Runner(others, changepoints, max_iter=max_iter, density_save=args.debug) + + survival = cfg.survival + + t0 = time.time() + print("Starting training session with a maximum of %d iterations and %d simulations per iteration" % (max_iter, nb_runs)) + if cfg.nothreads: + m = train(survival, habitat, cfg.energy, cfg.position, max_iter, nb_runs=nb_runs, runner=runner) + else: + m = train_threaded(survival, habitat, cfg.energy, cfg.position, max_iter, nb_runs=nb_runs, runner=runner, nb_threads=cfg.nbthreads) + print("Ending training session, total duration %fs" %(time.time() - t0)) + + cfg.metabolism = m + cfg.save() + + if args.out: + with open(args.out, 'w') as f: + f.write(str(m)) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/src/wozep.cpp b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/wozep.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9feedf6256463d71fda69eef694ae9cb013b8905 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/src/wozep.cpp @@ -0,0 +1,35 @@ +/* +* Disclaimer +* +* Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +* any and all intellectual property rights in the scrips and datafiles/databases are made available +* in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +* GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +* +* Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +* The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +* provided "as is", without warranty of any kind, express or implied, including but not limited to the +* warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +* the authors or copyright holders be liable for any claim, damages or other liability, whether in an +* action of contract, tort or otherwise, arising from, out of or in connection with the software or the +* use or other dealings in the software. +* +* Wageningen Marine Research +* January 2022 +*/ + +#include "lib/birds.h" +#include "lib/habitat.h" +#include "lib/index.h" + +using namespace wozep; + +int main(int argc, char **argv) { + WHabitat h = WHabitat(241, 345, 3000, true); + double m = h.mean(); + std::cout << "Mean: " << m << std::endl; + WBirds b = WBirds(10000, 0.945, &h, 1000); + b.random_positions(); + b.do_n_iters(10000); + std::cout << b.survival() << std::endl; +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4ibm/swig/wozep.i b/script_py_r/kec4_pyconversion-master/KEC4ibm/swig/wozep.i new file mode 100644 index 0000000000000000000000000000000000000000..78d0cbe3d0d73db768fd81dfac61d2dc2af4ff7b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4ibm/swig/wozep.i @@ -0,0 +1,14 @@ +/******************************************************************************* +swig module description file for habitat class +*******************************************************************************/ + +%module(threads="1") wozep +%include "std_string.i" + +%{ +#include "lib/habitat.h" +#include "lib/birds.h" +%} + +%include lib/habitat.h +%include lib/birds.h diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/.Rbuildignore b/script_py_r/kec4_pyconversion-master/KEC4popmodels/.Rbuildignore new file mode 100644 index 0000000000000000000000000000000000000000..6acdb63fc6f5983470339879a965c37cde6cc586 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/.Rbuildignore @@ -0,0 +1,6 @@ +^KEC4popmodels\.Rproj$ +^\.Rproj\.user$ +^doc$ +^Meta$ +^LICENSE\.md$ +^data-raw$ diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/.gitignore b/script_py_r/kec4_pyconversion-master/KEC4popmodels/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..582c62f4b115ba9acc5af665bcb8b3e99af1b190 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/.gitignore @@ -0,0 +1,19 @@ +*.Rproj +*.DS_Store +.Rproj.user +.Rhistory +.RData +.Ruserdata +*.log +devel.cpp +devel.R +scratch.R +DummyInputData.txt +output/ +IBMoutput/ +/doc/ +/Meta/ +src-i386/ +src-x64/ +*.pdf +*.docx \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/DESCRIPTION b/script_py_r/kec4_pyconversion-master/KEC4popmodels/DESCRIPTION new file mode 100644 index 0000000000000000000000000000000000000000..208ccef5cab42b2e6f93d437bc5e00507b20da7b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/DESCRIPTION @@ -0,0 +1,33 @@ +Package: KEC4popmodels +Title: KEC4 - Matrix Population Models for Seabirds +Version: 1.0.0 +Authors@R: + person(given = "Vincent", + family = "Hin", + role = c("aut", "cre"), + email = "vincent.hin@wur.nl", + comment = c(ORCID = "0000-0002-9497-1224")) +Description: Matrix population models to calculate impact of habitat loss from offshore wind farms (OWFs) on populations of several seabird species +License: GPL (>= 3) +Encoding: UTF-8 +LazyData: true +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.1.2 +LinkingTo: + Rcpp, + RcppArmadillo +Imports: + Rcpp, + yaml, + knitr +Depends: + R (>= 2.10), + knitr, + tidyverse, + numDeriv, + truncnorm +Suggests: + testthat, + knitr, + rmarkdown +VignetteBuilder: knitr diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa.txt b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa.txt new file mode 100644 index 0000000000000000000000000000000000000000..4792a8727d4fe3f2bcb132cb453b3d025f948d8c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa.txt @@ -0,0 +1,121 @@ +season batch species stat abundance Basic_2030 Rekenvariant_III Rekenvariant_II Rekenvariant_I International +1 "NAT" "Rissa tridactyla" mean 53103.08 24.12002921 81.89545773 71.09595279 68.19225458 "NA" +2 "NAT" "Rissa tridactyla" mean 68342.67 60.90325285 88.73934166 81.13479809 78.76011841 "NA" +3 "NAT" "Rissa tridactyla" mean 78921.4 60.66100606 102.9591223 94.76933873 91.26488522 "NA" +4 "NAT" "Rissa tridactyla" mean 52381.45 41.27531571 61.2008001 57.8104106 55.80037348 "NA" +5 "NAT" "Rissa tridactyla" mean 29888.59 34.97835614 58.24881491 52.49487865 49.2469555 "NA" +6 "NAT" "Rissa tridactyla" mean 33376.87 7.550299926 31.53223032 23.54064728 20.80824359 "NA" +1 "NAT" "Larus minutus" mean 1481.41 0.021549489 1.825930145 1.16539737 1.16539737 "NA" +2 "NAT" "Larus minutus" mean 8393.52 6.678919991 8.555130538 7.146238105 7.063331239 "NA" +3 "NAT" "Larus minutus" mean 2701.36 1.807404386 2.193346331 2.161745868 1.940708935 "NA" +4 "NAT" "Larus minutus" mean 6164.72 7.295548967 7.763523483 7.763523483 7.763523483 "NA" +5 "NAT" "Larus minutus" mean 57832.13 75.08274936 96.59767926 94.00372819 92.32658419 "NA" +6 "NAT" "Larus minutus" mean 93.52 0 0 0 0 "NA" +1 "NAT" "Stercorarius skua" mean 1364.08 1.274299512 4.542407214 3.979271852 3.73664799 "NA" +2 "NAT" "Stercorarius skua" mean 202.12 0.128422277 0.498564513 0.434875618 0.272693336 "NA" +3 "NAT" "Stercorarius skua" mean 281.4 0.455952637 0.4743329 0.4743329 0.4743329 "NA" +4 "NAT" "Stercorarius skua" mean 46.34 0.057529745 0.057529745 0.057529745 0.057529745 "NA" +5 "NAT" "Stercorarius skua" mean 45.43 0 0 0 0 "NA" +6 "NAT" "Stercorarius skua" mean 142.86 0.097383737 0.228089675 0.197106808 0.171812215 "NA" +1 "NAT" "Larus marinus" mean 7245.36 53.56712839 109.0335812 84.92916406 79.0050136 "NA" +2 "NAT" "Larus marinus" mean 16263.75 81.1001501 181.6703934 160.7045471 155.4236093 "NA" +3 "NAT" "Larus marinus" mean 14936.7 81.82053555 164.3375304 131.4523346 122.1422662 "NA" +4 "NAT" "Larus marinus" mean 9533.53 65.79631611 115.4976084 109.6154814 106.0079932 "NA" +5 "NAT" "Larus marinus" mean 3109.62 44.71725547 58.06439177 56.56984181 55.95074864 "NA" +6 "NAT" "Larus marinus" mean 6844.61 11.47273284 37.72485881 34.52596339 31.8791887 "NA" +1 "NAT" "Thalasseus sandvicensis" mean 22602.26 9.089013899 14.69960942 13.29601923 13.29601923 "NA" +2 "NAT" "Thalasseus sandvicensis" mean 91.64 0.032944626 0.093594638 0.093594638 0.093594638 "NA" +3 "NAT" "Thalasseus sandvicensis" mean 45.28 0 0.009516338 0.009516338 0.009516338 "NA" +4 "NAT" "Thalasseus sandvicensis" mean 55.33 0.048316657 0.048316657 0.048316657 0.048316657 "NA" +5 "NAT" "Thalasseus sandvicensis" mean 16513.59 16.31252131 19.62046597 19.40981725 19.40981725 "NA" +6 "NAT" "Thalasseus sandvicensis" mean 14548.96 6.57320929 6.859541379 6.859541379 6.859541379 "NA" +1 "NAT" "Morus bassanus" mean 26708.81 154.783255 310.7064589 276.6993319 261.6667176 "NA" +2 "NAT" "Morus bassanus" mean 31858.07 280.5925155 526.6880816 466.02901 432.2777568 "NA" +3 "NAT" "Morus bassanus" mean 18258.36 226.7156394 319.8378091 306.2053444 290.5016699 "NA" +4 "NAT" "Morus bassanus" mean 18804.13 205.7040181 259.2855745 254.7993326 252.6623666 "NA" +5 "NAT" "Morus bassanus" mean 11881.45 169.7718126 255.8327923 248.9399418 242.2113549 "NA" +6 "NAT" "Morus bassanus" mean 12540.23 145.7879208 252.7950674 218.5224373 210.85829 "NA" +1 "NAT" "Stercorarius parasiticus" mean 130.18 0.044499731 0.049543438 0.049520424 0.049485094 "NA" +2 "NAT" "Stercorarius parasiticus" mean 5.27 0 0 0 0 "NA" +3 "NAT" "Stercorarius parasiticus" mean 2.51 0.002615777 0.004265455 0.004265455 0.002615777 "NA" +4 "NAT" "Stercorarius parasiticus" mean 9.97 0.024497022 0.03016966 0.03016966 0.03016966 "NA" +5 "NAT" "Stercorarius parasiticus" mean 6.8 0.001427734 0.004319285 0.004319285 0.004319285 "NA" +6 "NAT" "Stercorarius parasiticus" mean 12.03 0 0 0 0 "NA" +1 "NAT" "Larus fuscus" mean 20206.5 16.43036543 32.22103559 29.55381961 28.51691463 "NA" +2 "NAT" "Larus fuscus" mean 5675.84 9.994905443 12.22197451 12.09284371 12.05739906 "NA" +3 "NAT" "Larus fuscus" mean 619.47 1.011859338 1.206692223 1.128810305 1.128519543 "NA" +4 "NAT" "Larus fuscus" mean 7783.81 13.4143436 16.9355714 16.11723484 15.91624611 "NA" +5 "NAT" "Larus fuscus" mean 19376.95 29.18280078 43.29387212 41.22042563 39.95557166 "NA" +6 "NAT" "Larus fuscus" mean 20552.64 26.69070752 47.39582313 44.30419658 41.62484733 "NA" +1 "NAT" "Sterna hirundo" mean 59092.68 34.18245143 44.79800215 43.00872822 42.65295719 "NA" +2 "NAT" "Sterna hirundo" mean 56.78 0.051083693 0.051083693 0.051083693 0.051083693 "NA" +3 "NAT" "Sterna hirundo" mean 0 0 0 0 0 "NA" +4 "NAT" "Sterna hirundo" mean 0 0 0 0 0 "NA" +5 "NAT" "Sterna hirundo" mean 11773.66 8.093300334 11.85302927 11.56924341 11.35379355 "NA" +6 "NAT" "Sterna hirundo" mean 5769.5 0.760455952 1.001870956 0.987407422 0.987407422 "NA" +1 "NAT" "Larus argentatus" mean 8674.12 27.92250147 39.48689552 33.84510196 32.32430857 "NA" +2 "NAT" "Larus argentatus" mean 21138.22 42.49218107 48.67979551 47.81938926 47.58809166 "NA" +3 "NAT" "Larus argentatus" mean 18140.03 20.72767157 36.31816448 32.96529348 31.2371152 "NA" +4 "NAT" "Larus argentatus" mean 14624.96 19.24117153 30.10268051 28.47430843 27.74420504 "NA" +5 "NAT" "Larus argentatus" mean 14086.37 52.11512044 56.30095273 56.26868625 56.16773887 "NA" +6 "NAT" "Larus argentatus" mean 8634.41 17.39037876 24.63963208 23.88605185 23.83580033 "NA" +1 "INT" "Rissa tridactyla" mean 251144.33 "NA" "NA" "NA" "NA" 160.9671026 +2 "INT" "Rissa tridactyla" mean 254033.29 "NA" "NA" "NA" "NA" 198.6413229 +3 "INT" "Rissa tridactyla" mean 444163.27 "NA" "NA" "NA" "NA" 347.6521041 +4 "INT" "Rissa tridactyla" mean 246114.67 "NA" "NA" "NA" "NA" 240.189282 +5 "INT" "Rissa tridactyla" mean 217118.65 "NA" "NA" "NA" "NA" 161.7746825 +6 "INT" "Rissa tridactyla" mean 244431.79 "NA" "NA" "NA" "NA" 159.0298606 +1 "INT" "Larus minutus" mean 14316.8 "NA" "NA" "NA" "NA" 4.57863408 +2 "INT" "Larus minutus" mean 18053.22 "NA" "NA" "NA" "NA" 12.6199365 +3 "INT" "Larus minutus" mean 4709.8 "NA" "NA" "NA" "NA" 5.720830661 +4 "INT" "Larus minutus" mean 11147.97 "NA" "NA" "NA" "NA" 22.06515109 +5 "INT" "Larus minutus" mean 55816.78 "NA" "NA" "NA" "NA" 94.2768165 +6 "INT" "Larus minutus" mean 2553.55 "NA" "NA" "NA" "NA" 2.531550474 +1 "INT" "Stercorarius skua" mean 12102.95 "NA" "NA" "NA" "NA" 13.44856654 +2 "INT" "Stercorarius skua" mean 3791.44 "NA" "NA" "NA" "NA" 5.014205301 +3 "INT" "Stercorarius skua" mean 644.77 "NA" "NA" "NA" "NA" 0.622375841 +4 "INT" "Stercorarius skua" mean 216.61 "NA" "NA" "NA" "NA" 0.991103671 +5 "INT" "Stercorarius skua" mean 1574.02 "NA" "NA" "NA" "NA" 3.004274484 +6 "INT" "Stercorarius skua" mean 5198.81 "NA" "NA" "NA" "NA" 6.115785272 +1 "INT" "Larus marinus" mean 45938.6 "NA" "NA" "NA" "NA" 296.7968735 +2 "INT" "Larus marinus" mean 67882.57 "NA" "NA" "NA" "NA" 621.6636014 +3 "INT" "Larus marinus" mean 87509.32 "NA" "NA" "NA" "NA" 503.9878921 +4 "INT" "Larus marinus" mean 92416.93 "NA" "NA" "NA" "NA" 453.8460749 +5 "INT" "Larus marinus" mean 21310.24 "NA" "NA" "NA" "NA" 152.6869536 +6 "INT" "Larus marinus" mean 22403.51 "NA" "NA" "NA" "NA" 145.0878704 +1 "INT" "Thalasseus sandvicensis" mean 18496.25 "NA" "NA" "NA" "NA" 14.45283135 +2 "INT" "Thalasseus sandvicensis" mean 310.29 "NA" "NA" "NA" "NA" 0.089776696 +3 "INT" "Thalasseus sandvicensis" mean 42.48 "NA" "NA" "NA" "NA" 0.032247035 +4 "INT" "Thalasseus sandvicensis" mean 129.35 "NA" "NA" "NA" "NA" 0.262745272 +5 "INT" "Thalasseus sandvicensis" mean 25881.09 "NA" "NA" "NA" "NA" 37.06447143 +6 "INT" "Thalasseus sandvicensis" mean 14830.62 "NA" "NA" "NA" "NA" 12.69903362 +1 "INT" "Morus bassanus" mean 162867.23 "NA" "NA" "NA" "NA" 1259.244229 +2 "INT" "Morus bassanus" mean 107631.13 "NA" "NA" "NA" "NA" 1419.623017 +3 "INT" "Morus bassanus" mean 83388.82 "NA" "NA" "NA" "NA" 712.2172577 +4 "INT" "Morus bassanus" mean 98020.44 "NA" "NA" "NA" "NA" 994.5872476 +5 "INT" "Morus bassanus" mean 91079.9 "NA" "NA" "NA" "NA" 999.8390643 +6 "INT" "Morus bassanus" mean 149912.9 "NA" "NA" "NA" "NA" 1615.943842 +1 "INT" "Stercorarius parasiticus" mean 3185.55 "NA" "NA" "NA" "NA" 1.152885312 +2 "INT" "Stercorarius parasiticus" mean 504.28 "NA" "NA" "NA" "NA" 0.071094081 +3 "INT" "Stercorarius parasiticus" mean 48.9 "NA" "NA" "NA" "NA" 0.034819305 +4 "INT" "Stercorarius parasiticus" mean 10.09 "NA" "NA" "NA" "NA" 0.027384673 +5 "INT" "Stercorarius parasiticus" mean 589.01 "NA" "NA" "NA" "NA" 0.320512676 +6 "INT" "Stercorarius parasiticus" mean 1530.78 "NA" "NA" "NA" "NA" 0.287731444 +1 "INT" "Larus fuscus" mean 72517.85 "NA" "NA" "NA" "NA" 114.322132 +2 "INT" "Larus fuscus" mean 18782.68 "NA" "NA" "NA" "NA" 35.61266091 +3 "INT" "Larus fuscus" mean 16359.22 "NA" "NA" "NA" "NA" 6.289566607 +4 "INT" "Larus fuscus" mean 49438.24 "NA" "NA" "NA" "NA" 64.4235639 +5 "INT" "Larus fuscus" mean 75350.67 "NA" "NA" "NA" "NA" 106.5000156 +6 "INT" "Larus fuscus" mean 69584.21 "NA" "NA" "NA" "NA" 113.9596831 +1 "INT" "Sterna hirundo" mean 74947.45 "NA" "NA" "NA" "NA" 46.65630451 +2 "INT" "Sterna hirundo" mean 214.32 "NA" "NA" "NA" "NA" 0.0259174 +3 "INT" "Sterna hirundo" mean 0 "NA" "NA" "NA" "NA" 0 +4 "INT" "Sterna hirundo" mean 11.19 "NA" "NA" "NA" "NA" 0 +5 "INT" "Sterna hirundo" mean 28542.73 "NA" "NA" "NA" "NA" 18.66241667 +6 "INT" "Sterna hirundo" mean 51816.75 "NA" "NA" "NA" "NA" 33.85031774 +1 "INT" "Larus argentatus" mean 34780.12 "NA" "NA" "NA" "NA" 60.38501354 +2 "INT" "Larus argentatus" mean 110283.13 "NA" "NA" "NA" "NA" 124.4499789 +3 "INT" "Larus argentatus" mean 79288.48 "NA" "NA" "NA" "NA" 103.7305437 +4 "INT" "Larus argentatus" mean 124964.43 "NA" "NA" "NA" "NA" 206.1561418 +5 "INT" "Larus argentatus" mean 31040.29 "NA" "NA" "NA" "NA" 89.38164639 +6 "INT" "Larus argentatus" mean 28466.44 "NA" "NA" "NA" "NA" 70.65724128 mean diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa_trekvogels.txt b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa_trekvogels.txt new file mode 100644 index 0000000000000000000000000000000000000000..a7950dd711c4db816b8db3a5f793b0a3fef2b509 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4.0_Results_collisions_BuWa_trekvogels.txt @@ -0,0 +1,17 @@ +season batch species stat abundance Basic_2030 Rekenvariant_III Rekenvariant_II Rekenvariant_I International +1 "NAT" "Branta bernicla" mean 247285.0296 22.77648903 45.32572396 43.63862525 38.85851224 "NA" +1 "NAT" "Calidris canutus" mean 672196.765 167.8526617 340.6530096 301.1394043 281.3826017 "NA" +1 "NAT" "Chlidonas niger" mean 285481.0419 8.83159637 17.86320059 15.79316387 14.75814551 "NA" +1 "NAT" "Cygnus columbianus" mean 17449.55987 2.313815423 4.565743621 4.260274458 3.802070712 "NA" +1 "NAT" "Numenius arquata" mean 302272.2621 90.14366329 181.9421922 160.9321874 150.427185 "NA" +1 "NAT" "Sturnus vulgaris" mean 18501265.82 3021.301402 6153.607136 5436.260879 5077.58775 "NA" +1 "NAT" "Tadorna tadorna" mean 302046.729 63.70303462 127.998687 113.3032852 105.9555843 "NA" +1 "NAT" "Limosa lapponica" mean 347670.0158 98.18475059 198.654789 175.655136 164.1553096 "NA" +1 "INT" "Branta bernicla" mean 247285.0296 "NA" "NA" "NA" "NA" 92.71526589 +1 "INT" "Calidris canutus" mean 672196.765 "NA" "NA" "NA" "NA" 1244.179673 +1 "INT" "Chlidonas niger" mean 285481.0419 "NA" "NA" "NA" "NA" 32.90285683 +1 "INT" "Cygnus columbianus" mean 17449.55987 "NA" "NA" "NA" "NA" 9.460510268 +1 "INT" "Numenius arquata" mean 302272.2621 "NA" "NA" "NA" "NA" 669.3136937 +1 "INT" "Sturnus vulgaris" mean 18501265.82 "NA" "NA" "NA" "NA" 22410.68227 +1 "INT" "Tadorna tadorna" mean 302046.729 "NA" "NA" "NA" "NA" 472.8122888 +1 "INT" "Limosa lapponica" mean 347670.0158 "NA" "NA" "NA" "NA" 728.8521306 \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_HabLoss_Casualties_Scenarios.txt b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_HabLoss_Casualties_Scenarios.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c06e06b83c071933d94dc37dd36050efdee1f24 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_HabLoss_Casualties_Scenarios.txt @@ -0,0 +1,85 @@ +"species" "season" "batch" "stat" "abundance" "Basic_2030" "Rekenvariant_III" "Rekenvariant_II" "Rekenvariant_I" "International" +"Alca torda" 1 "INT" "mean" 209751.4 NA NA NA NA 335.594724397394 +"Alca torda" 2 "INT" "mean" 129792.6 NA NA NA NA 286.653231298057 +"Alca torda" 3 "INT" "mean" 165211.4 NA NA NA NA 395.546819948261 +"Alca torda" 4 "INT" "mean" 178168.4 NA NA NA NA 661.127007611946 +"Alca torda" 5 "INT" "mean" 58448.8 NA NA NA NA 278.477470431187 +"Alca torda" 6 "INT" "mean" 227439 NA NA NA NA 652.238490282715 +"Alca torda" 1 "NAT" "mean" 1769.2 0.311101768368283 1.77175038793509 1.59439825945516 1.54386537813804 NA +"Alca torda" 2 "NAT" "mean" 61668.5 36.1130226462226 113.483312099752 98.7410163549784 94.2167192293408 NA +"Alca torda" 3 "NAT" "mean" 58750.4 38.7318952972464 105.549288241374 81.2811908457031 70.6443310994258 NA +"Alca torda" 4 "NAT" "mean" 59998.8 87.878696412068 144.272441863372 136.182788015079 128.870282080691 NA +"Alca torda" 5 "NAT" "mean" 4534.9 1.91827236033363 6.63616281456942 6.1401845376552 5.75218325667292 NA +"Alca torda" 6 "NAT" "mean" 275.4 0.0256759139193516 0.181238286845331 0.119889368995157 0.114632926144352 NA +"Fratercula arctica" 1 "INT" "mean" 143796.1 NA NA NA NA 246.715258084689 +"Fratercula arctica" 2 "INT" "mean" 58160.1 NA NA NA NA 25.3200479952813 +"Fratercula arctica" 3 "INT" "mean" 84409.6 NA NA NA NA 74.1594906843427 +"Fratercula arctica" 4 "INT" "mean" 128239.3 NA NA NA NA 26.8959279226028 +"Fratercula arctica" 5 "INT" "mean" 76787.7 NA NA NA NA 74.5852166628449 +"Fratercula arctica" 6 "INT" "mean" 231774.8 NA NA NA NA 260.132367936795 +"Fratercula arctica" 1 "NAT" "mean" 173.5 0 0.0190340226888657 0.0190340226888657 0.0190340226888657 NA +"Fratercula arctica" 2 "NAT" "mean" 1303.2 0.393899460758098 1.05943225625505 0.850223460625407 0.850223460625407 NA +"Fratercula arctica" 3 "NAT" "mean" 956.9 1.20787912193208 1.98851312848539 1.61948193978041 1.5552983253774 NA +"Fratercula arctica" 4 "NAT" "mean" 2214.3 1.562788794354 2.43778767698237 2.07601259635429 1.90772743211726 NA +"Fratercula arctica" 5 "NAT" "mean" 447.1 0.0195531037184701 1.13014505736798 0.483801810379635 0.483801810379635 NA +"Fratercula arctica" 6 "NAT" "mean" 11.7 0 0 0 0 NA +"Fulmarus glacialis" 1 "INT" "mean" 351118.9 NA NA NA NA 30.5647232943732 +"Fulmarus glacialis" 2 "INT" "mean" 309707 NA NA NA NA 23.1658219153954 +"Fulmarus glacialis" 3 "INT" "mean" 252988.6 NA NA NA NA 27.3100175289248 +"Fulmarus glacialis" 4 "INT" "mean" 349675.9 NA NA NA NA 27.238807168013 +"Fulmarus glacialis" 5 "INT" "mean" 319258.9 NA NA NA NA 23.5522686148633 +"Fulmarus glacialis" 6 "INT" "mean" 368438.6 NA NA NA NA 39.3754870094619 +"Fulmarus glacialis" 1 "NAT" "mean" 32106.9 2.49677754518044 6.89616387087433 5.90903131393649 5.50520599065282 NA +"Fulmarus glacialis" 2 "NAT" "mean" 25473.7 0.902567384704115 3.15619586907708 2.70006955161468 2.34969033345357 NA +"Fulmarus glacialis" 3 "NAT" "mean" 32296.2 3.6810145357389 7.04360248595627 6.12423441156493 5.59993363365279 NA +"Fulmarus glacialis" 4 "NAT" "mean" 50375.1 2.59566610024181 11.1744513900019 10.8269478000222 10.5940843932667 NA +"Fulmarus glacialis" 5 "NAT" "mean" 13717.7 1.11343727805077 2.31972624918475 1.97083392207867 1.6610005796131 NA +"Fulmarus glacialis" 6 "NAT" "mean" 25316.1 0.771794233908118 2.52623698934308 2.08691269668675 1.92175972384325 NA +"Gavia sp" 1 "INT" "mean" 339 NA NA NA NA 0.128738658574354 +"Gavia sp" 2 "INT" "mean" 7582.6 NA NA NA NA 16.0794615515376 +"Gavia sp" 3 "INT" "mean" 32198.3 NA NA NA NA 105.832626503462 +"Gavia sp" 4 "INT" "mean" 19750.8 NA NA NA NA 77.3944896956069 +"Gavia sp" 5 "INT" "mean" 28448.8 NA NA NA NA 67.2871164020126 +"Gavia sp" 6 "INT" "mean" 102.8 NA NA NA NA 0.153341033028746 +"Gavia sp" 1 "NAT" "mean" 30.6 0.106131110029916 0.106131110029916 0.106131110029916 0.106131110029916 NA +"Gavia sp" 2 "NAT" "mean" 2071.6 0.254580141770025 1.29861961508014 1.29861961508014 1.29861961508014 NA +"Gavia sp" 3 "NAT" "mean" 5679.9 2.58952030923729 4.46859171002445 3.28679463733034 3.28679463733034 NA +"Gavia sp" 4 "NAT" "mean" 5212 1.94745925552759 9.61057613324745 6.5895279561131 3.66613402999553 NA +"Gavia sp" 5 "NAT" "mean" 697 0.89878777453441 4.315614860574 3.65816540207479 3.02284606482599 NA +"Gavia sp" 6 "NAT" "mean" 0 0 0 0 0 NA +"Morus bassanus" 1 "INT" "mean" 162867.2 NA NA NA NA 40.3368481322082 +"Morus bassanus" 2 "INT" "mean" 107631.1 NA NA NA NA 57.9943517909137 +"Morus bassanus" 3 "INT" "mean" 83388.8 NA NA NA NA 29.9257641468178 +"Morus bassanus" 4 "INT" "mean" 98020.4 NA NA NA NA 39.2219446327602 +"Morus bassanus" 5 "INT" "mean" 91079.9 NA NA NA NA 37.4582241690018 +"Morus bassanus" 6 "INT" "mean" 149912.9 NA NA NA NA 45.7620493615811 +"Morus bassanus" 1 "NAT" "mean" 26708.8 3.43717393000132 8.10817246002684 6.81238325533794 6.44985269231127 NA +"Morus bassanus" 2 "NAT" "mean" 31858.1 9.46046348761536 19.4080020668146 16.698272119544 15.1924801454763 NA +"Morus bassanus" 3 "NAT" "mean" 18258.4 10.2220682033834 14.1257073808324 13.6360311818247 12.7476781816159 NA +"Morus bassanus" 4 "NAT" "mean" 18804.1 7.73315584419085 9.83531188193673 9.67563628034631 9.61462641455954 NA +"Morus bassanus" 5 "NAT" "mean" 11881.4 3.66759944871521 5.95492141439415 5.91337925669724 5.6663948139547 NA +"Morus bassanus" 6 "NAT" "mean" 12540.2 2.59030950196792 5.49823471288989 4.28150756159105 4.18543954547733 NA +"Thalasseus sandvicensis" 1 "INT" "mean" 18496.2 NA NA NA NA 6.58948712156608 +"Thalasseus sandvicensis" 2 "INT" "mean" 310.3 NA NA NA NA 0.137242781594396 +"Thalasseus sandvicensis" 3 "INT" "mean" 42.5 NA NA NA NA 0.0167894592228161 +"Thalasseus sandvicensis" 4 "INT" "mean" 129.4 NA NA NA NA 0.186755287211944 +"Thalasseus sandvicensis" 5 "INT" "mean" 25881.1 NA NA NA NA 32.8932707342401 +"Thalasseus sandvicensis" 6 "INT" "mean" 14830.6 NA NA NA NA 6.38057360913348 +"Thalasseus sandvicensis" 1 "NAT" "mean" 22602.3 4.63966406330739 7.88707438302609 7.76613666864268 7.76613666864268 NA +"Thalasseus sandvicensis" 2 "NAT" "mean" 91.6 0.0587824729050918 0.140782119553856 0.140782119553856 0.140782119553856 NA +"Thalasseus sandvicensis" 3 "NAT" "mean" 45.3 0 0.025363801997155 0.025363801997155 0.025363801997155 NA +"Thalasseus sandvicensis" 4 "NAT" "mean" 55.3 0.00576315492453321 0.00576315492453321 0.00576315492453321 0.00576315492453321 NA +"Thalasseus sandvicensis" 5 "NAT" "mean" 16513.6 12.0052572722152 15.1684958864091 14.7262777422113 14.7262777422113 NA +"Thalasseus sandvicensis" 6 "NAT" "mean" 14549 3.90272244072933 4.25600889265951 4.25600889265951 4.25600889265951 NA +"Uria aalge" 1 "INT" "mean" 1511771 NA NA NA NA 2017.78620271588 +"Uria aalge" 2 "INT" "mean" 1203124.6 NA NA NA NA 1729.16678368616 +"Uria aalge" 3 "INT" "mean" 943224.8 NA NA NA NA 2864.96390641869 +"Uria aalge" 4 "INT" "mean" 923944.8 NA NA NA NA 1709.42828451385 +"Uria aalge" 5 "INT" "mean" 771308.8 NA NA NA NA 2841.25490715184 +"Uria aalge" 6 "INT" "mean" 1677204.1 NA NA NA NA 2985.49824888118 +"Uria aalge" 1 "NAT" "mean" 227586.5 19.2573371480751 166.664608265486 113.744014338289 104.371925424425 NA +"Uria aalge" 2 "NAT" "mean" 221470.3 169.580682897888 316.28166153865 272.839078736458 255.792814621482 NA +"Uria aalge" 3 "NAT" "mean" 188760.4 176.821235854612 456.441900754081 320.550658992925 288.346367285648 NA +"Uria aalge" 4 "NAT" "mean" 128244.6 157.151297053279 257.277970132259 233.574766146721 222.603134730042 NA +"Uria aalge" 5 "NAT" "mean" 65560.7 61.5038239080139 179.593318266318 146.880061802869 126.886421573763 NA +"Uria aalge" 6 "NAT" "mean" 34860.9 4.10930483209938 42.3511047321188 26.3749451080481 24.6279086013235 NA diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_MB_IBM_mortality.txt b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_MB_IBM_mortality.txt new file mode 100644 index 0000000000000000000000000000000000000000..f491d87d3fc19444ed5333c07a40fbe3703738d0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_MB_IBM_mortality.txt @@ -0,0 +1,25 @@ +"species" "scenario" "stat" "batch" "n" "m" +"Morus bassanus" "International REAL" "m5_sz1" "INT" 6 0.0165161752940545 +"Morus bassanus" "International REAL" "m50_sz1" "INT" 6 0.00794957707627864 +"Morus bassanus" "International REAL" "m95_sz1" "INT" 6 0 +"Morus bassanus" "International WC" "m5_sz1" "INT" 6 0.0196585358148601 +"Morus bassanus" "International WC" "m50_sz1" "INT" 6 0.0107185128752321 +"Morus bassanus" "International WC" "m95_sz1" "INT" 6 0.00188608599426854 +"Morus bassanus" "Rekenvariant III REAL" "m5_sz1" "INT" 6 0.0108989540042358 +"Morus bassanus" "Rekenvariant III REAL" "m50_sz1" "INT" 6 0.00234197843930251 +"Morus bassanus" "Rekenvariant III REAL" "m95_sz1" "INT" 6 0 +"Morus bassanus" "Rekenvariant III WC" "m5_sz1" "INT" 6 0.0114476296878913 +"Morus bassanus" "Rekenvariant III WC" "m50_sz1" "INT" 6 0.00284476345004725 +"Morus bassanus" "Rekenvariant III WC" "m95_sz1" "INT" 6 0 +"Morus bassanus" "International REAL" "m5_sz8" "INT" 6 0.0498344093994568 +"Morus bassanus" "International REAL" "m50_sz8" "INT" 6 0.0378775099112917 +"Morus bassanus" "International REAL" "m95_sz8" "INT" 6 0.0257812009620298 +"Morus bassanus" "International WC" "m5_sz8" "INT" 6 0.0581196175723119 +"Morus bassanus" "International WC" "m50_sz8" "INT" 6 0.0458084079990183 +"Morus bassanus" "International WC" "m95_sz8" "INT" 6 0.0337043365112187 +"Morus bassanus" "Rekenvariant III REAL" "m5_sz8" "INT" 6 0.0219564753872754 +"Morus bassanus" "Rekenvariant III REAL" "m50_sz8" "INT" 6 0.0114819008815815 +"Morus bassanus" "Rekenvariant III REAL" "m95_sz8" "INT" 6 0.000535521555069529 +"Morus bassanus" "Rekenvariant III WC" "m5_sz8" "INT" 6 0.0224924433815035 +"Morus bassanus" "Rekenvariant III WC" "m50_sz8" "INT" 6 0.0117562171586988 +"Morus bassanus" "Rekenvariant III WC" "m95_sz8" "INT" 6 0.00100205701127531 diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_Total_Casualties_Scenarios.txt b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_Total_Casualties_Scenarios.txt new file mode 100644 index 0000000000000000000000000000000000000000..877a68078ad6dd03b5d59419a75af7815012ba98 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/KEC4_Total_Casualties_Scenarios.txt @@ -0,0 +1,73 @@ +"species" "season" "batch" "abundance" "stat" "International" "Basic_2030" "Rekenvariant_II" "Rekenvariant_III" "Rekenvariant_I" +"Morus bassanus" 1 "INT" 162867.2 "HabLoss" 40.3368481322082 NA NA NA NA +"Morus bassanus" 1 "INT" 162867.2 "Collisions" 1259.244229 NA NA NA NA +"Morus bassanus" 1 "INT" 162867.2 "Total" 1299.58107713221 NA NA NA NA +"Morus bassanus" 2 "INT" 107631.1 "HabLoss" 57.9943517909137 NA NA NA NA +"Morus bassanus" 2 "INT" 107631.1 "Collisions" 1419.623017 NA NA NA NA +"Morus bassanus" 2 "INT" 107631.1 "Total" 1477.61736879091 NA NA NA NA +"Morus bassanus" 3 "INT" 83388.8 "HabLoss" 29.9257641468178 NA NA NA NA +"Morus bassanus" 3 "INT" 83388.8 "Collisions" 712.2172577 NA NA NA NA +"Morus bassanus" 3 "INT" 83388.8 "Total" 742.143021846818 NA NA NA NA +"Morus bassanus" 4 "INT" 98020.4 "HabLoss" 39.2219446327602 NA NA NA NA +"Morus bassanus" 4 "INT" 98020.4 "Collisions" 994.5872476 NA NA NA NA +"Morus bassanus" 4 "INT" 98020.4 "Total" 1033.80919223276 NA NA NA NA +"Morus bassanus" 5 "INT" 91079.9 "HabLoss" 37.4582241690018 NA NA NA NA +"Morus bassanus" 5 "INT" 91079.9 "Collisions" 999.8390643 NA NA NA NA +"Morus bassanus" 5 "INT" 91079.9 "Total" 1037.297288469 NA NA NA NA +"Morus bassanus" 6 "INT" 149912.9 "HabLoss" 45.7620493615811 NA NA NA NA +"Morus bassanus" 6 "INT" 149912.9 "Collisions" 1615.943842 NA NA NA NA +"Morus bassanus" 6 "INT" 149912.9 "Total" 1661.70589136158 NA NA NA NA +"Morus bassanus" 1 "NAT" 26708.8 "HabLoss" NA 3.43717393000132 6.81238325533794 8.10817246002684 6.44985269231127 +"Morus bassanus" 1 "NAT" 26708.8 "Collisions" NA 154.783255 276.6993319 310.7064589 261.6667176 +"Morus bassanus" 1 "NAT" 26708.8 "Total" NA 158.220428930001 283.511715155338 318.814631360027 268.116570292311 +"Morus bassanus" 2 "NAT" 31858.1 "HabLoss" NA 9.46046348761536 16.698272119544 19.4080020668146 15.1924801454763 +"Morus bassanus" 2 "NAT" 31858.1 "Collisions" NA 280.5925155 466.02901 526.6880816 432.2777568 +"Morus bassanus" 2 "NAT" 31858.1 "Total" NA 290.052978987615 482.727282119544 546.096083666815 447.470236945476 +"Morus bassanus" 3 "NAT" 18258.4 "HabLoss" NA 10.2220682033834 13.6360311818247 14.1257073808324 12.7476781816159 +"Morus bassanus" 3 "NAT" 18258.4 "Collisions" NA 226.7156394 306.2053444 319.8378091 290.5016699 +"Morus bassanus" 3 "NAT" 18258.4 "Total" NA 236.937707603383 319.841375581825 333.963516480832 303.249348081616 +"Morus bassanus" 4 "NAT" 18804.1 "HabLoss" NA 7.73315584419085 9.67563628034631 9.83531188193673 9.61462641455954 +"Morus bassanus" 4 "NAT" 18804.1 "Collisions" NA 205.7040181 254.7993326 259.2855745 252.6623666 +"Morus bassanus" 4 "NAT" 18804.1 "Total" NA 213.437173944191 264.474968880346 269.120886381937 262.27699301456 +"Morus bassanus" 5 "NAT" 11881.4 "HabLoss" NA 3.66759944871521 5.91337925669724 5.95492141439415 5.6663948139547 +"Morus bassanus" 5 "NAT" 11881.4 "Collisions" NA 169.7718126 248.9399418 255.8327923 242.2113549 +"Morus bassanus" 5 "NAT" 11881.4 "Total" NA 173.439412048715 254.853321056697 261.787713714394 247.877749713955 +"Morus bassanus" 6 "NAT" 12540.2 "HabLoss" NA 2.59030950196792 4.28150756159105 5.49823471288989 4.18543954547733 +"Morus bassanus" 6 "NAT" 12540.2 "Collisions" NA 145.7879208 218.5224373 252.7950674 210.85829 +"Morus bassanus" 6 "NAT" 12540.2 "Total" NA 148.378230301968 222.803944861591 258.29330211289 215.043729545477 +"Thalasseus sandvicensis" 1 "INT" 18496.2 "HabLoss" 6.58948712156608 NA NA NA NA +"Thalasseus sandvicensis" 1 "INT" 18496.2 "Collisions" 14.45283135 NA NA NA NA +"Thalasseus sandvicensis" 1 "INT" 18496.2 "Total" 21.0423184715661 NA NA NA NA +"Thalasseus sandvicensis" 2 "INT" 310.3 "HabLoss" 0.137242781594396 NA NA NA NA +"Thalasseus sandvicensis" 2 "INT" 310.3 "Collisions" 0.089776696 NA NA NA NA +"Thalasseus sandvicensis" 2 "INT" 310.3 "Total" 0.227019477594396 NA NA NA NA +"Thalasseus sandvicensis" 3 "INT" 42.5 "HabLoss" 0.0167894592228161 NA NA NA NA +"Thalasseus sandvicensis" 3 "INT" 42.5 "Collisions" 0.032247035 NA NA NA NA +"Thalasseus sandvicensis" 3 "INT" 42.5 "Total" 0.0490364942228161 NA NA NA NA +"Thalasseus sandvicensis" 4 "INT" 129.4 "HabLoss" 0.186755287211944 NA NA NA NA +"Thalasseus sandvicensis" 4 "INT" 129.4 "Collisions" 0.262745272 NA NA NA NA +"Thalasseus sandvicensis" 4 "INT" 129.4 "Total" 0.449500559211944 NA NA NA NA +"Thalasseus sandvicensis" 5 "INT" 25881.1 "HabLoss" 32.8932707342401 NA NA NA NA +"Thalasseus sandvicensis" 5 "INT" 25881.1 "Collisions" 37.06447143 NA NA NA NA +"Thalasseus sandvicensis" 5 "INT" 25881.1 "Total" 69.9577421642401 NA NA NA NA +"Thalasseus sandvicensis" 6 "INT" 14830.6 "HabLoss" 6.38057360913348 NA NA NA NA +"Thalasseus sandvicensis" 6 "INT" 14830.6 "Collisions" 12.69903362 NA NA NA NA +"Thalasseus sandvicensis" 6 "INT" 14830.6 "Total" 19.0796072291335 NA NA NA NA +"Thalasseus sandvicensis" 1 "NAT" 22602.3 "HabLoss" NA 4.63966406330739 7.76613666864268 7.88707438302609 7.76613666864268 +"Thalasseus sandvicensis" 1 "NAT" 22602.3 "Collisions" NA 9.089013899 13.29601923 14.69960942 13.29601923 +"Thalasseus sandvicensis" 1 "NAT" 22602.3 "Total" NA 13.7286779623074 21.0621558986427 22.5866838030261 21.0621558986427 +"Thalasseus sandvicensis" 2 "NAT" 91.6 "HabLoss" NA 0.0587824729050918 0.140782119553856 0.140782119553856 0.140782119553856 +"Thalasseus sandvicensis" 2 "NAT" 91.6 "Collisions" NA 0.032944626 0.093594638 0.093594638 0.093594638 +"Thalasseus sandvicensis" 2 "NAT" 91.6 "Total" NA 0.0917270989050918 0.234376757553856 0.234376757553856 0.234376757553856 +"Thalasseus sandvicensis" 3 "NAT" 45.3 "HabLoss" NA 0 0.025363801997155 0.025363801997155 0.025363801997155 +"Thalasseus sandvicensis" 3 "NAT" 45.3 "Collisions" NA 0 0.009516338 0.009516338 0.009516338 +"Thalasseus sandvicensis" 3 "NAT" 45.3 "Total" NA 0 0.034880139997155 0.034880139997155 0.034880139997155 +"Thalasseus sandvicensis" 4 "NAT" 55.3 "HabLoss" NA 0.00576315492453321 0.00576315492453321 0.00576315492453321 0.00576315492453321 +"Thalasseus sandvicensis" 4 "NAT" 55.3 "Collisions" NA 0.048316657 0.048316657 0.048316657 0.048316657 +"Thalasseus sandvicensis" 4 "NAT" 55.3 "Total" NA 0.0540798119245332 0.0540798119245332 0.0540798119245332 0.0540798119245332 +"Thalasseus sandvicensis" 5 "NAT" 16513.6 "HabLoss" NA 12.0052572722152 14.7262777422113 15.1684958864091 14.7262777422113 +"Thalasseus sandvicensis" 5 "NAT" 16513.6 "Collisions" NA 16.31252131 19.40981725 19.62046597 19.40981725 +"Thalasseus sandvicensis" 5 "NAT" 16513.6 "Total" NA 28.3177785822152 34.1360949922113 34.7889618564091 34.1360949922113 +"Thalasseus sandvicensis" 6 "NAT" 14549 "HabLoss" NA 3.90272244072933 4.25600889265951 4.25600889265951 4.25600889265951 +"Thalasseus sandvicensis" 6 "NAT" 14549 "Collisions" NA 6.57320929 6.859541379 6.859541379 6.859541379 +"Thalasseus sandvicensis" 6 "NAT" 14549 "Total" NA 10.4759317307293 11.1155502716595 11.1155502716595 11.1155502716595 diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/LICENSE.md b/script_py_r/kec4_pyconversion-master/KEC4popmodels/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..175443ce8855883fcbf0122a6bfba12ac4cfb49d --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/LICENSE.md @@ -0,0 +1,595 @@ +GNU General Public License +========================== + +_Version 3, 29 June 2007_ +_Copyright © 2007 Free Software Foundation, Inc. <>_ + +Everyone is permitted to copy and distribute verbatim copies of this license +document, but changing it is not allowed. + +## Preamble + +The GNU General Public License is a free, copyleft license for software and other +kinds of works. + +The licenses for most software and other practical works are designed to take away +your freedom to share and change the works. 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Protecting Users' Legal Rights From Anti-Circumvention Law + +No covered work shall be deemed part of an effective technological measure under any +applicable law fulfilling obligations under article 11 of the WIPO copyright treaty +adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention +of such measures. + +When you convey a covered work, you waive any legal power to forbid circumvention of +technological measures to the extent such circumvention is effected by exercising +rights under this License with respect to the covered work, and you disclaim any +intention to limit operation or modification of the work as a means of enforcing, +against the work's users, your or third parties' legal rights to forbid circumvention +of technological measures. + +### 4. Conveying Verbatim Copies + +You may convey verbatim copies of the Program's source code as you receive it, in any +medium, provided that you conspicuously and appropriately publish on each copy an +appropriate copyright notice; keep intact all notices stating that this License and +any non-permissive terms added in accord with section 7 apply to the code; keep +intact all notices of the absence of any warranty; and give all recipients a copy of +this License along with the Program. + +You may charge any price or no price for each copy that you convey, and you may offer +support or warranty protection for a fee. + +### 5. 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But this requirement does not apply if +neither you nor any third party retains the ability to install modified object code +on the User Product (for example, the work has been installed in ROM). + +The requirement to provide Installation Information does not include a requirement to +continue to provide support service, warranty, or updates for a work that has been +modified or installed by the recipient, or for the User Product in which it has been +modified or installed. Access to a network may be denied when the modification itself +materially and adversely affects the operation of the network or violates the rules +and protocols for communication across the network. + +Corresponding Source conveyed, and Installation Information provided, in accord with +this section must be in a format that is publicly documented (and with an +implementation available to the public in source code form), and must require no +special password or key for unpacking, reading or copying. + +### 7. Additional Terms + +“Additional permissions” are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. Additional +permissions that are applicable to the entire Program shall be treated as though they +were included in this License, to the extent that they are valid under applicable +law. If additional permissions apply only to part of the Program, that part may be +used separately under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + +When you convey a copy of a covered work, you may at your option remove any +additional permissions from that copy, or from any part of it. (Additional +permissions may be written to require their own removal in certain cases when you +modify the work.) You may place additional permissions on material, added by you to a +covered work, for which you have or can give appropriate copyright permission. + +Notwithstanding any other provision of this License, for material you add to a +covered work, you may (if authorized by the copyright holders of that material) +supplement the terms of this License with terms: + +* **a)** Disclaiming warranty or limiting liability differently from the terms of +sections 15 and 16 of this License; or +* **b)** Requiring preservation of specified reasonable legal notices or author +attributions in that material or in the Appropriate Legal Notices displayed by works +containing it; or +* **c)** Prohibiting misrepresentation of the origin of that material, or requiring that +modified versions of such material be marked in reasonable ways as different from the +original version; or +* **d)** Limiting the use for publicity purposes of names of licensors or authors of the +material; or +* **e)** Declining to grant rights under trademark law for use of some trade names, +trademarks, or service marks; or +* **f)** Requiring indemnification of licensors and authors of that material by anyone +who conveys the material (or modified versions of it) with contractual assumptions of +liability to the recipient, for any liability that these contractual assumptions +directly impose on those licensors and authors. + +All other non-permissive additional terms are considered “further +restrictions” within the meaning of section 10. If the Program as you received +it, or any part of it, contains a notice stating that it is governed by this License +along with a term that is a further restriction, you may remove that term. If a +license document contains a further restriction but permits relicensing or conveying +under this License, you may add to a covered work material governed by the terms of +that license document, provided that the further restriction does not survive such +relicensing or conveying. + +If you add terms to a covered work in accord with this section, you must place, in +the relevant source files, a statement of the additional terms that apply to those +files, or a notice indicating where to find the applicable terms. + +Additional terms, permissive or non-permissive, may be stated in the form of a +separately written license, or stated as exceptions; the above requirements apply +either way. + +### 8. Termination + +You may not propagate or modify a covered work except as expressly provided under +this License. Any attempt otherwise to propagate or modify it is void, and will +automatically terminate your rights under this License (including any patent licenses +granted under the third paragraph of section 11). + +However, if you cease all violation of this License, then your license from a +particular copyright holder is reinstated **(a)** provisionally, unless and until the +copyright holder explicitly and finally terminates your license, and **(b)** permanently, +if the copyright holder fails to notify you of the violation by some reasonable means +prior to 60 days after the cessation. + +Moreover, your license from a particular copyright holder is reinstated permanently +if the copyright holder notifies you of the violation by some reasonable means, this +is the first time you have received notice of violation of this License (for any +work) from that copyright holder, and you cure the violation prior to 30 days after +your receipt of the notice. + +Termination of your rights under this section does not terminate the licenses of +parties who have received copies or rights from you under this License. If your +rights have been terminated and not permanently reinstated, you do not qualify to +receive new licenses for the same material under section 10. + +### 9. Acceptance Not Required for Having Copies + +You are not required to accept this License in order to receive or run a copy of the +Program. Ancillary propagation of a covered work occurring solely as a consequence of +using peer-to-peer transmission to receive a copy likewise does not require +acceptance. However, nothing other than this License grants you permission to +propagate or modify any covered work. These actions infringe copyright if you do not +accept this License. Therefore, by modifying or propagating a covered work, you +indicate your acceptance of this License to do so. + +### 10. Automatic Licensing of Downstream Recipients + +Each time you convey a covered work, the recipient automatically receives a license +from the original licensors, to run, modify and propagate that work, subject to this +License. You are not responsible for enforcing compliance by third parties with this +License. + +An “entity transaction” is a transaction transferring control of an +organization, or substantially all assets of one, or subdividing an organization, or +merging organizations. If propagation of a covered work results from an entity +transaction, each party to that transaction who receives a copy of the work also +receives whatever licenses to the work the party's predecessor in interest had or +could give under the previous paragraph, plus a right to possession of the +Corresponding Source of the work from the predecessor in interest, if the predecessor +has it or can get it with reasonable efforts. + +You may not impose any further restrictions on the exercise of the rights granted or +affirmed under this License. For example, you may not impose a license fee, royalty, +or other charge for exercise of rights granted under this License, and you may not +initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging +that any patent claim is infringed by making, using, selling, offering for sale, or +importing the Program or any portion of it. + +### 11. Patents + +A “contributor” is a copyright holder who authorizes use under this +License of the Program or a work on which the Program is based. The work thus +licensed is called the contributor's “contributor version”. + +A contributor's “essential patent claims” are all patent claims owned or +controlled by the contributor, whether already acquired or hereafter acquired, that +would be infringed by some manner, permitted by this License, of making, using, or +selling its contributor version, but do not include claims that would be infringed +only as a consequence of further modification of the contributor version. For +purposes of this definition, “control” includes the right to grant patent +sublicenses in a manner consistent with the requirements of this License. + +Each contributor grants you a non-exclusive, worldwide, royalty-free patent license +under the contributor's essential patent claims, to make, use, sell, offer for sale, +import and otherwise run, modify and propagate the contents of its contributor +version. + +In the following three paragraphs, a “patent license” is any express +agreement or commitment, however denominated, not to enforce a patent (such as an +express permission to practice a patent or covenant not to sue for patent +infringement). To “grant” such a patent license to a party means to make +such an agreement or commitment not to enforce a patent against the party. + +If you convey a covered work, knowingly relying on a patent license, and the +Corresponding Source of the work is not available for anyone to copy, free of charge +and under the terms of this License, through a publicly available network server or +other readily accessible means, then you must either **(1)** cause the Corresponding +Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the +patent license for this particular work, or **(3)** arrange, in a manner consistent with +the requirements of this License, to extend the patent license to downstream +recipients. “Knowingly relying” means you have actual knowledge that, but +for the patent license, your conveying the covered work in a country, or your +recipient's use of the covered work in a country, would infringe one or more +identifiable patents in that country that you have reason to believe are valid. + +If, pursuant to or in connection with a single transaction or arrangement, you +convey, or propagate by procuring conveyance of, a covered work, and grant a patent +license to some of the parties receiving the covered work authorizing them to use, +propagate, modify or convey a specific copy of the covered work, then the patent +license you grant is automatically extended to all recipients of the covered work and +works based on it. + +A patent license is “discriminatory” if it does not include within the +scope of its coverage, prohibits the exercise of, or is conditioned on the +non-exercise of one or more of the rights that are specifically granted under this +License. You may not convey a covered work if you are a party to an arrangement with +a third party that is in the business of distributing software, under which you make +payment to the third party based on the extent of your activity of conveying the +work, and under which the third party grants, to any of the parties who would receive +the covered work from you, a discriminatory patent license **(a)** in connection with +copies of the covered work conveyed by you (or copies made from those copies), or **(b)** +primarily for and in connection with specific products or compilations that contain +the covered work, unless you entered into that arrangement, or that patent license +was granted, prior to 28 March 2007. + +Nothing in this License shall be construed as excluding or limiting any implied +license or other defenses to infringement that may otherwise be available to you +under applicable patent law. + +### 12. No Surrender of Others' Freedom + +If conditions are imposed on you (whether by court order, agreement or otherwise) +that contradict the conditions of this License, they do not excuse you from the +conditions of this License. If you cannot convey a covered work so as to satisfy +simultaneously your obligations under this License and any other pertinent +obligations, then as a consequence you may not convey it at all. For example, if you +agree to terms that obligate you to collect a royalty for further conveying from +those to whom you convey the Program, the only way you could satisfy both those terms +and this License would be to refrain entirely from conveying the Program. + +### 13. Use with the GNU Affero General Public License + +Notwithstanding any other provision of this License, you have permission to link or +combine any covered work with a work licensed under version 3 of the GNU Affero +General Public License into a single combined work, and to convey the resulting work. +The terms of this License will continue to apply to the part which is the covered +work, but the special requirements of the GNU Affero General Public License, section +13, concerning interaction through a network will apply to the combination as such. + +### 14. Revised Versions of this License + +The Free Software Foundation may publish revised and/or new versions of the GNU +General Public License from time to time. Such new versions will be similar in spirit +to the present version, but may differ in detail to address new problems or concerns. + +Each version is given a distinguishing version number. If the Program specifies that +a certain numbered version of the GNU General Public License “or any later +version” applies to it, you have the option of following the terms and +conditions either of that numbered version or of any later version published by the +Free Software Foundation. If the Program does not specify a version number of the GNU +General Public License, you may choose any version ever published by the Free +Software Foundation. + +If the Program specifies that a proxy can decide which future versions of the GNU +General Public License can be used, that proxy's public statement of acceptance of a +version permanently authorizes you to choose that version for the Program. + +Later license versions may give you additional or different permissions. However, no +additional obligations are imposed on any author or copyright holder as a result of +your choosing to follow a later version. + +### 15. Disclaimer of Warranty + +THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. +EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER +EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE +QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE +DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +### 16. Limitation of Liability + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY +COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS +PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, +INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE +PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE +OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE +WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +### 17. Interpretation of Sections 15 and 16 + +If the disclaimer of warranty and limitation of liability provided above cannot be +given local legal effect according to their terms, reviewing courts shall apply local +law that most closely approximates an absolute waiver of all civil liability in +connection with the Program, unless a warranty or assumption of liability accompanies +a copy of the Program in return for a fee. + +_END OF TERMS AND CONDITIONS_ + +## How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest possible use to +the public, the best way to achieve this is to make it free software which everyone +can redistribute and change under these terms. + +To do so, attach the following notices to the program. It is safest to attach them +to the start of each source file to most effectively state the exclusion of warranty; +and each file should have at least the “copyright” line and a pointer to +where the full notice is found. + + + Copyright (C) + + 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 . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type 'show c' for details. + +The hypothetical commands `show w` and `show c` should show the appropriate parts of +the General Public License. Of course, your program's commands might be different; +for a GUI interface, you would use an “about box”. + +You should also get your employer (if you work as a programmer) or school, if any, to +sign a “copyright disclaimer” for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +<>. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider it +more useful to permit linking proprietary applications with the library. If this is +what you want to do, use the GNU Lesser General Public License instead of this +License. But first, please read +<>. diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryHist.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryHist.R new file mode 100644 index 0000000000000000000000000000000000000000..12475e1b035528733d340bd1820ad82c776e915a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryHist.R @@ -0,0 +1,213 @@ +################################################################################################################ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +################################################################################################################ + +## This script reads rds files of all species with population output and +## create a histogram of population growth rate wit ALI thresholds in subfolder /histograms + +## Created by : Vincent Hin (vincent.hin@wur.nl) +## Last modified : 25 Aug. 2021 +## Last run : 25 Okt. 2021 + +## KEC4popmodels R-package is available at +## https://git.wur.nl/ecodyn/KEC4popmodels +## Installation instructions are described in the README.md +library(KEC4popmodels) + +## Set name of output folder +outdir <- "output" + +## List subfolder in output folder +outflds <- list.files(outdir) + +## Import all Lambda Summary Tables +Lambda_summary <- + map_dfr(outflds, function(outfld) { + fln <- paste0(file.path(outdir, outfld), "/Lambda_summary_scns_", outfld, ".txt") + Lambda_sum <- read.table(fln, header = TRUE) + Lambda_sum$species <- outfld + if(outfld %in% c("Fratercula_arctica_2", "Fulmarus_glacialis_2")) { + Lambda_sum$ALI_Pt <- Lambda_sum$ALI10 + } else { + Lambda_sum$ALI_Pt <- Lambda_sum$ALI50 + } + Lambda_sum + }) + +## Import all stochastic Lambda values +Lambda_dists <- + lapply(outflds, function(outfld) { + fln <- paste0(file.path(outdir, outfld), "/Lambda_stoch_all_long_", outfld, ".rds") + #print(outfld) + Lambda_all <- readRDS(fln) + Lambda_all + }) +names(Lambda_dists) <- outflds + +## Loop across all species +for (species in outflds) +{ + print(species) + Lambda_stoch_all = Lambda_dists[[species]] + Lambda_summary_sp = Lambda_summary[Lambda_summary$species == species, ] + + ALI_median = Lambda_summary_sp[Lambda_summary_sp$scenario == "ALI_thres", ]$median + null_median = Lambda_summary_sp[Lambda_summary_sp$scenario == "null", ]$median + + ## Check all levels of stat and scenario + Lambda_stoch_all %>% + group_by(scenario, stat) %>% + summarize(n = n()) + + if ("Collisions" %in% Lambda_stoch_all$stat) + { + ## keep only combined lambdas + Lambda_stoch_all$stat = gsub("Total", "mean", Lambda_stoch_all$stat) + Lambda_summary_sp$stat = gsub("Total", "mean", Lambda_summary_sp$stat) + } + + Lambda_stoch_all$scenario <- gsub(x = Lambda_stoch_all$scenario, pattern = "_", replacement = " ") + Lambda_summary_sp$scenario <- gsub(x = Lambda_summary_sp$scenario, pattern = "_", replacement = " ") + + ## + # :-> --- Make histograms of stochastic Lambdas ---- + gg.hist <- + ggplot(mapping = aes(x = Lambda, y = ..density..)) + + geom_histogram(data = subset(Lambda_stoch_all, scenario == "null", -scenario), + position = 'identity', bins = 100, fill = 'black') + + geom_histogram(data = subset(Lambda_stoch_all, scenario != 'null' & scenario != 'ALI thres' & stat %in% c("null", "mean")), + aes(fill = scenario), + position = "identity", bins = 100, alpha = 0.5, show.legend = FALSE) + + geom_vline(xintercept = ALI_median, size = 0.5, col = 'red') + + geom_vline(xintercept = null_median, size = 0.5, col = 'black') + + geom_vline(data = subset(Lambda_summary_sp, scenario != 'null' & scenario != 'ALI thres' & stat %in% c("null", "mean")), + aes(xintercept = median, colour = scenario), + show.legend = FALSE, alpha = 1.0, size = 0.5) + + labs(fill = "Scenario") + + xlab("Population growth rate") + + ylab("Density") + + coord_cartesian(xlim = c(0.8,1.2)) + + facet_wrap(scenario ~ .) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) + + hist.fln <- paste0("Histogram_Lambda_", species, ".png") + if(!dir.exists("histograms")) dir.create("histograms") + hist.path <- file.path("./histograms/", hist.fln) + + ggsave(filename = hist.path, + plot = gg.hist, + device = "png", + width = 6, + height = 4, + dpi = 300) +} + +## ----------------------------------------- ## +## --- Make a histogram of MB IBM output --- ## +## ----------------------------------------- ## + +## Read data +Lambda_stoch_long <- readRDS("IBMoutput/Morus_bassanus/Lambda_stoch_all_long_Morus_bassanus.rds") +Lambda_det <- readRDS("IBMoutput/Morus_bassanus/Lambda_det_scn_Morus_bassanus.rds") +Lambda_sum <- read.table("IBMoutput/Morus_bassanus/Lambda_summary_scns_Morus_bassanus.txt", header = TRUE) + +## ALI & NULL median +ALI_median <- Lambda_sum[Lambda_sum$scenario == "ALI_thres", ]$median +null_median = Lambda_sum[Lambda_sum$scenario == "null", ]$median + +## Check all levels of stat and scenario +Lambda_stoch_long %>% + group_by(scenario, stat) %>% + summarize(n = n()) %>% View() + +## ---- +Lambda_stoch_plot <- + Lambda_stoch_long %>% + filter(scenario != 'ALI_thres' & stat %in% c('null', 'm50_sz1', 'm50_sz8')) %>% + mutate(scenario, scenario = gsub("Rekenvariant III", "Rekenvariant_III", scenario)) %>% + separate(scenario, into = c('owf', 'avoidance'), sep = ' ', remove = FALSE) %>% + separate(stat, into = c('CI', 'stepsize'), sep = '_', ) + +## +Lambda_sum_plot <- + Lambda_sum %>% + filter(scenario != "ALI_thres" & scenario != "null") %>% + mutate(scenario, scenario = gsub("Rekenvariant III", "Rekenvariant_III", scenario)) %>% + separate(scenario, into = c('owf', 'avoidance'), sep = ' ', remove = FALSE) %>% + separate(stat, into = c('CI', 'stepsize'), sep = '_', ) %>% + filter(CI == "m50") +## + +## +gg.hist <- + ggplot(mapping = aes(x = Lambda, y = ..density..)) + + geom_histogram(data = Lambda_stoch_plot %>% filter(owf == "null") %>% select(Lambda), + position = 'identity', bins = 100, fill = 'black') + + geom_histogram(data = Lambda_stoch_plot %>% filter(owf != 'null'), + aes(fill = owf), + position = "identity", bins = 100, alpha = 0.5, show.legend = FALSE) + + geom_vline(xintercept = ALI_median, size = 0.5, col = 'red') + + geom_vline(xintercept = null_median, size = 0.5, col = 'black') + + geom_vline(data = Lambda_sum_plot %>% filter(stepsize != "sz8"), + aes(xintercept = median, colour = owf), + show.legend = FALSE, alpha = 1.0, size = 0.5) + + labs(fill = "Scenario") + + xlab("Population growth rate") + + ylab("Density") + + coord_cartesian(xlim = c(0.8,1.2)) + + facet_grid(stepsize ~ scenario, + labeller = labeller(stepsize = c('sz1' = 'max. travel length 1', 'sz8' = 'max. travel length 8'), + scenario = c('International REAL' = 'International &\np = 0.8', + 'International WC' = 'International &\np = 1.0', + 'Rekenvariant_III REAL' = 'Rekenvariant III &\np = 0.8', + 'Rekenvariant_III WC' = 'Rekenvariant III &\np = 1.0'))) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) +## +ggsave(filename = "histograms/MB_IBM_PGR_Hist.png", plot = gg.hist, width = 6.1, height = 4, units = 'in', dpi = 300) + + +## +gg.hist_sz1 <- + ggplot(mapping = aes(x = Lambda, y = ..density..)) + + geom_histogram(data = Lambda_stoch_plot %>% filter(owf == "null") %>% select(Lambda), + position = 'identity', bins = 100, fill = 'black') + + geom_histogram(data = Lambda_stoch_plot %>% filter(owf != 'null' & stepsize != "sz8"), + aes(fill = owf), + position = "identity", bins = 100, alpha = 0.5, show.legend = FALSE) + + geom_vline(xintercept = ALI_median, size = 0.5, col = 'red') + + geom_vline(xintercept = null_median, size = 0.5, col = 'black') + + geom_vline(data = Lambda_sum_plot %>% filter(stepsize != "sz8"), + aes(xintercept = median, colour = owf), + show.legend = FALSE, alpha = 1.0, size = 0.5) + + labs(fill = "Scenario") + + xlab("Population growth rate") + + ylab("Density") + + coord_cartesian(xlim = c(0.8,1.2)) + + facet_grid(owf ~ avoidance, labeller = labeller(owf = c('International' = 'International', + 'Rekenvariant_III' = 'Rekenvariant III'), + avoidance = c('REAL' = 'p = 0.8', + 'WC' = 'p = 1.0'))) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) +## +ggsave(filename = "histograms/MB_IBM_PGR_Hist_sz1.png", plot = gg.hist_sz1, width = 4, height = 4, units = 'in', dpi = 300) +## diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryTable.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryTable.R new file mode 100644 index 0000000000000000000000000000000000000000..65a728640c61cc693a500609f371c1505f37a5b1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/Make_SummaryTable.R @@ -0,0 +1,185 @@ +################################################################################################################ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +################################################################################################################ + +## This script reads rds files of all species with population output and create a summary table + +## Created by : Vincent Hin (vincent.hin@wur.nl) +## Last modified : 25 Aug. 2021 +## Last run : 23 Aug. 2021 + +## KEC4popmodels R-package is available at +## https://git.wur.nl/ecodyn/KEC4popmodels +## Installation instructions are described in the README.md +library(KEC4popmodels) + +## set the order of the scenarios +scns_order <- c('Basic_2030', 'Rekenvariant_I', 'Rekenvariant_II', 'Rekenvariant_III', 'International') + +## Set name of output folder +outdir <- "output" + +## List subfolder in output folder +outflds <- list.files(outdir) + +## Set the names of the variables to put in Lambda summary table +Lambda_vars <- 'ALI_Pt' + +## Import all Lambda Summary Tables +Lambda_summary <- + map_dfr(outflds, function(outfld) { + fln <- paste0(file.path(outdir, outfld), "/Lambda_summary_scns_", outfld, ".txt") + Lambda_sum <- read.table(fln, header = TRUE) + Lambda_sum$species <- outfld + if(outfld %in% c("Fratercula_arctica_2", "Fulmarus_glacialis_2")) { + Lambda_sum$ALI_Pt <- Lambda_sum$ALI10 + } else { + Lambda_sum$ALI_Pt <- Lambda_sum$ALI50 + } + Lambda_sum + }) + +## Import all Lambda distributions +Lambda_dists <- + lapply(outflds, function(outfld) { + fln <- paste0(file.path(outdir, outfld), "/Lambda_stoch_all_long_", outfld, ".rds") + #print(outfld) + Lambda_all <- readRDS(fln) + Lambda_all + }) +names(Lambda_dists) <- outflds + +## Import all Lambda deterministic values +Lambda_dets <- + lapply(outflds, function(outfld) { + fln <- paste0(file.path(outdir, outfld), "/Lambda_det_scn_", outfld, ".rds") + #print(outfld) + Lambda_all <- readRDS(fln) + Lambda_all + }) +names(Lambda_dets) <- outflds + +## +Lambda_table <- + Lambda_summary %>% + select(species, scenario, stat, all_of(Lambda_vars)) %>% + filter(!(scenario %in% c('ALI_thres', 'null'))) %>% + mutate(stat, stat = recode(stat, 'mean' = 'HabLoss')) %>% + pivot_wider(id_cols = scenario, names_from = c(species, stat), values_from = ALI_Pt) %>% + arrange(factor(scenario, levels = scns_order)) %>% + knitr::kable(format = "markdown", caption = "Summary table of population models for 7 species") +## +fln <- "tables/Lambda_Summary_Table_7Species.Rmd" +cat(Lambda_table, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## Read the file with casualty data for species without population models +Casualties_NoPopModel <- read.table("KEC4_HabLoss_Casualties_Scenarios_NoPopModel.txt", header = T) + +## Calculate mortality rates +Mortality_Rates_NoPopModel <- + ConvertMortalityData(input = Casualties_NoPopModel) %>% + select(species, scenario, stat, batch, M_mean, N_max, m_mean_max_year) + +Mortality_Table_NoPopModel <- + Mortality_Rates_NoPopModel %>% + select(-stat) %>% + mutate(M_mean = ceiling(M_mean)) %>% + mutate(N_max = ceiling(N_max)) %>% + knitr::kable(format = "markdown", caption = "Mortality table for species with no population model") + +## Output the table +fln <- "tables/Table_KEC4.0_Mortality_NoPopModel.Rmd" +cat(Mortality_Table_NoPopModel, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") + +## Read the file with casualty data for species with population models +Casualties_PopModels <- read.table("KEC4_HabLoss_Casualties_Scenarios.txt", header = T) + +## Exclude Northern gannet and sandwich tern +## Calculate mortality rates +Mortality_Rates_PopModel <- + ConvertMortalityData(input = Casualties_PopModels) %>% + filter(!species %in% c("Thalasseus sandvicensis", "Morus bassanus")) %>% + select(species, scenario, stat, batch, M_mean, N_max, m_mean_max_year) +## + +## Read the file with casualty data for species with collision data +Casualties_withCollisions <- read.table("KEC4_Total_Casualties_Scenarios.txt", header = T) + +## Calculate mortality rates +Mortality_Rates_withCollisions <- + ConvertMortalityData(input = Casualties_withCollisions) %>% + select(species, scenario, stat, batch, M_mean, N_max, m_mean_max_year) + +# Rbind with Mortality tables +Mortality_Table <- + bind_rows(Mortality_Rates_NoPopModel, Mortality_Rates_PopModel, Mortality_Rates_withCollisions) %>% + select(-c(M_mean, N_max)) %>% + mutate(stat, stat = recode(stat, 'mean' = 'HabLoss')) %>% + mutate(m_mean_max_year = round(m_mean_max_year, 5)) %>% + mutate(species, species = recode(species, "Alca_torda" = "Alca torda", "Fratercula_arctica_2" = "Fratercula arctica", "Fulmarus_glacialis_2" = "Fulmarus glacialis", "Gavia_stellata" = "Gavia stellata", "Morus_bassanus" = "Morus bassanus", "Thalasseus_sandvicensis" = "Thalasseus sandvicensis", "Uria_aalge" = "Uria aalge")) %>% + pivot_wider(id_cols = c(scenario, batch), values_from = m_mean_max_year, names_from = c(species, stat)) %>% + arrange(factor(scenario, levels = scns_order)) %>% + knitr::kable(format = "markdown", caption = "Mortality table for all 10 'habitat loss' species") +## + +## Output the table +fln <- "tables/Table_KEC4.0_Mortality_10species.Rmd" +cat(Mortality_Table, file = fln, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + + +## Make a Table of ALI violation for IBM output for Morus bassanus +Lambda_sum <- read.table("IBMoutput/Morus_bassanus/Lambda_summary_scns_Morus_bassanus.txt", header = TRUE) +ALI_Pt <- "ALI50" + +IBM_ALI_Table <- + Lambda_sum %>% + filter(!(scenario %in% c('null', 'ALI_thres'))) %>% + select(scenario, stat, !!sym(ALI_Pt)) %>% + separate(stat, into = c('CI', 'stepsize'), sep = '_') %>% + filter(CI == "m50") %>% + select(-CI) %>% + mutate(scenario, scenario = gsub("Rekenvariant III", "Rekenvariant_III", scenario)) %>% + separate(scenario, into = c('scenario', 'avoidance'), sep = ' ') %>% + pivot_wider(values_from = ALI50, names_from = avoidance) %>% + arrange(stepsize, desc(scenario)) %>% + mutate(scenario, scenario = gsub("_", " ", scenario)) %>% + mutate(stepsize, stepsize = gsub("sz", "", stepsize)) %>% + rename('Max. travel length' = 'stepsize', 'realistic' = 'REAL', 'worst case' = 'WC') %>% + knitr::kable(format = 'markdown') +## +fln <- "tables/Table_KEC4.0_ALI_Violation_MB_IBMoutput.Rmd" +cat(IBM_ALI_Table, file = fln, sep = "\n") +rmarkdown::render(input = fln, output_format = "word_document") +rmarkdown::render(input = fln, output_format = "pdf_document") +## + + + + + + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/NAMESPACE b/script_py_r/kec4_pyconversion-master/KEC4popmodels/NAMESPACE new file mode 100644 index 0000000000000000000000000000000000000000..097685ea238e6b05e9e9a831c0a8090231430616 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/NAMESPACE @@ -0,0 +1,34 @@ +# Generated by roxygen2: do not edit by hand + +export(AllParSequence) +export(AnalyseMatrix) +export(AnalyseSensitivity) +export(AnalyzeMatrix) +export(Boxplot_Lambda) +export(CalculateALI) +export(ConvertMortalityData) +export(DrawParameters) +export(EigenDecomp) +export(FundamentalMatrix) +export(GenerationTime) +export(Histogram_Lambda) +export(KECdist) +export(Lambda) +export(LambdaThreshold) +export(MakeGenMatrix) +export(MakeMatrix) +export(MakeParSequence) +export(MaxReal) +export(MortProbToSurv) +export(MortRateToSurv) +export(Plot_AllParSeq) +export(PopProject) +export(ReadMatrix) +export(Sensitivity) +export(StableStage) +export(SurvToMortProb) +export(SurvToMortRate) +export(UpdateSurvival) +export(write_yamlopts) +importFrom(Rcpp,sourceCpp) +useDynLib(KEC4popmodels, .registration = TRUE) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Analysis.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Analysis.R new file mode 100644 index 0000000000000000000000000000000000000000..c0f094d0b70c2ce2d707e9e0408d8b1ac1b3c517 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Analysis.R @@ -0,0 +1,745 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +#' Calculate Lambda for a range of parameter values for all parameters of a species +#' +#' @param file Name of the YAML configuration file with matrix specification +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for +#' +#' @param parseq Parameter sequence, applied to all parameters except those excluded +#' +#' @param exclude Vector with parameters names that will be excluded +#' +#' @param verbose (TRUE/FALSE). Message printing to console (defaults to TRUE) +#' +#' @details wrapper function to apply \code{\link{MakeParSequence}} to all parameter of a species +#' +#' @export +AllParSequence <- function(file = NULL, folder = NULL, parseq = seq(0.01,1.0,0.1), exclude = NULL, verbose = TRUE){ + + conf <- ReadMatrix(file = file, folder = folder, verbose = verbose) + pars <- c(conf$SummerPars, conf$WinterPars) + pars <- pars[!names(pars) %in% exclude] + pars_nms <- names(pars) + pars_descr <- sapply(pars, "[[", 'descr') + + lst <- + lapply(as.list(pars_nms), function(i) { + + parseqlst <- list(parseq) + names(parseqlst) <- i + + MakeParSequence(file = file, parseq = parseqlst, verbose = FALSE) %>% + purrr::map(Lambda) %>% + purrr::map_df(as.data.frame, .id = "par") %>% + rlang::set_names(nm = c("par", "Lambda")) %>% + tidyr::separate(par, into = c("parameter", "value"), sep = " = ") %>% + dplyr::mutate('value' = as.numeric(value)) + }) + names(lst) <- pars_nms + + ## Add parameter descriptions to output list + lst <- mapply(function(x,y) { x$parsdescr <- y; return(x) }, x = lst, y = pars_descr, SIMPLIFY = FALSE) + + return(lst) +} +## + +#' A function to extract mortality probability from abundance and casualty data +#' +#' @param input A data.frame with number of OWF casualties. +#' +#' @details Input data.frame should at least contain the columns: +#' \describe{ +#' \item{season}{integer of season} +#' \item{null}{number of casualties per season without OWF mortality} +#' \item{abundance}{abundance estimate per season} +#' \item{species}{name of the species} +#' } +#' In addition to columns with number of casualties per scenario +#' +#' @seealso \code{\link{DummyInput}} +#' +#' @export +ConvertMortalityData <- function(input = NULL) { + + tidyr::pivot_longer(data = input, + cols = -c(species, season, batch, abundance, stat), + names_to = "scenario", + values_to = "casualties") %>% + filter(!is.na(casualties)) %>% + group_by(batch, species) %>% + dplyr::mutate(m_max_period = casualties / max(abundance)) %>% + dplyr::group_by(scenario, species, stat) %>% + dplyr::summarize(species = unique(species), + batch = unique(batch), + M_sum = sum(casualties), + M_mean = mean(casualties), + N_mean = mean(abundance), + N_max = max(abundance), + m_mean_max_period = mean(m_max_period), + n = n(), + .groups = 'drop') %>% + dplyr::mutate(m_sum_max = M_sum / N_max) %>% + dplyr::mutate(m_mean_max = M_mean / N_max) %>% + dplyr::mutate(m_mean_mean = M_mean / N_mean) %>% + dplyr::mutate(m_sum_mean = M_sum / N_mean) %>% + dplyr::mutate(m_mean_max_year = (1 - (1-m_mean_max_period)^(n))) %>% + dplyr::arrange(species) %>% + dplyr::relocate(species) +} +## + + +#' Calculate Acceptable Level of Impact for a species +#' +#' @param file Name of the YAML configuration file with matrix specification +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for +#' +#' @param threshold Fraction of impacted population to test against. +#' +#' @param N Number of replicate matrices +#' +#' @param summerpars (optional) A nested list with parameters for the reproduction (summer) matrix +#' +#' @param winterpars (optional) A nested list with parameters for the survival (winter) matrix +#' +#' @param verbose (TRUE/FALSE). Message printing to console (defaults to TRUE) +#' +#' @export +CalculateALI <- function(file = NULL, folder = NULL, threshold = 0.7, N = 1e4, summerpars = NULL, winterpars = NULL, verbose = TRUE) { + + ## Create NULL matrices + Lambda_null <- Lambda(MakeMatrix(file = file, folder = folder, N = N, stochastic = TRUE, summerpars = summerpars, winterpars = winterpars, verbose = verbose)) + + ## Calculate median of Lambda_null + Lambda_null_median <- median(Lambda_null, na.rm = T) + + ## Calculate median stochastic generation time + GenTime_median <- median(GenerationTime(MakeGenMatrix(file = file, folder = folder, N = N, stochastic = TRUE, summerpars = summerpars, winterpars = winterpars, verbose = verbose))) + + ## Calculate deterministic generation time + GenTime_det <- GenerationTime(MakeGenMatrix(file = file, folder = folder, N = 1, stochastic = FALSE, summerpars = summerpars, winterpars = winterpars, verbose = verbose)) + + ## Calculate reference time + Tref <- pmax(3 * GenTime_det, 10) + + ## Calculate lambda values that leads to 1-threshold reduction in population size after Tref years + Lambda_threshold <- Lambda_null * threshold^(1/Tref) + + ## Calculate median of Lambda_threshold + ## the probability of violating this median lambda_threshold will be assessed + Lambda_threshold_median <- median(Lambda_threshold, na.rm=TRUE) + + ## Check abundances after Tref years + N_null_Tref <- Lambda_null_median^Tref + N_threshold_Tref <- Lambda_threshold_median^Tref + X_diff_Tref <- 1 - N_threshold_Tref/N_null_Tref + + # Recalculate X for 30 years + N_null_30 <- Lambda_null_median^30 + N_threshold_30 <- Lambda_threshold_median^30 + X_diff_30 <- 1 - N_threshold_30 / N_null_30 + + # Calculate false positive of ALI + # Fraction of null simulations below the median threshold + P_falsepos <- sum(Lambda_null < Lambda_threshold_median) / length(Lambda_null) + + # Define thresholds + T10 <- 0.1 + T33 <- 0.33 + T50 <- 0.5 + T66 <- 0.66 + + # Calculate Y for each threshold + Y10 <- -(P_falsepos)/(T10-1) + Y33 <- -(P_falsepos)/(T33-1) + Y50 <- -(P_falsepos)/(T50-1) + Y66 <- -(P_falsepos)/(T66-1) + + # Replace NULL value of folder to prevent error + if(is.null(folder)) folder <- 'NULL' + + # Return all calculations + ALI_df <- data.frame("file" = file, + "folder" = folder, + "threshold" = threshold, + "Nrep" = N, + "Lambda_null_median" = Lambda_null_median, + "Lambda_threshold_median" = Lambda_threshold_median, + "X_30yrs" = X_diff_30, + "X_Tref" = X_diff_Tref, + "Tref" = Tref, + "GenTime_det" = GenTime_det, + "GenTime_median" = GenTime_median, + "N_null_30" = N_null_30, + "N_threshold_30"= N_threshold_30, + "P_falsepos" = P_falsepos, + "Y0.1" = Y10, + "Y0.33" = Y33, + "Y0.5" = Y50, + "Y0.66" = Y66) + + return(list('ALI_table' = ALI_df, 'Lambda_null' = Lambda_null, 'Lambda_threshold' = Lambda_threshold)) +} + +#' Run an analysis based on a configuration file +#' +#' @param config The name of the YAML configuration file +#' +#' @param folder Folder of `config` +#' +#' @details AnalyseMatrix runs a population model based on a configuration file and a input data file. +#' +#' @examples +#' # Run AnalyseMatrix for all runfiles with: +#' \dontrun{ +#' runfolder <- "runfiles" +#' map(as.list(list.files(path = runfolder)), AnalyseMatrix, folder = runfolder) +#' } +#' +#' @export +AnalyseMatrix <- function(config = NULL, folder = ".") { + # 0 :-> Check input + if(is.null(config)) stop("Please specify a YAML configuration file") + + ## --- INITIALIZE ----- + + # 1 :-> Get full file name and read configuration file + config <- gsub(".yaml|.yml", replacement = "", config) + fln <- normalizePath(file.path(folder, paste0(config, ".yaml")), mustWork = TRUE) + if(!file.exists(fln)) stop(paste0('File ', fln, ' not found')) + cfg <- yaml::read_yaml(fln) + + # 2 :-> Set default settings + if(is.null(cfg$seed)){ + cfg$seed <- floor(runif(1) * 1E8) + set.seed(cfg$seed) + } + + cfg$nrep <- as.numeric(cfg$nrep) + + # If NULL, set IBM to FALSE + if(is.null(cfg$IBM)) cfg$IBM <- FALSE + + with(cfg, { + + # 3 :-> Create folder to store output + outpath <- file.path(".", outfolder, matfile) + if(file.exists(outpath) & overwrite) unlink(file.path(outpath), recursive = TRUE, force = TRUE) + if(!file.exists(outpath)) dir.create(outpath, recursive = TRUE) + + # 4 :-> Read input with casualties and refine to species + dat.fln <- normalizePath(file.path(datafolder, datafile), mustWork = TRUE) + dat <- read.table(file = dat.fln, header = TRUE) + if(!species %in% dat$species) stop(paste0("Species ", species, " not found in input data")) + dat <- dat[dat$species == species,] + + # 5 :-> Convert number of casualties to population mortality estimates (if IBM ==) + if(!IBM) { + ## Create data.frame with annual mortality rates + dat.annual <- ConvertMortalityData(dat) + + if(save_objects){ + saveRDS(object = dat, file = file.path(outpath, paste0("Casualties_scenario_", matfile, ".rds"))) + saveRDS(object = dat.annual, file = file.path(outpath, paste0("Mortality_scenario_", matfile, ".rds"))) + } + } else { + dat.annual <- dat + if(save_objects){ + saveRDS(object = dat.annual, file = file.path(outpath, paste0("Mortality_scenario_", matfile, ".rds"))) + } + } + + ## --- ANALYSIS ----- + + # 6 :-> Sensitivity analysis + if(run_allparseq){ + parseq <- AllParSequence(file = matfile, parseq = seq(from = parseq$from, to = parseq$to, by = parseq$by), exclude = parseq$exclude) + } + + if(run_sensitivity & run_allparseq){ + parseq <- map(parseq, ~( mutate(., Sensitivity = Sensitivity(., file = matfile)))) + } + + if(run_allparseq & save_objects){ + saveRDS(parseq, file = file.path(outpath, paste0("AllParSequence_", matfile, ".rds"))) + } + + # 7 :-> Calculate ALI + ALI <- CalculateALI(file = matfile, threshold = ALI_threshold, N = nrep, verbose = FALSE) + + # 8 :-> Get default parameters. + # - conf : summer and winter projection matrices, parameter and stage names + # - AllPars : list of all model parameters (summer and winter projection matrices) + # - Spar : names of survival parameters + # - parvalues : list of survival parameters + # - Sparidx : index of Spar for each life stage + # - OWFvuln : relative vulnerability of each life stage (0-1) to OWF mortality. + conf <- ReadMatrix(file = matfile) + AllPars <- c(conf$SummerPars, conf$WinterPars) + parvalues <- AllPars[names(AllPars) %in% Spar] + if(length(parvalues) == 0) stop('Spar value not found') + + ## Convert to numeric + Sparidx <- as.integer(Sparidx) + OWFvuln <- as.numeric(OWFvuln) + + ## Check OWFvulnerability and add to parvalues list + if(length(Sparidx) != conf$nstages) stop('Length Sparidx does not equal number of life stages') + if(length(OWFvuln) != conf$nstages) stop('Length OWFvuln does not equal number of life stages') + if(length(unique(Sparidx)) != length(Spar)) stop('Unique values of Sparidx does not equal length of Spar') + + # 9 :-> Calculate mean stable stage distribution + StableDist <- rowMeans( + cbind(StableStage(A = MakeMatrix(file = matfile, verbose = FALSE)[[1]]), + StableStage(A = MakeMatrix(file = matfile, verbose = FALSE, postbreeding = TRUE)[[1]]))) + + # 10 :-> Scale stage-specific mortalities + + ## Create data.frame + Mort_Scaling <- bind_cols('LifeStage' = conf$stages, + 'StableDist_mean' = StableDist, + 'Sparidx' = Sparidx, + 'OWFvuln' = OWFvuln / max(OWFvuln)) + + ## Scale mortality or apply to specific life stages + if(scale_mortality){ + Mort_Scaling$OWFscalar <- Mort_Scaling$OWFvuln / sum(Mort_Scaling$StableDist_mean * Mort_Scaling$OWFvuln) + } else { + Mort_Scaling$OWFscalar <- Mort_Scaling$OWFvuln + } + + ## Aggregate mortality per survival parameter weighted by stable distribution and OWF vulnerability + Mort_Scaling <- + Mort_Scaling %>% + group_by(Sparidx) %>% + summarize(LifeStage = paste0(LifeStage, collapse = ","), + StableDist = sum(StableDist_mean), + OWFvuln = paste0(round(OWFvuln,2), collapse = ","), + OWFscalar = weighted.mean(OWFscalar, StableDist_mean)) %>% + mutate('Spar' = Spar) %>% + relocate(Spar) + ## + + ## Add OWFscalar to list of survival values + parvalues <- mapply(function(x, y) { x[['OWFscalar']] = y; x}, x = parvalues, y = Mort_Scaling$OWFscalar, SIMPLIFY = FALSE) + ## + + # 11 :-> Convert OWF mortality to survival + # scns.nms : names of all OWF scenarios, including 'null' scenario + # scns.stat : estimator of OWF mortality, including 'null' scenario + # mortcalc : the mortality calculate of choice (column name of ConverMortality output) + # mort.seq : vector with mortality rate per scenario / stat combination + # morttype : the type of mortality: "finite" of "rate" + # surv.seq : list with survival values for each survival parameter + # mort.scaled: list with scaled mortality values for each survival parameter + scns.nms <- c("null", dat.annual$scenario) + scns.stat <- c("null", dat.annual$stat) + mort.seq <- c(0, dat.annual[[mortcalc]]) + if(morttype == "finite"){ + surv.seq <- lapply(1:length(parvalues), function(i) parvalues[[i]]$mean * MortProbToSurv(mort.seq * parvalues[[i]]$OWFscalar)) + } else if (morttype == "rate") { + surv.seq <- lapply(1:length(parvalues), function(i) MortRateToSurv(SurvToMortRate(parvalues[[i]]$mean) + mort.seq * parvalues[[i]]$OWFscalar)) + } else { + stop("Please specifiy configuration option `morttype` as `finite` or `rate`") + } + names(surv.seq) <- Spar + + mort.scaled <- lapply(1:length(parvalues), function(i) mort.seq * parvalues[[i]]$OWFscalar) + names(mort.scaled) <- Spar + + ## Create data.frame with mortality scenarios + MortSurv_scns <- data.frame(scns.nms, scns.stat, mort.seq, as.data.frame(mort.scaled), as.data.frame(surv.seq), check.names = FALSE) + names(MortSurv_scns) <- c("scenario", "stat", "mort", paste0("mort.", names(mort.scaled)), paste0("survival.", names(surv.seq))) + + # 12 :-> --- DETERMINISTIC PGR ~ SCENARIOS ---- + APM_det_scn <- KEC4popmodels::MakeParSequence(file = matfile, N = 1, stochastic = FALSE, parseq = surv.seq, verbose = FALSE) + scns.statnames <- paste0(scns.nms, "_", scns.stat) + scns.statnames[1] <- "null" + names(APM_det_scn) <- scns.statnames + Lambda_det_scn <- purrr::map(APM_det_scn, KEC4popmodels::Lambda) + + if(save_objects){ + saveRDS(APM_det_scn, file = file.path(outpath, paste0("APM_det_scn_", matfile, ".rds"))) + saveRDS(Lambda_det_scn, file = file.path(outpath, paste0("Lambda_det_scn_", matfile, ".rds"))) + } + + # 13 :-> --- STOCHASTIC PGR ~ SCENARIO ---- + APM_stoch_scn <- KEC4popmodels::MakeParSequence(file = matfile, N = nrep, stochastic = TRUE, parseq = surv.seq, verbose = FALSE) + names(APM_stoch_scn) <- scns.statnames + Lambda_stoch_scn <- purrr::map(APM_stoch_scn, KEC4popmodels::Lambda) + Lambda_stoch_all <- c(Lambda_stoch_scn, list('ALI_thres' = ALI$Lambda_threshold)) + + + ## Convert Lambda_stoch to long data.frame + Lambda_stoch_all.longdf <- + map(Lambda_stoch_all, as.data.frame) %>% + map(set_names, nm = "Lambda") %>% + map2(., c(scns.nms, "ALI_thres"), ~ mutate(.x, "scenario" = .y)) %>% + map2(., .y = c(scns.stat, "null"), ~ mutate(.x, "stat" = .y)) %>% + bind_rows() %>% + relocate(scenario, stat) + ## + + ## Save to rds files + if(save_objects){ + saveRDS(APM_stoch_scn, file = file.path(outpath, paste0("APM_stoch_scn_", matfile, ".rds"))) + saveRDS(Lambda_stoch_all, file = file.path(outpath, paste0("Lambda_stoch_all_", matfile, ".rds"))) + saveRDS(Lambda_stoch_all.longdf, file = file.path(outpath, paste0("Lambda_stoch_all_long_", matfile, ".rds"))) + } + + # 14 :-> Summarize stochastic PGR and calculate ALI violation + Lambda_summary <- + Lambda_stoch_all.longdf %>% + group_by(scenario, stat) %>% + summarise(n = n(), + mean = mean(Lambda, na.rm = TRUE), + sd = sd(Lambda, na.rm = TRUE), + median = median(Lambda, na.rm = TRUE), + min = min(Lambda, na.rm = TRUE), + max = max(Lambda, na.rm = TRUE), + q05 = quantile(Lambda, 0.05, na.rm = TRUE), + q95 = quantile(Lambda, 0.95, na.rm = TRUE), + NAs = sum(is.na(Lambda)), + P_impact = sum(Lambda < ALI$ALI_table$Lambda_threshold_median, na.rm = TRUE) / n(), + .groups = 'drop') + + ## Extract false positive value from Lambda_summary df. + P_falsepos <- Lambda_summary %>% filter(scenario == "null") %>% pull(P_impact) + + ## Calculate Odds_impact. + Lambda_summary$Odds_impact <- pmax((Lambda_summary$P_impact - P_falsepos) / P_falsepos, 0.0) + Lambda_summary$P_causality <- Lambda_summary$Odds_impact / (1 + Lambda_summary$Odds_impact) + Lambda_summary$ALI10 <- Lambda_summary$P_impact > ALI$ALI_table$Y0.1 + Lambda_summary$ALI33 <- Lambda_summary$P_impact > ALI$ALI_table$Y0.33 + Lambda_summary$ALI50 <- Lambda_summary$P_impact > ALI$ALI_table$Y0.5 + Lambda_summary$ALI66 <- Lambda_summary$P_impact > ALI$ALI_table$Y0.66 + + ## Check for NAs + if(sum(Lambda_summary$NAs) > 0) warning(paste0(sum(Lambda_summary$NAs), " NAs produced during creation of stochastic matrices of ", matfile)) + + ## Add deterministic Lambda to summary + Lambda_det_scn.df <- data.frame(scenario = scns.nms, stat = scns.stat, det = unlist(Lambda_det_scn), row.names = NULL) + Lambda_summary <- merge(Lambda_det_scn.df, Lambda_summary, all = TRUE) + ## + + ## ----- OUTPUT ----- + + # 15 :-> Plot AllParSequence & Sensitivity output + if(run_allparseq & plot_parseq) { + + gg.parseq <- Plot_AllParSeq(parseq, yvar = "Lambda", species = species, pnts = plot_parseq_pnts) + parseq.fln <- paste0("AllParSequence_", matfile, ".", plotdev) + parseq.path <- file.path(outpath, parseq.fln) + + ggsave(filename = parseq.path, + plot = gg.parseq, + device = plotdev, + width = 6, + height = 5, + dpi = 300) + } + if(run_allparseq & run_sensitivity & plot_sensitivity) { + + gg.sens <- Plot_AllParSeq(parseq, yvar = "Sensitivity", species = species, pnts = plot_parseq_pnts) + parsens.fln <- paste0("Sensitivity_", matfile, ".", plotdev) + parsens.path <- file.path(outpath, parsens.fln) + + ggsave(filename = parsens.path, + plot = gg.sens, + device = plotdev, + width = 6, + height = 5, + dpi = 300) + } + + # 16 :-> --- Boxplot of stochastic Lambda ---- + if(make_boxplot) { + + gg.box <- Boxplot_Lambda(xvar = 'scenario', yvar = "Lambda", + subset(Lambda_stoch_all.longdf, stat %in% c("null", plot_stat)), + subset(Lambda_det_scn.df, stat %in% c("null", plot_stat)), + species = species, outlier.size = 0.2, size = 0.2, show.legend = FALSE) + gg.box <- gg.box + ggtitle(paste0(species, ": ", plot_stat)) + box.fln <- paste0("Boxplot_Lambda_", matfile, ".", plotdev) + box.path <- file.path(outpath, box.fln) + + ggsave(filename = box.path, + plot = gg.box, + device = plotdev, + width = 5, + height = 3, + dpi = 300) + } + + # 17 :-> --- Make histogram of stochastic Lambda ---- + if(make_histogram){ + + gg.hist <- + ggplot(mapping = aes(x = Lambda, y = ..density..)) + + geom_histogram(data = subset(Lambda_stoch_all.longdf, scenario == "null", -scenario), + position = 'identity', bins = 100, fill = 'black') + + geom_histogram(data = subset(Lambda_stoch_all.longdf, scenario != "null" & stat %in% c("null", plot_stat)), + aes(fill = scenario), + position = "identity", bins = 100, alpha = 0.5, show.legend = FALSE) + + geom_vline(xintercept = ALI$ALI_table$Lambda_threshold_median, size = 0.5, col = 'red') + + geom_vline(xintercept = ALI$ALI_table$Lambda_null_median, size = 0.5, col = 'black') + + geom_vline(data = subset(Lambda_summary, scenario != 'null' & stat %in% c("null", plot_stat)), + aes(xintercept = median, colour = scenario), + show.legend = FALSE, alpha = 1.0, size = 0.5) + + labs(fill = "Scenario") + + xlab("Population growth rate") + + ylab("Density") + + coord_cartesian(xlim = c(0.8,1.2)) + + facet_wrap(scenario ~ .) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) + + hist.fln <- paste0("Histogram_Lambda_", matfile, ".", plotdev) + hist.path <- file.path(outpath, hist.fln) + + ggsave(filename = hist.path, + plot = gg.hist, + device = plotdev, + width = 6, + height = 4, + dpi = 300) + } + + ## 18 :-> --- Write to csv ---- + if(write_table) { + write.table(x = Lambda_summary, file = file.path(outpath, paste0("Lambda_summary_scns_", matfile, ".txt")), row.names = FALSE) + write.table(x = dat.annual, file = file.path(outpath, paste0("Mortality_scenario_", matfile, ".txt")), row.names = FALSE) + if(!IBM) write.table(x = dat, file = file.path(outpath, paste0("Casualties_scenario_", matfile, ".txt")), row.names = FALSE) + } + + ## ----- REPORT ----- + + ## 19 :-> ---- Write a pdf / docx report + if(write_report){ + ## Write a Rmd report file + repfln <- file.path(outpath, paste0(matfile, "_report.Rmd")) + + ## :-> --- Header ---- + cat("---\n", file = repfln) + cat("title: KEC4popmodels report for ", paste0("*",species,"*"), "\n", file = repfln, append = T) + cat("date:", format(Sys.time(), "%a %b %d %H:%M:%S %Y"), "\n", file = repfln, append = T) + cat("author:", author, " (", affiliation, ")\n", file = repfln, append = T) + if(report_opts$landscape) cat("classoption: landscape\n", file = repfln, append = T) + cat("papersize:", report_opts$papersize, "\n", file = repfln, append = T) + cat("fontsize:", report_opts$fontsize, "\n", file = repfln, append = T) + cat("header-includes:\n - \\usepackage{caption}\n", file = repfln, append = T) + cat("---\n\n", file = repfln, append = T) + + ## Print configuration from runfile + cat("# Configuration\n\n ", file = repfln, append = T) + cat(" - configuration file \t\t:\t\t\t", config, "\n", file = repfln, append = T) + cat(" - configuration folder \t\t:\t\t\t", folder, "\n", file = repfln, append = T) + cat(paste0(" - ", names(cfg), "\t\t:\t\t\t", cfg, "\n"), file = repfln, append = T) + + ## :-> --- Casualties per scenario / season ---- + if(!IBM) { + cat("\n# Casualty data \n\n", file = repfln, append = T) + + ## Preparation data.frame + dat$species <- NULL + row.names(dat) <- NULL + names(dat)[1] <- "period" + + Casualty_table <- + dat %>% + mutate_if(is.numeric, ceiling) %>% + mutate(period, period = recode(period, '1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul")) %>% + knitr::kable(format = "markdown", + caption = "Casualties and abundance per batch, statistic and bimonthly period for each scenario") + cat(Casualty_table, sep="\n", file = repfln, append = T) + cat("\n", file = repfln, append = T) + } + + ## :-> --- Mortality and Survival ---- + cat("\n# Mortality and Survival data\n\n", file = repfln, append = T) + + if(IBM) { + MortCalc_caption <- "Mortality calculations. Estimated of annual mortality were derived from an Individual-Based Simulation model" + } else { + MortCalc_caption <- "Mortality calculations. Estimates of annual mortality calculated from number of casualties and abundance per bimonthly period. $M$ = mortality in number of casualties (individuals, rounded to highest integer), $N$ = abundance in number of individuals (rounded to highest integer), $m$ = annual mortality, $n$ = number of periods. Subscripts indicate type of operation ($mean$, $max$, or $sum$), *i.e.* $M_{mean}$ is the mean number of casualties averaged over all periods, $m_{mean,max}$ is the mortality calculated as the mean number of causalities ($M_{mean}$) divided by the max abundance ($N_{max}$)" + } + + MortCalc_table <- + dat.annual %>% + select(-species) %>% + mutate(across(matches("M_|N_", ignore.case = F), ceiling)) %>% + rename_with(~ gsub('^M_sum$', '$M_{sum}$', .x)) %>% + rename_with(~ gsub('^M_mean$', '$M_{mean}$', .x)) %>% + rename_with(~ gsub('^N_mean$', '$N_{mean}$', .x)) %>% + rename_with(~ gsub('^N_max$', '$N_{max}$', .x)) %>% + rename_with(~ gsub('^m_mean_max_period$', '$m_{mean,max,period}$', .x)) %>% + rename_with(~ gsub('^n$', '$n$', .x)) %>% + rename_with(~ gsub('^m_sum_max$', '$m_{sum,max}$', .x)) %>% + rename_with(~ gsub('^m_mean_max$', '$m_{mean,max}$', .x)) %>% + rename_with(~ gsub('^m_mean_mean$', '$m_{mean,mean}$', .x)) %>% + rename_with(~ gsub('^m_sum_mean$', '$m_{sum,mean}$', .x)) %>% + rename_with(~ gsub('^m_mean_max_year$', '$m_{mean,max,year}$', .x)) %>% + knitr::kable(format = "markdown", caption = MortCalc_caption) + cat(MortCalc_table, sep="\n", file = repfln, append = TRUE) + cat("\n", file = repfln, append = T) + + MortScale_table <- + Mort_Scaling %>% + mutate_if(is.numeric, ~round(., 5)) %>% + knitr::kable(format = "markdown", caption = "Scaling of mortality rates. Column `OWFvuln` represents vulnerability of each life stage to OWF induced mortality. `StableDist` is the stable proportion of each life stage. `OWFscalar` is the life stage specific multiplier that is applied to the additional OWF induced mortality.") + cat(MortScale_table, sep="\n", file = repfln, append = TRUE) + cat("\n", file = repfln, append = T) + + MortSurv_table <- + MortSurv_scns %>% + mutate_if(is.numeric, ~round(., 5)) %>% + knitr::kable(format = "markdown", caption = "Annual mortality and survival rates. The adopted mortality calculation is set by configuration option `mort_calc` and shown as `OWF annual mortality` in this table. `OWF annual mortality` is rescaled separately for each survival parameter using `OWFscalar` (Table 3). Configuration option `Sparidx` links each life stage to a survival parameter. Rescaled OWF annual mortalities are converted to annual survival rates. Depending on the configuration option `morttype` the scaled mortalities are treated as finite probabilities, or instantaneous rates.") + cat(MortSurv_table, sep="\n", file = repfln, append = T) + cat("\n", file = repfln, append = T) + + ## :-> --- ALI calculations ---- + cat("\n# ALI threshold calculation\n", file = repfln, append = T) + ALI_df <- t(data.frame( + 'ALI threshold' = ALI$ALI_table$threshold, + 'Generation time (deterministic)' = ALI$ALI_table$GenTime_det, + 'Generation time (median stochastic)' = ALI$ALI_table$GenTime_median, + 'Reference time ($T_{ref}$)' = ALI$ALI_table$Tref, + 'Lambda null (median stochastic)' = ALI$ALI_table$Lambda_null_median, + 'Lambda threshold (median stochastic)' = ALI$ALI_table$Lambda_threshold_median, + 'Abundance null 30y ' = ALI$ALI_table$N_null_30, + 'Abundance threshold 30y ' = ALI$ALI_table$N_threshold_30, + 'X threshold 30y' = ALI$ALI_table$X_30yrs, + 'X threshold Tref' = ALI$ALI_table$X_Tref, + 'P violation null' = ALI$ALI_table$P_falsepos, + 'Y 10%' = ALI$ALI_table$Y0.1, + 'Y 33%' = ALI$ALI_table$Y0.33, + 'Y 50%' = ALI$ALI_table$Y0.5, + 'Y 66%' = ALI$ALI_table$Y0.66, check.names = FALSE)) + ALI_table <- knitr::kable(ALI_df, format = "markdown", col.names = "Value", caption = "Calculation of the Acceptable Level of Impact (ALI) thresholds. The ALI threshold is set by the configuration option `ALI_threshold` and represents the threshold population abundance relative to an unimpacted population at the reference time period $T_{ref}$. $T_{ref}$ is either 10 years or 3 times the deterministic generation time, which ever is longest. `X threshold` is the relative decrease in population abundance at a specific time period (`X treshold Tref = 1 - ALI threshold`). `X` is recalculated for a time period of 30 years (`X threshold 30y`) from the unimpacted population abundance (`Abundance null 30y`) and the threshold population abundance (`Abundance threshold 30y`) after 30 years. `P violation null` represents the proportion of stochastic simulations of unimpacted population abundance that exceed the X threshold. The `Y` values are the threshold values for the chance that the impacted population abundance exceeds the X threshold for a given level of casuality. Causality levels are 10%, 33%, 50% and 66%.") + cat(ALI_table, sep = "\n", file = repfln, append = T) + cat("\n", file = repfln, append = T) + + ## :-> --- Lambda ~ scenarios ---- + cat("\n# Population growth rates\n\n", file = repfln, append = T) + Lambda_table <- + Lambda_summary %>% + mutate_if(is.numeric, ~round(., 4)) %>% + rename("P impact" = "P_impact") %>% + rename("Odds impact" = "Odds_impact") %>% + rename("P causality" = "P_causality") %>% + knitr::kable(format = "markdown", caption = "Summary statistics of population growth rates from stochastic annual projection matrices per scenario and their ALI violation for each level of casuality. `P_impact` represents the proportion of stochastic simulations of impacted population abundance that exceed the X threshold. Because the X threshold is defined as the median of threshold population growth rates `P_impact` is always 0.5 for the ALI threshold. In addition, `P_impact` for the null scenario should equal `P violation null` in the ALI table, but small difference may exists because of the stochastic nature of the calculations. `Odds_impact` and `P_causality` are the odds and probability that a violation of the X threshold results from an impact. Both variables are restricted to positive values. The last four columns show whether `P_causality` violates any of the four threshold causality levels.") + cat(Lambda_table, sep="\n", file = repfln, append = T) + cat("\n", file = repfln, append = T) + + ## :-> --- Summary table ---- + cat("\n# Summary table\n\n", file = repfln, append = T) + Y <- paste0('ALI', ALI_causality) + if(IBM){ + Summary_df <- merge(dat.annual %>% select(scenario, stat, batch), + MortSurv_scns %>% select(scenario, stat, mort, matches('survival')), by = c('scenario','stat'), all = TRUE) + Summary_df <- merge(Summary_df, Lambda_summary %>% select(scenario, stat, median, q05, q95, P_causality, !!sym(Y)), by = c('scenario','stat'), all = TRUE) + + Summary_table <- + Summary_df %>% + mutate(across((mort:P_causality), round, digits = 3)) %>% + rename('P causality' = 'P_causality', 'Lambda median' = 'median') %>% + set_names(nm = sub(pattern = "survival.", replacement = "survival ", names(.))) %>% + knitr::kable(format = "markdown", caption = "Summary") + } else { + Summary_df <- merge(dat.annual %>% select(scenario, stat, batch, M_mean, N_max), + MortSurv_scns %>% select(scenario, stat, mort, matches('survival')), by = c('scenario','stat'), all = TRUE) + Summary_df <- merge(Summary_df, Lambda_summary %>% select(scenario, stat, median, q05, q95, P_causality, !!sym(Y)), by = c('scenario','stat'), all = TRUE) + + Summary_table <- + Summary_df %>% + mutate(across(c(M_mean, N_max), ceiling)) %>% + mutate(across((mort:P_causality), round, digits = 3)) %>% + rename('P causality' = 'P_causality', 'Lambda median' = 'median', 'Mean casualties' = 'M_mean', 'Max abundance' = 'N_max') %>% + set_names(nm = sub(pattern = "survival.", replacement = "survival ", names(.))) %>% + knitr::kable(format = "markdown", caption = "Summary") + } + + cat(Summary_table, sep="\n", file = repfln, append = T) + cat("\\newpage", file = repfln, append = T) + ## + + ## :-> --- Histogram ---- + cat("\n# Figures\n", file = repfln, append = T) + ## Histogram + if(make_histogram) { + cat("```{r hist, fig.align = 'center', out.width = '80%', fig.cap = 'Histrogram of population growth rate. Within each panel the distribution of the unimpacted population growth rate (gray bars) is compared with the distribution of the impacted population growth rate (coloured bars). Vertical lines indicate median population growth rates for unimpacted (black) and impacted (coloured) populations and the ALI threshold population growth rate (red).', echo = FALSE}\n", file = repfln, append = T) + cat("knitr::include_graphics(path = hist.fln)\n", file = repfln, append = T) + cat("```\n", file = repfln, append = T) + } + + ## :-> --- Boxplot ---- + if(make_boxplot) { + cat("```{r boxplot, fig.align = 'center', out.width = '80%', fig.cap = 'Boxplot of population growth rate. Red triangles indicate mean population growth rates.', echo = FALSE}\n", file = repfln, append = T) + cat("knitr::include_graphics(path = box.fln)\n", file = repfln, append = T) + cat("```\n", file = repfln, append = T) + } + cat("\\newpage", file = repfln, append = T) + + ## :-> --- PGR ~ Parameter ---- + if(run_allparseq & plot_parseq) { + cat("\n# Parameter effect on population growth rate\n", file = repfln, append = T) + cat("```{r PGR_par, fig.align = 'center', out.width = '60%', fig.cap = 'Parameter effects on population growth rate', echo = FALSE}\n", file = repfln, append = T) + cat("knitr::include_graphics(path = parseq.fln)\n", file = repfln, append = T) + cat("```\n", file = repfln, append = T) + } + + ## :-> --- Sensitivity ~ Parameters ---- + if(run_allparseq & run_sensitivity & plot_sensitivity) { + cat("\n# Parameter sensitivity\n", file = repfln, append = T) + cat("```{r SENS_par, fig.align = 'center', fig.height = 2, out.width = '60%', fig.cap = 'Sensitivity of population growth rate', echo = FALSE}\n", file = repfln, append = T) + cat("knitr::include_graphics(path = parsens.fln)\n", file = repfln, append = T) + cat("```\n", file = repfln, append = T) + } + + ## :-> --- Render ---- + if(report_opts$pdf) rmarkdown::render(repfln, output_format = "pdf_document", clean = TRUE) + if(report_opts$docx) rmarkdown::render(repfln, output_format = "word_document", clean = TRUE) + } + }) +} + + +#' Run an analysis based on a configuration file +#' +#' @param config The name of the YAML configuration file +#' +#' @param folder Folder of `config` +#' +#' @examples +#' # Run AnalyzeMatrix for all runfiles with: +#' \dontrun{ +#' runfolder <- "runfiles" +#' map(as.list(list.files(path = runfolder)), AnalyzeMatrix, folder = runfolder) +#' } +#' +#' @details +#' Synonym of \code{\link{AnalyseMatrix}} +#' +#' @export +AnalyzeMatrix <- AnalyseMatrix diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/KEC4popmodels-package.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/KEC4popmodels-package.R new file mode 100644 index 0000000000000000000000000000000000000000..c7b12d8be455ee7918014bc10345f4d870fd0f55 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/KEC4popmodels-package.R @@ -0,0 +1,57 @@ +## usethis namespace: start +#' KEC4popmodels +#' +#' Matrix population models to calculate impact of habitat loss from offshore wind parks on selected seabird populations +#' +#' @section KEC4popmodels functions: +#' \describe{ +#' \item{\code{\link{AllParSequence}}}{} +#' \item{\code{\link{AnalyseMatrix}}}{} +#' \item{\code{\link{AnalyzeMatrix}}}{} +#' \item{\code{\link{Boxplot_Lambda}}}{} +#' \item{\code{\link{CalculateALI}}}{} +#' \item{\code{\link{ConvertMortalityData}}}{} +#' \item{\code{\link{DrawParameters}}}{} +#' \item{\code{\link{EigenDecomp}}}{Calculate eigenvalues and left and right eigenvectors for a square matrix A} +#' \item{\code{\link{FundamentalMatrix}}}{} +#' \item{\code{\link{GenerationTime}}}{Calculate the generation time based on the fertility and transition matrices} +#' \item{\code{\link{Histogram_Lambda}}}{} +#' \item{\code{\link{KECdist}}}{} +#' \item{\code{\link{Lambda}}}{Calculate the asymptotic population growth rate (Lambda) from a population projection matrix} +#' \item{\code{\link{LambdaThreshold}}}{} +#' \item{\code{\link{MakeGenMatrix}}}{Create the fertility, transition, fundamental and next generation matrix from a YAML configuration file} +#' \item{\code{\link{MakeMatrix}}}{Create the annual projection matrix from a YAML configuration file} +#' \item{\code{\link{MakeParSequence}}}{Create a list of matrices for a sequence of parameter values} +#' \item{\code{\link{MaxReal}}}{Return the real part of the largest eigenvalue} +#' \item{\code{\link{MortProbToSurv}}}{} +#' \item{\code{\link{MortRateToSurv}}}{} +#' \item{\code{\link{Plot_AllParSeq}}}{} +#' \item{\code{\link{PopProject}}}{Calculate population projection} +#' \item{\code{\link{ReadMatrix}}}{Read a YAML file with matrix specifications} +#' \item{\code{\link{Sensitivity}}}{} +#' \item{\code{\link{StableStage}}}{Calculate the stable population distribution from a population projection matrix} +#' \item{\code{\link{SurvToMortProb}}}{} +#' \item{\code{\link{SurvToMortRate}}}{} +#' \item{\code{\link{UpdateSurvival}}}{} +#' \item{\code{\link{write_yamlopts}}}{} +#' } +#' +#' @section KEC4popmodels seabird species: +#' Matrices and parameters for the following seabird species are included in the KEC4popmodels package +#' \describe{ +#' \item{Morus_bassanus}{Matrix for the Northern Gannet (\emph{Morus bassanus})} +#' \item{Alca_torda}{Matrix for the the Razorbill (\emph{Alca torda})} +#' \item{Gavia_stellata}{Matrix for the red-throated diver (\emph{Gavia stellata})} +#' \item{Thalasseus_sandvicensis}{Matrix for the sandwich tern (\emph{Thalasseus sandvicensis})} +#' \item{Uria_aalge}{Matrix for the common guillemot (\emph{Uria aalge})} +#' } +#' +#' @references +#' Van Kooten et al. (2019). The consequences of seabird habitat loss from offshore wind turbines: displacement and population level effects in 5 selected species. Wageningen University & Research report C088/18. \url{https://edepot.wur.nl/465541} +#' +#' @author Vincent Hin (Wageningen Marine Research) +#' @name KEC4popmodels +#' @useDynLib KEC4popmodels, .registration = TRUE +#' @importFrom Rcpp sourceCpp +## usethis namespace: end +NULL diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Lambda.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Lambda.R new file mode 100644 index 0000000000000000000000000000000000000000..e112e1ba64dfe2c811a9ee1ad21d132aa6ac4515 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Lambda.R @@ -0,0 +1,174 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +#' Calculate the asymptotic population growth rate (Lambda) from a population projection matrix +#' +#' @param A The population projection matrix, or a list with population projection matrices +#' +#' @param GenMat A nested listed generated by MakeGenMatrix containing list elements `FertilityMatrix`, `TransitionMatrix`, `FundamentalMatrix` and `NextGenMatrix`. +#' +#' @return A numeric vector with the real part of the largest eigenvalue of the annual projection matrix. +#' +#' @details In case `GenMat` is supplied, the annual projection matrix is calculated from the `TransitionMatrix` and the `FertilityMatrix`. Wrapper function that uses \code{\link{EigenDecomp}} and \code{\link{MaxReal}} +#' +#' @export +Lambda <- function(A = NULL, GenMat = NULL) { + + if(is.null(A) & !is.null(GenMat)) A <- mapply(`+`, GenMat$TransitionMatrix, GenMat$FertilityMatrix, SIMPLIFY = FALSE) + + # Handle according to object type + if(is.matrix(A)){ + + if(any(is.na(A))) return(NA) + + return(MaxReal(EigenDecomp(A)$Eigenvalues)) + + } else if (is.list(A)){ + + sapply(A, function(x){ + + if(any(is.na(x))){ + return(NA) + } else { + return(MaxReal(EigenDecomp(x)$Eigenvalues)) + } + }) + } else { + stop('Unable to handle ', class(A), ' specify as list of matrix') + } +} + +#' Calculate the asymptotic population growth rate that leads to a 'p' relative reduction after tref years from a list of transition and fertility matrices +#' +#' @param GenMat A list with matrices generated by MakeGenMatrix, containing the names elements `FertilityMatrix`, `TransitionMatrix`, `FundamentalMatrix` and `NextGenMatrix`. Projection matrix of unaffected population is the sum the Fertility and Transition matrices. +#' +#' @param tref Time period after which the population abundances are compared. If NULL, tref is set to 3 times the generation time or 10 years, which ever is largest. +#' +#' @param p Population abundance of the affected population (0 - 1) relative to the unaffected population (1) +#' +#' @return Returns a list with elements `Lambda_null`, `tref` and `Lambda_threshold` +#' +#' @export +LambdaThreshold <- function(GenMat = NULL, tref = NULL, p = 0.7) { + + ## Get Lambda of unaffected + LambdaNull <- Lambda(GenMat = GenMat) + + ## Calculate time reference from GenerationTime + if(is.null(tref)) tref <- pmax(GenerationTime(GenMat) * 3, 10) + + ## Calculate Lambda that leads to a p reduction after tref years + LambdaThreshold <- p^(1/tref) * LambdaNull + + return(list('Lambda_null' = LambdaNull, 'tref' = tref, 'Lambda_threshold' = LambdaThreshold)) +} + +#' Calculate the generation time based on the Fertility and Transition matrices +#' +#' @param GenMatrix A list generated by MakeGenMatrix with named elements `FertilityMatrix`, `TransitionMatrix` and `NextGenMatrix` +#' +#' @return A numeric vector with generation times +#' +#' @details Calculates the generation time according to eq. 5.73 in Caswell (2001) p. 129. +#' +#' @examples +#' library(magrittr) +#' MakeGenMatrix(file = "Alca_torda", N = 100, stochastic = TRUE) %>% +#' GenerationTime() %>% +#' hist() +#' +#' @export +GenerationTime <- function(GenMatrix = NULL){ + + Apm <- mapply(`+`, GenMatrix$FertilityMatrix, GenMatrix$TransitionMatrix, SIMPLIFY = FALSE) + lambda <- KEC4popmodels::Lambda(Apm) + R0 <- KEC4popmodels::Lambda(GenMatrix$NextGenMatrix) + + return(log(R0) / log(lambda)) +} + +#' Calculate sensitivity of population growth rate to a parameter +#' +#' @param parseq.df A data.frame with columns `parameter` and `value` (as returned by \code{\link{AllParSequence}}) +#' +#' @param file Name of the YAML configuration file with matrix specification (used in case parseq.df = NULL) +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used +#' +#' @param parameter The name of the parameter for which the sensitivity is calculated (used in case parseq.df = NULL) +#' +#' @param value Vector of parameter values at which the sensitivity is calculated (used in case parseq.df = NULL) +#' +#' @details Uses grad() function from numDeriv package. Function is vectorized on value. +#' +#' @return A numeric vector +#' +#' @export +Sensitivity <- function(parseq.df = NULL, file = NULL, folder = NULL, parameter = NULL, value = NULL) { + if(is.null(parseq.df) & is.null(parameter)) stop("Specify either an input data.frame `parseq.df` or a file with matrix specifications and a parameter name") + if(!is.null(parseq.df) & !is.data.frame(parseq.df)) stop("parseq.df should be a data.frame with columns `parameter` and `value`") + if(is.null(file)) stop("Please specify the name of a yaml config file") + + # Read the file with matrix specification + conf <- ReadMatrix(file, folder, verbose = FALSE) + # Parse matrices as expressions + Wexpr <- parse(text = conf$WinterMatrix) + Sexpr <- parse(text = conf$SummerMatrix) + + # Extract mean values of parameters + wintervalues <- lapply(conf$WinterPars, '[[', 'mean') + summervalues <- lapply(conf$SummerPars, '[[', 'mean') + + if(!is.null(parseq.df)){ + # Use value from data.frame + parameter <- unique(parseq.df$parameter) + if(length(parameter) > 1) stop("Multiple parameter names found in parseq.df") + value <- parseq.df$value + } else if(is.null(value)) { + # Use default parameter value + if(parameter %in% names(summervalues)){ + value <- summervalues[[parameter]] + } else if (parameter %in% names(wintervalues)){ + value <- wintervalues[[parameter]] + } else { + stop('Parameter ', parameter, ' not found') + } + } + + # Define a function to use in numDeriv::grad + f <- function(x) { + + if(parameter %in% names(summervalues)){ + summervalues[[parameter]] <- x + } else if (parameter %in% names(wintervalues)){ + wintervalues[[parameter]] <- x + } else { + stop('Parameter ', parameter, ' not found') + } + + Wmat <- KEC4popmodels:::EvalMatrix(Wexpr, parlist = wintervalues, stagenms = conf$stages)[[1]] + Smat <- KEC4popmodels:::EvalMatrix(Sexpr, parlist = summervalues, stagenms = conf$stages)[[1]] + Amat <- Wmat %*% Smat + + return(Lambda(Amat)) + } + sapply(value, numDeriv::grad, func = f) +} +## diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/MakeMatrix.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/MakeMatrix.R new file mode 100644 index 0000000000000000000000000000000000000000..b6379bb261b6b6b5725969c53afbdc41be257c0f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/MakeMatrix.R @@ -0,0 +1,429 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +#' Create the annual projection matrix from a YAML configuration file +#' +#' @param file Name of the YAML configuration file with matrix specification +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used +#' +#' @param N Number of matrices returned if tseq = NULL. Defaults to 1. +#' +#' @param tseq (optional). Create a time sequence of matrices. Overrides N +#' +#' @param stochastic (TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE) +#' +#' @param summerpars (optional) A nested list with parameters for the reproduction (summer) matrix +#' +#' @param winterpars (optional) A nested list with parameters for the survival (winter) matrix +#' +#' @param postbreeding (TRUE/FALSE) Creates a postbreeding matrix. By default (FALSE) the matrix represents a population just before breeding. +#' +#' @param verbose (TRUE/FALSE). Message printing to console (defaults to TRUE) +#' +#' @return Returns a list with N annual projection matrices +#' +#' @note +#' \describe{ +#' \item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +#' \item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +#' } +#' +#' @examples +#' MakeMatrix(file = "Morus_bassanus", N = 1000, stochastic = TRUE) +#' +#' @export +MakeMatrix <- function(file = NULL, folder = NULL, N = 1, tseq = NULL, stochastic = FALSE, summerpars = NULL, winterpars = NULL, postbreeding = FALSE, verbose = TRUE){ + + conf <- ReadMatrix(file = file, folder = folder, verbose = verbose) + + # Assign N if time sequence is given + if(!is.null(tseq)){ + if(verbose) message("Time sequence specified, overriding N argument") + N <- length(tseq) + } + + # Assign parameter values + if(is.null(summerpars)) summerpars <- conf$SummerPars + if(is.null(winterpars)) winterpars <- conf$WinterPars + + if(stochastic){ + + wintervalues <- DrawParameters(winterpars, N) + summervalues <- DrawParameters(summerpars, N) + + } else { + + # Extract parameter means + wintermeans <- lapply(winterpars, '[[', 'mean') + summermeans <- lapply(summerpars, '[[', 'mean') + + # !stochastic: Take mean values + wintervalues <- lapply(wintermeans, rep, times = N, SIMPLIFY = FALSE) + summervalues <- lapply(summermeans, rep, times = N, SIMPLIFY = FALSE) + } + + # Check for non-numeric parameters + if(!is.numeric(unlist(wintervalues))) stop('non-numeric winter parameters') + if(!is.numeric(unlist(summervalues))) stop('non-numeric summer parameters') + + # Parse the matrices into expressions + Wexpr <- parse(text = conf$WinterMatrix) + Sexpr <- parse(text = conf$SummerMatrix) + + # Evaluate the winter matrix for each replicate parameter set + Wmat <- KEC4popmodels:::EvalMatrix(Wexpr, parlist = wintervalues, stagenms = conf$stages) + + # Evaluate the summer matrix for each replicate simulation + Smat <- KEC4popmodels:::EvalMatrix(Sexpr, parlist = summervalues, stagenms = conf$stages) + + # Create list with annual projection matrices + if(postbreeding) { + Apm <- mapply(`%*%`, Smat, Wmat, SIMPLIFY = FALSE) + } else { + Apm <- mapply(`%*%`, Wmat, Smat, SIMPLIFY = FALSE) + } + + # Assign time stamps to Apm list + if(!is.null(tseq)) names(Apm) <- paste0('t = ', tseq) + + return(Apm) +} + +#' Create the fertility, transition, fundamental and next generation matrix from a YAML configuration file +#' +#' @param file Name of the YAML configuration file with matrix specification +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used +#' +#' @param N Number of matrices returned (defaults to 1) +#' +#' @param stochastic (TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE) +#' +#' @param summerpars (optional) A nested named list with parameters. Is combined with winterpars to create one parameter list. Parameter names should not overlap. +#' +#' @param winterpars (optional) A nested named list with parameters. Is combined with summerpars to create one parameter list. Parameter names should not overlap. +#' +#' @param verbose (TRUE/FALSE). Message printing to console (defaults to TRUE) +#' +#' @return Returns a nested list with N instances of each of the four matrices +#' +#' @note +#' \describe{ +#' \item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +#' \item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +#' } +#' +#' @examples +#' MakeGenMatrix(file = "Alca_torda", N = 1000, stochastic = TRUE) +#' +#' @export +MakeGenMatrix <- function(file = NULL, folder = NULL, N = 1, stochastic = FALSE, summerpars = NULL, winterpars = NULL, verbose = TRUE){ + + conf <- ReadMatrix(file = file, folder = folder, verbose = verbose) + + # Assign parameter values + if(is.null(summerpars)) summerpars <- conf$SummerPars + if(is.null(winterpars)) winterpars <- conf$WinterPars + + # Put all parameters in one list + pars <- c(winterpars, summerpars) + + if(stochastic){ + + parsvalues <- DrawParameters(pars, N) + + } else { + + # Extract parameter values + means <- lapply(pars, '[[', 'mean') + + # !stochastic: Take mean values + parsvalues <- lapply(means, rep, times = N, SIMPLIFY = FALSE) + } + + # Check for non-numeric parameters + if(!is.numeric(unlist(parsvalues))) stop('non-numeric parameter values') + + # Parse the matrices into expressions + Fexpr <- parse(text = conf$FertilityMatrix) + Texpr <- parse(text = conf$TransitionMatrix) + + # Evaluate the transition matrix for each replicate parameter set + Tmat <- KEC4popmodels:::EvalMatrix(Texpr, parlist = parsvalues, stagenms = conf$stages) + + ## Evaluate the summer matrix for each replicate simulation + Fmat <- KEC4popmodels:::EvalMatrix(Fexpr, parlist = parsvalues, stagenms = conf$stages) + + ## Get the fundamental matrix (see Caswell 2001. p. 112) + Nmat <- lapply(Tmat, FundamentalMatrix) + + ## Make the list with Next Generation Matrix (see Caswell 2001 p. 126) + Rmat <- mapply(`%*%`, Fmat, Nmat, SIMPLIFY = FALSE) + + return(list('FertilityMatrix' = Fmat, + 'TransitionMatrix' = Tmat, + 'FundamentalMatrix' = Nmat, + 'NextGenMatrix' = Rmat)) +} +## + +#' Create a list of matrices for a sequence of parameter values +#' +#' @param file Name of the YAML configuration file with matrix specification +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used +#' +#' @param N Number of matrices returned per value of the parameter (defaults to 1) +#' +#' @param stochastic (TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE) +#' +#' @param summerpars (optional) A nested list with parameters for the reproduction (summer) matrix +#' +#' @param winterpars (optional) A nested list with parameters for the survival (winter) matrix +#' +#' @param parseq A named list with parameter sequences. Multiple parameters can be change at once. Sequences need to be of equal length. +#' +#' @param verbose (TRUE/FALSE). Message printing to console (defaults to TRUE) +#' +#' @return Returns a nested list with N annual projection matrices per parameter value. List is named based on values of first element of `parseq` +#' +#' @note +#' \describe{ +#' \item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +#' \item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +#' } +#' +#' @examples +#' parseqlst <- list('FA' = seq(0.1,0.5,by=0.1), 'SA' = seq(0.7,0.9,by=0.05)) +#' MakeParSequence(file = "Morus_bassanus", stochastic = TRUE, N = 1000, parseq = parseqlst) %>% +#' map(Lambda) %>% +#' map_df(~as.data.frame(.x), .id = "par") %>% +#' set_names(nm = c("par", "Lambda")) %>% +#' ggplot(aes(x = par, y = Lambda, fill = par)) + +#' geom_boxplot() + theme_classic() +#' +#' @export +MakeParSequence <- function(file = NULL, folder = NULL, N = 1, stochastic = FALSE, summerpars = NULL, winterpars = NULL, parseq = NULL, verbose = TRUE){ + if(length(unique(sapply(parseq, length))) != 1) stop("Parameter sequences are of unequal length") + if(!is.list(parseq)) stop("`parseq` should be a named list with parameter sequences") + + conf <- ReadMatrix(file, folder, verbose = verbose) + + # Use default parameters if not specified + if(is.null(summerpars)) summerpars <- conf$SummerPars + if(is.null(winterpars)) winterpars <- conf$WinterPars + + pars <- c(summerpars, winterpars) + summeridx <- which(names(pars) %in% names(summerpars)) + winteridx <- which(names(pars) %in% names(winterpars)) + + matlst <- + lapply(1:length(parseq[[1]]), function(x) { + + # Set the mean values of all parameters in the parseq list + for(i in 1:length(parseq)) pars[[names(parseq[i])]]$mean <- parseq[[i]][x] + + MakeMatrix(file, folder, N = N, stochastic = stochastic, summerpars = pars[summeridx], winterpars = pars[winteridx], verbose = verbose) + }) + names(matlst) <- paste0(names(parseq[1]), " = ", parseq[[1]]) + + return(matlst) +} +## + +#' Read a YAML file with matrix specifications +#' +#' @param file Name of the YAML configuration file with matrix specifications +#' +#' @param folder Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for. +#' +#' @examples +#' ReadMatrix(file = "Morus_bassanus") +#' +#' @export +ReadMatrix <- function(file = NULL, folder = NULL, verbose = TRUE){ + if(is.null(file)) stop("Please specify the name of a yaml config file") + + # Get full file name + file <- gsub(".yaml|.yml", replacement = "", file) + if(is.null(folder)){ + if(verbose) message("ReadMatrix: reading internal file ", file, "\n") + fln <- system.file("extdata", paste0(file, ".yaml"), package = "KEC4popmodels") + } else { + if(verbose) message("ReadMatrix: reading file from user-defined folder\n") + fln <- normalizePath(file.path(folder, paste0(file, ".yaml")), mustWork = TRUE) + } + + # Read the configuration file + if(!file.exists(fln)) stop(paste0('File ', fln, ' not found')) + conf <- yaml::read_yaml(fln) + + return(conf) +} + +#' Evaluate a matrix from an expression +#' +#' @param matexpr An expression of a matrix +#' +#' @param parlist A list with parameters to evaluate `matexpr` +#' +#' @param stagenms A character vector with names of stages +#' +EvalMatrix <- function(matexpr = NULL, parlist = NULL, stagenms = NULL){ + if(!is.expression(matexpr)) stop('matexpr should be an expression') + if(!is.list(parlist)) stop('parlist should be a nested list with parameters') + if(!is.character(stagenms)) stop('stagenms should be a character vector with length equals to number of stages') + + # Number of stages is length of stagenms + nstages <- length(stagenms) + + # Number of matrices returned equals length of parameter vector + N <- length(parlist[[1]]) + + # Evaluate the matrix for each replicate parameter set + mat <- lapply(1:N, function(x) { + + # Extract parameter set + p <- lapply(parlist, '[[', x) + + # Evaluate matrix + m <- matrix(eval(matexpr, p), + nrow = nstages, + ncol = nstages, + byrow = TRUE, + dimnames = list(stagenms, stagenms)) + + # Check for na + if(any(is.na(m))) warning("Matrix with NA produced \n") + + return(m) + }) + return(mat) +} +## + +#' Wrapper function unpacks parameter list and calls \code{KECdist} function +#' +#' @param parlist A nested list with parameters. +#' +#' @param N Number of replicate draws +#' +#' @details In `parlist`, each parameter should at least contain `mean` and `sd` entry. Optionally, a distribution can be specified with named element `dist`, which can be `beta`, `gamma`, `normal`, `truncnormal` or `none`. If missing, `dist` is set to `beta`. For `none` the mean is replicated `N` times. For the truncated normal distribution (`truncnormal`) `lower` and `upper` bounds should be specified. +#' +#' @examples +#' parlist <- list('FA' = list('mean' = 2.32, 'sd' = 0.75, 'lower' = 1.5, 'upper' = 2.9, 'dist' = 'truncnormal'), +#' 'SA' = list('mean' = 0.956, 'sd' = 0.032)) +#' DrawParameters(parlist, N = 10) +#' +#' @export +DrawParameters <- function(parlist = NULL, N = 1){ + if(!is.list(parlist)) stop('parlist should be a nested list with parameters') + if(length(parlist) == 0) stop('Parameter list is empty') + if(is.null(parlist[[1]]$mean)) stop('Parameter should contain element `mean`') + if(is.null(parlist[[1]]$sd)) stop('Parameter should contain element `sd`') + + # Extract mean values; + means <- sapply(parlist, '[[', 'mean') + + # Extract standard deviation + sds <- sapply(parlist, '[[', 'sd') + + # Extract parameter distributions + dists <- sapply(parlist, function(x) ifelse(is.null(x[['dist']]), 'beta', x[['dist']])) + + # Extract upper and lower bounds + low <- sapply(parlist, function(x) ifelse(is.null(x[['lower']]), NA, x[['lower']])) + upp <- sapply(parlist, function(x) ifelse(is.null(x[['upper']]), NA, x[['upper']])) + + # Get parameters from KECdist function + pars <- mapply(KECdist, mu = means, sd = sds, dist = dists, lower = low, upper = upp, n = N, SIMPLIFY = FALSE, USE.NAMES = TRUE) + + return(pars) +} +## + +#' Sample parameters from a distribution +#' +#' @param mu parameter mean +#' +#' @param sd parameter standard deviation +#' +#' @param dist parameter distribution. Either `beta`, `gamma`, `normal`, `truncnormal` or `none`. +#' +#' @param n number of samples +#' +#' @param lower parameter lower bound. Only required if `dist = truncnormal` +#' +#' @param upper parameter upper bound. Only required if `dist = truncnormal` +#' +#' @details Sample parameters from a beta, gamma, normal or a truncated normal distribution. Beta distribution requires 0. < mu < 1. and 0. < sd < 0.5. Throws an error otherwise. Shape parameters of the beta distribution (\eqn{\alpha} and \eqn{\beta}) are calculated as \eqn{\alpha = \mu^2 (1-\mu)/(\sigma^2) - 1/\mu } and \eqn{\beta = \alpha (1/\mu - 1)}. In addition, for `dist = "beta"` parameters are restricted to <0.999 and >0.001. For the gamma distribution (`dist = "gamma"`) the shape parameter is \eqn{\alpha = \mu^2/sd} and the scale parameter is \eqn{\beta = (sd^2) / \mu}. If `dist = "normal"` invokes `rnorm` function with `mean = mu` and `sd = sd`. For the truncated normal (`dist = "truncnormal"`) supplied `mu` and `sd` are interpreted as the mean and standard deviation of the associated untruncated normal distribution and the sampled means and standard deviations might differ, depending on the values of `lower` and `upper`. For `dist == "none"` `mu` is replicated `n` times. +#' +#' @export +KECdist <- function(mu = NULL, sd = NULL, dist = NULL, n = 1, lower = NA, upper = NA) { + if(is.null(mu) | length(mu) > 1) stop("mu should be numeric of length 1") + if(is.null(sd) | length(sd) > 1) stop("sd should be numeric of length 1") + if(is.null(dist) | length(dist) > 1) stop("dist should be numeric of length 1") + if(is.null(lower) | length(lower) > 1) stop("lower should be numeric of length 1") + if(is.null(upper) | length(upper) > 1) stop("upper should be numeric of length 1") + + # Beta distribution + if(dist == "beta"){ + if(!(mu > 0. & mu < 1. & sd < 0.5 & sd > 0.)) stop("KECdist: conditions for beta distribution not met: mu = ", mu, ", sd = ", sd) + if( (mu - mu^2) < sd^2) stop("KECdist: condition for beta distribution not met: mu * (1 - mu) > sd^2, with mu = ", mu, ", sd = ", sd) + + alpha <- mu^2 * ((1-mu)/(sd^2) - 1/mu) + beta <- alpha * (1/mu - 1) + + val <- rbeta(n = n, shape1 = alpha, shape2 = beta) + val <- pmin(val, 0.999) + val <- pmax(val, 0.001) + + # Gamma distribution + } else if (dist == "gamma") { + + alpha <- (mu^2) / (sd^2) + beta <- (sd^2) / mu + + val <- rgamma(n = n, shape = alpha, scale = beta) + + # Normal distribution + } else if (dist == "normal") { + + val <- rnorm(n = n, mean = mu, sd = sd) + + # Truncated normal distribution + } else if (dist == "truncnormal") { + if(is.na(lower)) stop("Please specify lower parameter bound for use with `truncnormal`") + if(is.na(upper)) stop("Please specify upper parameter bound for use with `truncnormal`") + + val <- rtruncnorm(n = n, mean = mu, sd = sd, a = lower, b = upper) + + } else if (dist == "none") { + + val <- rep(mu, n) + + } else { + + stop("Distribution ", dist, " not available: use `beta`, `gamma`, `normal`, `truncnormal` or `none`") + + } + return(val) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Plotting.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Plotting.R new file mode 100644 index 0000000000000000000000000000000000000000..f9a591ed2cc427ba1851655e698899786c5d642a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Plotting.R @@ -0,0 +1,94 @@ +#' Make a boxplot of the population growth rate for each scenario +#' +#' @param xvar Name of x axis variable +#' +#' @param yvar Name of y axis variable +#' +#' @param Lambda_stoch A long data.frame with columns 'name' (scenario names) and 'value' (stochastic population growth rate) +#' +#' @param Lambda_det (optional) A long data.frame with columns 'name' (scenario names) and 'det' (deterministic population growth rates) +#' +#' @param species Name of species. Only used as title +#' +#' @details Additional arguments are passed on to geom_boxplot +#' +#' @export +Boxplot_Lambda <- function(xvar = 'name', yvar = 'value', Lambda_stoch = NULL, Lambda_det = NULL, species = "", ...) { + if(!is.data.frame(Lambda_stoch)) stop("Please specify Lambda_stoch data.frame") + + gg <- + Lambda_stoch %>% + ggplot(aes(x = !!sym(xvar), y = !!sym(yvar), fill = !!sym(xvar))) + + geom_boxplot(...) + + labs(fill = "Scenario") + xlab("Scenario") + ylab("Population growth rate") + ggtitle(species) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) + + if(!is.null(Lambda_det)) gg <- gg + geom_point(data = Lambda_det, aes(x = !!sym(xvar), y = det), colour = 'red', size = 2, shape = 18, show.legend = FALSE) + + return(gg) +} + +#' Make a histogram of the population growth rate for each scenario +#' +#' @param xvar Name of x axis variable +#' +#' @param Lambda_stoch A long data.frame with columns 'name' (scenario names) and 'value' (stochastic population growth rate) +#' +#' @param Lambda_det (optional) A long data.frame with columns 'name' (scenario names) and 'det' (deterministic population growth rates) +#' +#' @param species Name of species. Only used as title +#' +#' @details Additional arguments are passed on to geom_histogram +#' +#' @export +Histogram_Lambda <- function(Lambda_stoch = NULL, Lambda_det = NULL, xvar = 'value', facetvar = 'name', species = "", ...){ + if(!is.data.frame(Lambda_stoch)) stop("Please specify Lambda_stoch data.frame") + + gg <- + Lambda_stoch %>% + ggplot(aes(x = !!sym(xvar), y = ..density..)) + + geom_histogram(aes(fill = !!sym(facetvar)), position = "identity", ...) + + facet_wrap(facetvar) + + labs(fill = "Scenario") + xlab("Population growth rate") + ylab("Density") + ggtitle(species) + + theme_bw() + + theme(axis.text.x = element_text(size = 8, angle = 45, vjust = 0.5)) + + if(!is.null(Lambda_det)) gg <- gg + geom_vline(data = Lambda_det, aes(xintercept = det), colour = "black", size = 0.5, show.legend = FALSE) + + return(gg) +} + +#' Plot output from AllParSequence function +#' +#' @param `allparseq` A list or data.frame element returned by \code{\link{AllParSequence}} +#' +#' @param `yvar` Name of variable to plot on vertical axis (defaults to "Lambda") +#' +#' @param `species` Name of the species. Only used to created plot title. +#' +#' @param `pnts` (TRUE/FALSE) Plot individuals data points (defaults to FALSE) +#' +#' @examples +#' AllParSequence("Alca_torda", parseq = seq(0.01,0.99,0.01), verbose = FALSE) %>% +#' Plot_AllParSeq(species = "Alca_torda") +#' +#' @export +Plot_AllParSeq <- function(allparseq = NULL, yvar = "Lambda", species = "", facetvar = "parsdescr", pnts = FALSE, ...) { + if(!is.list(allparseq)) stop("Please specifiy a list with output from `AllParSequence`") + ylb <- switch(yvar, "Lambda" = "Population growth rate", "Sensitivity" = "Sensitivity", "") + + gg <- + allparseq %>% + bind_rows() %>% + ggplot(aes(x = value, y = !!sym(yvar), colour = !!sym(facetvar))) + + geom_line(...) + + facet_wrap(.data[[facetvar]] ~ .) + + xlab("Parameter value") + ylab(ylb) + + ggtitle(species) + + theme_bw() + ## + if(pnts) gg <- gg + geom_point(size = 0.25) + + return(gg) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/RcppExports.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/RcppExports.R new file mode 100644 index 0000000000000000000000000000000000000000..4b2a665fe9b5cdc1e6c811d3d832a66404339f08 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/RcppExports.R @@ -0,0 +1,94 @@ +# Generated by using Rcpp::compileAttributes() -> do not edit by hand +# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#' Calculate eigenvalues and left and right eigenvectors for a square matrix A +#' +#' @param A A square matrix +#' +#' @return Returns a list with elements 'Eigenvalues', 'Leftvectors' and 'Rightvectors' +#' +#' @author Vincent Hin - Wageningen Marine Research +#' +#' @source Uses RcppArmadillo function eig_gen(). See \url{http://arma.sourceforge.net/docs.html#eig_gen} +#' +#' @export +EigenDecomp <- function(A) { + .Call(`_KEC4popmodels_EigenDecomp`, A) +} + +#' Calculate the stable population distribution from a population projection matrix +#' +#' @param A A square matrix +#' +#' @return Return a row vector with the stable stage distribution +#' +#' @author Vincent Hin - Wageningen Marine Research +#' +#' @source http://arma.sourceforge.net/docs.html#eig_gen +#' +#' @export +StableStage <- function(A) { + .Call(`_KEC4popmodels_StableStage`, A) +} + +#' Return the real part of the largest eigenvalue +#' +#' @param A A square matrix with eigenvalues +#' +#' @author Vincent Hin - Wageningen Marine Research +#' +#' @source http://arma.sourceforge.net/docs.html#eig_gen +#' +#' @examples +#' MakeMatrix(file = "Morus_bassanus", N = 10000, stochastic = TRUE) %>% +#' map(EigenDecomp) %>% +#' map('Eigenvalues') %>% +#' map(MaxReal) %>% +#' unlist() +#' +#' @export +MaxReal <- function(A) { + .Call(`_KEC4popmodels_MaxReal`, A) +} + +#' Return the Fundamental Matrix from the Transition rate matrix +#' +#' @param Tmat A square matrix describing stage transitions +#' +#' @author Vincent Hin - Wageningen Marine Research +#' +#' @details see Caswell 2001 - Matrix Population Models p. 112 +#' +#' @export +FundamentalMatrix <- function(Tmat) { + .Call(`_KEC4popmodels_FundamentalMatrix`, Tmat) +} + +#' Calculate population projection +#' +#' @param A +#' The population projection matrix, or a list with annual projection matrices for a sequence of time steps +#' +#' @param n +#' A vector with initial densities +#' +#' @param niters +#' Number of iterations +#' +#' @return Returns a list with elements +#' \describe{ +#' \item{`ProjectionMatrix`}{A list with annual projection matrices} +#' \item{`StageVector`}{A matrix with stage vectors} +#' \item{`PopSize`}{A matrix with total population sizes} +#' \item{`PopChange`}{A matrix with annual growth factors} +#' } +#' +#' @author Vincent Hin - Wageningen Marine Research +#' +#' @source Rcpp version of R function popbio::pop.projection() +#' +#' @export +PopProject <- function(A, n, niter = 20L) { + .Call(`_KEC4popmodels_PopProject`, A, n, niter) +} + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Sensitivity.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Sensitivity.R new file mode 100644 index 0000000000000000000000000000000000000000..6f7eef4d2efea9121bc1ba5445a4ff81d8997eb6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/Sensitivity.R @@ -0,0 +1,252 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +#' Run a sensitivity analysis of based on a configuration file +#' +#' @param config The name of the YAML configuration file +#' +#' @param folder Folder of `config` +#' +#' @param statselect The level of the 'stat' column used for sensitivity analysis +#' +#' @return Returns a data.frame with sensitivity output. +#' +#' @details AnalyseSensitivity runs the population model for a range of parameter values based on a configuration file and a input data file. +#' +#' @export +AnalyseSensitivity <- function (config = NULL, folder = ".", statselect = NULL) { + if(is.null(config)) stop("Please specify a YAML configuration file") + if(is.null(statselect)) stop("`statselect` is empty") + + ## --- INITIALIZE ----- + + # 1 :-> Get full file name and read configuration file + config <- gsub(".yaml|.yml", replacement = "", config) + fln <- normalizePath(file.path(folder, paste0(config, ".yaml")), mustWork = TRUE) + if(!file.exists(fln)) stop(paste0('File ', fln, ' not found')) + cfg <- yaml::read_yaml(fln) + + # 2 :-> Set default settings + if(is.null(cfg$seed)){ + cfg$seed <- floor(runif(1) * 1E8) + set.seed(cfg$seed) + } + + cfg$nrep <- as.numeric(cfg$nrep) + + # If NULL, set IBM to FALSE + if(is.null(cfg$IBM)) cfg$IBM <- FALSE + + ## + with(cfg, { + + # :-> Create folder to store output + if(!is.null(outfolder)) { + outpath <- file.path(".", outfolder, matfile) + if(!file.exists(outpath)) dir.create(outpath, recursive = TRUE) + } + + # :-> Read input with casualties and refine to species + dat.fln <- normalizePath(file.path(datafolder, datafile), mustWork = TRUE) + dat <- read.table(file = dat.fln, header = TRUE) + if(!species %in% dat$species) stop(paste0("Species ", species, " not found in input data")) + + ## Refine data based on species and 'statselect' + dat <- dat[dat$species == species & dat$stat == statselect,] + if(nrow(dat) == 0) stop("No casualty data after selection") + + # 5 :-> Convert number of casualties to population mortality estimates + if(!IBM) { + ## Create data.frame with annual mortality rates + dat.annual <- ConvertMortalityData(dat) + } else { + dat.annual <- dat + } + + # Create names + # scns.nms : names of all OWF scenarios, including 'null' scenario + # scns.stat : estimator of OWF mortality, including 'null' scenario + # mort.seq : vector with mortality rate per scenario / stat combination + # scns.statnames : combination of scenario names and stat names + scns.nms <- c("null", dat.annual$scenario) + scns.stat <- c("null", dat.annual$stat) + mort.seq <- c(0, dat.annual[[mortcalc]]) + scns.statnames <- paste0(scns.nms, "_", scns.stat) + scns.statnames[1] <- "null" + + ## --- ANALYSIS ----- + + # 8 :-> Get default parameters. + # - conf : summer and winter projection matrices, parameter and stage names + # - AllPars : list of all DEFAULT model parameters (summer and winter projection matrices) + # - ParTable : data.frame with all parameter combinations + # - Spar : names of survival parameters + # - parvalues : list of survival parameters + # - Sparidx : index of Spar for each life stage + # - OWFvuln : relative vulnerability of each life stage (0-1) to OWF mortality. + conf <- ReadMatrix(file = matfile) + AllPars <- c(conf$SummerPars, conf$WinterPars) + Sparidx <- as.integer(Sparidx) + OWFvuln <- as.numeric(OWFvuln) + + ## Check OWFvulnerability + if(length(Sparidx) != conf$nstages) stop('Length Sparidx does not equal number of life stages') + if(length(OWFvuln) != conf$nstages) stop('Length OWFvuln does not equal number of life stages') + if(length(unique(Sparidx)) != length(Spar)) stop('Unique values of Sparidx does not equal length of Spar') + + # Create a table with parameter ranges + ParTable <- + map_dfr(names(AllPars), function(x) { + parseq <- seq(AllPars[[all_of(x)]]$min, AllPars[[all_of(x)]]$max, AllPars[[all_of(x)]]$step) + if(max(parseq) != AllPars[[all_of(x)]]$max) parseq <- c(parseq, AllPars[[all_of(x)]]$max) + tibble('parname' = x, 'parvalue' = parseq) + }) + + # Split into list by row + ParList <- split(ParTable, 1:nrow(ParTable)) + + ParResults <- map_dfr(ParList, function(p) { + + cat(p$parname, "=", p$parvalue, "\n") + + # Reassign default parameters + Pars <- AllPars + # Override default mean parameter new DEFAULT from p element (ParList) + # NOTE, Pars, SummerPars and WinterPars are new default parameter (NULL scenario) + Pars[[p$parname]]$mean <- p$parvalue + SummerPars <- Pars[names(conf$SummerPars)] + WinterPars <- Pars[names(conf$WinterPars)] + + # Recalculate ALI with new default parameters + ALI <- CalculateALI(file = matfile, threshold = ALI_threshold, N = nrep, summerpars = SummerPars, winterpars = WinterPars, verbose = FALSE) + + # Get new default survival parameters (NULL scenario) + parvalues <- Pars[names(Pars) %in% Spar] + if(length(parvalues) == 0) stop('Spar value not found') + + # 9 :-> Calculate mean stable stage distribution + StableDist <- rowMeans( + cbind(StableStage(A = MakeMatrix(file = matfile, summerpars = SummerPars, winterpars = WinterPars, verbose = FALSE)[[1]]), + StableStage(A = MakeMatrix(file = matfile, summerpars = SummerPars, winterpars = WinterPars, verbose = FALSE, postbreeding = TRUE)[[1]]))) + + # 10 :-> Scale stage-specific mortalities + + ## Create data.frame + Mort_Scaling <- bind_cols('LifeStage' = conf$stages, + 'StableDist_mean' = StableDist, + 'Sparidx' = Sparidx, + 'OWFvuln' = OWFvuln / max(OWFvuln)) + + ## Scale mortality or apply to specific life stages + if(scale_mortality){ + Mort_Scaling$OWFscalar <- Mort_Scaling$OWFvuln / sum(Mort_Scaling$StableDist_mean * Mort_Scaling$OWFvuln) + } else { + Mort_Scaling$OWFscalar <- Mort_Scaling$OWFvuln + } + + ## Aggregate mortality per survival parameter weighted by stable distribution and OWF vulnerability + Mort_Scaling <- + Mort_Scaling %>% + group_by(Sparidx) %>% + summarize(LifeStage = paste0(LifeStage, collapse = ","), + StableDist = sum(StableDist_mean), + OWFvuln = paste0(round(OWFvuln,2), collapse = ","), + OWFscalar = weighted.mean(OWFscalar, StableDist_mean)) %>% + mutate('Spar' = Spar) %>% + relocate(Spar) + ## + + ## Add OWFscalar to list of survival values + parvalues <- mapply(function(x, y) { x[['OWFscalar']] = y; x}, x = parvalues, y = Mort_Scaling$OWFscalar, SIMPLIFY = FALSE) + ## + + # 11 :-> Convert OWF mortality to survival + # mortcalc : the mortality calculate of choice (column name of ConverMortality output) + # morttype : the type of mortality: "finite" of "rate" + # surv.seq : list with survival values for each survival parameter + # mort.scaled: list with scaled mortality values for each survival parameter + if(morttype == "finite"){ + surv.seq <- lapply(1:length(parvalues), function(i) parvalues[[i]]$mean * MortProbToSurv(mort.seq * parvalues[[i]]$OWFscalar)) + } else if (morttype == "rate") { + surv.seq <- lapply(1:length(parvalues), function(i) MortRateToSurv(SurvToMortRate(parvalues[[i]]$mean) + mort.seq * parvalues[[i]]$OWFscalar)) + } else { + stop("Please specifiy configuration option `morttype` as `finite` or `rate`") + } + names(surv.seq) <- Spar + + mort.scaled <- lapply(1:length(parvalues), function(i) mort.seq * parvalues[[i]]$OWFscalar) + names(mort.scaled) <- Spar + + ## Create data.frame with mortality scenarios + MortSurv_scns <- data.frame(scns.nms, scns.stat, mort.seq, as.data.frame(mort.scaled), as.data.frame(surv.seq), check.names = FALSE) + names(MortSurv_scns) <- c("scenario", "stat", "mort", paste0("mort.", names(mort.scaled)), paste0("survival.", names(surv.seq))) + + # 13 :-> --- STOCHASTIC PGR ~ SCENARIO ---- + Lambda_stoch_scn <- purrr::map( + KEC4popmodels::MakeParSequence(file = matfile, + N = nrep, + stochastic = TRUE, + summerpars = SummerPars, + winterpars = WinterPars, + parseq = surv.seq, + verbose = FALSE), + KEC4popmodels::Lambda) + names(Lambda_stoch_scn) <- scns.statnames + + ## Count NAs per scenario + NAcount <- map(Lambda_stoch_scn, ~ sum(is.na(.))) + + ## Calculate the median of Lambda + Lambda_median <- map(Lambda_stoch_scn, median, na.rm = TRUE) + + # Calculate P_impact for each scenario and bind with ParTable row + Pimpact <- map(Lambda_stoch_scn, ~ sum(. < ALI$ALI_table$Lambda_threshold_median, na.rm = TRUE) / length(.) ) + + # Create df + ParRow <- + data.frame('Scenario' = scns.statnames, + 'Lambda_median' = do.call('rbind', Lambda_median), + 'NAcount' = do.call('rbind', NAcount), + 'Pimpact' = do.call('rbind', Pimpact)) %>% + bind_cols(p, .) %>% + bind_cols(., 'Lambda_threshold_median' = ALI$ALI_table$Lambda_threshold_median) %>% + bind_cols(., 'Pfalsepos' = ALI$ALI_table$P_falsepos) + ## + return(ParRow) + }, .id = 'iteration') + + # Define thresholds + ParResults$ALI_threshold <- -(ParResults$Pfalsepos)/(ALI_causality/100-1) + ParResults$ALI_violation <- ParResults$Pimpact > ParResults$ALI_threshold + + ## Save output to .rds is outfolder is defined + if(!is.null(outpath)) { + fln <- file.path(outpath, paste0("OWF_sensitivity_", matfile, "_", statselect, ".rds")) + if(file.exists(fln) & !overwrite) { + warning("File ", fln, " already exists. Specify different `outfolder` or set overwrite = `TRUE`") + } else { + message("Saving sensitivity output to file ", fln) + saveRDS(ParResults, file = fln) + } + } + # + return(ParResults) + }) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/data.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/data.R new file mode 100644 index 0000000000000000000000000000000000000000..94bbaae8b2315a125af2b0468d2f5697b106309c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/data.R @@ -0,0 +1,40 @@ +###################################################################### +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +###################################################################### + +#' KEC4species +#' +#' Names of all species that are part of the KEC4popmodels package +#' +#' @format Character vector +"KEC4species" + +#' KEC4matfiles +#' +#' Names of all the YAML configuration files with matrix descriptions that are part of the KEC4popmodels package +#' +#' @format Character vector +"KEC4matfiles" + +#' DummyInput +#' +#' A dummy data set with number of birds displaced per season and scenario +#' +#' @format A data.frame +"DummyInput" diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/utils.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/utils.R new file mode 100644 index 0000000000000000000000000000000000000000..79bb53c8b79d0606b1356e9dc6ab6316a56c0f51 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/R/utils.R @@ -0,0 +1,122 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +#' Re-calculate annual survival probability +#' +#' @param survival Baseline annual survival probability +#' +#' @param mort Additional monthly mortality rate +#' +#' @param months Number of months per year with additional mortality +#' +#' @description +#' Calculate the annual survival from baseline survival and additional +#' mortality applied for a certain number of months per year +#' +#' @return +#' Annual survival probability (including additional mortality) +#' +#' @export +UpdateSurvival <- function(survival = NULL, months = NULL, mort = NULL){ + + # Calculate monthly survival + Smonth <- survival^(1/12) + + # Calculate increased mortality rate + m <- SurvToMortRate(Smonth) + mort + + Sannual <- MortToSurvProb(m * months + SurvToMortRate(Smonth)*(12-months)) + + return(Sannual) +} + +#' Calculate survival from mortality rate +#' +#' @details +#' m = -log(S) +#' +#' @export +SurvToMortRate <- function(S = NULL) +{ + -log(S) +} + +#' Calculate survival from finite mortality probability +#' +#' @details +#' m = 1 - S +#' +#' @export +SurvToMortProb <- function(S = NULL) +{ + 1 - S +} + +#' Calculate mortality rate from survival +#' +#' @details +#' S = exp(-m) +#' +#' @export +MortRateToSurv <- function(m = NULL) +{ + exp(-m) +} + +#' Calculate mortality probability from survival +#' +#' @details +#' S = 1 - m +#' +#' @export +MortProbToSurv <- function(m = NULL) +{ + 1 - m +} + +#' Change a configuration option in multiple YAML runfiles +#' +#' @param folder Folder where to look for YAML runfiles +#' +#' @param exclude Names of YAML files to exclude. +#' +#' @param cfg A named vector of length 1 with the yaml option that needs to be replaced in all the files +#' +#' @details Uses pattern matching to exclude YAML files. File names listed under `exclude` do not need to be exact. +#' +#' @export +write_yamlopts <- function(folder = NULL, exclude = NULL, cfg = NULL) { + if(is.null(folder)) stop("Please specifiy a folder with yaml files") + + fls <- list.files(path = folder, pattern = "yaml", full.names = TRUE) + if(!is.null(exclude)) fls <- fls[!grepl(pattern = exclude, fls)] + + lapply(as.list(fls), function(i) { + + lns <- readLines(i) + ptr <- paste0("(^[[:space:]]+|^)", names(cfg), "[[:space:]]+:") + idx <- which(grepl(ptr, lns)) + if(length(idx) == 0) stop("Config option ", names(cfg), " not found") + + lns[idx] <- paste0(names(cfg), "\t\t: ", cfg) + + writeLines(text = lns, con = i) + }) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/README.md b/script_py_r/kec4_pyconversion-master/KEC4popmodels/README.md new file mode 100644 index 0000000000000000000000000000000000000000..79251a8939a650687c24d00a5472dcf5c28956b8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/README.md @@ -0,0 +1,25 @@ +## About KEC4popmodels +Matrix population models to calculate impact of habitat loss and collison risks from offshore wind farms (OWFs) on populations of several seabird species. Code is wrapped into an R package that can be installed from source. Installation required a C++ compiler, which is provided by Rtools for Windows machines. Installation instructions are below. + +## Installation instructions + +### Installing Rtools +Rtools is needed to build R packages with C/C++/Fortran code from source and can be installed from: [https://cran.r-project.org/bin/windows/Rtools/]. Please make sure to add Rtools to the PATH variable in the `~.Renviron` file. Instructions are in the link above. The value of the PATH variable can inspected by running in R: `Sys.getenv('PATH')`, which should contain `"C:\\rtools40\\usr\\bin;` if Rtools is installed in the default directory. The installation and configuration of Rtools can be checked with the function `pkgbuild::has_rtools()` from the R-package `pkgbuild`. + +### Installing the KEC4popmodels R-package + + - Clone this repository to your local machine, which should create a top-level folder called `KEC4popmodels` + - Open a new R / Rstudio session and set the working directory to the cloned KEC4popmodels folder + - Execute the following command, which should install the package into the R's default package directory: + + `install.packages(pkgs = getwd(), repos = NULL, type = 'source', INSTALL_opts = c("--with-keep.source", "--no-multiarch"))` + + - Alternatively, you can install the package from the command line / terminal by navigating to the top-level directory (KEC4popmodels) and running: + + `R CMD INSTALL --with-keep.source --no-multiarch .` + +### Instructions for code development and package maintenance +This R-package was created using the `devtools` R-package. For more information see [https://r-pkgs.org/index.html]. Please adhere to the practice of package building as described on this website. It is advised to create a new Rstudio project within the KEC4popmodels directory. This allows rebuilding and installing the package from Rstudio using `Build` -> `Install and Restart`, or from the Build pane clicking `Install and Restart`. Documentation should be build separately using `Build` -> `Document`, which is also available in the `More` drop-down menu of the Build pane. The package also contains several test functions that check the correct implementation the matrices that are available within the package. These tests can be run from Rstudio with `Build` -> `Test Package` or again from the `More` dropdown menu in the Build pane. + +### Troubleshooting +In case of problems feel free to send an email to [vincent.hin@wur.nl] diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/DummyInput.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/DummyInput.R new file mode 100644 index 0000000000000000000000000000000000000000..287fa854b6e1211d1446f7b4591521e970c189d6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/DummyInput.R @@ -0,0 +1,69 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +MakeDummyInput <- function(species = "Morus_bassanus") { + + # some random scenarios + scenarios <- c("NAT_2030", "NAT_2030plus", "INT_2030") + + # seasons + seasons <- 1:6 + + casualties <- expand.grid('scenario' = scenarios, 'season' = seasons) + + # data subset + casualties$batch <- NA + casualties$batch[grepl("NAT", casualties$scenario)] <- "NAT" + casualties$batch[grepl("INT", casualties$scenario)] <- "INT" + + casualties$middle <- 0 + casualties$middle[casualties$scenario == "NAT_2030"] <- runif(n = length(seasons), min = 30, max = 80) + casualties$middle[casualties$scenario == "NAT_2030plus"] <- runif(n = length(seasons), min = 50, max = 100) + casualties$middle[casualties$scenario == "INT_2030"] <- runif(n = length(seasons), min = 70, max = 150) + casualties$middle <- ceiling(casualties$middle) + casualties$lower <- casualties$middle * 0.5 + casualties$upper <- casualties$middle * 1.5 + + casualties <- + casualties %>% + tidyr::pivot_longer(cols = c(middle, lower, upper), names_to = 'stat') %>% + tidyr::pivot_wider(names_from = "scenario", values_from = 'value') + + abund <- expand.grid('season' = seasons, batch = c("NAT", "INT")) + abund$abundance <- NA + abund$abundance[abund$batch == "NAT"] <- floor(runif(n = length(seasons), min = 1e3, max = 1e4)) + abund$abundance[abund$batch == "INT"] <- floor(runif(n = length(seasons), min = 5e4, max = 2e5)) + + dat <- merge(abund, casualties) + dat$species <- species + + dat <- + dat %>% + dplyr::relocate(species) %>% + arrange(species, season, batch, stat) + + return(dat) +} +## + +set.seed(10062019) +DummyInput <- purrr::map_dfr(as.list(KEC4species), ~MakeDummyInput(.)) +usethis::use_data(DummyInput, overwrite = TRUE) +write.table(DummyInput, file = "DummyInputData.txt", row.names = FALSE) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/KEC4species.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/KEC4species.R new file mode 100644 index 0000000000000000000000000000000000000000..f3cbfdb792ca75f835410f857200bf4e67377ea2 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data-raw/KEC4species.R @@ -0,0 +1,30 @@ +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + +KEC4species <- c("Alca torda" ,"Branta bernicla", "Calidris canutus", "Chlidonas niger", + "Cygnus columbianus" ,"Fratercula arctica", "Fulmarus glacialis", "Gavia stellata", + "Larus argentatus", "Larus fuscus", "Larus marinus", "Morus bassanus", "Numenius arquata", + "Rissa tridactyla" ,"Stercorarius parasiticus", "Stercorarius skua", "Sterna hirundo", + "Sturnus vulgaris" ,"Tadorna tadorna", "Thalasseus sandvicensis", "Uria aalge") +usethis::use_data(KEC4species, overwrite = TRUE) + +KEC4matfiles <- list.files(path = "inst/extdata/") +KEC4matfiles <- KEC4matfiles[!KEC4matfiles == "Orcinus_orca.yaml"] +usethis::use_data(KEC4matfiles, overwrite = TRUE) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/DummyInput.rda b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/DummyInput.rda new file mode 100644 index 0000000000000000000000000000000000000000..cf942f90e28509a00d2c1c823a6b6328519c0f4f Binary files /dev/null and b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/DummyInput.rda differ diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4matfiles.rda b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4matfiles.rda new file mode 100644 index 0000000000000000000000000000000000000000..405164233a7005864a91b5b6b3dd28dba92eacb4 Binary files /dev/null and b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4matfiles.rda differ diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4species.rda b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4species.rda new file mode 100644 index 0000000000000000000000000000000000000000..49a6c17e2cbcd75ab03a0408628b22c1242dd6d9 Binary files /dev/null and b/script_py_r/kec4_pyconversion-master/KEC4popmodels/data/KEC4species.rda differ diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/CITATION b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/CITATION new file mode 100644 index 0000000000000000000000000000000000000000..06566937f4fc8ae94c1ecb4126bf988288309388 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/CITATION @@ -0,0 +1,15 @@ +citHeader("To cite KEC4popmodels in publications use:") + +citEntry(entry = "Manual", + title = "KEC4popmodels: Matrix models to assess mortality effects of Offshore Wind Parks on seabird populations", + author = personList(as.person("Vincent Hin")), + year = "2021", + organization = "Wageningen Marine Research", + address = "IJmuiden, The Netherlands", + url = "https://git.wur.nl/ecodyn/KEC4popmodels", + textVersion = + paste("Vincent Hin (2021).", + "KEC4popmodels: Matrix population models to assess mortality effects of Offshore Wind Parks on seabird populations.", + "Wageningen Marine Research, IJmuiden, The Netherlands.", + "URL: https://git.wur.nl/ecodyn/KEC4popmodels") +) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Alca_torda.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Alca_torda.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b2e8fe08f39a6e4f9f7f889e54f739cbceecb81c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Alca_torda.yaml @@ -0,0 +1,110 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The Razorbill - Alca torda - Alk +# +# Matrix structure and parameters are taken from: +# Van Kooten et al. 2019. The consequences of seabird habitat loss from offshore wind turbines, version 2 +# Displacement and population level effects in 5 selected species +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 8 April 2021 +# Date last modified : 19 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Alca torda + +# Number of life stages +nstages: 3 + +## Names of life stages +# J01 - age class 0 and 1 +# A234 - non breeding individuals aged 2, 3 and 4 +# AB - breeding adults +stages: ["J01", "A234", "AB"] + +## WinterMatrix contains the following parameters: +# S01 - Yearly survival of individuals of age 0 and 1 +# SA - Yearly survival of individuals of age > 1 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J01 A234 AB +WinterMatrix: c( S01*(1-S01)/(1-S01^2), 0, 0, + S01^2*(1-S01)/(1-S01^2), SA*(1-SA^2)/(1-SA^3), 0, + 0, SA^3*(1-SA)/(1-SA^3), SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S01 : + mean : 0.643 + sd : 0.048 + descr : immature survival + SA : + mean : 0.909 + sd : 0.0678 + descr : adult survival + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J01 A234 AB +SummerMatrix: c( 1.0, 0, (FA/2)*(1-PF), + 0, 1.0, 0, + 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.550 + sd : 0.138 + descr : Breeding success + PF : + mean : 0.03 + sd : 0.125 + descr : Prob. floater + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J01 A234 AB +FertilityMatrix: c( 0, 0, (FA/2)*(1-PF)*S01*(1-S01)/(1-S01^2), + 0, 0, (FA/2)*(1-PF)*S01^2*(1-S01)/(1-S01^2), + 0, 0, 0.0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J01 A234 AB +TransitionMatrix: c( S01*(1-S01)/(1-S01^2), 0, 0, + S01^2*(1-S01)/(1-S01^2), SA*(1-SA^2)/(1-SA^3), 0, + 0, SA^3*(1-SA)/(1-SA^3), SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Branta_bernicla.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Branta_bernicla.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bf034ee24921f1717df029c5d767e26959eccf7b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Branta_bernicla.yaml @@ -0,0 +1,108 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Brent goose - Branta bernicla - Rotgans +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 11 June 2021 +# Version : 0.999 + +# Species name +species_name: Branta_bernicla + +# Number of life stages +nstages: 3 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# A - breeding adults +stages: ["J0","J1", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 6 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 A +WinterMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.51 # Sedinger et al 2007 + sd : 0.05 # guesstimate + SJ : + mean : 0.849 # weighted average, immature survival; + sd : 0.05 # guesstimate + SA : + mean : 0.868 # weighted average based on several estimates + sd : 0.03 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF), + 0, 1, 0, + 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.588117635 # based on Nolet et al 2017; correcting for survival in first 6 months; times two to get number of fledglings per pair (incl nonreaders) +# mean : 0.23805 # =0.17/((0.51)^0.5) # Nolet et al.; data 2006-2016; 0.17 1yr old indiv per adult; (longer time period 0.21;) correcting for survival in first 6 months + sd : 0.1 # estimate based on literature + PF : + mean : 0.0001 # I assumed this parameter very small. In the previous population models, we assumed no floaters, as the metric for fecundity is different (counts in wintering grounds) + sd : 0.00001 # I assumed this parameter very small. In the previous population models, we assumed no floaters, as the metric for fecundity is different (counts in wintering grounds) + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +FertilityMatrix: c( 0, 0, 0, + 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +TransitionMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Calidris_canutus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Calidris_canutus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f7263b903eb4c367789536e4b24e2731a3aef585 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Calidris_canutus.yaml @@ -0,0 +1,107 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Red knot -  Calidris canutus - Kanoet, Kanoetstrandloper +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 01 June 2021 +# Date last modified : 01 June 2021 +# Version : 0.999 + +# Species name +species_name: Calidris_canutus + +# Number of life stages +nstages: 3 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# A - breeding adults +stages: ["J0","J1", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 2 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 A +WinterMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.782 # weighted estimate + sd : 0.03 # guesstimate + SJ : + mean : 0.842 # immature survival is assumed to be the same as adult survival + sd : 0.01 # immature survival is assumed to be the same as adult survival + SA : + mean : 0.842 # weighted average based on several estimates - Spaans et al. 2011, Brochard et al. 2002, Rakhimberdiev et al. 2015 + sd : 0.01 # Rakhimberdiev et al. 2015 + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF), + 0, 1, 0, + 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.284 # Meltofte 08 reports 0.25 juv per adult during winter - divided by the sqrt of annual first-year survival (to get the first six months), this gives 0.284 + sd : 0.03 # guesstimate based on Atkinson 03. Some of the variance is caused by survival during the first six months + PF : + mean : 0.1 # guesstimate + sd : 0.03 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +FertilityMatrix: c( 0, 0, 0, + 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +TransitionMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Chlidonas_niger.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Chlidonas_niger.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7867465271c00c58b8fd15f5cb61c21155b9b32a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Chlidonas_niger.yaml @@ -0,0 +1,115 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Black tern -  Chlidonas niger - Zwarte stern +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 31 May 2021 +# Date last modified : 11 June 2021 +# Version : 0.999 + +# Species name +species_name: Chlidonas_niger + +# Number of life stages +nstages: 4 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 (third year) [some ind can take part in breeding, but rarely successful] +# A - breeding adults +stages: ["J0","J1", "J2", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1 and 2, +# SA - Yearly survival of individuals of age > 2 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 A +WinterMatrix: c( 0, 0, 0, 0, + S0, 0, 0, 0, + 0, SJ, 0, 0, + 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.595 # van der Winden & Horssen (2008) + sd : 0.05 # guesstimate + SJ : + mean : 0.595 # van der Winden & Horssen (2008) + sd : 0.05 # guesstimate + SA : + mean : 0.846 # van der Winden & Horssen (2008) + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF1), (FA/2)*(1-PF2), + 0, 1, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.93 # weighted average June21 + sd : 0.1 # guesstimate + PF1 : + mean : 0.8 # guesstimate; this is for individuals after their third year (age 3 onwards) + sd : 0.05 # guesstimate + PF2 : + mean : 0.1 # guesstimate; this is for individuals after their third year (age 3 onwards) + sd : 0.05 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A1 A2 +FertilityMatrix: c( 0, 0, 0, 0, + 0, 0, (FA/2)*(1-PF1)*S0, (FA/2)*(1-PF2)*S0, + 0, 0, 0, 0, + 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + + +# J0 J1 A1 A2 +TransitionMatrix: c( 0, 0, 0, 0, + S0, 0, 0, 0, + 0, SJ, 0, 0, + 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Cygnus_columbianus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Cygnus_columbianus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33101eb2da213ccd23c4733e647f6f83385195c1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Cygnus_columbianus.yaml @@ -0,0 +1,110 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Bewick's swan; Tundra swan -  Cygnus_columbianus - Kleine zwaan +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 01 June 2021 +# Date last modified : 01 June 2021 +# Version : 0.999 + +# Species name +species_name: Cygnus_columbianus + +# Number of life stages +nstages: 3 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# A - breeding adults +stages: ["J0","J1", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 2 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 A +WinterMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + #mean : 0.84 # weighted average, updated pars June 2021 + mean : 0.908 # Nuijten et al 2020 + sd : 0.05 # guesstimate + SJ : + #mean : 0.88 # weighted average, updated pars June 2021 + mean : 0.936 # Nuijten et al 2020 + sd : 0.05 # guesstimate + SA : + #mean : 0.85 # weighted average, updated pars June 2021 + mean : 0.873 # Nuijten et al 2020 + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF), + 0, 1, 0, + 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.278158228 # Nuijten et al 2020: 0.189 female fl per bp in wintering grounds; correction for adult survival during first six months (as reported in Nuijten); *2 to get number of chicks per bp, this is divided by 2 in the matrix +# mean_entireperiod : 0.353182293 # Nuijten et al 2020: 0.189 female fl per bp in wintering grounds; correction for adult survival during first six months (as reported in Nuijten); *2 to get number of chicks per bp, this is divided by 2 in the matrix + sd : 0.1 # estimate based on literature + PF : + mean : 0.0001 # taken very small, because fecundity is based on percentage juveniles per adult, and nonbreeders are included in this measure + sd : 0.00001 # + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +FertilityMatrix: c( 0, 0, 0, + 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +TransitionMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3ba61d3b041e1c23179e955f1491a28467b260d4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica.yaml @@ -0,0 +1,156 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Atlantic Puffin - Fratercula arctica - Papegaaiduiker -- +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 3 June 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Fratercula arctica + +# Number of life stages +nstages: 9 + +# Names of life stages +stages: ["J0", "J1", "J2", "J3", "J4", "J5", "J6", "J7", "B"] + +## WinterMatrix contains the following parameters: +# S03 - Survival of immature individuals with age 0 - 3 +# S4 - Survival of immature individuals with age 4 +# S5 - Survival of immature individuals with age 5 +# SA - Survival of adults +# Pi - Probability of recruiting to the breeding stage of class i +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 J7 B +WinterMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, + S03, 0, 0, 0, 0, 0, 0, 0, 0, + 0, S03, 0, 0, 0, 0, 0, 0, 0, + 0, 0, S03, 0, 0, 0, 0, 0, 0, + 0, 0, 0, (1-P3)*S03, 0, 0, 0, 0, 0, + 0, 0, 0, 0, (1-P4)*S4, 0, 0, 0, 0, + 0, 0, 0, 0, 0, (1-P5)*S5, 0, 0, 0, + 0, 0, 0, 0, 0, 0, (1-P6)*SA, 0, 0, + 0, 0, 0, P3*S03, P4*S4, P5*S5, P6*SA, SA, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S03 : + mean : 0.71 + sd : 0.11 + descr : Survival age 0-3 + S4 : + mean : 0.78 + sd : 0.092 + descr : Survival age 4 + S5 : + mean : 0.80 + sd : 0.083 + descr : Survival age 5 + SA : + mean : 0.93 + sd : 0.057 + descr : Adult survival + P3 : + mean : 0.046 + sd : 0.01 + descr : Prob. recruitment age 3 + P4 : + mean : 0.16 + sd : 0.01 + descr : Prob. recruitment age 4 + P5 : + mean : 0.28 + sd : 0.01 + descr : Prob. recruitment age 5 + P6 : + mean : 0.6 + sd : 0.01 + descr : Prob. recruitment age 6 + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Incidence of missed (prob. of floater) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 J7 B +SummerMatrix: c(1.0, 0, 0, 0, 0, 0, 0, 0, (1-PF)*FA/2, + 0, 1.0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1.0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 1.0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1.0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1.0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1.0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 1.0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1.0) + + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.70 + sd : 0.11 + descr : Breeding success + PF : + mean : 0.078 + sd : 0.01 + descr : Prob. floater adults + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 J6 J7 B +FertilityMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, (1-PF)*FA/2*S03, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 J6 J7 B +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, + S03, 0, 0, 0, 0, 0, 0, 0, 0, + 0, S03, 0, 0, 0, 0, 0, 0, 0, + 0, 0, S03, 0, 0, 0, 0, 0, 0, + 0, 0, 0, (1-P3)*S03, 0, 0, 0, 0, 0, + 0, 0, 0, 0, (1-P4)*S4, 0, 0, 0, 0, + 0, 0, 0, 0, 0, (1-P5)*S5, 0, 0, 0, + 0, 0, 0, 0, 0, 0, (1-P6)*SA, 0, 0, + 0, 0, 0, P3*S03, P4*S4, P5*S5, P6*SA, SA, SA) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_2.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae4043c62f3f827d539ed8ef893049ba2d44f1f4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_2.yaml @@ -0,0 +1,125 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Atlantic Puffin - Fratercula arctica - Papegaaiduiker -- +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 10 June 2021 +# Date last modified : 20 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Fratercula arctica + +# Number of life stages +nstages: 4 + +# Names of life stages +stages: ["J03", "J4", "J5", "A"] + +## WinterMatrix contains the following parameters: +# S03 - Survival of immature individuals with age 0 - 3 +# S4 - Survival of immature individuals with age 4 +# S5 - Survival of immature individuals with age 5 +# SA - Survival of adults +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J03 J4 J5 A +WinterMatrix: c( (1-S03^3)*S03/(1-S03^4), 0, 0, 0, + S03^4*(1-S03)/(1-S03^4), 0, 0, 0, + 0, S4, 0, 0, + 0, 0, S5, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S03 : + mean : 0.71 + sd : 0.11 + descr : Survival age 0-3 + S4 : + mean : 0.78 + sd : 0.092 + descr : Survival age 4 + S5 : + mean : 0.80 + sd : 0.083 + descr : Survival age 5 + SA : + mean : 0.93 + sd : 0.057 + descr : Adult survival + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Incidence of missed (prob. of floater) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J03 J4 J5 A +SummerMatrix: c( 1, (1-PF4)*FA/2, (1-PF5)*FA/2, (1-PF)*FA/2, + 0, 1, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.70 + sd : 0.11 + descr : Breeding success + PF4 : + mean : 0.6 + sd : 0.01 + descr : Prob. floater age 4 + PF5 : + mean : 0.3 + sd : 0.01 + descr : Prob. floater age 5 + PF : + mean : 0.078 + sd : 0.01 + descr : Prob. floater adults + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J03 J4 J5 A +FertilityMatrix: c( 0, (1-S03^3)/(1-S03^4)*S03*(1-PF4)*FA/2, (1-S03^3)/(1-S03^4)*S03*(1-PF5)*FA/2, (1-S03^3)/(1-S03^4)*S03*(1-PF)*FA/2, + 0, S03^4*(1-S03)/(1-S03^4)*(1-PF4)*FA/2, S03^4*(1-S03)/(1-S03^4)*(1-PF5)*FA/2, S03^4*(1-S03)/(1-S03^4)*(1-PF)*FA/2, + 0, 0, 0, 0, + 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J03 J4 J5 A +TransitionMatrix: c( (1-S03^3)/(1-S03^4)*S03, 0, 0, 0, + S03^4*(1-S03)/(1-S03^4), 0, 0, 0, + 0, S4, 0, 0, + 0, 0, S5, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_3.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..35fd7f3312e744138cbb29f85311794c39bd57cd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fratercula_arctica_3.yaml @@ -0,0 +1,141 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Atlantic Puffin - Fratercula arctica - Papegaaiduiker -- +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 10 June 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Fratercula arctica + +# Number of life stages +nstages: 7 + +# Names of life stages +stages: ["J0", "J1", "J2", "J3", "J4", "J5", "B"] + +## WinterMatrix contains the following parameters: +# S03 - Survival of immature individuals with age 0 - 3 +# S4 - Survival of immature individuals with age 4 +# S5 - Survival of immature individuals with age 5 +# SA - Survival of adults +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 J4 J5 B +WinterMatrix: c( 0, 0, 0, 0, 0, 0, 0, + S03, 0, 0, 0, 0, 0, 0, + 0, S03, 0, 0, 0, 0, 0, + 0, 0, S03, 0, 0, 0, 0, + 0, 0, 0, S03, 0, 0, 0, + 0, 0, 0, 0, S4, 0, 0, + 0, 0, 0, 0, 0, S5, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S03 : + mean : 0.71 + sd : 0.11 + descr : Survival age 0-3 + S4 : + mean : 0.78 + sd : 0.092 + descr : Survival age 4 + S5 : + mean : 0.80 + sd : 0.083 + descr : Survival age 5 + SA : + mean : 0.93 + sd : 0.057 + descr : Adult survival + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF4 - Probability of being a floater (not reproducing) for age 4 +# PF5 - Probability of being a floater (not reproducing) for age 5 +# PF - Probability of being a floater (not reproducing) for adults +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 J4 J5 B +SummerMatrix: c(1, 0, 0, 0, (1-PF4)*FA/2, (1-PF5)*FA/2, (1-PF)*FA/2, + 0, 1, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 1, 0, 0, 0, + 0, 0, 0, 0, 1, 0, 0, + 0, 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 0, 1) + + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.70 + sd : 0.11 + descr : Breeding success + PF4 : + mean : 0.6 + sd : 0.01 + descr : Prob. floater age 4 + PF5 : + mean : 0.3 + sd : 0.01 + descr : Prob. floater age 5 + PF : + mean : 0.078 + sd : 0.01 + descr : Prob. floater adults + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 B +FertilityMatrix: c( 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, (1-PF4)*FA/2*S03, (1-PF5)*FA/2*S03, (1-PF)*FA/2*S03, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 B +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, 0, + S03, 0, 0, 0, 0, 0, 0, + 0, S03, 0, 0, 0, 0, 0, + 0, 0, S03, 0, 0, 0, 0, + 0, 0, 0, S03, 0, 0, 0, + 0, 0, 0, 0, S4, 0, 0, + 0, 0, 0, 0, 0, S5, SA) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dccba12ed8ee3d2736bc162bfde39685617fd7a0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis.yaml @@ -0,0 +1,180 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The northern fulmar - Fulmarus glacialis - Noordse stormvogel -- +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 1 June 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Fulmarus glacialis + +# Number of life stages +nstages: 14 + +# Names of life stages +stages: ["J0", "J1", "J2", "J3", "J4", "J5", "J6", "J7", "J8", "J9", "J10", "J11", "J12", "B"] + +## WinterMatrix contains the following parameters: +# SJ - Immature survival (for stage J0 - J5) +# SA - Adult survival (for stage J6 - J12 & B) +# Pi - Probability of recruiting to the breeding stage of class i +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 B +WinterMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, SA*(1-P5), 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, SA*(1-P6), 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, SA*(1-P7), 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P8), 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P9), 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P10), 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P11), 0, 0, + 0, 0, 0, 0, 0, SA*P5, SA*P6, SA*P7, SA*P8, SA*P9, SA*P10, SA*P11, SA, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + SJ : + mean : 0.884 + sd : 0.054 + descr : Immmature survival + SA : + mean : 0.936 + sd : 0.055 + descr : Adult survival + P5 : + mean : 0.125 + sd : 0.01 + descr : Prob. recruitment age 5 + P6 : + mean : 0.25 + sd : 0.01 + descr : Prob. recruitment age 6 + P7 : + mean : 0.375 + sd : 0.01 + descr : Prob. recruitment age 7 + P8 : + mean : 0.5 + sd : 0.01 + descr : Prob. recruitment age 8 + P9 : + mean : 0.625 + sd : 0.01 + descr : Prob. recruitment age 9 + P10 : + mean : 0.75 + sd : 0.01 + descr : Prob. recruitment age 10 + P11 : + mean : 0.875 + sd : 0.01 + descr : Prob. recruitment age 11 + + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Incidence of missed breeding (prob. floater) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 B +SummerMatrix: c( 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, (1-PF)*(FA/2), + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.42 + sd : 0.13 + descr : Breeding success + PF : + mean : 0.304 + sd : 0.113 + descr : Prob. floater + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + + +# J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 B +FertilityMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, SJ*(1-PF)*(FA/2), + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 B +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, SJ, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, SA*(1-P5), 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, SA*(1-P6), 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, SA*(1-P7), 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P8), 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P9), 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P10), 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, SA*(1-P11), 0, 0, + 0, 0, 0, 0, 0, SA*P5, SA*P6, SA*P7, SA*P8, SA*P9, SA*P10, SA*P11, SA, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis_2.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ff416fa665815fce506c4f5444795d056f27aa28 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Fulmarus_glacialis_2.yaml @@ -0,0 +1,119 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The northern fulmar - Fulmarus glacialis - Noordse stormvogel -- +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 15 June 2021 +# Date last modified : 20 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Fulmarus glacialis + +# Number of life stages +nstages: 3 + +## Names of life stages +# J - juveniles +# APB - pre-breeding adults +# AB - breeding adults +stages: ["J", "APB", "AB"] + +## WinterMatrix contains the following parameters: +# SJ - Yearly survival of immature individuals +# SA - Yearly survival of adult individuals (pre-breeding & breeding) +# NJ - Number of juvenile age classes +# NPB - Number of pre-breeding age classes +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J APB AB +WinterMatrix: c(SJ*(1-SJ^(NJ-1))/(1-SJ^NJ), 0, 0, + SJ^NJ*(1-SJ)/(1-SJ^NJ), SA*(1-SA^(NPB-1))/(1-SA^NPB), 0, + 0, (SA^NPB)*(1-SA)/(1-SA^NPB), SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + SJ : + mean : 0.884 + sd : 0.054 + descr : Immature survival + SA : + mean : 0.936 + sd : 0.055 + descr : Adult survival + NJ : + mean : 6.0 + sd : 0.0 + descr : Number of immature years + dist : none + NPB : + mean : 4.0 + sd : 0.0 + descr : Number of pre-breeding years + dist : none + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Incidence of missed breeding (prob. floater) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J APB AB +SummerMatrix: c( 1.0, 0, (FA/2)*(1-PF), + 0, 1.0, 0, + 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.42 + sd : 0.13 + descr : Breeding success + PF : + mean : 0.304 + sd : 0.113 + descr : Prob. floater + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J APB AB +FertilityMatrix: c( 0, 0, (FA/2)*(1-PF)*SJ*(1-SJ^(NJ-1))/(1-SJ^NJ), + 0, 0, (FA/2)*(1-PF)*SJ^NJ*(1-SJ)/(1-SJ^NJ), + 0, 0, 0.0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J APB AB +TransitionMatrix: c(SJ*(1-SJ^(NJ-1))/(1-SJ^NJ), 0, 0, + SJ^NJ*(1-SJ)/(1-SJ^NJ), SA*(1-SA^(NPB-1))/(1-SA^NPB), 0, + 0, (SA^NPB)*(1-SA)/(1-SA^NPB), SA) + diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Gavia_stellata.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Gavia_stellata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..21f9925f235c56a1aa1b0f419f4c59aaf97155fd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Gavia_stellata.yaml @@ -0,0 +1,110 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The Red-Throated Diver - Gavia stellata - Roodkeelduiker +# +# Matrix structure and parameters are taken from: +# Van Kooten et al. 2019. The consequences of seabird habitat loss from offshore wind turbines, version 2 +# Displacement and population level effects in 5 selected species +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 7 April 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Gavia stellata + +# Number of life stages +nstages: 3 + +## Names of life stages +# J01 - age class 0 and 1 +# A2 - pre-breeding adult age class (??) +# AB - breeding adult age class +stages: ["J01", "A2", "AB"] + +## WinterMatrix contains the following parameters: +# P1 - Fraction remaining in age class 01 +# G1 - Fraction transiting to age class A2 +# SA - Survival probability of adults +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J01 A2 AB +WinterMatrix: c( SJ * (1 - SJ^(2-1))/(1 - SJ^2), 0, 0, + SJ^2*(1 - SJ)/(1 - SJ^2), 0, 0, + 0, SA, SA) + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + SJ : + mean : 0.61 + sd : 0.0141 + descr : Survival age 0-1 + SA : + mean : 0.861 + sd : 0.132 + descr : Adult survival + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J01 A2 AB +SummerMatrix: c( 1.0, 0, FA*(1-PF), + 0, 1.0, 0, + 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.381 + sd : 0.0905 + descr : Breeding success (fem) + PF : + mean : 0.05 + sd : 0.125 + descr : Prob. floater + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J01 A2 AB +FertilityMatrix: c( 0, 0, SJ*(1-SJ^(2-1))/(1-SJ^2)*FA*(1-PF), + 0, 0, SJ^2*(1-SJ)/(1-SJ^2)*FA*(1-PF), + 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J01 A2 AB +TransitionMatrix: c( SJ*(1-SJ^(2-1))/(1-SJ^2), 0, 0, + SJ^2*(1-SJ)/(1-SJ^2), 0, 0, + 0, SA, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_argentatus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_argentatus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..38c0538854f3312c2fe0ec503d64f1597c9a8ac6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_argentatus.yaml @@ -0,0 +1,124 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Herring gull - Larus argentatus - Zilvermeeuw +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Larus argentatus + +# Number of life stages +nstages: 5 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# A - breeding adults +stages: ["J0","J1", "J2","J3", "A"] + +## WinterMatrix contains the following parameters: + +# SJ0 - Yearly survival of individuals of age 0 +# SJ1 - Yearly survival of individuals of age 1, +# SJ2 - Yearly survival of individuals of age 2, +# SJ3 - Yearly survival of individuals of age 3, +# SA - Yearly survival of individuals of age > 3 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 A +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ1, 0, 0, 0, + 0, 0, SJ2, 0, 0, + 0, 0, 0, SJ3, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.375 # weighted average based on several estimates + sd : 0.06 # Camphuysen 2013 + SJ1 : + mean : 0.8 # weighted estimate + sd : 0.052 # Camphuysen 2013 + SJ2 : + mean : 0.8 # weighted estimate + sd : 0.052 # Camphuysen 2013 + SJ3 : + mean : 0.8 # weighted estimate + sd : 0.052 # Camphuysen 2013 + SA : + mean : 0.8646 # weighted average based on several estimates + sd : 0.03 # based on several estimates + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 A +SummerMatrix: c( 1, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, + 0, 0, 1, 0, 0, + 0, 0, 0, 1, 0, + 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.8532 # weighted average, male + female fledglings + sd : 0.2 + PF : + mean : 0.10 # estimate + sd : 0.05 # estimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +FertilityMatrix: c(0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ1, 0, 0, 0, + 0, 0, SJ2, 0, 0, + 0, 0, 0, SJ3, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_fuscus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_fuscus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..79679b743a2ab053a17a8d52483dfed97fe55234 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_fuscus.yaml @@ -0,0 +1,125 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Lesser black-backed gull - Larus fuscus - Kleine mantelmeeuw +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Larus fuscus + +# Number of life stages +nstages: 6 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# J4 - age class 4 +# A - breeding adults +stages: ["J0","J1", "J2","J3", "J4","A"] + +## WinterMatrix contains the following parameters: + +###### CHECK: yearly survival, or for 6 months?? #### +###### with this setup survival is 100% in summer matrix, and all mortality occurs in the winter matrix ##### +###### alternative: square root of annual survival gives survival for 6 months #### + +# SJ0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, 2, 3, and 4 +# SA - Yearly survival of individuals of age > 4 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 J4 A +WinterMatrix: c( 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, + 0, 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.521 # weighted average based on several estimates + sd : 0.0375 # Camphuysen 2013 + SJ : + mean : 0.856 + sd : 0.052 # Camphuysen 2013 + SA : + mean : 0.914 # weighted average based on several estimates + sd : 0.02 # based on several estimates + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 J4 A +SummerMatrix: c( 1, 0, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, + 0, 0, 0, 1, 0, 0, + 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.807 # weighted average + sd : 0.18 + PF : + mean : 0.435 # average Callidine&Harris (2008) and Camphuysen (2013) + sd : 0.1 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 A +FertilityMatrix: c(0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 A +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, + 0, 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_marinus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_marinus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6214715c666a985efa50e0270728d812f9598bcc --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Larus_marinus.yaml @@ -0,0 +1,116 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Great black-backed gull - Larus marinus - Grote mantelmeeuw +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Larus marinus + +# Number of life stages +nstages: 5 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# A - breeding adults +stages: ["J0","J1", "J2","J3", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, 2, and 3, +# SA - Yearly survival of individuals of age > 3 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 A +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.34 # average Collier et al 2020 (0.3) and weighted avg HG (0.375) + sd : 0.05 # guesstimate + SJ : + mean : 0.8 # Collier et al 2020 + sd : 0.03 # guesstimate + SA : + mean : 0.86 # weighted average based on several estimates + sd : 0.02 # Barret et al 15 + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 A +SummerMatrix: c( 1, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, + 0, 0, 1, 0, 0, + 0, 0, 0, 1, 0, + 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.8 # TEMP; NOG AANPASSEN!!! 0.98 = weighted average, male + female fledglings + sd : 0.2 # TEMP; NOG AANPASSEN!!! ; 0.4 = estimate based on literature, large variation + PF : + mean : 0.1 # estimate + sd : 0.05 # estimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +FertilityMatrix: c(0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Morus_bassanus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Morus_bassanus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97a7e6df9d0348adf8793488e291203dc872907b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Morus_bassanus.yaml @@ -0,0 +1,153 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The Northern Gannet - Morus Bassanus - Jan van Gent -- +# +# Matrix structure and parameters are taken from: +# Van Kooten et al. 2019. The consequences of seabird habitat loss from offshore wind turbines, version 2 +# Displacement and population level effects in 5 selected species +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 7 April 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Morus bassanus + +# Number of life stages +nstages: 6 + +# Names of life stages +stages: ["J0", "I1", "I2", "I3", "A4", "AB"] + +## WinterMatrix contains the following parameters: +# S0 to Si - Survival of age class 0 - i +# SA - Survival of adults +# PF - Probability of becoming a floater (skipping reproduction) +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 I1 I2 I3 A4 AB +WinterMatrix: c( 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, + 0, S1, 0, 0, 0, 0, + 0, 0, S2, 0, 0, 0, + 0, 0, 0, S3, 0, 0, + 0, 0, 0, 0, SA, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S0 : + mean : 0.481 + sd : 0.0853 + min : 0.2 + max : 0.6 + step : 0.1 + descr : Survival age 0 + S1 : + mean : 0.816 + sd : 0.0393 + min : 0.2 + max : 0.95 + step : 0.1 + descr : Survival age 1 + S2 : + mean : 0.884 + sd : 0.0293 + min : 0.4 + max : 0.95 + step : 0.1 + descr : Survival age 2 + S3 : + mean : 0.887 + sd : 0.0301 + min : 0.4 + max : 0.95 + step : 0.1 + descr : Survival age 3 + SA : + mean : 0.918 + sd : 0.0199 + min : 0.6 + max : 0.98 + step : 0.01 + descr : Adult survival + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Probability of becoming a floater (skipping reproduction next season) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 I1 I2 I3 A4 AB +SummerMatrix: c(1.0, 0, 0, 0, 0, FA/2*(1-PF), + 0, 1.0, 0, 0, 0, 0, + 0, 0, 1.0, 0, 0, 0, + 0, 0, 0, 1.0, 0, 0, + 0, 0, 0, 0, 1.0, 0, + 0, 0, 0, 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.7 + sd : 0.082 + min : 0.2 + max : 0.9 + step : 0.1 + descr : Breeding success + PF : + mean : 0.05 + sd : 0.125 + min : 0.02 + max : 0.52 + step : 0.05 + descr : Prob. floater + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 I1 I2 I3 A4 AB +FertilityMatrix: c( 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, FA/2*(1-PF)*S0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 I1 I2 I3 A4 AB +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, + 0, S1, 0, 0, 0, 0, + 0, 0, S2, 0, 0, 0, + 0, 0, 0, S3, 0, 0, + 0, 0, 0, 0, SA, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Numenius_arquata.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Numenius_arquata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f7392b88dfc2aa1a9731cfcc6c64cc16c7be9d8 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Numenius_arquata.yaml @@ -0,0 +1,106 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Eurasian curlew -  Numenius arquata - Wulp +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 11 June 2021 +# Version : 0.999 + +# Species name +species_name: Numenius_arquata + +# Number of life stages +nstages: 3 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# A - breeding adults +stages: ["J0","J1", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 2 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 A +WinterMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.5595 # Collier 20, Gerritsen 21 + sd : 0.05 # guesstimate + SJ : + mean : 0.771 # Collier 20, Gerritsen 21 + sd : 0.05 # guesstimate + SA : + mean : 0.912 # Collier 20, Gerritsen 21, Robinson 20 + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF), + 0, 1, 0, + 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.34 # review Roodbergen et al 2012 + sd : 0.1 # estimate based on literature + PF : + mean : 0.1 # guesstimate + sd : 0.05 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +FertilityMatrix: c( 0, 0, 0, + 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +TransitionMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Orcinus_orca.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Orcinus_orca.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f5ef59b50d6dafeb8b45c26e05a2387ea8cf966 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Orcinus_orca.yaml @@ -0,0 +1,150 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The Killer Whale - Orcinus orca - Orka +# +# Matrix structure and parameters are taken from: +# Caswell (2001) Matrix Population Models: construction, analysis and interpretation. +# 2nd edition Sinauer Associates Inc. Sunderland. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 7 July 2021 +# Date last modified : 7 July 2021 +# Version : 0.999 + +# Species name +species_name: Orcinus orca + +# Number of life stages +nstages: 4 + +## Names of life stages +# N1 - yearlings +# N2 - juveniles +# N3 - breeding females +# N4 - postreproductive females +stages: ["N1", "N2", "N3", "N4"] + +## WinterMatrix contains the following parameters: +# S1 - Survival of yearlings +# P2 - Fraction remaining in stage N2 +# G2 - Fraction transiting to stage N3 +# P3 - Fraction remaining in stage N3 +# G3 - Fraction transiting to stage N4 +# S4 - Survival probability of post-reproductive females +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# N1 N2 N3 N4 +#WinterMatrix: c( 0, 0, 0, 0, +# S1, P2, 0, 0, +# 0, G2, P3, 0, +# 0, 0, G3, S4) +# N1 N2 N3 N4 +WinterMatrix: c( 1, 0, 0, 0, + 0, 1, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1) + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S1 : + mean : 0.9775 + sd : 0.0 + P2 : + mean : 0.9111 + sd : 0.0 + G2 : + mean : 0.0736 + sd : 0.0 + P3 : + mean : 0.9534 + sd : 0.0 + G3 : + mean : 0.0452 + sd : 0.0 + S4 : + mean : 0.9804 + sd : 0.0 + +## SummerMatrix contains the following parameters: +# F2 - Fecundity of juvenile females +# F3 - Fecundity of breeding females +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# N1 N2 N3 N4 +# SummerMatrix: c( 1, F2, F3, 0, +# 0, 1, 0, 0, +# 0, 0, 1, 0, +# 0, 0, 0, 1) + +SummerMatrix: c( 0, F2, F3, 0, + S1, P2, 0, 0, + 0, G2, P3, 0, + 0, 0, G3, S4) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + S1 : + mean : 0.9775 + sd : 0.0 + P2 : + mean : 0.9111 + sd : 0.0 + G2 : + mean : 0.0736 + sd : 0.0 + P3 : + mean : 0.9534 + sd : 0.0 + G3 : + mean : 0.0452 + sd : 0.0 + S4 : + mean : 0.9804 + sd : 0.0 + F2 : + mean : 0.0043 + sd : 0.0 + F3 : + mean : 0.1132 + sd : 0.0 + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# N1 N2 N3 N4 +FertilityMatrix: c( 0, F2, F3, 0, + 0, 0, 0, 0, + 0, 0, 0, 0, + 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# N1 N2 N3 N4 +TransitionMatrix: c( 0, 0, 0, 0, + S1, P2, 0, 0, + 0, G2, P3, 0, + 0, 0, G3, S4) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Rissa_tridactyla.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Rissa_tridactyla.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ec2dc1a07e33095f2dc37980c18d6724c57eae0b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Rissa_tridactyla.yaml @@ -0,0 +1,116 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Black-legged kittiwake - Rissa tridactyla - Drieteenmeeuw +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Rissa tridactyla + +# Number of life stages +nstages: 5 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# A - breeding adults +stages: ["J0","J1", "J2","J3", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1,2,3, +# SA - Yearly survival of individuals of age > 3 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 A +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.79 # [1] Coulson & White (1959); [2] Horswill & Robinson (2015); + sd : 0.05 # guesstimate + SJ : + mean : 0.7 # immature survival; [1] Coulson & White (1959);[3] Thomas & Coulson (1988); same as previous estimate; + sd : 0.04 # guesstimate, based on reported estimates + SA : + mean : 0.854 # 210607 weighted average based on updated parameters KEC4.0 + sd : 0.05 # Horswill & Robinson 2015 + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 A +SummerMatrix: c( 1, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, + 0, 0, 1, 0, 0, + 0, 0, 0, 1, 0, + 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.66 # weighted average 210628, male + female fledglings + sd : 0.2 # estimate based on literature, large variation + PF : + mean : 0.10 # guesstimate + sd : 0.05 # estimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +FertilityMatrix: c(0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_parasiticus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_parasiticus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..76ca22abe33e5a0f2e9880d4b6798068f92ba3bd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_parasiticus.yaml @@ -0,0 +1,117 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Arctic skua / Parasitic jaeger - Stercorarius parasiticus - Kleine jager +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Stercorarius_parasiticus + +# Number of life stages +nstages: 5 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# A - breeding adults +stages: ["J0","J1", "J2", "J3", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 4 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 A +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.57 # update 210608 + sd : 0.05 # guesstimate + SJ : + mean : 0.77 # immature survival; + sd : 0.05 # guesstimate + SA : + mean : 0.9 # weighted average based on several estimates + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 A +SummerMatrix: c( 1, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, + 0, 0, 1, 0, 0, + 0, 0, 0, 1, 0, + 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.488 # weighted avg + sd : 0.1 # estimate based on literature + PF : + mean : 0.25 # I assumed this parameter very small. In the previous population models, we assumed no floaters, as the metric for fecundity is different (counts in wintering grounds) + sd : 0.05 # I assumed this parameter very small. In the previous population models, we assumed no floaters, as the metric for fecundity is different (counts in wintering grounds) + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +FertilityMatrix: c( 0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_skua.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_skua.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7ba8244f34f23aeb8357798343daaa7192e8117c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Stercorarius_skua.yaml @@ -0,0 +1,136 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Great skua - Stercorarius skua (also referred to as Catharacta skua) - Grote jager +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 26 May 2021 +# Version : 0.999 + +# Species name +species_name: Stercorarius_skua + +# Number of life stages +nstages: 8 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# J4 - age class 4 +# J5 - age class 5 +# J6 - age class 6 +# A - breeding adults +stages: ["J0","J1", "J2","J3","J4","J5","J6", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ1 - Yearly survival of individuals of age 1, +# SJ2 - Yearly survival of individuals of age 2, +# SJ3 - Yearly survival of individuals of age 3, +# SJ4 - Yearly survival of individuals of age 4, +# SJ5 - Yearly survival of individuals of age 5, +# SJ6 - Yearly survival of individuals of age 6, +# SA - Yearly survival of individuals of age > 6 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 A +WinterMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, 0, 0, + 0, 0, 0, 0, SJ, 0, 0, 0, + 0, 0, 0, 0, 0, SJ, 0, 0, + 0, 0, 0, 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.97 # update 210628 + sd : 0.05 # guesstimate + SJ : + mean : 0.78 # immature survival; update 210608 + sd : 0.05 # guesstimate + SA : + mean : 0.882 # weighted average; update 210608 + sd : 0.055 # Catry et al 1998 + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 J4 J5 J6 A +SummerMatrix: c( 1, 0, 0, 0, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 1, 0, 0, 0, 0, + 0, 0, 0, 0, 1, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 0, + 0, 0, 0, 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.536 # weighted average selected sources, male + female fledglings + sd : 0.3 # estimate based on literature + PF : + mean : 0.089 # Catry et al. (1998); + sd : 0.01 # estimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 J6 A +FertilityMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 J4 J5 J6 A +TransitionMatrix: c( 0, 0, 0, 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, 0, 0, 0, + 0, 0, SJ, 0, 0, 0, 0, 0, + 0, 0, 0, SJ, 0, 0, 0, 0, + 0, 0, 0, 0, SJ, 0, 0, 0, + 0, 0, 0, 0, 0, SJ, 0, 0, + 0, 0, 0, 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sterna_hirundo.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sterna_hirundo.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f1d34e4b3e31beb64348206c6581233bc7be019 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sterna_hirundo.yaml @@ -0,0 +1,118 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Common tern -  Sterna hirundo - Visdief +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 01 June 2021 +# Date last modified : 01 June 2021 +# Version : 0.999 + +# Species name +species_name: Sterna_hirundo + +# Number of life stages +nstages: 5 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# J3 - age class 3 +# A - breeding adults +stages: ["J0","J1","J2","J3","A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# S1 - Yearly survival of individuals of age 1, +# S2 - Yearly survival of individuals of age 2, +# S3 - Yearly survival of individuals of age 3, +# SA - Yearly survival of individuals of age 4 onwards +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 J3 A +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.685 # Coulson & White (1959); [2] Horswill & Robinson (2015); + sd : 0.05 # guesstimate + SJ : + mean : 0.72 # weighted average; updated parameters June 2021 + sd : 0.05 # guesstimate + SA : + mean : 0.915 # weighted average; updated parameters June 2021 + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 J3 A +SummerMatrix: c( 1, 0, 0, 0, (FA/2)*(1-PF), + 0, 1, 0, 0, 0, + 0, 0, 1, 0, 0, + 0, 0, 0, 1, 0, + 0, 0, 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 0.646 # weighted average + sd : 0.2 # based on literature + PF : + mean : 0.1 # Becker and Ludwigs 04 + sd : 0.03 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +FertilityMatrix: c( 0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 J3 A +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, SJ, 0, 0, 0, + 0, 0, SJ, 0, 0, + 0, 0, 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sturnus_vulgaris.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sturnus_vulgaris.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f8586e618849dc72cabb1d9808a8df77c0e99a96 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Sturnus_vulgaris.yaml @@ -0,0 +1,97 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Common starling - Sturnus vulgaris - Spreeuw +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 01 June 2021 +# Version : 0.999 + +# Species name +species_name: Sturnus_vulgaris + +# Number of life stages +nstages: 2 + +## Names of life stages. +# J0 - age class 0 +# A - breeding adults +stages: ["J0", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SA - Yearly survival of individuals of age > 1 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 A +WinterMatrix: c( 0, 0, + S0, SA) + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.102 # Versluijs et al 2016, Schippers et al 2020 + sd : 0.034 # guesstimate + SA : + mean : 0.607 # Versluijs et al 2016, Schippers et al 2020 + sd : 0.151 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 A +SummerMatrix: c( 1, (FA/2)*(1-PF), + 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 4.43 # Versluijs et al 2016, Schippers et al 2020, parameter is corrected for second clutches (N fledglings per breeding pair per season) + sd : 0.075 # estimate based on literature + dist : normal + PF : + mean : 0.01 # I assumed this parameter very small. In Schippers et al., no floaters are assumed. + sd : 0.001 # I assumed this parameter very small. See above. + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 A +FertilityMatrix: c( 0, 0, + 0, (FA/2)*(1-PF)*S0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 A +TransitionMatrix: c( 0, 0, + S0, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Tadorna_tadorna.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Tadorna_tadorna.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e35f0f3b929d81c879e62c1d107547fedd07db29 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Tadorna_tadorna.yaml @@ -0,0 +1,108 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Common shelduck - Tadorna tadorna - Bergeend +# +# Matrix structure and parameters are taken from: +# Potiek et al. 2019. Effects of turbine collision mortality on population dynamics of 13 bird species. +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research; Astrid Potiek, Bureau Waardenburg +# Date created : 26 May 2021 +# Date last modified : 11 June 2021 +# Version : 0.999 + +# Species name +species_name: Tadorna_tadorna + +# Number of life stages +nstages: 3 + +## Names of life stages. +# J0 - age class 0 +# J1 - age class 1 +# A - breeding adults +stages: ["J0","J1", "A"] + +## WinterMatrix contains the following parameters: + +# S0 - Yearly survival of individuals of age 0 +# SJ - Yearly survival of individuals of age 1, +# SA - Yearly survival of individuals of age > 2 +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 A +WinterMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) + + +## WinterPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +WinterPars: + S0 : + mean : 0.25 # Patterson et al 1983: 16.6% survival to age 2; we assume 25% survival between 0 and 1, and 67% survival between 1 and 2 + sd : 0.05 # guesstimate + SJ : + mean : 0.67 # weighted average, immature survival; + sd : 0.05 # guesstimate + SA : + mean : 0.873 # weighted av, Patterson et al 1983; Robinson 2015 + sd : 0.05 # + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged female chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 A +SummerMatrix: c( 1, 0, (FA/2)*(1-PF), + 0, 1, 0, + 0, 0, 1) + +## SummerPars is a nested named list with parameters values +## For each parameter a mean and a sd value should be supplied +## Expressions will be evaluated by the MakeMatrix function, +## but all components should be supplied +## NOTE!: Values of parameters without variance should be entered +## directly into the matrix! Variance cannot be zero! +SummerPars: + FA : + mean : 3.7 # Lensink 2001 successful breeding pairs; 25% floaters gives the estimate by Lensink 2001 over all pairs + sd : 0.1 # estimate based on literature + dist : normal + PF : + mean : 0.35 # 25% unsuccessful breeders based on Lensink 2001; 10% floaters guesstimate + #mean : 0.25 # based on Lensink 2001 + sd : 0.05 # guesstimate + + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +FertilityMatrix: c( 0, 0, 0, + 0, 0, (FA/2)*(1-PF) * S0, + 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 A +TransitionMatrix: c( 0, 0, 0, + S0, 0, 0, + 0, SJ, SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Thalasseus_sandvicensis.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Thalasseus_sandvicensis.yaml new file mode 100644 index 0000000000000000000000000000000000000000..931db7e63cb6472024e69dc17aa87b9510088c3a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Thalasseus_sandvicensis.yaml @@ -0,0 +1,115 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# Sandwich Tern - Thalasseus sandvicensis - Grote stern +# +# Matrix structure and parameters are taken from: +# Van Kooten et al. 2019. The consequences of seabird habitat loss from offshore wind turbines, version 2 +# Displacement and population level effects in 5 selected species +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 7 April 2021 +# Date last modified : 19 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Thalasseus sandvicensis + +# Number of life stages +nstages: 4 + +# Names of life stages +stages: ["J0", "J12", "A34", "AB"] + +## WinterMatrix contains the following parameters: +# S0 - Survival of age class 0 +# S12 - Survival of age class 1 and 2 +# SA - Survival of adults +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J12 A34 AB +WinterMatrix: c( 0, 0, 0, 0, + S0, S12*(1-S12)/(1-S12^2), 0, 0, + 0, S12^2*(1-S12)/(1-S12^2), SA*(1-SA)/(1-SA^2), 0, + 0, 0, SA^2*(1-SA)/(1-SA^2), SA) + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S0 : + mean : 0.508 + sd : 0.0917 + descr : Survival age 0 + S12 : + mean : 0.777 + sd : 0.0518 + descr : Survival age 1-2 + SA : + mean : 0.942 + sd : 0.108 + descr : Adult survival + +## SummerMatrix can contain the following parameters: +# FA - Fecundity (mean number of fledged female!! chicks per female per year) +# PF - Incidence of missed breeding (prob. floater) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J12 A34 AB +SummerMatrix: c(1.0, 0, (0.3*FA)*(1-PF), FA*(1-PF), + 0, 1.0, 0, 0, + 0, 0, 1.0, 0, + 0, 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.325 + sd : 0.160 + descr : Breeding success (fem) + PF : + mean : 0.1 + sd : 0.125 + descr : Prob. floater + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J12 A34 AB +FertilityMatrix: c(0, 0, 0, 0, + 0, 0, (0.3*FA)*(1-PF)*S0, FA*(1-PF)*S0, + 0, 0, 0, 0, + 0, 0, 0, 0) + + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J12 A34 AB +TransitionMatrix: c( 0, 0, 0, 0, + S0, S12*(1-S12)/(1-S12^2), 0, 0, + 0, S12^2*(1-S12)/(1-S12^2), SA*(1-SA)/(1-SA^2), 0, + 0, 0, SA^2*(1-SA)/(1-SA^2), SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Uria_aalge.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Uria_aalge.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c064180c08a1c2620fd1a167ceb24f13e04cecb --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/inst/extdata/Uria_aalge.yaml @@ -0,0 +1,130 @@ +# YAML file specifying the Winter and Summer transition matrices for: +# +# The Common Guillemot - Uria aalge - Zeekoet +# +# Matrix structure and parameters are taken from: +# Van Kooten et al. 2019. The consequences of seabird habitat loss from offshore wind turbines, version 2 +# Displacement and population level effects in 5 selected species +# +# Use the function: MakeMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 7 April 2021 +# Date last modified : 2 Aug. 2021 +# Version : 0.999 + +# Species name +species_name: Uria aalge + +# Number of life stages +nstages: 5 + +## Names of life stages +# J0 - age class 0 +# J1 - age class 1 +# J2 - age class 2 +# A345 - age classes 3,4 and 5 (pre-breeding adults) +# AB - breeding adults +stages: ["J0", "J1", "J2", "A345", "AB"] + +## WinterMatrix contains the following parameters: +# S0 - Fraction remaining in age class 01 +# S1 - Fraction transiting to age class A2 +# S3 - Survival probability of adults +# P4 - Fraction that remains in pre-breeding adult class +# G4 - Fraction that transits to adult breeding class +# SA - Adult survival +# +# These should be specified as a WinterPars vector that is specifically +# applied to the WinterMatrix and can differ from the SummerMatrix parameters + +# J0 J1 J2 A345 AB +WinterMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, S1, 0, 0, 0, + 0, 0, S2, SA*(1 - SA^(3-1))/(1 - SA^3), 0, + 0, 0, 0, SA^3*(1 - SA)/(1 - SA^3), SA) + +## WinterPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +WinterPars: + S0 : + mean : 0.608 + sd : 0.132 + descr : Survival age 0 + S1 : + mean : 0.774 + sd : 0.112 + descr : Survival age 1 + S2 : + mean : 0.858 + sd : 0.0736 + descr : Survival age 2 + SA : + mean : 0.949 + sd : 0.0447 + descr : Adult survival + +## SummerMatrix contains the following parameters: +# FA - Fecundity (mean number of fledged chicks per female per year) +# PF - Probability of being a floater (not reproducing) +# +# These should be specified as a SummerPars vector that is specifically +# applied to the SummerMatrix and can differ from the WinterMatrix parameters + +# J0 J1 J2 A345 AB +SummerMatrix: c( 1.0, 0, 0, 0, (FA/2)*(1-PF), + 0, 1.0, 0, 0, 0, + 0, 0, 1.0, 0, 0, + 0, 0, 0, 1.0, 0, + 0, 0, 0, 0, 1.0) + +## SummerPars is a nested named list with parameters values +## For each parameter a `mean` and a `sd` value should be supplied +## Expressions will be evaluated by the MakeMatrix family of functions, +## but all components should be supplied +## Parameter distribution can be assigned with element `dist`, which can be +## `beta` (default), `normal`, `truncnormal`, `gamma` or `none`. +## `beta` requires 0. < `mean` < 1.0 and 0. < `sd` < 0.5. +## `truncnormal` requires `lower` and `upper` values. +SummerPars: + FA : + mean : 0.664 + sd : 0.149 + descr : Breeding success + PF : + mean : 0.07 + sd : 0.03 + descr : Prob. floater + +## The Fertility Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in reproduction +## Both Summer and Winter parameters are applied to the Fertility Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 A345 AB +FertilityMatrix: c( 0, 0, 0, 0, 0, + 0, 0, 0, 0, (FA/2)*(1-PF)*S0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0) + +## The Transition Matrix contains all the elements +## of the Annual Projection Matrix `A` involved in transitions between stages +## Both Summer and Winter parameters are applied to the Transition Matrix +## NOTE that: A = F + T +## (Caswell 2001. p. 110. eq. 5.1) + +# J0 J1 J2 A345 AB +TransitionMatrix: c( 0, 0, 0, 0, 0, + S0, 0, 0, 0, 0, + 0, S1, 0, 0, 0, + 0, 0, S2, SA*(1 - SA^(3-1))/(1 - SA^3), 0, + 0, 0, 0, SA^3*(1 - SA)/(1 - SA^3), SA) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AllParSequence.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AllParSequence.Rd new file mode 100644 index 0000000000000000000000000000000000000000..8531dd866be763e6e10fa4f74d33bbd52a55b2ce --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AllParSequence.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Analysis.R +\name{AllParSequence} +\alias{AllParSequence} +\title{Calculate Lambda for a range of parameter values for all parameters of a species} +\usage{ +AllParSequence( + file = NULL, + folder = NULL, + parseq = seq(0.01, 1, 0.1), + exclude = NULL, + verbose = TRUE +) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specification} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for} + +\item{parseq}{Parameter sequence, applied to all parameters except those excluded} + +\item{exclude}{Vector with parameters names that will be excluded} + +\item{verbose}{(TRUE/FALSE). Message printing to console (defaults to TRUE)} +} +\description{ +Calculate Lambda for a range of parameter values for all parameters of a species +} +\details{ +wrapper function to apply \code{\link{MakeParSequence}} to all parameter of a species +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2ece9dcdc85f9bd571a49f6fca640729b1ec923a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseMatrix.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Analysis.R +\name{AnalyseMatrix} +\alias{AnalyseMatrix} +\title{Run an analysis based on a configuration file} +\usage{ +AnalyseMatrix(config = NULL, folder = ".") +} +\arguments{ +\item{config}{The name of the YAML configuration file} + +\item{folder}{Folder of \code{config}} +} +\description{ +Run an analysis based on a configuration file +} +\details{ +AnalyseMatrix runs a population model based on a configuration file and a input data file. +} +\examples{ +# Run AnalyseMatrix for all runfiles with: +\dontrun{ +runfolder <- "runfiles" +map(as.list(list.files(path = runfolder)), AnalyseMatrix, folder = runfolder) +} + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseSensitivity.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseSensitivity.Rd new file mode 100644 index 0000000000000000000000000000000000000000..5fc4c789d5caa355581d31ed514bf6fe8f057149 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyseSensitivity.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Sensitivity.R +\name{AnalyseSensitivity} +\alias{AnalyseSensitivity} +\title{Run a sensitivity analysis of based on a configuration file} +\usage{ +AnalyseSensitivity(config = NULL, folder = ".", statselect = NULL) +} +\arguments{ +\item{config}{The name of the YAML configuration file} + +\item{folder}{Folder of \code{config}} + +\item{statselect}{The level of the 'stat' column used for sensitivity analysis} +} +\value{ +Returns a data.frame with sensitivity output. +} +\description{ +Run a sensitivity analysis of based on a configuration file +} +\details{ +AnalyseSensitivity runs the population model for a range of parameter values based on a configuration file and a input data file. +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyzeMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyzeMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..89eebf69c277a0da2444b4f7176591d93a2d4e43 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/AnalyzeMatrix.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Analysis.R +\name{AnalyzeMatrix} +\alias{AnalyzeMatrix} +\title{Run an analysis based on a configuration file} +\usage{ +AnalyzeMatrix(config = NULL, folder = ".") +} +\arguments{ +\item{config}{The name of the YAML configuration file} + +\item{folder}{Folder of \code{config}} +} +\description{ +Run an analysis based on a configuration file +} +\details{ +Synonym of \code{\link{AnalyseMatrix}} +} +\examples{ +# Run AnalyzeMatrix for all runfiles with: +\dontrun{ +runfolder <- "runfiles" +map(as.list(list.files(path = runfolder)), AnalyzeMatrix, folder = runfolder) +} + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Boxplot_Lambda.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Boxplot_Lambda.Rd new file mode 100644 index 0000000000000000000000000000000000000000..4ebd266f607aa22720cb69b366f6fcb5127dc92b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Boxplot_Lambda.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Plotting.R +\name{Boxplot_Lambda} +\alias{Boxplot_Lambda} +\title{Make a boxplot of the population growth rate for each scenario} +\usage{ +Boxplot_Lambda( + xvar = "name", + yvar = "value", + Lambda_stoch = NULL, + Lambda_det = NULL, + species = "", + ... +) +} +\arguments{ +\item{xvar}{Name of x axis variable} + +\item{yvar}{Name of y axis variable} + +\item{Lambda_stoch}{A long data.frame with columns 'name' (scenario names) and 'value' (stochastic population growth rate)} + +\item{Lambda_det}{(optional) A long data.frame with columns 'name' (scenario names) and 'det' (deterministic population growth rates)} + +\item{species}{Name of species. Only used as title} +} +\description{ +Make a boxplot of the population growth rate for each scenario +} +\details{ +Additional arguments are passed on to geom_boxplot +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/CalculateALI.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/CalculateALI.Rd new file mode 100644 index 0000000000000000000000000000000000000000..b365b8aa3f3995a6fcf6f35384e5dd171c3c26d4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/CalculateALI.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Analysis.R +\name{CalculateALI} +\alias{CalculateALI} +\title{Calculate Acceptable Level of Impact for a species} +\usage{ +CalculateALI( + file = NULL, + folder = NULL, + threshold = 0.7, + N = 10000, + summerpars = NULL, + winterpars = NULL, + verbose = TRUE +) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specification} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for} + +\item{threshold}{Fraction of impacted population to test against.} + +\item{N}{Number of replicate matrices} + +\item{summerpars}{(optional) A nested list with parameters for the reproduction (summer) matrix} + +\item{winterpars}{(optional) A nested list with parameters for the survival (winter) matrix} + +\item{verbose}{(TRUE/FALSE). Message printing to console (defaults to TRUE)} +} +\description{ +Calculate Acceptable Level of Impact for a species +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ConvertMortalityData.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ConvertMortalityData.Rd new file mode 100644 index 0000000000000000000000000000000000000000..d52bedfee1b85b03dfcbaed6a9621c570fd0146c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ConvertMortalityData.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Analysis.R +\name{ConvertMortalityData} +\alias{ConvertMortalityData} +\title{A function to extract mortality probability from abundance and casualty data} +\usage{ +ConvertMortalityData(input = NULL) +} +\arguments{ +\item{input}{A data.frame with number of OWF casualties.} +} +\description{ +A function to extract mortality probability from abundance and casualty data +} +\details{ +Input data.frame should at least contain the columns: +\describe{ +\item{season}{integer of season} +\item{null}{number of casualties per season without OWF mortality} +\item{abundance}{abundance estimate per season} +\item{species}{name of the species} +} +In addition to columns with number of casualties per scenario +} +\seealso{ +\code{\link{DummyInput}} +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DrawParameters.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DrawParameters.Rd new file mode 100644 index 0000000000000000000000000000000000000000..93e696cb64222e894d22af36ec5ac568ee8a2681 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DrawParameters.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{DrawParameters} +\alias{DrawParameters} +\title{Wrapper function unpacks parameter list and calls \code{KECdist} function} +\usage{ +DrawParameters(parlist = NULL, N = 1) +} +\arguments{ +\item{parlist}{A nested list with parameters.} + +\item{N}{Number of replicate draws} +} +\description{ +Wrapper function unpacks parameter list and calls \code{KECdist} function +} +\details{ +In \code{parlist}, each parameter should at least contain \code{mean} and \code{sd} entry. Optionally, a distribution can be specified with named element \code{dist}, which can be \code{beta}, \code{gamma}, \code{normal}, \code{truncnormal} or \code{none}. If missing, \code{dist} is set to \code{beta}. For \code{none} the mean is replicated \code{N} times. For the truncated normal distribution (\code{truncnormal}) \code{lower} and \code{upper} bounds should be specified. +} +\examples{ +parlist <- list('FA' = list('mean' = 2.32, 'sd' = 0.75, 'lower' = 1.5, 'upper' = 2.9, 'dist' = 'truncnormal'), + 'SA' = list('mean' = 0.956, 'sd' = 0.032)) +DrawParameters(parlist, N = 10) + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DummyInput.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DummyInput.Rd new file mode 100644 index 0000000000000000000000000000000000000000..7f3a3301624b651dae33a9cf3952d52dfb870fea --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/DummyInput.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{DummyInput} +\alias{DummyInput} +\title{DummyInput} +\format{ +A data.frame +} +\usage{ +DummyInput +} +\description{ +A dummy data set with number of birds displaced per season and scenario +} +\keyword{datasets} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EigenDecomp.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EigenDecomp.Rd new file mode 100644 index 0000000000000000000000000000000000000000..46ec2cb269250f5e802ba9458dcca766fee66fde --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EigenDecomp.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{EigenDecomp} +\alias{EigenDecomp} +\title{Calculate eigenvalues and left and right eigenvectors for a square matrix A} +\source{ +Uses RcppArmadillo function eig_gen(). See \url{http://arma.sourceforge.net/docs.html#eig_gen} +} +\usage{ +EigenDecomp(A) +} +\arguments{ +\item{A}{A square matrix} +} +\value{ +Returns a list with elements 'Eigenvalues', 'Leftvectors' and 'Rightvectors' +} +\description{ +Calculate eigenvalues and left and right eigenvectors for a square matrix A +} +\author{ +Vincent Hin - Wageningen Marine Research +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EvalMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EvalMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..e02bc43ebcaa221650832cb533a38be06386f6e0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/EvalMatrix.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{EvalMatrix} +\alias{EvalMatrix} +\title{Evaluate a matrix from an expression} +\usage{ +EvalMatrix(matexpr = NULL, parlist = NULL, stagenms = NULL) +} +\arguments{ +\item{matexpr}{An expression of a matrix} + +\item{parlist}{A list with parameters to evaluate \code{matexpr}} + +\item{stagenms}{A character vector with names of stages} +} +\description{ +Evaluate a matrix from an expression +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/FundamentalMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/FundamentalMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..9f7838a27e19b1a7a3faa5fcfecd939c91441bc6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/FundamentalMatrix.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{FundamentalMatrix} +\alias{FundamentalMatrix} +\title{Return the Fundamental Matrix from the Transition rate matrix} +\usage{ +FundamentalMatrix(Tmat) +} +\arguments{ +\item{Tmat}{A square matrix describing stage transitions} +} +\description{ +Return the Fundamental Matrix from the Transition rate matrix +} +\details{ +see Caswell 2001 - Matrix Population Models p. 112 +} +\author{ +Vincent Hin - Wageningen Marine Research +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/GenerationTime.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/GenerationTime.Rd new file mode 100644 index 0000000000000000000000000000000000000000..417632bc9af2e15a592ab5bc8c75ec2b46b12b95 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/GenerationTime.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Lambda.R +\name{GenerationTime} +\alias{GenerationTime} +\title{Calculate the generation time based on the Fertility and Transition matrices} +\usage{ +GenerationTime(GenMatrix = NULL) +} +\arguments{ +\item{GenMatrix}{A list generated by MakeGenMatrix with named elements \code{FertilityMatrix}, \code{TransitionMatrix} and \code{NextGenMatrix}} +} +\value{ +A numeric vector with generation times +} +\description{ +Calculate the generation time based on the Fertility and Transition matrices +} +\details{ +Calculates the generation time according to eq. 5.73 in Caswell (2001) p. 129. +} +\examples{ +library(magrittr) +MakeGenMatrix(file = "Alca_torda", N = 100, stochastic = TRUE) \%>\% +GenerationTime() \%>\% +hist() + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Histogram_Lambda.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Histogram_Lambda.Rd new file mode 100644 index 0000000000000000000000000000000000000000..518330c73384513a2ff17e352b3005e2c733dbfb --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Histogram_Lambda.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Plotting.R +\name{Histogram_Lambda} +\alias{Histogram_Lambda} +\title{Make a histogram of the population growth rate for each scenario} +\usage{ +Histogram_Lambda( + Lambda_stoch = NULL, + Lambda_det = NULL, + xvar = "value", + facetvar = "name", + species = "", + ... +) +} +\arguments{ +\item{Lambda_stoch}{A long data.frame with columns 'name' (scenario names) and 'value' (stochastic population growth rate)} + +\item{Lambda_det}{(optional) A long data.frame with columns 'name' (scenario names) and 'det' (deterministic population growth rates)} + +\item{xvar}{Name of x axis variable} + +\item{species}{Name of species. Only used as title} +} +\description{ +Make a histogram of the population growth rate for each scenario +} +\details{ +Additional arguments are passed on to geom_histogram +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4matfiles.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4matfiles.Rd new file mode 100644 index 0000000000000000000000000000000000000000..88b037976c839c32b2ec70d6ad8c85dbccfb23e9 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4matfiles.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{KEC4matfiles} +\alias{KEC4matfiles} +\title{KEC4matfiles} +\format{ +Character vector +} +\usage{ +KEC4matfiles +} +\description{ +Names of all the YAML configuration files with matrix descriptions that are part of the KEC4popmodels package +} +\keyword{datasets} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4popmodels.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4popmodels.Rd new file mode 100644 index 0000000000000000000000000000000000000000..0823fab1bd3eb5c34c1db64c7f15ec098319321d --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4popmodels.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/KEC4popmodels-package.R +\name{KEC4popmodels} +\alias{KEC4popmodels} +\title{KEC4popmodels} +\description{ +Matrix population models to calculate impact of habitat loss from offshore wind parks on selected seabird populations +} +\section{KEC4popmodels functions}{ + +\describe{ +\item{\code{\link{AllParSequence}}}{} +\item{\code{\link{AnalyseMatrix}}}{} +\item{\code{\link{AnalyzeMatrix}}}{} +\item{\code{\link{Boxplot_Lambda}}}{} +\item{\code{\link{CalculateALI}}}{} +\item{\code{\link{ConvertMortalityData}}}{} +\item{\code{\link{DrawParameters}}}{} +\item{\code{\link{EigenDecomp}}}{Calculate eigenvalues and left and right eigenvectors for a square matrix A} +\item{\code{\link{FundamentalMatrix}}}{} +\item{\code{\link{GenerationTime}}}{Calculate the generation time based on the fertility and transition matrices} +\item{\code{\link{Histogram_Lambda}}}{} +\item{\code{\link{KECdist}}}{} +\item{\code{\link{Lambda}}}{Calculate the asymptotic population growth rate (Lambda) from a population projection matrix} +\item{\code{\link{LambdaThreshold}}}{} +\item{\code{\link{MakeGenMatrix}}}{Create the fertility, transition, fundamental and next generation matrix from a YAML configuration file} +\item{\code{\link{MakeMatrix}}}{Create the annual projection matrix from a YAML configuration file} +\item{\code{\link{MakeParSequence}}}{Create a list of matrices for a sequence of parameter values} +\item{\code{\link{MaxReal}}}{Return the real part of the largest eigenvalue} +\item{\code{\link{MortProbToSurv}}}{} +\item{\code{\link{MortRateToSurv}}}{} +\item{\code{\link{Plot_AllParSeq}}}{} +\item{\code{\link{PopProject}}}{Calculate population projection} +\item{\code{\link{ReadMatrix}}}{Read a YAML file with matrix specifications} +\item{\code{\link{Sensitivity}}}{} +\item{\code{\link{StableStage}}}{Calculate the stable population distribution from a population projection matrix} +\item{\code{\link{SurvToMortProb}}}{} +\item{\code{\link{SurvToMortRate}}}{} +\item{\code{\link{UpdateSurvival}}}{} +\item{\code{\link{write_yamlopts}}}{} +} +} + +\section{KEC4popmodels seabird species}{ + +Matrices and parameters for the following seabird species are included in the KEC4popmodels package +\describe{ +\item{Morus_bassanus}{Matrix for the Northern Gannet (\emph{Morus bassanus})} +\item{Alca_torda}{Matrix for the the Razorbill (\emph{Alca torda})} +\item{Gavia_stellata}{Matrix for the red-throated diver (\emph{Gavia stellata})} +\item{Thalasseus_sandvicensis}{Matrix for the sandwich tern (\emph{Thalasseus sandvicensis})} +\item{Uria_aalge}{Matrix for the common guillemot (\emph{Uria aalge})} +} +} + +\references{ +Van Kooten et al. (2019). The consequences of seabird habitat loss from offshore wind turbines: displacement and population level effects in 5 selected species. Wageningen University & Research report C088/18. \url{https://edepot.wur.nl/465541} +} +\author{ +Vincent Hin (Wageningen Marine Research) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4species.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4species.Rd new file mode 100644 index 0000000000000000000000000000000000000000..4e516141dea8d4fe0ca24bc4337bf319cfb1b537 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KEC4species.Rd @@ -0,0 +1,16 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{KEC4species} +\alias{KEC4species} +\title{KEC4species} +\format{ +Character vector +} +\usage{ +KEC4species +} +\description{ +Names of all species that are part of the KEC4popmodels package +} +\keyword{datasets} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KECdist.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KECdist.Rd new file mode 100644 index 0000000000000000000000000000000000000000..288b940988ce19e67dd5bc0693c9d15333d8e664 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/KECdist.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{KECdist} +\alias{KECdist} +\title{Sample parameters from a distribution} +\usage{ +KECdist(mu = NULL, sd = NULL, dist = NULL, n = 1, lower = NA, upper = NA) +} +\arguments{ +\item{mu}{parameter mean} + +\item{sd}{parameter standard deviation} + +\item{dist}{parameter distribution. Either \code{beta}, \code{gamma}, \code{normal}, \code{truncnormal} or \code{none}.} + +\item{n}{number of samples} + +\item{lower}{parameter lower bound. Only required if \code{dist = truncnormal}} + +\item{upper}{parameter upper bound. Only required if \code{dist = truncnormal}} +} +\description{ +Sample parameters from a distribution +} +\details{ +Sample parameters from a beta, gamma, normal or a truncated normal distribution. Beta distribution requires 0. < mu < 1. and 0. < sd < 0.5. Throws an error otherwise. Shape parameters of the beta distribution (\eqn{\alpha} and \eqn{\beta}) are calculated as \eqn{\alpha = \mu^2 (1-\mu)/(\sigma^2) - 1/\mu } and \eqn{\beta = \alpha (1/\mu - 1)}. In addition, for \code{dist = "beta"} parameters are restricted to <0.999 and >0.001. For the gamma distribution (\code{dist = "gamma"}) the shape parameter is \eqn{\alpha = \mu^2/sd} and the scale parameter is \eqn{\beta = (sd^2) / \mu}. If \code{dist = "normal"} invokes \code{rnorm} function with \code{mean = mu} and \code{sd = sd}. For the truncated normal (\code{dist = "truncnormal"}) supplied \code{mu} and \code{sd} are interpreted as the mean and standard deviation of the associated untruncated normal distribution and the sampled means and standard deviations might differ, depending on the values of \code{lower} and \code{upper}. For \code{dist == "none"} \code{mu} is replicated \code{n} times. +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Lambda.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Lambda.Rd new file mode 100644 index 0000000000000000000000000000000000000000..6cea05c54586250bb938a81a84dbbd38e4da8257 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Lambda.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Lambda.R +\name{Lambda} +\alias{Lambda} +\title{Calculate the asymptotic population growth rate (Lambda) from a population projection matrix} +\usage{ +Lambda(A = NULL, GenMat = NULL) +} +\arguments{ +\item{A}{The population projection matrix, or a list with population projection matrices} + +\item{GenMat}{A nested listed generated by MakeGenMatrix containing list elements \code{FertilityMatrix}, \code{TransitionMatrix}, \code{FundamentalMatrix} and \code{NextGenMatrix}.} +} +\value{ +A numeric vector with the real part of the largest eigenvalue of the annual projection matrix. +} +\description{ +Calculate the asymptotic population growth rate (Lambda) from a population projection matrix +} +\details{ +In case \code{GenMat} is supplied, the annual projection matrix is calculated from the \code{TransitionMatrix} and the \code{FertilityMatrix}. Wrapper function that uses \code{\link{EigenDecomp}} and \code{\link{MaxReal}} +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/LambdaThreshold.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/LambdaThreshold.Rd new file mode 100644 index 0000000000000000000000000000000000000000..ee1ac1821fdaf5ebcb49bf2370cb08fa47e318ed --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/LambdaThreshold.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Lambda.R +\name{LambdaThreshold} +\alias{LambdaThreshold} +\title{Calculate the asymptotic population growth rate that leads to a 'p' relative reduction after tref years from a list of transition and fertility matrices} +\usage{ +LambdaThreshold(GenMat = NULL, tref = NULL, p = 0.7) +} +\arguments{ +\item{GenMat}{A list with matrices generated by MakeGenMatrix, containing the names elements \code{FertilityMatrix}, \code{TransitionMatrix}, \code{FundamentalMatrix} and \code{NextGenMatrix}. Projection matrix of unaffected population is the sum the Fertility and Transition matrices.} + +\item{tref}{Time period after which the population abundances are compared. If NULL, tref is set to 3 times the generation time or 10 years, which ever is largest.} + +\item{p}{Population abundance of the affected population (0 - 1) relative to the unaffected population (1)} +} +\value{ +Returns a list with elements \code{Lambda_null}, \code{tref} and \code{Lambda_threshold} +} +\description{ +Calculate the asymptotic population growth rate that leads to a 'p' relative reduction after tref years from a list of transition and fertility matrices +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeGenMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeGenMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..f2b25656925488ec16366defa8a7782a67fb4765 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeGenMatrix.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{MakeGenMatrix} +\alias{MakeGenMatrix} +\title{Create the fertility, transition, fundamental and next generation matrix from a YAML configuration file} +\usage{ +MakeGenMatrix( + file = NULL, + folder = NULL, + N = 1, + stochastic = FALSE, + summerpars = NULL, + winterpars = NULL, + verbose = TRUE +) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specification} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used} + +\item{N}{Number of matrices returned (defaults to 1)} + +\item{stochastic}{(TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE)} + +\item{summerpars}{(optional) A nested named list with parameters. Is combined with winterpars to create one parameter list. Parameter names should not overlap.} + +\item{winterpars}{(optional) A nested named list with parameters. Is combined with summerpars to create one parameter list. Parameter names should not overlap.} + +\item{verbose}{(TRUE/FALSE). Message printing to console (defaults to TRUE)} +} +\value{ +Returns a nested list with N instances of each of the four matrices +} +\description{ +Create the fertility, transition, fundamental and next generation matrix from a YAML configuration file +} +\note{ +\describe{ +\item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +\item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +} +} +\examples{ +MakeGenMatrix(file = "Alca_torda", N = 1000, stochastic = TRUE) + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2cc6165e1d8494376ef746cfa2fd13a6867cc86e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeMatrix.Rd @@ -0,0 +1,53 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{MakeMatrix} +\alias{MakeMatrix} +\title{Create the annual projection matrix from a YAML configuration file} +\usage{ +MakeMatrix( + file = NULL, + folder = NULL, + N = 1, + tseq = NULL, + stochastic = FALSE, + summerpars = NULL, + winterpars = NULL, + postbreeding = FALSE, + verbose = TRUE +) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specification} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used} + +\item{N}{Number of matrices returned if tseq = NULL. Defaults to 1.} + +\item{tseq}{(optional). Create a time sequence of matrices. Overrides N} + +\item{stochastic}{(TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE)} + +\item{summerpars}{(optional) A nested list with parameters for the reproduction (summer) matrix} + +\item{winterpars}{(optional) A nested list with parameters for the survival (winter) matrix} + +\item{postbreeding}{(TRUE/FALSE) Creates a postbreeding matrix. By default (FALSE) the matrix represents a population just before breeding.} + +\item{verbose}{(TRUE/FALSE). Message printing to console (defaults to TRUE)} +} +\value{ +Returns a list with N annual projection matrices +} +\description{ +Create the annual projection matrix from a YAML configuration file +} +\note{ +\describe{ +\item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +\item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +} +} +\examples{ +MakeMatrix(file = "Morus_bassanus", N = 1000, stochastic = TRUE) + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeParSequence.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeParSequence.Rd new file mode 100644 index 0000000000000000000000000000000000000000..c2239a37608654e24f262430d24b554e0ec92884 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MakeParSequence.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{MakeParSequence} +\alias{MakeParSequence} +\title{Create a list of matrices for a sequence of parameter values} +\usage{ +MakeParSequence( + file = NULL, + folder = NULL, + N = 1, + stochastic = FALSE, + summerpars = NULL, + winterpars = NULL, + parseq = NULL, + verbose = TRUE +) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specification} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used} + +\item{N}{Number of matrices returned per value of the parameter (defaults to 1)} + +\item{stochastic}{(TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE)} + +\item{summerpars}{(optional) A nested list with parameters for the reproduction (summer) matrix} + +\item{winterpars}{(optional) A nested list with parameters for the survival (winter) matrix} + +\item{parseq}{A named list with parameter sequences. Multiple parameters can be change at once. Sequences need to be of equal length.} + +\item{verbose}{(TRUE/FALSE). Message printing to console (defaults to TRUE)} +} +\value{ +Returns a nested list with N annual projection matrices per parameter value. List is named based on values of first element of \code{parseq} +} +\description{ +Create a list of matrices for a sequence of parameter values +} +\note{ +\describe{ +\item{Run \code{\link{KEC4species}} for the species that are part of the KEC4popmodels package}{} +\item{Run \code{\link{KEC4matfiles}} for the names of the matrix files available in KEC4popmodels package}{} +} +} +\examples{ +parseqlst <- list('FA' = seq(0.1,0.5,by=0.1), 'SA' = seq(0.7,0.9,by=0.05)) +MakeParSequence(file = "Morus_bassanus", stochastic = TRUE, N = 1000, parseq = parseqlst) \%>\% + map(Lambda) \%>\% + map_df(~as.data.frame(.x), .id = "par") \%>\% + set_names(nm = c("par", "Lambda")) \%>\% + ggplot(aes(x = par, y = Lambda, fill = par)) + + geom_boxplot() + theme_classic() + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MaxReal.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MaxReal.Rd new file mode 100644 index 0000000000000000000000000000000000000000..0dc361651e91db24e2c7c47a61d54ad29c6ba239 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MaxReal.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{MaxReal} +\alias{MaxReal} +\title{Return the real part of the largest eigenvalue} +\source{ +http://arma.sourceforge.net/docs.html#eig_gen +} +\usage{ +MaxReal(A) +} +\arguments{ +\item{A}{A square matrix with eigenvalues} +} +\description{ +Return the real part of the largest eigenvalue +} +\examples{ +MakeMatrix(file = "Morus_bassanus", N = 10000, stochastic = TRUE) \%>\% +map(EigenDecomp) \%>\% +map('Eigenvalues') \%>\% +map(MaxReal) \%>\% +unlist() + +} +\author{ +Vincent Hin - Wageningen Marine Research +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortProbToSurv.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortProbToSurv.Rd new file mode 100644 index 0000000000000000000000000000000000000000..509d0e205a6bf7db2976fee708265bb6c5404bdd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortProbToSurv.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{MortProbToSurv} +\alias{MortProbToSurv} +\title{Calculate mortality probability from survival} +\usage{ +MortProbToSurv(m = NULL) +} +\description{ +Calculate mortality probability from survival +} +\details{ +S = 1 - m +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortRateToSurv.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortRateToSurv.Rd new file mode 100644 index 0000000000000000000000000000000000000000..ae69bd2c060d1521ee2c799a4905f0a168eacaae --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/MortRateToSurv.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{MortRateToSurv} +\alias{MortRateToSurv} +\title{Calculate mortality rate from survival} +\usage{ +MortRateToSurv(m = NULL) +} +\description{ +Calculate mortality rate from survival +} +\details{ +S = exp(-m) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Plot_AllParSeq.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Plot_AllParSeq.Rd new file mode 100644 index 0000000000000000000000000000000000000000..536980307e83526e0da1e1dc96c0441be5032858 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Plot_AllParSeq.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Plotting.R +\name{Plot_AllParSeq} +\alias{Plot_AllParSeq} +\title{Plot output from AllParSequence function} +\usage{ +Plot_AllParSeq( + allparseq = NULL, + yvar = "Lambda", + species = "", + facetvar = "parsdescr", + pnts = FALSE, + ... +) +} +\arguments{ +\item{`allparseq`}{A list or data.frame element returned by \code{\link{AllParSequence}}} + +\item{`yvar`}{Name of variable to plot on vertical axis (defaults to "Lambda")} + +\item{`species`}{Name of the species. Only used to created plot title.} + +\item{`pnts`}{(TRUE/FALSE) Plot individuals data points (defaults to FALSE)} +} +\description{ +Plot output from AllParSequence function +} +\examples{ +AllParSequence("Alca_torda", parseq = seq(0.01,0.99,0.01), verbose = FALSE) \%>\% +Plot_AllParSeq(species = "Alca_torda") + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/PopProject.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/PopProject.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2cdacab7c7e06014315c51af7a0eda3afc191c7c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/PopProject.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{PopProject} +\alias{PopProject} +\title{Calculate population projection} +\source{ +Rcpp version of R function popbio::pop.projection() +} +\usage{ +PopProject(A, n, niter = 20L) +} +\arguments{ +\item{A}{The population projection matrix, or a list with annual projection matrices for a sequence of time steps} + +\item{n}{A vector with initial densities} + +\item{niters}{Number of iterations} +} +\value{ +Returns a list with elements +\describe{ +\item{\code{ProjectionMatrix}}{A list with annual projection matrices} +\item{\code{StageVector}}{A matrix with stage vectors} +\item{\code{PopSize}}{A matrix with total population sizes} +\item{\code{PopChange}}{A matrix with annual growth factors} +} +} +\description{ +Calculate population projection +} +\author{ +Vincent Hin - Wageningen Marine Research +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ReadMatrix.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ReadMatrix.Rd new file mode 100644 index 0000000000000000000000000000000000000000..618801d9107f938d5a7176dbf0cd8b1986fe9703 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/ReadMatrix.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/MakeMatrix.R +\name{ReadMatrix} +\alias{ReadMatrix} +\title{Read a YAML file with matrix specifications} +\usage{ +ReadMatrix(file = NULL, folder = NULL, verbose = TRUE) +} +\arguments{ +\item{file}{Name of the YAML configuration file with matrix specifications} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is searched for.} +} +\description{ +Read a YAML file with matrix specifications +} +\examples{ +ReadMatrix(file = "Morus_bassanus") + +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Sensitivity.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Sensitivity.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2e18f2341505d4c50f2c6d800b78da3dfd606191 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/Sensitivity.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/Lambda.R +\name{Sensitivity} +\alias{Sensitivity} +\title{Calculate sensitivity of population growth rate to a parameter} +\usage{ +Sensitivity( + parseq.df = NULL, + file = NULL, + folder = NULL, + parameter = NULL, + value = NULL +) +} +\arguments{ +\item{parseq.df}{A data.frame with columns \code{parameter} and \code{value} (as returned by \code{\link{AllParSequence}})} + +\item{file}{Name of the YAML configuration file with matrix specification (used in case parseq.df = NULL)} + +\item{folder}{Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used} + +\item{parameter}{The name of the parameter for which the sensitivity is calculated (used in case parseq.df = NULL)} + +\item{value}{Vector of parameter values at which the sensitivity is calculated (used in case parseq.df = NULL)} +} +\value{ +A numeric vector +} +\description{ +Calculate sensitivity of population growth rate to a parameter +} +\details{ +Uses grad() function from numDeriv package. Function is vectorized on value. +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/StableStage.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/StableStage.Rd new file mode 100644 index 0000000000000000000000000000000000000000..8e25a4baa226b8f0e4b45ded16305f0d1a5a80bd --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/StableStage.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{StableStage} +\alias{StableStage} +\title{Calculate the stable population distribution from a population projection matrix} +\source{ +http://arma.sourceforge.net/docs.html#eig_gen +} +\usage{ +StableStage(A) +} +\arguments{ +\item{A}{A square matrix} +} +\value{ +Return a row vector with the stable stage distribution +} +\description{ +Calculate the stable population distribution from a population projection matrix +} +\author{ +Vincent Hin - Wageningen Marine Research +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortProb.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortProb.Rd new file mode 100644 index 0000000000000000000000000000000000000000..d481f3afee36ef209dc8213f17efe2312004cf1f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortProb.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{SurvToMortProb} +\alias{SurvToMortProb} +\title{Calculate survival from finite mortality probability} +\usage{ +SurvToMortProb(S = NULL) +} +\description{ +Calculate survival from finite mortality probability +} +\details{ +m = 1 - S +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortRate.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortRate.Rd new file mode 100644 index 0000000000000000000000000000000000000000..ef4acd8b208b73db5fc934619e279f08153a0713 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/SurvToMortRate.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{SurvToMortRate} +\alias{SurvToMortRate} +\title{Calculate survival from mortality rate} +\usage{ +SurvToMortRate(S = NULL) +} +\description{ +Calculate survival from mortality rate +} +\details{ +m = -log(S) +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/UpdateSurvival.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/UpdateSurvival.Rd new file mode 100644 index 0000000000000000000000000000000000000000..2a86e9306f145367c7bf7c9190c0662e3ca7fea3 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/UpdateSurvival.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{UpdateSurvival} +\alias{UpdateSurvival} +\title{Re-calculate annual survival probability} +\usage{ +UpdateSurvival(survival = NULL, months = NULL, mort = NULL) +} +\arguments{ +\item{survival}{Baseline annual survival probability} + +\item{months}{Number of months per year with additional mortality} + +\item{mort}{Additional monthly mortality rate} +} +\value{ +Annual survival probability (including additional mortality) +} +\description{ +Calculate the annual survival from baseline survival and additional +mortality applied for a certain number of months per year +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/write_yamlopts.Rd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/write_yamlopts.Rd new file mode 100644 index 0000000000000000000000000000000000000000..b2b6b04ad9bbe5da2a706f32ecf05b4f7cc77113 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/man/write_yamlopts.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils.R +\name{write_yamlopts} +\alias{write_yamlopts} +\title{Change a configuration option in multiple YAML runfiles} +\usage{ +write_yamlopts(folder = NULL, exclude = NULL, cfg = NULL) +} +\arguments{ +\item{folder}{Folder where to look for YAML runfiles} + +\item{exclude}{Names of YAML files to exclude.} + +\item{cfg}{A named vector of length 1 with the yaml option that needs to be replaced in all the files} +} +\description{ +Change a configuration option in multiple YAML runfiles +} +\details{ +Uses pattern matching to exclude YAML files. File names listed under \code{exclude} do not need to be exact. +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/run_KEC4popmodels.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/run_KEC4popmodels.R new file mode 100644 index 0000000000000000000000000000000000000000..e8e22da0483c5658019c5213b19a45e762043b08 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/run_KEC4popmodels.R @@ -0,0 +1,34 @@ +################################################################################################################ +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +################################################################################################################ + +## Installation instructions for the KEC4popmodels R package are described in the README.md +library('KEC4popmodels') + +## Function KEC4popmodels::AnalyseMatrix performs analysis based on a YAML runfile +## YAML runfiles are stored at https://git.wur.nl/ecodyn/KEC4popmodels +## and copied in .runfiles/ for convenience +runfiles <- list.files("./runfiles/") + +## AnalyseMatrix requires input data with number of casualties per OWF scenario and abundance data for each species + +## Following call applies AnalyseMatrix to all runfiles: +## For each runfile a subfolder is created within /output and a pdf and docx report is generated +sapply(runfiles, AnalyseMatrix, folder = "runfiles/") diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Alca_torda.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Alca_torda.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5b2ac15548f8a8e4c9241e2582cfd09bdbf8ff75 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Alca_torda.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# The Razorbill - Alca torda - Alk +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 July 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Alca torda + +# Name of the YAML file that contains the matrix of `species` +matfile : Alca_torda + +# Name of text file with input data +datafile : KEC4_HabLoss_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S01, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [1., 1., 1.] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 2] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : mean +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fratercula_arctica.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fratercula_arctica.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45d2088f6c7ea12311d3d3e17f5510750838b261 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fratercula_arctica.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# Atlantic Puffin - Fratercula arctica - Papegaaiduiker -- +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 July 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Fratercula arctica + +# Name of the YAML file that contains the matrix of `species` +matfile : Fratercula_arctica_2 + +# Name of text file with input data +datafile : KEC4_HabLoss_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S03, S4, S5, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [1., 1., 1., 1.] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 3, 4] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.85 + +# Casuality threshold of ALI (Y%) +ALI_causality : 10 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : mean +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fulmarus_glacialis.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fulmarus_glacialis.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b56835b3ef5c92f6155d6877a0979ac17f6d694f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Fulmarus_glacialis.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# The northern fulmar - Fulmarus glacialis - Noordse stormvogel -- +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 July 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Fulmarus glacialis + +# Name of the YAML file that contains the matrix of `species` +matfile : Fulmarus_glacialis_2 + +# Name of text file with input data +datafile : KEC4_HabLoss_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [SJ, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [1., 1., 1.] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 2] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.85 + +# Casuality threshold of ALI (Y%) +ALI_causality : 10 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : [NJ, NPB] + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : mean +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Gavia_stellata.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Gavia_stellata.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ff255f8670ed1b39f261be490fa5fdbd3486150 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Gavia_stellata.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# The Red-Throated Diver - Gavia stellata - Roodkeelduiker +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 July 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Gavia sp + +# Name of the YAML file that contains the matrix of `species` +matfile : Gavia_stellata + +# Name of text file with input data +datafile : KEC4_HabLoss_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [SJ, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [1., 1., 1.] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 2] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : mean +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus.yaml new file mode 100644 index 0000000000000000000000000000000000000000..94d68e6f7e830cf8dac5c341fd5deafe342a49c6 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# The Northern Gannet - Morus Bassanus - Jan van Gent -- +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 Jan. 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Morus bassanus + +# Name of the YAML file that contains the matrix of `species` +matfile : Morus_bassanus + +# Name of text file with input data +datafile : KEC4_Total_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S0, S1, S2, S3, SA] + +# Rescale stage-specific mortalities (TRUE) or apply to each life stage only (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [0.47, 0.45, 0.90, 0.89, 0.88, 1.20] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 3, 4, 5, 5] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Abundance threshold of ALI (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : Total +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus_IBM.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus_IBM.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb3cdae61f4e7f9edbe0b8a4e0af1527a7af1184 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Morus_bassanus_IBM.yaml @@ -0,0 +1,105 @@ +# YAML configuration file for: +# +# The Northern Gannet - Morus Bassanus - Jan van Gent -- +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 11 Aug. 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Morus bassanus + +# Name of the YAML file that contains the matrix of `species` +matfile : Morus_bassanus + +# Name of text file with input data +datafile : KEC4_MB_IBM_mortality.txt + +# Is the output derived from an IBM +IBM : TRUE + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : IBMoutput + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S0, S1, S2, S3, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [0.47, 0.45, 0.90, 0.89, 0.88, 1.20] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 3, 4, 5, 5] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : FALSE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : FALSE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : m50_sz1 +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Thalasseus_sandvicensis.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Thalasseus_sandvicensis.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d79d4be05556adb972951e96725598ab54407b1f --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Thalasseus_sandvicensis.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# Sandwich Tern - Thalasseus sandvicensis - Grote stern +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 11 Aug. 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Thalasseus sandvicensis + +# Name of the YAML file that contains the matrix of `species` +matfile : Thalasseus_sandvicensis + +# Name of text file with input data +datafile : KEC4_Total_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S0, S12, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [0., 0., 1.0, 1.0] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 3, 3] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : Total +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Uria_aalge.yaml b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Uria_aalge.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2afa33c6c524f0a7332d22ff335927d0757c1f39 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/runfiles/run_Uria_aalge.yaml @@ -0,0 +1,102 @@ +# YAML configuration file for: +# +# The Common Guillemot - Uria aalge - Zeekoet +# +# Use the function: AnalyseMatrix of the KEC4popmodels R-package to read this file +# +# Author : Vincent Hin, Wageningen Marine Research +# Date created : 17 June 2021 +# Date last modified : 13 July 2021 +# Version : 0.999 + +# Author of this YAML file +author : Vincent Hin + +# Affiliation of author +affiliation : Wageningen Marine Research + +# Name of the species +species : Uria aalge + +# Name of the YAML file that contains the matrix of `species` +matfile : Uria_aalge + +# Name of text file with input data +datafile : KEC4_HabLoss_Casualties_Scenarios.txt + +# Name of folder with input data +datafolder : "." + +# Name of top-level folder to store output in +# A subfolder with species name is created within this folder +outfolder : output + +# Overwrite existing output in output subfolder +overwrite : TRUE + +# All survival parameters +# in order of the life stages +Spar : [S0, S1, S2, SA] + +# Scaling mortality (TRUE) or apply to life stage onlye (FALSE) +scale_mortality : FALSE + +# The relative vulnerability of each life stage (0-1) +# Use zero for life stages unaffected by OWF mortality +OWFvuln : [1., 1., 1., 1., 1.] + +# Index of Spar for each life stage (starting 1) +# Should be same length as OWFvuln +Sparidx : [1, 2, 3, 4, 4] + +# Type of mortality calculation from casualties per period / scenario +mortcalc : m_mean_max_year + +# Are calculated mortalities finite probabilities `finite` or instanteous rates `rate` +morttype : finite + +# Threshold for ALI calculation (1 - X%/100) +ALI_threshold : 0.7 + +# Casuality threshold of ALI (Y%) +ALI_causality : 50 + +# Number of replicates for stochastic population growth rate +nrep : 1e+05 + +# Seed of random number generator +seed : NULL + +# Run `AllParSequence` (calculates Lambda as a function of all parameters) +run_allparseq : TRUE + +# Define the parameter sequence for `AllParSequence` +# `exclude` option can used to exlude parameter from analysis +# Use `[par1, par2]` to specify multiple parameters +parseq : + from : 0.01 + to : 0.99 + by : 0.01 + exclude : NULL + +# Run `Sensitivity` (calculates Sensitivy of Lambda for all parameters) +# Requires `run_allparseq = TRUE` +run_sensitivity : TRUE + +# Output options +make_boxplot : TRUE +make_histogram : TRUE +plot_stat : mean +plot_parseq : TRUE +plot_parseq_pnts : FALSE +plot_sensitivity : TRUE +write_table : TRUE +write_report : TRUE +report_opts : + pdf : TRUE + docx : TRUE + papersize : a3 + fontsize : 10pt + landscape : FALSE +save_objects : TRUE +plotdev : png diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/.gitignore b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..22034c461fce9171ea1d649f15429426e0eb8d10 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/.gitignore @@ -0,0 +1,3 @@ +*.o +*.so +*.dll diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars new file mode 100644 index 0000000000000000000000000000000000000000..d715e498f8bcc9e0a78ea23ffaea76c49faac7d4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars @@ -0,0 +1,3 @@ +CXX_STD = CXX11 +PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) +PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars.win b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars.win new file mode 100644 index 0000000000000000000000000000000000000000..d715e498f8bcc9e0a78ea23ffaea76c49faac7d4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/Makevars.win @@ -0,0 +1,3 @@ +CXX_STD = CXX11 +PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) +PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/MatrixAnalysis.cpp b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/MatrixAnalysis.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d4f4d258551e1c466b0eb720733939aedda123e4 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/MatrixAnalysis.cpp @@ -0,0 +1,246 @@ +/* +# ---------------------------------------------------------------------- # +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +# ---------------------------------------------------------------------- # + */ + +#include + +using namespace Rcpp; + +//' Calculate eigenvalues and left and right eigenvectors for a square matrix A +//' +//' @param A A square matrix +//' +//' @return Returns a list with elements 'Eigenvalues', 'Leftvectors' and 'Rightvectors' +//' +//' @author Vincent Hin - Wageningen Marine Research +//' +//' @source Uses RcppArmadillo function eig_gen(). See \url{http://arma.sourceforge.net/docs.html#eig_gen} +//' +//' @export +// [[Rcpp::export]] +List EigenDecomp(arma::mat A) { + + arma::cx_vec eigval; + arma::cx_mat leigvec; + arma::cx_mat reigvec; + + arma::eig_gen(eigval, leigvec, reigvec, A); + + return List::create(Named("Eigenvalues") = eigval, + Named("Leftvectors") = leigvec, + Named("Rightvectors") = reigvec); + +} + +//' Calculate the stable population distribution from a population projection matrix +//' +//' @param A A square matrix +//' +//' @return Return a row vector with the stable stage distribution +//' +//' @author Vincent Hin - Wageningen Marine Research +//' +//' @source http://arma.sourceforge.net/docs.html#eig_gen +//' +//' @export +// [[Rcpp::export]] +NumericVector StableStage(arma::mat A) { + + arma::cx_vec eigval; + arma::cx_mat leigvec; + arma::cx_mat reigvec; + + arma::eig_gen(eigval, leigvec, reigvec, A); + + arma::mat re = real(eigval); + arma::uword idx = re.index_max(); + arma::vec reigreal = real(reigvec.col(idx)); + + NumericVector ss = wrap(reigreal / arma::accu(reigreal)); + + return ss; +} + + +//' Return the real part of the largest eigenvalue +//' +//' @param A A square matrix with eigenvalues +//' +//' @author Vincent Hin - Wageningen Marine Research +//' +//' @source http://arma.sourceforge.net/docs.html#eig_gen +//' +//' @examples +//' MakeMatrix(file = "Morus_bassanus", N = 10000, stochastic = TRUE) %>% +//' map(EigenDecomp) %>% +//' map('Eigenvalues') %>% +//' map(MaxReal) %>% +//' unlist() +//' +//' @export +// [[Rcpp::export]] +double MaxReal(arma::cx_mat A) { + + arma::mat re = real(A); + + double max_val = re.max(); + + return max_val; +} + +//' Return the Fundamental Matrix from the Transition rate matrix +//' +//' @param Tmat A square matrix describing stage transitions +//' +//' @author Vincent Hin - Wageningen Marine Research +//' +//' @details see Caswell 2001 - Matrix Population Models p. 112 +//' +//' @export +// [[Rcpp::export]] +arma::mat FundamentalMatrix(arma::mat Tmat) { + + arma::mat Id = eye( size(Tmat) ); + + return(arma::inv(Id - Tmat)); +} + + +//' Calculate population projection +//' +//' @param A +//' The population projection matrix, or a list with annual projection matrices for a sequence of time steps +//' +//' @param n +//' A vector with initial densities +//' +//' @param niters +//' Number of iterations +//' +//' @return Returns a list with elements +//' \describe{ +//' \item{`ProjectionMatrix`}{A list with annual projection matrices} +//' \item{`StageVector`}{A matrix with stage vectors} +//' \item{`PopSize`}{A matrix with total population sizes} +//' \item{`PopChange`}{A matrix with annual growth factors} +//' } +//' +//' @author Vincent Hin - Wageningen Marine Research +//' +//' @source Rcpp version of R function popbio::pop.projection() +//' +//' @export +// [[Rcpp::export]] +List PopProject(RObject A, arma::vec n, arma::uword niter = 20) { + + // Define constants + bool tseq; // If A is a list: use different matrix per time step and set seq = true + List Alst; // Define list to hold all matrices + arma::mat Amat; // Define Armadillo matrix to hold matrix of current iteration. + CharacterVector rownms; // Rownames for vector of difference + CharacterVector rownms_long; // Rownames for population vector and stage matrix + + // Determine the type of A and set constants + // From: https://teuder.github.io/rcpp4everyone_en/200_robject.html + if(is(A)){ + if(Rf_isMatrix(A)){ + tseq = false; + + // Convert A to arma matrix + Amat = as(A); + + // Add A matrix to Alist for output + Alst.push_back(Amat); + + // Create row names + rownms_long.push_back("t = 0"); + for(arma::uword k = 1; k <= niter; k++){ + String str("t = "); + str += std::to_string(k); + rownms.push_back(str); + rownms_long.push_back(str); + } + } else { + Rcout << "NumericVector\n"; + stop("Please specify A as a List or a NumericMatrix"); + } + } else if(is(A)) { + Rcout << "DataFrame\n"; + stop("Please specify A as a list or a NumericMatrix"); + } else if(is(A)) { + tseq = true; + + // Convert A to a list + Alst = as(A); + + // Copy row names from the list names + rownms = Alst.names(); + rownms_long = Alst.names(); + + // Add add name for first row + rownms_long.push_front("initial"); + + // Override number of iterations based on length of matrix list A + niter = Alst.size(); + + // Assign first element of Alst to Amat + Amat = as(Alst[0]); + + } else { + stop("Please specify A as a list or a NumericMatrix"); + } + + // Define arma matrices to hold results + arma::uword ncols = Amat.n_cols; + arma::mat stage(niter + 1, ncols); + arma::vec pop(niter + 1); + arma::vec diff(niter); + arma::uword i = 0; + + while (i < niter){ + if(tseq) Amat = as(Alst[i]); + stage.row(i) = arma::conv_to< arma::rowvec >::from(n); + pop(i) = sum(n); + if (i > 0) { + diff(i - 1) = pop(i) / pop(i-1); + } + n = Amat * n; + i++; + } + stage.row(i) = arma::conv_to< arma::rowvec >::from(n); + pop(i) = sum(n); + diff(i - 1) = pop(i) / pop(i-1); + + // Convert to Rcpp matrices and set names + NumericMatrix popmat = wrap(pop); + rownames(popmat) = rownms_long; + + NumericMatrix stagemat = wrap(stage); + rownames(stagemat) = rownms_long; + + NumericMatrix diffmat = wrap(diff); + rownames(diffmat) = rownms; + + return List::create(Named("ProjectionMatrix") = Alst, + Named("StageVector") = stagemat, + Named("PopSize") = popmat, + Named("PopChange") = diffmat); +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/RcppExports.cpp b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/RcppExports.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a23800e62a2d8a4970ed9fd4c2566ad43d52b231 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/src/RcppExports.cpp @@ -0,0 +1,84 @@ +// Generated by using Rcpp::compileAttributes() -> do not edit by hand +// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#include +#include + +using namespace Rcpp; + +#ifdef RCPP_USE_GLOBAL_ROSTREAM +Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); +Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); +#endif + +// EigenDecomp +List EigenDecomp(arma::mat A); +RcppExport SEXP _KEC4popmodels_EigenDecomp(SEXP ASEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat >::type A(ASEXP); + rcpp_result_gen = Rcpp::wrap(EigenDecomp(A)); + return rcpp_result_gen; +END_RCPP +} +// StableStage +NumericVector StableStage(arma::mat A); +RcppExport SEXP _KEC4popmodels_StableStage(SEXP ASEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat >::type A(ASEXP); + rcpp_result_gen = Rcpp::wrap(StableStage(A)); + return rcpp_result_gen; +END_RCPP +} +// MaxReal +double MaxReal(arma::cx_mat A); +RcppExport SEXP _KEC4popmodels_MaxReal(SEXP ASEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::cx_mat >::type A(ASEXP); + rcpp_result_gen = Rcpp::wrap(MaxReal(A)); + return rcpp_result_gen; +END_RCPP +} +// FundamentalMatrix +arma::mat FundamentalMatrix(arma::mat Tmat); +RcppExport SEXP _KEC4popmodels_FundamentalMatrix(SEXP TmatSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat >::type Tmat(TmatSEXP); + rcpp_result_gen = Rcpp::wrap(FundamentalMatrix(Tmat)); + return rcpp_result_gen; +END_RCPP +} +// PopProject +List PopProject(RObject A, arma::vec n, arma::uword niter); +RcppExport SEXP _KEC4popmodels_PopProject(SEXP ASEXP, SEXP nSEXP, SEXP niterSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< RObject >::type A(ASEXP); + Rcpp::traits::input_parameter< arma::vec >::type n(nSEXP); + Rcpp::traits::input_parameter< arma::uword >::type niter(niterSEXP); + rcpp_result_gen = Rcpp::wrap(PopProject(A, n, niter)); + return rcpp_result_gen; +END_RCPP +} + +static const R_CallMethodDef CallEntries[] = { + {"_KEC4popmodels_EigenDecomp", (DL_FUNC) &_KEC4popmodels_EigenDecomp, 1}, + {"_KEC4popmodels_StableStage", (DL_FUNC) &_KEC4popmodels_StableStage, 1}, + {"_KEC4popmodels_MaxReal", (DL_FUNC) &_KEC4popmodels_MaxReal, 1}, + {"_KEC4popmodels_FundamentalMatrix", (DL_FUNC) &_KEC4popmodels_FundamentalMatrix, 1}, + {"_KEC4popmodels_PopProject", (DL_FUNC) &_KEC4popmodels_PopProject, 3}, + {NULL, NULL, 0} +}; + +RcppExport void R_init_KEC4popmodels(DllInfo *dll) { + R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); + R_useDynamicSymbols(dll, FALSE); +} diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat.R new file mode 100644 index 0000000000000000000000000000000000000000..961e998ce7722620f31e369c2725fcb65c3850c9 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat.R @@ -0,0 +1,4 @@ +library(testthat) +library(KEC4popmodels) + +test_check("KEC4popmodels") diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Caswell.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Caswell.R new file mode 100644 index 0000000000000000000000000000000000000000..c87e40160bb9a7aec3b0ff76df1a4617a2b38294 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Caswell.R @@ -0,0 +1,89 @@ +###################################################################### +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +###################################################################### + +test_that("Check Transition matrix Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 117: + T_orca <- matrix(c(0,0,0,0,0.9775,0.9111,0,0,0,0.0736,0.9534,0,0,0,0.0452,0.9804), nrow = 4, ncol = 4, byrow = T) + Tmat <- MakeGenMatrix(file = "Orcinus_orca")$TransitionMatrix[[1]] + attributes(Tmat)$dimnames <- NULL + + testthat::expect_identical(T_orca, Tmat) +}) + +test_that("Check Fertility matrix Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 117: + F_orca <- matrix(c(0,0.0043,0.1132,0,0,0,0,0,0,0,0,0,0,0,0,0), nrow = 4, ncol = 4, byrow = T) + Fmat <- MakeGenMatrix(file = "Orcinus_orca")$FertilityMatrix[[1]] + attributes(Fmat)$dimnames <- NULL + + testthat::expect_identical(F_orca, Fmat) +}) + + +test_that("Check Fundamental matrix Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 119: + N_orca <- matrix(c(1.00,0,0,0,10.99,11.25,0,0,17.37,17.77,21.46,0,40.05,40.97,49.49,51.02), nrow = 4, ncol = 4, byrow = T) + Nmat <- MakeGenMatrix(file = "Orcinus_orca")$FundamentalMatrix[[1]] + attributes(Nmat)$dimnames <- NULL + + testthat::expect_equal(N_orca, Nmat, tolerance = 1e-4) + +}) + +test_that("Check Next Generation Matrix Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 127: + R_orca <- matrix(c(2.0131,2.0595,2.4292,0,0,0,0,0,0,0,0,0,0,0,0,0), nrow = 4, ncol = 4, byrow = T) + Rmat <- MakeGenMatrix(file = "Orcinus_orca")$NextGenMatrix[[1]] + attributes(Rmat)$dimnames <- NULL + + testthat::expect_equal(R_orca, Rmat, tolerance = 1e-4) + +}) + +test_that("Check R0 Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 129: + LRO_orca <- 2.0131 + Rmat <- MakeGenMatrix(file = "Orcinus_orca")$NextGenMatrix[[1]] + + testthat::expect_equal(LRO_orca, Lambda(Rmat), tolerance = 1e-4) +}) + +test_that("Check Generation Time Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 119: + Tgen_orca <- 27.85 + GenTime <- GenerationTime(MakeGenMatrix(file = "Orcinus_orca")) + + testthat::expect_equal(Tgen_orca, GenTime, tolerance = 1e-4) +}) + +test_that("Check Lambda Orcinus orca against Caswell (2001) MPM", { + + ## From Caswell (2001) Chapter 5, p. 119: + Lambda_orca <- 1.0254 + Lambda <- Lambda(MakeMatrix(file = "Orcinus_orca")) + + testthat::expect_equal(Lambda_orca, Lambda, tolerance = 1e-4) +}) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-CheckMatrices.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-CheckMatrices.R new file mode 100644 index 0000000000000000000000000000000000000000..c002e7f6131b1501f4919016b0f9269947a0427b --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-CheckMatrices.R @@ -0,0 +1,30 @@ +###################################################################### +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +###################################################################### + +test_that("Check whether Fertility and Transition matrices are equal to Annual Projection Matrices", { + + for(k in KEC4matfiles) { + + mat <- MakeMatrix(file = k) + genmat <- MakeGenMatrix(file = k) + expect_equal(genmat$FertilityMatrix[[1]] + genmat$TransitionMatrix[[1]], mat[[1]]) + + } +}) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Lambda.R b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Lambda.R new file mode 100644 index 0000000000000000000000000000000000000000..d84724c320b30d2ecdc832182f7f458216fdbbc9 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/tests/testthat/test-Lambda.R @@ -0,0 +1,29 @@ +###################################################################### +# +# This file is part of KEC4popmodels R-package +# (https://git.wur.nl/ecodyn/KEC4popmodels). +# Copyright (c) 2021 Wageningen Marine Research +# +# 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, version 3. +# +# 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 . +# +###################################################################### + +test_that("Check Lambda function against popbio::lambda", { + + for(k in KEC4matfiles) { + mat <- MakeMatrix(file = k)[[1]] + lambda1 <- KEC4popmodels::Lambda(mat) + lambda2 <- popbio::lambda(mat) + expect_equal(lambda1, lambda2) + } +}) diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/.gitignore b/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..2c6ab8d7f287ba75429ff212b1fa82b995cfebe5 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/.gitignore @@ -0,0 +1,3 @@ +*.html +*.R +*_cache/ diff --git a/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/KEC4popmodels.Rmd b/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/KEC4popmodels.Rmd new file mode 100644 index 0000000000000000000000000000000000000000..3650116f6d120947b16f1214b698cbb7c9aad34e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/KEC4popmodels/vignettes/KEC4popmodels.Rmd @@ -0,0 +1,277 @@ +--- +title: "R-package KEC4popmodels" +author: "Vincent Hin" +date: "23 June 2021" +output: pdf_document +vignette: > + %\VignetteIndexEntry{R-package KEC4popmodels} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE, eval = TRUE, cache = TRUE, collapse = TRUE, comment = "#>") +``` + +This document outlines the functionality of the KEC4popmodels package. Installation instructions are described in the README file. Once installed, the package can be loaded (attached) with: + +```{r message=FALSE, warning=FALSE} +library(KEC4popmodels, quietly = TRUE) +``` + +This also attaches the packages collected within `tidyverse`. + +The KEC4species 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, version 3. 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 \url{http://www.gnu.org/licenses/} + +# KEC4species +Species-specific projection matrices for several seabird species are part of the `KEC4popmodels` package and stored as YAML configuration files. The data object `KEC4species` contains the names of all the species that are part of the `KEC4popmodels` package and `KEC4matfiles` contains the names of all the matrix specification files that come with the package. A single species can have multiple matrix specification files. + +```{r} +KEC4species +KEC4matfiles +``` + +# Functions to generate matrices + +The `KEC4popmodels` package contains several functions to generate matrices for YAML configuration files. + +## ReadMatrix +The function `ReadMatrix` reads a YAML configuration file with matrix specifications and returns all elements as a list. This function is used within functions that generate matrices (`MakeMatrix`, `MakeGenMatrix`, and `MakeParSequence`, see below), but can also be used directly, for example to obtain default parameter values of a certain species. Parameters are stored as nested lists, with each parameter having a `mean` and `sd` entry. The list elements returned by `ReadMatrix` correspond directly to the entries in the YAML configuration file and are listed below. In addition, the structure of a parameter list is displayed. + +```{r} +cfg <- ReadMatrix(file = "Alca_torda") +names(cfg) +cfg$SummerPars +``` + +## MakeMatrix +The `MakeMatrix()` function can be used to read and evaluate the matrix for a particular species and takes as arguments: + + - `file`: Name of the YAML configuration file with matrix specification + - `folder`: Name of the folder where file is located. Defaults to NULL, in which case an internally stored file is used + - `N`: Number of matrices returned if tseq = NULL. Defaults to 1. + - `tseq`: (optional). Create a time sequence of matrices. Overrides N. + - `stochastic`: (TRUE/FALSE). Sample parameters from a beta distribution (defaults to FALSE) + - `summerpars`: (optional) A nested list with parameters for the reproduction (summer) matrix + - `winterpars` (optional) A nested list with parameters for the survival (winter) matrix + - `verbose` (TRUE/FALSE). Message printing to console (defaults to TRUE) + +`MakeMatrix` returns a list with annual projection matrices (APM). Run `?(MakeMatrix)` for more information. The only required argument is the name of the YAML file that stores the specifications of the summer and winter matrices. Default parameter values are also stored in the each YAML file and are used in case the arguments `summerpars` and `winterpars` remain unspecified. + +```{r} +MakeMatrix(file = "Alca_torda") +``` + +Alternatively, one can use the `MakeMatrix` function to generate a user-defined number of matrices with parameter values sampled from a beta distribution. To generate 3 instances of a stochastic annual projection matrix run: + +```{r} +MakeMatrix(file = "Alca_torda", N = 3, stochastic = TRUE) +``` + +Annual projection matrices can also be generated as a time series, by using the `tseq` argument. Specifying `tseq` overrides the `N` argument. For example: + +```{r} +MakeMatrix(file = "Alca_torda", tseq = seq(2021,2023,by=1), stochastic = TRUE) +``` + +Finally, summer and winter parameters can be specified directly using the function arguments `summerpars` and `winterpars`. This will override the default behavior to use parameters from the YAML configuration file. Parameter should be specified as a nested list with a `mean` and `sd` value for each parameter (see ReadMatrix()). + +## MakeGenMatrix +The `MakeGenMatrix` function is similar to the `MakeMatrix` function, except that it returns 4 different matrices: the transition, fertility, next generation and fundamental matrix. `MakeGenMatrix` returns a nested list, with a user-specified number of matrices for each type (1 by default): + +```{r} +MakeGenMatrix(file = "Alca_torda") +``` + +Note that the annual projection matrix ($\mathbf{A}$) as returned by `MakeMatrix` should equal the sum of the transition ($\mathbf{T}$) and fertility ($\mathbf{F}$) matrices, as returned by `MakeGenMatrix`, *i.e.* $\mathbf{A = T + F}$. The user can specify the number of matrices returned (`N`), whether or not the parameters are drawn from a beta distribution (`stochastic`), and the winter and summer parameters (with `summerpars` and `winterpars` arguments). + +## MakeParSequence +The `MakeParSequence` function creates `N` matrices for a range of parameter values and can be used to investigate the effect of a change in parameter values on the population growth rate. Per parameter value, matrices can be stochastic, in which case `MakeParSequence` varies the mean from the beta distribution from which the parameters are drawn, or not (the default), in which case it uses the specified parameter value. In the following example we change the breeding success parameter $F_A$. The parameter name and the sequence of values need to be supplied as a named list. + +```{r} +parseqlst <- list("FA" = c(0.5,0.6)) +MakeParSequence(file = "Alca_torda", parseq = parseqlst, verbose = F) +``` +Note that only the matrix entries that relate to breeding success have changed. Alternatively, one can draw parameters from a beta distribution with `stochastic = TRUE`: + +```{r} +MakeParSequence(file = "Alca_torda", N = 2, stochastic = TRUE, parseq = parseqlst, verbose = F) +``` +Now the output is a list of length 2 for each parameter value. + +The `MakeParSequence` function allows to change multiple parameters at once. This is achieved by extending the `parseq` list, as in the following example. Note that all sequences need to be of equal length. The output list with annual projection matrices is named after the first element of the `parseq` input list. + +```{r} +parseqlst <- list("FA" = c(0.5,0.6), "SA" = c(0.9,0.95)) +MakeParSequence(file = "Alca_torda", parseq = parseqlst, verbose = F) +``` +Here we can see that both the elements relating to breeding success ($F_A$) and adult survival rates ($S_A$) have changed simultaneously. + +# Functions to analyse matrices + +## EigenDecomp +`EigenDecomp` performs an Eigen decomposition of a square matrix `A` by using RcppArmadillo. It returns a named list with the eigenvalues, and left and right eigenvectors. Note the its single argument should be a matrix, and not a list of matrices. Subsetting is necessary when `MakeMatrix` is used to generate `A`. + +```{r} +APM <- MakeMatrix('Alca_torda')[[1]] +eig <- EigenDecomp(A = APM) +eig +``` + +Note that `EigenDecomp` returns a list with complex matrices. + +## MaxReal +`MaxReal` extracts the real part of the largest value of a complex matrix and can be used together with `EigenDecomp` to obtain the asymptotic population growth rate ($\lambda$, the real part of the largest eigenvalue): + +```{r} +MaxReal(eig$Eigenvalues) +``` + +## Lambda +For convenience, the function `Lambda` combines both `EigenDecomp` and `MaxReal` to obtain the real part of the largest eigenvalue of a square matrix $\mathbf{A}$. When $\mathbf{A}$ equals the annual projection matrix, the real part of the largest eigenvalue corresponds to the asymptotic population growth rate ($\lambda$). In addition, `Lambda` can also be used on a list of matrices (as opposed to `EigenDecomp`) and on the output of `MakeGenMatrix`. In the latter case, `Lambda` calculates the annual projection matrix by adding the transition and fertility matrices. When used on a list of matrices, `Lambda` returns a numeric vector of length equal to the number of matrices it receives. + +```{r} +APMs <- MakeMatrix('Alca_torda', N = 5, stochastic = TRUE) +Lambda(APMs) +``` + +## StableStage +`StableStage` returns the stable distribution of a square matrix $\mathbf{A}$. The stable distribution corresponds to the fixed relative abundance of the stages in case the population grows at its asymptotic growth rate ($\lambda$). `StableStage` does not work on a list of matrices, but might do so in the future. + +```{r} +StableStage(APM) +``` + +## GenerationTime +`GenerationTime` calculates the generation time based on the fertility and transition matrices according to eq. 5.73 in Caswell (2001, p. 129), where generation time ($T_G$) is defined as: +$$ +T_G = \frac{\text{ln}(\lambda)}{\text{ln}(R_0)} +$$ +Here, $R_0$ is the per generation growth rate, or the expected number of offspring produced by an individual during its lifetime (Caswell 2001). $R_0$ equals the dominant eigenvalue of the next generation matrix $\mathbf{R}$, which is calculated by the `MakeGenMatrix` function as the matrix product of the fertility matrix $\mathbf{F}$ and the fundamental matrix $\mathbf{N}$. Because its dependency on $R_0$, the `GenerationTime` function requires output from `MakeGenMatrix`, which contains all the matrices required to calculate the generation time. + +```{r} +GenMat <- MakeGenMatrix("Alca_torda") +GenerationTime(GenMat) +``` + +## PopProject +The function `PopProject` runs a population projection, which basically involves iterating the equation: +$$ +\mathbf{n}(t+1) = \mathbf{A} \mathbf{n}(t) +$$ +where $\mathbf{n}(t)$ is the population stage vector at time $t$ and $\mathbf{A}$ the population projection matrix. As input, `PopProject` requires a projection matrix `A`, a vector with initial population abundances `n` and the number iterations `niters`. `PopProject` produces a list with 4 elements: + + - `ProjectionMatrix` is a list with the projection matrices that were specified as input + - `StageVectors` is a matrix with for each iteration (in rows) the stage-specific abundances (in columns) + - `PopSize` is a single-column matrix with for each iteration (in rows) the total population abundance + - `PopChange` is a single-column matrix with for each iteration (in rows) the relative change in total population abundance + +```{r} +APM +n <- as.numeric(StableStage(APM)) * 1000 +PopProject(APM, n, niter = 5) +``` + +It's also possible to pass a list with projection matrices to `PopProject`, in which case it will use a different matrix each time step. As described above, such a list can be produced with the function `MakeMatrix` by using the `tseq` argument. This allows to run a stochastic population projection: + +```{r} +APMseq <- MakeMatrix("Alca_torda", tseq = 2021:2023, stochastic = TRUE) +PopProject(APMseq, n) +``` +Note that in this case, `PopProject` outputs the different projection matrices under the list element called `ProjectionMatrix`. + +## Sensitivity +The function `Sensitivity` calculates the derivative of the population growth rate ($\lambda$) with respect to a parameter at a specified value of that parameter. The main arguments are the name of the YAML configuration file with matrix specifications, the name of the parameter to calculate the sensitivity for, and the value of the parameter at which the sensitivity is evaluated. Note that the function is vectorized on `value` and a range of parameter values can therefore be supplied: +```{r} +Sensitivity(file = "Alca_torda", parameter = "FA", value = seq(0.1,1.0,0.1)) +``` + +## AllParSequence +The function `AllParSequence` is a helper function that calculates the deterministic population growth rate $\lambda$ for a range of parameter values for all parameters of a particular species. It's argument `parseq` specifies the parameter range and is applied to all parameters, except those that are excluded with the `exclude` argument. In the following chunk, we calculate the population growth rate of *Alca torda* for all parameters, except the breeding success parameter: +```{r} +parseq <- AllParSequence("Alca_torda", parseq = seq(0.6,0.9,by=0.1), exclude = "FA") +parseq +``` + +Subsequently, we can add a column with the sensitivity of $\lambda$ by passing the output of `AllParSequence()` to the `Sensitivity()` function, which reads its `parameter` and `value` arguments from the `data.frame` produced by `AllParSequence`. Because `AllParSequence` produces a list with data.frames, one for each parameter, we have to invoke `purrr::map()` to pass each of those data.frames to `Sensitivity()`. The function `dplyr::mutate` adds a column with the output of `Sensitivity` to each data.frame in the list: + +```{r} +map(parseq, ~( mutate(., sensitivity = Sensitivity(., file = "Alca_torda")))) +``` + +# Plotting functions + +The `KEC4popmodels` package contains three functions to plot output, which make use of the `ggplot2` package. + +## Boxplot_Lambda +`Boxplot_Lambda` creates a boxplot of the distribution of population growth rate for different scenarios. Its first argument `Lambda_stoch` is a long data.frame with columns `name` (scenario names) and `value` ($\lambda$). In addition, a long data.frame with deterministic population growth rate can be specified as `Lambda_det`. These values will be plotted as points with each boxplot. + +```{r, fig.width=3, fig.height=3, fig.align=2} +PGRdet <- MakeMatrix("Alca_torda") %>% Lambda() +PGRstoch <- MakeMatrix("Alca_torda", N = 1e4, stochastic = TRUE) %>% Lambda() +Boxplot_Lambda(Lambda_stoch = data.frame('name' = "null", 'value' = PGRstoch), + Lambda_det = data.frame('name' = "null", 'det' = PGRdet)) +``` + +## Histogram_Lambda +`Histrogram_Lambda` is similar to `Boxplot_Lambda`, except that it creates a histogram for each scenario in column `name`, instead of a boxplot. + +```{r, fig.width=4, fig.height=3, fig.align=2} +Histogram_Lambda(Lambda_stoch = data.frame('name' = "null", 'value' = PGRstoch), + Lambda_det = data.frame('name' = "null", 'det' = PGRdet)) +``` + +## Plot_AllParSeq +`Plot_AllParSeq` can be used to plot the output of `AllParSequence`. It creates a faceted plot with a panel for each parameter. + +```{r, fig.width=5, fig.height=3.5, fig.align=2} +allparseq <- AllParSequence("Alca_torda", parseq = seq(0.01,0.99,0.01)) +Plot_AllParSeq(allparseq, species = "Alca torda") +``` + +The optional argument `yvar` can be used to specify the variable that should be plotted on the vertical axis. Using `yvar` we can also plot the sensitivity of $\lambda$ with `Plot_AllParSeq()`: + +```{r, fig.width=5, fig.height=3.5, fig.align=2} +allparseq <- map(allparseq, ~( mutate(., Sensitivity = Sensitivity(., file = "Alca_torda")))) +Plot_AllParSeq(allparseq, yvar = 'Sensitivity', species = "Alca torda") +``` + +# Matrix Analysis + +## Stochastic population projection +Using `purrr::map`, several functions of the `KEC4popmodels` package can be chained together to run and plot a stochastic population projection. In the below example we run 100 stochastic projections across 30 years. Subsequently, we can repeat this procedure, but with modified parameter values to study the effect of additional mortality on the projected population abundance. We assumed a 5 per cent decrease in adult annual survival. Note that the output of `PopProject` is not stored and passed directly to the plotting function. For sake of repeatability it is best practice to store all output before plotting. + +```{r, results=FALSE, fig.width=5, fig.height=5, fig.align=2} +# initialize +tstart <- 1 +tend <- 30 +nrep <- 100 +Nini <- 1e3 +nini <- as.numeric(StableStage(MakeMatrix("Alca_torda")[[1]])) * Nini + +# Make a plot window +plot(c(tstart,tend+1), c(1e2,5e3), type = 'n', + xlab = "Time index", ylab = "Population abundance") +# Read matrix, run projection and plot population sizes +map(1:nrep, ~MakeMatrix("Alca_torda", + tseq = seq(tstart,tend,1), + stochastic = TRUE, + verbose = FALSE)) %>% + map(~PopProject(., nini)) %>% + map("PopSize") %>% + map(~lines(.[,1], col = 'black')) + +# Read parameters and adjusted adult survival +wintpars <- ReadMatrix("Alca_torda")$WinterPars +wintpars$SA$mean <- wintpars$SA$mean * 0.95 + +# Repeat analysis with adjusted adult survival +map(1:nrep, ~MakeMatrix("Alca_torda", + tseq = seq(tstart,tend,1), + stochastic = TRUE, + winterpars = wintpars, + verbose = FALSE)) %>% + map(~PopProject(., nini)) %>% + map("PopSize") %>% + map(~lines(.[,1], col = adjustcolor('red', alpha.f = 0.5))) +``` diff --git a/script_py_r/kec4_pyconversion-master/LICENSE b/script_py_r/kec4_pyconversion-master/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..c6cf44a82104c868b31e2026f5542b7d3fd1fce0 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) 2021 Wal, Jan Tjalling van der + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. 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But first, please read +. diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_HabLoss.R b/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_HabLoss.R new file mode 100644 index 0000000000000000000000000000000000000000..c9fdd06546e7bb50c421bc94308ab39aedb6bbdc --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_HabLoss.R @@ -0,0 +1,540 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +##################################################################### +library(tidyverse) +rm(list = ls()) + +## ----- READ INPUT ----- +# owp_habloss$countSDHabLoss = gemiddelde dichtheid * oppervlakte van het (geplande) windmolenpark, rekening houdend met de ScaleFactor (wanneer de oppervlakte een zoekgebied beschrijft ipv een daadwerkelijk oppervlak) per species per seizoen +owp_habloss <- read.csv("input/OWP_SB_HabLoss.csv", sep = ";") +# owp_species --> soorten met $RDRS --> een maat voor hoeveel sterfte er mogelijk optreed per soort door parken +owp_species <- read.csv("input/OWP_SB_Species.csv", sep = ";") +# owp_scenarios: lijst met welke parken tot welke scenarios behoren (_Final heeft correcte namen) +owp_scenarios <- read.csv("input/OWP_SB_Scenarios.csv", sep = ";") +# owp_abundance: totale vogelaantallen per soort, per seizoen, internationaal en nationaal +owp_abundance <- read.csv("input/OWP_SB_Abundance.csv", sep = ";") +# owp_attributes: info over verschillende windparken & zoekgebieden +owp_attributes <- read.csv("input/OWP_Attributes.csv") + +## Fix messed up names :-( +owp_species <- owp_species %>% rename("EUring" = contains("EUring")) +owp_scenarios <- owp_scenarios %>% rename("Scenario_name" = contains("Scenario"), "Batch" = contains("batch")) +owp_abundance <- owp_abundance %>% rename("Batch"= contains("Batch")) + +head(owp_habloss) +head(owp_species) +head(owp_scenarios) +head(owp_abundance) +head(owp_attributes) + +## ----- MERGE DATA ----- + +## Add column Include4Habloss, WMR_PopModel, RDRS and species to owp_habloss +owp_habloss <- left_join(owp_habloss,owp_species[,c("EUring", "Do4HabLoss","WMR_PopModel","RDRS","ScientificName")],by="EUring") %>% + rename("Include4HabLoss" = "Do4HabLoss", + "species" = "ScientificName") + +## Add column CountryName en Verwachte met owp_attributes$UniekID +owp_habloss <- left_join(owp_habloss,owp_attributes[,c("UniekID", "Country.Na", "Verwachte", "Wind.Far_1")], by="UniekID") %>% + rename("CountryName" = "Country.Na") +owp_habloss$CountryName <- trimws(owp_habloss$CountryName) + +## Calculate Casualties HabitatLoss by multiplying number of birds per park times the risk factor +owp_habloss <- owp_habloss %>% mutate(casualtiesHabLoss = countSBHabLoss * RDRS) + +## Add and fill column per OWP scenario +# change direction +owp_scenarios.long <- owp_scenarios %>% pivot_wider(names_from = Scenario_name, values_from = Batch) + +## Add scenarios to habloss df +owp_habloss <- left_join(owp_habloss, owp_scenarios.long, by = "UniekID") + +## Convert Batch to TRUE / FALSE in Scenario Columns +owp_habloss <- owp_habloss %>% + mutate(across(unique(owp_scenarios$Scenario_name), ~ifelse(.x %in% c("INT", "NAT"), TRUE, FALSE))) + +## Remove whitespaces from names and species +names(owp_habloss) <- gsub(" ", "", names(owp_habloss)) +owp_habloss$species <- trimws(owp_habloss$species) +owp_habloss$UniekID <- trimws(owp_habloss$UniekID) +owp_habloss$Wind.Far_1 <- trimws(owp_habloss$Wind.Far_1) + +## Which OWP are included in a national scenario? +owp_habloss$included_nat <- (owp_habloss$basic_2030 + owp_habloss$Rekenvariant_III + owp_habloss$Rekenvariant_II + owp_habloss$Rekenvariant_I) +owp_habloss$included_nat <- ifelse(owp_habloss$included_nat > 0, TRUE, FALSE) +head(owp_habloss) + +# check if there is NAs in casualtiesHabLoss --> only starling +owp_habloss %>% + filter(is.na(casualtiesHabLoss)) %>% + distinct(species) +## + +## +Scn_basic2030 <- + owp_habloss %>% + filter(basic_2030 == TRUE & Batch == "NAT" & Include4HabLoss == 1) %>% + group_by(EUring, species, Season, Batch) %>% + summarize(Sum_CasualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(Scenario = "Basic_2030") +## + +## +Scn_RekenvariantIII <- + owp_habloss %>% + filter(Rekenvariant_III == TRUE & Batch == "NAT" & Include4HabLoss == 1) %>% + group_by(EUring, species, Season, Batch) %>% + summarize(Sum_CasualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(Scenario = "Rekenvariant_III") +## + +## +Scn_RekenvariantII <- + owp_habloss %>% + filter(Rekenvariant_II == TRUE & Batch == "NAT" & Include4HabLoss == 1) %>% + group_by(EUring, species, Season, Batch) %>% + summarize(Sum_CasualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(Scenario = "Rekenvariant_II") +## + +## +Scn_RekenvariantI <- + owp_habloss %>% + filter(Rekenvariant_I == TRUE & Batch == "NAT" & Include4HabLoss == 1) %>% + group_by(EUring, species, Season, Batch) %>% + summarize(Sum_CasualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(Scenario = "Rekenvariant_I") +## + +## +Scn_int30 <- + owp_habloss %>% + filter(international == TRUE & Batch == "INT" & Include4HabLoss == 1) %>% + group_by(EUring, species, Season, Batch) %>% + summarize(Sum_CasualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(Scenario = "International") +## + +## Rbind different scenarios and add species names +scns_habloss <- bind_rows(Scn_basic2030, Scn_RekenvariantI, Scn_RekenvariantII, Scn_RekenvariantIII, Scn_int30) +rm(Scn_basic2030, Scn_RekenvariantI, Scn_RekenvariantII, Scn_RekenvariantIII, Scn_int30) + +## Add abundance per species, Season and Batch +scns_alldata <- left_join(scns_habloss, owp_abundance) + +## Add WMR_PopModels variable +scns_alldata <- left_join(scns_alldata, owp_species[,c('EUring', 'WMR_PopModel')], by = 'EUring') +## + +## ---------------------- ## +## ----- STORE DATA ----- +## ---------------------- ## + +## Save scns_alldata & owp_habloss to rds for easy use later +saveRDS(owp_habloss, file = "rds/owp_habloss.rds") +saveRDS(scns_alldata, file = "rds/scns_alldata.rds") + +## Write scenario table to csv +write.csv(scns_alldata, file = "output/HabLoss_Casualties_Scenarios.csv", row.names = FALSE) + +## ----------------- ## +## ----- TABLES ---- +## ----------------- ## +scns_order <- c('Basic_2030', 'Rekenvariant_I', 'Rekenvariant_II', 'Rekenvariant_III', 'International') + +OWF_scns_Table <- + left_join(owp_scenarios.long, + owp_attributes[,c('UniekID', 'Wind.Far_1', 'Country.Na', 'Verwachte', 'Capacity.M')], by = "UniekID") %>% + relocate(UniekID, Wind.Far_1, Country.Na, Verwachte, Capacity.M, basic_2030, Rekenvariant_I, Rekenvariant_II, Rekenvariant_III, international) %>% + mutate(across(matches("Rekenvariant|basic|international"), ~ifelse(!is.na(.x), "x", ""))) %>% + rename('Country' = 'Country.Na', 'OWF name' = 'Wind.Far_1', 'Expected date' = 'Verwachte', 'Capacity (MJ)' = 'Capacity.M', 'Basic 2030 Snc' = 'basic_2030', 'Rekenvariant I scn' = 'Rekenvariant_I', 'Rekenvariant II scn' = 'Rekenvariant_II', 'Rekenvariant III scn' = 'Rekenvariant_III', 'International scn' = 'international') %>% + knitr::kable(format = "markdown", caption = "OWF information") +## +fln <- "output/Table_KEC4_OWF_info_scenarios.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(OWF_scns_Table, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## CASUALTIES PER SPECIES (rows), SCENARIO (rows) AND SEASON (columns) +scns_table <- + scns_alldata %>% + select(-c(WMR_PopModel, abundance)) %>% + mutate(Scenario, Scenario = gsub("_", " ", Scenario)) %>% + mutate(Season, Season = recode(Season, '1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul")) %>% + pivot_wider(id_cols = c(EUring, species, Batch, Scenario), values_from = Sum_CasualtiesHabLoss, names_from = Season) %>% + rowwise() %>% + mutate(Mean = mean(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + mutate(Total = sum(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + arrange(species, desc(Batch), factor(Scenario, levels = scns_order)) %>% + knitr::kable(format = "markdown", caption = "Number of casualties per scenario, season, species and batch") +## +fln <- "output/Table_KEC4.0_Casualties_Scenarios.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(scns_table, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## SCENARIO TOTALS +scns_totals <- + scns_alldata %>% + select(-c(WMR_PopModel, abundance)) %>% + group_by(species, Batch, Scenario) %>% + summarise(Sum_CasualtiesHabLoss = sum(Sum_CasualtiesHabLoss)) %>% + mutate(Scenario, Scenario = recode(Scenario, 'nat_30_basic' = "NAT 30 basic", 'nat_30_fallback' = "NAT 30 fallback", 'nat_30_favoured' = "NAT 30 favoured", 'nat_30_restricted' = "NAT 30 restricted", 'int_30' = "INT 30")) %>% + pivot_wider(id_cols = c(Batch, Scenario), values_from = Sum_CasualtiesHabLoss, names_from = species) %>% + rowwise() %>% + mutate(Total = sum(c_across(where(is.numeric)))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + arrange(desc(Batch), factor(Scenario, levels = scns_order)) %>% + knitr::kable(format = "markdown", caption = "Number of casualties per scenario, species") +## +fln <- "output/Table_KEC4.0_Casualties_Scenarios_Totals.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(scns_totals, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## Dutch OWPs Table Casualties per Season, BATCH = NAT, Scenario OWPs only +owp_table_NATBatch_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & Batch == "NAT" & CountryName == "Netherlands" & included_nat == TRUE) %>% + select(EUring, species, Season, UniekID, casualtiesHabLoss, Wind.Far_1) %>% + mutate(casualtiesHabLoss = round(casualtiesHabLoss, digits = 1)) %>% + mutate(Season, Season = recode(Season, '1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul")) %>% + pivot_wider(id_cols = c(UniekID, EUring, species, Wind.Far_1), values_from = casualtiesHabLoss, names_from = Season) %>% + rowwise() %>% + mutate(Total = sum(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + rename("OWP name" = "Wind.Far_1") %>% + relocate("OWP name", .after = "UniekID") %>% + arrange(str_sort(UniekID), species) %>% + knitr::kable(format = "markdown", caption = "Number of casualties per OWP, species and season. National data (subset = 'NAT'). National scenario OWPs only") +## +fln <- "output/Table_KEC4.0_Casualties_BatchNAT_ScenarioOWPs.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(owp_table_NATBatch_ScenarioOWPs, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## Create a sorted vector with UniekID names: +sort_IDs <- str_sort(unique(owp_habloss$UniekID), numeric = T) + +## Create tibble with Total casualties per Dutch OWP, Both NAT and INT batch +owp_habloss_NLtot <- + owp_habloss %>% + filter(Include4HabLoss == 1 & CountryName == "Netherlands" & included_nat == TRUE) %>% + group_by(UniekID, Wind.Far_1, EUring, species, Batch) %>% + summarize(Sum_casualtiesHabLoss = sum(casualtiesHabLoss), .groups = "drop") %>% + arrange(factor(UniekID, levels = sort_IDs)) + +## DUTCH OWP Table Casualties per Year, BATCH = NAT, Scenario OWPs only +totals_dutchOWP_perspecies_BatchNAT_ScenarioOWPs <- + owp_habloss_NLtot %>% + filter(Batch == "NAT") %>% + pivot_wider(id_cols = c(UniekID, Wind.Far_1), values_from = Sum_casualtiesHabLoss, names_from = species) %>% + bind_rows(summarise(., across(where(is.numeric), sum), across(where(is.character), ~ "Total"), across(where(is.logical), ~ "NA"), .groups = "drop")) %>% + rowwise() %>% + mutate(Total = sum(c_across(where(is.numeric)))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + knitr::kable(format = "markdown", caption = "Number of casualties per species for Dutch OWP areas. National data (subset = 'NAT'). National scenario OWPs only") +## +fln <- "output/Table_KEC4.0_TotalCasualties_DutchOWP_perSpecies_BatchNAT_ScenarioOWPs.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(totals_dutchOWP_perspecies_BatchNAT_ScenarioOWPs, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## DUTCH OWP Table Casualties per Year, BATCH = INT, Scenario OWPs only +totals_dutchOWP_perspecies_BatchINT_ScenarioOWPs <- + owp_habloss_NLtot %>% + filter(Batch == "INT") %>% + pivot_wider(id_cols = c(UniekID, Wind.Far_1), values_from = Sum_casualtiesHabLoss, names_from = species) %>% + bind_rows(summarise(., across(where(is.numeric), sum), across(where(is.character), ~ "Total"), across(where(is.logical), ~ "NA"), .groups = "drop")) %>% + rowwise() %>% + mutate(Total = sum(c_across(where(is.numeric)))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + knitr::kable(format = "markdown", caption = "Number of casualties per species for Dutch OWP areas. International data (subset = 'INT'). National scenario OWPs only") +## +fln <- "output/Table_KEC4.0_TotalCasualties_DutchOWP_perSpecies_BatchINT_ScenarioOWPs.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(totals_dutchOWP_perspecies_BatchINT_ScenarioOWPs, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## INTERNATIONAL OWP Table Casualties per Season, BATCH = INT, International scenario OWPs only +Int_owp_table_ScenarioOWPs <- + owp_habloss %>% + filter(Batch == "INT" & Include4HabLoss == 1 & int_30 == TRUE) %>% + select(EUring, species, Season, UniekID, casualtiesHabLoss, CountryName) %>% + mutate(EUring = as.character(EUring)) %>% + mutate(Season, Season = recode(Season, '1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul")) %>% + pivot_wider(id_cols = c(UniekID, EUring, species, CountryName), values_from = casualtiesHabLoss, names_from = Season) %>% + rowwise() %>% + mutate(Total = sum(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + relocate(CountryName) %>% + arrange(CountryName, str_sort(UniekID), species) %>% + knitr::kable(format = "markdown", caption = "Number of casualties from habitat loss per OWP, species and season. International data (batch = 'INT')") +## +fln <- "output/Table_KEC4.0_Casualties_IntOWPs_BatchINT_ScenarioOWPs.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(Int_owp_table_ScenarioOWPs, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + +## Convert to wide data.frame +Casualties_Scenarios_WMRPopModel <- + scns_alldata %>% + filter(WMR_PopModel == 1) %>% + select(-c(EUring, WMR_PopModel)) %>% + pivot_wider(names_from = Scenario, values_from = Sum_CasualtiesHabLoss) %>% + rename('season' = 'Season') %>% + rename('batch' = 'Batch') %>% + mutate('stat' = 'mean') %>% + relocate(stat, .after = batch) %>% + arrange(species, batch, season) +## + +## Export for use in KEC4popmodels +write.table(Casualties_Scenarios_WMRPopModel, file = "output/KEC4_HabLoss_Casualties_Scenarios.txt", quote = TRUE, row.names = FALSE) +## + +## Convert to wide data.frame +Casualties_Scenarios_NoPopModel <- + scns_alldata %>% + filter(WMR_PopModel == 0) %>% + select(-c(EUring, WMR_PopModel)) %>% + pivot_wider(names_from = Scenario, values_from = Sum_CasualtiesHabLoss) %>% + rename('season' = 'Season') %>% + rename('batch' = 'Batch') %>% + mutate('stat' = 'mean') %>% + relocate(stat, .after = batch) %>% + arrange(species, batch, season) +## + +## Export for use in KEC4popmodels +write.table(Casualties_Scenarios_NoPopModel, file = "output/KEC4_HabLoss_Casualties_Scenarios_NoPopModel.txt", quote = TRUE, row.names = FALSE) +## + +## --- CHECKING ---- +owp_habloss %>% + group_by(Batch, EUring, Season) %>% + summarize(n = n()) %>% + pivot_wider(names_from = EUring, values_from = n) %>% + View() +## + +## --- PLOTTING ---- +scn_labels <- c("basic_2030" = "Basic 2030", + "Rekenvariant_III" = "Rekenvariant III", + "Rekenvariant_II" = "Rekenvariant II", + "international" = "International", + "Rekenvariant_I" = "Rekenvariant I") + +theme_WMR <- theme_classic() + + theme(text = element_text(size = 8), + title = element_text(size = 9), + rect = element_rect(size = 0.5), + line = element_line(size = 0.5)) + +## Define colours (from: https://www.datanovia.com/en/blog/ggplot-colors-best-tricks-you-will-love/) +cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73", + "#F0E442", "#0072B2", "#D55E00", "#CC79A7") + +country_colours <- c("Netherlands" = cbp2[2], + "Belgium" = cbp2[7], + "United Kingdom" = cbp2[6], + "Germany" = cbp2[5], + "Denmark" = cbp2[4]) +## + +## Make plot of Casualties per season, scenario and species +gg.Casualties_Habloss_Season_Batch <- + scns_alldata %>% + ggplot(aes(x = Season, y = Sum_CasualtiesHabLoss, colour = Scenario)) + + geom_point() + geom_line() + + facet_wrap("species", scales = "free") + + scale_colour_discrete(type = cbp2, labels = scn_labels) + + ylab("Number of Casualties") + xlab("Season") + + theme_WMR + + theme(strip.text.x = element_text(size = 9, face = "italic")) +## +ggsave("output/Casualties_Habloss.pdf", gg.Casualties_Habloss_Season_Batch, width = 9, height = 6) +ggsave("output/Casualties_Habloss.png", gg.Casualties_Habloss_Season_Batch, width = 9, height = 6, units = 'in', dpi = 300) +## + +## Make plot of Abundance per season, batch and species +gg.Abundance_Season_Batch <- + scns_alldata %>% + ggplot(aes(x = Season, y = abundance, colour = Batch)) + + geom_point() + geom_line() + + facet_wrap("species", scales = "free") + + scale_colour_discrete(type = cbp2, name = 'Subset') + + ylab("Abundance") + xlab("Season") + + theme_WMR + + theme(strip.text.x = element_text(size = 9, face = "italic")) +## +ggsave("output/Species_Abundance.pdf", gg.Abundance_Season_Batch, width = 9, height = 6) +ggsave("output/Species_Abundance.png", gg.Abundance_Season_Batch, width = 9, height = 6, units = 'in', dpi = 300) +## + +## +gg.Casualties_OWP_Species_BatchINT_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & Batch == "INT" & international == TRUE) %>% + group_by(Batch, EUring, species, UniekID, CountryName) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(UniekID = fct_reorder(UniekID, desc(Sum_Casualties))) %>% + ggplot(aes(x = UniekID, y = Sum_Casualties, fill = CountryName)) + + geom_col(colour = "black") + + facet_wrap("species", ncol = 1, scales = "free") + + scale_fill_manual(values = country_colours) + + xlab("OWP ID") + ylab("Casualties from habitat displacement") + + ggtitle("Batch = 'INT', OWPs of the INT 30 scenario only") + + theme_WMR + + theme(axis.text.x = element_text(size = 7, angle = 90), + strip.text.x = element_text(size = 9, face = "italic")) +## +ggsave("output/Casualties_OWP_Species_INT.pdf", gg.Casualties_OWP_Species_BatchINT_ScenarioOWPs, width = 15, height = 20) +ggsave("output/Casualties_OWP_Species_INT.png", gg.Casualties_OWP_Species_BatchINT_ScenarioOWPs, width = 15, height = 20, units = 'in', dpi = 300) +## + +## +gg.Casualties_OWP_Species_BatchNAT_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & Batch == "NAT" & CountryName == "Netherlands" & included_nat == TRUE) %>% + group_by(EUring, species, UniekID) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(UniekID = factor(UniekID, levels = str_sort(unique(.$UniekID), numeric = T))) %>% + ggplot(aes(x = UniekID, y = Sum_Casualties)) + + geom_col(colour = "black", fill = cbp2[6], size = 0.1) + + facet_wrap("species", ncol = 1, scales = "free") + + xlab("OWP ID") + ylab("Casualties from habitat displacement") + + theme_WMR + + theme(axis.text.x = element_text(angle = 90), + strip.text.x = element_text(size = 9, face = "italic")) +## +ggsave("output/Casualties_OWP_Species_NAT.pdf", gg.Casualties_OWP_Species_BatchNAT_ScenarioOWPs, width = 4, height = 10.6) +ggsave("output/Casualties_OWP_Species_NAT.png", gg.Casualties_OWP_Species_BatchNAT_ScenarioOWPs, width = 4, height = 10.6, units = 'in', dpi = 300) +## + +## +gg.Casualties_OWP_Countries_BatchINT_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & Batch == "INT" & international == TRUE) %>% + group_by(Batch, species, EUring, UniekID, CountryName) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(UniekID = fct_reorder(UniekID, desc(Sum_Casualties))) %>% + ggplot(aes(x = UniekID, y = Sum_Casualties, fill = species)) + + geom_col(colour = "black", size = 0.1) + + scale_fill_discrete(type = cbp2) + + facet_wrap("CountryName", ncol = 1, scales = "free") + + xlab("OWP ID") + ylab("Casualties from habitat displacement") + + ggtitle("Batch = 'INT', OWPs of the INT 30 scenario only") + + theme_WMR + + theme(axis.text.x = element_text(size = 6, angle = 90)) +## +ggsave("output/Casualties_OWP_Countries_INT.pdf", gg.Casualties_OWP_Countries_BatchINT_ScenarioOWPs, width = 6, height = 10) +ggsave("output/Casualties_OWP_Countries_INT.png", gg.Casualties_OWP_Countries_BatchINT_ScenarioOWPs, width = 6, height = 10, units = 'in', dpi = 300) +## + +## +gg.Casualties_Dutch_OWPs_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & CountryName == "Netherlands" & included_nat == TRUE) %>% + group_by(Batch, species, EUring, UniekID) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + mutate(UniekID = fct_reorder(UniekID, desc(Sum_Casualties))) %>% + ggplot(aes(x = UniekID, y = Sum_Casualties, fill = Batch)) + + geom_col(colour = "black", size = 0.1, position = position_dodge2(width = 0.9)) + + facet_wrap("species", ncol = 1, scales = "free") + + xlab("OWP ID") + ylab("Annual casualties from habitat displacement") + + ggtitle("Dutch OWP. Comparison between 'NAT' and 'INT' data. OWPs from national scenarios only") + + theme_WMR + + theme(axis.text.x = element_text(size = 6, angle = 90), axis.text.y = element_text(size = 6, angle = 90)) +## +ggsave("output/Casualties_Dutch_OWPs.pdf", gg.Casualties_Dutch_OWPs_ScenarioOWPs, width = 5, height = 10) +ggsave("output/Casualties_Dutch_OWPs.png", gg.Casualties_Dutch_OWPs_ScenarioOWPs, width = 5, height = 10, units = 'in', dpi = 300) +## + +## +gg.Casualties_Dutch_Years_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & CountryName == "Netherlands" & Batch == "NAT" & included_nat == TRUE) %>% + group_by(Batch, species, Verwachte, EUring, UniekID) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + ggplot(aes(x = factor(Verwachte), y = Sum_Casualties, fill = factor(UniekID))) + + geom_col(colour = "black", size = 0.1) + + scale_fill_discrete(name = "OWP id") + + facet_wrap("species", ncol = 1, scales = "free") + + xlab("Year") + ylab("Annual casualties from habitat displacement") + + ggtitle("Dutch OWPs, National data ('NAT')") + + theme_WMR + + theme(axis.text.x = element_text(size = 7, angle = 90), axis.text.y = element_text(size = 7)) +## +ggsave("output/Casualties_Dutch_Years.pdf", gg.Casualties_Dutch_Years_ScenarioOWPs, width = 5, height = 10) +ggsave("output/Casualties_Dutch_Years.png", gg.Casualties_Dutch_Years_ScenarioOWPs, width = 5, height = 10, units = 'in', dpi = 300) +## + +## +gg.Casualties_Dutch_OWPs_AllSpecies_ScenarioOWPs <- + owp_habloss %>% + filter(Include4HabLoss == 1 & CountryName == "Netherlands" & Batch == "NAT" & included_nat == TRUE) %>% + group_by(Batch, species, EUring, UniekID) %>% + summarize(Sum_Casualties = sum(casualtiesHabLoss), .groups = "drop") %>% + ggplot(aes(x = reorder(UniekID, -Sum_Casualties), y = Sum_Casualties, fill = species)) + + geom_col(colour = "black", size = 0) + + # facet_wrap("species", ncol = 1, scales = "free") + + xlab("OWP ID") + ylab("Annual casualties from habitat displacement") + + ggtitle("Dutch OWPs, National data ('NAT')") + + theme_WMR + + theme(axis.text.x = element_text(size = 6, angle = 90)) +## +ggsave("output/Casualties_Dutch_OWPs_AllSpecies.pdf", gg.Casualties_Dutch_OWPs_AllSpecies_ScenarioOWPs, width = 7, height = 4) +ggsave("output/Casualties_Dutch_OWPs_AllSpecies.png", gg.Casualties_Dutch_OWPs_AllSpecies_ScenarioOWPs, width = 7, height = 4, units = 'in', dpi = 300) +## diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_Habloss_Collisions.R b/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_Habloss_Collisions.R new file mode 100644 index 0000000000000000000000000000000000000000..6aa6a663266564768a2a5c334dbd89c92560e60a --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/OWP_SB_Habloss_Collisions.R @@ -0,0 +1,138 @@ +##################################################################### +# +# Disclaimer +# +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# +# Wageningen Marine Research +# January 2022 +##################################################################### +library(tidyverse) +# rm(list = ls()) + +## --- PLOTTING ---- + +## Define colours (from: https://www.datanovia.com/en/blog/ggplot-colors-best-tricks-you-will-love/) +cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73", + "#F0E442", "#0072B2", "#D55E00", "#CC79A7") + +country_colours <- c("Netherlands" = cbp2[2], + "Belgium" = cbp2[7], + "United Kingdom" = cbp2[6], + "Germany" = cbp2[5], + "Denmark" = cbp2[4]) + +scn_labels <- c("Basic_2030" = "Basic 2030", + "Rekenvariant_III" = "Rekenvariant III", + "Rekenvariant_II" = "Rekenvariant II", + "International" = "International", + "Rekenvariant_I" = "Rekenvariant I") + +scns_order <- c('Basic_2030', 'Rekenvariant_I', 'Rekenvariant_II', 'Rekenvariant_III', 'International') + +theme_WMR <- theme_classic() + + theme(text = element_text(size = 8), + title = element_text(size = 9), + rect = element_rect(size = 0.5), + line = element_line(size = 0.5)) + +## Script to Add Casualties from Displacement & Collisons for Northern Gannet and Sandwich Tern +HabLoss <- read.table("output/KEC4_HabLoss_Casualties_Scenarios.txt", header = TRUE) +HabLoss$species <- trimws(HabLoss$species) +Collisions <- read.table("input/KEC4.0_Results_collisions_BuWa.txt", header = TRUE) +Collisions$species <- trimws(Collisions$species) +Collisions <- Collisions %>% + rename('Basic_2030' = 'nvic_nat_30_basic', + 'Rekenvariant_III' = 'nvic_nat_30_favoured', + 'Rekenvariant_II' = 'nvic_nat_30_fallback', + 'Rekenvariant_I' = 'nvic_nat_30_restricted', + 'International' = 'nvic_int_30') + +HabLoss_long <- + HabLoss %>% + filter(species %in% c("Morus bassanus", "Thalasseus sandvicensis") & stat == "mean") %>% + pivot_longer(cols = matches("Basic_2030|Rekenvariant|International"), names_to = "Scenario", values_to = "HabLoss") %>% + filter(!is.na(HabLoss)) + +Collisions_long <- + Collisions %>% + select(-abundance) %>% + filter(species %in% c("Morus bassanus", "Thalasseus sandvicensis") & stat == "mean") %>% + pivot_longer(cols = matches("Basic_2030|Rekenvariant|International"), names_to = "Scenario", values_to = "Collisions") %>% + filter(!is.na(Collisions)) + +## +AllCasualties <- + left_join(HabLoss_long, Collisions_long, by = c('species', 'season', 'Scenario', 'batch', 'stat')) %>% + arrange(species, batch, Scenario) + +## Calculate all Casualties +AllCasualties$Total <- AllCasualties$HabLoss + AllCasualties$Collisions + +## ---- EXPORT ---- + +## Convert to wide data.frame for KEC4popmodels +## Replaces 'stat' with source of casualties +AllCasualties_wide <- + AllCasualties %>% + select(-stat) %>% + pivot_longer(cols = c(HabLoss, Collisions, Total), names_to = "stat") %>% + pivot_wider(names_from = Scenario, values_from = value) +## + +## Export for use in KEC4popmodels +write.table(AllCasualties_wide, file = "output/KEC4_Total_Casualties_Scenarios.txt", quote = TRUE, row.names = FALSE) +## + +## Convert to table for use in report +AllCasualties.Table <- + AllCasualties %>% + select(-c(abundance, stat, batch)) %>% + pivot_longer(cols = c(HabLoss, Collisions, Total), names_to = "stat") %>% + mutate(season, season = recode(season, '1' = "Aug + Sep", '2' = "Okt + Nov", '3' = "Dec + Jan", '4' = "Feb + Mar", '5' = "Apr + May", '6' = "Jun + Jul")) %>% + mutate(Scenario, Scenario = gsub("_", " ", Scenario)) %>% + pivot_wider(id_cols = c(Scenario, species, stat), values_from = value, names_from = season) %>% + arrange(species, factor(Scenario, levels = scns_order)) %>% + rowwise() %>% + mutate(Mean = mean(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + mutate(Total = sum(c_across(`Aug + Sep`:`Jun + Jul`))) %>% + mutate(across(where(is.numeric), round, 1)) %>% + knitr::kable(format = "markdown", caption = "Number of casualties from collisions and habitat loss per scenario, species and season.") + +fln <- "output/Table_KEC4.0_AllCasualties_Scenarios_Totals.Rmd" +cat("---\n", file = fln) +cat("classoption: landscape\n", file = fln, append = TRUE) +cat("papersize: a3\n", file = fln, append = TRUE) +cat("---\n", file = fln, append = TRUE) +cat(AllCasualties.Table, file = fln, append = TRUE, sep = "\n") +rmarkdown::render(fln, output_format = "word_document") +rmarkdown::render(fln, output_format = "pdf_document") +## + + +## ---- PLOTTING ---- +gg.AllCasualties <- + AllCasualties %>% + pivot_longer(cols = c(HabLoss, Collisions, Total)) %>% + ggplot(aes(x = season, value, colour = name)) + + geom_point() + geom_line() + + scale_colour_discrete(type = cbp2[c(2,3,1)], name = "Source") + + facet_grid(species ~ Scenario, scales = "free", + labeller = labeller(Scenario = scn_labels)) + + ylab("Number of casualties") + xlab("Bimonthly period") + + theme_classic() +## +ggsave("output/KEC4_AllCasualties_Scenarios.pdf", width = 10, height = 4) +ggsave("output/KEC4_AllCasualties_Scenarios.png", width = 10, height = 4) +## diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/input/KEC4.0_Results_collisions_BuWa.txt b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/KEC4.0_Results_collisions_BuWa.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac8d785028765fc95d193dfe1630fd9877e5acf1 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/KEC4.0_Results_collisions_BuWa.txt @@ -0,0 +1,361 @@ +season batch species abundance nvic_nat_30_basic nvic_nat_30_favoured nvic_nat_30_fallback nvic_nat_30_restricted nvic_int_30 stat +1 "NAT" "Rissa tridactyla" 53103.08 24.12002921 81.89545773 71.09595279 68.19225458 "NA" "mean" +2 "NAT" "Rissa tridactyla" 68342.67 60.90325285 88.73934166 81.13479809 78.76011841 "NA" "mean" +3 "NAT" "Rissa tridactyla" 78921.4 60.66100606 102.9591223 94.76933873 91.26488522 "NA" "mean" +4 "NAT" "Rissa tridactyla" 52381.45 41.27531571 61.2008001 57.8104106 55.80037348 "NA" "mean" +5 "NAT" "Rissa tridactyla" 29888.59 34.97835614 58.24881491 52.49487865 49.2469555 "NA" "mean" +6 "NAT" "Rissa tridactyla" 33376.87 7.550299926 31.53223032 23.54064728 20.80824359 "NA" "mean" +1 "NAT" "Larus minutus" 1481.41 0.021549489 1.825930145 1.16539737 1.16539737 "NA" "mean" +2 "NAT" "Larus minutus" 8393.52 6.678919991 8.555130538 7.146238105 7.063331239 "NA" "mean" +3 "NAT" "Larus minutus" 2701.36 1.807404386 2.193346331 2.161745868 1.940708935 "NA" "mean" +4 "NAT" "Larus minutus" 6164.72 7.295548967 7.763523483 7.763523483 7.763523483 "NA" "mean" +5 "NAT" "Larus minutus" 57832.13 75.08274936 96.59767926 94.00372819 92.32658419 "NA" "mean" +6 "NAT" "Larus minutus" 93.52 0 0 0 0 "NA" "mean" +1 "NAT" "Stercorarius skua" 1364.08 1.274299512 4.542407214 3.979271852 3.73664799 "NA" "mean" +2 "NAT" "Stercorarius skua" 202.12 0.128422277 0.498564513 0.434875618 0.272693336 "NA" "mean" +3 "NAT" "Stercorarius skua" 281.4 0.455952637 0.4743329 0.4743329 0.4743329 "NA" "mean" +4 "NAT" "Stercorarius skua" 46.34 0.057529745 0.057529745 0.057529745 0.057529745 "NA" "mean" +5 "NAT" "Stercorarius skua" 45.43 0 0 0 0 "NA" "mean" +6 "NAT" "Stercorarius skua" 142.86 0.097383737 0.228089675 0.197106808 0.171812215 "NA" "mean" +1 "NAT" "Larus marinus" 7245.36 53.56712839 109.0335812 84.92916406 79.0050136 "NA" "mean" +2 "NAT" "Larus marinus" 16263.75 81.1001501 181.6703934 160.7045471 155.4236093 "NA" "mean" +3 "NAT" "Larus marinus" 14936.7 81.82053555 164.3375304 131.4523346 122.1422662 "NA" "mean" +4 "NAT" "Larus marinus" 9533.53 65.79631611 115.4976084 109.6154814 106.0079932 "NA" "mean" +5 "NAT" "Larus marinus" 3109.62 44.71725547 58.06439177 56.56984181 55.95074864 "NA" "mean" +6 "NAT" "Larus marinus" 6844.61 11.47273284 37.72485881 34.52596339 31.8791887 "NA" "mean" +1 "NAT" "Thalasseus sandvicensis" 22602.26 9.089013899 14.69960942 13.29601923 13.29601923 "NA" "mean" +2 "NAT" "Thalasseus sandvicensis" 91.64 0.032944626 0.093594638 0.093594638 0.093594638 "NA" "mean" +3 "NAT" "Thalasseus sandvicensis" 45.28 0 0.009516338 0.009516338 0.009516338 "NA" "mean" +4 "NAT" "Thalasseus sandvicensis" 55.33 0.048316657 0.048316657 0.048316657 0.048316657 "NA" "mean" +5 "NAT" "Thalasseus sandvicensis" 16513.59 16.31252131 19.62046597 19.40981725 19.40981725 "NA" "mean" +6 "NAT" "Thalasseus sandvicensis" 14548.96 6.57320929 6.859541379 6.859541379 6.859541379 "NA" "mean" +1 "NAT" "Morus bassanus" 26708.81 154.783255 310.7064589 276.6993319 261.6667176 "NA" "mean" +2 "NAT" "Morus bassanus" 31858.07 280.5925155 526.6880816 466.02901 432.2777568 "NA" "mean" +3 "NAT" "Morus bassanus" 18258.36 226.7156394 319.8378091 306.2053444 290.5016699 "NA" "mean" +4 "NAT" "Morus bassanus" 18804.13 205.7040181 259.2855745 254.7993326 252.6623666 "NA" "mean" +5 "NAT" "Morus bassanus" 11881.45 169.7718126 255.8327923 248.9399418 242.2113549 "NA" "mean" +6 "NAT" "Morus bassanus" 12540.23 145.7879208 252.7950674 218.5224373 210.85829 "NA" "mean" +1 "NAT" "Stercorarius parasiticus" 130.18 0.044499731 0.049543438 0.049520424 0.049485094 "NA" "mean" +2 "NAT" "Stercorarius parasiticus" 5.27 0 0 0 0 "NA" "mean" +3 "NAT" "Stercorarius parasiticus" 2.51 0.002615777 0.004265455 0.004265455 0.002615777 "NA" "mean" +4 "NAT" "Stercorarius parasiticus" 9.97 0.024497022 0.03016966 0.03016966 0.03016966 "NA" "mean" +5 "NAT" "Stercorarius parasiticus" 6.8 0.001427734 0.004319285 0.004319285 0.004319285 "NA" "mean" +6 "NAT" "Stercorarius parasiticus" 12.03 0 0 0 0 "NA" "mean" +1 "NAT" "Larus fuscus" 20206.5 16.43036543 32.22103559 29.55381961 28.51691463 "NA" "mean" +2 "NAT" "Larus fuscus" 5675.84 9.994905443 12.22197451 12.09284371 12.05739906 "NA" "mean" +3 "NAT" "Larus fuscus" 619.47 1.011859338 1.206692223 1.128810305 1.128519543 "NA" "mean" +4 "NAT" "Larus fuscus" 7783.81 13.4143436 16.9355714 16.11723484 15.91624611 "NA" "mean" +5 "NAT" "Larus fuscus" 19376.95 29.18280078 43.29387212 41.22042563 39.95557166 "NA" "mean" +6 "NAT" "Larus fuscus" 20552.64 26.69070752 47.39582313 44.30419658 41.62484733 "NA" "mean" +1 "NAT" "Sterna hirundo" 59092.68 34.18245143 44.79800215 43.00872822 42.65295719 "NA" "mean" +2 "NAT" "Sterna hirundo" 56.78 0.051083693 0.051083693 0.051083693 0.051083693 "NA" "mean" +3 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "mean" +4 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "mean" +5 "NAT" "Sterna hirundo" 11773.66 8.093300334 11.85302927 11.56924341 11.35379355 "NA" "mean" +6 "NAT" "Sterna hirundo" 5769.5 0.760455952 1.001870956 0.987407422 0.987407422 "NA" "mean" +1 "NAT" "Larus argentatus" 8674.12 27.92250147 39.48689552 33.84510196 32.32430857 "NA" "mean" +2 "NAT" "Larus argentatus" 21138.22 42.49218107 48.67979551 47.81938926 47.58809166 "NA" "mean" +3 "NAT" "Larus argentatus" 18140.03 20.72767157 36.31816448 32.96529348 31.2371152 "NA" "mean" +4 "NAT" "Larus argentatus" 14624.96 19.24117153 30.10268051 28.47430843 27.74420504 "NA" "mean" +5 "NAT" "Larus argentatus" 14086.37 52.11512044 56.30095273 56.26868625 56.16773887 "NA" "mean" +6 "NAT" "Larus argentatus" 8634.41 17.39037876 24.63963208 23.88605185 23.83580033 "NA" "mean" +1 "INT" "Rissa tridactyla" 251144.33 "NA" "NA" "NA" "NA" 160.9671026 "mean" +2 "INT" "Rissa tridactyla" 254033.29 "NA" "NA" "NA" "NA" 198.6413229 "mean" +3 "INT" "Rissa tridactyla" 444163.27 "NA" "NA" "NA" "NA" 347.6521041 "mean" +4 "INT" "Rissa tridactyla" 246114.67 "NA" "NA" "NA" "NA" 240.189282 "mean" +5 "INT" "Rissa tridactyla" 217118.65 "NA" "NA" "NA" "NA" 161.7746825 "mean" +6 "INT" "Rissa tridactyla" 244431.79 "NA" "NA" "NA" "NA" 159.0298606 "mean" +1 "INT" "Larus minutus" 14316.8 "NA" "NA" "NA" "NA" 4.57863408 "mean" +2 "INT" "Larus minutus" 18053.22 "NA" "NA" "NA" "NA" 12.6199365 "mean" +3 "INT" "Larus minutus" 4709.8 "NA" "NA" "NA" "NA" 5.720830661 "mean" +4 "INT" "Larus minutus" 11147.97 "NA" "NA" "NA" "NA" 22.06515109 "mean" +5 "INT" "Larus minutus" 55816.78 "NA" "NA" "NA" "NA" 94.2768165 "mean" +6 "INT" "Larus minutus" 2553.55 "NA" "NA" "NA" "NA" 2.531550474 "mean" +1 "INT" "Stercorarius skua" 12102.95 "NA" "NA" "NA" "NA" 13.44856654 "mean" +2 "INT" "Stercorarius skua" 3791.44 "NA" "NA" "NA" "NA" 5.014205301 "mean" +3 "INT" "Stercorarius skua" 644.77 "NA" "NA" "NA" "NA" 0.622375841 "mean" +4 "INT" "Stercorarius skua" 216.61 "NA" "NA" "NA" "NA" 0.991103671 "mean" +5 "INT" "Stercorarius skua" 1574.02 "NA" "NA" "NA" "NA" 3.004274484 "mean" +6 "INT" "Stercorarius skua" 5198.81 "NA" "NA" "NA" "NA" 6.115785272 "mean" +1 "INT" "Larus marinus" 45938.6 "NA" "NA" "NA" "NA" 296.7968735 "mean" +2 "INT" "Larus marinus" 67882.57 "NA" "NA" "NA" "NA" 621.6636014 "mean" +3 "INT" "Larus marinus" 87509.32 "NA" "NA" "NA" "NA" 503.9878921 "mean" +4 "INT" "Larus marinus" 92416.93 "NA" "NA" "NA" "NA" 453.8460749 "mean" +5 "INT" "Larus marinus" 21310.24 "NA" "NA" "NA" "NA" 152.6869536 "mean" +6 "INT" "Larus marinus" 22403.51 "NA" "NA" "NA" "NA" 145.0878704 "mean" +1 "INT" "Thalasseus sandvicensis" 18496.25 "NA" "NA" "NA" "NA" 14.45283135 "mean" +2 "INT" "Thalasseus sandvicensis" 310.29 "NA" "NA" "NA" "NA" 0.089776696 "mean" +3 "INT" "Thalasseus sandvicensis" 42.48 "NA" "NA" "NA" "NA" 0.032247035 "mean" +4 "INT" "Thalasseus sandvicensis" 129.35 "NA" "NA" "NA" "NA" 0.262745272 "mean" +5 "INT" "Thalasseus sandvicensis" 25881.09 "NA" "NA" "NA" "NA" 37.06447143 "mean" +6 "INT" "Thalasseus sandvicensis" 14830.62 "NA" "NA" "NA" "NA" 12.69903362 "mean" +1 "INT" "Morus bassanus" 162867.23 "NA" "NA" "NA" "NA" 1259.244229 "mean" +2 "INT" "Morus bassanus" 107631.13 "NA" "NA" "NA" "NA" 1419.623017 "mean" +3 "INT" "Morus bassanus" 83388.82 "NA" "NA" "NA" "NA" 712.2172577 "mean" +4 "INT" "Morus bassanus" 98020.44 "NA" "NA" "NA" "NA" 994.5872476 "mean" +5 "INT" "Morus bassanus" 91079.9 "NA" "NA" "NA" "NA" 999.8390643 "mean" +6 "INT" "Morus bassanus" 149912.9 "NA" "NA" "NA" "NA" 1615.943842 "mean" +1 "INT" "Stercorarius parasiticus" 3185.55 "NA" "NA" "NA" "NA" 1.152885312 "mean" +2 "INT" "Stercorarius parasiticus" 504.28 "NA" "NA" "NA" "NA" 0.071094081 "mean" +3 "INT" "Stercorarius parasiticus" 48.9 "NA" "NA" "NA" "NA" 0.034819305 "mean" +4 "INT" "Stercorarius parasiticus" 10.09 "NA" "NA" "NA" "NA" 0.027384673 "mean" +5 "INT" "Stercorarius parasiticus" 589.01 "NA" "NA" "NA" "NA" 0.320512676 "mean" +6 "INT" "Stercorarius parasiticus" 1530.78 "NA" "NA" "NA" "NA" 0.287731444 "mean" +1 "INT" "Larus fuscus" 72517.85 "NA" "NA" "NA" "NA" 114.322132 "mean" +2 "INT" "Larus fuscus" 18782.68 "NA" "NA" "NA" "NA" 35.61266091 "mean" +3 "INT" "Larus fuscus" 16359.22 "NA" "NA" "NA" "NA" 6.289566607 "mean" +4 "INT" "Larus fuscus" 49438.24 "NA" "NA" "NA" "NA" 64.4235639 "mean" +5 "INT" "Larus fuscus" 75350.67 "NA" "NA" "NA" "NA" 106.5000156 "mean" +6 "INT" "Larus fuscus" 69584.21 "NA" "NA" "NA" "NA" 113.9596831 "mean" +1 "INT" "Sterna hirundo" 74947.45 "NA" "NA" "NA" "NA" 46.65630451 "mean" +2 "INT" "Sterna hirundo" 214.32 "NA" "NA" "NA" "NA" 0.0259174 "mean" +3 "INT" "Sterna hirundo" 0 "NA" "NA" "NA" "NA" 0 "mean" +4 "INT" "Sterna hirundo" 11.19 "NA" "NA" "NA" "NA" 0 "mean" +5 "INT" "Sterna hirundo" 28542.73 "NA" "NA" "NA" "NA" 18.66241667 "mean" +6 "INT" "Sterna hirundo" 51816.75 "NA" "NA" "NA" "NA" 33.85031774 "mean" +1 "INT" "Larus argentatus" 34780.12 "NA" "NA" "NA" "NA" 60.38501354 "mean" +2 "INT" "Larus argentatus" 110283.13 "NA" "NA" "NA" "NA" 124.4499789 "mean" +3 "INT" "Larus argentatus" 79288.48 "NA" "NA" "NA" "NA" 103.7305437 "mean" +4 "INT" "Larus argentatus" 124964.43 "NA" "NA" "NA" "NA" 206.1561418 "mean" +5 "INT" "Larus argentatus" 31040.29 "NA" "NA" "NA" "NA" 89.38164639 "mean" +6 "INT" "Larus argentatus" 28466.44 "NA" "NA" "NA" "NA" 70.65724128 "mean" +1 "NAT" "Rissa tridactyla" 53103.08 23.37493565 79.67794294 68.9758258 66.07934192 "NA" "lower25perc" +2 "NAT" "Rissa tridactyla" 68342.67 59.86287712 87.44339538 79.92247888 77.55626449 "NA" "lower25perc" +3 "NAT" "Rissa tridactyla" 78921.4 59.61479864 101.3974198 93.2874123 89.79803585 "NA" "lower25perc" +4 "NAT" "Rissa tridactyla" 52381.45 40.50441822 60.27518322 56.90763599 54.90576584 "NA" "lower25perc" +5 "NAT" "Rissa tridactyla" 29888.59 34.21926416 57.27968553 51.58980015 48.36325959 "NA" "lower25perc" +6 "NAT" "Rissa tridactyla" 33376.87 7.225074021 30.68879262 22.84769913 20.13508086 "NA" "lower25perc" +1 "NAT" "Larus minutus" 1481.41 0.020380468 1.750834545 1.106164133 1.106164133 "NA" "lower25perc" +2 "NAT" "Larus minutus" 8393.52 6.59329861 8.423495729 7.059038101 6.976324507 "NA" "lower25perc" +3 "NAT" "Larus minutus" 2701.36 1.781788193 2.162876126 2.131355659 1.914529341 "NA" "lower25perc" +4 "NAT" "Larus minutus" 6164.72 7.181003746 7.648418688 7.648418688 7.648418688 "NA" "lower25perc" +5 "NAT" "Larus minutus" 57832.13 74.02766452 95.37336413 92.79292584 91.12147535 "NA" "lower25perc" +6 "NAT" "Larus minutus" 93.52 0 0 0 0 "NA" "lower25perc" +1 "NAT" "Stercorarius skua" 1364.08 1.140963004 3.987481263 3.449447092 3.211614241 "NA" "lower25perc" +2 "NAT" "Stercorarius skua" 202.12 0.108433413 0.436804218 0.376027099 0.238246211 "NA" "lower25perc" +3 "NAT" "Stercorarius skua" 281.4 0.414228247 0.432181879 0.432181879 0.432181879 "NA" "lower25perc" +4 "NAT" "Stercorarius skua" 46.34 0.04927763 0.04927763 0.04927763 0.04927763 "NA" "lower25perc" +5 "NAT" "Stercorarius skua" 45.43 0 0 0 0 "NA" "lower25perc" +6 "NAT" "Stercorarius skua" 142.86 0.082916026 0.201476289 0.172081347 0.147903644 "NA" "lower25perc" +1 "NAT" "Larus marinus" 7245.36 48.19051729 97.15288046 76.86587695 71.23211771 "NA" "lower25perc" +2 "NAT" "Larus marinus" 16263.75 71.76073852 162.1533274 142.7258279 137.5469523 "NA" "lower25perc" +3 "NAT" "Larus marinus" 14936.7 72.49002547 146.819438 118.5972543 109.7365654 "NA" "lower25perc" +4 "NAT" "Larus marinus" 9533.53 57.48288495 103.3009083 97.60667035 94.07061606 "NA" "lower25perc" +5 "NAT" "Larus marinus" 3109.62 39.6745 52.40582272 50.93722239 50.32259381 "NA" "lower25perc" +6 "NAT" "Larus marinus" 6844.61 9.678420112 31.2032828 28.10796325 25.53307091 "NA" "lower25perc" +1 "NAT" "Thalasseus sandvicensis" 22602.26 8.884362545 14.38081151 12.99990918 12.99990918 "NA" "lower25perc" +2 "NAT" "Thalasseus sandvicensis" 91.64 0.031138514 0.088165148 0.088165148 0.088165148 "NA" "lower25perc" +3 "NAT" "Thalasseus sandvicensis" 45.28 0 0.008790218 0.008790218 0.008790218 "NA" "lower25perc" +4 "NAT" "Thalasseus sandvicensis" 55.33 0.04572211 0.04572211 0.04572211 0.04572211 "NA" "lower25perc" +5 "NAT" "Thalasseus sandvicensis" 16513.59 15.92338601 19.21038142 19.00011644 19.00011644 "NA" "lower25perc" +6 "NAT" "Thalasseus sandvicensis" 14548.96 6.355858844 6.641617593 6.641617593 6.641617593 "NA" "lower25perc" +1 "NAT" "Morus bassanus" 26708.81 145.8602329 293.9105131 261.1160987 246.3316282 "NA" "lower25perc" +2 "NAT" "Morus bassanus" 31858.07 260.963725 497.6286065 439.2187908 406.2030279 "NA" "lower25perc" +3 "NAT" "Morus bassanus" 18258.36 210.9358272 302.0352897 288.5803714 273.1150855 "NA" "lower25perc" +4 "NAT" "Morus bassanus" 18804.13 192.4477857 244.7291702 240.2666179 238.1350314 "NA" "lower25perc" +5 "NAT" "Morus bassanus" 11881.45 158.7532073 242.3476729 235.5148515 228.8437123 "NA" "lower25perc" +6 "NAT" "Morus bassanus" 12540.23 137.1755277 239.4818149 206.7958516 199.2167256 "NA" "lower25perc" +1 "NAT" "Stercorarius parasiticus" 130.18 0.040707288 0.045720137 0.045697118 0.04566179 "NA" "lower25perc" +2 "NAT" "Stercorarius parasiticus" 5.27 0 0 0 0 "NA" "lower25perc" +3 "NAT" "Stercorarius parasiticus" 2.51 0.002393294 0.003985375 0.003985375 0.002393294 "NA" "lower25perc" +4 "NAT" "Stercorarius parasiticus" 9.97 0.023423881 0.028991497 0.028991497 0.028991497 "NA" "lower25perc" +5 "NAT" "Stercorarius parasiticus" 6.8 0.001308459 0.004053827 0.004053827 0.004053827 "NA" "lower25perc" +6 "NAT" "Stercorarius parasiticus" 12.03 0 0 0 0 "NA" "lower25perc" +1 "NAT" "Larus fuscus" 20206.5 15.38309142 30.12123594 27.55647668 26.53551684 "NA" "lower25perc" +2 "NAT" "Larus fuscus" 5675.84 9.199274916 11.37546011 11.2469117 11.21151099 "NA" "lower25perc" +3 "NAT" "Larus fuscus" 619.47 0.926163736 1.11719565 1.041334587 1.041043845 "NA" "lower25perc" +4 "NAT" "Larus fuscus" 7783.81 12.53995214 15.9852113 15.18797459 14.98827396 "NA" "lower25perc" +5 "NAT" "Larus fuscus" 19376.95 27.22475399 40.82482021 38.80335359 37.55813012 "NA" "lower25perc" +6 "NAT" "Larus fuscus" 20552.64 24.47158434 44.27014878 41.27001099 38.66123359 "NA" "lower25perc" +1 "NAT" "Sterna hirundo" 59092.68 32.92509625 43.4250178 41.6456396 41.29026127 "NA" "lower25perc" +2 "NAT" "Sterna hirundo" 56.78 0.04921427 0.04921427 0.04921427 0.04921427 "NA" "lower25perc" +3 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "lower25perc" +4 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "lower25perc" +5 "NAT" "Sterna hirundo" 11773.66 7.812262198 11.52635309 11.24361262 11.02876685 "NA" "lower25perc" +6 "NAT" "Sterna hirundo" 5769.5 0.737836859 0.976306685 0.961877594 0.961877594 "NA" "lower25perc" +1 "NAT" "Larus argentatus" 8674.12 25.36962567 36.1990817 31.09019398 29.61219584 "NA" "lower25perc" +2 "NAT" "Larus argentatus" 21138.22 38.28494446 44.35615218 43.50448423 43.2738186 "NA" "lower25perc" +3 "NAT" "Larus argentatus" 18140.03 18.27694946 32.95641134 29.7764802 28.0958454 "NA" "lower25perc" +4 "NAT" "Larus argentatus" 14624.96 17.5320662 27.67958857 26.10780172 25.38923474 "NA" "lower25perc" +5 "NAT" "Larus argentatus" 14086.37 46.94416695 51.06934232 51.037086 50.93623724 "NA" "lower25perc" +6 "NAT" "Larus argentatus" 8634.41 15.62566885 22.41947137 21.67880405 21.62861004 "NA" "lower25perc" +1 "INT" "Rissa tridactyla" 251144.33 "NA" "NA" "NA" "NA" 146.0964097 "lower25perc" +2 "INT" "Rissa tridactyla" 254033.29 "NA" "NA" "NA" "NA" 184.679784 "lower25perc" +3 "INT" "Rissa tridactyla" 444163.27 "NA" "NA" "NA" "NA" 325.2547307 "lower25perc" +4 "INT" "Rissa tridactyla" 246114.67 "NA" "NA" "NA" "NA" 224.6230491 "lower25perc" +5 "INT" "Rissa tridactyla" 217118.65 "NA" "NA" "NA" "NA" 151.1480877 "lower25perc" +6 "INT" "Rissa tridactyla" 244431.79 "NA" "NA" "NA" "NA" 147.6171386 "lower25perc" +1 "INT" "Larus minutus" 14316.8 "NA" "NA" "NA" "NA" 4.50395308 "lower25perc" +2 "INT" "Larus minutus" 18053.22 "NA" "NA" "NA" "NA" 12.51343959 "lower25perc" +3 "INT" "Larus minutus" 4709.8 "NA" "NA" "NA" "NA" 5.679460613 "lower25perc" +4 "INT" "Larus minutus" 11147.97 "NA" "NA" "NA" "NA" 21.90505275 "lower25perc" +5 "INT" "Larus minutus" 55816.78 "NA" "NA" "NA" "NA" 93.33104721 "lower25perc" +6 "INT" "Larus minutus" 2553.55 "NA" "NA" "NA" "NA" 2.481499766 "lower25perc" +1 "INT" "Stercorarius skua" 12102.95 "NA" "NA" "NA" "NA" 12.63962762 "lower25perc" +2 "INT" "Stercorarius skua" 3791.44 "NA" "NA" "NA" "NA" 4.632672761 "lower25perc" +3 "INT" "Stercorarius skua" 644.77 "NA" "NA" "NA" "NA" 0.579296941 "lower25perc" +4 "INT" "Stercorarius skua" 216.61 "NA" "NA" "NA" "NA" 0.911830563 "lower25perc" +5 "INT" "Stercorarius skua" 1574.02 "NA" "NA" "NA" "NA" 2.640058801 "lower25perc" +6 "INT" "Stercorarius skua" 5198.81 "NA" "NA" "NA" "NA" 5.565368596 "lower25perc" +1 "INT" "Larus marinus" 45938.6 "NA" "NA" "NA" "NA" 282.2749476 "lower25perc" +2 "INT" "Larus marinus" 67882.57 "NA" "NA" "NA" "NA" 588.1737562 "lower25perc" +3 "INT" "Larus marinus" 87509.32 "NA" "NA" "NA" "NA" 481.1667716 "lower25perc" +4 "INT" "Larus marinus" 92416.93 "NA" "NA" "NA" "NA" 432.0558862 "lower25perc" +5 "INT" "Larus marinus" 21310.24 "NA" "NA" "NA" "NA" 146.429408 "lower25perc" +6 "INT" "Larus marinus" 22403.51 "NA" "NA" "NA" "NA" 137.4895888 "lower25perc" +1 "INT" "Thalasseus sandvicensis" 18496.25 "NA" "NA" "NA" "NA" 14.22039668 "lower25perc" +2 "INT" "Thalasseus sandvicensis" 310.29 "NA" "NA" "NA" "NA" 0.085394735 "lower25perc" +3 "INT" "Thalasseus sandvicensis" 42.48 "NA" "NA" "NA" "NA" 0.030281505 "lower25perc" +4 "INT" "Thalasseus sandvicensis" 129.35 "NA" "NA" "NA" "NA" 0.25604373 "lower25perc" +5 "INT" "Thalasseus sandvicensis" 25881.09 "NA" "NA" "NA" "NA" 36.56949035 "lower25perc" +6 "INT" "Thalasseus sandvicensis" 14830.62 "NA" "NA" "NA" "NA" 12.48856763 "lower25perc" +1 "INT" "Morus bassanus" 162867.23 "NA" "NA" "NA" "NA" 1229.041756 "lower25perc" +2 "INT" "Morus bassanus" 107631.13 "NA" "NA" "NA" "NA" 1374.634777 "lower25perc" +3 "INT" "Morus bassanus" 83388.82 "NA" "NA" "NA" "NA" 690.4504504 "lower25perc" +4 "INT" "Morus bassanus" 98020.44 "NA" "NA" "NA" "NA" 964.0006144 "lower25perc" +5 "INT" "Morus bassanus" 91079.9 "NA" "NA" "NA" "NA" 971.793061 "lower25perc" +6 "INT" "Morus bassanus" 149912.9 "NA" "NA" "NA" "NA" 1570.169461 "lower25perc" +1 "INT" "Stercorarius parasiticus" 3185.55 "NA" "NA" "NA" "NA" 1.135055758 "lower25perc" +2 "INT" "Stercorarius parasiticus" 504.28 "NA" "NA" "NA" "NA" 0.069347007 "lower25perc" +3 "INT" "Stercorarius parasiticus" 48.9 "NA" "NA" "NA" "NA" 0.033860353 "lower25perc" +4 "INT" "Stercorarius parasiticus" 10.09 "NA" "NA" "NA" "NA" 0.026423606 "lower25perc" +5 "INT" "Stercorarius parasiticus" 589.01 "NA" "NA" "NA" "NA" 0.309848829 "lower25perc" +6 "INT" "Stercorarius parasiticus" 1530.78 "NA" "NA" "NA" "NA" 0.27943922 "lower25perc" +1 "INT" "Larus fuscus" 72517.85 "NA" "NA" "NA" "NA" 111.0027486 "lower25perc" +2 "INT" "Larus fuscus" 18782.68 "NA" "NA" "NA" "NA" 34.22281254 "lower25perc" +3 "INT" "Larus fuscus" 16359.22 "NA" "NA" "NA" "NA" 5.983881277 "lower25perc" +4 "INT" "Larus fuscus" 49438.24 "NA" "NA" "NA" "NA" 61.99331943 "lower25perc" +5 "INT" "Larus fuscus" 75350.67 "NA" "NA" "NA" "NA" 103.4967073 "lower25perc" +6 "INT" "Larus fuscus" 69584.21 "NA" "NA" "NA" "NA" 110.1946634 "lower25perc" +1 "INT" "Sterna hirundo" 74947.45 "NA" "NA" "NA" "NA" 45.81386916 "lower25perc" +2 "INT" "Sterna hirundo" 214.32 "NA" "NA" "NA" "NA" 0.025173827 "lower25perc" +3 "INT" "Sterna hirundo" 0 "NA" "NA" "NA" "NA" 0 "lower25perc" +4 "INT" "Sterna hirundo" 11.19 "NA" "NA" "NA" "NA" 0 "lower25perc" +5 "INT" "Sterna hirundo" 28542.73 "NA" "NA" "NA" "NA" 18.32018921 "lower25perc" +6 "INT" "Sterna hirundo" 51816.75 "NA" "NA" "NA" "NA" 32.8638145 "lower25perc" +1 "INT" "Larus argentatus" 34780.12 "NA" "NA" "NA" "NA" 57.61038085 "lower25perc" +2 "INT" "Larus argentatus" 110283.13 "NA" "NA" "NA" "NA" 117.6102095 "lower25perc" +3 "INT" "Larus argentatus" 79288.48 "NA" "NA" "NA" "NA" 99.19180586 "lower25perc" +4 "INT" "Larus argentatus" 124964.43 "NA" "NA" "NA" "NA" 190.6374292 "lower25perc" +5 "INT" "Larus argentatus" 31040.29 "NA" "NA" "NA" "NA" 85.68334775 "lower25perc" +6 "INT" "Larus argentatus" 28466.44 "NA" "NA" "NA" "NA" 67.77323903 "lower25perc" +1 "NAT" "Rissa tridactyla" 53103.08 24.86512275 84.11297246 73.2160798 70.30516728 "NA" "upper25perc" +2 "NAT" "Rissa tridactyla" 68342.67 61.94362848 90.03528802 82.34711732 79.96397231 "NA" "upper25perc" +3 "NAT" "Rissa tridactyla" 78921.4 61.70721356 104.5208242 96.2512651 92.73173455 "NA" "upper25perc" +4 "NAT" "Rissa tridactyla" 52381.45 42.04621318 62.12641698 58.71318521 56.69498116 "NA" "upper25perc" +5 "NAT" "Rissa tridactyla" 29888.59 35.73744804 59.21794427 53.39995705 50.13065141 "NA" "upper25perc" +6 "NAT" "Rissa tridactyla" 33376.87 7.875525839 32.37566798 24.23359547 21.48140634 "NA" "upper25perc" +1 "NAT" "Larus minutus" 1481.41 0.022718512 1.901025735 1.224630607 1.224630607 "NA" "upper25perc" +2 "NAT" "Larus minutus" 8393.52 6.76454137 8.686765351 7.233438099 7.150337973 "NA" "upper25perc" +3 "NAT" "Larus minutus" 2701.36 1.833020587 2.223816534 2.192136081 1.966888539 "NA" "upper25perc" +4 "NAT" "Larus minutus" 6164.72 7.410094194 7.878628272 7.878628272 7.878628272 "NA" "upper25perc" +5 "NAT" "Larus minutus" 57832.13 76.13783428 97.82199447 95.21453056 93.53169305 "NA" "upper25perc" +6 "NAT" "Larus minutus" 93.52 0 0 0 0 "NA" "upper25perc" +1 "NAT" "Stercorarius skua" 1364.08 1.407636016 5.097333157 4.509096608 4.261681739 "NA" "upper25perc" +2 "NAT" "Stercorarius skua" 202.12 0.148411147 0.560324802 0.493724141 0.307140469 "NA" "upper25perc" +3 "NAT" "Stercorarius skua" 281.4 0.497677033 0.516483921 0.516483921 0.516483921 "NA" "upper25perc" +4 "NAT" "Stercorarius skua" 46.34 0.06578185 0.06578185 0.06578185 0.06578185 "NA" "upper25perc" +5 "NAT" "Stercorarius skua" 45.43 0 0 0 0 "NA" "upper25perc" +6 "NAT" "Stercorarius skua" 142.86 0.111851454 0.254703071 0.222132273 0.195720796 "NA" "upper25perc" +1 "NAT" "Larus marinus" 7245.36 58.94373951 120.9142815 92.99245125 86.77790949 "NA" "upper25perc" +2 "NAT" "Larus marinus" 16263.75 90.43956168 201.1874586 178.6832661 173.3002657 "NA" "upper25perc" +3 "NAT" "Larus marinus" 14936.7 91.15104553 181.855622 144.3074157 134.5479666 "NA" "upper25perc" +4 "NAT" "Larus marinus" 9533.53 74.10974725 127.6943077 121.6242917 117.9453699 "NA" "upper25perc" +5 "NAT" "Larus marinus" 3109.62 49.760011 63.72296088 62.20246121 61.57890339 "NA" "upper25perc" +6 "NAT" "Larus marinus" 6844.61 13.26704549 44.2464348 40.94396355 38.22530649 "NA" "upper25perc" +1 "NAT" "Thalasseus sandvicensis" 22602.26 9.293665255 15.01840729 13.59212922 13.59212922 "NA" "upper25perc" +2 "NAT" "Thalasseus sandvicensis" 91.64 0.034750746 0.099024132 0.099024132 0.099024132 "NA" "upper25perc" +3 "NAT" "Thalasseus sandvicensis" 45.28 0 0.010242462 0.010242462 0.010242462 "NA" "upper25perc" +4 "NAT" "Thalasseus sandvicensis" 55.33 0.05091121 0.05091121 0.05091121 0.05091121 "NA" "upper25perc" +5 "NAT" "Thalasseus sandvicensis" 16513.59 16.70165659 20.03055058 19.81951796 19.81951796 "NA" "upper25perc" +6 "NAT" "Thalasseus sandvicensis" 14548.96 6.790559736 7.077465167 7.077465167 7.077465167 "NA" "upper25perc" +1 "NAT" "Morus bassanus" 26708.81 163.7062771 327.5024049 292.2825653 277.0018078 "NA" "upper25perc" +2 "NAT" "Morus bassanus" 31858.07 300.221307 555.7475575 492.8392292 458.3524861 "NA" "upper25perc" +3 "NAT" "Morus bassanus" 18258.36 242.4954508 337.6403283 323.8303166 307.8882545 "NA" "upper25perc" +4 "NAT" "Morus bassanus" 18804.13 218.9602503 273.8419798 269.3320481 267.1897026 "NA" "upper25perc" +5 "NAT" "Morus bassanus" 11881.45 180.7904187 269.3179111 262.3650325 255.5789977 "NA" "upper25perc" +6 "NAT" "Morus bassanus" 12540.23 154.4003143 266.1083191 230.2490224 222.4998544 "NA" "upper25perc" +1 "NAT" "Stercorarius parasiticus" 130.18 0.048292172 0.053366743 0.053343722 0.05330839 "NA" "upper25perc" +2 "NAT" "Stercorarius parasiticus" 5.27 0 0 0 0 "NA" "upper25perc" +3 "NAT" "Stercorarius parasiticus" 2.51 0.002838266 0.004545545 0.004545545 0.002838266 "NA" "upper25perc" +4 "NAT" "Stercorarius parasiticus" 9.97 0.025570159 0.031347823 0.031347823 0.031347823 "NA" "upper25perc" +5 "NAT" "Stercorarius parasiticus" 6.8 0.001547001 0.004584753 0.004584753 0.004584753 "NA" "upper25perc" +6 "NAT" "Stercorarius parasiticus" 12.03 0 0 0 0 "NA" "upper25perc" +1 "NAT" "Larus fuscus" 20206.5 17.47763938 34.32083526 31.55116252 30.49831236 "NA" "upper25perc" +2 "NAT" "Larus fuscus" 5675.84 10.79053596 13.06848889 12.9387757 12.90328721 "NA" "upper25perc" +3 "NAT" "Larus fuscus" 619.47 1.097554944 1.29618879 1.216286033 1.215995235 "NA" "upper25perc" +4 "NAT" "Larus fuscus" 7783.81 14.28873506 17.8859315 17.04649501 16.84421824 "NA" "upper25perc" +5 "NAT" "Larus fuscus" 19376.95 31.14084761 45.76292399 43.63749761 42.35301328 "NA" "upper25perc" +6 "NAT" "Larus fuscus" 20552.64 28.90983066 50.52149742 47.33838221 44.58846101 "NA" "upper25perc" +1 "NAT" "Sterna hirundo" 59092.68 35.43980655 46.1709866 44.3718168 44.01565313 "NA" "upper25perc" +2 "NAT" "Sterna hirundo" 56.78 0.05295311 0.05295311 0.05295311 0.05295311 "NA" "upper25perc" +3 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "upper25perc" +4 "NAT" "Sterna hirundo" 0 0 0 0 0 "NA" "upper25perc" +5 "NAT" "Sterna hirundo" 11773.66 8.374338462 12.17970551 11.89487418 11.67882035 "NA" "upper25perc" +6 "NAT" "Sterna hirundo" 5769.5 0.783075041 1.027435235 1.012937246 1.012937246 "NA" "upper25perc" +1 "NAT" "Larus argentatus" 8674.12 30.47537733 42.7747093 36.60001002 35.03642136 "NA" "upper25perc" +2 "NAT" "Larus argentatus" 21138.22 46.69941774 53.00343882 52.13429437 51.9023648 "NA" "upper25perc" +3 "NAT" "Larus argentatus" 18140.03 23.17839374 39.67991766 36.1541068 34.378385 "NA" "upper25perc" +4 "NAT" "Larus argentatus" 14624.96 20.9502768 32.52577243 30.84081508 30.09917526 "NA" "upper25perc" +5 "NAT" "Larus argentatus" 14086.37 57.28607385 61.53256308 61.5002866 61.39924056 "NA" "upper25perc" +6 "NAT" "Larus argentatus" 8634.41 19.15508875 26.85979283 26.09329975 26.04299056 "NA" "upper25perc" +1 "INT" "Rissa tridactyla" 251144.33 "NA" "NA" "NA" "NA" 175.8377963 "upper25perc" +2 "INT" "Rissa tridactyla" 254033.29 "NA" "NA" "NA" "NA" 212.602862 "upper25perc" +3 "INT" "Rissa tridactyla" 444163.27 "NA" "NA" "NA" "NA" 370.0494773 "upper25perc" +4 "INT" "Rissa tridactyla" 246114.67 "NA" "NA" "NA" "NA" 255.7555149 "upper25perc" +5 "INT" "Rissa tridactyla" 217118.65 "NA" "NA" "NA" "NA" 172.4012783 "upper25perc" +6 "INT" "Rissa tridactyla" 244431.79 "NA" "NA" "NA" "NA" 170.4425834 "upper25perc" +1 "INT" "Larus minutus" 14316.8 "NA" "NA" "NA" "NA" 4.65331508 "upper25perc" +2 "INT" "Larus minutus" 18053.22 "NA" "NA" "NA" "NA" 12.72643341 "upper25perc" +3 "INT" "Larus minutus" 4709.8 "NA" "NA" "NA" "NA" 5.762200707 "upper25perc" +4 "INT" "Larus minutus" 11147.97 "NA" "NA" "NA" "NA" 22.22524945 "upper25perc" +5 "INT" "Larus minutus" 55816.78 "NA" "NA" "NA" "NA" 95.22258579 "upper25perc" +6 "INT" "Larus minutus" 2553.55 "NA" "NA" "NA" "NA" 2.581601174 "upper25perc" +1 "INT" "Stercorarius skua" 12102.95 "NA" "NA" "NA" "NA" 14.25750538 "upper25perc" +2 "INT" "Stercorarius skua" 3791.44 "NA" "NA" "NA" "NA" 5.395737839 "upper25perc" +3 "INT" "Stercorarius skua" 644.77 "NA" "NA" "NA" "NA" 0.665454739 "upper25perc" +4 "INT" "Stercorarius skua" 216.61 "NA" "NA" "NA" "NA" 1.070376777 "upper25perc" +5 "INT" "Stercorarius skua" 1574.02 "NA" "NA" "NA" "NA" 3.368490159 "upper25perc" +6 "INT" "Stercorarius skua" 5198.81 "NA" "NA" "NA" "NA" 6.666201944 "upper25perc" +1 "INT" "Larus marinus" 45938.6 "NA" "NA" "NA" "NA" 311.3187984 "upper25perc" +2 "INT" "Larus marinus" 67882.57 "NA" "NA" "NA" "NA" 655.1534458 "upper25perc" +3 "INT" "Larus marinus" 87509.32 "NA" "NA" "NA" "NA" 526.8090124 "upper25perc" +4 "INT" "Larus marinus" 92416.93 "NA" "NA" "NA" "NA" 475.6362638 "upper25perc" +5 "INT" "Larus marinus" 21310.24 "NA" "NA" "NA" "NA" 158.9445 "upper25perc" +6 "INT" "Larus marinus" 22403.51 "NA" "NA" "NA" "NA" 152.6861512 "upper25perc" +1 "INT" "Thalasseus sandvicensis" 18496.25 "NA" "NA" "NA" "NA" 14.68526592 "upper25perc" +2 "INT" "Thalasseus sandvicensis" 310.29 "NA" "NA" "NA" "NA" 0.094158665 "upper25perc" +3 "INT" "Thalasseus sandvicensis" 42.48 "NA" "NA" "NA" "NA" 0.034212575 "upper25perc" +4 "INT" "Thalasseus sandvicensis" 129.35 "NA" "NA" "NA" "NA" 0.26944681 "upper25perc" +5 "INT" "Thalasseus sandvicensis" 25881.09 "NA" "NA" "NA" "NA" 37.55945245 "upper25perc" +6 "INT" "Thalasseus sandvicensis" 14830.62 "NA" "NA" "NA" "NA" 12.90949957 "upper25perc" +1 "INT" "Morus bassanus" 162867.23 "NA" "NA" "NA" "NA" 1289.446704 "upper25perc" +2 "INT" "Morus bassanus" 107631.13 "NA" "NA" "NA" "NA" 1464.611263 "upper25perc" +3 "INT" "Morus bassanus" 83388.82 "NA" "NA" "NA" "NA" 733.9840656 "upper25perc" +4 "INT" "Morus bassanus" 98020.44 "NA" "NA" "NA" "NA" 1025.173882 "upper25perc" +5 "INT" "Morus bassanus" 91079.9 "NA" "NA" "NA" "NA" 1027.885067 "upper25perc" +6 "INT" "Morus bassanus" 149912.9 "NA" "NA" "NA" "NA" 1661.718219 "upper25perc" +1 "INT" "Stercorarius parasiticus" 3185.55 "NA" "NA" "NA" "NA" 1.170714862 "upper25perc" +2 "INT" "Stercorarius parasiticus" 504.28 "NA" "NA" "NA" "NA" 0.072841153 "upper25perc" +3 "INT" "Stercorarius parasiticus" 48.9 "NA" "NA" "NA" "NA" 0.035778267 "upper25perc" +4 "INT" "Stercorarius parasiticus" 10.09 "NA" "NA" "NA" "NA" 0.028345734 "upper25perc" +5 "INT" "Stercorarius parasiticus" 589.01 "NA" "NA" "NA" "NA" 0.331176531 "upper25perc" +6 "INT" "Stercorarius parasiticus" 1530.78 "NA" "NA" "NA" "NA" 0.29602366 "upper25perc" +1 "INT" "Larus fuscus" 72517.85 "NA" "NA" "NA" "NA" 117.6415154 "upper25perc" +2 "INT" "Larus fuscus" 18782.68 "NA" "NA" "NA" "NA" 37.00250926 "upper25perc" +3 "INT" "Larus fuscus" 16359.22 "NA" "NA" "NA" "NA" 6.595251943 "upper25perc" +4 "INT" "Larus fuscus" 49438.24 "NA" "NA" "NA" "NA" 66.85380837 "upper25perc" +5 "INT" "Larus fuscus" 75350.67 "NA" "NA" "NA" "NA" 109.5033247 "upper25perc" +6 "INT" "Larus fuscus" 69584.21 "NA" "NA" "NA" "NA" 117.7247026 "upper25perc" +1 "INT" "Sterna hirundo" 74947.45 "NA" "NA" "NA" "NA" 47.49873984 "upper25perc" +2 "INT" "Sterna hirundo" 214.32 "NA" "NA" "NA" "NA" 0.026660973 "upper25perc" +3 "INT" "Sterna hirundo" 0 "NA" "NA" "NA" "NA" 0 "upper25perc" +4 "INT" "Sterna hirundo" 11.19 "NA" "NA" "NA" "NA" 0 "upper25perc" +5 "INT" "Sterna hirundo" 28542.73 "NA" "NA" "NA" "NA" 19.00464419 "upper25perc" +6 "INT" "Sterna hirundo" 51816.75 "NA" "NA" "NA" "NA" 34.8368209 "upper25perc" +1 "INT" "Larus argentatus" 34780.12 "NA" "NA" "NA" "NA" 63.15964615 "upper25perc" +2 "INT" "Larus argentatus" 110283.13 "NA" "NA" "NA" "NA" 131.2897485 "upper25perc" +3 "INT" "Larus argentatus" 79288.48 "NA" "NA" "NA" "NA" 108.2692821 "upper25perc" +4 "INT" "Larus argentatus" 124964.43 "NA" "NA" "NA" "NA" 221.6748548 "upper25perc" +5 "INT" "Larus argentatus" 31040.29 "NA" "NA" "NA" "NA" 93.07994505 "upper25perc" +6 "INT" "Larus argentatus" 28466.44 "NA" "NA" "NA" "NA" 73.54124357 "upper25perc" \ No newline at end of file diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_Attributes.csv b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_Attributes.csv new file mode 100644 index 0000000000000000000000000000000000000000..534974f926ebf6853b0944d76178cb8bc94f6a70 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_Attributes.csv @@ -0,0 +1,179 @@ +fid,UniekID,Wind Farm,Country Na,Wind Far_1,Other Name,Windfarm S,Round/Cate,Verwachte,Analyst Ce,Capacity M,Turbine MW,Num Turbin,Total Turb,Hub Height,Rotor Diam,Foundation,Water Dept,Water De_1,Area (SqKm,ingeschatt,Lat,Lon,ScaleFactor +"1",BE01,BE01,Belgium,Thornton Bank phase I,"Zone 1, C-Power",Fully Commissioned,Concession identified through decree of May 2004,"2008",High,30,5.075,"6",157,94,"126",Grounded: Gravity-Base,20,20,4.09801766598395,,51.54389659,2.93845124,1 +"2",BE02,BE02,Belgium,Northwind,"Eldepasco, Zone 2 and Bank Zonder Naam",Fully Commissioned,Concession identified through decree of May 2004,"2013",High,216,3,"72",127,71,"112",Grounded: Monopile,15.3,23,23.1412703583795,,51.61941118,2.90184956,1 +"3",BE03,BE03,Belgium,Belwind,Zone 3 and Bligh Bank,Fully Commissioned,Concession identified through decree of May 2004,"2009",High,165,3,"55",117,72,"90",Grounded: Monopile,11.6,20,23.5853382156077,,51.67021111,2.8028375,1 +"4",BE04,BE04,Belgium,Norther,"North Sea Power, Norther",Fully Commissioned,Concession identified through decree of May 2004,"2018",High,369.6,8.4,"44",183,101,"164",Grounded: Monopile,13,26,53.6995040258063,,51.52694081,3.01316418,1 +"5",BE05,BE05,Belgium,Rentel,,Fully Commissioned,Concession identified through decree of May 2004,"2017",High,309,7,"42",183,106,"154",Grounded: Monopile,23.5,34,34.4043899275409,,51.59134824,2.94456284,1 +"6",BE06,BE06,Belgium,Seamade (SeaStar),Zone 6,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,252,8.4,"30",192.5,109,"167",Grounded: Monopile,20,26.5,28.5648839669269,,51.63418002,2.8616514,1 +"7",BE07,BE07,Belgium,Seamade (Mermaid),Zone 7,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,235,8.4,"28",192.5,109,"167",Grounded: Monopile,26,26,25.368742853723,,51.71861135,2.73948958,1 +"8",BE08,BE08,Belgium,Nobelwind,"Belwind Phase 2, Zone 3 +Bligh Bank 2",Fully Commissioned,Concession identified through decree of May 2004,"2016",High,165,3.3,"50",135,79,"112",Grounded: Monopile,20,33,41.9892144324826,,51.66439838,2.81648834,1 +"9",BE09,BE09,Belgium,Thornton Bank phase II,"Zone 1, C-Power 2",Fully Commissioned,Concession identified through decree of May 2004,"2010",High,184.5,6.15,"30",158,95,"126",Grounded: Jacket (Piled),5.9,20,26.5927646792896,,51.5564308,2.96915138,1 +"10",BE10,BE10,Belgium,Thornton Bank phase III,"Zone 1, C-Power 2",Fully Commissioned,Concession identified through decree of May 2004,"2011",High,110.7,6.15,"18",158,95,"126",Grounded: Jacket (Piled),10.2,21.5,16.6876085150946,,51.54027173,2.92176148,1 +"11",BE12,BE12,Belgium,Northwester 2,Zone 7,Fully Commissioned,Concession identified through decree of May 2004,"2019",High,219,9.5,"23",187,105,"164",Grounded: Monopile,24.5,37,21.815423556101,,51.68675369,2.75288457,1 +"12",BE14,BE14,Belgium,Princess Elisabeth - Noordhinder Noord - 2023 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",Development Zone,2023 Tender,"2025",Medium,700,12,"59",245,135,"220",Grounded: Not Specified,8.1,33,62.102782485355,70,51.62110382,2.55674602,1.12716366640267 +"13",BE15,BE15,Belgium,Princess Elisabeth - Fairybank/Nordhinder Zuid - 2025 Tender,"Prinses Elisabeth Zone, Fairybank, Noordhinder North, Noordhinder South",Development Zone,2025 Tender,"2027",Medium,1400,15,"94",261,143,"236",Grounded: Not Specified,4.9,35,271.782754436073,140,51.47308888,2.43931753,0.515117304961048 +"14",DE01,DE01,Germany,Alpha Ventus,Borkum West I,Fully Commissioned,,"2008",High,60,5,"12",151,92,"120",Various,28,30,8.73610717264621,,54.01089678,6.60654332,1 +"15",DE02,DE02,Germany,DanTysk,"Dan Tysk, Dantysk",Fully Commissioned,,"2013",High,288,3.6,"80",148,88,"120",Grounded: Monopile,21,29,88.8172063358872,,55.13772274,7.20076354,1 +"16",DE03,DE03,Germany,Borkum Riffgrund 3,"German Cluster Offshore Windfarm 1, Borkum Riffgrund West 1, Borkum Riffgrund West 2, OWP West.",Consent Authorised,WindSeeG: 1st and 2nd Tender,"2023",High,900,11,"81",225,125,"200",Grounded: Not Specified,29,31,91.8298599417256,,54.04622646,6.19331525,1 +"17",DE04,DE04,Germany,Borkum Riffgrund 1,,Fully Commissioned,,"2013",High,312,4,"78",147,87,"120",Various,23,29,48.2847375393535,,53.96734348,6.55413942,1 +"18",DE05,DE05,Germany,Amrumbank West,,Fully Commissioned,,"2013",High,302,3.775,"80",150,90,"120",Grounded: Monopile,20,25,43.4191769691324,,54.52297276,7.70565387,1 +"19",DE06,DE06,Germany,Nordsee Ost,Innogy Nordsee Ost,Fully Commissioned,,"2012",High,295.2,6.15,"48",155,92,"126",Grounded: Jacket (Piled),22,25,48.7213168587201,,54.44455387,7.68215399,1 +"20",DE07,DE07,Germany,Meerwind Süd/Ost,Meerwind,Fully Commissioned,,"2012",High,288,3.6,"80",149,89,"120",Grounded: Monopile,24,27,54.8039338907529,,54.39216259,7.70413509,1 +"21",DE08,DE08,Germany,Butendiek,Offshore-Bürgerpark Butendiek,Fully Commissioned,,"2014",High,288,3.6,"80",150,90,"120",Grounded: Monopile,18.4,21,44.660141589676,,55.0174822,7.77401776,1 +"22",DE09,DE09,Germany,Global Tech I,GT1,Fully Commissioned,,"2012",High,400,5,"80",140,82,"116",Grounded: Tripod,38,41,54.483795206958,,54.50891743,6.36543494,1 +"23",DE0H,DE0H,Germany,Gode Wind 3,"Gode Wind 03, Gode Wind 04, Gode Wind III, Gode Wind IV",Consent Authorised,WindSeeG: 1st and 2nd Tender,"2023",High,241.75,11,"22",225,125,"200",Grounded: Not Specified,30,34,31.3867870820095,,54.03746874,7.10462029,1 +"24",DE0K,DE0K,Germany,Trianel Windpark Borkum II,"Borkum West II Phase 2, TWB II",Fully Commissioned,,"2018",High,203,6.344,"32",180.5,104.5,"152",Grounded: Monopile,28,33,48.7855502076397,,54.05034154,6.46366893,1 +"25",DE11,DE11,Germany,Hohe See,Hochsee Windpark 'Nordsee',Fully Commissioned,,"2018",High,497,7,"71",182,105,"154",Grounded: Monopile,39,40,55.1726071810185,,54.44334719,6.32994366,1 +"26",DE12,DE12,Germany,Sandbank,Sandbank24,Fully Commissioned,,"2015",High,288,4,"72",159.75,94.75,"130",Grounded: Monopile,25.5,29,73.2423494876009,,55.19962637,6.85239725,1 +"27",DE13,DE13,Germany,Gode Wind 1 and 2,Gode Wind I and II,Fully Commissioned,,"2015",High,582,6.264,"97",188,111,"154",Grounded: Monopile,28,34,96.5321161487852,,54.0412232,6.99450814,1 +"28",DE19,DE19,Germany,EnBW He Dreiht,Hochsee Windpark He dreiht,Consent Authorised,WindSeeG: 1st Tender,"2024",High,900,10,"70",218,121.5,"193",Grounded: Monopile,37,40,82.3177185612138,,54.36529145,6.18647301,1 +"29",DE20,DE20,Germany,Nordergründe,"OWP Nordergründe, Nordergrunde",Fully Commissioned,,"2016",High,110.7,6.15,"18",147,84,"126",Grounded: Monopile,3,11,7.86280046325181,,53.83495012,8.16918216,1 +"30",DE21,DE21,Germany,Riffgat,"OWP Riffgat, Borkum Riffgat",Fully Commissioned,,"2012",High,108,3.6,"30",150,90,"120",Grounded: Monopile,18,23,12.9300481517701,,53.69242975,6.47544661,1 +"31",DE23,DE23,Germany,BARD Offshore 1,BARD,Fully Commissioned,,"2010",High,400,5,"80",151,90,"122",Grounded: Tripile,39,41,76.8533771850899,,54.35537441,5.98085192,1 +"32",DE24,DE24,Germany,Deutsche Bucht,"DBU, Debu",Fully Commissioned,,"2018",High,252,8.4,"31",190,108,"164",Grounded: Monopile,38,40,27.4123599580331,,54.30539739,5.79302719,1 +"33",DE26,DE26,Germany,Merkur,MEG Offshore I,Fully Commissioned,,"2017",High,396,6,"66",177.6,102.6,"150",Grounded: Monopile,27,33,54.5600187813524,,54.04306092,6.55370567,1 +"34",DE27,DE27,Germany,Trianel Windpark Borkum I,"Borkum West II Phase 1, Trianel Borkum, Borkum Phase I",Fully Commissioned,,"2011",High,200,5,"40",148,90,"116",Grounded: Tripod,28,33,37.8189198765473,,54.04635684,6.45749314,1 +"35",DE28,DE28,Germany,Nordsee One,"Innogy Nordsee 1 , Innogy Nordsee 1 Phase 1, North Sea Windpower 3 NSWP3",Fully Commissioned,,"2015",High,332.1,6.15,"54",153,90,"126",Grounded: Monopile,28,29,43.6622276002561,,53.98161225,6.81734194,1 +"36",DE2Q,DE2Q,Germany,N-3.5,,Development Zone,German Central Tender 2023,"2027",Medium,420,15,"28",261,143,"236",Grounded: Not Specified,29,33,44.7708371818327,42,54.0256931,6.84904222,0.938110668545705 +"37",DE2R,DE2R,Germany,N-3.6,,Development Zone,German Central Tender 2023,"2027",Medium,480,15,"32",261,143,"236",Grounded: Not Specified,30,34,46.7608366857453,48,54.04565357,6.77231105,1.02650002442391 +"38",DE2S,DE2S,Germany,N-3.7,,Development Zone,German Central Tender 2021,"2025",Medium,225,15,"15",261,143,"236",Grounded: Not Specified,30,32,27.0263032542515,22.5,54.04490205,7.062126,0.83252229460796 +"39",DE2T,DE2T,Germany,N-3.8,,Development Zone,German Central Tender 2021,"2025",Medium,433,15,"29",261,143,"236",Grounded: Not Specified,33,33,42.6105049248221,43.3,54.06796156,6.89332102,1.01618134017408 +"40",DE2V,DE2V,Germany,N-6.6,,Development Zone,German Central Tender 2024,"2028",Medium,630,15,"42",261,143,"236",Grounded: Not Specified,38,39,61.3119297220673,63,54.26591893,5.95355963,1.02753249303333 +"41",DE2W,DE2W,Germany,N-6.7,,Development Zone,German Central Tender 2024,"2028",Medium,270,15,"18",261,143,"236",Grounded: Not Specified,40,40,40.9442832662726,27,54.36063549,5.84539799,0.659432717979483 +"42",DE2X,DE2X,Germany,N-7.2,,Development Zone,German Central Tender 2022,"2026",Medium,930,15,"62",261,143,"236",Grounded: Not Specified,37,39,96.2171463264876,93,54.28834022,6.22358211,0.966563690056126 +"43",DE2Z,DE2Z,Germany,N-8.4,,Development Zone,German Central Tender,"2026",Medium,425,15,"28",261,143,"236",Grounded: Not Specified,40,40,49.3677304236695,42.5,54.53463794,6.31355851,0.860886243610325 +"44",DE30,DE30,Germany,Borkum Riffgrund 2,,Fully Commissioned,,"2018",High,450,8.3,"56",187,105,"164",Various,25,29,56.6031857121368,,53.95177099,6.48745918,1 +"45",DE33,DE33,Germany,Kaskasi,"Kaskasi II, (formerly) Hochsee testfeld Helgoland",Pre-Construction,WindSeeG: 2nd Tender,"2021",High,342,9,"38",191,107.5,"167",Grounded: Monopile,22,26,27.4162253268616,,54.48914974,7.69545631,1 +"46",DE36,DE36,Germany,Veja Mate,,Fully Commissioned,,"2016",High,402,6,"67",180.28,103.28,"154",Grounded: Monopile,39,41,68.8073232177383,,54.31808719,5.87282159,1 +"47",DE39,DE39,Germany,Albatros,"Hohe Extension, Albatros 1",Fully Commissioned,,"2019",High,112,7,"16",182,105,"154",Grounded: Monopile,40,40,18.7958298442165,,54.48600809,6.24913654,1 +"48",DE3A,DE3A,Germany,N-9.1,,Development Zone,German Central Tender 2024,"2028",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,42,126.574980829048,100,54.43751005,5.76512543,0.790045547271775 +"49",DE3B,DE3B,Germany,N-9.2,,Development Zone,German Central Tender 2024,"2028",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,41,127.145180387325,100,54.52131578,5.89977262,0.786502482401362 +"50",DE3C,DE3C,Germany,N-10.1,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"57",261,143,"236",Grounded: Not Specified,41,42,116.801604170185,100,54.60193314,6.14335151,0.856152624875733 +"51",DE3D,DE3D,Germany,N-11-1,,Development Zone,German Central Tender 2026+,"2031",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,39,42,395.617774789928,100,54.78220476,6.37218683,0.252769229221563 +"52",DE3E,DE3E,Germany,N-12.1,,Development Zone,German Central Tender 2026+,"2034",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914841,100,54.84468775,6.14651684,0.183574489386104 +"53",DE3I,DE3I,Germany,N-10.2,,Development Zone,German Central Tender 2025,"2029",Medium,700,15,"47",261,143,"236",Grounded: Not Specified,41,41,116.356321492184,70,54.67504415,5.98511505,0.60160031790539 +"54",DE3J,DE3J,Germany,N-12.2,,Development Zone,German Central Tender 2026+,"2034",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914841,100,54.84468775,6.14651684,0.183574489386104 +"55",DE3K,DE3K,Germany,N-12.3,,Development Zone,German Central Tender 2026+,"2033",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914845,100,54.84468775,6.14651684,0.183574489386103 +"56",DE3M,DE3M,Germany,N-12.4,,Development Zone,German Central Tender 2026+,"2033",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,544.737998914845,100,54.84468775,6.14651684,0.183574489386103 +"57",DE3O,DE3O,Germany,N-11-2,,Development Zone,German Central Tender 2026+,"2031",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,39,42,395.617774789928,100,54.78220476,6.37218683,0.252769229221563 +"58",DE3Q,DE3Q,Germany,N-13-2,,Development Zone,German Central Tender 2026+,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,40,48,301.898512939832,100,55.06058697,6.35809751,0.331237140011782 +"59",DE3R,DE3R,Germany,N-13-3,,Development Zone,German Central Tender 2026+,"2030",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,40,48,301.898512939832,100,55.06058697,6.35809751,0.331237140011782 +"60",DE3S,DE3S,Germany,N-9.3,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,40,43,127.595385492944,100,54.43628044,5.63087554,0.783727402160089 +"61",DE3T,DE3T,Germany,N-9.4,,Development Zone,German Central Tender 2025,"2029",Medium,1000,15,"67",261,143,"236",Grounded: Not Specified,41,42,123.131784626929,100,54.55953326,5.75514749,0.812137989415042 +"62",DK03,DK03,Denmark,Horns Rev 1,,Fully Commissioned,,"2002",High,160,2,"80",110,70,"80",Grounded: Monopile,6,11,30.6443531449027,,55.48610077,7.84038064,1 +"63",DK0V,DK0V,Denmark,Nordsøen - Tender 1,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,27.5,43,416.515741865753,100,56.549218,6.2643305,0.240086964185452 +"64",DK0W,DK0W,Denmark,Nordsøen - Tender 3,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,38,44,400.963310746687,100,56.30297162,6.41826499,0.249399377249197 +"65",DK0X,DK0X,Denmark,Nordsøen - Tender 2,,Development Zone,,"2032",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,35,42,438.789688729757,100,56.63111021,6.67237267,0.227899612430474 +"66",DK0Y,DK0Y,Denmark,Nordsøen - Tender 4,,Development Zone,,"2035",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,32,48,343.30791832147,100,56.7921246,6.32260056,0.291283697996039 +"67",DK0Z,DK0Z,Denmark,Nordsøen - Tender 5,,Development Zone,,"2035",Low,1000,20,"50",305,165,"280",Grounded: Not Specified,34,43,362.352413339133,100,56.29716697,6.08264947,0.275974427984306 +"68",DK10,DK10,Denmark,Horns Rev 2,,Fully Commissioned,Tender (DK),"2008",High,209.3,2.3,"91",114.5,68,"93",Grounded: Monopile,9,17,47.2499961623854,,55.60042043,7.58252166,1 +"69",DK19,DK19,Denmark,Horns Rev 3,Horns Rev A HR3-HR4,Fully Commissioned,Tender (DK),"2017",High,406.7,8.3,"49",187,102,"164",Grounded: Monopile,10.6,19.1,140.32545500473,,55.69409292,7.65486583,1 +"70",DK1A,DK1A,Denmark,Nordsøen - Tender 6,,Development Zone,,"2036",,1000,20,"50",305,165,"280",Grounded: Not Specified,30.5,40,372.436596776018,100,56.53221977,7.06288008,0.268502077576817 +"71",DK1B,DK1B,Denmark,Nordsøen - Tender 7,,Development Zone,,"2037",,1000,20,"50",305,165,"280",Grounded: Not Specified,34,40,357.128848779714,100,56.32274482,6.84800141,0.280010982987494 +"72",DK1C,DK1C,Denmark,Nordsøen - Tender 8,,Development Zone,,"2038",,1000,20,"50",305,165,"280",Grounded: Not Specified,36,40,347.303012493645,100,56.08000108,6.55868117,0.28793300490542 +"73",DK1D,DK1D,Denmark,Nordsøen - Tender 9,,Development Zone,,"2039",,1000,20,"50",305,165,"280",Grounded: Not Specified,37,42,339.036488523039,100,56.035332,6.06920374,0.294953503192635 +"74",DK1E,DK1E,Denmark,Nordsøen - Tender 10,,Development Zone,,"2032",,1000,20,"50",305,165,"280",Grounded: Not Specified,42,45,324.045207650715,100,55.82309559,6.29156517,0.308598916567805 +"75",DK22,DK22,Denmark,Thor - 2020 Tender,Horns Rev 4,Development Zone,2019 Tender (DK),"2025",Medium,1000,16,"75",261,143,"236",Grounded: Not Specified,27,34,490.795015669501,100,56.32540117,7.65243573,0.203751050453494 +"76",DK48,DK48,Denmark,Vesterhav Nord/Syd,North Sea (south),Consent Authorised,Nearshore Tender (DK),"2022",Medium,344,8.4,"41",193,109,"167",Grounded: Monopile,16,24,146.87543752293,34.4,56.37756229,8.02229099,0.234212068267912 +"77",UK04,UK04,United Kingdom,Dudgeon,,Fully Commissioned,Round 2,"2016",High,402,6,"67",187,110,"154",Grounded: Monopile,11.5,23.5,72.2836989959603,,53.26535507,1.38071537,1 +"78",UK05,UK05,United Kingdom,Greater Gabbard,GGOWL,Fully Commissioned,Round 2,"2009",High,504,3.6,"140",131,77.5,"107",Grounded: Monopile,3.7,37,180.487713677604,,51.88398379,1.93583846,1 +"79",UK07,UK07,United Kingdom,Gunfleet Sands,GFS,Fully Commissioned,Round 1,"2008",High,172.8,3.6,"48",128.5,75,"107",Grounded: Monopile,0,12.9,26.8600264044145,,51.73015694,1.22922906,1 +"80",UK0V,UK0V,United Kingdom,Dogger Bank B,"Creyke Beck B, Zone 3",Pre-Construction,Round 3,"2023",High,1200,13,"95",260,150,"220",Grounded: Monopile,20,33,649.041485906262,,54.97737666,1.67972877,1 +"81",UK10,UK10,United Kingdom,Humber Gateway,,Fully Commissioned,Round 2,"2013",High,219,3,"73",136,80,"112",Grounded: Monopile,10.6,16.2,38.776871919882,,53.6442876,0.29312324,1 +"82",UK11,UK11,United Kingdom,Inner Dowsing,,Fully Commissioned,Round 1,"2007",High,97.2,3.6,"27",133.5,80,"107",Grounded: Monopile,6.4,7.9,16.5219908613015,,53.18907175,0.44525315,1 +"83",UK12,UK12,United Kingdom,Kentish Flats,,Fully Commissioned,Round 1,"2004",High,90,3,"30",115,70,"90",Grounded: Monopile,3,4.5,17.5088015412025,,51.4604165,1.0939165,1 +"84",UK13,UK13,United Kingdom,Lincs,,Fully Commissioned,Round 2,"2011",High,270,3.6,"75",145.25,85.25,"120",Grounded: Monopile,7.6,16.4,57.6548427538279,,53.19047417,0.49160338,1 +"85",UK14,UK14,United Kingdom,London Array,,Fully Commissioned,Round 2,"2011",High,630,3.6,"175",147,87,"120",Grounded: Monopile,0,23,128.468897937706,,51.62680883,1.49516009,1 +"86",UK15,UK15,United Kingdom,Lynn,,Fully Commissioned,Round 1,"2007",High,97.2,3.6,"27",133.5,80,"107",Grounded: Monopile,7,11,14.4793360621024,,53.13666862,0.4521628,1 +"87",UK18,UK18,United Kingdom,Race Bank,ROW01,Fully Commissioned,Round 2,"2016",High,573.3,6.3,"91",187,110,"154",Grounded: Monopile,6,23,82.3752465649017,,53.27657921,0.84137697,1 +"88",UK1F,UK1F,United Kingdom,Dogger Bank C,"Teesside A, Zone 3, Lackenby A (Tranche B)",Consent Authorised,Round 3,"2024",High,1200,14,"95",248,138,"220",Grounded: Monopile,21,30,610.388995188024,,55.03972759,2.8226577,1 +"89",UK1G,UK1G,United Kingdom,Sofia,"Zone 3, Lackenby B (Tranche B) +Dogger Bank - Teesside B",Consent Authorised,Round 3,"2024",High,1400,14,"100",252,141,"222",Grounded: Monopile,21,33,642.918801505068,140,54.98993661,2.2285549,0.217756891962501 +"90",UK1J,UK1J,United Kingdom,Hornsea Project Four,"Zone 4, Hornsea Three SPC 6",Concept/Early Planning,Round 3,"2031",Medium,1000,20,"50",305,165,"280",Grounded: Not Specified,24.5,55,534.545090118118,100,54.03860767,1.2710722,0.187074957470665 +"91",UK1K,UK1K,United Kingdom,Hornsea Project Three,"Zone 4, Hornsea Three SPC 8",Consent Authorised,Round 3,"2026",Medium,2400,11,"231",205,123,"164",Grounded: Not Specified,26,73,758.101979826106,240,53.87299926,2.53716073,0.316580099230253 +"92",UK1U,UK1U,United Kingdom,Hornsea Project Two,"Zone 4, Hornsea Project 2 (Optimus and Breesea)",Under Construction,Round 3,"2020",High,1386,8.4,"165",204.4,120.9,"167",Grounded: Monopile,25,67,528.863231533713,,53.94023544,1.6876632,1 +"93",UK23,UK23,United Kingdom,Scroby Sands,,Fully Commissioned,Round 1,"2003",High,60,2,"30",100,60,"80",Grounded: Monopile,0,10.1,9.77479075043837,,52.64572523,1.78781929,1 +"94",UK27,UK27,United Kingdom,Sheringham Shoal,,Fully Commissioned,Round 2,"2009",High,316.8,3.6,"88",135.25,81.75,"107",Grounded: Monopile,14,23,49.6562543312242,,53.13557362,1.14747958,1 +"95",UK28,UK28,United Kingdom,Teesside,Redcar,Fully Commissioned,Round 1,"2012",High,62.1,2.3,"27",126.5,80,"93",Grounded: Monopile,6,18,9.81006268476869,,54.64531751,-1.09408429,1 +"96",UK29,UK29,United Kingdom,Thanet,,Fully Commissioned,Round 2,"2009",High,300,3,"100",115,70,"90",Grounded: Monopile,14.4,23,47.5555281705209,,51.4304064,1.63370997,1 +"97",UK2Q,UK2Q,United Kingdom,East Anglia Hub - ONE North,"EA1N, East Anglia Hub",Consent Application Submitted,Round 3,"2024",Medium,800,14,"58",300,140,"222",Grounded: Monopile,32,59,238.527559732585,80,52.38105836,2.43113765,0.335391013473196 +"98",UK2Y,UK2Y,United Kingdom,ForthWind Offshore Wind Demonstration Project Phase 2,2B Energy Powerblock,Concept/Early Planning,,,,53,12,"7",225,125,"200",Various,10,24,15.7504030795213,,56.13760438,-3.02423266,1 +"99",UK30,UK30,United Kingdom,Triton Knoll,,Partial Generation/Under Construction,Round 2,"2020",High,857,9.5,"90",189,107,"164",Grounded: Monopile,12.3,30,177.385780666715,,53.47846028,0.83858297,1 +"100",UK34,UK34,United Kingdom,Westermost Rough,,Fully Commissioned,Round 2,"2014",High,210,6,"35",177,102,"154",Grounded: Monopile,12,22,47.5170331104598,,53.80588225,0.1498239,1 +"101",UK39,UK39,United Kingdom,East Anglia Hub - TWO,"East Anglia Array, Zone 5, Norfolk, EA2, +East Anglia Hub",Consent Application Submitted,Round 3,"2025",Medium,900,14,"65",251,140,"222",Grounded: Monopile,30,73,251.054341394847,90,52.13269299,2.20666027,0.358488124523018 +"102",UK3F,UK3F,United Kingdom,Scottish Sectoral Marine Plan - E3,,Development Zone,ScotWind,"2030",Low,1000,20,"50",305,165,"280",Floating: Not Specified,54,125,519.726845258834,100,56.99776921,-1.48641529,0.192408764165719 +"103",UK3G,UK3G,United Kingdom,Scottish Sectoral Marine Plan - E2,,Development Zone,ScotWind,"2035",,2000,20,"100",305,165,"280",Floating: Not Specified,63,93,1390.9806175828,200,57.34306553,-0.08353952,0.14378345569441 +"104",UK3I,UK3I,United Kingdom,Scottish Sectoral Marine Plan - E1,,Development Zone,ScotWind,"2035",,3000,20,"150",305,165,"280",Floating: Not Specified,62,148,3871.86458599749,300,56.71435246,-0.42248326,0.0774820485935751 +"105",UK3L,UK3L,United Kingdom,Scottish Sectoral Marine Plan - NE6,,Development Zone,ScotWind,"2034",Low,2000,20,"100",305,165,"280",Floating: Not Specified,58,125,755.472167218755,200,58.05810167,-1.65680883,0.264735100349617 +"106",UK3N,UK3N,United Kingdom,Scottish Sectoral Marine Plan - NE7,,Development Zone,ScotWind,"2032",Low,3000,20,"150",305,165,"280",Floating: Not Specified,91,119,740.45486193662,300,58.16211001,-0.62257398,0.405156364582935 +"107",UK3O,UK3O,United Kingdom,Scottish Sectoral Marine Plan - NE8,,Development Zone,ScotWind,"2037",,1000,20,"50",305,165,"280",Floating: Not Specified,74,102,382.721944887829,100,58.41112453,-1.2334218,0.261286297626097 +"108",UK3P,UK3P,United Kingdom,Scottish Sectoral Marine Plan - NE3,,Development Zone,ScotWind,"2036",,1000,20,"50",305,165,"280",Floating: Not Specified,69,90,300.194859592604,100,58.51271028,-2.1427869,0.333116963214196 +"109",UK3Q,UK3Q,United Kingdom,Scottish Sectoral Marine Plan - NE4,,Development Zone,ScotWind,"2037",,1000,20,"50",305,165,"280",Grounded: Not Specified,43,82,492.21662335689,100,58.2005869,-2.49270311,0.20316258178768 +"110",UK40,UK40,United Kingdom,Moray East,"Zone 1, Moray Central A, Moray Firth Eastern Development Area, Stevenson, Telford and MacColl.",Under Construction,Round 3,"2019",High,950,9.5,"100",280,105,"164",Grounded: Jacket (Piled),39,50,336.350852052243,,58.1882694,-2.72036686,1 +"111",UK44,UK44,United Kingdom,Seagreen,"Zone 2, Firth of Forth Alpha-Bravo; Seagreen Alpha; Seagreen Bravo, Seagreen Phase 1",Pre-Construction,Round 3,"2021",High,1140,10,"114",205,123,"164",Grounded: Jacket (Suction Bucket),39,61,403.267713739498,,56.59691754,-1.71957856,1 +"112",UK47,UK47,United Kingdom,Aberdeen Offshore Wind Farm (EOWDC),"Aberdeen Bay, European Offshore Wind Deployment Centre - EOWDC",Fully Commissioned,,"2018",High,93.2,8.8,"11",191,109,"164",Grounded: Jacket (Suction Bucket),20,30,30.5718540080577,,57.22570891,-1.99650044,1 +"113",UK4F,UK4F,United Kingdom,Race Bank Extension,,Concept/Early Planning,2017 Extension Opportunity,"2029",Medium,573,15,"38",261,143,"236",Grounded: Not Specified,3.3,21.5,178.26729188464,57.3,53.29354357,0.91555526,0.32142744411622 +"114",UK4G,UK4G,United Kingdom,Dudgeon Extension,,Concept/Early Planning,2017 Extension Opportunity,"2028",Medium,402,20,"115",305,165,"280",Grounded: Not Specified,10.6,31,146.614960129056,40.2,53.27882195,1.37380062,0.274187572431998 +"115",UK4H,UK4H,United Kingdom,Sheringham Shoal Extension,,Concept/Early Planning,2017 Extension Opportunity,"2028",Medium,317,20,"16",305,165,"280",Grounded: Not Specified,13.6,21,119.751080038085,31.7,53.16698375,1.16306147,0.264715775339298 +"116",UK4I,UK4I,United Kingdom,Five Estuaries,Galloper Extension,Concept/Early Planning,2017 Extension Opportunity,"2030",Medium,353,20,"18",305,165,"280",Grounded: Not Specified,31,57,207.206788243746,35.3,51.90139244,2.16543274,0.170361214027772 +"117",UK4J,UK4J,United Kingdom,North Falls,Greater Gabbard Extension,Concept/Early Planning,2017 Extension Opportunity,"2029",Medium,504,15,"34",261,143,"236",Grounded: Not Specified,9.5,59,213.009465825662,50.4,51.75373073,1.89921265,0.236609203279492 +"118",UK4N,UK4N,United Kingdom,Kincardine - Phase 2,KOWL,Under Construction,Scottish Floating Demonstration,"2020",High,48,9.6,"5",180,104,"164",Floating: Semi-Submersible Platform - Steel,61,100,36.5205208383717,,57.00674209,-1.85390541,1 +"119",UK4P,UK4P,United Kingdom,Seagreen 1A,"Seagreen 1A, SG1A, Seagreen Extension",Consent Authorised,Round 3,"2024",Medium,360,10,"36",205,123,"164",Grounded: Jacket (Piled),44,54,74.5673233992684,36,56.56191878,-1.86222668,0.482785198112035 +"120",UK4X,UK4X,United Kingdom,Round 4 - Area 1 (RWE Renewables),,Concept/Early Planning,Leasing Round 4,"2035",Medium,1500,20,"75",305,165,"280",Grounded: Not Specified,14,36,538.167469692955,150,54.62499758,1.47870583,0.2787236472795 +"121",UK4Y,UK4Y,United Kingdom,Round 4 - Area 2 (RWE Renewables),,Concept/Early Planning,Leasing Round 4,"2035",,1500,20,"75",305,165,"280",Grounded: Not Specified,14,33,542.209354015866,150,54.50075208,1.90376825,0.276645909719239 +"122",UK4Z,UK4Z,United Kingdom,Round 4 - Area 3 (GIG & Total),,Concept/Early Planning,Leasing Round 4,"2034",Medium,1500,20,"75",305,165,"280",Grounded: Not Specified,7,37,547.613942487847,150,53.5683674,1.28480603,0.273915597032719 +"123",UK53,UK53,United Kingdom,Beatrice,BOWL,Fully Commissioned,Scottish Territorial Waters 1,"2017",High,588,7,"84",198.4,110,"154",Grounded: Jacket (Piled),35,50,156.977052985059,,58.25402325,-2.88454523,1 +"124",UK54,UK54,United Kingdom,Inch Cape,ICOL,Consent Authorised,Scottish Territorial Waters 1,"2026",Medium,1000,15,"72",291,,"250",Grounded: Monopile,36,54,179.93149120997,100,56.49461093,-2.19559965,0.555767082946618 +"125",UK56,UK56,United Kingdom,Neart na Gaoithe,NnG,Under Construction,Scottish Territorial Waters 1,"2020",High,448,8,"54",208,125,"167",Grounded: Jacket (Piled),44,56,126.294724989167,,56.26767895,-2.25005051,1 +"126",UK60,UK60,United Kingdom,Kentish Flats Extension,Kentish Flats 2,Fully Commissioned,Round 2.5,"2015",High,49.5,3.3,"15",139.6,83.6,"112",Grounded: Monopile,1,4,18.2587656002391,,51.45071428,1.07998646,1 +"127",UK62,UK62,United Kingdom,Galloper,,Fully Commissioned,Round 2.5,"2016",High,353,6,"56",180.5,103.5,"154",Grounded: Monopile,4,50,150.765237754745,,51.89314988,2.03450963,1 +"128",UK64,UK64,United Kingdom,East Anglia ONE,"East Anglia Array, Zone 5, Norfolk, EA ONE",Fully Commissioned,Round 3,"2018",High,714,7,"102",167,90,"154",Grounded: Jacket (Piled),29.5,41,190.525909758042,,52.23270284,2.49853336,1 +"129",UK66,UK66,United Kingdom,East Anglia Hub - THREE,"East Anglia Array, Zone 5, Norfolk, EA3, +East Anglia Hub,",Consent Authorised,Round 3,"2023",Medium,1400,14,"100",251,140,"222",Grounded: Monopile,24.5,48,347.52477375532,140,52.66515209,2.85566697,0.402848978181247 +"130",UK67,UK67,United Kingdom,Norfolk Vanguard,East Anglia Array,Consent Application Submitted,Round 3,"2025",Medium,1800,20,"158",305,198.5,"303",Grounded: Not Specified,21.5,40,665.369908154925,180,52.87021588,2.69987804,0.270526210749658 +"131",UK69,UK69,United Kingdom,Norfolk Boreas,East Anglia Six,Consent Application Submitted,Round 3,"2027",Medium,1800,20,"158",350,,"303",Grounded: Not Specified,22.5,41,782.568852318447,180,53.03851213,2.94162843,0.230011710109251 +"132",UK70,UK70,United Kingdom,Blyth Offshore Demonstrator Phase 1,"Blyth Offshore Demonstrator Project - Array 2, NaREC Offshore Wind Demonstration Project, BOD",Fully Commissioned,Demonstration,"2017",High,41.5,8.3,"5",191.5,109.5,"164",Grounded: Gravity-Base,29,39,9.75287302016675,,55.13097973,-1.40106756,1 +"133",UK74,UK74,United Kingdom,Berwick Bank,"Zone 2, Firth of Forth Phase 2 Charlie-Delta-Echo, Seagreen Phase 2",Concept/Early Planning,Round 3,"2028",Medium,2300,20,"115",305,165,"280",Grounded: Not Specified,39,64,717.976920511343,,56.29191816,-1.46712425,1 +"134",UK75,UK75,United Kingdom,Marr Bank,"Zone 2, Firth of Forth Phase 3 Foxtrot-Golf, Seagreen Phase 3",Concept/Early Planning,Round 3,"2031",Medium,1850,20,"93",305,165,"280",Grounded: Not Specified,32,64,852.098582558707,,56.34493328,-1.79275443,1 +"135",UK76,UK76,United Kingdom,Hywind Scotland Pilot Park,"Hywind II, Hywind 2, Buchan Deep",Fully Commissioned,Scottish Floating Demonstration,"2017",High,30,6,"5",178,101,"154",Floating: Spar Floater - Steel,96,110,23.7028961583754,,57.48425352,-1.36262218,1 +"136",UK77,UK77,United Kingdom,Moray West,"Zone 1, Moray Firth Western Development Area",Consent Authorised,Round 3,"2024",Medium,950,15,"85",265,,"230",Grounded: Not Specified,35,52,261.978624301432,95,58.09724802,-3.00714744,0.362625005201544 +"137",UK79,UK79,United Kingdom,Blyth Offshore Demonstrator Phase 2,"Blyth Offshore Demonstrator Project - Array 3A & 4 +NaREC Offshore Wind Demonstration Project",Consent Authorised,Demonstration,"2024",Medium,58.4,14,"5",258,140,"236",Floating: Semi-Submersible Platform,43,54,30.3366065021439,,55.18510691,-1.35296017,1 +"138",UK80,UK80,United Kingdom,Dogger Bank A,"Creyke Beck A, Zone 3, Project One",Pre-Construction,Round 3,"2022",High,1200,13,"95",260,150,"220",Grounded: Monopile,18.3,25,564.618967790395,,54.76947456,1.90895964,1 +"139",UK81,UK81,United Kingdom,Hornsea Project One,"Zone 4, Subzone 1, Heron Wind & Njord",Fully Commissioned,Round 3,"2018",High,1218,7,"174",200,113,"154",Grounded: Monopile,23.5,37,462.931508996161,,53.88330242,1.92187445,1 +"140","1","1",Netherlands,Borssele 2,Orsted,Fully Commissioned,2016 Tender,"2020",High,376,8,"47",192,108,"167",Grounded: Monopile,15.6,38,81.2314890278253,,51.68301223,3.06622564,1 +"141","2","2",Netherlands,Borssele 3,Blauwwind,Fully Commissioned,2016 Tender,"2019",High,366,9.5,"39",190,108,"164",Grounded: Monopile,14.3,38,91.7480853589124,,51.69975608,2.92823038,1 +"142","3","3",Netherlands,Borssele 4 - Blauwwind,Blauwwind,Fully Commissioned,2016 Tender,"2019",High,366,9.5,"39",190,108,"164",Grounded: Monopile,14.3,38,97.0265440084513,,51.69975608,2.92823038,1 +"143","4","4",Netherlands,Borssele Site V -Two towers,Two Towers,Fully Commissioned,2016 Tender - Demonstrator,"2020",High,19,9.5,"2",190,108,"164",Grounded: Monopile,22,24,3.06602636784179,,51.70915697,3.00330596,1 +"144","5","5",Netherlands,Egmond aan Zee,"OWEZ, Offshore Windpark Egmond aan Zee(demonstratieproject)",Fully Commissioned,Round 1 (NL),"2006",High,108,3,"36",115,70,"90",Grounded: Monopile,15,18,38.3221488638661,,52.60621656,4.41925086,1 +"145","6","6",Netherlands,Prinses Amaliawindpark,"Q7, Prinses Amalia Wind Park",Fully Commissioned,Round 1 (NL),"2006",High,120,2,"60",99,59,"80",Grounded: Monopile,19,24,31.0515035941986,,52.58788938,4.22381982,1 +"146","7","7",Netherlands,Eneco Luchterduinen,Luchterduinen,Fully Commissioned,Round 2 (NL),"2014",High,129,3,"43",137,81,"112",Grounded: Monopile,18,22,25.5882253902796,,52.40509242,4.16285737,1 +"147","8","8",Netherlands,Gemini Zee energie,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",Fully Commissioned,Round 2 (NL),"2015",High,300,4,"75",154,89,"130",Grounded: Monopile,32,34,46.389100525219,,54.03650301,5.96493437,1 +"148","9","9",Netherlands,Gemini Buitengaats,"Buitengaats & ZeeEnergie, BARD-Nederland Offshore, GWS Offshore NL1, 64 (ID assigned by authority),",Fully Commissioned,Round 2 (NL),"2015",High,300,4,"75",154,89,"130",Grounded: Monopile,32,34,47.5115496229591,,54.03650301,5.96493437,1 +"149","10","10",Netherlands,Hollandse Kust Zuid Holland IV,Vattenfall,Pre-Construction,2018 Tender,"2022",High,385,11,"70",251,121,"193",Grounded: Monopile,17,22,116.58357444553,,52.27960249,4.08390741,1 +"150","11","11",Netherlands,Hollandse Kust Zuid Holland III,Vattenfall,Development Zone,2018 Tender,"2022",High,385,11,"70",251,121,"193",Grounded: Monopile,17,22,69.8275750029334,,52.27960249,4.08390741,1 +"151","12","12",Netherlands,Hollandse Kust Zuid Holland II,Vattenfall,Pre-Construction,2017 Tender,"2021",High,385,11,"70",251,121,"193",Grounded: Monopile,17.9,22,62.4433020185146,,52.33407403,4.01082547,1 +"152","13","13",Netherlands,Hollandse Kust Zuid Holland I,Vattenfall,Pre-Construction,2017 Tender,"2021",High,385,11,"70",251,121,"193",Grounded: Monopile,17.9,22,89.627387342407,,52.33407403,4.01082547,1 +"153","14","14",Netherlands,Borssele 1,Orsted,Fully Commissioned,2016 Tender,"2020",High,376,8,"47",192,108,"167",Grounded: Monopile,15.6,38,82.8536979641582,,51.68301223,3.06622564,1 +"154","15","15",Netherlands,Hollandse Kust Noord (Tender 2019),"HKN, CrossWind",Pre-Construction,2019 Tender,"2022",High,700,11,"69",225,121,"200",Grounded: Monopile,19,24,153.883793981307,,52.71410564,4.26460906,1 +"155",bw4,bw4,Netherlands,Ten noorden van de Waddeneilanden - (Tender 2022),Object ID 4,Development Zone,2030 Roadmap,"2026",Medium,700,15,"47",,,,Grounded: Not Specified,34,37,155.60756600162,70,54.02257343,5.65156705,0.449849591499113 +"156",bw6,bw6,Netherlands,IJmuiden Ver,,Development Zone,2030 Roadmap,"2027",Medium,4000,15,"267",261,143,"236",Grounded: Not Specified,23,41,461.233617921657,400,52.865641,3.53421658,0.867239473571812 +"157",bw7,bw7,Netherlands,Hollandse Kust West - (Tender 2020/2021),"HKW, Hollandse Kust West VI, Hollandse Kust West VII",Development Zone,2030 Roadmap,"2024",Medium,1400,12,"117",245,135,"220",Grounded: Not Specified,19.1,31,219.226094600169,140,52.62929154,3.71622035,0.638610108232491 +"158",bw8,bw8,Netherlands,Hollandse Kust West zuidelijke punt,,Development Zone,,"2028",Medium,700,15,"47",261,143,"236",Grounded: Monopile,,,158.335030973185,70,52.5522,3.6218,0.442100522984425 +"159","258","258",Netherlands,Zoekgebied 1 Noord,,Development Zone,,"2030",Medium,4000,20,"200",305,165,"280",Grounded: Monopile,,,644.723429440967,400,53.431,3.2233,0.620421070080912 +"160","259","259",Netherlands,zoekgebied 3,,Development Zone,,"2034",Low,2000,20,"100",305,165,"280",Grounded: Monopile,,,319.505487063697,200,53.7831,3.473,0.625967340461128 +"161","261","261",Netherlands,Zoekgebied 8,,Development Zone,,"2036",Low,2000,20,"100",305,165,"280",Grounded: Monopile,,,209.21013121624,200,53.3739,4.0859,0.955976648154192 +"162","262","262",Netherlands,Zoekgebied 5 middenberm,,Development Zone,,"2030",Medium,2000,20,"100",305,165,"280",Grounded: Monopile,,,399.743058191578,200,54.3947,5.0373,0.500321383702802 +"163",263k,"263",Netherlands,Zoekgebied 5 Oost klein,,Development Zone,,"2029",Medium,2000,15,"134",261,143,"236",Grounded: Monopile,,,725.409010174026,200,54.254,5.6158,0.27570652858588 +"164",263o,"263",Netherlands,Zoekgebied 5 Oost origineel,,Development Zone,,"2029",Medium,4000,15,"267",261,143,"236",Grounded: Monopile,,,725.409010174026,400,54.1496,5.6547,0.55141305717176 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sha256:39950af63e11d5708655f3eed11b636e9f493e8ab0b605e190ddf06d09c10341 +size 2009542 diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_SB_Scenarios.csv b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_SB_Scenarios.csv new file mode 100644 index 0000000000000000000000000000000000000000..127639d7a191914596f23646a87c031377d99044 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_SB_Scenarios.csv @@ -0,0 +1,221 @@ +Scenario_name;UniekID;Batch +basic_2030;1;NAT +basic_2030;2;NAT +basic_2030;3;NAT +basic_2030;4;NAT +basic_2030;5;NAT +basic_2030;6;NAT +basic_2030;7;NAT +basic_2030;8;NAT +basic_2030;9;NAT +basic_2030;10;NAT +basic_2030;11;NAT +basic_2030;12;NAT +basic_2030;13;NAT +basic_2030;14;NAT +basic_2030;15;NAT +basic_2030;bw4;NAT +basic_2030;bw6;NAT +basic_2030;bw7;NAT +Rekenvariant_III;14;NAT +Rekenvariant_III;1;NAT +Rekenvariant_III;2;NAT +Rekenvariant_III;3;NAT +Rekenvariant_III;4;NAT +Rekenvariant_III;5;NAT 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--- /dev/null +++ b/script_py_r/kec4_pyconversion-master/OWP_casualties/input/OWP_SB_Species.csv @@ -0,0 +1,20 @@ +EUring;ScientificName;Do4HabLoss;NLname;ENname;Iter;RDRS;WMR_PopModel +59; Gavia sp;1;Duiker spec.;Diver sp;0;0.08;1 +720; Phalacrocorax carbo;1;Aalscholver;Cormorant;0;0.012;0 +2060; Somateria mollissima;1;Eidereend;Eider duck;0;0.048;0 +2130; Melanitta nigra;1;Zw. Zeeëend;Common Scoter;0;0.08;0 +15820; Sturnus vulgaris;0;Spreeuw;Common Starling;0;NA;0 +5670; Stercorarius parasiticus;0;Kleine Jager;Arctic Skua;0;0.008;0 +5690; Stercorarius skua;0;Grote Jager;Great Skua;0;0.008;0 +5780; Hydrocoloeus minutus;0;Dwergmeeuw;Little Gull;0;0.012;0 +6110; Thalasseus sandvicensis;1;Grote Stern;Sandwich Tern;0;0.024;1 +6169; Sterna paradisaea+hirundo;0;"""Noordse Dief""";"""Commic Tern""";0;0.024;0 +6340; Uria aalge;1;Zeekoet;Common Guillemot;0;0.036;1 +6360; Alca torda;1;Alk;Razorbill;0;0.036;1 +6540; Fratercula arctica;1;Papegaaiduiker;Atlantic Puffin;0;0.024;1 +220; Fulmarus glacialis;1;Noordse Stormvogel;Northern Fulmar;1;0.004;1 +710; Morus bassanus;1;Jan-van-Gent;Gannet;1;0.008;1 +6020; Rissa tridactyla;0;Drieeteenmeeuw;Kittiwake;1;0.016;0 +5910; Larus fuscus;0;Kleine Mantelmeeuw;Lesser black-backed Gull;2;0.008;0 +5920; Larus argentatus;0;Zilvermeeuw;Herring Gull;2;0.008;0 +6000; Larus marinus;0;Grote Mantelmeeuw;Great Black-backed Gull;2;0.016;0 diff --git a/script_py_r/kec4_pyconversion-master/OWP_casualties/output/.gitkeep b/script_py_r/kec4_pyconversion-master/OWP_casualties/output/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/script_py_r/kec4_pyconversion-master/README.md b/script_py_r/kec4_pyconversion-master/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c9b071a2787464d88476fa8e863abb6f75a3878c --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/README.md @@ -0,0 +1,52 @@ +--- +title: "KEC4: Preprocessing maps of Northern Gannet abundance for use in KEC4 IBM" +--- + +This folder contains the necessary files to create the maps required for the Individual Based Model (IBM) of the Northern Gannet used in the KEC4 project. This IBM is used to estimate the additional mortality from habitat loss due to avoidance of Offshore Wind Farms (OWFs). This IBM was originally created for a separate Wind-op-Zee project (WOZEP) and described in: + + Van Kooten, T., Soudijn, F., Tulp, T., Chen, C., Benden D., Leopold, M. + *"The consequences of seabird habitat loss from offshore wind turbines; Displacement and population level effects in 5 selected species"* + Wageningen Marine Research (University & Research centre), Wageningen Marine Research report C063/19. 116 pp. + +R-script that process the files in this folder are stored in the KEC4ibm repository at: [https://git.wur.nl/ecodyn/KEC4ibm/-/tree/master/src/R] and copied here for convenience. + +### Content of R-Files + - The file **prep_map.R** reads input csv files of the NS grid layout (5kmX5km) and Northern Gannet abundances per grid cell and creates one .mat file per season and batch, which are stored in /matfiles. + - The file **plot_map.R** reads the worked grids from prep_map.R and makes plot for checking. + - The file **prep_OWF_scenario.R** reads input csv files with OWF data and creates maps for OWF scenarios + - The file **combine_OWF_predicted.R** reads the maps with OWF scenarios created in **prep_OWF_scenario.R** and the maps with predicted northern gannet abundances created in **prep_map.R** to create habitat quality maps for the IBM. + - The file **plot_matfiles.R** make a plot of each map in /matfiles subfolder + +### Content of Folders + - **/csv** contains input csv files (provided by Jan Tjalling vd Wal) + - **/rds** contains R data objects files (.rds) with the worked grid and merged data.frames. + - **/matfiles** contains the matrices (maps) used in the IBM. There are two matrices per season and batch and scenario. + - **\"MB_predicted_Grid_5km_....\"**: the matrix with predicted abundances. + - **\"MB_presence_Grid_5km_...\"** : 0/1 matrix indicating whether an abundance measurement exists for that grid cell, with 0 indicating abundance was NOT predicted (NA). A 1 indicates abundance was predicted (note that this prediction can be equal to zero). + - **/figures** contains all the figures produced by plot_map.R + +### Layout of the matrices in .matfiles + +Matrix lay-out + + - Easting in columns + - Northing in rows + +| |EASTING:| 1 | 2 | .. | 219 | +|:---:| ---:|:---:|:---:|:---:|:---:| +|**N**| 284| E1N284 | E2N284 | ... | E219N284 +|**O**| . | . | . | ... | . +|**R**| . | . | . | ... | . +|**T**| . | . | . | ... | . +|**H**| . | . | . | ... | . +|**I**| . | . | . | ... | . +|**N**| 2 | E1N2 | E2N2 | ... | E219N2 +|**G**| 1 | E1N1 | E2N1 | ... | E219N1 + + +### Dimensions of the habitat map +The dimension of the KEC4 North Sea grid (in **csv/windsp5c_vectorgrid.csv**) are + + - 219 columns (Easting) + - 284 rows (Northing) + - 219 x 284 = 62196 grid cells diff --git a/script_py_r/kec4_pyconversion-master/disclaimer.txt b/script_py_r/kec4_pyconversion-master/disclaimer.txt new file mode 100644 index 0000000000000000000000000000000000000000..12bdbc74f064a306741b1f84d637d4a8852eb47e --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/disclaimer.txt @@ -0,0 +1,14 @@ +Disclaimer +Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +any and all intellectual property rights in the scrips and datafiles/databases are made available +in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +provided "as is", without warranty of any kind, express or implied, including but not limited to the +warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +the authors or copyright holders be liable for any claim, damages or other liability, whether in an +action of contract, tort or otherwise, arising from, out of or in connection with the software or the +use or other dealings in the software. +Wageningen Marine Research +January 2022 diff --git a/script_py_r/kec4_pyconversion-master/pyQgis_test.py b/script_py_r/kec4_pyconversion-master/pyQgis_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4d8fa32d27ab9ec0b5f9b44fdc16102246bc5664 --- /dev/null +++ b/script_py_r/kec4_pyconversion-master/pyQgis_test.py @@ -0,0 +1,70 @@ +# Importing sys and os first. +# Copied from https://anitagraser.com/2019/03/03/stand-alone-pyqgis-scripts-with-osgeo4w/ )( +# Where this is part of a comment from Nguyen Thi van Thao (2019-08-05, 07:06). +# And based on his text this could/should also work Visual Studio Code. + +# Disclaimer +# Wageningen Marine Research (“WMR”) institute within Stichting Wageningen Research, is the owner of +# any and all intellectual property rights in the scrips and datafiles/databases are made available +# in the WoZEP-repository as part of the KEC4-project. They are released to the public domain under a +# GNU-GPL-v3-license for scripts and a CC-BY-SA-v4-license for datafiles/databases. +# Please provide proper references to the source(s) when using the scripts and/or datafiles/databases. +# The scripts and datafiles/databases made available by WMR via the WoZEP repository (“software”) are +# provided "as is", without warranty of any kind, express or implied, including but not limited to the +# warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall +# the authors or copyright holders be liable for any claim, damages or other liability, whether in an +# action of contract, tort or otherwise, arising from, out of or in connection with the software or the +# use or other dealings in the software. +# Wageningen Marine Research +# January 2022 + + +import sys +import os +# The use these to extend and set some properties correctly for Python+QGis +sys.path.extend([r'C:\OSGeo4W64\apps\qgis-ltr\python',r'C:\OSGeo4W64\apps\Python37\lib\site-packages']) +os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = r'C:\OSGeo4W64\apps\Qt5\plugins' +os.environ['PATH'] += r';C:\OSGeo4W64\apps\qgis\bin;C:\OSGeo4W64\apps\Qt5\bin' +# And this as well. +sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python') +sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins') +sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins\processing') +# Now continue with what needs to be accomplished + +from qgis.core import QgsApplication, QgsProcessingFeedback +from qgis.analysis import QgsNativeAlgorithms + +#QgsApplication.setPrefixPath(r'C:\OSGeo4W64\apps\qgis-dev', True) +QgsApplication.setPrefixPath(r'C:\OSGeo4W64\apps\qgis-ltr', True) +qgs = QgsApplication([], False) +qgs.initQgis() + +# Add the path to processing so we can import it next +#sys.path.append(r'C:\OSGeo4W64\apps\qgis-dev\python\plugins') +sys.path.append(r'C:\OSGeo4W64\apps\qgis-ltr\python\plugins') +# Imports usually should be at the top of a script but this unconventional +# order is necessary here because QGIS has to be initialized first +# 20210426: previously this was okay importing 'processing', not any more +# 20210426: 'Processing' instead of 'processing' works for the first import line +# 20210426: The second still reports an error, not necessarily for itself, but for import _ssl. +# 20210426: 'shadowing'? +import processing +from processing.core.Processing import processing + +# processing.initialize() +QgsApplication.processingRegistry().addProvider(QgsNativeAlgorithms()) +feedback = QgsProcessingFeedback() + +# rivers = r'D:\Documents\Geodata\NaturalEarthData\Natural_Earth_quick_start\10m_physical\ne_10m_rivers_lake_centerlines.shp' +rivers = r'C:\Users\wal031\Downloads\ne_10m_rivers_lake_centerlines\ne_10m_rivers_lake_centerlines.shp' +# output = r'D:\Documents\Geodata\temp\danube3.shp' +output = r'C:\Users\wal031\Downloads\danube3.shp' +expression = "name LIKE '%Danube%'" + +danube = processing.run( + 'native:extractbyexpression', + {'INPUT': rivers, 'EXPRESSION': expression, 'OUTPUT': output}, + feedback=feedback +)['OUTPUT'] + +print(danube) \ No newline at end of file