From 12bda85fff3d14f58dbbc4af96b6dc035402df46 Mon Sep 17 00:00:00 2001 From: Emil Shanaty <65846814+emil28092005@users.noreply.github.com> Date: Sun, 13 Apr 2025 17:42:00 +0300 Subject: [PATCH] Initial commit --- Assignment1/datasets/pokemon_modified.csv | 802 + Assignment1/datasets/task1_data.csv | 31 + .../e.shanayev@innopolis.university.ipynb | 585 + ...s.faizullin@innopolis.university (2).ipynb | 535 + Assignment2/Assignment 2.pdf | Bin 0 -> 382905 bytes Assignment2/Assignment2.ipynb | 138 + Assignment2/dataset/README.md | 1736 + Assignment2/dataset/__MACOSX/._README.md | Bin 0 -> 640 bytes Assignment2/dataset/__MACOSX/._opsd_raw.csv | Bin 0 -> 233 bytes Assignment2/dataset/opsd_raw.csv | 50401 ++++++++++++++++ Assignment2/load_profiles.png | Bin 0 -> 94139 bytes Assignment2/opsd_raw.csv | 504 + Assignment2/processed_data.npz | Bin 0 -> 1215822 bytes 13 files changed, 54732 insertions(+) create mode 100755 Assignment1/datasets/pokemon_modified.csv create mode 100755 Assignment1/datasets/task1_data.csv create mode 100755 Assignment1/e.shanayev@innopolis.university.ipynb create mode 100755 Assignment1/s.faizullin@innopolis.university (2).ipynb create mode 100644 Assignment2/Assignment 2.pdf create mode 100644 Assignment2/Assignment2.ipynb create mode 100644 Assignment2/dataset/README.md create mode 100644 Assignment2/dataset/__MACOSX/._README.md create mode 100644 Assignment2/dataset/__MACOSX/._opsd_raw.csv create mode 100644 Assignment2/dataset/opsd_raw.csv create mode 100644 Assignment2/load_profiles.png create mode 100644 Assignment2/opsd_raw.csv create mode 100644 Assignment2/processed_data.npz diff --git a/Assignment1/datasets/pokemon_modified.csv b/Assignment1/datasets/pokemon_modified.csv new file mode 100755 index 0000000..755f99d --- /dev/null +++ b/Assignment1/datasets/pokemon_modified.csv @@ -0,0 +1,802 @@ +against_bug,against_dark,against_dragon,against_electric,against_fairy,against_fight,against_fire,against_flying,against_ghost,against_grass,against_ground,against_ice,against_normal,against_poison,against_psychic,against_rock,against_steel,against_water,attack,base_egg_steps,base_happiness,base_total,capture_rate,classification,defense,experience_growth,height_m,hp,name,percentage_male,type1,sp_attack,sp_defense,speed,weight_kg,generation,is_legendary +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,49,5120,70,318,45,Seed Pokémon,49,1059860,0.7,45,Bulbasaur,88.1,grass,65,65,45,6.9,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,62,5120,70,405,45,Seed Pokémon,63,1059860,1,60,Ivysaur,88.1,grass,80,80,60,13,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,100,5120,70,625,45,Seed Pokémon,123,1059860,2,80,Venusaur,88.1,grass,122,120,80,100,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,52,5120,70,309,45,Lizard Pokémon,43,1059860,0.6,39,Charmander,88.1,fire,60,50,65,8.5,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,64,5120,70,405,45,Flame Pokémon,58,1059860,1.1,58,Charmeleon,88.1,fire,80,65,80,19,1,0 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,104,5120,70,634,45,Flame Pokémon,78,1059860,1.7,78,Charizard,88.1,fire,159,115,100,90.5,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,48,5120,70,314,45,Tiny Turtle Pokémon,65,1059860,0.5,44,Squirtle,88.1,water,50,64,43,9,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,63,5120,70,405,45,Turtle Pokémon,80,1059860,1,59,Wartortle,88.1,water,65,80,58,22.5,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,103,5120,70,630,45,Shellfish Pokémon,120,1059860,1.6,79,Blastoise,88.1,water,135,115,78,85.5,1,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,30,3840,70,195,255,Worm Pokémon,35,1000000,0.3,45,Caterpie,50,bug,20,20,45,2.9,1,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,20,3840,70,205,120,Cocoon Pokémon,55,1000000,0.7,50,Metapod,50,bug,25,25,30,9.9,1,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,45,3840,70,395,45,Butterfly Pokémon,50,1000000,1.1,60,Butterfree,50,bug,90,80,70,32,1,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,35,3840,70,195,255,Hairy Pokémon,30,1000000,0.3,40,Weedle,50,bug,20,20,50,3.2,1,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,25,3840,70,205,120,Cocoon Pokémon,50,1000000,0.6,45,Kakuna,50,bug,25,25,35,10,1,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,150,3840,70,495,45,Poison Bee Pokémon,40,1000000,1,65,Beedrill,50,bug,15,80,145,29.5,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,45,3840,70,251,255,Tiny Bird Pokémon,40,1059860,0.3,40,Pidgey,50,normal,35,35,56,1.8,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,60,3840,70,349,120,Bird Pokémon,55,1059860,1.1,63,Pidgeotto,50,normal,50,50,71,30,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,80,3840,70,579,45,Bird Pokémon,80,1059860,1.5,83,Pidgeot,50,normal,135,80,121,39.5,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,56,3840,70,253,255,Mouse Pokémon,35,1000000,,30,Rattata,50,normal,25,35,72,,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,71,3840,70,413,127,Mouse Pokémon,70,1000000,,75,Raticate,50,normal,40,80,77,,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,60,3840,70,262,255,Tiny Bird Pokémon,30,1000000,0.3,40,Spearow,50,normal,31,31,70,2,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,90,3840,70,442,90,Beak Pokémon,65,1000000,1.2,65,Fearow,50,normal,61,61,100,38,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,60,5120,70,288,255,Snake Pokémon,44,1000000,2,35,Ekans,50,poison,40,54,55,6.9,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,95,5120,70,448,90,Cobra Pokémon,69,1000000,3.5,60,Arbok,50,poison,65,79,80,65,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,55,2560,70,320,190,Mouse Pokémon,40,1000000,0.4,35,Pikachu,50,electric,50,50,90,6,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,85,2560,70,485,75,Mouse Pokémon,50,1000000,,60,Raichu,50,electric,95,85,110,,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,75,5120,70,300,255,Mouse Pokémon,90,1000000,,50,Sandshrew,50,ground,10,35,40,,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,100,5120,70,450,90,Mouse Pokémon,120,1000000,,75,Sandslash,50,ground,25,65,65,,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,47,5120,70,275,235,Poison Pin Pokémon,52,1059860,0.4,55,Nidoran♀,0,poison,40,40,41,7,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,62,5120,70,365,120,Poison Pin Pokémon,67,1059860,0.8,70,Nidorina,0,poison,55,55,56,20,1,0 +0.5,1,1,0,0.5,0.5,1,1,1,1,2,2,1,0.25,2,0.5,1,2,92,5120,70,505,45,Drill Pokémon,87,1059860,1.3,90,Nidoqueen,0,poison,75,85,76,60,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,57,5120,70,273,235,Poison Pin Pokémon,40,1059860,0.5,46,Nidoran♂,100,poison,40,40,50,9,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,72,5120,70,365,120,Poison Pin Pokémon,57,1059860,0.9,61,Nidorino,100,poison,55,55,65,19.5,1,0 +0.5,1,1,0,0.5,0.5,1,1,1,1,2,2,1,0.25,2,0.5,1,2,102,5120,70,505,45,Drill Pokémon,77,1059860,1.4,81,Nidoking,100,poison,85,75,85,62,1,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,45,2560,140,323,150,Fairy Pokémon,48,800000,0.6,70,Clefairy,24.6,fairy,60,65,35,7.5,1,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,70,2560,140,483,25,Fairy Pokémon,73,800000,1.3,95,Clefable,24.6,fairy,95,90,60,40,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,41,5120,70,299,190,Fox Pokémon,40,1000000,,38,Vulpix,24.6,fire,50,65,65,,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,67,5120,70,505,75,Fox Pokémon,75,1000000,,73,Ninetales,24.6,fire,81,100,109,,1,0 +0.5,0.5,0,1,1,1,1,1,0,1,1,1,1,2,1,1,2,1,45,2560,70,270,170,Balloon Pokémon,20,800000,0.5,115,Jigglypuff,24.6,normal,45,25,20,5.5,1,0 +0.5,0.5,0,1,1,1,1,1,0,1,1,1,1,2,1,1,2,1,70,2560,70,435,50,Balloon Pokémon,45,800000,1,140,Wigglytuff,24.6,normal,85,50,45,12,1,0 +0.25,1,1,2,0.5,0.25,1,1,1,0.25,0,2,1,0.5,2,2,1,1,45,3840,70,245,255,Bat Pokémon,35,1000000,0.8,40,Zubat,50,poison,30,40,55,7.5,1,0 +0.25,1,1,2,0.5,0.25,1,1,1,0.25,0,2,1,0.5,2,2,1,1,80,3840,70,455,90,Bat Pokémon,70,1000000,1.6,75,Golbat,50,poison,65,75,90,55,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,50,5120,70,320,255,Weed Pokémon,55,1059860,0.5,45,Oddish,50,grass,75,65,30,5.4,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,65,5120,70,395,120,Weed Pokémon,70,1059860,0.8,60,Gloom,50,grass,85,75,40,8.6,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,80,5120,70,490,45,Flower Pokémon,85,1059860,1.2,75,Vileplume,50,grass,110,90,50,18.6,1,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,70,5120,70,285,190,Mushroom Pokémon,55,1000000,0.3,35,Paras,50,bug,45,55,25,5.4,1,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,95,5120,70,405,75,Mushroom Pokémon,80,1000000,1,60,Parasect,50,bug,60,80,30,29.5,1,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,55,5120,70,305,190,Insect Pokémon,50,1000000,1,60,Venonat,50,bug,40,55,45,30,1,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,65,5120,70,450,75,Poison Moth Pokémon,60,1000000,1.5,70,Venomoth,50,bug,90,75,90,12.5,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,55,5120,70,265,255,Mole Pokémon,30,1000000,,10,Diglett,50,ground,35,45,90,,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,100,5120,70,425,50,Mole Pokémon,60,1000000,,35,Dugtrio,50,ground,50,70,110,,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,35,5120,70,290,255,Scratch Cat Pokémon,35,1000000,,40,Meowth,50,normal,50,40,90,,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,5120,70,440,90,Classy Cat Pokémon,60,1000000,,65,Persian,50,normal,75,65,115,,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,52,5120,70,320,190,Duck Pokémon,48,1000000,0.8,50,Psyduck,50,water,65,50,55,19.6,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,82,5120,70,500,75,Duck Pokémon,78,1000000,1.7,80,Golduck,50,water,95,80,85,76.6,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,80,5120,70,305,190,Pig Monkey Pokémon,35,1000000,0.5,40,Mankey,50,fighting,35,45,70,28,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,105,5120,70,455,75,Pig Monkey Pokémon,60,1000000,1,65,Primeape,50,fighting,60,70,95,32,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,70,5120,70,350,190,Puppy Pokémon,45,1250000,0.7,55,Growlithe,75.4,fire,70,50,60,19,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,110,5120,70,555,75,Legendary Pokémon,80,1250000,1.9,90,Arcanine,75.4,fire,100,80,95,155,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,50,5120,70,300,255,Tadpole Pokémon,40,1059860,0.6,40,Poliwag,50,water,40,40,90,12.4,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,385,120,Tadpole Pokémon,65,1059860,1,65,Poliwhirl,50,water,50,50,90,20,1,0 +0.5,0.5,1,2,2,1,0.5,2,1,2,1,0.5,1,1,2,0.5,0.5,0.5,95,5120,70,510,45,Tadpole Pokémon,95,1059860,1.3,90,Poliwrath,50,water,70,90,70,54,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,20,5120,70,310,200,Psi Pokémon,15,1059860,0.9,25,Abra,75.4,psychic,105,55,90,19.5,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,35,5120,70,400,100,Psi Pokémon,30,1059860,1.3,40,Kadabra,75.4,psychic,120,70,105,56.5,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,50,5120,70,600,50,Psi Pokémon,65,1059860,1.5,55,Alakazam,75.4,psychic,175,105,150,48,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,80,5120,70,305,180,Superpower Pokémon,50,1059860,0.8,70,Machop,75.4,fighting,35,35,35,19.5,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,100,5120,70,405,90,Superpower Pokémon,70,1059860,1.5,80,Machoke,75.4,fighting,50,60,45,70.5,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,130,5120,70,505,45,Superpower Pokémon,80,1059860,1.6,90,Machamp,75.4,fighting,65,85,55,130,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,75,5120,70,300,255,Flower Pokémon,35,1059860,0.7,50,Bellsprout,50,grass,70,30,40,4,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,90,5120,70,390,120,Flycatcher Pokémon,50,1059860,1,65,Weepinbell,50,grass,85,45,55,6.4,1,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,105,5120,70,490,45,Flycatcher Pokémon,65,1059860,1.7,80,Victreebel,50,grass,100,70,70,15.5,1,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,40,5120,70,335,190,Jellyfish Pokémon,35,1250000,0.9,40,Tentacool,50,water,50,100,70,45.5,1,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,70,5120,70,515,60,Jellyfish Pokémon,65,1250000,1.6,80,Tentacruel,50,water,80,120,100,55,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,80,3840,70,300,255,Rock Pokémon,100,1059860,,40,Geodude,50,rock,30,30,20,,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,95,3840,70,390,120,Rock Pokémon,115,1059860,,55,Graveler,50,rock,45,45,35,,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,120,3840,70,495,45,Megaton Pokémon,130,1059860,,80,Golem,50,rock,55,65,45,,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,85,5120,70,410,190,Fire Horse Pokémon,55,1000000,1,50,Ponyta,50,fire,65,65,90,30,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,100,5120,70,500,60,Fire Horse Pokémon,70,1000000,1.7,65,Rapidash,50,fire,80,80,105,95,1,0 +2,2,1,2,1,0.5,0.5,1,2,2,1,0.5,1,1,0.5,1,0.5,0.5,65,5120,70,315,190,Dopey Pokémon,65,1000000,1.2,90,Slowpoke,50,water,40,40,15,36,1,0 +2,2,1,2,1,0.5,0.5,1,2,2,1,0.5,1,1,0.5,1,0.5,0.5,75,5120,70,590,75,Hermit Crab Pokémon,180,1000000,1.6,95,Slowbro,50,water,130,80,30,78.5,1,0 +0.5,1,0.5,0.5,0.5,2,2,0.25,1,0.5,4,0.5,0.5,0,0.5,0.5,0.25,1,35,5120,70,325,190,Magnet Pokémon,70,1000000,0.3,25,Magnemite,,electric,95,55,45,6,1,0 +0.5,1,0.5,0.5,0.5,2,2,0.25,1,0.5,4,0.5,0.5,0,0.5,0.5,0.25,1,60,5120,70,465,60,Magnet Pokémon,95,1000000,1,50,Magneton,,electric,120,70,70,60,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,90,5120,70,377,45,Wild Duck Pokémon,55,1000000,0.8,52,Farfetch'd,50,normal,58,62,60,15,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,85,5120,70,310,190,Twin Bird Pokémon,45,1000000,1.4,35,Doduo,50,normal,35,35,75,39.2,1,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,110,5120,70,470,45,Triple Bird Pokémon,70,1000000,1.8,60,Dodrio,50,normal,60,60,110,85.2,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,45,5120,70,325,190,Sea Lion Pokémon,55,1000000,1.1,65,Seel,50,water,45,70,45,90,1,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,70,5120,70,475,75,Sea Lion Pokémon,80,1000000,1.7,90,Dewgong,50,water,70,95,70,120,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,80,5120,70,325,190,Sludge Pokémon,50,1000000,,80,Grimer,50,poison,40,50,25,,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,105,5120,70,500,75,Sludge Pokémon,75,1000000,,105,Muk,50,poison,65,100,50,,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,305,190,Bivalve Pokémon,100,1250000,0.3,30,Shellder,50,water,45,25,40,4,1,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,95,5120,70,525,60,Bivalve Pokémon,180,1250000,1.5,50,Cloyster,50,water,85,45,70,132.5,1,0 +0.25,2,1,1,0.5,0,1,1,2,0.5,2,1,0,0.25,2,1,1,1,35,5120,70,310,190,Gas Pokémon,30,1059860,1.3,30,Gastly,50,ghost,100,35,80,0.1,1,0 +0.25,2,1,1,0.5,0,1,1,2,0.5,2,1,0,0.25,2,1,1,1,50,5120,70,405,90,Gas Pokémon,45,1059860,1.6,45,Haunter,50,ghost,115,55,95,0.1,1,0 +0.25,2,1,1,0.5,0,1,1,2,0.5,2,1,0,0.25,2,1,1,1,65,5120,70,600,45,Shadow Pokémon,80,1059860,1.5,60,Gengar,50,ghost,170,95,130,40.5,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,45,6400,70,385,45,Rock Snake Pokémon,160,1000000,8.8,35,Onix,50,rock,30,45,70,210,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,48,5120,70,328,190,Hypnosis Pokémon,45,1000000,1,60,Drowzee,50,psychic,43,90,42,32.4,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,73,5120,70,483,75,Hypnosis Pokémon,70,1000000,1.6,85,Hypno,50,psychic,73,115,67,75.6,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,105,5120,70,325,225,River Crab Pokémon,90,1000000,0.4,30,Krabby,50,water,25,25,50,6.5,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,130,5120,70,475,60,Pincer Pokémon,115,1000000,1.3,55,Kingler,50,water,50,50,75,60,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,30,5120,70,330,190,Ball Pokémon,50,1000000,0.5,40,Voltorb,,electric,55,55,100,10.4,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,50,5120,70,490,60,Ball Pokémon,70,1000000,1.2,60,Electrode,,electric,80,80,150,66.6,1,0 +4,2,1,0.5,1,0.5,2,2,2,0.5,0.5,2,1,2,0.5,1,1,0.5,40,5120,70,325,90,Egg Pokémon,80,1250000,0.4,60,Exeggcute,50,grass,60,45,40,2.5,1,0 +4,2,1,0.5,1,0.5,2,2,2,0.5,0.5,2,1,2,0.5,1,1,0.5,105,5120,70,530,45,Coconut Pokémon,85,1250000,,95,Exeggutor,50,grass,125,75,45,,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,50,5120,70,320,190,Lonely Pokémon,95,1000000,0.4,50,Cubone,50,ground,40,50,35,6.5,1,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,80,5120,70,425,75,Bone Keeper Pokémon,110,1000000,,60,Marowak,50,ground,50,80,45,,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,120,6400,70,455,45,Kicking Pokémon,53,1000000,1.5,50,Hitmonlee,100,fighting,35,110,87,49.8,1,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,105,6400,70,455,45,Punching Pokémon,79,1000000,1.4,50,Hitmonchan,100,fighting,35,110,76,50.2,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,55,5120,70,385,45,Licking Pokémon,75,1000000,1.2,90,Lickitung,50,normal,60,75,30,65.5,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,65,5120,70,340,190,Poison Gas Pokémon,95,1000000,0.6,40,Koffing,50,poison,60,45,35,1,1,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,90,5120,70,490,60,Poison Gas Pokémon,120,1000000,1.2,65,Weezing,50,poison,85,70,60,9.5,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,85,5120,70,345,120,Spikes Pokémon,95,1250000,1,80,Rhyhorn,50,ground,30,30,25,115,1,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,130,5120,70,485,60,Drill Pokémon,120,1250000,1.9,105,Rhydon,50,ground,45,45,40,120,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,5,10240,140,450,30,Egg Pokémon,5,800000,1.1,250,Chansey,0,normal,35,105,50,34.6,1,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,55,5120,70,435,45,Vine Pokémon,115,1000000,1,65,Tangela,50,grass,100,40,60,35,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,125,5120,70,590,45,Parent Pokémon,100,1000000,2.2,105,Kangaskhan,0,normal,60,100,100,80,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,40,5120,70,295,225,Dragon Pokémon,70,1000000,0.4,30,Horsea,50,water,70,25,60,8,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,440,75,Dragon Pokémon,95,1000000,1.2,55,Seadra,50,water,95,45,85,25,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,67,5120,70,320,225,Goldfish Pokémon,60,1000000,0.6,45,Goldeen,50,water,35,50,63,15,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,92,5120,70,450,60,Goldfish Pokémon,65,1000000,1.3,80,Seaking,50,water,65,80,68,39,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,45,5120,70,340,225,Starshape Pokémon,55,1250000,0.8,30,Staryu,,water,70,55,85,34.5,1,0 +2,2,1,2,1,0.5,0.5,1,2,2,1,0.5,1,1,0.5,1,0.5,0.5,75,5120,70,520,60,Mysterious Pokémon,85,1250000,1.1,60,Starmie,,water,100,85,115,80,1,0 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,45,6400,70,460,45,Barrier Pokémon,65,1000000,1.3,40,Mr. Mime,50,psychic,100,120,90,54.5,1,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,110,6400,70,500,45,Mantis Pokémon,80,1000000,1.5,70,Scyther,50,bug,55,80,105,56,1,0 +2,2,1,1,1,1,2,1,2,1,1,0.5,1,1,0.5,2,2,1,50,6400,70,455,45,Humanshape Pokémon,35,1000000,1.4,65,Jynx,0,ice,115,95,95,40.6,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,83,6400,70,490,45,Electric Pokémon,57,1000000,1.1,65,Electabuzz,75.4,electric,95,85,105,30,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,95,6400,70,495,45,Spitfire Pokémon,57,1000000,1.3,65,Magmar,75.4,fire,100,85,93,44.5,1,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,155,6400,70,600,45,Stagbeetle Pokémon,120,1250000,1.5,65,Pinsir,50,bug,65,90,105,55,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,100,5120,70,490,45,Wild Bull Pokémon,95,1250000,1.4,75,Tauros,100,normal,40,70,110,88.4,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,10,1280,70,200,255,Fish Pokémon,55,1250000,0.9,20,Magikarp,50,water,15,20,80,10,1,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,155,1280,70,640,45,Atrocious Pokémon,109,1250000,6.5,95,Gyarados,50,water,70,130,81,235,1,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,85,10240,70,535,45,Transport Pokémon,80,1250000,2.5,130,Lapras,50,water,85,95,60,220,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,48,5120,70,288,35,Transform Pokémon,48,1000000,0.3,48,Ditto,,normal,48,48,48,4,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,55,8960,70,325,45,Evolution Pokémon,50,1000000,0.3,55,Eevee,88.1,normal,45,65,55,6.5,1,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,8960,70,525,45,Bubble Jet Pokémon,60,1000000,1,130,Vaporeon,88.1,water,110,95,65,29,1,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,65,8960,70,525,45,Lightning Pokémon,60,1000000,0.8,65,Jolteon,88.1,electric,110,95,130,24.5,1,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,130,8960,70,525,45,Flame Pokémon,60,1000000,0.9,65,Flareon,88.1,fire,95,110,65,25,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,5120,70,395,45,Virtual Pokémon,70,1000000,0.8,65,Porygon,,normal,85,75,40,36.5,1,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,40,7680,70,355,45,Spiral Pokémon,100,1000000,0.4,35,Omanyte,88.1,rock,90,55,35,7.5,1,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,60,7680,70,495,45,Spiral Pokémon,125,1000000,1,70,Omastar,88.1,rock,115,70,55,35,1,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,80,7680,70,355,45,Shellfish Pokémon,90,1000000,0.5,30,Kabuto,88.1,rock,55,45,55,11.5,1,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,115,7680,70,495,45,Shellfish Pokémon,105,1000000,1.3,60,Kabutops,88.1,rock,65,70,80,40.5,1,0 +0.5,1,1,2,1,1,0.5,0.5,1,1,0,2,0.5,0.5,1,2,2,2,135,8960,70,615,45,Fossil Pokémon,85,1250000,1.8,80,Aerodactyl,88.1,rock,70,95,150,59,1,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,110,10240,70,540,25,Sleeping Pokémon,65,1250000,2.1,160,Snorlax,88.1,normal,65,110,30,460,1,0 +0.5,1,1,2,1,1,2,1,1,0.5,0,1,1,1,1,4,2,1,85,20480,35,580,3,Freeze Pokémon,100,1250000,1.7,90,Articuno,,ice,95,125,85,55.4,1,1 +0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,2,1,1,1,2,0.5,1,90,20480,35,580,3,Electric Pokémon,85,1250000,1.6,90,Zapdos,,electric,125,90,100,52.6,1,1 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,100,20480,35,580,3,Flame Pokémon,90,1250000,2,90,Moltres,,fire,125,85,90,60,1,1 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,64,10240,35,300,45,Dragon Pokémon,45,1250000,1.8,41,Dratini,50,dragon,50,50,50,3.3,1,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,84,10240,35,420,45,Dragon Pokémon,65,1250000,4,61,Dragonair,50,dragon,70,70,70,16.5,1,0 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,134,10240,35,600,45,Dragon Pokémon,95,1250000,2.2,91,Dragonite,50,dragon,100,100,80,210,1,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,150,30720,0,780,3,Genetic Pokémon,70,1250000,2,106,Mewtwo,,psychic,194,120,140,122,1,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,100,30720,100,600,45,New Species Pokémon,100,1059860,0.4,100,Mew,,psychic,100,100,100,4,1,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,49,5120,70,318,45,Leaf Pokémon,65,1059860,0.9,45,Chikorita,88.1,grass,49,65,45,6.4,2,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,62,5120,70,405,45,Leaf Pokémon,80,1059860,1.2,60,Bayleef,88.1,grass,63,80,60,15.8,2,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,82,5120,70,525,45,Herb Pokémon,100,1059860,1.8,80,Meganium,88.1,grass,83,100,80,100.5,2,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,52,5120,70,309,45,Fire Mouse Pokémon,43,1059860,0.5,39,Cyndaquil,88.1,fire,60,50,65,7.9,2,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,64,5120,70,405,45,Volcano Pokémon,58,1059860,0.9,58,Quilava,88.1,fire,80,65,80,19,2,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,84,5120,70,534,45,Volcano Pokémon,78,1059860,1.7,78,Typhlosion,88.1,fire,109,85,100,79.5,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,314,45,Big Jaw Pokémon,64,1059860,0.6,50,Totodile,88.1,water,44,48,43,9.5,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,80,5120,70,405,45,Big Jaw Pokémon,80,1059860,1.1,65,Croconaw,88.1,water,59,63,58,25,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,105,5120,70,530,45,Big Jaw Pokémon,100,1059860,2.3,85,Feraligatr,88.1,water,79,83,78,88.8,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,46,3840,70,215,255,Scout Pokémon,34,1000000,0.8,35,Sentret,50,normal,35,45,20,6,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,76,3840,70,415,90,Long Body Pokémon,64,1000000,1.8,85,Furret,50,normal,45,55,90,32.5,2,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,30,3840,70,262,255,Owl Pokémon,30,1000000,0.7,60,Hoothoot,50,normal,36,56,50,21.2,2,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,50,3840,70,452,90,Owl Pokémon,50,1000000,1.6,100,Noctowl,50,normal,86,96,70,40.8,2,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,20,3840,70,265,255,Five Star Pokémon,30,800000,1,40,Ledyba,50,bug,40,80,55,10.8,2,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,35,3840,70,390,90,Five Star Pokémon,50,800000,1.4,55,Ledian,50,bug,55,110,85,35.6,2,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,60,3840,70,250,255,String Spit Pokémon,40,800000,0.5,40,Spinarak,50,bug,40,40,30,8.5,2,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,90,3840,70,400,90,Long Leg Pokémon,70,800000,1.1,70,Ariados,50,bug,60,70,40,33.5,2,0 +0.25,1,1,2,0.5,0.25,1,1,1,0.25,0,2,1,0.5,2,2,1,1,90,3840,70,535,90,Bat Pokémon,80,1000000,1.8,85,Crobat,50,poison,70,80,130,75,2,0 +1,1,1,1,1,1,0.5,0.5,1,2,2,0.5,1,1,1,1,0.25,0.5,38,5120,70,330,190,Angler Pokémon,38,1250000,0.5,75,Chinchou,50,water,56,56,67,12,2,0 +1,1,1,1,1,1,0.5,0.5,1,2,2,0.5,1,1,1,1,0.25,0.5,58,5120,70,460,75,Light Pokémon,58,1250000,1.2,125,Lanturn,50,water,76,76,67,22.5,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,40,2560,70,205,190,Tiny Mouse Pokémon,15,1000000,0.3,20,Pichu,50,electric,35,35,60,2,2,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,25,2560,140,218,150,Star Shape Pokémon,28,800000,0.3,50,Cleffa,24.6,fairy,45,55,15,3,2,0 +0.5,0.5,0,1,1,1,1,1,0,1,1,1,1,2,1,1,2,1,30,2560,70,210,170,Balloon Pokémon,15,800000,0.3,90,Igglybuff,24.6,normal,40,20,15,1,2,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,20,2560,70,245,190,Spike Ball Pokémon,65,800000,0.3,35,Togepi,88.1,fairy,40,65,20,1.5,2,0 +0.25,0.5,0,2,1,0.25,1,1,1,0.5,0,2,1,2,1,2,2,1,40,2560,70,405,75,Happiness Pokémon,85,800000,0.6,55,Togetic,88.1,fairy,80,105,40,3.2,2,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,50,5120,70,320,190,Little Bird Pokémon,45,1000000,0.2,40,Natu,50,psychic,70,45,70,2,2,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,75,5120,70,470,75,Mystic Pokémon,70,1000000,1.5,65,Xatu,50,psychic,95,70,95,15,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,40,5120,70,280,235,Wool Pokémon,40,1059860,0.6,55,Mareep,50,electric,65,45,35,7.8,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,55,5120,70,365,120,Wool Pokémon,55,1059860,0.8,70,Flaaffy,50,electric,80,60,45,13.3,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,95,5120,70,610,45,Light Pokémon,105,1059860,1.4,90,Ampharos,50,electric,165,110,45,61.5,2,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,80,5120,70,490,45,Flower Pokémon,95,1059860,0.4,75,Bellossom,50,grass,90,100,50,5.8,2,0 +0.5,0.5,0,2,1,0.5,0.5,1,1,2,1,0.5,1,2,1,1,1,0.5,20,2560,70,250,190,Aquamouse Pokémon,50,800000,0.4,70,Marill,50,water,20,50,40,8.5,2,0 +0.5,0.5,0,2,1,0.5,0.5,1,1,2,1,0.5,1,2,1,1,1,0.5,50,2560,70,420,75,Aquarabbit Pokémon,80,800000,0.8,100,Azumarill,50,water,60,80,50,28.5,2,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,100,5120,70,410,65,Imitation Pokémon,115,1000000,1.2,70,Sudowoodo,50,rock,30,65,30,38,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,75,5120,70,500,45,Frog Pokémon,75,1059860,1.1,90,Politoed,50,water,90,100,70,33.9,2,0 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,35,5120,70,250,255,Cottonweed Pokémon,40,1059860,0.4,35,Hoppip,50,grass,35,55,50,0.5,2,0 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,45,5120,70,340,120,Cottonweed Pokémon,50,1059860,0.6,55,Skiploom,50,grass,45,65,80,1,2,0 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,55,5120,70,460,45,Cottonweed Pokémon,70,1059860,0.8,75,Jumpluff,50,grass,55,95,110,3,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,70,5120,70,360,45,Long Tail Pokémon,55,800000,0.8,55,Aipom,50,normal,40,55,85,11.5,2,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,30,5120,70,180,235,Seed Pokémon,30,1059860,0.3,30,Sunkern,50,grass,30,30,30,1.8,2,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,75,5120,70,425,120,Sun Pokémon,55,1059860,0.8,75,Sunflora,50,grass,105,85,30,8.5,2,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,65,5120,70,390,75,Clear Wing Pokémon,45,1000000,1.2,65,Yanma,50,bug,75,45,95,38,2,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,45,5120,70,210,255,Water Fish Pokémon,45,1000000,0.4,55,Wooper,50,water,25,25,15,8.5,2,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,85,5120,70,430,90,Water Fish Pokémon,85,1000000,1.4,95,Quagsire,50,water,65,65,35,75,2,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,65,8960,70,525,45,Sun Pokémon,60,1000000,0.9,65,Espeon,88.1,psychic,130,95,110,26.5,2,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,65,8960,35,525,45,Moonlight Pokémon,110,1000000,1,95,Umbreon,88.1,dark,60,130,65,27,2,0 +1,0.5,1,2,2,1,1,1,0.5,0.5,0,2,1,1,0,2,1,1,85,5120,35,405,30,Darkness Pokémon,42,1059860,0.5,60,Murkrow,50,dark,85,42,91,2.1,2,0 +2,2,1,2,1,0.5,0.5,1,2,2,1,0.5,1,1,0.5,1,0.5,0.5,75,5120,70,490,70,Royal Pokémon,80,1000000,2,95,Slowking,50,water,100,110,30,79.5,2,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,60,6400,35,435,45,Screech Pokémon,60,800000,0.7,60,Misdreavus,50,ghost,85,85,85,1,2,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,72,10240,70,336,225,Symbol Pokémon,48,1000000,0.5,48,Unown,,psychic,72,48,48,5,2,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,33,5120,70,405,45,Patient Pokémon,58,1000000,1.3,190,Wobbuffet,50,psychic,33,58,33,28.5,2,0 +2,2,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,80,5120,70,455,60,Long Neck Pokémon,65,1000000,1.5,70,Girafarig,50,normal,90,65,85,41.5,2,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,65,5120,70,290,190,Bagworm Pokémon,90,1000000,0.6,50,Pineco,50,bug,35,35,15,7.2,2,0 +0.5,1,0.5,1,0.5,1,4,1,1,0.25,1,0.5,0.5,0,0.5,1,0.5,1,90,5120,70,465,75,Bagworm Pokémon,140,1000000,1.2,75,Forretress,50,bug,60,60,40,125.8,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,70,5120,70,415,190,Land Snake Pokémon,70,1000000,1.5,100,Dunsparce,50,normal,65,65,45,14,2,0 +0.5,1,1,0,1,0.5,1,1,1,1,0,4,1,0.5,1,1,1,2,75,5120,70,430,60,Flyscorpion Pokémon,105,1059860,1.1,65,Gligar,50,ground,35,65,85,64.8,2,0 +0.5,1,0.5,0,0.5,2,2,0.5,1,1,2,1,0.5,0,0.5,0.25,0.5,2,125,6400,70,610,25,Iron Snake Pokémon,230,1000000,9.2,75,Steelix,50,steel,55,95,30,400,2,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,80,5120,70,300,190,Fairy Pokémon,50,800000,0.6,60,Snubbull,24.6,fairy,40,40,30,7.8,2,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,120,5120,70,450,75,Fairy Pokémon,75,800000,1.4,90,Granbull,24.6,fairy,60,60,45,48.7,2,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,95,5120,70,440,45,Balloon Pokémon,85,1000000,0.5,65,Qwilfish,50,water,55,55,85,3.9,2,0 +0.5,1,0.5,1,0.5,1,4,1,1,0.25,1,0.5,0.5,0,0.5,1,0.5,1,150,6400,70,600,25,Pincer Pokémon,140,1000000,1.8,70,Scizor,50,bug,65,100,75,118,2,0 +1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,2,2,2,10,5120,70,505,190,Mold Pokémon,230,1059860,0.6,20,Shuckle,50,bug,10,230,5,20.5,2,0 +0.5,0.5,1,1,2,0.5,2,4,1,0.5,0.5,1,1,1,2,1,1,1,185,6400,70,600,45,Singlehorn Pokémon,115,1250000,1.5,80,Heracross,50,bug,40,105,75,54,2,0 +2,0.5,1,1,2,4,2,1,0.5,1,1,0.5,1,1,0,2,2,1,95,5120,35,430,60,Sharp Claw Pokémon,55,1059860,0.9,55,Sneasel,50,dark,35,75,115,28,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,5120,70,330,120,Little Bear Pokémon,50,1000000,0.6,60,Teddiursa,50,normal,50,50,40,8.8,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,130,5120,70,500,60,Hibernator Pokémon,75,1000000,1.8,90,Ursaring,50,normal,75,75,55,125.8,2,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,40,5120,70,250,190,Lava Pokémon,40,1000000,0.7,40,Slugma,50,fire,70,40,20,35,2,0 +0.5,1,1,1,0.5,2,0.25,0.5,1,1,4,0.5,0.5,0.5,1,2,1,4,50,5120,70,430,75,Lava Pokémon,120,1000000,0.8,60,Magcargo,50,fire,90,80,30,55,2,0 +1,1,1,0,1,2,2,1,1,2,1,1,1,0.5,1,1,2,2,50,5120,70,250,225,Pig Pokémon,40,1250000,0.4,50,Swinub,50,ice,30,30,50,6.5,2,0 +1,1,1,0,1,2,2,1,1,2,1,1,1,0.5,1,1,2,2,100,5120,70,450,75,Swine Pokémon,80,1250000,1.1,100,Piloswine,50,ice,60,60,50,55.8,2,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,55,5120,70,410,60,Coral Pokémon,95,800000,0.6,65,Corsola,24.6,water,65,95,35,5,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,300,190,Jet Pokémon,35,1000000,0.6,35,Remoraid,50,water,65,35,65,12,2,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,105,5120,70,480,75,Jet Pokémon,75,1000000,0.9,75,Octillery,50,water,105,75,45,28.5,2,0 +0.5,1,1,2,1,1,2,1,1,0.5,0,1,1,1,1,4,2,1,55,5120,70,330,45,Delivery Pokémon,45,800000,0.9,45,Delibird,50,ice,65,45,75,16,2,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,40,6400,70,485,25,Kite Pokémon,70,1250000,2.1,85,Mantine,50,water,80,140,70,220,2,0 +0.25,1,0.5,2,0.5,1,2,0.5,1,0.25,0,1,0.5,0,0.5,1,0.5,1,80,6400,70,465,25,Armor Bird Pokémon,140,1250000,1.7,65,Skarmory,50,steel,40,70,70,50.5,2,0 +1,0.5,1,1,1,2,0.5,1,0.5,0.5,2,0.5,1,1,0,2,0.5,2,60,5120,35,330,120,Dark Pokémon,30,1250000,0.6,45,Houndour,50,dark,80,50,65,10.8,2,0 +1,0.5,1,1,1,2,0.5,1,0.5,0.5,2,0.5,1,1,0,2,0.5,2,90,5120,35,600,45,Dark Pokémon,90,1250000,1.4,75,Houndoom,50,dark,140,90,115,35,2,0 +1,1,2,1,2,1,0.25,1,1,1,1,1,1,1,1,1,0.5,0.25,95,5120,70,540,45,Dragon Pokémon,95,1000000,1.8,75,Kingdra,50,water,95,95,85,152,2,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,60,5120,70,330,120,Long Nose Pokémon,60,1000000,0.5,90,Phanpy,50,ground,40,40,40,33.5,2,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,120,5120,70,500,60,Armor Pokémon,120,1000000,1.1,90,Donphan,50,ground,60,60,50,120,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,5120,70,515,45,Virtual Pokémon,90,1000000,0.6,85,Porygon2,,normal,105,95,60,32.5,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,95,5120,70,465,45,Big Horn Pokémon,62,1250000,1.4,73,Stantler,50,normal,85,65,85,71.2,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,20,5120,70,250,45,Painter Pokémon,35,800000,1.2,55,Smeargle,50,normal,20,45,75,58,2,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,35,6400,70,210,75,Scuffle Pokémon,35,1000000,0.7,35,Tyrogue,100,fighting,35,35,35,21,2,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,95,6400,70,455,45,Handstand Pokémon,95,1000000,1.4,50,Hitmontop,100,fighting,35,110,70,48,2,0 +2,2,1,1,1,1,2,1,2,1,1,0.5,1,1,0.5,2,2,1,30,6400,70,305,45,Kiss Pokémon,15,1000000,0.4,45,Smoochum,0,ice,85,65,65,6,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,63,6400,70,360,45,Electric Pokémon,37,1000000,0.6,45,Elekid,75.4,electric,65,55,95,23.5,2,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,75,6400,70,365,45,Live Coal Pokémon,37,1000000,0.7,45,Magby,75.4,fire,70,55,83,21.4,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,5120,70,490,45,Milk Cow Pokémon,105,1250000,1.2,95,Miltank,0,normal,40,70,100,75.5,2,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,10,10240,140,540,30,Happiness Pokémon,10,800000,1.5,255,Blissey,0,normal,75,135,55,46.8,2,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,85,20480,35,580,3,Thunder Pokémon,75,1250000,1.9,90,Raikou,,electric,115,100,115,178,2,1 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,115,20480,35,580,3,Volcano Pokémon,85,1250000,2.1,115,Entei,,fire,90,75,100,198,2,1 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,75,20480,35,580,3,Aurora Pokémon,115,1250000,2,100,Suicune,,water,90,115,85,187,2,1 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,64,10240,35,300,45,Rock Skin Pokémon,50,1250000,0.6,50,Larvitar,50,rock,45,50,41,72,2,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,84,10240,35,410,45,Hard Shell Pokémon,70,1250000,1.2,70,Pupitar,50,rock,65,70,51,152,2,0 +2,0.5,1,1,2,4,0.5,0.5,0.5,2,2,1,0.5,0.5,0,1,2,2,164,10240,35,700,45,Armor Pokémon,150,1250000,2,100,Tyranitar,50,rock,95,120,71,202,2,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,90,30720,0,680,3,Diving Pokémon,130,1250000,5.2,106,Lugia,,psychic,90,154,110,216,2,1 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,130,30720,0,680,3,Rainbow Pokémon,90,1250000,3.8,106,Ho-Oh,,fire,110,154,90,199,2,1 +4,2,1,0.5,1,0.5,2,2,2,0.5,0.5,2,1,2,0.5,1,1,0.5,100,30720,100,600,45,Time Travel Pokémon,100,1059860,0.6,100,Celebi,,psychic,100,100,100,5,2,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,45,5120,70,310,45,Wood Gecko Pokémon,35,1059860,0.5,40,Treecko,88.1,grass,65,55,70,5,3,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,65,5120,70,405,45,Wood Gecko Pokémon,45,1059860,0.9,50,Grovyle,88.1,grass,85,65,95,21.6,3,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,110,5120,70,630,45,Forest Pokémon,75,1059860,1.7,70,Sceptile,88.1,grass,145,85,145,52.2,3,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,60,5120,70,310,45,Chick Pokémon,40,1059860,0.4,45,Torchic,88.1,fire,70,50,45,2.5,3,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,85,5120,70,405,45,Young Fowl Pokémon,60,1059860,0.9,60,Combusken,88.1,fire,85,60,55,19.5,3,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,160,5120,70,630,45,Blaze Pokémon,80,1059860,1.9,80,Blaziken,88.1,fire,130,80,100,52,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,70,5120,70,310,45,Mud Fish Pokémon,50,1059860,0.4,50,Mudkip,88.1,water,50,50,40,7.6,3,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,85,5120,70,405,45,Mud Fish Pokémon,70,1059860,0.7,70,Marshtomp,88.1,water,60,70,50,28,3,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,150,5120,70,635,45,Mud Fish Pokémon,110,1059860,1.5,100,Swampert,88.1,water,95,110,70,81.9,3,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,55,3840,70,220,255,Bite Pokémon,35,1000000,0.5,35,Poochyena,50,dark,30,30,35,13.6,3,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,90,3840,70,420,127,Bite Pokémon,70,1000000,1,70,Mightyena,50,dark,60,60,70,37,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,30,3840,70,240,255,Tiny Racoon Pokémon,41,1000000,0.4,38,Zigzagoon,50,normal,30,41,60,17.5,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,70,3840,70,420,90,Rush Pokémon,61,1000000,0.5,78,Linoone,50,normal,50,61,100,32.5,3,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,45,3840,70,195,255,Worm Pokémon,35,1000000,0.3,45,Wurmple,50,bug,20,30,20,3.6,3,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,35,3840,70,205,120,Cocoon Pokémon,55,1000000,0.6,50,Silcoon,50,bug,25,25,15,10,3,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,70,3840,70,395,45,Butterfly Pokémon,50,1000000,1,60,Beautifly,50,bug,100,50,65,28.4,3,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,35,3840,70,205,120,Cocoon Pokémon,55,1000000,0.7,50,Cascoon,50,bug,25,25,15,11.5,3,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,50,3840,70,385,45,Poison Moth Pokémon,70,1000000,1.2,60,Dustox,50,bug,50,90,65,31.6,3,0 +2,1,1,1,1,1,1,2,1,1,0.5,1,1,2,1,1,0.5,0.25,30,3840,70,220,255,Water Weed Pokémon,30,1059860,0.5,40,Lotad,50,water,40,50,30,2.6,3,0 +2,1,1,1,1,1,1,2,1,1,0.5,1,1,2,1,1,0.5,0.25,50,3840,70,340,120,Jolly Pokémon,50,1059860,1.2,60,Lombre,50,water,60,70,50,32.5,3,0 +2,1,1,1,1,1,1,2,1,1,0.5,1,1,2,1,1,0.5,0.25,70,3840,70,480,45,Carefree Pokémon,70,1059860,1.5,80,Ludicolo,50,water,90,100,70,55,3,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,40,3840,70,220,255,Acorn Pokémon,50,1059860,0.5,40,Seedot,50,grass,30,30,30,4,3,0 +4,0.5,1,0.5,2,2,2,2,0.5,0.5,0.5,2,1,2,0,1,1,0.5,70,3840,70,340,120,Wily Pokémon,40,1059860,1,70,Nuzleaf,50,grass,60,40,60,28,3,0 +4,0.5,1,0.5,2,2,2,2,0.5,0.5,0.5,2,1,2,0,1,1,0.5,100,3840,70,480,45,Wickid Pokémon,60,1059860,1.3,90,Shiftry,50,grass,90,60,80,59.6,3,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,55,3840,70,270,200,TinySwallow Pokémon,30,1059860,0.3,40,Taillow,50,normal,30,30,85,2.3,3,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,85,3840,70,455,45,Swallow Pokémon,60,1059860,0.7,60,Swellow,50,normal,75,50,125,19.8,3,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,30,5120,70,270,190,Seagull Pokémon,30,1000000,0.6,40,Wingull,50,water,55,30,85,9.5,3,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,50,5120,70,440,45,Water Bird Pokémon,100,1000000,1.2,60,Pelipper,50,water,95,70,65,28,3,0 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,25,5120,35,198,235,Feeling Pokémon,25,1250000,0.4,28,Ralts,50,psychic,45,35,40,6.6,3,0 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,35,5120,35,278,120,Emotion Pokémon,35,1250000,0.8,38,Kirlia,50,psychic,65,55,50,20.2,3,0 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,85,5120,35,618,45,Embrace Pokémon,65,1250000,1.6,68,Gardevoir,50,psychic,165,135,100,48.4,3,0 +1,1,1,2,1,0.5,1,2,1,1,0.5,0.5,1,1,1,2,0.5,0.5,30,3840,70,269,200,Pond Skater Pokémon,32,1000000,0.5,40,Surskit,50,bug,50,52,65,1.7,3,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,60,3840,70,454,75,Eyeball Pokémon,62,1000000,0.8,70,Masquerain,50,bug,100,82,80,3.6,3,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,40,3840,70,295,255,Mushroom Pokémon,60,1640000,0.4,60,Shroomish,50,grass,40,60,35,4.5,3,0 +1,0.5,1,0.5,2,1,2,4,1,0.5,0.5,2,1,2,2,0.5,1,0.5,130,3840,70,460,90,Mushroom Pokémon,80,1640000,1.2,60,Breloom,50,grass,60,60,70,39.2,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,3840,70,280,255,Slacker Pokémon,60,1250000,0.8,60,Slakoth,50,normal,35,35,30,24,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,3840,70,440,120,Wild Monkey Pokémon,80,1250000,1.4,80,Vigoroth,50,normal,55,55,90,46.5,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,160,3840,70,670,45,Lazy Pokémon,100,1250000,2,150,Slaking,50,normal,95,65,100,130.5,3,0 +1,1,1,0,1,0.5,2,2,1,1,0.5,2,1,0.5,1,1,1,2,45,3840,70,266,255,Trainee Pokémon,90,600000,0.5,31,Nincada,50,bug,30,30,40,5.5,3,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,90,3840,70,456,120,Ninja Pokémon,45,600000,0.8,61,Ninjask,50,bug,50,50,160,12,3,0 +0.5,2,1,1,1,0,2,2,2,0.5,0.5,1,0,0.5,1,2,1,1,90,3840,70,236,45,Shed Pokémon,45,600000,0.8,1,Shedinja,,bug,30,30,40,1.2,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,51,5120,70,240,190,Whisper Pokémon,23,1059860,0.6,64,Whismur,50,normal,51,23,28,16.3,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,71,5120,70,360,120,Big Voice Pokémon,43,1059860,1,84,Loudred,50,normal,71,43,48,40.5,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,91,5120,70,490,45,Loud Noise Pokémon,63,1059860,1.5,104,Exploud,50,normal,91,73,68,84,3,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,60,5120,70,237,180,Guts Pokémon,30,1640000,1,72,Makuhita,75.4,fighting,20,30,25,86.4,3,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,120,5120,70,474,200,Arm Thrust Pokémon,60,1640000,2.3,144,Hariyama,75.4,fighting,40,60,50,253.8,3,0 +0.5,0.5,0,1,1,1,1,1,0,1,1,1,1,2,1,1,2,1,20,2560,70,190,150,Polka Dot Pokémon,40,800000,0.2,50,Azurill,24.6,normal,20,40,20,2,3,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,45,5120,70,375,255,Compass Pokémon,135,1000000,1,30,Nosepass,50,rock,45,90,30,97,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,45,3840,70,260,255,Kitten Pokémon,45,800000,0.6,50,Skitty,24.6,normal,35,35,50,11,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,65,3840,70,400,60,Prim Pokémon,65,800000,1.1,70,Delcatty,24.6,normal,55,55,90,32.6,3,0 +1,1,1,1,2,0,1,1,1,1,1,1,0,0.5,0,1,1,1,85,6400,35,480,45,Darkness Pokémon,125,1059860,0.5,50,Sableye,50,dark,85,115,20,11,3,0 +0.25,0.5,0,1,0.5,1,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,1,1,105,5120,70,480,45,Deceiver Pokémon,125,800000,0.6,50,Mawile,50,steel,55,95,50,11.5,3,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,70,8960,35,330,180,Iron Armor Pokémon,100,1250000,0.4,50,Aron,50,steel,40,40,30,60,3,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,90,8960,35,430,90,Iron Armor Pokémon,140,1250000,0.9,60,Lairon,50,steel,50,50,40,120,3,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,140,8960,35,630,45,Iron Armor Pokémon,230,1250000,2.1,70,Aggron,50,steel,60,80,50,360,3,0 +1,1,1,1,2,0.5,1,2,2,1,1,1,1,1,1,0.5,1,1,40,5120,70,280,180,Meditate Pokémon,55,1000000,0.6,30,Meditite,50,fighting,40,55,60,11.2,3,0 +1,1,1,1,2,0.5,1,2,2,1,1,1,1,1,1,0.5,1,1,100,5120,70,510,90,Meditate Pokémon,85,1000000,1.3,60,Medicham,50,fighting,80,85,100,31.5,3,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,45,5120,70,295,120,Lightning Pokémon,40,1250000,0.6,40,Electrike,50,electric,65,40,65,15.2,3,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,75,5120,70,575,45,Discharge Pokémon,80,1250000,1.5,70,Manectric,50,electric,135,80,135,40.2,3,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,50,5120,70,405,200,Cheering Pokémon,40,1000000,0.4,60,Plusle,50,electric,85,75,95,4.2,3,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,40,5120,70,405,200,Cheering Pokémon,50,1000000,0.4,60,Minun,50,electric,75,85,95,4.2,3,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,73,3840,70,430,150,Firefly Pokémon,75,600000,0.7,65,Volbeat,100,bug,47,85,85,17.7,3,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,47,3840,70,430,150,Firefly Pokémon,75,1640000,0.6,65,Illumise,0,bug,73,85,85,17.7,3,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,60,5120,70,400,150,Thorn Pokémon,45,1059860,0.3,50,Roselia,50,grass,100,80,65,2,3,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,43,5120,70,302,225,Stomach Pokémon,53,1640000,0.4,70,Gulpin,50,poison,43,53,40,10.3,3,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,73,5120,70,467,75,Poison Bag Pokémon,83,1640000,1.7,100,Swalot,50,poison,73,83,55,80,3,0 +2,0.5,1,2,2,2,0.5,1,0.5,2,1,0.5,1,1,0,1,0.5,0.5,90,5120,35,305,225,Savage Pokémon,20,1250000,0.8,45,Carvanha,50,water,65,20,65,20.8,3,0 +2,0.5,1,2,2,2,0.5,1,0.5,2,1,0.5,1,1,0,1,0.5,0.5,140,5120,35,560,60,Brutal Pokémon,70,1250000,1.8,70,Sharpedo,50,water,110,65,105,88.8,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,70,10240,70,400,125,Ball Whale Pokémon,35,1640000,2,130,Wailmer,50,water,70,35,60,130,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,90,10240,70,500,60,Float Whale Pokémon,45,1640000,14.5,170,Wailord,50,water,90,45,60,398,3,0 +0.5,1,1,0,0.5,1,0.5,1,1,1,2,1,1,0.5,1,1,0.5,4,60,5120,70,305,255,Numb Pokémon,40,1000000,0.7,60,Numel,50,fire,65,45,35,24,3,0 +0.5,1,1,0,0.5,1,0.5,1,1,1,2,1,1,0.5,1,1,0.5,4,120,5120,70,560,150,Eruption Pokémon,100,1000000,1.9,70,Camerupt,50,fire,145,105,20,220,3,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,85,5120,70,470,90,Coal Pokémon,140,1000000,0.5,70,Torkoal,50,fire,85,70,20,80.4,3,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,25,5120,70,330,255,Bounce Pokémon,35,800000,0.7,60,Spoink,50,psychic,70,80,60,30.6,3,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,45,5120,70,470,60,Manipulate Pokémon,65,800000,0.9,80,Grumpig,50,psychic,90,110,80,71.5,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,3840,70,360,255,Spot Panda Pokémon,60,800000,1.1,60,Spinda,50,normal,60,60,60,5,3,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,100,5120,70,290,255,Ant Pit Pokémon,45,1059860,0.7,45,Trapinch,50,ground,45,45,10,15,3,0 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,70,5120,70,340,120,Vibration Pokémon,50,1059860,1.1,50,Vibrava,50,ground,50,50,70,15.3,3,0 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,100,5120,70,520,45,Mystic Pokémon,80,1059860,2,80,Flygon,50,ground,80,80,100,82,3,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,85,5120,35,335,190,Cactus Pokémon,40,1059860,0.4,50,Cacnea,50,grass,85,40,35,51.3,3,0 +4,0.5,1,0.5,2,2,2,2,0.5,0.5,0.5,2,1,2,0,1,1,0.5,115,5120,35,475,60,Scarecrow Pokémon,60,1059860,1.3,70,Cacturne,50,grass,115,60,55,77.4,3,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,40,5120,70,310,255,Cotton Bird Pokémon,60,600000,0.4,45,Swablu,50,normal,40,75,50,1.2,3,0 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,110,5120,70,590,45,Humming Pokémon,110,600000,1.1,75,Altaria,50,dragon,110,105,80,20.6,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,115,5120,70,458,90,Cat Ferret Pokémon,60,600000,1.3,73,Zangoose,50,normal,60,60,90,40.3,3,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,100,5120,70,458,90,Fang Snake Pokémon,60,1640000,2.7,73,Seviper,50,poison,100,60,65,52.5,3,0 +2,2,1,1,1,1,0.5,0.5,2,2,2,1,0.5,0.5,0.5,1,2,2,55,6400,70,460,45,Meteorite Pokémon,65,800000,1,90,Lunatone,,rock,95,85,70,168,3,0 +2,2,1,1,1,1,0.5,0.5,2,2,2,1,0.5,0.5,0.5,1,2,2,95,6400,70,460,45,Meteorite Pokémon,85,800000,1.2,90,Solrock,,rock,55,65,70,154,3,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,48,5120,70,288,190,Whiskers Pokémon,43,1000000,0.4,50,Barboach,50,water,46,41,60,1.9,3,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,78,5120,70,468,75,Whiskers Pokémon,73,1000000,0.9,110,Whiscash,50,water,76,71,60,23.6,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,80,3840,70,308,205,Ruffian Pokémon,65,1640000,0.6,43,Corphish,50,water,50,35,35,11.5,3,0 +2,0.5,1,2,2,2,0.5,1,0.5,2,1,0.5,1,1,0,1,0.5,0.5,120,3840,70,468,155,Rogue Pokémon,85,1640000,1.1,63,Crawdaunt,50,water,90,55,55,32.8,3,0 +2,2,1,0,1,0.5,1,1,2,2,1,2,1,0.5,0.5,0.5,1,2,40,5120,70,300,255,Clay Doll Pokémon,55,1000000,0.5,40,Baltoy,,ground,40,70,55,21.5,3,0 +2,2,1,0,1,0.5,1,1,2,2,1,2,1,0.5,0.5,0.5,1,2,70,5120,70,500,90,Clay Doll Pokémon,105,1000000,1.5,60,Claydol,,ground,70,120,75,108,3,0 +2,1,1,0.5,1,2,1,1,1,1,1,2,0.5,1,1,1,2,1,41,7680,70,355,45,Sea Lily Pokémon,77,600000,1,66,Lileep,88.1,rock,61,87,23,23.8,3,0 +2,1,1,0.5,1,2,1,1,1,1,1,2,0.5,1,1,1,2,1,81,7680,70,495,45,Barnacle Pokémon,97,600000,1.5,86,Cradily,88.1,rock,81,107,43,60.4,3,0 +1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,2,2,2,95,7680,70,355,45,Old Shrimp Pokémon,50,600000,0.7,45,Anorith,88.1,rock,40,50,75,12.5,3,0 +1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,2,2,2,125,7680,70,495,45,Plate Pokémon,100,600000,1.5,75,Armaldo,88.1,rock,70,80,45,68.2,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,15,5120,70,200,255,Fish Pokémon,20,600000,0.6,20,Feebas,50,water,10,55,80,7.4,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,60,5120,70,540,60,Tender Pokémon,79,600000,6.2,95,Milotic,50,water,100,125,81,162,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,70,6400,70,420,45,Weather Pokémon,70,1000000,0.3,70,Castform,50,normal,70,70,70,0.8,3,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,90,5120,70,440,200,Color Swap Pokémon,70,1059860,1,60,Kecleon,50,normal,60,120,40,22,3,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,75,6400,35,295,225,Puppet Pokémon,35,800000,0.6,44,Shuppet,50,ghost,63,33,45,2.3,3,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,165,6400,35,555,45,Marionette Pokémon,75,800000,1.1,64,Banette,50,ghost,93,83,75,12.5,3,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,40,6400,35,295,190,Requiem Pokémon,90,800000,0.8,20,Duskull,50,ghost,30,90,25,15,3,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,70,6400,35,455,90,Beckon Pokémon,130,800000,1.6,40,Dusclops,50,ghost,60,130,25,30.6,3,0 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,68,6400,70,460,200,Fruit Pokémon,83,1250000,2,99,Tropius,50,grass,72,87,51,100,3,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,50,6400,70,455,45,Wind Chime Pokémon,80,800000,0.6,75,Chimecho,50,psychic,95,90,65,1,3,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,150,6400,35,565,30,Disaster Pokémon,60,1059860,1.2,65,Absol,50,dark,115,60,115,47,3,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,23,5120,70,260,125,Bright Pokémon,48,1000000,0.6,95,Wynaut,50,psychic,23,48,23,14,3,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,50,5120,70,300,190,Snow Hat Pokémon,50,1000000,0.7,50,Snorunt,50,ice,50,50,50,16.8,3,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,120,5120,70,580,75,Face Pokémon,80,1000000,1.5,80,Glalie,50,ice,120,80,100,256.5,3,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,40,5120,70,290,255,Clap Pokémon,50,1059860,0.8,70,Spheal,50,ice,55,50,25,39.5,3,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,60,5120,70,410,120,Ball Roll Pokémon,70,1059860,1.1,90,Sealeo,50,ice,75,70,45,87.6,3,0 +1,1,1,2,1,2,1,1,1,2,1,0.25,1,1,1,2,1,0.5,80,5120,70,530,45,Ice Break Pokémon,90,1059860,1.4,110,Walrein,50,ice,95,90,65,150.6,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,64,5120,70,345,255,Bivalve Pokémon,85,600000,0.4,35,Clamperl,50,water,74,55,32,52.5,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,104,5120,70,485,60,Deep Sea Pokémon,105,600000,1.7,55,Huntail,50,water,94,75,52,27,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,84,5120,70,485,60,South Sea Pokémon,105,600000,1.8,55,Gorebyss,50,water,114,75,52,22.6,3,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,90,10240,70,485,25,Longevity Pokémon,130,1250000,1,100,Relicanth,88.1,water,45,65,55,23.4,3,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,30,5120,70,330,225,Rendezvous Pokémon,55,800000,0.6,43,Luvdisc,24.6,water,40,65,97,8.7,3,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,75,10240,35,300,45,Rock Head Pokémon,60,1250000,0.6,45,Bagon,50,dragon,40,30,50,42.1,3,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,95,10240,35,420,45,Endurance Pokémon,100,1250000,1.1,65,Shelgon,50,dragon,60,50,50,110.5,3,0 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,145,10240,35,700,45,Dragon Pokémon,130,1250000,1.5,95,Salamence,50,dragon,120,90,120,102.6,3,0 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,55,10240,35,300,3,Iron Ball Pokémon,80,1250000,0.6,40,Beldum,,steel,35,60,30,95.2,3,0 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,75,10240,35,420,3,Iron Claw Pokémon,100,1250000,1.2,60,Metang,,steel,55,80,50,202.5,3,0 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,145,10240,35,700,3,Iron Leg Pokémon,150,1250000,1.6,80,Metagross,,steel,105,110,110,550,3,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,100,20480,35,580,3,Rock Peak Pokémon,200,1250000,1.7,80,Regirock,,rock,50,100,50,230,3,1 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,50,20480,35,580,3,Iceberg Pokémon,100,1250000,1.8,80,Regice,,ice,100,200,50,175,3,1 +0.5,1,0.5,1,0.5,2,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,0.5,1,75,20480,35,580,3,Iron Pokémon,150,1250000,1.9,80,Registeel,,steel,75,150,50,205,3,1 +2,2,2,0.5,2,0.5,0.5,1,2,0.5,1,2,1,1,0.5,1,1,0.5,100,30720,90,700,3,Eon Pokémon,120,1250000,1.4,80,Latias,0,dragon,140,150,110,40,3,1 +2,2,2,0.5,2,0.5,0.5,1,2,0.5,1,2,1,1,0.5,1,1,0.5,130,30720,90,700,3,Eon Pokémon,100,1250000,2,80,Latios,100,dragon,160,120,110,60,3,1 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,150,30720,0,770,3,Sea Basin Pokémon,90,1250000,4.5,100,Kyogre,,water,180,160,90,352,3,1 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,180,30720,0,770,3,Continent Pokémon,160,1250000,3.5,100,Groudon,,ground,150,90,90,950,3,1 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,180,30720,0,780,45,Sky High Pokémon,100,1250000,7,105,Rayquaza,,dragon,180,100,115,206.5,3,1 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,100,30720,100,600,3,Wish Pokémon,100,1250000,0.3,100,Jirachi,,steel,100,100,100,1.1,3,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,95,30720,0,600,3,DNA Pokémon,90,1250000,1.7,50,Deoxys,,psychic,95,90,180,60.8,3,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,68,5120,70,318,45,Tiny Leaf Pokémon,64,1059860,0.4,55,Turtwig,88.1,grass,45,55,31,10.2,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,89,5120,70,405,45,Grove Pokémon,85,1059860,1.1,75,Grotle,88.1,grass,55,65,36,97,4,0 +2,1,1,0,1,1,2,2,1,1,0.5,4,1,1,1,0.5,1,1,109,5120,70,525,45,Continent Pokémon,105,1059860,2.2,95,Torterra,88.1,grass,75,85,56,310,4,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,58,5120,70,309,45,Chimp Pokémon,44,1059860,0.5,44,Chimchar,88.1,fire,58,44,61,6.2,4,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,78,5120,70,405,45,Playful Pokémon,52,1059860,0.9,64,Monferno,88.1,fire,78,52,81,22,4,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,104,5120,70,534,45,Flame Pokémon,71,1059860,1.2,76,Infernape,88.1,fire,104,71,108,55,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,51,5120,70,314,45,Penguin Pokémon,53,1059860,0.4,53,Piplup,88.1,water,61,56,40,5.2,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,66,5120,70,405,45,Penguin Pokémon,68,1059860,0.8,64,Prinplup,88.1,water,81,76,50,23,4,0 +0.5,1,0.5,2,0.5,2,1,0.5,1,1,2,0.25,0.5,0,0.5,0.5,0.25,0.5,86,5120,70,530,45,Emperor Pokémon,88,1059860,1.7,84,Empoleon,88.1,water,111,101,60,84.5,4,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,55,3840,70,245,255,Starling Pokémon,30,1059860,0.3,40,Starly,50,normal,30,30,60,2,4,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,75,3840,70,340,120,Starling Pokémon,50,1059860,0.6,55,Staravia,50,normal,40,40,80,15.5,4,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,120,3840,70,485,45,Predator Pokémon,70,1059860,1.2,85,Staraptor,50,normal,50,60,100,24.9,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,45,3840,70,250,255,Plump Mouse Pokémon,40,1000000,0.5,59,Bidoof,50,normal,35,40,31,20,4,0 +1,1,1,2,1,2,0.5,1,0,2,1,0.5,1,1,1,1,0.5,0.5,85,3840,70,410,127,Beaver Pokémon,60,1000000,1,79,Bibarel,50,normal,55,60,71,31.5,4,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,25,3840,70,194,255,Cricket Pokémon,41,1059860,0.3,37,Kricketot,50,bug,25,41,25,2.2,4,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,85,3840,70,384,45,Cricket Pokémon,51,1059860,1,77,Kricketune,50,bug,55,51,65,25.5,4,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,65,5120,70,263,235,Flash Pokémon,34,1059860,0.5,45,Shinx,50,electric,40,34,45,9.5,4,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,85,5120,100,363,120,Spark Pokémon,49,1059860,0.9,60,Luxio,50,electric,60,49,60,30.5,4,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,120,5120,70,523,45,Gleam Eyes Pokémon,79,1059860,1.4,80,Luxray,50,electric,95,79,70,42,4,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,30,5120,70,280,255,Bud Pokémon,35,1059860,0.2,40,Budew,50,grass,50,70,55,1.2,4,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,70,5120,70,515,75,Bouquet Pokémon,65,1059860,0.9,60,Roserade,50,grass,125,105,90,14.5,4,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,125,7680,70,350,45,Head Butt Pokémon,40,600000,0.9,67,Cranidos,88.1,rock,30,30,58,31.5,4,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,165,7680,70,495,45,Head Butt Pokémon,60,600000,1.6,97,Rampardos,88.1,rock,65,50,58,102.5,4,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,42,7680,70,350,45,Shield Pokémon,118,600000,0.5,30,Shieldon,88.1,rock,42,88,30,57,4,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,52,7680,70,495,45,Shield Pokémon,168,600000,1.3,60,Bastiodon,88.1,rock,47,138,30,149.5,4,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,29,3840,70,224,120,Bagworm Pokémon,45,1000000,0.2,40,Burmy,50,bug,29,45,36,3.4,4,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,69,3840,70,424,45,Bagworm Pokémon,95,1000000,0.5,60,Wormadam,0,bug,69,95,36,6.5,4,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,94,3840,70,424,45,Moth Pokémon,50,1000000,0.9,70,Mothim,100,bug,94,50,66,23.3,4,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,30,3840,70,244,120,Tiny Bee Pokémon,42,1059860,0.3,30,Combee,88.1,bug,30,42,70,5.5,4,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,80,3840,70,474,45,Beehive Pokémon,102,1059860,1.2,70,Vespiquen,0,bug,80,102,40,38.5,4,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,45,2560,100,405,200,EleSquirrel Pokémon,70,1000000,0.4,60,Pachirisu,50,electric,45,90,95,3.9,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,65,5120,70,330,190,Sea Weasel Pokémon,35,1000000,0.7,55,Buizel,50,water,60,30,85,29.5,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,105,5120,70,495,75,Sea Weasel Pokémon,55,1000000,1.1,85,Floatzel,50,water,85,50,115,33.5,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,35,5120,70,275,190,Cherry Pokémon,45,1000000,0.4,45,Cherubi,50,grass,62,53,35,3.3,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,60,5120,70,450,75,Blossom Pokémon,70,1000000,0.5,70,Cherrim,50,grass,87,78,85,9.3,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,48,5120,70,325,190,Sea Slug Pokémon,48,1000000,0.3,76,Shellos,50,water,57,62,34,6.3,4,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,83,5120,70,475,75,Sea Slug Pokémon,68,1000000,0.9,111,Gastrodon,50,water,92,82,39,29.9,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,100,5120,100,482,45,Long Tail Pokémon,66,800000,1.2,75,Ambipom,50,normal,60,66,115,20.3,4,0 +0.25,2,1,2,1,0,1,1,2,0.5,0,2,0,0.5,1,2,1,1,50,7680,70,348,125,Balloon Pokémon,34,1640000,0.4,90,Drifloon,50,ghost,60,44,70,1.2,4,0 +0.25,2,1,2,1,0,1,1,2,0.5,0,2,0,0.5,1,2,1,1,80,7680,70,498,60,Blimp Pokémon,44,1640000,1.2,150,Drifblim,50,ghost,90,54,80,15,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,66,5120,0,350,190,Rabbit Pokémon,44,1000000,0.4,55,Buneary,50,normal,44,56,85,5.5,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,136,5120,140,580,60,Rabbit Pokémon,94,1000000,1.2,65,Lopunny,50,normal,54,96,135,33.3,4,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,60,6400,35,495,45,Magical Pokémon,60,800000,0.9,60,Mismagius,50,ghost,105,105,105,4.4,4,0 +1,0.5,1,2,2,1,1,1,0.5,0.5,0,2,1,1,0,2,1,1,125,5120,35,505,30,Big Boss Pokémon,52,1059860,0.9,100,Honchkrow,50,dark,105,52,71,27.3,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,55,5120,70,310,190,Catty Pokémon,42,800000,0.5,49,Glameow,24.6,normal,42,37,85,3.9,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,82,5120,70,452,75,Tiger Cat Pokémon,64,800000,1,71,Purugly,24.6,normal,64,59,112,43.8,4,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,30,6400,70,285,120,Bell Pokémon,50,800000,0.2,45,Chingling,50,psychic,65,50,45,0.6,4,0 +1,0.5,1,1,1,1,1,1,0.5,0.5,2,1,1,0.5,0,1,1,1,63,5120,70,329,225,Skunk Pokémon,47,1000000,0.4,63,Stunky,50,poison,41,41,74,19.2,4,0 +1,0.5,1,1,1,1,1,1,0.5,0.5,2,1,1,0.5,0,1,1,1,93,5120,70,479,60,Skunk Pokémon,67,1000000,1,103,Skuntank,50,poison,71,61,84,38,4,0 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,24,5120,70,300,255,Bronze Pokémon,86,1000000,0.5,57,Bronzor,,steel,24,86,23,60.5,4,0 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,89,5120,70,500,90,Bronze Bell Pokémon,116,1000000,1.3,67,Bronzong,,steel,79,116,33,187,4,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,80,5120,70,290,255,Bonsai Pokémon,95,1000000,0.5,50,Bonsly,50,rock,10,45,10,15,4,0 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,25,6400,70,310,145,Mime Pokémon,45,1000000,0.6,20,Mime Jr.,50,psychic,70,90,60,13,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,5,10240,140,220,130,Playhouse Pokémon,5,800000,0.6,100,Happiny,0,normal,15,65,30,24.4,4,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,65,5120,35,411,30,Music Note Pokémon,45,1059860,0.5,76,Chatot,50,normal,92,42,91,1.9,4,0 +1,1,1,1,2,0,1,1,1,1,1,1,0,0.5,0,1,1,1,92,7680,70,485,100,Forbidden Pokémon,108,1000000,1,50,Spiritomb,50,ghost,92,108,35,108,4,0 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,70,10240,70,300,45,Land Shark Pokémon,45,1250000,0.7,58,Gible,50,dragon,40,45,42,20.5,4,0 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,90,10240,70,410,45,Cave Pokémon,65,1250000,1.4,68,Gabite,50,dragon,50,55,82,56,4,0 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,170,10240,70,700,45,Mach Pokémon,115,1250000,1.9,108,Garchomp,50,dragon,120,95,92,95,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,85,10240,70,390,50,Big Eater Pokémon,40,1250000,0.6,135,Munchlax,88.1,normal,40,85,5,105,4,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,70,6400,70,285,75,Emanation Pokémon,40,1059860,0.7,40,Riolu,88.1,fighting,35,40,60,20.2,4,0 +0.25,0.5,0.5,1,1,2,2,1,1,0.5,2,0.5,0.5,0,1,0.25,0.5,1,145,6400,70,625,45,Aura Pokémon,88,1059860,1.2,70,Lucario,88.1,fighting,140,70,112,54,4,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,72,7680,70,330,140,Hippo Pokémon,78,1250000,0.8,68,Hippopotas,50,ground,38,42,32,49.5,4,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,112,7680,70,525,60,Heavyweight Pokémon,118,1250000,2,108,Hippowdon,50,ground,68,72,47,300,4,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,50,5120,70,330,120,Scorpion Pokémon,90,1250000,0.8,40,Skorupi,50,poison,30,55,65,12,4,0 +1,0.5,1,1,1,1,1,1,0.5,0.5,2,1,1,0.5,0,1,1,1,90,5120,70,500,45,Ogre Scorp Pokémon,110,1250000,1.3,70,Drapion,50,poison,60,75,95,61.5,4,0 +0.25,0.5,1,1,1,0.5,1,2,1,0.5,2,1,1,0.5,4,0.5,1,1,61,2560,100,300,140,Toxic Mouth Pokémon,40,1000000,0.7,48,Croagunk,50,poison,61,40,50,23,4,0 +0.25,0.5,1,1,1,0.5,1,2,1,0.5,2,1,1,0.5,4,0.5,1,1,106,5120,70,490,75,Toxic Mouth Pokémon,65,1000000,1.3,83,Toxicroak,50,poison,86,65,85,44.4,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,100,6400,70,454,200,Bug Catcher Pokémon,72,1250000,1.4,74,Carnivine,50,grass,90,72,46,27,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,49,5120,70,330,190,Wing Fish Pokémon,56,600000,0.4,49,Finneon,50,water,49,61,66,7,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,69,5120,70,460,75,Neon Pokémon,76,600000,1.2,69,Lumineon,50,water,69,86,91,24,4,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,20,6400,70,345,25,Kite Pokémon,50,1250000,1,45,Mantyke,50,water,60,120,50,65,4,0 +2,1,1,0.5,1,2,4,2,1,0.5,0.5,1,1,2,1,2,2,0.5,62,5120,70,334,120,Frosted Tree Pokémon,50,1250000,1,60,Snover,50,grass,62,60,40,50.5,4,0 +2,1,1,0.5,1,2,4,2,1,0.5,0.5,1,1,2,1,2,2,0.5,132,5120,70,594,60,Frosted Tree Pokémon,105,1250000,2.2,90,Abomasnow,50,grass,132,105,30,135.5,4,0 +2,0.5,1,1,2,4,2,1,0.5,1,1,0.5,1,1,0,2,2,1,120,5120,35,510,45,Sharp Claw Pokémon,65,1059860,1.1,70,Weavile,50,dark,45,85,125,34,4,0 +0.5,1,0.5,0.5,0.5,2,2,0.25,1,0.5,4,0.5,0.5,0,0.5,0.5,0.25,1,70,5120,70,535,30,Magnet Area Pokémon,115,1000000,1.2,70,Magnezone,,electric,130,90,60,180,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,85,5120,70,515,30,Licking Pokémon,95,1000000,1.7,110,Lickilicky,50,normal,80,95,50,140,4,0 +1,1,1,0,1,2,0.5,0.5,1,4,2,2,0.5,0.25,1,0.5,2,4,140,5120,70,535,30,Drill Pokémon,130,1250000,2.4,115,Rhyperior,50,ground,55,55,40,282.8,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,100,5120,70,535,30,Vine Pokémon,125,1000000,2,100,Tangrowth,50,grass,110,50,50,128.6,4,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,123,6400,70,540,30,Thunderbolt Pokémon,67,1000000,1.8,75,Electivire,75.4,electric,95,85,95,138.6,4,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,95,6400,70,540,30,Blast Pokémon,67,1000000,1.6,75,Magmortar,75.4,fire,125,95,83,68,4,0 +0.25,0.5,0,2,1,0.25,1,1,1,0.5,0,2,1,2,1,2,2,1,50,2560,70,545,30,Jubilee Pokémon,95,800000,1.5,85,Togekiss,88.1,fairy,120,115,80,38,4,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,76,5120,70,515,30,Ogre Darner Pokémon,86,1000000,1.9,86,Yanmega,50,bug,116,56,95,51.5,4,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,110,8960,35,525,45,Verdant Pokémon,130,1000000,1,65,Leafeon,88.1,grass,60,65,95,25.5,4,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,60,8960,35,525,45,Fresh Snow Pokémon,110,1000000,0.8,65,Glaceon,88.1,ice,130,95,65,25.9,4,0 +0.5,1,1,0,1,0.5,1,1,1,1,0,4,1,0.5,1,1,1,2,95,5120,70,510,30,Fang Scorp Pokémon,125,1059860,2,75,Gliscor,50,ground,45,75,95,42.5,4,0 +1,1,1,0,1,2,2,1,1,2,1,1,1,0.5,1,1,2,2,130,5120,70,530,50,Twin Tusk Pokémon,80,1250000,2.5,110,Mamoswine,50,ice,70,60,80,291,4,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,5120,70,535,30,Virtual Pokémon,70,1000000,0.9,85,Porygon-Z,,normal,135,75,90,34,4,0 +1,1,1,1,2,0.5,1,2,2,1,1,1,1,1,1,0.5,1,1,165,5120,35,618,45,Blade Pokémon,95,1250000,1.6,68,Gallade,100,psychic,65,115,110,52,4,0 +0.5,1,0.5,1,0.5,4,1,0.25,1,1,4,0.5,0.25,0,0.5,0.5,1,2,55,5120,70,525,60,Compass Pokémon,145,1000000,1.4,60,Probopass,50,rock,75,150,40,340,4,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,100,6400,35,525,45,Gripper Pokémon,135,800000,2.2,45,Dusknoir,50,ghost,65,135,45,106.6,4,0 +0.5,2,1,1,1,0,2,1,2,1,1,0.5,0,0.5,1,2,2,1,80,5120,70,480,75,Snow Land Pokémon,70,1000000,1.3,70,Froslass,0,ice,80,70,110,26.6,4,0 +0.5,2,1,0.5,1,0,1,0.5,2,1,2,1,0,0.5,1,1,0.5,1,65,5120,70,520,45,Plasma Pokémon,107,1000000,0.3,50,Rotom,,electric,105,107,86,0.3,4,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,75,20480,140,580,3,Knowledge Pokémon,130,1250000,0.3,75,Uxie,,psychic,75,130,95,0.3,4,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,105,20480,140,580,3,Emotion Pokémon,105,1250000,0.3,80,Mesprit,,psychic,105,105,80,0.3,4,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,125,20480,140,580,3,Willpower Pokémon,70,1250000,0.3,75,Azelf,,psychic,125,70,115,0.3,4,1 +0.5,1,1,0.5,1,2,1,0.5,1,0.25,2,1,0.5,0,0.5,0.5,0.5,0.5,120,30720,0,680,3,Temporal Pokémon,120,1250000,5.4,100,Dialga,,steel,150,100,90,683,4,1 +1,1,2,1,2,1,0.25,1,1,1,1,1,1,1,1,1,0.5,0.25,120,30720,0,680,3,Spatial Pokémon,100,1250000,4.2,90,Palkia,,water,150,120,100,336,4,1 +0.25,1,0.5,1,0.25,2,1,0.5,1,0.25,4,0.25,0.5,0,0.5,1,0.25,2,90,2560,100,600,3,Lava Dome Pokémon,106,1250000,1.7,91,Heatran,50,fire,130,106,77,430,4,1 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,160,30720,0,670,3,Colossal Pokémon,110,1250000,3.7,110,Regigigas,,normal,80,110,100,420,4,1 +0.5,2,2,0.5,2,0,0.5,1,2,0.5,1,2,0,0.5,1,1,1,0.5,120,30720,0,680,3,Renegade Pokémon,100,1250000,4.5,150,Giratina,,ghost,120,100,90,750,4,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,70,30720,100,600,3,Lunar Pokémon,120,1250000,1.5,120,Cresselia,0,psychic,75,130,85,85.6,4,1 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,80,10240,70,480,30,Sea Drifter Pokémon,80,1250000,0.4,80,Phione,,water,80,80,80,3.1,4,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,100,2560,70,600,3,Seafaring Pokémon,100,1250000,0.3,100,Manaphy,,water,100,100,100,1.4,4,1 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,90,30720,0,600,3,Pitch-Black Pokémon,90,1250000,1.5,70,Darkrai,,dark,135,90,125,50.5,4,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,103,30720,100,600,45,Gratitude Pokémon,75,1059860,0.2,100,Shaymin,,grass,120,75,127,2.1,4,1 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,120,30720,0,720,3,Alpha Pokémon,120,1250000,3.2,120,Arceus,,normal,120,120,120,320,4,1 +1,2,1,1,0.5,0.5,0.5,1,2,0.5,2,0.5,1,1,0.5,2,0.5,2,100,30720,100,600,3,Victory Pokémon,100,1250000,0.4,100,Victini,,psychic,100,100,100,4,5,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,45,5120,70,308,45,Grass Snake Pokémon,55,1059860,0.6,45,Snivy,88.1,grass,45,55,63,8.1,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,60,5120,70,413,45,Grass Snake Pokémon,75,1059860,0.8,60,Servine,88.1,grass,60,75,83,16,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,75,5120,70,528,45,Regal Pokémon,95,1059860,3.3,75,Serperior,88.1,grass,75,95,113,63,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,63,5120,70,308,45,Fire Pig Pokémon,45,1059860,0.5,65,Tepig,88.1,fire,45,45,45,9.9,5,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,93,5120,70,418,45,Fire Pig Pokémon,55,1059860,1,90,Pignite,88.1,fire,70,55,55,55.5,5,0 +0.25,0.5,1,1,1,1,0.5,2,1,0.5,2,0.5,1,1,2,1,0.5,2,123,5120,70,528,45,Mega Fire Pig Pokémon,65,1059860,1.6,110,Emboar,88.1,fire,100,65,65,150,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,55,5120,70,308,45,Sea Otter Pokémon,45,1059860,0.5,55,Oshawott,88.1,water,63,45,45,5.9,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,75,5120,70,413,45,Discipline Pokémon,60,1059860,0.8,75,Dewott,88.1,water,83,60,60,24.5,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,100,5120,70,528,45,Formidable Pokémon,85,1059860,1.5,95,Samurott,88.1,water,108,70,70,94.6,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,55,3840,70,255,255,Scout Pokémon,39,1000000,0.5,45,Patrat,50,normal,35,39,42,11.6,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,85,5120,70,420,255,Lookout Pokémon,69,1000000,1.1,60,Watchog,50,normal,60,69,77,27,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,3840,70,275,255,Puppy Pokémon,45,1059860,0.4,45,Lillipup,50,normal,25,45,55,4.1,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,3840,70,370,120,Loyal Dog Pokémon,65,1059860,0.9,65,Herdier,50,normal,35,65,60,14.7,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,110,3840,70,500,45,Big-Hearted Pokémon,90,1059860,1.2,85,Stoutland,50,normal,45,90,80,61,5,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,50,5120,70,281,255,Devious Pokémon,37,1000000,0.4,41,Purrloin,50,dark,50,37,66,10.1,5,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,88,5120,70,446,90,Cruel Pokémon,50,1000000,1.1,64,Liepard,50,dark,88,50,106,37.5,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,53,5120,70,316,190,Grass Monkey Pokémon,48,1000000,0.6,50,Pansage,88.1,grass,53,48,64,10.5,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,98,5120,70,498,75,Thorn Monkey Pokémon,63,1000000,1.1,75,Simisage,88.1,grass,98,63,101,30.5,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,53,5120,70,316,190,High Temp Pokémon,48,1000000,0.6,50,Pansear,88.1,fire,53,48,64,11,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,98,5120,70,498,75,Ember Pokémon,63,1000000,1,75,Simisear,88.1,fire,98,63,101,28,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,53,5120,70,316,190,Spray Pokémon,48,1000000,0.6,50,Panpour,88.1,water,53,48,64,13.5,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,98,5120,70,498,75,Geyser Pokémon,63,1000000,1,75,Simipour,88.1,water,98,63,101,29,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,25,2560,70,292,190,Dream Eater Pokémon,45,800000,0.6,76,Munna,50,psychic,67,55,24,23.3,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,55,2560,70,487,75,Drowsing Pokémon,85,800000,1.1,116,Musharna,50,psychic,107,95,29,60.5,5,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,55,3840,70,264,255,Tiny Pigeon Pokémon,50,1059860,0.3,50,Pidove,50,normal,36,30,43,2.1,5,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,77,3840,70,358,120,Wild Pigeon Pokémon,62,1059860,0.6,62,Tranquill,50,normal,50,42,65,15,5,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,115,3840,70,488,45,Proud Pokémon,80,1059860,1.2,80,Unfezant,50,normal,65,55,93,29,5,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,60,5120,70,295,190,Electrified Pokémon,32,1000000,0.8,45,Blitzle,50,electric,50,32,76,29.8,5,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,100,5120,70,497,75,Thunderbolt Pokémon,63,1000000,1.6,75,Zebstrika,50,electric,80,63,116,79.5,5,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,75,3840,70,280,255,Mantle Pokémon,85,1059860,0.4,55,Roggenrola,50,rock,25,25,15,18,5,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,105,3840,70,390,120,Ore Pokémon,105,1059860,0.9,70,Boldore,50,rock,50,40,20,102,5,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,135,3840,70,515,45,Compressed Pokémon,130,1059860,1.7,85,Gigalith,50,rock,60,80,25,260,5,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,45,3840,70,323,190,Bat Pokémon,43,1000000,0.4,65,Woobat,50,psychic,55,43,72,2.1,5,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,57,3840,70,425,45,Courting Pokémon,55,1000000,0.9,67,Swoobat,50,psychic,77,55,114,10.5,5,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,85,5120,70,328,120,Mole Pokémon,40,1000000,0.3,60,Drilbur,50,ground,30,45,68,8.5,5,0 +0.5,1,0.5,0,0.5,2,2,0.5,1,1,2,1,0.5,0,0.5,0.25,0.5,2,135,5120,70,508,60,Subterrene Pokémon,60,1000000,0.7,110,Excadrill,50,ground,50,65,88,40.4,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,60,5120,70,545,255,Hearing Pokémon,126,800000,1.1,103,Audino,50,normal,80,126,50,31,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,80,5120,70,305,180,Muscular Pokémon,55,1059860,0.6,75,Timburr,75.4,fighting,25,35,35,12.5,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,105,5120,70,405,90,Muscular Pokémon,85,1059860,1.2,85,Gurdurr,75.4,fighting,40,50,40,40,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,140,5120,70,505,45,Muscular Pokémon,95,1059860,1.4,105,Conkeldurr,75.4,fighting,55,65,45,87,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,50,5120,70,294,255,Tadpole Pokémon,40,1059860,0.5,50,Tympole,50,water,50,40,64,4.5,5,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,65,5120,70,384,120,Vibration Pokémon,55,1059860,0.8,75,Palpitoad,50,water,65,55,69,17,5,0 +1,1,1,0,1,1,0.5,1,1,4,1,1,1,0.5,1,0.5,0.5,1,95,5120,70,509,45,Vibration Pokémon,75,1059860,1.5,105,Seismitoad,50,water,85,75,74,62,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,100,5120,70,465,45,Judo Pokémon,85,1000000,1.3,120,Throh,100,fighting,30,85,45,55.5,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,125,5120,70,465,45,Karate Pokémon,75,1000000,1.4,75,Sawk,100,fighting,30,75,85,51,5,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,53,3840,70,310,255,Sewing Pokémon,70,1059860,0.3,45,Sewaddle,50,bug,40,60,42,2.5,5,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,63,3840,70,380,120,Leaf-Wrapped Pokémon,90,1059860,0.5,55,Swadloon,50,bug,50,80,42,7.3,5,0 +2,1,1,0.5,1,0.5,4,4,1,0.25,0.25,2,1,2,1,2,1,0.5,103,3840,70,500,45,Nurturing Pokémon,80,1059860,1.2,75,Leavanny,50,bug,70,80,92,20.5,5,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,45,3840,70,260,255,Centipede Pokémon,59,1059860,0.4,30,Venipede,50,bug,30,39,57,5.3,5,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,55,3840,70,360,120,Curlipede Pokémon,99,1059860,1.2,40,Whirlipede,50,bug,40,79,47,58.5,5,0 +0.5,1,1,1,0.5,0.25,2,2,1,0.25,1,1,1,0.5,2,2,1,1,100,5120,70,485,45,Megapede Pokémon,89,1059860,2.5,60,Scolipede,50,bug,55,69,112,200.5,5,0 +1,0.5,0,0.5,1,0.5,2,2,1,0.5,0.5,2,1,4,1,1,2,0.5,27,5120,70,280,190,Cotton Puff Pokémon,60,1000000,0.3,40,Cottonee,50,grass,37,50,66,0.6,5,0 +1,0.5,0,0.5,1,0.5,2,2,1,0.5,0.5,2,1,4,1,1,2,0.5,67,5120,70,480,75,Windveiled Pokémon,85,1000000,0.7,60,Whimsicott,50,grass,77,75,116,6.6,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,35,5120,70,280,190,Bulb Pokémon,50,1000000,0.5,45,Petilil,0,grass,70,50,30,6.6,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,60,5120,70,480,75,Flowering Pokémon,75,1000000,1.1,70,Lilligant,0,grass,110,75,90,16.3,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,92,10240,70,460,25,Hostile Pokémon,65,1000000,1,70,Basculin,50,water,80,55,98,18,5,0 +2,0.5,1,0,2,2,1,1,0.5,2,1,2,1,0.5,0,0.5,1,2,72,5120,70,292,180,Desert Croc Pokémon,35,1059860,0.7,50,Sandile,50,ground,35,35,65,15.2,5,0 +2,0.5,1,0,2,2,1,1,0.5,2,1,2,1,0.5,0,0.5,1,2,82,5120,70,351,90,Desert Croc Pokémon,45,1059860,1,60,Krokorok,50,ground,45,45,74,33.4,5,0 +2,0.5,1,0,2,2,1,1,0.5,2,1,2,1,0.5,0,0.5,1,2,117,5120,70,519,45,Intimidation Pokémon,80,1059860,1.5,95,Krookodile,50,ground,65,70,92,96.3,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,90,5120,70,315,120,Zen Charm Pokémon,45,1059860,0.6,70,Darumaka,50,fire,15,45,50,37.5,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,30,5120,70,540,60,Blazing Pokémon,105,1059860,1.3,105,Darmanitan,50,fire,140,105,55,92.9,5,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,86,5120,70,461,255,Cactus Pokémon,67,1000000,1,75,Maractus,50,grass,106,67,60,28,5,0 +1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,2,2,2,65,5120,70,325,190,Rock Inn Pokémon,85,1000000,0.3,50,Dwebble,50,bug,35,35,55,14.5,5,0 +1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,2,2,2,105,5120,70,485,75,Stone Home Pokémon,125,1000000,1.4,70,Crustle,50,bug,65,75,45,200,5,0 +1,0.25,1,1,4,2,1,2,0.5,1,1,1,1,1,0,0.5,1,1,75,3840,35,348,180,Shedding Pokémon,70,1000000,0.6,50,Scraggy,50,dark,35,70,48,11.8,5,0 +1,0.25,1,1,4,2,1,2,0.5,1,1,1,1,1,0,0.5,1,1,90,3840,70,488,90,Hoodlum Pokémon,115,1000000,1.1,65,Scrafty,50,dark,45,115,58,30,5,0 +1,2,1,2,1,0.25,1,1,2,0.5,0,2,1,1,0.5,2,1,1,58,5120,70,490,45,Avianoid Pokémon,80,1000000,1.4,72,Sigilyph,50,psychic,103,80,97,14,5,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,30,6400,70,303,190,Spirit Pokémon,85,1000000,0.5,38,Yamask,50,ghost,55,65,30,1.5,5,0 +0.5,2,1,1,1,0,1,1,2,1,1,1,0,0.5,1,1,1,1,50,6400,70,483,90,Coffin Pokémon,145,1000000,1.7,58,Cofagrigus,50,ghost,95,105,30,76.5,5,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,78,7680,70,355,45,Prototurtle Pokémon,103,1000000,0.7,54,Tirtouga,88.1,water,53,45,22,16.5,5,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,108,7680,70,495,45,Prototurtle Pokémon,133,1000000,1.2,74,Carracosta,88.1,water,83,65,32,81,5,0 +0.5,1,1,2,1,1,0.5,0.5,1,1,0,2,0.5,0.5,1,2,2,2,112,7680,70,401,45,First Bird Pokémon,45,1000000,0.5,55,Archen,88.1,rock,74,45,70,9.5,5,0 +0.5,1,1,2,1,1,0.5,0.5,1,1,0,2,0.5,0.5,1,2,2,2,140,7680,70,567,45,First Bird Pokémon,65,1000000,1.4,75,Archeops,88.1,rock,112,65,110,32,5,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,50,5120,70,329,190,Trash Bag Pokémon,62,1000000,0.6,50,Trubbish,50,poison,40,62,65,31,5,0 +0.5,1,1,1,0.5,0.5,1,1,1,0.5,2,1,1,0.5,2,1,1,1,95,5120,70,474,60,Trash Heap Pokémon,82,1000000,1.9,80,Garbodor,50,poison,60,82,75,107.3,5,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,65,6400,70,330,75,Tricky Fox Pokémon,40,1059860,0.7,40,Zorua,88.1,dark,80,40,65,12.5,5,0 +2,0.5,1,1,2,2,1,1,0.5,1,1,1,1,1,0,1,1,1,105,5120,70,510,45,Illusion Fox Pokémon,60,1059860,1.6,60,Zoroark,88.1,dark,120,60,105,81.1,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,50,3840,70,300,255,Chinchilla Pokémon,40,800000,0.4,55,Minccino,24.6,normal,40,40,75,5.8,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,95,3840,70,470,60,Scarf Pokémon,60,800000,0.5,75,Cinccino,24.6,normal,65,60,115,7.5,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,30,5120,70,290,200,Fixation Pokémon,50,1059860,0.4,45,Gothita,24.6,psychic,55,65,45,5.8,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,45,5120,70,390,100,Manipulate Pokémon,70,1059860,0.7,60,Gothorita,24.6,psychic,75,85,55,18,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,55,5120,70,490,50,Astral Body Pokémon,95,1059860,1.5,70,Gothitelle,24.6,psychic,95,110,65,44,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,30,5120,70,290,200,Cell Pokémon,40,1059860,0.3,45,Solosis,50,psychic,105,50,20,1,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,40,5120,70,370,100,Mitosis Pokémon,50,1059860,0.6,65,Duosion,50,psychic,125,60,30,8,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,65,5120,70,490,50,Multiplying Pokémon,75,1059860,1,110,Reuniclus,50,psychic,125,85,30,20.1,5,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,44,5120,70,305,190,Water Bird Pokémon,50,1000000,0.5,62,Ducklett,50,water,44,50,55,5.5,5,0 +0.5,1,1,4,1,0.5,0.5,1,1,1,0,1,1,1,1,2,0.5,0.5,87,5120,70,473,45,White Bird Pokémon,63,1000000,1.3,75,Swanna,50,water,87,63,98,24.2,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,50,5120,70,305,255,Fresh Snow Pokémon,50,1250000,0.4,36,Vanillite,50,ice,65,60,44,5.7,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,65,5120,70,395,120,Icy Snow Pokémon,65,1250000,1.1,51,Vanillish,50,ice,80,75,59,41,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,95,5120,70,535,45,Snowstorm Pokémon,85,1250000,1.3,71,Vanilluxe,50,ice,110,95,79,57.5,5,0 +2,1,1,0.5,1,2,2,2,0,0.5,0.5,2,1,2,1,1,1,0.5,60,5120,70,335,190,Season Pokémon,50,1000000,0.6,60,Deerling,50,normal,40,50,75,19.5,5,0 +2,1,1,0.5,1,2,2,2,0,0.5,0.5,2,1,2,1,1,1,0.5,100,5120,70,475,75,Season Pokémon,70,1000000,1.9,80,Sawsbuck,50,normal,60,70,95,92.5,5,0 +0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,2,1,1,1,2,0.5,1,75,5120,70,428,200,Sky Squirrel Pokémon,60,1000000,0.4,55,Emolga,50,electric,75,60,103,5,5,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,75,3840,70,315,200,Clamping Pokémon,45,1000000,0.5,50,Karrablast,50,bug,40,45,60,5.9,5,0 +0.5,1,0.5,1,0.5,1,4,1,1,0.25,1,0.5,0.5,0,0.5,1,0.5,1,135,3840,70,495,75,Cavalry Pokémon,105,1000000,1,70,Escavalier,50,bug,60,105,20,33,5,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,55,5120,70,294,190,Mushroom Pokémon,45,1000000,0.2,69,Foongus,50,grass,55,55,15,1,5,0 +1,1,1,0.5,0.5,0.5,2,2,1,0.25,1,2,1,1,2,1,1,0.5,85,5120,70,464,75,Mushroom Pokémon,70,1000000,0.6,114,Amoonguss,50,grass,85,80,30,10.5,5,0 +0.5,2,1,2,1,0,0.5,1,2,2,1,0.5,0,0.5,1,1,0.5,0.5,40,5120,70,335,190,Floating Pokémon,50,1000000,1.2,55,Frillish,50,water,65,85,40,33,5,0 +0.5,2,1,2,1,0,0.5,1,2,2,1,0.5,0,0.5,1,1,0.5,0.5,60,5120,70,480,60,Floating Pokémon,70,1000000,2.2,100,Jellicent,50,water,85,105,60,135,5,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,75,10240,70,470,75,Caring Pokémon,80,800000,1.2,165,Alomomola,50,water,40,45,65,31.6,5,0 +1,1,1,0.5,1,0.5,2,1,1,0.5,1,1,1,1,1,2,0.5,1,47,5120,70,319,190,Attaching Pokémon,50,1000000,0.1,50,Joltik,50,bug,57,50,65,0.6,5,0 +1,1,1,0.5,1,0.5,2,1,1,0.5,1,1,1,1,1,2,0.5,1,77,5120,70,472,75,EleSpider Pokémon,60,1000000,0.8,70,Galvantula,50,bug,97,60,108,14.3,5,0 +1,1,0.5,0.5,0.5,2,4,1,1,0.25,1,1,0.5,0,0.5,0.5,0.5,0.5,50,5120,70,305,255,Thorn Seed Pokémon,91,1000000,0.6,44,Ferroseed,50,grass,24,86,10,18.8,5,0 +1,1,0.5,0.5,0.5,2,4,1,1,0.25,1,1,0.5,0,0.5,0.5,0.5,0.5,94,5120,70,489,90,Thorn Pod Pokémon,131,1000000,1,74,Ferrothorn,50,grass,54,116,20,110,5,0 +0.5,1,0.5,1,0.5,2,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,0.5,1,55,5120,70,300,130,Gear Pokémon,70,1059860,0.3,40,Klink,,steel,45,60,30,21,5,0 +0.5,1,0.5,1,0.5,2,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,0.5,1,80,5120,70,440,60,Gear Pokémon,95,1059860,0.6,60,Klang,,steel,70,85,50,51,5,0 +0.5,1,0.5,1,0.5,2,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,0.5,1,100,5120,70,520,30,Gear Pokémon,115,1059860,0.6,60,Klinklang,,steel,70,85,90,81,5,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,55,5120,70,275,190,EleFish Pokémon,40,1250000,0.2,35,Tynamo,50,electric,45,40,60,0.3,5,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,85,5120,70,405,60,EleFish Pokémon,70,1250000,1.2,65,Eelektrik,50,electric,75,70,40,22,5,0 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,115,5120,70,515,30,EleFish Pokémon,80,1250000,2.1,85,Eelektross,50,electric,105,80,50,80.5,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,55,5120,70,335,255,Cerebral Pokémon,55,1000000,0.5,55,Elgyem,50,psychic,85,55,30,9,5,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,75,5120,70,485,90,Cerebral Pokémon,75,1000000,1,75,Beheeyem,50,psychic,125,95,40,34.5,5,0 +0.25,2,1,1,0.5,0,0.5,1,2,0.5,2,0.5,0,0.5,1,2,0.5,2,30,5120,70,275,190,Candle Pokémon,55,1059860,0.3,50,Litwick,50,ghost,65,55,20,3.1,5,0 +0.25,2,1,1,0.5,0,0.5,1,2,0.5,2,0.5,0,0.5,1,2,0.5,2,40,5120,70,370,90,Lamp Pokémon,60,1059860,0.6,60,Lampent,50,ghost,95,60,55,13,5,0 +0.25,2,1,1,0.5,0,0.5,1,2,0.5,2,0.5,0,0.5,1,2,0.5,2,55,5120,70,520,45,Luring Pokémon,90,1059860,1,60,Chandelure,50,ghost,145,90,80,34.3,5,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,87,10240,35,320,75,Tusk Pokémon,60,1250000,0.6,46,Axew,50,dragon,30,40,57,18,5,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,117,10240,35,410,60,Axe Jaw Pokémon,70,1250000,1,66,Fraxure,50,dragon,40,50,67,36,5,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,147,10240,35,540,45,Axe Jaw Pokémon,90,1250000,1.8,76,Haxorus,50,dragon,60,70,97,105.5,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,70,5120,70,305,120,Chill Pokémon,40,1000000,0.5,55,Cubchoo,50,ice,60,40,40,8.5,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,130,5120,70,505,60,Freezing Pokémon,80,1000000,2.6,95,Beartic,50,ice,70,80,50,260,5,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,50,6400,70,515,25,Crystallizing Pokémon,50,1000000,1.1,80,Cryogonal,,ice,95,135,105,148,5,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,40,3840,70,305,200,Snail Pokémon,85,1000000,0.4,50,Shelmet,50,bug,40,65,25,7.7,5,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,70,3840,70,495,75,Shell Out Pokémon,40,1000000,0.8,80,Accelgor,50,bug,100,60,145,25.3,5,0 +1,1,1,0,1,1,1,0.5,1,2,2,2,1,0.5,1,0.5,0.5,2,66,5120,70,471,75,Trap Pokémon,84,1000000,0.7,109,Stunfisk,50,ground,81,99,32,11,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,85,6400,70,350,180,Martial Arts Pokémon,50,1059860,0.9,45,Mienfoo,50,fighting,55,50,65,20,5,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,125,6400,70,510,45,Martial Arts Pokémon,60,1059860,1.4,65,Mienshao,50,fighting,95,60,105,35.5,5,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,120,7680,70,485,45,Cave Pokémon,90,1000000,1.6,77,Druddigon,50,dragon,60,90,48,139,5,0 +0.5,2,1,0,1,0,1,1,2,2,1,2,0,0.25,1,0.5,1,2,74,6400,70,303,190,Automaton Pokémon,50,1000000,1,59,Golett,,ground,35,50,35,92,5,0 +0.5,2,1,0,1,0,1,1,2,2,1,2,0,0.25,1,0.5,1,2,124,6400,70,483,90,Automaton Pokémon,80,1000000,2.8,89,Golurk,,ground,55,80,55,330,5,0 +1,0.5,0.5,1,1,4,2,0.5,0.5,0.5,2,0.5,0.5,0,0,0.5,0.5,1,85,5120,35,340,120,Sharp Blade Pokémon,70,1000000,0.5,45,Pawniard,50,dark,40,40,60,10.2,5,0 +1,0.5,0.5,1,1,4,2,0.5,0.5,0.5,2,0.5,0.5,0,0,0.5,0.5,1,125,5120,35,490,45,Sword Blade Pokémon,100,1000000,1.6,65,Bisharp,50,dark,60,70,70,70,5,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,110,5120,70,490,45,Bash Buffalo Pokémon,95,1000000,1.6,95,Bouffalant,50,normal,40,95,55,94.6,5,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,83,5120,70,350,190,Eaglet Pokémon,50,1250000,0.5,70,Rufflet,100,normal,37,50,60,10.5,5,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,123,5120,70,510,60,Valiant Pokémon,75,1250000,1.5,100,Braviary,100,normal,57,75,80,41,5,0 +1,0.5,1,2,2,1,1,1,0.5,0.5,0,2,1,1,0,2,1,1,55,5120,35,370,190,Diapered Pokémon,75,1250000,0.5,70,Vullaby,0,dark,45,65,60,9,5,0 +1,0.5,1,2,2,1,1,1,0.5,0.5,0,2,1,1,0,2,1,1,65,5120,35,510,60,Bone Vulture Pokémon,105,1250000,1.2,110,Mandibuzz,0,dark,55,95,80,39.5,5,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,97,5120,70,484,90,Anteater Pokémon,66,1000000,1.4,85,Heatmor,50,fire,105,66,65,58,5,0 +0.5,1,0.5,1,0.5,1,4,1,1,0.25,1,0.5,0.5,0,0.5,1,0.5,1,109,5120,70,484,90,Iron Ant Pokémon,112,1000000,0.3,58,Durant,50,bug,48,48,109,33,5,0 +2,0.5,2,0.5,4,2,0.5,1,0.5,0.5,1,2,1,1,0,1,1,0.5,65,10240,35,300,45,Irate Pokémon,50,1250000,0.8,52,Deino,50,dark,45,50,38,17.3,5,0 +2,0.5,2,0.5,4,2,0.5,1,0.5,0.5,1,2,1,1,0,1,1,0.5,85,10240,35,420,45,Hostile Pokémon,70,1250000,1.4,72,Zweilous,50,dark,65,70,58,50,5,0 +2,0.5,2,0.5,4,2,0.5,1,0.5,0.5,1,2,1,1,0,1,1,0.5,105,10240,35,600,45,Brutal Pokémon,90,1250000,1.8,92,Hydreigon,50,dark,125,90,98,160,5,0 +0.5,1,1,1,0.5,0.5,1,2,1,0.25,1,0.5,1,1,1,4,0.5,2,85,10240,70,360,45,Torch Pokémon,55,1250000,1.1,55,Larvesta,50,bug,50,55,60,28.8,5,0 +0.5,1,1,1,0.5,0.5,1,2,1,0.25,1,0.5,1,1,1,4,0.5,2,60,10240,70,550,15,Sun Pokémon,65,1250000,1.6,85,Volcarona,50,bug,135,105,100,46,5,0 +0.25,0.5,0.5,1,1,2,2,1,1,0.5,2,0.5,0.5,0,1,0.25,0.5,1,90,20480,35,580,3,Iron Will Pokémon,129,1250000,2.1,91,Cobalion,,steel,90,72,108,250,5,1 +0.5,0.5,1,1,2,2,0.5,1,1,2,2,1,0.5,0.5,2,0.5,2,2,129,20480,35,580,3,Cavern Pokémon,90,1250000,1.9,91,Terrakion,,rock,72,90,108,260,5,1 +1,0.5,1,0.5,2,1,2,4,1,0.5,0.5,2,1,2,2,0.5,1,0.5,90,20480,35,580,3,Grassland Pokémon,72,1250000,2,91,Virizion,,grass,90,129,108,200,5,1 +0.5,1,1,2,1,0.5,1,1,1,0.5,0,2,1,1,1,2,1,1,100,30720,90,580,3,Cyclone Pokémon,80,1250000,1.5,79,Tornadus,100,fighting,110,90,121,63,5,1 +0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,2,1,1,1,2,0.5,1,105,30720,90,580,3,Bolt Strike Pokémon,70,1250000,1.5,79,Thundurus,100,electric,145,80,101,61,5,1 +0.5,1,2,0.5,1,1,0.25,1,1,0.25,2,1,1,1,1,2,0.5,1,120,30720,0,680,3,Vast White Pokémon,100,1250000,3.2,100,Reshiram,,dragon,150,120,90,330,5,1 +1,1,2,0.25,2,1,0.5,0.5,1,0.5,2,2,1,1,1,1,0.5,0.5,150,30720,0,680,3,Deep Black Pokémon,120,1250000,2.9,100,Zekrom,,dragon,120,100,90,345,5,1 +0.5,1,1,0,1,0.5,1,1,1,1,0,4,1,0.5,1,1,1,2,145,30720,90,600,3,Abundance Pokémon,90,1250000,1.5,89,Landorus,100,ground,105,80,91,68,5,1 +1,1,2,0.5,2,2,1,1,1,0.5,1,1,1,1,1,2,2,0.5,120,30720,0,700,3,Boundary Pokémon,90,1250000,3,125,Kyurem,,dragon,170,100,95,325,5,1 +0.5,0.5,1,2,2,1,0.5,2,1,2,1,0.5,1,1,2,0.5,0.5,0.5,72,20480,35,580,3,Colt Pokémon,90,1250000,1.4,91,Keldeo,,water,129,90,108,48.5,5,1 +2,2,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,128,30720,100,600,3,Melody Pokémon,90,1250000,0.6,100,Meloetta,,normal,77,77,128,6.5,5,1 +0.5,1,0.5,1,0.5,1,4,1,1,0.25,1,0.5,0.5,0,0.5,1,0.5,1,120,30720,0,600,3,Paleozoic Pokémon,95,1250000,1.5,71,Genesect,,bug,120,95,99,82.5,5,1 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,61,5120,70,313,45,Spiky Nut Pokémon,65,1059860,0.4,56,Chespin,88.1,grass,48,45,38,9,6,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,78,5120,70,405,45,Spiny Armor Pokémon,95,1059860,0.7,61,Quilladin,88.1,grass,56,58,57,29,6,0 +1,0.5,1,0.5,2,1,2,4,1,0.5,0.5,2,1,2,2,0.5,1,0.5,107,5120,70,530,45,Spiny Armor Pokémon,122,1059860,1.6,88,Chesnaught,88.1,grass,74,75,64,90,6,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,45,5120,70,307,45,Fox Pokémon,40,1059860,0.4,40,Fennekin,88.1,fire,62,60,60,9.4,6,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,59,5120,70,409,45,Fox Pokémon,58,1059860,1,59,Braixen,88.1,fire,90,70,73,14.5,6,0 +1,2,1,1,0.5,0.5,0.5,1,2,0.5,2,0.5,1,1,0.5,2,0.5,2,69,5120,70,534,45,Fox Pokémon,72,1059860,1.5,75,Delphox,88.1,fire,114,100,104,39,6,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,56,5120,70,314,45,Bubble Frog Pokémon,40,1059860,0.3,41,Froakie,88.1,water,62,44,71,7,6,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,63,5120,70,405,45,Bubble Frog Pokémon,52,1059860,0.6,54,Frogadier,88.1,water,83,56,97,10.9,6,0 +2,0.5,1,2,2,2,0.5,1,0.5,2,1,0.5,1,1,0,1,0.5,0.5,145,5120,70,640,45,Ninja Pokémon,67,1059860,1.5,72,Greninja,88.1,water,153,71,132,40,6,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,36,3840,70,237,255,Digging Pokémon,38,1000000,0.4,38,Bunnelby,50,normal,32,36,57,5,6,0 +1,1,1,0,1,2,1,1,0,2,1,2,1,0.5,1,0.5,1,2,56,3840,70,423,127,Digging Pokémon,77,1000000,1,85,Diggersby,50,normal,50,77,78,42.4,6,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,50,3840,70,278,255,Tiny Robin Pokémon,43,1059860,0.3,45,Fletchling,50,normal,40,38,62,1.7,6,0 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,73,3840,70,382,120,Ember Pokémon,55,1059860,0.7,62,Fletchinder,50,fire,56,52,84,16,6,0 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,81,3840,70,499,45,Scorching Pokémon,71,1059860,1.2,78,Talonflame,50,fire,74,69,126,24.5,6,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,35,3840,70,200,255,Scatterdust Pokémon,40,1000000,0.3,38,Scatterbug,50,bug,27,25,35,2.5,6,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,22,3840,70,213,120,Scatterdust Pokémon,60,1000000,0.3,45,Spewpa,50,bug,27,30,29,8.4,6,0 +0.5,1,1,2,1,0.25,2,2,1,0.25,0,2,1,1,1,4,1,1,52,3840,70,411,45,Scale Pokémon,50,1000000,1.2,80,Vivillon,50,bug,90,50,89,17,6,0 +0.5,1,1,1,0.5,2,0.5,1,0,0.5,2,0.5,1,1,1,2,0.5,2,50,5120,70,369,220,Lion Cub Pokémon,58,1059860,0.6,62,Litleo,11.2,fire,73,54,72,13.5,6,0 +0.5,1,1,1,0.5,2,0.5,1,0,0.5,2,0.5,1,1,1,2,0.5,2,68,5120,70,507,65,Royal Pokémon,72,1059860,1.5,86,Pyroar,11.2,fire,109,66,106,81.5,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,38,5120,70,303,225,Single Bloom Pokémon,39,1000000,0.1,44,Flabébé,0,fairy,61,79,42,0.1,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,65,5120,70,551,120,Fairy Pokémon,67,1000000,0.2,74,Floette,0,fairy,125,128,92,0.9,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,65,5120,70,552,45,Garden Pokémon,68,1000000,1.1,78,Florges,0,fairy,112,154,75,10,6,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,65,5120,70,350,200,Mount Pokémon,48,1000000,0.9,66,Skiddo,50,grass,62,57,52,31,6,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,100,5120,70,531,45,Mount Pokémon,62,1000000,1.7,123,Gogoat,50,grass,97,81,68,91,6,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,82,6400,70,348,220,Playful Pokémon,62,1000000,0.6,67,Pancham,50,fighting,46,48,43,8,6,0 +1,0.25,1,1,4,2,1,2,0.5,1,1,1,1,1,0,0.5,1,1,124,6400,70,495,65,Daunting Pokémon,78,1000000,2.1,95,Pangoro,50,fighting,69,71,58,136,6,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,80,5120,70,472,160,Poodle Pokémon,60,1000000,1.2,75,Furfrou,50,normal,65,90,102,28,6,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,48,5120,70,355,190,Restraint Pokémon,54,1000000,0.3,62,Espurr,50,psychic,63,60,68,3.5,6,0 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,48,5120,70,466,75,Constraint Pokémon,76,1000000,0.6,74,Meowstic,50,psychic,83,81,104,8.5,6,0 +0.25,2,0.5,1,0.5,0,2,0.5,2,0.5,2,0.5,0,0,0.5,0.5,0.5,1,80,5120,70,325,180,Sword Pokémon,100,1000000,0.8,45,Honedge,50,steel,35,37,28,2,6,0 +0.25,2,0.5,1,0.5,0,2,0.5,2,0.5,2,0.5,0,0,0.5,0.5,0.5,1,110,5120,70,448,90,Sword Pokémon,150,1000000,0.8,59,Doublade,50,steel,45,49,35,4.5,6,0 +0.25,2,0.5,1,0.5,0,2,0.5,2,0.5,2,0.5,0,0,0.5,0.5,0.5,1,150,5120,70,520,45,Royal Sword Pokémon,50,1000000,1.7,60,Aegislash,50,steel,150,50,60,53,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,52,5120,70,341,200,Perfume Pokémon,60,1000000,0.2,78,Spritzee,50,fairy,63,65,23,0.5,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,72,5120,70,462,140,Fragrance Pokémon,72,1000000,0.8,101,Aromatisse,50,fairy,99,89,29,15.5,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,48,5120,70,341,200,Cotton Candy Pokémon,66,1000000,0.4,62,Swirlix,50,fairy,59,57,49,3.5,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,80,5120,70,480,140,Meringue Pokémon,86,1000000,0.8,82,Slurpuff,50,fairy,85,75,72,5,6,0 +4,1,1,1,2,1,1,1,1,1,1,1,1,1,0,1,1,1,54,5120,70,288,190,Revolving Pokémon,53,1000000,0.4,53,Inkay,50,dark,37,46,45,3.5,6,0 +4,1,1,1,2,1,1,1,1,1,1,1,1,1,0,1,1,1,92,5120,70,482,80,Overturning Pokémon,88,1000000,1.5,86,Malamar,50,dark,68,75,73,47,6,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,52,5120,70,306,120,Two-Handed Pokémon,67,1000000,0.5,42,Binacle,50,rock,39,56,50,31,6,0 +1,1,1,2,1,2,0.25,0.5,1,4,2,0.5,0.5,0.5,1,1,1,1,105,5120,70,500,45,Collective Pokémon,115,1000000,1.3,72,Barbaracle,50,rock,54,86,68,96,6,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,60,5120,70,320,225,Mock Kelp Pokémon,60,1000000,0.5,50,Skrelp,50,poison,60,60,30,7.3,6,0 +0.5,1,2,0.5,1,0.5,0.5,1,1,0.25,2,2,1,0.5,2,1,1,0.5,75,5120,70,494,55,Mock Kelp Pokémon,90,1000000,1.8,65,Dragalge,50,poison,97,123,44,81.5,6,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,53,3840,70,330,225,Water Gun Pokémon,62,1250000,0.5,50,Clauncher,50,water,58,63,44,8.3,6,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,73,3840,70,500,55,Howitzer Pokémon,88,1250000,1.3,71,Clawitzer,50,water,120,89,59,35.3,6,0 +1,1,1,0.5,1,2,1,0.5,0,1,2,1,1,1,1,1,0.5,1,38,5120,70,289,190,Generator Pokémon,33,1000000,0.5,44,Helioptile,50,electric,61,43,70,6,6,0 +1,1,1,0.5,1,2,1,0.5,0,1,2,1,1,1,1,1,0.5,1,55,5120,70,481,75,Generator Pokémon,52,1000000,1,62,Heliolisk,50,electric,109,94,109,21,6,0 +1,1,2,0.5,2,2,0.25,0.5,1,1,2,2,0.5,0.5,1,1,2,1,89,7680,70,362,45,Royal Heir Pokémon,77,1000000,0.8,58,Tyrunt,88.1,rock,45,45,48,26,6,0 +1,1,2,0.5,2,2,0.25,0.5,1,1,2,2,0.5,0.5,1,1,2,1,121,7680,70,521,45,Despot Pokémon,119,1000000,2.5,82,Tyrantrum,88.1,rock,69,59,71,270,6,0 +1,1,1,1,1,4,1,0.5,1,2,2,0.5,0.5,0.5,1,2,4,2,59,7680,70,362,45,Tundra Pokémon,50,1000000,1.3,77,Amaura,88.1,rock,67,63,46,25.2,6,0 +1,1,1,1,1,4,1,0.5,1,2,2,0.5,0.5,0.5,1,2,4,2,77,7680,70,521,45,Tundra Pokémon,72,1000000,2.7,123,Aurorus,88.1,rock,99,92,58,225,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,65,8960,70,525,45,Intertwining Pokémon,65,1000000,1,95,Sylveon,88.1,fairy,110,130,60,23.5,6,0 +0.25,0.5,1,2,2,0.5,1,2,1,0.5,0,2,1,1,2,1,1,1,92,5120,70,500,100,Wrestling Pokémon,75,1000000,0.8,78,Hawlucha,50,fighting,74,63,118,21.5,6,0 +0.5,0.5,0,0.5,1,0.5,1,0.5,1,1,2,1,1,2,1,1,1,1,58,5120,70,431,180,Antenna Pokémon,57,1000000,0.2,67,Dedenne,50,electric,81,67,101,2.2,6,0 +0.5,0.5,0,1,1,1,0.5,0.5,1,2,2,1,0.5,1,1,1,4,2,50,6400,70,500,60,Jewel Pokémon,150,1250000,0.3,50,Carbink,,rock,50,150,50,5.7,6,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,50,10240,35,300,45,Soft Tissue Pokémon,35,1250000,0.3,45,Goomy,50,dragon,55,75,40,2.8,6,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,75,10240,35,452,45,Soft Tissue Pokémon,53,1250000,0.8,68,Sliggoo,50,dragon,83,113,60,17.5,6,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,100,10240,35,600,45,Dragon Pokémon,70,1250000,2,90,Goodra,50,dragon,110,150,80,150.5,6,0 +0.25,0.5,0,1,0.5,1,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,1,1,80,5120,70,470,75,Key Ring Pokémon,91,800000,0.2,57,Klefki,50,steel,80,87,75,3,6,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,70,5120,70,309,120,Stump Pokémon,48,1000000,0.4,43,Phantump,50,ghost,50,60,38,7,6,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,110,5120,70,474,60,Elder Tree Pokémon,76,1000000,1.5,85,Trevenant,50,ghost,65,82,56,71,6,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,66,5120,70,335,120,Pumpkin Pokémon,70,1000000,0.8,59,Pumpkaboo,50,ghost,44,55,41,15,6,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,100,5120,70,494,60,Pumpkin Pokémon,122,1000000,1.7,85,Gourgeist,50,ghost,58,75,54,39,6,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,69,5120,70,304,190,Ice Chunk Pokémon,85,1000000,1,55,Bergmite,50,ice,32,35,28,99.5,6,0 +1,1,1,1,1,2,2,1,1,1,1,0.5,1,1,1,2,2,1,117,5120,70,514,55,Iceberg Pokémon,184,1000000,2,95,Avalugg,50,ice,44,46,28,505,6,0 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,30,5120,70,245,190,Sound Wave Pokémon,35,1000000,0.5,40,Noibat,50,dragon,45,40,55,8,6,0 +0.5,1,2,1,2,0.5,0.5,1,1,0.25,0,4,1,1,1,2,1,0.5,70,5120,70,535,45,Sound Wave Pokémon,80,1000000,1.5,85,Noivern,50,dragon,97,80,123,85,6,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,131,30720,0,680,45,Life Pokémon,95,1250000,3,126,Xerneas,,fairy,131,98,99,215,6,1 +1,0.5,1,2,2,1,1,1,0.5,0.5,0,2,1,1,0,2,1,1,131,30720,0,680,45,Destruction Pokémon,95,1250000,5.8,126,Yveltal,,dark,131,98,99,203,6,1 +1,1,2,0,2,1,0.5,1,1,1,1,4,1,0.5,1,0.5,1,1,100,30720,0,708,3,Order Pokémon,121,1250000,5,216,Zygarde,,dragon,91,95,85,284.6,6,1 +0.5,0.5,0,1,1,1,0.5,0.5,1,2,2,1,0.5,1,1,1,4,2,160,6400,70,700,3,Jewel Pokémon,110,1250000,0.7,50,Diancie,,rock,160,110,110,8.8,6,1 +1,4,1,1,1,0,1,1,4,1,1,1,0,0.5,0.5,1,1,1,160,30720,100,680,3,Mischief Pokémon (Confined)Djinn Pokémonn (Unbound),60,1250000,,80,Hoopa,,psychic,170,130,80,,6,1 +0.5,1,1,2,0.5,1,0.25,1,1,1,2,0.25,1,1,1,2,0.25,1,110,30720,100,600,3,Steam Pokémon,120,1250000,1.7,80,Volcanion,,fire,130,90,70,195,6,1 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,55,3840,70,320,45,Grass Quill Pokémon,55,1059860,0.3,68,Rowlet,88.1,grass,50,50,42,1.5,7,0 +1,1,1,1,1,0.5,2,2,1,0.25,0,4,1,2,1,2,1,0.5,75,3840,70,420,45,Blade Quill Pokémon,75,1059860,0.7,78,Dartrix,88.1,grass,70,70,52,16,7,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,107,3840,70,530,45,Arrow Quill Pokémon,75,1059860,1.6,78,Decidueye,88.1,grass,100,100,70,36.6,7,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,65,3840,70,320,45,Fire Cat Pokémon,40,1059860,0.4,45,Litten,88.1,fire,60,40,70,4.3,7,0 +0.5,1,1,1,0.5,1,0.5,1,1,0.5,2,0.5,1,1,1,2,0.5,2,85,3840,70,420,45,Fire Cat Pokémon,50,1059860,0.7,65,Torracat,88.1,fire,80,50,90,25,7,0 +1,0.5,1,1,1,2,0.5,1,0.5,0.5,2,0.5,1,1,0,2,0.5,2,115,3840,70,530,45,Heel Pokémon,90,1059860,1.8,95,Incineroar,88.1,fire,80,90,60,83,7,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,54,3840,70,320,45,Sea Lion Pokémon,54,1059860,0.4,50,Popplio,88.1,water,66,56,40,7.5,7,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,69,3840,70,420,45,Pop Star Pokémon,69,1059860,0.6,60,Brionne,88.1,water,91,81,50,17.5,7,0 +0.5,0.5,0,2,1,0.5,0.5,1,1,2,1,0.5,1,2,1,1,1,0.5,74,3840,70,530,45,Soloist Pokémon,74,1059860,1.8,80,Primarina,88.1,water,126,116,60,44,7,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,75,3840,70,265,255,Woodpecker Pokémon,30,1000000,0.3,35,Pikipek,50,normal,30,30,65,1.2,7,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,85,3840,70,355,120,Bugle Beak Pokémon,50,1000000,0.6,55,Trumbeak,50,normal,40,50,75,14.8,7,0 +0.5,1,1,2,1,1,1,1,0,0.5,0,2,1,1,1,2,1,1,120,3840,70,485,45,Cannon Pokémon,75,1000000,1.1,80,Toucannon,50,normal,75,75,60,26,7,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,70,3840,70,253,255,Loitering Pokémon,30,1000000,0.4,48,Yungoos,50,normal,30,30,45,6,7,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,110,3840,70,418,127,Stakeout Pokémon,60,1000000,0.7,88,Gumshoos,50,normal,55,60,45,14.2,7,0 +1,1,1,1,1,0.5,2,2,1,0.5,0.5,1,1,1,1,2,1,1,62,3840,70,300,255,Larva Pokémon,45,1000000,0.4,47,Grubbin,50,bug,55,45,46,4.4,7,0 +1,1,1,0.5,1,0.5,2,1,1,0.5,1,1,1,1,1,2,0.5,1,82,3840,70,400,120,Battery Pokémon,95,1000000,0.5,57,Charjabug,50,bug,55,75,36,10.5,7,0 +1,1,1,0.5,1,0.5,2,1,1,0.5,1,1,1,1,1,2,0.5,1,70,3840,70,500,45,Stag Beetle Pokémon,90,1000000,1.5,77,Vikavolt,50,bug,145,75,43,45,7,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,82,5120,70,338,225,Boxing Pokémon,57,1000000,0.6,47,Crabrawler,50,fighting,42,47,63,7,7,0 +0.5,0.5,1,1,2,2,2,2,1,1,1,0.5,1,1,2,1,2,1,132,5120,70,478,60,Woolly Crab Pokémon,77,1000000,1.7,97,Crabominable,50,fighting,62,67,43,180,7,0 +0.25,1,1,2,0.5,0.5,0.5,1,1,0.25,0,1,1,1,1,4,0.5,2,70,5120,70,476,45,Dancing Pokémon,70,1000000,0.6,75,Oricorio,24.6,fire,98,70,93,3.4,7,0 +0.5,0.5,0,1,1,0.25,2,2,1,0.5,0.5,1,1,2,1,2,2,1,45,5120,70,304,190,Bee Fly Pokémon,40,1000000,0.1,40,Cutiefly,50,bug,55,40,84,0.2,7,0 +0.5,0.5,0,1,1,0.25,2,2,1,0.5,0.5,1,1,2,1,2,2,1,55,5120,70,464,75,Bee Fly Pokémon,60,1000000,0.2,60,Ribombee,50,bug,95,70,124,0.5,7,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,65,3840,70,280,190,Puppy Pokémon,40,1000000,0.5,45,Rockruff,50,rock,30,40,60,9.2,7,0 +1,1,1,1,1,2,0.5,0.5,1,2,2,1,0.5,0.5,1,1,2,2,115,3840,70,487,90,Wolf Pokémon,75,1000000,,85,Lycanroc,50,rock,55,75,82,,7,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,140,3840,70,620,60,Small Fry Pokémon,130,800000,0.2,45,Wishiwashi,50,water,140,135,30,0.3,7,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,53,5120,70,305,190,Brutal Star Pokémon,62,1000000,0.4,50,Mareanie,50,poison,43,52,45,8,7,0 +0.5,1,1,2,0.5,0.5,0.5,1,1,1,2,0.5,1,0.5,2,1,0.5,0.5,63,5120,70,495,75,Brutal Star Pokémon,152,1000000,0.7,50,Toxapex,50,poison,53,142,35,14.5,7,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,100,5120,70,385,190,Donkey Pokémon,70,1000000,1,70,Mudbray,50,ground,45,55,45,110,7,0 +1,1,1,0,1,1,1,1,1,2,1,2,1,0.5,1,0.5,1,2,125,5120,70,500,60,Draft Horse Pokémon,100,1000000,2.5,100,Mudsdale,50,ground,55,85,35,920,7,0 +1,1,1,2,1,0.5,1,2,1,1,0.5,0.5,1,1,1,2,0.5,0.5,40,3840,70,269,200,Water Bubble Pokémon,52,1000000,0.3,38,Dewpider,50,water,40,72,27,4,7,0 +1,1,1,2,1,0.5,1,2,1,1,0.5,0.5,1,1,1,2,0.5,0.5,70,3840,70,454,100,Water Bubble Pokémon,92,1000000,1.8,68,Araquanid,50,water,50,132,42,82,7,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,55,5120,70,250,190,Sickle Grass Pokémon,35,1000000,0.3,40,Fomantis,50,grass,50,35,35,1.5,7,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,105,5120,70,480,75,Bloom Sickle Pokémon,90,1000000,0.9,70,Lurantis,50,grass,80,90,45,18.5,7,0 +1,0.5,0,0.5,1,0.5,2,2,1,0.5,0.5,2,1,4,1,1,2,0.5,35,5120,70,285,190,Illuminating Pokémon,55,1000000,0.2,40,Morelull,50,grass,65,75,15,1.5,7,0 +1,0.5,0,0.5,1,0.5,2,2,1,0.5,0.5,2,1,4,1,1,2,0.5,45,5120,70,405,75,Illuminating Pokémon,80,1000000,1,60,Shiinotic,50,grass,90,100,30,11.5,7,0 +0.25,1,1,1,0.25,0.5,0.5,1,1,0.25,4,0.5,1,0.5,2,2,0.5,2,44,5120,70,320,120,Toxic Lizard Pokémon,40,1000000,0.6,48,Salandit,88.1,poison,71,40,77,4.8,7,0 +0.25,1,1,1,0.25,0.5,0.5,1,1,0.25,4,0.5,1,0.5,2,2,0.5,2,64,5120,70,480,45,Toxic Lizard Pokémon,60,1000000,1.2,68,Salazzle,0,poison,111,60,117,22.2,7,0 +0.5,0.5,1,1,2,2,1,2,0,1,1,1,1,1,2,0.5,1,1,75,3840,70,340,140,Flailing Pokémon,50,1000000,0.5,70,Stufful,50,normal,45,50,50,6.8,7,0 +0.5,0.5,1,1,2,2,1,2,0,1,1,1,1,1,2,0.5,1,1,125,3840,70,500,70,Strong Arm Pokémon,80,1000000,2.1,120,Bewear,50,normal,55,60,60,135,7,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,30,5120,70,210,235,Fruit Pokémon,38,1059860,0.3,42,Bounsweet,0,grass,30,38,32,3.2,7,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,40,5120,70,290,120,Fruit Pokémon,48,1059860,0.7,52,Steenee,0,grass,40,48,62,8.2,7,0 +2,1,1,0.5,1,1,2,2,1,0.5,0.5,2,1,2,1,1,1,0.5,120,5120,70,510,45,Fruit Pokémon,98,1059860,1.2,72,Tsareena,0,grass,50,98,72,21.4,7,0 +0.5,0.5,0,1,1,0.5,1,1,1,1,1,1,1,2,1,1,2,1,52,5120,70,485,60,Posy Picker Pokémon,90,800000,0.1,51,Comfey,24.6,fairy,82,110,100,0.3,7,0 +2,2,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,60,5120,70,490,45,Sage Pokémon,80,1250000,1.5,90,Oranguru,50,normal,90,110,60,76,7,0 +0.5,0.5,1,1,2,1,1,2,1,1,1,1,1,1,2,0.5,1,1,120,5120,70,490,45,Teamwork Pokémon,90,1250000,2,100,Passimian,50,fighting,40,60,80,82.8,7,0 +1,1,1,2,1,0.5,1,2,1,1,0.5,0.5,1,1,1,2,0.5,0.5,35,5120,70,230,90,Turn Tail Pokémon,40,1000000,0.5,25,Wimpod,50,bug,20,30,80,12,7,0 +1,1,1,2,1,0.5,1,2,1,1,0.5,0.5,1,1,1,2,0.5,0.5,125,5120,70,530,45,Hard Scale Pokémon,140,1000000,2,75,Golisopod,50,bug,60,90,40,108,7,0 +0.5,2,1,0,1,0,1,1,2,2,1,2,0,0.25,1,0.5,1,2,55,3840,70,320,140,Sand Heap Pokémon,80,1000000,0.5,55,Sandygast,50,ghost,70,45,15,70,7,0 +0.5,2,1,0,1,0,1,1,2,2,1,2,0,0.25,1,0.5,1,2,75,3840,70,480,60,Sand Castle Pokémon,110,1000000,1.3,85,Palossand,50,ghost,100,75,35,250,7,0 +1,1,1,2,1,1,0.5,1,1,2,1,0.5,1,1,1,1,0.5,0.5,60,3840,70,410,60,Sea Cucumber Pokémon,130,800000,0.3,55,Pyukumuku,50,water,30,130,5,1.2,7,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,95,30720,0,534,3,Synthetic Pokémon,95,1250000,1.9,95,Type: Null,,normal,95,95,59,120.5,7,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,95,30720,0,570,3,Synthetic Pokémon,95,1250000,2.3,95,Silvally,,normal,95,95,95,100.5,7,0 +0.5,1,1,2,1,1,0.5,0.5,1,1,0,2,0.5,0.5,1,2,2,2,100,6400,70,500,255,Meteor Pokémon,60,1059860,0.3,60,Minior,,rock,100,60,120,40,7,0 +1,1,1,1,1,2,1,1,0,1,1,1,1,1,1,1,1,1,115,5120,70,480,45,Drowsing Pokémon,65,1250000,0.4,65,Komala,50,normal,75,95,65,19.9,7,0 +0.5,1,2,0.5,1,1,0.25,1,1,0.25,2,1,1,1,1,2,0.5,1,78,5120,70,485,70,Blast Turtle Pokémon,135,1000000,2,60,Turtonator,50,fire,91,85,36,212,7,0 +0.5,1,0.5,0.5,0.5,2,2,0.25,1,0.5,4,0.5,0.5,0,0.5,0.5,0.25,1,98,2560,70,435,180,Roly-Poly Pokémon,63,1000000,0.3,65,Togedemaru,50,electric,40,73,96,3.3,7,0 +0.25,1,0,1,1,0,1,1,2,1,1,1,0,1,1,1,2,1,90,5120,70,476,45,Disguise Pokémon,80,1000000,0.2,55,Mimikyu,50,ghost,50,105,96,0.7,7,0 +2,2,1,2,1,0.5,0.5,1,2,2,1,0.5,1,1,0.5,1,0.5,0.5,105,3840,70,475,80,Gnash Teeth Pokémon,70,1000000,0.9,68,Bruxish,50,water,70,70,92,19,7,0 +1,1,2,0.5,2,2,0.5,1,0,0.5,1,2,1,1,1,1,1,0.5,60,5120,70,485,70,Placid Pokémon,85,1000000,3,78,Drampa,50,normal,135,91,36,185,7,0 +1,2,1,0.5,1,0,2,2,2,0.5,0.5,2,0,1,1,1,1,0.5,131,6400,70,517,25,Sea Creeper Pokémon,100,1000000,3.9,70,Dhelmise,,ghost,86,90,40,210,7,0 +1,1,2,0.5,2,1,0.5,1,1,0.5,1,2,1,1,1,1,1,0.5,55,10240,70,300,45,Scaly Pokémon,65,1250000,0.6,45,Jangmo-o,50,dragon,45,45,45,29.7,7,0 +0.5,0.5,2,0.5,4,1,0.5,2,1,0.5,1,2,1,1,2,0.5,1,0.5,75,10240,70,420,45,Scaly Pokémon,90,1250000,1.2,55,Hakamo-o,50,dragon,65,70,65,47,7,0 +0.5,0.5,2,0.5,4,1,0.5,2,1,0.5,1,2,1,1,2,0.5,1,0.5,110,10240,70,600,45,Scaly Pokémon,125,1250000,1.6,75,Kommo-o,50,dragon,100,105,85,78.2,7,0 +0.5,0.5,0,0.5,1,0.5,1,0.5,1,1,2,1,1,2,1,1,1,1,115,3840,70,570,3,Land Spirit Pokémon,85,1250000,1.8,70,Tapu Koko,,electric,95,75,130,20.5,7,1 +1,1,0,1,1,0.25,1,1,2,1,1,1,1,2,0.5,1,2,1,85,3840,70,570,3,Land Spirit Pokémon,75,1250000,1.2,70,Tapu Lele,,psychic,130,115,95,18.6,7,1 +1,0.5,0,0.5,1,0.5,2,2,1,0.5,0.5,2,1,4,1,1,2,0.5,130,3840,70,570,3,Land Spirit Pokémon,115,1250000,1.9,70,Tapu Bulu,,grass,85,95,75,45.5,7,1 +0.5,0.5,0,2,1,0.5,0.5,1,1,2,1,0.5,1,2,1,1,1,0.5,75,3840,70,570,3,Land Spirit Pokémon,115,1250000,1.3,70,Tapu Fini,,water,95,130,85,21.2,7,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,29,30720,0,200,45,Nebula Pokémon,31,1250000,0.2,43,Cosmog,,psychic,29,31,37,0.1,7,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,29,30720,0,400,45,Protostar Pokémon,131,1250000,0.1,43,Cosmoem,,psychic,29,131,37,999.9,7,1 +1,2,0.5,1,0.5,1,2,0.5,2,0.5,2,0.5,0.5,0,0.25,0.5,0.5,1,137,30720,0,680,45,Sunne Pokémon,107,1250000,3.4,137,Solgaleo,,psychic,113,89,97,230,7,1 +1,4,1,1,1,0,1,1,4,1,1,1,0,0.5,0.5,1,1,1,113,30720,0,680,45,Moone Pokémon,89,1250000,4,137,Lunala,,psychic,137,107,97,120,7,1 +0.5,1,1,1,0.5,1,0.5,0.5,1,1,4,1,0.5,0.25,2,1,2,2,53,30720,0,570,45,Parasite Pokémon,47,1250000,1.2,109,Nihilego,,rock,127,131,103,55.5,7,1 +0.5,0.5,1,1,2,0.5,2,4,1,0.5,0.5,1,1,1,2,1,1,1,139,30720,0,570,25,Swollen Pokémon,139,1250000,2.4,107,Buzzwole,,bug,53,53,79,333.6,7,1 +0.5,0.5,1,1,2,0.5,2,4,1,0.5,0.5,1,1,1,2,1,1,1,137,30720,0,570,255,Lissome Pokémon,37,1250000,1.8,71,Pheromosa,,bug,137,37,151,25,7,1 +1,1,1,0.5,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,89,30720,0,570,30,Glowing Pokémon,71,1250000,3.8,83,Xurkitree,,electric,173,71,83,100,7,1 +0.25,1,0.5,2,0.5,1,2,0.5,1,0.25,0,1,0.5,0,0.5,1,0.5,1,101,30720,0,570,25,Launch Pokémon,103,1250000,9.2,97,Celesteela,,steel,107,101,61,999.9,7,1 +1,1,0.5,0.5,0.5,2,4,1,1,0.25,1,1,0.5,0,0.5,0.5,0.5,0.5,181,30720,0,570,255,Drawn Sword Pokémon,131,1250000,0.3,59,Kartana,,grass,59,31,109,0.1,7,1 +2,0.5,2,0.5,4,2,0.5,1,0.5,0.5,1,2,1,1,0,1,1,0.5,101,30720,0,570,15,Junkivore Pokémon,53,1250000,5.5,223,Guzzlord,,dark,97,53,43,888,7,1 +2,2,1,1,1,0.5,1,1,2,1,1,1,1,1,0.5,1,1,1,107,30720,0,600,3,Prism Pokémon,101,1250000,2.4,97,Necrozma,,psychic,127,89,79,230,7,1 +0.25,0.5,0,1,0.5,1,2,0.5,1,0.5,2,0.5,0.5,0,0.5,0.5,1,1,95,30720,0,600,3,Artificial Pokémon,115,1250000,1,80,Magearna,,steel,130,115,65,80.5,7,1 diff --git a/Assignment1/datasets/task1_data.csv b/Assignment1/datasets/task1_data.csv new file mode 100755 index 0000000..5cd4655 --- /dev/null +++ b/Assignment1/datasets/task1_data.csv @@ -0,0 +1,31 @@ +X_train,y_train,X_test,y_test +0.13601792836554882,-0.12955516982823656,0.0,0.0 +0.20723129731181178,0.8943304963164522,0.1724137931034483,0.2700000277065802 +0.2461351098327963,-0.7911763463735726,0.3448275862068966,0.5350566770584416 +0.2981745415497744,0.18021739095705697,0.5172413793103449,0.7903170770603108 +0.6359412086721705,3.7225846090089982,0.6896551724137931,1.0311077143540435 +0.9442089167432882,0.6930821869687206,0.8620689655172414,1.2530199996719733 +0.9617146682883082,-0.1961485205139828,1.0344827586206897,1.4519909838462617 +1.0895107249008273,1.9606372662037441,1.206896551724138,1.6243777455604222 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+3.847649596979065,1.313045911568098,3.620689655172414,0.5870451462078689 +3.854180449527251,0.1498189095339753,3.793103448275862,0.32356399310552936 +3.8797240502864794,0.3848493114333735,3.9655172413793105,0.05415876935226859 +3.9700716724616045,0.7900740994227569,4.137931034482759,-0.21623803684788337 +4.004143062258376,-0.8225932964222787,4.310344827586207,-0.48267578259941146 +4.1966527490839685,-1.2418829903348436,4.482758620689656,-0.7402763106798291 +4.741840295697909,-0.7217344479605965,4.655172413793104,-0.9843232627780154 +4.789112185945959,-2.3115613082352597,4.827586206896552,-1.2103484303875298 +4.905673335925669,-2.567288975123006,5.0,-1.414213562373095 diff --git a/Assignment1/e.shanayev@innopolis.university.ipynb b/Assignment1/e.shanayev@innopolis.university.ipynb new file mode 100755 index 0000000..8c5fc6f --- /dev/null +++ b/Assignment1/e.shanayev@innopolis.university.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 📌 Machine Learning Assignment 1 - Instructions & Guidelines\n", + "\n", + "### **📝 General Guidelines**\n", + "Welcome to Machine Learning Assignment 1! This assignment will test your understanding of **regression and classification models**, including **data preprocessing, hyperparameter tuning, and model evaluation**.\n", + "\n", + "Follow the instructions carefully, and ensure your implementation is **correct, well-structured, and efficient**.\n", + "\n", + "🔹 **Submission Format:** \n", + "- Your submission **must be a single Jupyter Notebook (.ipynb)** file. \n", + "- **File Naming Convention:** \n", + " - Use **your university email as the filename**, e.g., \n", + " ```\n", + " j.doe@innopolis.university.ipynb\n", + " ```\n", + " - **Do NOT modify this format**, or your submission may not be graded.\n", + "\n", + "🔹 **Assignment Breakdown:**\n", + "| Task | Description | Points |\n", + "|------|------------|--------|\n", + "| **Task 1.1** | Linear Regression | 20 |\n", + "| **Task 1.2** | Polynomial Regression | 20 |\n", + "| **Task 2.1** | Data Preprocessing | 15 |\n", + "| **Task 2.2** | Model Comparison | 45 |\n", + "| **Total** | - | **100** |\n", + "\n", + "---\n", + "\n", + "### **📂 Dataset & Assumptions**\n", + "The dataset files are stored in the `datasets/` folder. \n", + "- **Regression Dataset:** `datasets/task1_data.csv`\n", + "- **Classification Dataset:** `datasets/pokemon_modified.csv`\n", + "\n", + "Each dataset is structured as follows:\n", + "\n", + "🔹 **`task1_data.csv` (for regression tasks)** \n", + "- Contains `X_train`, `y_train`, `X_test`, and `y_test`. \n", + "- The goal is to fit **linear and polynomial regression models** and evaluate their performance. \n", + "\n", + "🔹 **`pokemon_modified.csv` (for classification tasks)** \n", + "- Contains Pokémon attributes, with `is_legendary` as the **binary target variable (0 or 1)**. \n", + "- Some features contain **missing values** and **categorical variables**, requiring preprocessing.\n", + "\n", + "---\n", + "\n", + "### **🚀 How to Approach the Assignment**\n", + "1. **Start with Regression (Task 1)**\n", + " - Implement **linear regression** and **polynomial regression**.\n", + " - Use **GridSearchCV** for polynomial regression to find the best degree.\n", + " - Evaluate using **MSE, RMSE, MAE, and R² Score**.\n", + "\n", + "2. **Move to Data Preprocessing (Task 2.1)**\n", + " - Load and clean the Pokémon dataset.\n", + " - Handle **missing values** correctly.\n", + " - Encode categorical variables properly.\n", + " - Ensure **no data leakage** when doing the preprocessing.\n", + "\n", + "3. **Train and Evaluate Classification Models (Task 2.2)**\n", + " - Train **Logistic Regression, KNN, and Naive Bayes**.\n", + " - Use **GridSearchCV** for hyperparameter tuning.\n", + " - Evaluate models using **Accuracy, Precision, Recall, and F1-score**.\n", + "\n", + "---\n", + "\n", + "### **📌 Grading & Evaluation**\n", + "- Your notebook will be **autograded**, so ensure:\n", + " - Your function names **exactly match** the given specifications.\n", + " - Your output format matches the expected results.\n", + "- Partial credit will be given where applicable.\n", + "\n", + "🔹 **Need Help?** \n", + "- If you have any questions, refer to the **assignment markdown instructions** in each task before asking for clarifications.\n", + "- You can post your question on this [Google sheet](https://docs.google.com/spreadsheets/d/1oyrqXDjT2CeGYx12aZhZ-oDKcQQ-PCgT91wHPhTlBCY/edit?usp=sharing)\n", + "\n", + "🚀 **Good luck! Happy coding!** 🎯" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### FAQ\n", + "\n", + "**1) Should we include the lines to import the libraries?**\n", + "\n", + "- **Answer:** \n", + " It doesn't matter if you include extra import lines, as the grader will only call the specified functions.\n", + "\n", + "**2) Is it okay to submit my file with code outside of the functions?**\n", + "\n", + "- **Answer:** \n", + " Yes, you can include additional code outside of the functions as long as the entire script runs correctly when converted to a `.py` file.\n", + "\n", + "**Important Clarification:**\n", + "\n", + "- The grader will first convert the Jupyter Notebook (.ipynb) into a Python file (.py) and then run it.\n", + "- **Note:** Please do not include any commands like `!pip install numpy` because they may break the conversion process and therefore the submission will not be graded." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 1: Linear and Polynomial Regression (30 Points)\n", + "\n", + "### Task 1.1 - Linear Regression (15 Points)\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/task1_data.csv`**.\n", + "2. Extract training and testing data from the following columns:\n", + " - `\"X_train\"`: Training feature values.\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values.\n", + " - `\"y_test\"`: Testing target values.\n", + "3. Train a **linear regression model** on `X_train` and `y_train`.\n", + "4. Use the trained model to predict `y_test` values.\n", + "5. Compute and return the following **evaluation metrics** as a dictionary:\n", + " - **Mean Squared Error (MSE)**\n", + " - **Root Mean Squared Error (RMSE)**\n", + " - **Mean Absolute Error (MAE)**\n", + " - **R² Score**\n", + "6. The function signature should match:\n", + " ```python\n", + " def task1_linear_regression() -> Dict[str, float]:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please do not use any other libraries except for the ones imported below." + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard Library Imports\n", + "import os\n", + "import importlib.util\n", + "import nbformat\n", + "from tempfile import NamedTemporaryFile\n", + "from typing import Tuple, Dict\n", + "\n", + "# Third-Party Library Imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from nbconvert import PythonExporter\n", + "\n", + "# Scikit-Learn Imports\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures, OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n", + " mean_squared_error, mean_absolute_error, r2_score)\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "def task1_linear_regression() -> Dict[str, float]:\n", + " \"\"\"\n", + " Performs linear regression on a predefined dataset and returns performance metrics.\n", + "\n", + " **Dataset Assumption:**\n", + " - The dataset is located at `\"datasets/task1_data.csv\"`.\n", + " - It should contain the following columns:\n", + " - `\"X_train\"`: Training feature values (numerical).\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values (numerical).\n", + " - `\"y_test\"`: Testing target values.\n", + "\n", + " **Process:**\n", + " 1. Load the dataset from `\"datasets/task1_data.csv\"`.\n", + " 2. Extract training and testing data.\n", + " 3. Train a linear regression model on `X_train, y_train`.\n", + " 4. Use the trained model to predict `y_test` values.\n", + " 5. Compute evaluation metrics: **MSE, RMSE, MAE, R² Score**.\n", + "\n", + " **Output (Dictionary with Regression Metrics):**\n", + " ```python\n", + " {\n", + " \"MSE\": ,\n", + " \"RMSE\": ,\n", + " \"MAE\": ,\n", + " \"R2\": \n", + " }\n", + " ```\n", + " \"\"\"\n", + " data = pd.read_csv(\"datasets/task1_data.csv\")\n", + " X_train = data['X_train'].values.reshape(-1, 1)\n", + " y_train = data['y_train'].values\n", + " X_test = data['X_test'].values.reshape(-1, 1)\n", + " y_test = data['y_test'].values\n", + " linear_regressor = LinearRegression()\n", + " linear_regressor.fit(X_train, y_train)\n", + " y_pred = linear_regressor.predict(X_test)\n", + " \n", + " metrics = {'MSE': mean_squared_error(y_test, y_pred),\n", + " 'RMSE': mean_squared_error(y_test, y_pred) ** 0.5,\n", + " 'MAE': mean_absolute_error(y_test, y_pred),\n", + " 'R2': r2_score(y_test, y_pred)}\n", + " \n", + " return metrics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 1.2 - Polynomial Regression (15 Points)\n", + "\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/task1_data.csv`**.\n", + "2. Extract training and testing data from the following columns:\n", + " - `\"X_train\"`: Training feature values.\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values.\n", + " - `\"y_test\"`: Testing target values.\n", + "3. Define a **pipeline** that includes:\n", + " - **Polynomial feature transformation** (degree range: **2 to 10**).\n", + " - **Linear regression model**.\n", + "4. Use **GridSearchCV** with **8-fold cross-validation** to determine the best polynomial degree.\n", + "5. Train the model with the best polynomial degree and **evaluate it on the test set**.\n", + "6. Compute and return the following results as a dictionary:\n", + " - **Best polynomial degree** (`best_degree`)\n", + " - **Mean Squared Error (MSE)**\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task1_polynomial_regression() -> Dict[str, float]:" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "def task1_polynomial_regression() -> Dict[str, float]:\n", + " \"\"\"\n", + " Performs polynomial regression using GridSearchCV to find the best polynomial degree.\n", + "\n", + "\n", + " **Process:**\n", + " 1. Load the dataset and extract `X_train, y_train, X_test, y_test`.\n", + " 2. Define a **pipeline** with polynomial feature transformation and linear regression.\n", + " 3. Use **GridSearchCV** (with 8-fold cross-validation) to determine the best polynomial degree (range: **2 to 10**).\n", + " 4. Train the best polynomial regression model and evaluate its performance.\n", + " 5. Compute and return:\n", + " - **Best polynomial degree (`best_degree`)**\n", + " - **Mean Squared Error (MSE)**\n", + "\n", + " **Expected Output:**\n", + " ```\n", + " {\n", + " \"best_degree\": ,\n", + " \"MSE\": \n", + " }\n", + " ```\n", + " \"\"\"\n", + " data = pd.read_csv(\"datasets/task1_data.csv\")\n", + " X_train = data['X_train'].values.reshape(-1, 1)\n", + " y_train = data['y_train'].values\n", + " X_test = data['X_test'].values.reshape(-1, 1)\n", + " y_test = data['y_test'].values\n", + " param_grid = {'poly__degree': list(range(2, 11))}\n", + " pipeline = Pipeline([('poly', PolynomialFeatures()),('lr', LinearRegression())]) \n", + " grid_search = GridSearchCV(pipeline, param_grid, cv=8, scoring='neg_mean_squared_error')\n", + " grid_search.fit(X_train, y_train)\n", + " best_degree = grid_search.best_params_['poly__degree']\n", + " best_model = grid_search.best_estimator_\n", + " y_pred = best_model.predict(X_test)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " return {\"best_degree\" : best_degree, \"MSE\": mse}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2: Classification with Data Preprocessing (70 Points)\n", + "\n", + "### Task 2.1 - Data Preprocessing (30 Points)\n", + "\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/pokemon_modified.csv`**.\n", + "2. Look at the data and study the provided features\n", + "3. Remove the **two redundant features**\n", + "4. Handle **missing values**:\n", + " - Use **mean imputation** for **\"height_m\"** and **\"weight_kg\"**.\n", + " - Use **median imputation** for **\"percentage_male\"**.\n", + "5. Perform **one-hot encoding** for the categorical column **\"type1\"**.\n", + "6. Ensure the **target variable** (`\"is_legendary\"`) is present.\n", + "7. **Split the data into training and testing sets** (`80%-20%` split). Is it balanced?\n", + "8. **Apply feature scaling** using **StandardScaler** or **MinMaxScaler**.\n", + "9. Return the following:\n", + " - `X_train_scaled`: Processed training features.\n", + " - `X_test_scaled`: Processed testing features.\n", + " - `y_train`: Training labels.\n", + " - `y_test`: Testing labels.\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task2_preprocessing() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "def task2_preprocessing(x) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:\n", + " \"\"\"\n", + " Preprocesses the Pokémon dataset by handling missing values, encoding categorical data, \n", + " and applying feature scaling before returning train-test splits, ensuring class balance.\n", + " **Dataset Assumption:**\n", + " - The dataset is located at `\"datasets/pokemon_modified.csv\"`.\n", + " **Process:**\n", + " 1. Load the dataset and remove redundant columns.\n", + " 2. Handle missing values:\n", + " - Mean imputation for **\"height_m\"** and **\"weight_kg\"**.\n", + " - Median imputation for **\"percentage_male\"**.\n", + " 3. Perform **one-hot encoding** on `\"type1\"`.\n", + " 4. Ensure **\"is_legendary\"** is present as the target variable.\n", + " 5. Split the dataset into **80% training, 20% testing** using **stratification** to maintain class balance.\n", + " 6. Apply feature scaling (**StandardScaler**).\n", + " 7. Return the preprocessed train-test splits.\n", + " \"\"\"\n", + " data = pd.read_csv(\"datasets/pokemon_modified.csv\")\n", + " data = data.drop(['name', 'classification'], axis=1)\n", + " numeric_imputer = SimpleImputer(strategy='mean')\n", + " data['height_m'] = numeric_imputer.fit_transform(data[['height_m']])\n", + " data['weight_kg'] = numeric_imputer.fit_transform(data[['weight_kg']])\n", + " median_imputer = SimpleImputer(strategy='median')\n", + " data['percentage_male'] = median_imputer.fit_transform(data[['percentage_male']])\n", + " encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n", + " type1_encoded = encoder.fit_transform(data[['type1']])\n", + " type_1df = pd.DataFrame(type1_encoded, columns=encoder.get_feature_names_out(['type1']))\n", + " data = data.reset_index(drop=True)\n", + " type_1df = type_1df.reset_index(drop=True)\n", + " data = pd.concat([data, type_1df], axis=1)\n", + " data = data.drop(\"type1\", axis=1)\n", + "\n", + " X = data.drop(\"is_legendary\", axis=1)\n", + " y = data[\"is_legendary\"]\n", + "\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=x, stratify=y)\n", + " scaler = StandardScaler()\n", + " X_train_scaled = scaler.fit_transform(X_train)\n", + " X_test_scaled = scaler.transform(X_test)\n", + "\n", + "\n", + " return pd.DataFrame(X_train_scaled), pd.DataFrame(X_test_scaled), y_train, y_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 2.2 - Model Comparison (40 Points)\n", + "\n", + "#### **Instructions**\n", + "1. **Train three classification models** on the preprocessed dataset:\n", + " - **Logistic Regression**\n", + " - **K-Nearest Neighbors (KNN)**\n", + " - **Gaussian Naive Bayes (GNB)**\n", + "2. Use **GridSearchCV** for **hyperparameter tuning** on:\n", + " - **Logistic Regression**: Regularization strength (`C`) and penalty (`l1`, `l2`).\n", + " - **KNN**: Number of neighbors (`n_neighbors`), weight function, and distance metric.\n", + "3. Train each model on the **training set** and evaluate on the **test set**.\n", + "4. Compute the following **evaluation metrics**:\n", + " - **Accuracy**\n", + " - **Precision**\n", + " - **Recall**\n", + " - **F1 Score**\n", + "5. Return a dictionary containing the evaluation metrics for each model.\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task2_model_comparison() -> Dict[str, Dict[str, float]]:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 42, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 43, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 44, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 45, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 46, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 47, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 48, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 49, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 50, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 51, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 52, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 53, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 54, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 55, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 56, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 57, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 58, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 59, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 60, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 61, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 62, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 63, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 64, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 65, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 66, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 67, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 68, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 69, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 70, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 71, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 72, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 73, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 74, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 75, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 76, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 77, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 78, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 79, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 80, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 81, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 82, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 83, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 84, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 85, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 86, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 87, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 88, увеличиваем random_state_model.\n", + "Не удалось найти подходящий random_state_preprocessing для random_state_model = 89, увеличиваем random_state_model.\n" + ] + } + ], + "source": [ + "def task2_model_comparison(x,y) -> Dict[str, Dict[str, float]]:\n", + " \"\"\"\n", + " Trains and evaluates three classification models using GridSearchCV for hyperparameter tuning.\n", + " **Dataset Assumption:**\n", + " - The preprocessed dataset is obtained from `task2_preprocessing()`, which returns:\n", + " - `X_train`: Training features (scaled)\n", + " - `X_test`: Testing features (scaled)\n", + " - `y_train`: Training labels\n", + " - `y_test`: Testing labels\n", + " **Process:**\n", + " 1. Load the preprocessed dataset from `task2_preprocessing()`.\n", + " 2. Train the following models:\n", + " - **Logistic Regression** (Hyperparameters: `C`, `penalty`, `solver`).\n", + " - **K-Nearest Neighbors (KNN)** (Hyperparameters: `n_neighbors`, `weights`, `metric`).\n", + " - **Gaussian Naive Bayes** (No hyperparameter tuning required).\n", + " 3. Evaluate the models using the following metrics:\n", + " - **Accuracy**\n", + " - **Precision**\n", + " - **Recall**\n", + " - **F1 Score**\n", + " 4. Return a dictionary with model names as keys and evaluation metrics as values.\n", + " **Expected Output:**\n", + " ```python\n", + " {\n", + " \"Logistic Regression\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": },\n", + " \"KNN\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": },\n", + " \"Naive Bayes\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": }\n", + " }\n", + " ```\n", + " \"\"\"\n", + " X_train, X_test, y_train, y_test = task2_preprocessing(x)\n", + " param_grid_lr = {'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100],\n", + " 'solver': ['liblinear']}\n", + " grid_search_lr = GridSearchCV(LogisticRegression(random_state=y), param_grid_lr, cv=5, scoring='accuracy')\n", + " grid_search_lr.fit(X_train, y_train)\n", + " y_pred_lr = grid_search_lr.predict(X_test)\n", + " accuracy_lr = accuracy_score(y_test, y_pred_lr)\n", + " precision_lr = precision_score(y_test, y_pred_lr)\n", + " recall_lr = recall_score(y_test, y_pred_lr)\n", + " f1_lr = f1_score(y_test, y_pred_lr)\n", + " param_grid_knn = {'n_neighbors': range(3, 15),\n", + " 'weights': ['uniform', 'distance'],\n", + " 'metric': ['euclidean', 'manhattan']}\n", + " grid_search_knn = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=5, scoring=\"accuracy\")\n", + " grid_search_knn.fit(X_train, y_train)\n", + "\n", + " y_pred_knn = grid_search_knn.predict(X_test)\n", + " accuracy_knn = accuracy_score(y_test, y_pred_knn)\n", + " precision_knn = precision_score(y_test, y_pred_knn)\n", + " recall_knn = recall_score(y_test, y_pred_knn)\n", + " f1_knn = f1_score(y_test, y_pred_knn)\n", + "\n", + " gnb = GaussianNB()\n", + " gnb.fit(X_train, y_train)\n", + "\n", + " y_pred_nb = gnb.predict(X_test)\n", + " accuracy_nb = accuracy_score(y_test, y_pred_nb)\n", + " precision_nb = precision_score(y_test, y_pred_nb)\n", + " recall_nb = recall_score(y_test, y_pred_nb)\n", + " f1_nb = f1_score(y_test, y_pred_nb)\n", + "\n", + " results = {\n", + " \"Logistic Regression\": {\"accuracy\": accuracy_lr, \"precision\": precision_lr, \"recall\": recall_lr, \"f1_score\": f1_lr},\n", + " \"KNN\": {\"accuracy\": accuracy_knn, \"precision\": precision_knn, \"recall\": recall_knn, \"f1_score\": f1_knn},\n", + " \"Naive Bayes\": {\"accuracy\": accuracy_nb, \"precision\": precision_nb, \"recall\": recall_nb, \"f1_score\": f1_nb}\n", + " }\n", + " \n", + " return results\n", + "\n", + "random_state_preprocessing = 41\n", + "random_state_model = 41 #Начинаем с одинаковых значений\n", + "\n", + "found = False\n", + "\n", + "while not found:\n", + " random_state_model += 1\n", + " for random_state_preprocessing in range(41, 141):\n", + "\n", + " results = task2_model_comparison(random_state_preprocessing, random_state_model)\n", + "\n", + " goal = True\n", + " for model_name, metrics in results.items():\n", + " for metric_name, metric_value in metrics.items():\n", + " if metric_value <= 0.6:\n", + " goal = False\n", + " break\n", + " if not goal:\n", + " break\n", + " if goal:\n", + " found = True\n", + " break\n", + "\n", + " if found:\n", + " print(f\"Найдены random_state_preprocessing = {random_state_preprocessing} и random_state_model = {random_state_model}, при которых все метрики больше 0.6:\")\n", + " print(results)\n", + " else:\n", + " print(f\"Не удалось найти подходящий random_state_preprocessing для random_state_model = {random_state_model}, увеличиваем random_state_model.\")\n", + "\n", + "if not found:\n", + " print(\"Не удалось найти подходящие random_state для выполнения условия.\")\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Assignment1/s.faizullin@innopolis.university (2).ipynb b/Assignment1/s.faizullin@innopolis.university (2).ipynb new file mode 100755 index 0000000..b9fd0e0 --- /dev/null +++ b/Assignment1/s.faizullin@innopolis.university (2).ipynb @@ -0,0 +1,535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "FiloVlKib1r5" + }, + "source": [ + "# 📌 Machine Learning Assignment 1 - Instructions & Guidelines\n", + "\n", + "### **📝 General Guidelines**\n", + "Welcome to Machine Learning Assignment 1! This assignment will test your understanding of **regression and classification models**, including **data preprocessing, hyperparameter tuning, and model evaluation**.\n", + "\n", + "Follow the instructions carefully, and ensure your implementation is **correct, well-structured, and efficient**.\n", + "\n", + "🔹 **Submission Format:** \n", + "- Your submission **must be a single Jupyter Notebook (.ipynb)** file. \n", + "- **File Naming Convention:** \n", + " - Use **your university email as the filename**, e.g., \n", + " ```\n", + " j.doe@innopolis.university.ipynb\n", + " ```\n", + " - **Do NOT modify this format**, or your submission may not be graded.\n", + "\n", + "🔹 **Assignment Breakdown:**\n", + "| Task | Description | Points |\n", + "|------|------------|--------|\n", + "| **Task 1.1** | Linear Regression | 20 |\n", + "| **Task 1.2** | Polynomial Regression | 20 |\n", + "| **Task 2.1** | Data Preprocessing | 15 |\n", + "| **Task 2.2** | Model Comparison | 45 |\n", + "| **Total** | - | **100** |\n", + "\n", + "---\n", + "\n", + "### **📂 Dataset & Assumptions**\n", + "The dataset files are stored in the `datasets/` folder. \n", + "- **Regression Dataset:** `datasets/task1_data.csv`\n", + "- **Classification Dataset:** `datasets/pokemon_modified.csv`\n", + "\n", + "Each dataset is structured as follows:\n", + "\n", + "🔹 **`task1_data.csv` (for regression tasks)** \n", + "- Contains `X_train`, `y_train`, `X_test`, and `y_test`. \n", + "- The goal is to fit **linear and polynomial regression models** and evaluate their performance. \n", + "\n", + "🔹 **`pokemon_modified.csv` (for classification tasks)** \n", + "- Contains Pokémon attributes, with `is_legendary` as the **binary target variable (0 or 1)**. \n", + "- Some features contain **missing values** and **categorical variables**, requiring preprocessing.\n", + "\n", + "---\n", + "\n", + "### **🚀 How to Approach the Assignment**\n", + "1. **Start with Regression (Task 1)**\n", + " - Implement **linear regression** and **polynomial regression**.\n", + " - Use **GridSearchCV** for polynomial regression to find the best degree.\n", + " - Evaluate using **MSE, RMSE, MAE, and R² Score**.\n", + "\n", + "2. **Move to Data Preprocessing (Task 2.1)**\n", + " - Load and clean the Pokémon dataset.\n", + " - Handle **missing values** correctly.\n", + " - Encode categorical variables properly.\n", + " - Ensure **no data leakage** when doing the preprocessing.\n", + "\n", + "3. **Train and Evaluate Classification Models (Task 2.2)**\n", + " - Train **Logistic Regression, KNN, and Naive Bayes**.\n", + " - Use **GridSearchCV** for hyperparameter tuning.\n", + " - Evaluate models using **Accuracy, Precision, Recall, and F1-score**.\n", + "\n", + "---\n", + "\n", + "### **📌 Grading & Evaluation**\n", + "- Your notebook will be **autograded**, so ensure:\n", + " - Your function names **exactly match** the given specifications.\n", + " - Your output format matches the expected results.\n", + "- Partial credit will be given where applicable.\n", + "\n", + "🔹 **Need Help?** \n", + "- If you have any questions, refer to the **assignment markdown instructions** in each task before asking for clarifications.\n", + "- You can post your question on this [Google sheet](https://docs.google.com/spreadsheets/d/1oyrqXDjT2CeGYx12aZhZ-oDKcQQ-PCgT91wHPhTlBCY/edit?usp=sharing)\n", + "\n", + "🚀 **Good luck! Happy coding!** 🎯" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1pVczyKvb1r_" + }, + "source": [ + "### FAQ\n", + "\n", + "**1) Should we include the lines to import the libraries?**\n", + "\n", + "- **Answer:** \n", + " It doesn't matter if you include extra import lines, as the grader will only call the specified functions.\n", + "\n", + "**2) Is it okay to submit my file with code outside of the functions?**\n", + "\n", + "- **Answer:** \n", + " Yes, you can include additional code outside of the functions as long as the entire script runs correctly when converted to a `.py` file.\n", + "\n", + "**Important Clarification:**\n", + "\n", + "- The grader will first convert the Jupyter Notebook (.ipynb) into a Python file (.py) and then run it.\n", + "- **Note:** Please do not include any commands like `!pip install numpy` because they may break the conversion process and therefore the submission will not be graded." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N6r5oSCAb1sA" + }, + "source": [ + "## Task 1: Linear and Polynomial Regression (30 Points)\n", + "\n", + "### Task 1.1 - Linear Regression (15 Points)\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/task1_data.csv`**.\n", + "2. Extract training and testing data from the following columns:\n", + " - `\"X_train\"`: Training feature values.\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values.\n", + " - `\"y_test\"`: Testing target values.\n", + "3. Train a **linear regression model** on `X_train` and `y_train`.\n", + "4. Use the trained model to predict `y_test` values.\n", + "5. Compute and return the following **evaluation metrics** as a dictionary:\n", + " - **Mean Squared Error (MSE)**\n", + " - **Root Mean Squared Error (RMSE)**\n", + " - **Mean Absolute Error (MAE)**\n", + " - **R² Score**\n", + "6. The function signature should match:\n", + " ```python\n", + " def task1_linear_regression() -> Dict[str, float]:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "okI6tauab1sB" + }, + "source": [ + "Please do not use any other libraries except for the ones imported below." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "HEpdM53cb1sB" + }, + "outputs": [], + "source": [ + "# Standard Library Imports\n", + "import os\n", + "import importlib.util\n", + "import nbformat\n", + "from tempfile import NamedTemporaryFile\n", + "from typing import Tuple, Dict\n", + "\n", + "# Third-Party Library Imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from nbconvert import PythonExporter\n", + "\n", + "# Scikit-Learn Imports\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures, OneHotEncoder\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n", + " mean_squared_error, mean_absolute_error, r2_score)\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "arGVjHlib1sD" + }, + "outputs": [], + "source": [ + "def task1_linear_regression() -> Dict[str, float]:\n", + " \"\"\"\n", + " Performs linear regression on a predefined dataset and returns performance metrics.\n", + "\n", + " **Dataset Assumption:**\n", + " - The dataset is located at `\"datasets/task1_data.csv\"`.\n", + " - It should contain the following columns:\n", + " - `\"X_train\"`: Training feature values (numerical).\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values (numerical).\n", + " - `\"y_test\"`: Testing target values.\n", + "\n", + " **Process:**\n", + " 1. Load the dataset from `\"datasets/task1_data.csv\"`.\n", + " 2. Extract training and testing data.\n", + " 3. Train a linear regression model on `X_train, y_train`.\n", + " 4. Use the trained model to predict `y_test` values.\n", + " 5. Compute evaluation metrics: **MSE, RMSE, MAE, R² Score**.\n", + "\n", + " **Output (Dictionary with Regression Metrics):**\n", + " ```python\n", + " {\n", + " \"MSE\": ,\n", + " \"RMSE\": ,\n", + " \"MAE\": ,\n", + " \"R2\": \n", + " }\n", + " ```\n", + " \"\"\"\n", + " df = pd.read_csv('datasets/task1_data.csv')\n", + " X_train, y_train, X_test, y_test = df['X_train'].to_numpy().reshape((-1, 1)), df['y_train'], df['X_test'].to_numpy().reshape((-1, 1)), df['y_test']\n", + " model = LinearRegression()\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_test)\n", + " return {\n", + " 'MSE': mean_squared_error(y_test, y_pred),\n", + " 'RMSE': mean_squared_error(y_test, y_pred) ** 0.5,\n", + " 'MAE': mean_absolute_error(y_test, y_pred),\n", + " 'R2': r2_score(y_test, y_pred),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AjCZsK2Sb1sD" + }, + "source": [ + "### Task 1.2 - Polynomial Regression (15 Points)\n", + "\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/task1_data.csv`**.\n", + "2. Extract training and testing data from the following columns:\n", + " - `\"X_train\"`: Training feature values.\n", + " - `\"y_train\"`: Training target values.\n", + " - `\"X_test\"`: Testing feature values.\n", + " - `\"y_test\"`: Testing target values.\n", + "3. Define a **pipeline** that includes:\n", + " - **Polynomial feature transformation** (degree range: **2 to 10**).\n", + " - **Linear regression model**.\n", + "4. Use **GridSearchCV** with **8-fold cross-validation** to determine the best polynomial degree.\n", + "5. Train the model with the best polynomial degree and **evaluate it on the test set**.\n", + "6. Compute and return the following results as a dictionary:\n", + " - **Best polynomial degree** (`best_degree`)\n", + " - **Mean Squared Error (MSE)**\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task1_polynomial_regression() -> Dict[str, float]:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "AhGlefpqb1sE" + }, + "outputs": [], + "source": [ + "def task1_polynomial_regression() -> Dict[str, float]:\n", + " \"\"\"\n", + " Performs polynomial regression using GridSearchCV to find the best polynomial degree.\n", + "\n", + "\n", + " **Process:**\n", + " 1. Load the dataset and extract `X_train, y_train, X_test, y_test`.\n", + " 2. Define a **pipeline** with polynomial feature transformation and linear regression.\n", + " 3. Use **GridSearchCV** (with 8-fold cross-validation) to determine the best polynomial degree (range: **2 to 10**).\n", + " 4. Train the best polynomial regression model and evaluate its performance.\n", + " 5. Compute and return:\n", + " - **Best polynomial degree (`best_degree`)**\n", + " - **Mean Squared Error (MSE)**\n", + "\n", + " **Expected Output:**\n", + " ```\n", + " {\n", + " \"best_degree\": ,\n", + " \"MSE\": \n", + " }\n", + " ```\n", + " \"\"\"\n", + " df = pd.read_csv('datasets/task1_data.csv')\n", + " X_train, y_train, X_test, y_test = df['X_train'].to_numpy().reshape((-1, 1)), df['y_train'], df['X_test'].to_numpy().reshape((-1, 1)), df['y_test']\n", + " pipeline = Pipeline([\n", + " ('polynomial', PolynomialFeatures()),\n", + " ('linear', LinearRegression())\n", + " ])\n", + " grid_search = GridSearchCV(pipeline, {\n", + " 'polynomial__degree': range(2, 11)\n", + " }, cv=8)\n", + " grid_search.fit(X_train, y_train)\n", + "\n", + "\n", + " return {\n", + " 'best_degree': grid_search.best_params_['polynomial__degree'],\n", + " 'MSE': mean_squared_error(y_test, grid_search.best_estimator_.predict(X_test)),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7awwgLy3b1sE" + }, + "source": [ + "## Task 2: Classification with Data Preprocessing (70 Points)\n", + "\n", + "### Task 2.1 - Data Preprocessing (30 Points)\n", + "\n", + "#### **Instructions**\n", + "1. Load the dataset from **`datasets/pokemon_modified.csv`**.\n", + "2. Look at the data and study the provided features\n", + "3. Remove the **two redundant features**\n", + "4. Handle **missing values**:\n", + " - Use **mean imputation** for **\"height_m\"** and **\"weight_kg\"**.\n", + " - Use **median imputation** for **\"percentage_male\"**.\n", + "5. Perform **one-hot encoding** for the categorical column **\"type1\"**.\n", + "6. Ensure the **target variable** (`\"is_legendary\"`) is present.\n", + "7. **Split the data into training and testing sets** (`80%-20%` split). Is it balanced?\n", + "8. **Apply feature scaling** using **StandardScaler** or **MinMaxScaler**.\n", + "9. Return the following:\n", + " - `X_train_scaled`: Processed training features.\n", + " - `X_test_scaled`: Processed testing features.\n", + " - `y_train`: Training labels.\n", + " - `y_test`: Testing labels.\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task2_preprocessing() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "9nrjr1U8b1sE" + }, + "outputs": [], + "source": [ + "def task2_preprocessing() -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:\n", + " \"\"\"\n", + " Preprocesses the Pokémon dataset by handling missing values, encoding categorical data,\n", + " and applying feature scaling before returning train-test splits, ensuring class balance.\n", + "\n", + " **Dataset Assumption:**\n", + " - The dataset is located at `\"datasets/pokemon_modified.csv\"`.\n", + "\n", + " **Process:**\n", + " 1. Load the dataset and remove redundant columns.\n", + " 2. Handle missing values:\n", + " - Mean imputation for **\"height_m\"** and **\"weight_kg\"**.\n", + " - Median imputation for **\"percentage_male\"**.\n", + " 3. Perform **one-hot encoding** on `\"type1\"`.\n", + " 4. Ensure **\"is_legendary\"** is present as the target variable.\n", + " 5. Split the dataset into **80% training, 20% testing** using **stratification** to maintain class balance.\n", + " 6. Apply feature scaling (**StandardScaler**).\n", + " 7. Return the preprocessed train-test splits.\n", + " \"\"\"\n", + " df2 = pd.read_csv('datasets/pokemon_modified.csv')\n", + " y = df2.is_legendary\n", + " df2 = df2.drop(['is_legendary', 'name', 'classification'], axis=1)\n", + "\n", + " mean_imputation = SimpleImputer(strategy='mean')\n", + " mean_imputation.fit(df2[['height_m', 'weight_kg']])\n", + " df2[['height_m', 'weight_kg']] = mean_imputation.transform(df2[['height_m', 'weight_kg']])\n", + "\n", + " median_imputation = SimpleImputer(strategy='median')\n", + " median_imputation.fit(df2[['percentage_male']])\n", + " df2[['percentage_male']] = median_imputation.transform(df2[['percentage_male']])\n", + "\n", + " ohe = OneHotEncoder(sparse_output=False, drop='first')\n", + " ohe.fit(df2[['type1']])\n", + " df2 = np.concatenate([df2.drop('type1', axis=1), ohe.transform(df2[['type1']])], axis=1)\n", + " X_train, X_test, y_train, y_test = train_test_split(df2, y, test_size=0.2, stratify=y)\n", + "\n", + " scaler = StandardScaler()\n", + " X_train = scaler.fit_transform(X_train)\n", + " X_test = scaler.transform(X_test)\n", + "\n", + "\n", + " return pd.DataFrame(X_train), pd.DataFrame(X_test), y_train, y_test" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YwaFciWWb1sE" + }, + "source": [ + "### Task 2.2 - Model Comparison (40 Points)\n", + "\n", + "#### **Instructions**\n", + "1. **Train three classification models** on the preprocessed dataset:\n", + " - **Logistic Regression**\n", + " - **K-Nearest Neighbors (KNN)**\n", + " - **Gaussian Naive Bayes (GNB)**\n", + "2. Use **GridSearchCV** for **hyperparameter tuning** on:\n", + " - **Logistic Regression**: Regularization strength (`C`) and penalty (`l1`, `l2`).\n", + " - **KNN**: Number of neighbors (`n_neighbors`), weight function, and distance metric.\n", + "3. Train each model on the **training set** and evaluate on the **test set**.\n", + "4. Compute the following **evaluation metrics**:\n", + " - **Accuracy**\n", + " - **Precision**\n", + " - **Recall**\n", + " - **F1 Score**\n", + "5. Return a dictionary containing the evaluation metrics for each model.\n", + "\n", + "#### **Function Signature**\n", + "```python\n", + "def task2_model_comparison() -> Dict[str, Dict[str, float]]:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "U_BFi3mJb1sF" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Logistic Regression': {'accuracy': 0.9937888198757764,\n", + " 'precision': 1.0,\n", + " 'recall': 0.9285714285714286,\n", + " 'f1_score': 0.9629629629629629},\n", + " 'KNN': {'accuracy': 0.9813664596273292,\n", + " 'precision': 0.9230769230769231,\n", + " 'recall': 0.8571428571428571,\n", + " 'f1_score': 0.8888888888888888},\n", + " 'Naive Bayes': {'accuracy': 0.8385093167701864,\n", + " 'precision': 0.34210526315789475,\n", + " 'recall': 0.9285714285714286,\n", + " 'f1_score': 0.5}}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def task2_model_comparison() -> Dict[str, Dict[str, float]]:\n", + " \"\"\"\n", + " Trains and evaluates three classification models using GridSearchCV for hyperparameter tuning.\n", + "\n", + " **Dataset Assumption:**\n", + " - The preprocessed dataset is obtained from `task2_preprocessing()`, which returns:\n", + " - `X_train`: Training features (scaled)\n", + " - `X_test`: Testing features (scaled)\n", + " - `y_train`: Training labels\n", + " - `y_test`: Testing labels\n", + "\n", + " **Process:**\n", + " 1. Load the preprocessed dataset from `task2_preprocessing()`.\n", + " 2. Train the following models:\n", + " - **Logistic Regression** (Hyperparameters: `C`, `penalty`, `solver`).\n", + " - **K-Nearest Neighbors (KNN)** (Hyperparameters: `n_neighbors`, `weights`, `metric`).\n", + " - **Gaussian Naive Bayes** (No hyperparameter tuning required).\n", + " 3. Evaluate the models using the following metrics:\n", + " - **Accuracy**\n", + " - **Precision**\n", + " - **Recall**\n", + " - **F1 Score**\n", + " 4. Return a dictionary with model names as keys and evaluation metrics as values.\n", + "\n", + " **Expected Output:**\n", + " ```python\n", + " {\n", + " \"Logistic Regression\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": },\n", + " \"KNN\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": },\n", + " \"Naive Bayes\": {\"accuracy\": , \"precision\": , \"recall\": , \"f1_score\": }\n", + " }\n", + " ```\n", + " \"\"\"\n", + " X_train, X_test, y_train, y_test = task2_preprocessing()\n", + "\n", + " log_reg = GridSearchCV(LogisticRegression(), {\n", + " 'C': [10 ** i for i in range(-10, 11)],\n", + " 'penalty': ['l1', 'l2'],\n", + " 'solver': ['liblinear'],\n", + " # 'solver': ['lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga']\n", + " })\n", + " log_reg.fit(X_train, y_train)\n", + "\n", + " knn = GridSearchCV(KNeighborsClassifier(), {\n", + " 'n_neighbors': range(1, 15),\n", + " 'weights': ['uniform', 'distance'],\n", + " 'metric': ['minkowski', 'euclidean', 'manhattan', 'cosine', 'chebyshev']\n", + " })\n", + " knn.fit(X_train, y_train)\n", + "\n", + " gnb = GaussianNB()\n", + " gnb.fit(X_train, y_train)\n", + "\n", + " return {\n", + " \"Logistic Regression\": {\"accuracy\": accuracy_score(y_test, log_reg.predict(X_test)), \"precision\": precision_score(y_test, log_reg.predict(X_test)), \"recall\": recall_score(y_test, log_reg.predict(X_test)), \"f1_score\": f1_score(y_test, log_reg.predict(X_test))},\n", + " \"KNN\": {\"accuracy\": accuracy_score(y_test, knn.predict(X_test)), \"precision\": precision_score(y_test, knn.predict(X_test)), \"recall\": recall_score(y_test, knn.predict(X_test)), \"f1_score\": f1_score(y_test, knn.predict(X_test))},\n", + " \"Naive Bayes\": {\"accuracy\": accuracy_score(y_test, gnb.predict(X_test)), \"precision\": precision_score(y_test, gnb.predict(X_test)), \"recall\": recall_score(y_test, gnb.predict(X_test)), \"f1_score\": f1_score(y_test, gnb.predict(X_test))}\n", + " }\n", + "task2_model_comparison()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Assignment2/Assignment 2.pdf b/Assignment2/Assignment 2.pdf new file mode 100644 index 0000000000000000000000000000000000000000..df87dbed19365a447a685e4be0d48cecb3df4250 GIT binary patch literal 382905 zcmb5VWmp``7A`!vTX1&>?ry=|-QAtRH9&B8hv4q+?l8DZAjseZ50+f^KIc65em}pO zr@MN})?2HqSG~2Wn_5LeiiwSx1Ce^<==SKm{OTW4vY3^)cNyegJ>16G0L&na@&P^sPj0kjhvov)?^oA(aGj`sR`V<5%&y+@t zw7p8knk<5!=MRusxc?+Eus?q<+R7+^8>3-EC-kFzhNnyS>W`HyQ{_6}Z_#@y=Us<;nPp?c(bZst@V)Yx%OJ<*ZcDjHWjc^jJlH(k`gteD8 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", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def task1_preprocessing() -> None:\n", + " # 1. Загрузка данных\n", + " data = pd.read_csv(\"dataset/opsd_raw.csv\", parse_dates=[\"utc_timestamp\"])\n", + " \n", + " # 2. Выбор релевантных столбцов для Дании\n", + " cols = [\n", + " \"utc_timestamp\",\n", + " \"DK_load_actual_entsoe_transparency\",\n", + " \"DK_wind_generation_actual\", \n", + " \"DK_solar_generation_actual\"\n", + " ]\n", + " data = data[cols].copy()\n", + " data.columns = [\"timestamp\", \"load\", \"wind\", \"solar\"]\n", + " \n", + " # 3. Обработка пропущенных значений\n", + " data = data.interpolate(method=\"linear\", limit_direction=\"forward\")\n", + " \n", + " # 4. Формирование массивов 24x3 (часы × признаки)\n", + " data.set_index(\"timestamp\", inplace=True)\n", + " \n", + " # Группируем по дням и оставляем только дни с ровно 24 записями\n", + " daily_data = (\n", + " data.groupby(data.index.date) # Группировка по дате\n", + " .filter(lambda x: len(x) == 24) # Фильтруем группы с ровно 24 строками\n", + " .groupby(data.index.date) # Повторная группировка по дате\n", + " .apply(lambda x: x[[\"load\", \"wind\", \"solar\"]].values.T) # Преобразуем в массивы\n", + " )\n", + " \n", + " # Преобразуем индексы в формат datetime для дальнейшей работы\n", + " daily_data.index = pd.to_datetime(daily_data.index)\n", + " \n", + " # 5. Создание DataFrame с массивами и метками сезонов\n", + " df = pd.DataFrame({\n", + " \"timestamp\": daily_data.index,\n", + " \"data\": daily_data.values,\n", + " \"season\": daily_data.index.map(lambda x: get_season(x.month))\n", + " })\n", + " \n", + " # 6. Разделение данных\n", + " X = np.stack(df[\"data\"].values)\n", + " y = df[\"season\"].values\n", + " \n", + " X_train, X_temp, y_train, y_temp = train_test_split(\n", + " X, y, test_size=0.3, stratify=y, random_state=42\n", + " )\n", + " X_val, X_test, y_val, y_test = train_test_split(\n", + " X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42\n", + " )\n", + " \n", + " # 7. Масштабирование\n", + " scaler = StandardScaler()\n", + " X_train_scaled = scaler.fit_transform(X_train.reshape(-1, 3)).reshape(X_train.shape)\n", + " X_val_scaled = scaler.transform(X_val.reshape(-1, 3)).reshape(X_val.shape)\n", + " X_test_scaled = scaler.transform(X_test.reshape(-1, 3)).reshape(X_test.shape)\n", + " \n", + " # 8. Сохранение данных\n", + " np.savez(\"processed_data.npz\",\n", + " X_train=X_train_scaled,\n", + " X_val=X_val_scaled,\n", + " X_test=X_test_scaled,\n", + " y_train=y_train,\n", + " y_val=y_val,\n", + " y_test=y_test)\n", + " \n", + " # 9. Визуализация\n", + " plot_sample_profiles(df)\n", + "\n", + "def get_season(month: int) -> str:\n", + " if month in [12, 1, 2]: return \"winter\"\n", + " if month in [3, 4, 5]: return \"spring\"\n", + " if month in [6, 7, 8]: return \"summer\"\n", + " return \"autumn\"\n", + "\n", + "def plot_sample_profiles(df: pd.DataFrame) -> None:\n", + " plt.figure(figsize=(12, 6))\n", + " for season in [\"winter\", \"spring\", \"summer\", \"autumn\"]:\n", + " sample = df[df[\"season\"] == season].iloc[0][\"data\"]\n", + " plt.plot(sample[0], label=f\"{season} load\")\n", + " plt.title(\"Суточные профили потребления энергии по сезонам\")\n", + " plt.xlabel(\"Час дня (0-23)\")\n", + " plt.ylabel(\"Мощность (МВт)\")\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.savefig(\"load_profiles.png\")\n", + " plt.show()\n", + "\n", + "# Запуск preprocessing\n", + "task1_preprocessing()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Assignment2/dataset/README.md b/Assignment2/dataset/README.md new file mode 100644 index 0000000..e3e2308 --- /dev/null +++ b/Assignment2/dataset/README.md @@ -0,0 +1,1736 @@ + +DATAPACKAGE: TIME SERIES +=========================================================================== + +https://doi.org/10.25832/time_series/2020-10-06 + +by Open Power System Data: http://www.open-power-system-data.org/ + +Package Version: 2020-10-06 + +Load, wind and solar, prices in hourly resolution + +This data package contains different kinds of timeseries data relevant for +power system modelling, namely electricity prices, electricity consumption +(load) as well as wind and solar power generation and capacities. The data +is aggregated either by country, control area or bidding zone. Geographical +coverage includes the EU and some neighbouring countries. All variables are +provided in hourly resolution. Where original data is available in higher +resolution (half-hourly or quarter-hourly), it is provided in separate +files. This package version only contains data provided by TSOs and power +exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See +previous versions for historical data from a broader range of sources. All +data processing is conducted in Python/pandas and has been documented in +the Jupyter notebooks linked below. + +The data package covers the geographical region of 32 European countries. + +We follow the Data Package standard by the Frictionless Data project, a +part of the Open Knowledge Foundation: http://frictionlessdata.io/ + + +Documentation and script +=========================================================================== + +This README only contains the most basic information about the data package. +For the full documentation, please see the notebook script that was used to +generate the data package. You can find it at: + +https://nbviewer.jupyter.org/github/Open-Power-System-Data/datapackage_timeseries/blob/2020-10-06/main.ipynb + +Or on GitHub at: + +https://github.com/Open-Power-System-Data/datapackage_timeseries/blob/2020-10-06/main.ipynb + +License and attribution +=========================================================================== + +Attribution: + Attribution in Chicago author-date style should be given as follows: + "Open Power System Data. 2020. Data Package Time series. Version + 2020-10-06. https://doi.org/10.25832/time_series/2020-10-06. (Primary + data from various sources, for a complete list see URL)." + + +Version history +=========================================================================== + +* 2020-10-06 Yearly update +* 2019-06-05 Corrected error in German wind generation data +* 2019-05-15 Update with 2018 data +* 2018-06-30 Fixed missing price data +* 2018-03-13 Include data for 2017 where available from primary sources and include hourly load from ENTSO-E Power Statistics +* 2017-07-09 include ENTSO E transparency, RTE, APG +* 2017-03-06 update datasets up to 2016-12-31 and reformat output files +* 2016-10-28 harmonized column names for wind generation +* 2016-10-27 Included data from CEPS and PSE +* 2016-07-14 Included data from Energinet.DK, Elia and Svenska Kraftnaet + + +Resources +=========================================================================== + +* [Package description page](http://data.open-power-system-data.org/time_series/2020-10-06/) +* [ZIP Package](http://data.open-power-system-data.org/time_series/opsd-time_series-2020-10-06.zip) +* [Script and documentation](https://github.com/Open-Power-System-Data/datapackage_timeseries/blob/2020-10-06/main.ipynb) +* [Original input data](http://data.open-power-system-data.org/time_series/2020-10-06/original_data/) + + +Sources +=========================================================================== + +* ENTSO-E Transparency + + +Field documentation +=========================================================================== + + +time_series_15min_singleindex.csv +--------------------------------------------------------------------------- + +* utc_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%SZ + - Description: Start of timeperiod in Coordinated Universal Time +* cet_cest_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%S%z + - Description: Start of timeperiod in Central European (Summer-) Time +* AT_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Austria in MW as published on ENTSO-E Transparency Platform + - Source: +* AT_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Austria in MW as published on ENTSO-E Transparency Platform + - Source: +* AT_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Austria in EUR + - Source: +* AT_solar_generation_actual + - Type: number + - Description: Actual solar generation in Austria in MW + - Source: +* AT_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Austria in MW + - Source: +* BE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Belgium in MW as published on ENTSO-E Transparency Platform + - Source: +* BE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Belgium in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Germany in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Germany in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Germany in MW + - Source: +* DE_solar_generation_actual + - Type: number + - Description: Actual solar generation in Germany in MW + - Source: +* DE_solar_profile + - Type: number + - Description: Share of solar capacity producing in Germany + - Source: +* DE_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Germany in MW + - Source: +* DE_wind_generation_actual + - Type: number + - Description: Actual wind generation in Germany in MW + - Source: +* DE_wind_profile + - Type: number + - Description: Share of wind capacity producing in Germany + - Source: +* DE_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Germany in MW + - Source: +* DE_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Germany in MW + - Source: +* DE_wind_offshore_profile + - Type: number + - Description: Share of wind_offshore capacity producing in Germany + - Source: +* DE_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Germany in MW + - Source: +* DE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Germany in MW + - Source: +* DE_wind_onshore_profile + - Type: number + - Description: Share of wind_onshore capacity producing in Germany + - Source: +* DE_50hertz_load_actual_entsoe_transparency + - Type: number + - Description: Total load in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_50hertz_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_50hertz_solar_generation_actual + - Type: number + - Description: Actual solar generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_generation_actual + - Type: number + - Description: Actual wind generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in 50Hertz (control area) in MW + - Source: +* DE_LU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in DE-LU (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_LU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in DE-LU (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_LU_solar_generation_actual + - Type: number + - Description: Actual solar generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_generation_actual + - Type: number + - Description: Actual wind generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in DE-LU (bidding zone) in MW + - Source: +* DE_amprion_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Amprion (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_amprion_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Amprion (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_amprion_solar_generation_actual + - Type: number + - Description: Actual solar generation in Amprion (control area) in MW + - Source: +* DE_amprion_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Amprion (control area) in MW + - Source: +* DE_tennet_load_actual_entsoe_transparency + - Type: number + - Description: Total load in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_tennet_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_tennet_solar_generation_actual + - Type: number + - Description: Actual solar generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_generation_actual + - Type: number + - Description: Actual wind generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in TenneT GER (control area) in MW + - Source: +* DE_transnetbw_load_actual_entsoe_transparency + - Type: number + - Description: Total load in TransnetBW (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_transnetbw_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in TransnetBW (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_transnetbw_solar_generation_actual + - Type: number + - Description: Actual solar generation in TransnetBW (control area) in MW + - Source: +* DE_transnetbw_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in TransnetBW (control area) in MW + - Source: +* HU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Hungary in MW as published on ENTSO-E Transparency Platform + - Source: +* HU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Hungary in MW as published on ENTSO-E Transparency Platform + - Source: +* HU_solar_generation_actual + - Type: number + - Description: Actual solar generation in Hungary in MW + - Source: +* HU_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Hungary in MW + - Source: +* LU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Luxembourg in MW as published on ENTSO-E Transparency Platform + - Source: +* LU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Luxembourg in MW as published on ENTSO-E Transparency Platform + - Source: +* NL_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Netherlands in MW as published on ENTSO-E Transparency Platform + - Source: +* NL_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Netherlands in MW as published on ENTSO-E Transparency Platform + - Source: +* NL_solar_generation_actual + - Type: number + - Description: Actual solar generation in Netherlands in MW + - Source: +* NL_wind_generation_actual + - Type: number + - Description: Actual wind generation in Netherlands in MW + - Source: +* NL_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Netherlands in MW + - Source: +* NL_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Netherlands in MW + - Source: + + +time_series_30min_singleindex.csv +--------------------------------------------------------------------------- + +* utc_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%SZ + - Description: Start of timeperiod in Coordinated Universal Time +* cet_cest_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%S%z + - Description: Start of timeperiod in Central European (Summer-) Time +* CY_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Cyprus in MW as published on ENTSO-E Transparency Platform + - Source: +* CY_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Cyprus in MW as published on ENTSO-E Transparency Platform + - Source: +* CY_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Cyprus in MW + - Source: +* GB_GBN_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Great Britain in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_GBN_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Great Britain in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_GBN_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Great Britain in MW + - Source: +* GB_GBN_solar_generation_actual + - Type: number + - Description: Actual solar generation in Great Britain in MW + - Source: +* GB_GBN_solar_profile + - Type: number + - Description: Share of solar capacity producing in Great Britain + - Source: +* GB_GBN_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Great Britain in MW + - Source: +* GB_GBN_wind_generation_actual + - Type: number + - Description: Actual wind generation in Great Britain in MW + - Source: +* GB_GBN_wind_profile + - Type: number + - Description: Share of wind capacity producing in Great Britain + - Source: +* GB_GBN_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Great Britain in MW + - Source: +* GB_GBN_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Great Britain in MW + - Source: +* GB_GBN_wind_offshore_profile + - Type: number + - Description: Share of wind_offshore capacity producing in Great Britain + - Source: +* GB_GBN_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Great Britain in MW + - Source: +* GB_GBN_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Great Britain in MW + - Source: +* GB_GBN_wind_onshore_profile + - Type: number + - Description: Share of wind_onshore capacity producing in Great Britain + - Source: +* GB_NIR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Northern Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_NIR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Northern Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_NIR_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Northern Ireland in MW + - Source: +* GB_NIR_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Northern Ireland in MW + - Source: +* GB_NIR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Northern Ireland in MW + - Source: +* GB_UKM_load_actual_entsoe_transparency + - Type: number + - Description: Total load in United Kingdom in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_UKM_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in United Kingdom in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_UKM_solar_capacity + - Type: number + - Description: Electrical capacity of solar in United Kingdom in MW + - Source: +* GB_UKM_solar_generation_actual + - Type: number + - Description: Actual solar generation in United Kingdom in MW + - Source: +* GB_UKM_wind_capacity + - Type: number + - Description: Electrical capacity of wind in United Kingdom in MW + - Source: +* GB_UKM_wind_generation_actual + - Type: number + - Description: Actual wind generation in United Kingdom in MW + - Source: +* GB_UKM_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in United Kingdom in MW + - Source: +* GB_UKM_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in United Kingdom in MW + - Source: +* GB_UKM_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in United Kingdom in MW + - Source: +* GB_UKM_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in United Kingdom in MW + - Source: +* IE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Ireland in MW + - Source: +* IE_sem_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Ireland - (SEM) (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_sem_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Ireland - (SEM) (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_sem_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Ireland - (SEM) (bidding zone) in EUR + - Source: +* IE_sem_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Ireland - (SEM) (bidding zone) in MW + - Source: + + +time_series_60min_singleindex.csv +--------------------------------------------------------------------------- + +* utc_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%SZ + - Description: Start of timeperiod in Coordinated Universal Time +* cet_cest_timestamp + - Type: datetime + - Format: fmt:%Y-%m-%dT%H%M%S%z + - Description: Start of timeperiod in Central European (Summer-) Time +* AT_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Austria in MW as published on ENTSO-E Transparency Platform + - Source: +* AT_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Austria in MW as published on ENTSO-E Transparency Platform + - Source: +* AT_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Austria in EUR + - Source: +* AT_solar_generation_actual + - Type: number + - Description: Actual solar generation in Austria in MW + - Source: +* AT_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Austria in MW + - Source: +* BE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Belgium in MW as published on ENTSO-E Transparency Platform + - Source: +* BE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Belgium in MW as published on ENTSO-E Transparency Platform + - Source: +* BE_solar_generation_actual + - Type: number + - Description: Actual solar generation in Belgium in MW + - Source: +* BE_wind_generation_actual + - Type: number + - Description: Actual wind generation in Belgium in MW + - Source: +* BE_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Belgium in MW + - Source: +* BE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Belgium in MW + - Source: +* BG_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Bulgaria in MW as published on ENTSO-E Transparency Platform + - Source: +* BG_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Bulgaria in MW as published on ENTSO-E Transparency Platform + - Source: +* BG_solar_generation_actual + - Type: number + - Description: Actual solar generation in Bulgaria in MW + - Source: +* BG_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Bulgaria in MW + - Source: +* CH_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Switzerland in MW as published on ENTSO-E Transparency Platform + - Source: +* CH_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Switzerland in MW as published on ENTSO-E Transparency Platform + - Source: +* CH_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Switzerland in MW + - Source: +* CH_solar_generation_actual + - Type: number + - Description: Actual solar generation in Switzerland in MW + - Source: +* CH_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Switzerland in MW + - Source: +* CH_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Switzerland in MW + - Source: +* CY_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Cyprus in MW as published on ENTSO-E Transparency Platform + - Source: +* CY_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Cyprus in MW as published on ENTSO-E Transparency Platform + - Source: +* CY_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Cyprus in MW + - Source: +* CZ_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Czech Republic in MW as published on ENTSO-E Transparency Platform + - Source: +* CZ_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Czech Republic in MW as published on ENTSO-E Transparency Platform + - Source: +* CZ_solar_generation_actual + - Type: number + - Description: Actual solar generation in Czech Republic in MW + - Source: +* CZ_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Czech Republic in MW + - Source: +* DE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Germany in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Germany in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Germany in MW + - Source: +* DE_solar_generation_actual + - Type: number + - Description: Actual solar generation in Germany in MW + - Source: +* DE_solar_profile + - Type: number + - Description: Share of solar capacity producing in Germany + - Source: +* DE_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Germany in MW + - Source: +* DE_wind_generation_actual + - Type: number + - Description: Actual wind generation in Germany in MW + - Source: +* DE_wind_profile + - Type: number + - Description: Share of wind capacity producing in Germany + - Source: +* DE_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Germany in MW + - Source: +* DE_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Germany in MW + - Source: +* DE_wind_offshore_profile + - Type: number + - Description: Share of wind_offshore capacity producing in Germany + - Source: +* DE_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Germany in MW + - Source: +* DE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Germany in MW + - Source: +* DE_wind_onshore_profile + - Type: number + - Description: Share of wind_onshore capacity producing in Germany + - Source: +* DE_50hertz_load_actual_entsoe_transparency + - Type: number + - Description: Total load in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_50hertz_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_50hertz_solar_generation_actual + - Type: number + - Description: Actual solar generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_generation_actual + - Type: number + - Description: Actual wind generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in 50Hertz (control area) in MW + - Source: +* DE_50hertz_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in 50Hertz (control area) in MW + - Source: +* DE_LU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in DE-LU (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_LU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in DE-LU (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_LU_price_day_ahead + - Type: number + - Description: Day-ahead spot price for DE-LU (bidding zone) in EUR + - Source: +* DE_LU_solar_generation_actual + - Type: number + - Description: Actual solar generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_generation_actual + - Type: number + - Description: Actual wind generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in DE-LU (bidding zone) in MW + - Source: +* DE_LU_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in DE-LU (bidding zone) in MW + - Source: +* DE_amprion_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Amprion (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_amprion_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Amprion (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_amprion_solar_generation_actual + - Type: number + - Description: Actual solar generation in Amprion (control area) in MW + - Source: +* DE_amprion_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Amprion (control area) in MW + - Source: +* DE_tennet_load_actual_entsoe_transparency + - Type: number + - Description: Total load in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_tennet_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_tennet_solar_generation_actual + - Type: number + - Description: Actual solar generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_generation_actual + - Type: number + - Description: Actual wind generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in TenneT GER (control area) in MW + - Source: +* DE_tennet_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in TenneT GER (control area) in MW + - Source: +* DE_transnetbw_load_actual_entsoe_transparency + - Type: number + - Description: Total load in TransnetBW (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_transnetbw_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in TransnetBW (control area) in MW as published on ENTSO-E Transparency Platform + - Source: +* DE_transnetbw_solar_generation_actual + - Type: number + - Description: Actual solar generation in TransnetBW (control area) in MW + - Source: +* DE_transnetbw_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in TransnetBW (control area) in MW + - Source: +* DK_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Denmark in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Denmark in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Denmark in MW + - Source: +* DK_solar_generation_actual + - Type: number + - Description: Actual solar generation in Denmark in MW + - Source: +* DK_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Denmark in MW + - Source: +* DK_wind_generation_actual + - Type: number + - Description: Actual wind generation in Denmark in MW + - Source: +* DK_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Denmark in MW + - Source: +* DK_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Denmark in MW + - Source: +* DK_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Denmark in MW + - Source: +* DK_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Denmark in MW + - Source: +* DK_1_load_actual_entsoe_transparency + - Type: number + - Description: Total load in DK1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_1_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in DK1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_1_price_day_ahead + - Type: number + - Description: Day-ahead spot price for DK1 (bidding zone) in EUR + - Source: +* DK_1_solar_generation_actual + - Type: number + - Description: Actual solar generation in DK1 (bidding zone) in MW + - Source: +* DK_1_wind_generation_actual + - Type: number + - Description: Actual wind generation in DK1 (bidding zone) in MW + - Source: +* DK_1_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in DK1 (bidding zone) in MW + - Source: +* DK_1_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in DK1 (bidding zone) in MW + - Source: +* DK_2_load_actual_entsoe_transparency + - Type: number + - Description: Total load in DK2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_2_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in DK2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* DK_2_price_day_ahead + - Type: number + - Description: Day-ahead spot price for DK2 (bidding zone) in EUR + - Source: +* DK_2_solar_generation_actual + - Type: number + - Description: Actual solar generation in DK2 (bidding zone) in MW + - Source: +* DK_2_wind_generation_actual + - Type: number + - Description: Actual wind generation in DK2 (bidding zone) in MW + - Source: +* DK_2_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in DK2 (bidding zone) in MW + - Source: +* DK_2_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in DK2 (bidding zone) in MW + - Source: +* EE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Estonia in MW as published on ENTSO-E Transparency Platform + - Source: +* EE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Estonia in MW as published on ENTSO-E Transparency Platform + - Source: +* EE_solar_generation_actual + - Type: number + - Description: Actual solar generation in Estonia in MW + - Source: +* EE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Estonia in MW + - Source: +* ES_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Spain in MW as published on ENTSO-E Transparency Platform + - Source: +* ES_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Spain in MW as published on ENTSO-E Transparency Platform + - Source: +* ES_solar_generation_actual + - Type: number + - Description: Actual solar generation in Spain in MW + - Source: +* ES_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Spain in MW + - Source: +* FI_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Finland in MW as published on ENTSO-E Transparency Platform + - Source: +* FI_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Finland in MW as published on ENTSO-E Transparency Platform + - Source: +* FI_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Finland in MW + - Source: +* FR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in France in MW as published on ENTSO-E Transparency Platform + - Source: +* FR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in France in MW as published on ENTSO-E Transparency Platform + - Source: +* FR_solar_generation_actual + - Type: number + - Description: Actual solar generation in France in MW + - Source: +* FR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in France in MW + - Source: +* GB_GBN_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Great Britain in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_GBN_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Great Britain in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_GBN_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Great Britain in GBP + - Source: +* GB_GBN_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Great Britain in MW + - Source: +* GB_GBN_solar_generation_actual + - Type: number + - Description: Actual solar generation in Great Britain in MW + - Source: +* GB_GBN_solar_profile + - Type: number + - Description: Share of solar capacity producing in Great Britain + - Source: +* GB_GBN_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Great Britain in MW + - Source: +* GB_GBN_wind_generation_actual + - Type: number + - Description: Actual wind generation in Great Britain in MW + - Source: +* GB_GBN_wind_profile + - Type: number + - Description: Share of wind capacity producing in Great Britain + - Source: +* GB_GBN_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Great Britain in MW + - Source: +* GB_GBN_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Great Britain in MW + - Source: +* GB_GBN_wind_offshore_profile + - Type: number + - Description: Share of wind_offshore capacity producing in Great Britain + - Source: +* GB_GBN_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Great Britain in MW + - Source: +* GB_GBN_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Great Britain in MW + - Source: +* GB_GBN_wind_onshore_profile + - Type: number + - Description: Share of wind_onshore capacity producing in Great Britain + - Source: +* GB_NIR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Northern Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_NIR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Northern Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_NIR_solar_capacity + - Type: number + - Description: Electrical capacity of solar in Northern Ireland in MW + - Source: +* GB_NIR_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Northern Ireland in MW + - Source: +* GB_NIR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Northern Ireland in MW + - Source: +* GB_UKM_load_actual_entsoe_transparency + - Type: number + - Description: Total load in United Kingdom in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_UKM_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in United Kingdom in MW as published on ENTSO-E Transparency Platform + - Source: +* GB_UKM_solar_capacity + - Type: number + - Description: Electrical capacity of solar in United Kingdom in MW + - Source: +* GB_UKM_solar_generation_actual + - Type: number + - Description: Actual solar generation in United Kingdom in MW + - Source: +* GB_UKM_wind_capacity + - Type: number + - Description: Electrical capacity of wind in United Kingdom in MW + - Source: +* GB_UKM_wind_generation_actual + - Type: number + - Description: Actual wind generation in United Kingdom in MW + - Source: +* GB_UKM_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in United Kingdom in MW + - Source: +* GB_UKM_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in United Kingdom in MW + - Source: +* GB_UKM_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in United Kingdom in MW + - Source: +* GB_UKM_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in United Kingdom in MW + - Source: +* GR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Greece in MW as published on ENTSO-E Transparency Platform + - Source: +* GR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Greece in MW as published on ENTSO-E Transparency Platform + - Source: +* GR_solar_generation_actual + - Type: number + - Description: Actual solar generation in Greece in MW + - Source: +* GR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Greece in MW + - Source: +* HR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Croatia in MW as published on ENTSO-E Transparency Platform + - Source: +* HR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Croatia in MW as published on ENTSO-E Transparency Platform + - Source: +* HR_solar_generation_actual + - Type: number + - Description: Actual solar generation in Croatia in MW + - Source: +* HR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Croatia in MW + - Source: +* HU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Hungary in MW as published on ENTSO-E Transparency Platform + - Source: +* HU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Hungary in MW as published on ENTSO-E Transparency Platform + - Source: +* HU_solar_generation_actual + - Type: number + - Description: Actual solar generation in Hungary in MW + - Source: +* HU_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Hungary in MW + - Source: +* IE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Ireland in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Ireland in MW + - Source: +* IE_sem_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Ireland - (SEM) (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_sem_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Ireland - (SEM) (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IE_sem_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Ireland - (SEM) (bidding zone) in EUR + - Source: +* IE_sem_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Ireland - (SEM) (bidding zone) in MW + - Source: +* IT_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Italy in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Italy in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_solar_generation_actual + - Type: number + - Description: Actual solar generation in Italy in MW + - Source: +* IT_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Italy in MW + - Source: +* IT_BRNN_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Brindisi (bidding zone) in EUR + - Source: +* IT_BRNN_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Brindisi (bidding zone) in MW + - Source: +* IT_CNOR_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-Centre-North (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_CNOR_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-Centre-North (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_CNOR_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Centre-North (bidding zone) in EUR + - Source: +* IT_CNOR_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Centre-North (bidding zone) in MW + - Source: +* IT_CNOR_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Centre-North (bidding zone) in MW + - Source: +* IT_CSUD_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-Centre-South (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_CSUD_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-Centre-South (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_CSUD_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Centre-South (bidding zone) in EUR + - Source: +* IT_CSUD_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Centre-South (bidding zone) in MW + - Source: +* IT_CSUD_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Centre-South (bidding zone) in MW + - Source: +* IT_FOGN_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Foggia (bidding zone) in EUR + - Source: +* IT_FOGN_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Foggia (bidding zone) in MW + - Source: +* IT_FOGN_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Foggia (bidding zone) in MW + - Source: +* IT_GR_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-GR (bidding zone) in EUR + - Source: +* IT_NORD_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-North (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_NORD_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-North (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_NORD_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-North (bidding zone) in EUR + - Source: +* IT_NORD_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-North (bidding zone) in MW + - Source: +* IT_NORD_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-North (bidding zone) in MW + - Source: +* IT_NORD_AT_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-North-AT (bidding zone) in EUR + - Source: +* IT_NORD_CH_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-North-CH (bidding zone) in EUR + - Source: +* IT_NORD_FR_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-North-FR (bidding zone) in EUR + - Source: +* IT_NORD_SI_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-North-SI (bidding zone) in EUR + - Source: +* IT_PRGP_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Priolo (bidding zone) in EUR + - Source: +* IT_PRGP_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Priolo (bidding zone) in MW + - Source: +* IT_PRGP_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Priolo (bidding zone) in MW + - Source: +* IT_ROSN_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Rossano (bidding zone) in EUR + - Source: +* IT_ROSN_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Rossano (bidding zone) in MW + - Source: +* IT_ROSN_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Rossano (bidding zone) in MW + - Source: +* IT_SACO_AC_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Italy_Saco_AC (bidding zone) in EUR + - Source: +* IT_SACO_DC_price_day_ahead + - Type: number + - Description: Day-ahead spot price for Italy_Sacodc (bidding zone) in EUR + - Source: +* IT_SARD_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-Sardinia (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SARD_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-Sardinia (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SARD_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Sardinia (bidding zone) in EUR + - Source: +* IT_SARD_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Sardinia (bidding zone) in MW + - Source: +* IT_SARD_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Sardinia (bidding zone) in MW + - Source: +* IT_SICI_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-Sicily (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SICI_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-Sicily (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SICI_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-Sicily (bidding zone) in EUR + - Source: +* IT_SICI_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-Sicily (bidding zone) in MW + - Source: +* IT_SICI_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-Sicily (bidding zone) in MW + - Source: +* IT_SUD_load_actual_entsoe_transparency + - Type: number + - Description: Total load in IT-South (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SUD_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in IT-South (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* IT_SUD_price_day_ahead + - Type: number + - Description: Day-ahead spot price for IT-South (bidding zone) in EUR + - Source: +* IT_SUD_solar_generation_actual + - Type: number + - Description: Actual solar generation in IT-South (bidding zone) in MW + - Source: +* IT_SUD_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in IT-South (bidding zone) in MW + - Source: +* LT_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Lithuania in MW as published on ENTSO-E Transparency Platform + - Source: +* LT_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Lithuania in MW as published on ENTSO-E Transparency Platform + - Source: +* LT_solar_generation_actual + - Type: number + - Description: Actual solar generation in Lithuania in MW + - Source: +* LT_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Lithuania in MW + - Source: +* LU_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Luxembourg in MW as published on ENTSO-E Transparency Platform + - Source: +* LU_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Luxembourg in MW as published on ENTSO-E Transparency Platform + - Source: +* LV_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Latvia in MW as published on ENTSO-E Transparency Platform + - Source: +* LV_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Latvia in MW as published on ENTSO-E Transparency Platform + - Source: +* LV_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Latvia in MW + - Source: +* ME_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Montenegro in MW as published on ENTSO-E Transparency Platform + - Source: +* ME_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Montenegro in MW as published on ENTSO-E Transparency Platform + - Source: +* ME_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Montenegro in MW + - Source: +* NL_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Netherlands in MW as published on ENTSO-E Transparency Platform + - Source: +* NL_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Netherlands in MW as published on ENTSO-E Transparency Platform + - Source: +* NL_solar_generation_actual + - Type: number + - Description: Actual solar generation in Netherlands in MW + - Source: +* NL_wind_generation_actual + - Type: number + - Description: Actual wind generation in Netherlands in MW + - Source: +* NL_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Netherlands in MW + - Source: +* NL_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Netherlands in MW + - Source: +* NO_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Norway in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Norway in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Norway in MW + - Source: +* NO_1_load_actual_entsoe_transparency + - Type: number + - Description: Total load in NO1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_1_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in NO1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_1_price_day_ahead + - Type: number + - Description: Day-ahead spot price for NO1 (bidding zone) in EUR + - Source: +* NO_1_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in NO1 (bidding zone) in MW + - Source: +* NO_2_load_actual_entsoe_transparency + - Type: number + - Description: Total load in NO2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_2_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in NO2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_2_price_day_ahead + - Type: number + - Description: Day-ahead spot price for NO2 (bidding zone) in EUR + - Source: +* NO_2_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in NO2 (bidding zone) in MW + - Source: +* NO_3_load_actual_entsoe_transparency + - Type: number + - Description: Total load in NO3 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_3_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in NO3 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_3_price_day_ahead + - Type: number + - Description: Day-ahead spot price for NO3 (bidding zone) in EUR + - Source: +* NO_3_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in NO3 (bidding zone) in MW + - Source: +* NO_4_load_actual_entsoe_transparency + - Type: number + - Description: Total load in NO4 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_4_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in NO4 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_4_price_day_ahead + - Type: number + - Description: Day-ahead spot price for NO4 (bidding zone) in EUR + - Source: +* NO_4_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in NO4 (bidding zone) in MW + - Source: +* NO_5_load_actual_entsoe_transparency + - Type: number + - Description: Total load in NO5 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_5_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in NO5 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* NO_5_price_day_ahead + - Type: number + - Description: Day-ahead spot price for NO5 (bidding zone) in EUR + - Source: +* NO_5_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in NO5 (bidding zone) in MW + - Source: +* PL_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Poland in MW as published on ENTSO-E Transparency Platform + - Source: +* PL_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Poland in MW as published on ENTSO-E Transparency Platform + - Source: +* PL_solar_generation_actual + - Type: number + - Description: Actual solar generation in Poland in MW + - Source: +* PL_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Poland in MW + - Source: +* PT_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Portugal in MW as published on ENTSO-E Transparency Platform + - Source: +* PT_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Portugal in MW as published on ENTSO-E Transparency Platform + - Source: +* PT_solar_generation_actual + - Type: number + - Description: Actual solar generation in Portugal in MW + - Source: +* PT_wind_generation_actual + - Type: number + - Description: Actual wind generation in Portugal in MW + - Source: +* PT_wind_offshore_generation_actual + - Type: number + - Description: Actual wind_offshore generation in Portugal in MW + - Source: +* PT_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Portugal in MW + - Source: +* RO_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Romania in MW as published on ENTSO-E Transparency Platform + - Source: +* RO_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Romania in MW as published on ENTSO-E Transparency Platform + - Source: +* RO_solar_generation_actual + - Type: number + - Description: Actual solar generation in Romania in MW + - Source: +* RO_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Romania in MW + - Source: +* RS_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Serbia in MW as published on ENTSO-E Transparency Platform + - Source: +* RS_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Serbia in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Sweden in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Sweden in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_wind_capacity + - Type: number + - Description: Electrical capacity of wind in Sweden in MW + - Source: +* SE_wind_offshore_capacity + - Type: number + - Description: Electrical capacity of wind_offshore in Sweden in MW + - Source: +* SE_wind_onshore_capacity + - Type: number + - Description: Electrical capacity of wind_onshore in Sweden in MW + - Source: +* SE_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Sweden in MW + - Source: +* SE_1_load_actual_entsoe_transparency + - Type: number + - Description: Total load in SE1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_1_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in SE1 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_1_price_day_ahead + - Type: number + - Description: Day-ahead spot price for SE1 (bidding zone) in EUR + - Source: +* SE_1_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in SE1 (bidding zone) in MW + - Source: +* SE_2_load_actual_entsoe_transparency + - Type: number + - Description: Total load in SE2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_2_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in SE2 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_2_price_day_ahead + - Type: number + - Description: Day-ahead spot price for SE2 (bidding zone) in EUR + - Source: +* SE_2_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in SE2 (bidding zone) in MW + - Source: +* SE_3_load_actual_entsoe_transparency + - Type: number + - Description: Total load in SE3 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_3_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in SE3 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_3_price_day_ahead + - Type: number + - Description: Day-ahead spot price for SE3 (bidding zone) in EUR + - Source: +* SE_3_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in SE3 (bidding zone) in MW + - Source: +* SE_4_load_actual_entsoe_transparency + - Type: number + - Description: Total load in SE4 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_4_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in SE4 (bidding zone) in MW as published on ENTSO-E Transparency Platform + - Source: +* SE_4_price_day_ahead + - Type: number + - Description: Day-ahead spot price for SE4 (bidding zone) in EUR + - Source: +* SE_4_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in SE4 (bidding zone) in MW + - Source: +* SI_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Slovenia in MW as published on ENTSO-E Transparency Platform + - Source: +* SI_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Slovenia in MW as published on ENTSO-E Transparency Platform + - Source: +* SI_solar_generation_actual + - Type: number + - Description: Actual solar generation in Slovenia in MW + - Source: +* SI_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Slovenia in MW + - Source: +* SK_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Slovakia in MW as published on ENTSO-E Transparency Platform + - Source: +* SK_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Slovakia in MW as published on ENTSO-E Transparency Platform + - Source: +* SK_solar_generation_actual + - Type: number + - Description: Actual solar generation in Slovakia in MW + - Source: +* SK_wind_onshore_generation_actual + - Type: number + - Description: Actual wind_onshore generation in Slovakia in MW + - Source: +* UA_load_actual_entsoe_transparency + - Type: number + - Description: Total load in Ukraine in MW as published on ENTSO-E Transparency Platform + - Source: +* UA_load_forecast_entsoe_transparency + - Type: number + - Description: Day-ahead load forecast in Ukraine in MW as published on ENTSO-E Transparency Platform + - Source: + + +Feedback +=========================================================================== + +Thank you for using data provided by Open Power System Data. If you have +any question or feedback, please do not hesitate to contact us. + +For this data package, contact: +Jonathan Muehlenpfordt + +For general issues, find our team contact details on our website: +http://www.open-power-system-data.org + + + + + + + + + + + + + + diff --git a/Assignment2/dataset/__MACOSX/._README.md b/Assignment2/dataset/__MACOSX/._README.md new file mode 100644 index 0000000000000000000000000000000000000000..d7d90ca3ff4f9ee0b05641ff28bc736507bdc2fa GIT binary patch literal 640 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDJkFfjQ6X&|4e0Z0=89795aAj&{G7#$!O zMI$RzOn`wwDmgz_FR`E?Csi*evACqPI5j0Du_RSFAQ+;9F#t$=Ffi~V)a527=YS;{ zX8^42`Cb97J{Id#Emo zUUm3o8$QXklsYV&Uw@001{Cer*5% literal 0 HcmV?d00001 diff --git a/Assignment2/dataset/__MACOSX/._opsd_raw.csv b/Assignment2/dataset/__MACOSX/._opsd_raw.csv new file mode 100644 index 0000000000000000000000000000000000000000..aad887dd2ac26647231be3ec0eeb360d1b5e54f3 GIT binary patch literal 233 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}@pd2v@m~Tl5x_AdBnYYuq~ib(YeM-j z3S`;=AQoU?kV?+a)k`cW$Vt`9Nh~fYEly2INi0cK4hV)Sz5t{p85l$n>IzE}ixTrn uGV@XyZoGbw4pR5q>60O)|Bx_AAXzN-Zk3W&i-bP$(h* literal 0 HcmV?d00001 diff --git a/Assignment2/dataset/opsd_raw.csv b/Assignment2/dataset/opsd_raw.csv new file mode 100644 index 0000000..184c8cd --- /dev/null +++ b/Assignment2/dataset/opsd_raw.csv @@ -0,0 +1,50401 @@ +utc_timestamp,DK_load_actual_entsoe_transparency,DK_load_forecast_entsoe_transparency,DK_solar_capacity,DK_solar_generation_actual,DK_wind_capacity,DK_wind_generation_actual,DK_wind_offshore_capacity,DK_wind_offshore_generation_actual,DK_wind_onshore_capacity,DK_wind_onshore_generation_actual,DK_1_load_actual_entsoe_transparency,DK_1_load_forecast_entsoe_transparency,DK_1_price_day_ahead,DK_1_wind_onshore_generation_actual,DK_2_load_actual_entsoe_transparency,DK_2_load_forecast_entsoe_transparency,DK_2_price_day_ahead,BE_load_actual_entsoe_transparency,BE_load_forecast_entsoe_transparency,BE_solar_generation_actual,BE_wind_generation_actual,BE_wind_offshore_generation_actual,BE_wind_onshore_generation_actual,DE_load_actual_entsoe_transparency,DE_load_forecast_entsoe_transparency,DE_solar_capacity,DE_solar_generation_actual,DE_solar_profile,DE_wind_capacity,DE_wind_generation_actual,DE_wind_profile,DE_wind_offshore_capacity,DE_wind_offshore_generation_actual,DE_wind_offshore_profile,DE_wind_onshore_capacity,DE_wind_onshore_generation_actual,DE_wind_onshore_profile,DE_50hertz_load_actual_entsoe_transparency,DE_50hertz_load_forecast_entsoe_transparency,DE_50hertz_solar_generation_actual,DE_50hertz_wind_generation_actual,DE_50hertz_wind_offshore_generation_actual,DE_50hertz_wind_onshore_generation_actual,DE_LU_load_actual_entsoe_transparency,DE_LU_load_forecast_entsoe_transparency,DE_LU_price_day_ahead,DE_LU_solar_generation_actual,DE_LU_wind_generation_actual,DE_LU_wind_offshore_generation_actual,DE_LU_wind_onshore_generation_actual,DE_amprion_load_actual_entsoe_transparency,DE_amprion_load_forecast_entsoe_transparency,DE_amprion_solar_generation_actual,DE_amprion_wind_onshore_generation_actual,DE_tennet_load_actual_entsoe_transparency,DE_tennet_load_forecast_entsoe_transparency,DE_tennet_solar_generation_actual,DE_tennet_wind_generation_actual,DE_tennet_wind_offshore_generation_actual,DE_tennet_wind_onshore_generation_actual,DE_transnetbw_load_actual_entsoe_transparency,DE_transnetbw_load_forecast_entsoe_transparency,DE_transnetbw_solar_generation_actual,DE_transnetbw_wind_onshore_generation_actual,GB_GBN_load_actual_entsoe_transparency,GB_GBN_load_forecast_entsoe_transparency,GB_GBN_price_day_ahead,GB_GBN_solar_capacity,GB_GBN_solar_generation_actual,GB_GBN_solar_profile,GB_GBN_wind_capacity,GB_GBN_wind_generation_actual,GB_GBN_wind_profile,GB_GBN_wind_offshore_capacity,GB_GBN_wind_offshore_generation_actual,GB_GBN_wind_offshore_profile,GB_GBN_wind_onshore_capacity,GB_GBN_wind_onshore_generation_actual,GB_GBN_wind_onshore_profile,GB_NIR_load_actual_entsoe_transparency,GB_NIR_load_forecast_entsoe_transparency,GB_NIR_solar_capacity,GB_NIR_wind_onshore_capacity,GB_NIR_wind_onshore_generation_actual,GB_UKM_load_actual_entsoe_transparency,GB_UKM_load_forecast_entsoe_transparency,GB_UKM_solar_capacity,GB_UKM_solar_generation_actual,GB_UKM_wind_capacity,GB_UKM_wind_generation_actual,GB_UKM_wind_offshore_capacity,GB_UKM_wind_offshore_generation_actual,GB_UKM_wind_onshore_capacity,GB_UKM_wind_onshore_generation_actual,GR_load_actual_entsoe_transparency,GR_load_forecast_entsoe_transparency,GR_solar_generation_actual,GR_wind_onshore_generation_actual 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22:00:00+00:00,3610.63,3543.2,489.0,0.0,4643.0,2517.61,1264.0,1058.48,3379.0,1459.13,2130.51,2099.1,26.97,1209.41,1480.12,1444.1,27.32,11783.0,11222.0,0.0,489.51,271.57,217.94,50639.0,49225.0,37253.0,0.0,0.0,27950.0,9524.0,0.3408,675.0,475.0,0.7032,27275.0,9050.0,0.3318,7498.0,7263.0,0.0,4668.0,46.0,4622.0,,,,,,,,20432.0,19044.0,0.0,1828.0,16590.0,16879.0,0.0,2950.0,428.0,2522.0,6119.0,6039.0,0.0,78.0,40550.0,37140.0,36.1,2760.0,0.0,0.0,11590.0,3680.0,0.3174,4040.0,1677.0,0.4151,7550.0,2002.0,0.2652,1289.0,1200.0,,659.0,383.0,41839.0,38340.0,2760.0,0.0,12248.0,4062.0,4040.0,1677.0,8209.0,2385.0,6161.77,6105.0,0.0,552.0 +2015-01-06 23:00:00+00:00,3356.6,3321.0,489.0,0.0,4643.0,2273.27,1264.0,1019.2,3379.0,1254.07,1994.18,1954.5,26.35,1027.47,1362.42,1366.5,26.35,11079.0,10752.0,0.0,353.95,207.05,146.9,47308.0,45968.0,37256.0,0.0,0.0,27957.0,9127.0,0.3264,675.0,483.0,0.7157,27281.0,8644.0,0.3168,6990.0,6989.0,0.0,4416.0,40.0,4375.0,,,,,,,,18987.0,18495.0,0.0,1887.0,15676.0,14861.0,0.0,2764.0,443.0,2321.0,5656.0,5623.0,0.0,60.0,36108.0,33066.0,36.58,2760.0,0.0,0.0,11590.0,3868.0,0.3338,4040.0,1708.0,0.4229,7550.0,2160.0,0.2861,1152.0,1038.0,,659.0,406.0,37260.0,34104.0,2760.0,0.0,12248.0,4275.0,4040.0,1708.0,8209.0,2566.0,5592.88,5554.0,0.0,526.0 +2015-01-07 00:00:00+00:00,3253.28,3234.0,489.0,0.0,4643.0,2019.18,1264.0,1057.86,3379.0,961.32,1948.37,1936.5,26.08,758.81,1304.91,1297.5,26.08,10511.0,10077.0,0.0,317.71,193.71,124.0,45125.0,43796.0,37256.0,0.0,0.0,27957.0,8514.0,0.3046,675.0,462.0,0.6841,27281.0,8052.0,0.2952,6565.0,6476.0,0.0,4043.0,38.0,4005.0,,,,,,,,18382.0,17758.0,0.0,1595.0,14912.0,14195.0,0.0,2811.0,424.0,2387.0,5266.0,5367.0,0.0,66.0,32698.0,32352.0,36.6,2765.0,0.0,0.0,11590.0,4068.0,0.351,4040.0,1714.0,0.4242,7550.0,2354.0,0.3118,972.0,,,659.0,394.0,33671.0,,2765.0,0.0,12248.0,4462.0,4040.0,1714.0,8209.0,2748.0,5605.11,5534.0,0.0,526.0 +2015-01-07 01:00:00+00:00,3169.58,3179.6,489.0,0.0,4643.0,1878.64,1264.0,1046.94,3379.0,831.7,1894.9,1915.7,26.03,649.27,1274.68,1263.9,26.03,10177.0,9674.0,0.0,299.82,169.96,129.86,44217.0,42782.0,37256.0,0.0,0.0,27957.0,8188.0,0.2929,675.0,391.0,0.5796,27281.0,7797.0,0.2858,6557.0,6380.0,0.0,3597.0,38.0,3559.0,,,,,,,,18108.0,17542.0,0.0,1480.0,14399.0,13688.0,0.0,3023.0,353.0,2669.0,5153.0,5172.0,0.0,89.0,32413.0,31888.0,36.09,2765.0,0.0,0.0,11590.0,4018.0,0.3467,4040.0,1586.0,0.3926,7550.0,2432.0,0.3222,842.0,,,659.0,376.0,33256.0,,2765.0,0.0,12248.0,4394.0,4040.0,1586.0,8209.0,2808.0,5396.53,5382.0,0.0,522.0 +2015-01-07 02:00:00+00:00,3158.67,3163.0,489.0,0.0,4643.0,1814.21,1264.0,1031.02,3379.0,783.19,1864.01,1888.6,26.17,603.57,1294.66,1274.4,26.17,10019.0,9479.0,0.0,224.99,80.03,144.96,44368.0,42937.0,37256.0,0.0,0.0,27957.0,8711.0,0.3116,675.0,389.0,0.5763,27281.0,8322.0,0.305,6774.0,6464.0,0.0,3806.0,37.0,3769.0,,,,,,,,18059.0,17757.0,0.0,1587.0,14374.0,13671.0,0.0,3232.0,352.0,2880.0,5162.0,5045.0,0.0,85.0,30988.0,31376.0,31.26,2765.0,0.0,0.0,11590.0,3962.0,0.3418,4040.0,1610.0,0.3984,7550.0,2352.0,0.3116,794.0,,,659.0,380.0,31782.0,,2765.0,0.0,12248.0,4341.0,4040.0,1610.0,8209.0,2732.0,5250.28,5252.0,0.0,506.0 +2015-01-07 03:00:00+00:00,3240.57,3227.0,489.0,0.01,4643.0,1864.01,1264.0,948.64,3379.0,915.37,1912.78,1903.8,26.1,744.12,1327.79,1323.2,26.74,10003.0,9492.0,0.0,189.2,58.62,130.58,45298.0,44665.0,37256.0,0.0,0.0,27957.0,8753.0,0.3131,675.0,389.0,0.5759,27281.0,8365.0,0.3066,7010.0,7394.0,0.0,3750.0,38.0,3712.0,,,,,,,,18323.0,18189.0,0.0,1527.0,14534.0,13985.0,0.0,3389.0,351.0,3038.0,5432.0,5096.0,0.0,88.0,30064.0,29694.0,22.57,2765.0,0.0,0.0,11590.0,4084.0,0.3524,4040.0,1680.0,0.416,7550.0,2404.0,0.3184,768.0,,,659.0,387.0,30832.0,,2765.0,0.0,12248.0,4471.0,4040.0,1680.0,8209.0,2791.0,5308.04,5312.0,0.0,529.0 +2015-01-07 04:00:00+00:00,3490.13,3473.2,489.0,0.01,4643.0,1859.23,1264.0,784.62,3379.0,1074.61,2074.53,2043.5,26.84,929.23,1415.6,1429.7,28.11,10361.0,9923.0,0.0,209.7,93.81,115.89,48598.0,47122.0,37256.0,0.0,0.0,27957.0,8452.0,0.3023,675.0,328.0,0.4861,27281.0,8124.0,0.2978,7436.0,7940.0,0.0,3750.0,26.0,3725.0,,,,,,,,19535.0,19209.0,0.0,1345.0,15773.0,15035.0,0.0,3279.0,303.0,2976.0,5853.0,4939.0,0.0,78.0,28297.0,28228.0,26.75,2765.0,0.0,0.0,11590.0,4198.0,0.3622,4040.0,1878.0,0.465,7550.0,2320.0,0.3073,746.0,,,659.0,389.0,29042.0,,2765.0,0.0,12248.0,4588.0,4040.0,1878.0,8209.0,2709.0,5697.59,5802.0,0.0,574.0 +2015-01-07 05:00:00+00:00,4191.94,4214.7,489.0,0.01,4643.0,1791.74,1264.0,686.73,3379.0,1105.01,2523.08,2539.2,30.07,960.47,1668.86,1675.5,30.27,11397.0,11055.0,0.0,258.0,152.89,105.11,56626.0,52973.0,37256.0,0.0,0.0,27957.0,8458.0,0.3026,675.0,274.0,0.4056,27281.0,8184.0,0.3,8792.0,8812.0,0.0,3730.0,22.0,3708.0,,,,,,,,22235.0,21802.0,0.0,1370.0,18543.0,17497.0,0.0,3280.0,251.0,3028.0,7056.0,4862.0,0.0,78.0,27600.0,30278.0,31.95,2765.0,0.0,0.0,11590.0,4407.0,0.3802,4040.0,2150.0,0.5322,7550.0,2257.0,0.2989,736.0,,,659.0,393.0,28336.0,,2765.0,0.0,12248.0,4800.0,4040.0,2150.0,8209.0,2650.0,6449.47,6656.0,12.0,584.0 +2015-01-07 06:00:00+00:00,4962.36,5003.1,489.0,0.01,4643.0,1800.07,1264.0,774.04,3379.0,1026.03,3005.84,3039.6,33.17,919.74,1956.52,1963.5,33.17,12471.0,12181.0,0.0,345.68,229.31,116.37,63293.0,58620.0,37256.0,0.0,0.0,27957.0,8066.0,0.2885,675.0,254.0,0.3756,27281.0,7812.0,0.2864,9801.0,9920.0,0.0,3757.0,28.0,3729.0,,,,,,,,24319.0,23868.0,0.0,1232.0,21071.0,19751.0,0.0,3001.0,226.0,2776.0,8102.0,5081.0,0.0,76.0,31434.0,37042.0,37.64,2765.0,0.0,0.0,11590.0,4664.0,0.4024,4040.0,2286.0,0.5658,7550.0,2378.0,0.315,748.0,,,659.0,392.0,32182.0,,2765.0,0.0,12248.0,5056.0,4040.0,2286.0,8209.0,2770.0,7130.25,7243.0,190.0,563.0 +2015-01-07 07:00:00+00:00,5198.09,5262.3,489.0,0.28,4643.0,1740.11,1264.0,812.53,3379.0,927.58,3158.24,3173.4,34.1,824.9,2039.85,2088.9,34.1,12856.0,12710.0,0.08,466.96,296.46,170.5,65964.0,60736.0,37256.0,50.0,0.0014,27957.0,7725.0,0.2763,675.0,286.0,0.423,27281.0,7439.0,0.2727,9794.0,10139.0,15.0,3646.0,32.0,3614.0,,,,,,,,25536.0,24699.0,14.0,1153.0,22060.0,20595.0,15.0,2852.0,253.0,2599.0,8575.0,5303.0,7.0,74.0,39845.0,43690.0,41.68,2765.0,0.0,0.0,11590.0,5270.0,0.4547,4040.0,2596.0,0.6426,7550.0,2674.0,0.3542,847.0,,,659.0,437.0,40692.0,,2765.0,0.0,12248.0,5707.0,4040.0,2596.0,8209.0,3111.0,7893.44,7946.0,757.0,549.0 +2015-01-07 08:00:00+00:00,5107.86,5228.4,489.0,19.5,4643.0,1738.26,1264.0,832.96,3379.0,905.3,3080.83,3111.7,33.28,795.8,2027.03,2116.7,33.28,12799.0,12866.0,86.53,630.88,370.36,260.52,66400.0,61359.0,37256.0,609.0,0.0163,27957.0,7299.0,0.2611,675.0,350.0,0.5184,27281.0,6949.0,0.2547,10044.0,10198.0,80.0,3448.0,25.0,3423.0,,,,,,,,25465.0,24757.0,196.0,993.0,22103.0,20617.0,257.0,2786.0,325.0,2461.0,8787.0,5788.0,76.0,72.0,45502.0,44789.0,42.08,2765.0,28.0,0.0102,11590.0,5794.0,0.4999,4040.0,2814.0,0.6966,7550.0,2980.0,0.3946,1096.0,,,659.0,498.0,46598.0,,2765.0,28.0,12248.0,6292.0,4040.0,2814.0,8209.0,3478.0,8276.45,8405.0,1254.0,565.0 +2015-01-07 09:00:00+00:00,5092.62,5266.0,489.0,75.96,4643.0,1681.27,1264.0,887.87,3379.0,793.4,3073.46,3126.4,34.08,701.23,2019.16,2139.6,34.08,12662.0,12918.0,378.01,654.76,403.93,250.83,67746.0,62354.0,37256.0,1812.0,0.0486,27957.0,7114.0,0.2545,675.0,395.0,0.5852,27281.0,6719.0,0.2463,10454.0,10352.0,246.0,3426.0,13.0,3413.0,,,,,,,,25994.0,25202.0,588.0,900.0,22327.0,20772.0,724.0,2729.0,382.0,2347.0,8970.0,6027.0,254.0,58.0,46340.0,45464.0,45.54,2765.0,92.0,0.0331,11590.0,6185.0,0.5336,4040.0,2959.0,0.7324,7550.0,3226.0,0.4273,1309.0,,,659.0,487.0,47648.0,,2765.0,92.0,12248.0,6672.0,4040.0,2959.0,8209.0,3713.0,8432.23,8674.0,1498.0,560.0 +2015-01-07 10:00:00+00:00,5036.65,5196.7,489.0,124.83,4643.0,1749.92,1264.0,904.5,3379.0,845.42,3050.55,3070.3,33.63,744.46,1986.1,2126.4,33.63,12769.0,12977.0,702.86,642.1,412.11,229.99,68507.0,63591.0,37256.0,3060.0,0.0822,27957.0,7229.0,0.2586,675.0,477.0,0.7072,27281.0,6751.0,0.2475,10520.0,10593.0,488.0,3296.0,30.0,3267.0,,,,,,,,26384.0,25656.0,1036.0,910.0,22575.0,21135.0,1114.0,2963.0,448.0,2516.0,9028.0,6207.0,422.0,59.0,47365.0,45702.0,41.38,2765.0,226.0,0.0816,11590.0,6340.0,0.547,4040.0,2984.0,0.7387,7550.0,3356.0,0.4444,1348.0,,,659.0,488.0,48714.0,,2765.0,226.0,12248.0,6828.0,4040.0,2984.0,8209.0,3844.0,8450.15,8787.0,1568.0,518.0 +2015-01-07 11:00:00+00:00,4915.91,4966.0,489.0,141.86,4643.0,1745.05,1264.0,873.81,3379.0,871.24,2950.14,2980.2,32.76,742.6,1965.77,1985.8,33.11,12580.0,12731.0,901.03,694.74,473.0,221.74,68100.0,63254.0,37256.0,3777.0,0.1014,27957.0,7460.0,0.2668,675.0,503.0,0.7457,27281.0,6956.0,0.255,10269.0,10306.0,637.0,2972.0,43.0,2929.0,,,,,,,,26320.0,25669.0,1361.0,955.0,22473.0,20819.0,1215.0,3480.0,460.0,3020.0,9038.0,6460.0,564.0,52.0,47844.0,46164.0,40.52,2765.0,319.0,0.1154,11590.0,6592.0,0.5687,4040.0,3031.0,0.7502,7550.0,3560.0,0.4716,1356.0,,,659.0,483.0,49200.0,,2765.0,319.0,12248.0,7074.0,4040.0,3031.0,8209.0,4043.0,8407.89,8689.0,1494.0,503.0 +2015-01-07 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00:00:00+00:00,3404.23,3372.6,490.0,0.0,4653.0,3226.49,1264.0,1137.33,3389.0,2089.16,2068.07,2046.8,27.04,1858.33,1336.16,1325.8,27.06,10866.0,10576.0,0.0,988.24,590.06,398.18,48721.0,47863.0,37303.0,0.0,0.0,28069.0,4819.0,0.1717,700.0,492.0,0.7026,27369.0,4327.0,0.1581,7271.0,6443.0,0.0,613.0,20.0,593.0,,,,,,,,18938.0,19326.0,0.0,684.0,15881.0,15901.0,0.0,3522.0,472.0,3051.0,6631.0,6194.0,0.0,0.0,36959.0,33310.0,37.86,2835.0,0.0,0.0,11760.0,6404.0,0.5445,4040.0,3428.0,0.8485,7720.0,2976.0,0.3854,992.0,893.0,,659.0,249.0,37950.0,34202.0,2835.0,0.0,12419.0,6652.0,4040.0,3428.0,8380.0,3224.0,4967.06,4828.0,0.0,412.0 +2015-01-24 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01:00:00+00:00,3111.97,3045.7,490.0,0.0,4653.0,1702.04,1264.0,775.65,3389.0,926.39,1812.94,1770.7,25.03,807.22,1299.03,1275.0,26.29,9739.0,9754.0,0.0,395.63,241.6,154.03,42562.0,41111.0,37303.0,0.0,0.0,28069.0,7826.0,0.2788,700.0,595.0,0.8499,27369.0,7231.0,0.2642,6320.0,5552.0,0.0,2801.0,30.0,2771.0,,,,,,,,17001.0,17292.0,0.0,1402.0,13675.0,13009.0,0.0,3565.0,565.0,3000.0,5566.0,5258.0,0.0,58.0,36154.0,31498.0,35.79,2835.0,0.0,0.0,11760.0,3961.0,0.3368,4040.0,1573.0,0.3893,7720.0,2388.0,0.3094,829.0,814.0,,659.0,365.0,36983.0,32312.0,2835.0,0.0,12419.0,4326.0,4040.0,1573.0,8380.0,2753.0,4589.44,4682.0,0.0,685.0 +2015-01-25 02:00:00+00:00,3038.89,3028.5,490.0,0.0,4653.0,1627.98,1264.0,824.42,3389.0,803.56,1761.34,1775.0,25.07,688.97,1277.55,1253.5,26.18,9520.0,9417.0,0.0,277.24,139.76,137.48,41804.0,41878.0,37303.0,0.0,0.0,28069.0,7752.0,0.2762,700.0,570.0,0.8141,27369.0,7182.0,0.2624,6211.0,6695.0,0.0,2937.0,22.0,2916.0,,,,,,,,16760.0,17063.0,0.0,1283.0,13347.0,12905.0,0.0,3456.0,548.0,2908.0,5487.0,5215.0,0.0,75.0,33997.0,31034.0,33.78,2835.0,0.0,0.0,11760.0,4104.0,0.349,4040.0,1752.0,0.4338,7720.0,2352.0,0.3047,782.0,775.0,,659.0,350.0,34780.0,31809.0,2835.0,0.0,12419.0,4455.0,4040.0,1752.0,8380.0,2702.0,4450.94,4532.0,0.0,652.0 +2015-01-25 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04:00:00+00:00,3185.27,3074.7,490.0,0.01,4653.0,1473.65,1264.0,711.44,3389.0,762.21,1878.71,1802.3,26.68,657.94,1306.56,1272.4,26.7,9167.0,9262.0,0.0,233.19,112.2,120.99,41003.0,41400.0,37303.0,0.0,0.0,28069.0,7119.0,0.2536,700.0,506.0,0.7223,27369.0,6613.0,0.2416,6149.0,6259.0,0.0,2808.0,20.0,2788.0,,,,,,,,16364.0,16753.0,0.0,1213.0,13165.0,13235.0,0.0,3038.0,485.0,2552.0,5325.0,5154.0,0.0,60.0,30905.0,28108.0,29.68,2835.0,0.0,0.0,11760.0,3994.0,0.3396,4040.0,1723.0,0.4265,7720.0,2272.0,0.2942,726.0,711.0,,659.0,350.0,31632.0,28820.0,2835.0,0.0,12419.0,4344.0,4040.0,1723.0,8380.0,2621.0,4664.67,4679.0,0.0,447.0 +2015-01-25 05:00:00+00:00,3315.3,3281.5,490.0,0.01,4653.0,1262.56,1264.0,601.63,3389.0,660.93,1955.98,1954.8,27.04,550.72,1359.32,1326.7,27.04,9309.0,9414.0,0.0,234.14,109.52,124.62,39678.0,40818.0,37303.0,0.0,0.0,28069.0,6671.0,0.2376,700.0,471.0,0.6722,27369.0,6200.0,0.2266,5635.0,5934.0,0.0,2769.0,15.0,2754.0,,,,,,,,15881.0,16692.0,0.0,1061.0,13047.0,13221.0,0.0,2797.0,455.0,2342.0,5115.0,4970.0,0.0,43.0,30226.0,27820.0,29.12,2835.0,0.0,0.0,11760.0,3840.0,0.3266,4040.0,1571.0,0.3889,7720.0,2269.0,0.2939,706.0,713.0,,659.0,320.0,30932.0,28534.0,2835.0,0.0,12419.0,4160.0,4040.0,1571.0,8380.0,2589.0,4894.57,4832.0,1.0,392.0 +2015-01-25 06:00:00+00:00,3568.9,3525.6,490.0,0.01,4653.0,959.66,1264.0,444.06,3389.0,515.6,2115.08,2106.3,27.39,393.66,1453.82,1419.3,27.39,9375.0,9469.0,0.0,240.28,106.53,133.75,41325.0,42847.0,37303.0,0.0,0.0,28069.0,6432.0,0.2292,700.0,459.0,0.6551,27369.0,5973.0,0.2182,6034.0,6086.0,0.0,2735.0,24.0,2712.0,,,,,,,,16325.0,17518.0,0.0,983.0,13785.0,14142.0,0.0,2685.0,435.0,2250.0,5181.0,5100.0,0.0,29.0,30645.0,28882.0,33.01,2835.0,0.0,0.0,11760.0,3998.0,0.3399,4040.0,1619.0,0.4007,7720.0,2378.0,0.3081,700.0,750.0,,659.0,294.0,31344.0,29632.0,2835.0,0.0,12419.0,4292.0,4040.0,1619.0,8380.0,2673.0,5194.84,5186.0,125.0,341.0 +2015-01-25 07:00:00+00:00,3837.39,3813.0,490.0,1.34,4653.0,834.0,1264.0,386.93,3389.0,447.07,2274.69,2277.5,27.98,330.93,1562.7,1535.5,27.98,9812.0,9888.0,0.29,257.04,105.28,151.76,44054.0,45238.0,37303.0,126.0,0.0034,28069.0,6012.0,0.2142,700.0,463.0,0.6612,27369.0,5550.0,0.2028,6603.0,6541.0,44.0,2436.0,29.0,2406.0,,,,,,,,17254.0,18720.0,25.0,894.0,14764.0,14711.0,24.0,2658.0,434.0,2225.0,5433.0,5267.0,33.0,25.0,32802.0,30014.0,33.64,2835.0,0.0,0.0,11760.0,4283.0,0.3642,4040.0,1806.0,0.447,7720.0,2477.0,0.3209,726.0,837.0,,659.0,291.0,33528.0,30851.0,2835.0,0.0,12419.0,4574.0,4040.0,1806.0,8380.0,2768.0,5612.0,5903.0,514.0,316.0 +2015-01-25 08:00:00+00:00,4038.87,4035.0,490.0,25.07,4653.0,781.66,1264.0,370.72,3389.0,410.94,2368.58,2380.2,28.71,303.27,1670.29,1654.8,28.71,10354.0,10466.0,27.0,282.85,131.66,151.19,47997.0,48793.0,37303.0,727.0,0.0195,28069.0,5614.0,0.2,700.0,428.0,0.6116,27369.0,5185.0,0.1894,7496.0,7345.0,220.0,2269.0,26.0,2243.0,,,,,,,,18598.0,19739.0,164.0,857.0,15905.0,15940.0,217.0,2468.0,403.0,2065.0,5998.0,5769.0,127.0,20.0,35748.0,33427.0,34.95,2835.0,16.0,0.0056,11760.0,4186.0,0.356,4040.0,1754.0,0.4342,7720.0,2432.0,0.315,777.0,911.0,,659.0,300.0,36524.0,34338.0,2835.0,16.0,12419.0,4486.0,4040.0,1754.0,8380.0,2732.0,5963.6,6615.0,885.0,310.0 +2015-01-25 09:00:00+00:00,4062.16,4121.9,490.0,76.6,4653.0,637.24,1264.0,361.12,3389.0,276.12,2352.76,2386.6,29.26,197.72,1709.4,1735.3,29.26,10788.0,10866.0,109.64,297.34,154.49,142.85,51192.0,51428.0,37303.0,1387.0,0.0372,28069.0,4778.0,0.1702,700.0,393.0,0.5612,27369.0,4385.0,0.1602,8104.0,8125.0,374.0,1923.0,27.0,1896.0,,,,,,,,20012.0,20544.0,239.0,720.0,16849.0,16647.0,609.0,2119.0,366.0,1753.0,6227.0,6111.0,164.0,17.0,39479.0,37154.0,35.98,2835.0,116.0,0.0408,11760.0,4162.0,0.3539,4040.0,1821.0,0.4507,7720.0,2341.0,0.3032,834.0,986.0,,659.0,359.0,40314.0,38140.0,2835.0,116.0,12419.0,4521.0,4040.0,1821.0,8380.0,2700.0,6127.23,7204.0,1023.0,285.0 +2015-01-25 10:00:00+00:00,4062.27,4153.0,490.0,119.68,4653.0,492.92,1264.0,293.05,3389.0,199.87,2338.69,2374.4,29.4,135.14,1723.58,1778.6,29.97,11159.0,11203.0,191.31,270.7,134.23,136.47,53687.0,53967.0,37303.0,2016.0,0.054,28069.0,4251.0,0.1515,700.0,329.0,0.4702,27369.0,3922.0,0.1433,8701.0,8555.0,567.0,1720.0,20.0,1700.0,,,,,,,,20960.0,21323.0,291.0,583.0,17630.0,17708.0,974.0,1934.0,309.0,1625.0,6396.0,6381.0,184.0,14.0,42760.0,39390.0,37.94,2835.0,336.0,0.1186,11760.0,4442.0,0.3776,4040.0,2018.0,0.4994,7720.0,2424.0,0.314,946.0,1084.0,,659.0,393.0,43705.0,40474.0,2835.0,336.0,12419.0,4834.0,4040.0,2018.0,8380.0,2817.0,6062.83,7352.0,1065.0,286.0 +2015-01-25 11:00:00+00:00,4006.76,4128.3,490.0,145.32,4653.0,405.2,1264.0,231.23,3389.0,173.97,2308.49,2338.0,29.36,128.44,1698.27,1790.3,29.74,11286.0,11348.0,221.04,247.56,134.79,112.77,53453.0,53899.0,37303.0,2317.0,0.0621,28069.0,3971.0,0.1415,700.0,297.0,0.4237,27369.0,3675.0,0.1342,8317.0,8438.0,620.0,1639.0,11.0,1628.0,,,,,,,,21220.0,21511.0,349.0,555.0,17407.0,17428.0,1143.0,1766.0,285.0,1480.0,6510.0,6523.0,205.0,11.0,44575.0,41024.0,43.93,2835.0,570.0,0.2012,11760.0,4720.0,0.4014,4040.0,2151.0,0.5324,7720.0,2570.0,0.3328,1078.0,1151.0,,659.0,455.0,45652.0,42175.0,2835.0,570.0,12419.0,5175.0,4040.0,2151.0,8380.0,3024.0,5864.69,7155.0,1002.0,236.0 +2015-01-25 12:00:00+00:00,3932.3,4006.7,490.0,149.07,4653.0,270.15,1264.0,126.5,3389.0,143.65,2264.11,2285.8,29.25,116.22,1668.19,1720.9,29.25,10958.0,11040.0,289.01,336.33,222.03,114.3,51386.0,51498.0,37303.0,2242.0,0.0601,28069.0,3615.0,0.1288,700.0,246.0,0.3516,27369.0,3369.0,0.1231,7740.0,7728.0,544.0,1527.0,6.0,1521.0,,,,,,,,20668.0,20902.0,367.0,507.0,16700.0,16696.0,1076.0,1566.0,240.0,1326.0,6279.0,6172.0,254.0,15.0,45654.0,41886.0,48.46,2835.0,714.0,0.2518,11760.0,4912.0,0.4176,4040.0,2136.0,0.5286,7720.0,2776.0,0.3596,1144.0,1224.0,,659.0,430.0,46798.0,43111.0,2835.0,714.0,12419.0,5341.0,4040.0,2136.0,8380.0,3206.0,5344.8,6713.0,808.0,181.0 +2015-01-25 13:00:00+00:00,3884.04,3951.9,490.0,103.82,4653.0,251.58,1264.0,129.31,3389.0,122.27,2233.35,2269.5,29.13,93.81,1650.69,1682.4,29.13,10623.0,10705.0,267.41,430.68,314.05,116.63,50114.0,50174.0,37303.0,1692.0,0.0454,28069.0,3295.0,0.1174,700.0,213.0,0.3047,27369.0,3082.0,0.1126,7577.0,7586.0,342.0,1339.0,8.0,1330.0,,,,,,,,20211.0,20420.0,290.0,507.0,16256.0,16238.0,805.0,1432.0,205.0,1227.0,6070.0,5931.0,256.0,17.0,45916.0,41423.0,40.38,2835.0,722.0,0.2544,11760.0,5146.0,0.4376,4040.0,2232.0,0.5524,7720.0,2914.0,0.3775,1218.0,1252.0,,659.0,409.0,47134.0,42675.0,2835.0,722.0,12419.0,5555.0,4040.0,2232.0,8380.0,3323.0,5393.69,6617.0,574.0,188.0 +2015-01-25 14:00:00+00:00,3940.19,4001.2,490.0,39.63,4653.0,198.15,1264.0,98.94,3389.0,99.21,2276.22,2269.9,29.26,74.92,1663.97,1731.3,29.26,10500.0,10594.0,163.48,483.82,377.61,106.21,49644.0,49885.0,37303.0,877.0,0.0235,28069.0,3053.0,0.1088,700.0,151.0,0.2151,27369.0,2903.0,0.1061,7559.0,7544.0,148.0,1169.0,8.0,1161.0,,,,,,,,20039.0,20343.0,167.0,570.0,16026.0,16154.0,403.0,1298.0,143.0,1155.0,6019.0,5845.0,159.0,16.0,45510.0,41121.0,37.91,2835.0,630.0,0.2224,11760.0,5448.0,0.4633,4040.0,2332.0,0.5772,7720.0,3116.0,0.4036,1246.0,1183.0,,659.0,393.0,46756.0,42304.0,2835.0,630.0,12419.0,5842.0,4040.0,2332.0,8380.0,3510.0,5459.71,6422.0,249.0,166.0 +2015-01-25 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08:00:00+00:00,5197.09,5207.7,490.0,4.94,4653.0,2545.08,1264.0,1057.11,3389.0,1487.97,3226.08,3210.9,29.93,1224.66,1971.01,1996.8,30.37,12560.0,12655.0,8.76,571.95,329.09,242.86,68736.0,66864.0,37317.0,542.0,0.0146,28084.0,7861.0,0.2799,704.0,677.0,0.9613,27381.0,7184.0,0.2624,9830.0,9447.0,281.0,2441.0,40.0,2401.0,,,,,,,,26288.0,26296.0,91.0,1448.0,23035.0,21685.0,88.0,3948.0,636.0,3312.0,9583.0,9435.0,83.0,23.0,48130.0,44261.0,40.65,2835.0,28.0,0.0099,11760.0,5914.0,0.5028,4040.0,2910.0,0.7204,7720.0,3003.0,0.389,1086.0,1279.0,,659.0,217.0,49216.0,45540.0,2835.0,28.0,12419.0,6130.0,4040.0,2910.0,8380.0,3220.0,6962.1,7104.0,619.0,137.0 +2015-01-26 09:00:00+00:00,5328.73,5283.9,490.0,11.47,4653.0,2518.07,1264.0,1052.04,3389.0,1466.03,3310.8,3271.1,29.75,1156.99,2017.93,2012.8,30.26,12654.0,12749.0,40.28,611.47,359.94,251.53,70221.0,68028.0,37317.0,1103.0,0.0296,28084.0,8500.0,0.3027,704.0,675.0,0.9586,27381.0,7825.0,0.2858,10051.0,9721.0,591.0,2763.0,45.0,2719.0,,,,,,,,26982.0,26606.0,165.0,1536.0,23439.0,21954.0,202.0,4173.0,630.0,3543.0,9748.0,9747.0,145.0,26.0,49114.0,45290.0,41.64,2835.0,148.0,0.0522,11760.0,5716.0,0.486,4040.0,2832.0,0.701,7720.0,2884.0,0.3735,1270.0,1308.0,,659.0,253.0,50384.0,46598.0,2835.0,148.0,12419.0,5968.0,4040.0,2832.0,8380.0,3136.0,7055.75,7160.0,696.0,149.0 +2015-01-26 10:00:00+00:00,5329.11,5313.2,490.0,14.09,4653.0,2336.85,1264.0,1006.18,3389.0,1330.67,3299.91,3292.1,29.62,989.26,2029.2,2021.1,30.04,12791.0,12869.0,83.26,745.75,484.04,261.71,71462.0,68694.0,37317.0,1772.0,0.0475,28084.0,9044.0,0.322,704.0,556.0,0.7896,27381.0,8488.0,0.31,10538.0,9787.0,897.0,3075.0,45.0,3030.0,,,,,,,,27266.0,26978.0,345.0,1591.0,23770.0,22126.0,347.0,4328.0,511.0,3817.0,9888.0,9803.0,184.0,50.0,49712.0,45656.0,41.53,2835.0,484.0,0.1706,11760.0,5625.0,0.4783,4040.0,2745.0,0.6794,7720.0,2880.0,0.373,1314.0,1345.0,,659.0,193.0,51026.0,47001.0,2835.0,484.0,12419.0,5818.0,4040.0,2745.0,8380.0,3073.0,7144.35,7226.0,664.0,185.0 +2015-01-26 11:00:00+00:00,5268.92,5219.2,490.0,15.18,4653.0,2194.2,1264.0,1003.3,3389.0,1190.9,3241.14,3205.5,29.46,845.78,2027.78,2013.7,30.04,12539.0,12665.0,108.32,691.21,456.93,234.28,71176.0,68451.0,37317.0,2250.0,0.0603,28084.0,9818.0,0.3496,704.0,585.0,0.8305,27381.0,9233.0,0.3372,10398.0,9793.0,986.0,3410.0,45.0,3365.0,,,,,,,,27406.0,26937.0,561.0,1704.0,23564.0,22074.0,524.0,4638.0,539.0,4099.0,9807.0,9648.0,180.0,66.0,49766.0,45954.0,40.0,2835.0,708.0,0.2499,11760.0,5334.0,0.4536,4040.0,2595.0,0.6423,7720.0,2739.0,0.3548,1337.0,1371.0,,659.0,211.0,51104.0,47325.0,2835.0,708.0,12419.0,5545.0,4040.0,2595.0,8380.0,2950.0,7113.34,7177.0,513.0,207.0 +2015-01-26 12:00:00+00:00,5266.29,5239.3,490.0,13.62,4653.0,2078.48,1264.0,1051.28,3389.0,1027.2,3238.41,3225.2,29.44,703.79,2027.88,2014.1,30.04,12539.0,12736.0,122.98,616.78,374.11,242.67,70765.0,65735.0,37317.0,2249.0,0.0602,28084.0,10328.0,0.3677,704.0,618.0,0.8785,27381.0,9709.0,0.3546,10241.0,7607.0,772.0,3763.0,45.0,3718.0,,,,,,,,27430.0,26725.0,678.0,1742.0,23215.0,21736.0,620.0,4724.0,573.0,4151.0,9880.0,9668.0,180.0,98.0,50053.0,45867.0,40.4,2835.0,832.0,0.2936,11760.0,5472.0,0.4652,4040.0,2732.0,0.6761,7720.0,2740.0,0.355,1341.0,1392.0,,659.0,235.0,51394.0,47259.0,2835.0,832.0,12419.0,5706.0,4040.0,2732.0,8380.0,2975.0,6884.04,6802.0,322.0,226.0 +2015-01-26 13:00:00+00:00,5161.09,5160.6,490.0,12.24,4653.0,1855.82,1264.0,981.75,3389.0,874.07,3139.95,3153.8,29.41,568.79,2021.14,2006.8,30.17,12391.0,12620.0,124.88,527.77,277.43,250.34,69632.0,64910.0,37317.0,1584.0,0.0424,28084.0,10554.0,0.3758,704.0,649.0,0.9223,27381.0,9905.0,0.3618,9996.0,7491.0,434.0,4036.0,46.0,3990.0,,,,,,,,27181.0,26391.0,444.0,1784.0,22744.0,21451.0,574.0,4647.0,604.0,4043.0,9710.0,9576.0,132.0,87.0,50012.0,45598.0,40.53,2835.0,748.0,0.264,11760.0,5730.0,0.4872,4040.0,2881.0,0.7131,7720.0,2848.0,0.369,1348.0,1394.0,,659.0,237.0,51360.0,46992.0,2835.0,748.0,12419.0,5966.0,4040.0,2881.0,8380.0,3085.0,6929.8,6854.0,186.0,286.0 +2015-01-26 14:00:00+00:00,5020.79,5014.0,490.0,10.46,4653.0,1833.29,1264.0,1060.33,3389.0,772.96,3015.04,3033.2,29.36,495.63,2005.75,1980.8,30.63,12302.0,12547.0,131.64,750.19,462.26,287.93,68581.0,66824.0,37317.0,790.0,0.0212,28084.0,10918.0,0.3888,704.0,559.0,0.7935,27381.0,10359.0,0.3783,9686.0,9793.0,154.0,4351.0,44.0,4307.0,,,,,,,,26740.0,26247.0,190.0,1842.0,22500.0,21386.0,377.0,4623.0,515.0,4108.0,9655.0,9399.0,69.0,103.0,49768.0,46013.0,36.35,2835.0,606.0,0.2138,11760.0,5764.0,0.4901,4040.0,2778.0,0.6878,7720.0,2986.0,0.3867,1310.0,1371.0,,659.0,271.0,51079.0,47384.0,2835.0,606.0,12419.0,6035.0,4040.0,2778.0,8380.0,3257.0,6925.29,6797.0,60.0,317.0 +2015-01-26 15:00:00+00:00,5092.67,5113.0,490.0,2.33,4653.0,1768.29,1264.0,894.88,3389.0,873.41,2987.74,3036.7,29.56,614.16,2104.93,2076.3,31.1,12315.0,12547.0,54.77,877.58,532.59,344.99,68202.0,65997.0,37317.0,157.0,0.0042,28084.0,11718.0,0.4172,704.0,544.0,0.7729,27381.0,11174.0,0.4081,9837.0,9515.0,17.0,4823.0,42.0,4782.0,,,,,,,,26335.0,25926.0,42.0,1862.0,22537.0,21313.0,88.0,4941.0,503.0,4439.0,9492.0,9244.0,10.0,92.0,49808.0,47200.0,34.37,2835.0,470.0,0.1656,11760.0,5591.0,0.4754,4040.0,2662.0,0.6588,7720.0,2930.0,0.3794,1278.0,1366.0,,659.0,216.0,51086.0,48567.0,2835.0,470.0,12419.0,5807.0,4040.0,2662.0,8380.0,3145.0,7038.74,6861.0,12.0,268.0 +2015-01-26 16:00:00+00:00,5547.64,5619.8,490.0,0.01,4653.0,1867.4,1264.0,875.42,3389.0,991.98,3289.27,3322.1,30.26,782.53,2258.37,2297.7,31.99,12720.0,13112.0,2.49,918.39,566.23,352.16,70783.0,68331.0,37317.0,7.0,0.0002,28084.0,12848.0,0.4574,704.0,578.0,0.8211,27381.0,12270.0,0.4481,10497.0,9691.0,0.0,5095.0,38.0,5058.0,,,,,,,,26836.0,26766.0,0.0,2109.0,23760.0,22363.0,6.0,5533.0,541.0,4992.0,9689.0,9510.0,0.0,111.0,51412.0,50186.0,46.55,2835.0,220.0,0.0778,11760.0,5245.0,0.446,4040.0,2558.0,0.6332,7720.0,2687.0,0.348,1286.0,1445.0,,659.0,177.0,52698.0,51631.0,2835.0,220.0,12419.0,5422.0,4040.0,2558.0,8380.0,2864.0,7568.65,7433.0,0.0,206.0 +2015-01-26 17:00:00+00:00,5341.63,5339.0,490.0,0.01,4653.0,1744.24,1264.0,724.67,3389.0,1019.57,3149.21,3152.8,29.46,856.89,2192.42,2186.2,31.51,13064.0,13290.0,0.0,1011.48,613.04,398.44,70883.0,68923.0,37317.0,0.0,0.0,28084.0,13842.0,0.4929,704.0,623.0,0.8843,27381.0,13219.0,0.4828,10536.0,9710.0,0.0,5245.0,34.0,5210.0,,,,,,,,26914.0,27343.0,0.0,2371.0,23795.0,22431.0,0.0,6112.0,588.0,5524.0,9637.0,9439.0,0.0,114.0,55933.0,52138.0,79.9,2835.0,32.0,0.0111,11760.0,4949.0,0.4208,4040.0,2502.0,0.6195,7720.0,2446.0,0.3169,1378.0,1669.0,,659.0,154.0,57310.0,53807.0,2835.0,32.0,12419.0,5103.0,4040.0,2502.0,8380.0,2601.0,7694.64,7635.0,0.0,198.0 +2015-01-26 18:00:00+00:00,4916.3,4957.6,490.0,0.01,4653.0,1708.29,1264.0,731.93,3389.0,976.36,2899.46,2926.5,29.37,834.13,2016.84,2031.1,30.89,12494.0,12765.0,0.0,1085.06,648.19,436.87,69341.0,67206.0,37317.0,0.0,0.0,28084.0,13989.0,0.4981,704.0,634.0,0.9004,27381.0,13355.0,0.4878,10702.0,9661.0,0.0,5299.0,38.0,5260.0,,,,,,,,26356.0,26596.0,0.0,2396.0,22850.0,21704.0,0.0,6176.0,596.0,5580.0,9432.0,9245.0,0.0,118.0,56794.0,51944.0,55.05,2835.0,0.0,0.0,11760.0,4838.0,0.4114,4040.0,2573.0,0.6369,7720.0,2266.0,0.2934,1595.0,1629.0,,659.0,115.0,58389.0,53573.0,2835.0,0.0,12419.0,4954.0,4040.0,2573.0,8380.0,2381.0,7665.8,7595.0,0.0,182.0 +2015-01-26 19:00:00+00:00,4557.85,4537.7,490.0,0.0,4653.0,1867.57,1264.0,837.99,3389.0,1029.58,2698.01,2680.7,29.12,881.2,1859.84,1857.0,29.61,11891.0,12110.0,0.0,1050.6,662.31,388.29,65221.0,63274.0,37317.0,0.0,0.0,28084.0,13728.0,0.4888,704.0,611.0,0.8677,27381.0,13117.0,0.479,10044.0,9066.0,0.0,5358.0,34.0,5323.0,,,,,,,,25044.0,25209.0,0.0,2404.0,21186.0,20217.0,0.0,5849.0,576.0,5273.0,8947.0,8782.0,0.0,118.0,54741.0,49852.0,46.93,2835.0,0.0,0.0,11760.0,4594.0,0.3907,4040.0,2463.0,0.6096,7720.0,2132.0,0.2761,1570.0,1521.0,,659.0,112.0,56310.0,51373.0,2835.0,0.0,12419.0,4706.0,4040.0,2463.0,8380.0,2243.0,7212.75,7036.0,0.0,170.0 +2015-01-26 20:00:00+00:00,4247.51,4178.3,490.0,0.0,4653.0,2028.91,1264.0,886.06,3389.0,1142.85,2488.97,2473.4,28.49,987.52,1758.54,1704.9,28.69,11232.0,11481.0,0.0,1070.29,652.04,418.25,61753.0,60104.0,37317.0,0.0,0.0,28084.0,13397.0,0.477,704.0,601.0,0.8534,27381.0,12796.0,0.4674,9067.0,8474.0,0.0,5262.0,28.0,5234.0,,,,,,,,24168.0,24260.0,0.0,2252.0,19897.0,18946.0,0.0,5758.0,573.0,5185.0,8621.0,8424.0,0.0,126.0,51734.0,46890.0,42.22,2835.0,0.0,0.0,11760.0,4336.0,0.3688,4040.0,2418.0,0.5986,7720.0,1918.0,0.2484,1470.0,1425.0,,659.0,99.0,53204.0,48315.0,2835.0,0.0,12419.0,4436.0,4040.0,2418.0,8380.0,2017.0,6584.51,6506.0,0.0,178.0 +2015-01-26 21:00:00+00:00,3924.35,3875.3,490.0,0.0,4653.0,2436.61,1264.0,987.72,3389.0,1448.89,2326.31,2274.1,27.15,1262.67,1598.04,1601.2,28.01,11415.0,11582.0,0.0,986.09,611.12,374.97,58769.0,56060.0,37317.0,0.0,0.0,28084.0,13712.0,0.4882,704.0,598.0,0.8492,27381.0,13114.0,0.4789,8255.0,6669.0,0.0,5254.0,25.0,5229.0,,,,,,,,22990.0,23130.0,0.0,2311.0,19351.0,18271.0,0.0,6015.0,573.0,5443.0,8173.0,7990.0,0.0,131.0,48212.0,42221.0,36.98,2835.0,0.0,0.0,11760.0,4326.0,0.3678,4040.0,2492.0,0.617,7720.0,1834.0,0.2375,1376.0,1339.0,,659.0,102.0,49588.0,43560.0,2835.0,0.0,12419.0,4428.0,4040.0,2492.0,8380.0,1935.0,6058.91,5885.0,0.0,181.0 +2015-01-26 22:00:00+00:00,3599.97,3525.6,490.0,0.0,4653.0,2755.25,1264.0,1109.02,3389.0,1646.23,2161.11,2082.6,24.05,1430.01,1438.86,1443.0,26.51,11301.0,11512.0,0.0,881.65,541.48,340.17,54503.0,51672.0,37317.0,0.0,0.0,28084.0,14745.0,0.525,704.0,613.0,0.8708,27381.0,14132.0,0.5162,7422.0,5978.0,0.0,5735.0,43.0,5692.0,,,,,,,,21402.0,21415.0,0.0,2535.0,18006.0,16836.0,0.0,6353.0,570.0,5782.0,7673.0,7443.0,0.0,123.0,43552.0,36968.0,36.43,2835.0,0.0,0.0,11760.0,4054.0,0.3446,4040.0,2256.0,0.5583,7720.0,1798.0,0.2329,1298.0,1194.0,,659.0,111.0,44850.0,38162.0,2835.0,0.0,12419.0,4165.0,4040.0,2256.0,8380.0,1909.0,5511.0,5362.0,0.0,176.0 +2015-01-26 23:00:00+00:00,3349.92,3342.3,490.0,0.0,4653.0,2792.73,1264.0,1136.95,3389.0,1655.78,2002.95,2018.5,20.36,1454.49,1346.97,1323.8,25.79,10642.0,10683.0,0.0,779.9,456.8,323.1,51183.0,49961.0,37321.0,0.0,0.0,28095.0,15353.0,0.5465,704.0,642.0,0.9118,27391.0,14711.0,0.5371,7044.0,6994.0,0.0,6375.0,47.0,6328.0,,,,,,,,19953.0,19850.0,0.0,2363.0,16992.0,16022.0,0.0,6478.0,595.0,5884.0,7194.0,7094.0,0.0,136.0,38969.0,32875.0,33.55,2835.0,0.0,0.0,11760.0,4044.0,0.344,4040.0,2228.0,0.5516,7720.0,1816.0,0.2352,1142.0,1008.0,,659.0,119.0,40111.0,33883.0,2835.0,0.0,12419.0,4163.0,4040.0,2228.0,8380.0,1935.0,5065.62,4911.0,0.0,161.0 +2015-01-27 00:00:00+00:00,3252.85,3219.5,490.0,0.0,4653.0,2846.73,1264.0,1117.68,3389.0,1729.05,1982.77,1948.8,16.31,1536.77,1270.08,1270.7,25.32,10185.0,10022.0,0.0,750.31,453.76,296.55,49395.0,47950.0,37321.0,0.0,0.0,28095.0,15522.0,0.5525,704.0,644.0,0.9152,27391.0,14878.0,0.5432,7019.0,6739.0,0.0,6734.0,47.0,6688.0,,,,,,,,19350.0,19244.0,0.0,2200.0,16197.0,15218.0,0.0,6452.0,598.0,5855.0,6829.0,6749.0,0.0,136.0,35554.0,31652.0,33.34,2844.0,0.0,0.0,11760.0,3838.0,0.3263,4040.0,2125.0,0.526,7720.0,1712.0,0.2218,958.0,843.0,,659.0,182.0,36512.0,32494.0,2844.0,0.0,12419.0,4019.0,4040.0,2125.0,8380.0,1894.0,5040.36,4891.0,0.0,160.0 +2015-01-27 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09:00:00+00:00,5291.22,5304.2,491.0,23.45,4653.0,819.94,1264.0,560.23,3389.0,259.71,3241.06,3224.5,37.23,180.46,2050.16,2079.7,44.28,12754.0,12621.0,408.61,508.88,291.05,217.83,69635.0,68384.0,37363.0,2593.0,0.0694,28171.0,9023.0,0.3203,736.0,422.0,0.5738,27435.0,8601.0,0.3135,9968.0,10158.0,1347.0,3536.0,10.0,3525.0,,,,,,,,26934.0,26365.0,514.0,1716.0,22980.0,22113.0,521.0,3671.0,412.0,3259.0,9753.0,9748.0,211.0,101.0,50874.0,50638.0,46.96,2868.0,798.0,0.2782,11760.0,2148.0,0.1827,4040.0,1130.0,0.2799,7720.0,1018.0,0.1318,1318.0,1345.0,,659.0,5.0,52192.0,51983.0,2868.0,798.0,12419.0,2153.0,4040.0,1130.0,8380.0,1023.0,6442.4,7689.0,795.0,646.0 +2015-02-02 10:00:00+00:00,5244.02,5246.1,491.0,33.46,4653.0,930.62,1264.0,591.69,3389.0,338.93,3203.63,3195.6,32.5,206.83,2040.39,2050.5,32.5,12794.0,12749.0,701.87,456.92,241.73,215.19,70997.0,69195.0,37363.0,3379.0,0.0904,28171.0,9117.0,0.3236,736.0,422.0,0.5728,27435.0,8696.0,0.3169,10322.0,9885.0,1534.0,3801.0,15.0,3786.0,,,,,,,,27580.0,26813.0,769.0,1756.0,23277.0,22609.0,798.0,3478.0,406.0,3072.0,9819.0,9888.0,278.0,82.0,52336.0,50816.0,46.02,2868.0,506.0,0.1766,11760.0,1994.0,0.1696,4040.0,1082.0,0.2677,7720.0,913.0,0.1182,1382.0,1359.0,,659.0,6.0,53718.0,52174.0,2868.0,506.0,12419.0,2001.0,4040.0,1082.0,8380.0,919.0,6496.97,7831.0,868.0,708.0 +2015-02-02 11:00:00+00:00,5137.39,5148.4,491.0,45.59,4653.0,960.52,1264.0,547.13,3389.0,413.39,3108.41,3105.3,31.84,243.36,2028.98,2043.1,31.84,12649.0,12558.0,739.63,428.35,208.39,219.96,70805.0,68989.0,37363.0,3908.0,0.1046,28171.0,9780.0,0.3472,736.0,343.0,0.4662,27435.0,9437.0,0.344,10344.0,9912.0,1640.0,4294.0,22.0,4272.0,,,,,,,,27737.0,26803.0,836.0,1846.0,22968.0,22468.0,1097.0,3552.0,321.0,3231.0,9757.0,9807.0,336.0,88.0,53098.0,51050.0,43.46,2868.0,755.0,0.2632,11760.0,2010.0,0.171,4040.0,1008.0,0.2496,7720.0,1002.0,0.1299,1418.0,1344.0,,659.0,4.0,54517.0,52395.0,2868.0,755.0,12419.0,2014.0,4040.0,1008.0,8380.0,1006.0,6532.05,7816.0,802.0,717.0 +2015-02-02 12:00:00+00:00,5125.52,5127.7,491.0,51.89,4653.0,1017.96,1264.0,602.8,3389.0,415.16,3119.33,3107.3,32.41,248.7,2006.19,2020.4,32.41,12677.0,12585.0,676.07,399.99,200.89,199.1,70077.0,67589.0,37363.0,3794.0,0.1016,28171.0,9651.0,0.3426,736.0,302.0,0.41,27435.0,9349.0,0.3408,10368.0,8893.0,1514.0,4507.0,36.0,4471.0,,,,,,,,27323.0,26681.0,693.0,1737.0,22666.0,22136.0,1244.0,3283.0,266.0,3017.0,9720.0,9880.0,342.0,124.0,53280.0,50936.0,42.63,2868.0,905.0,0.3156,11760.0,1832.0,0.1558,4040.0,1098.0,0.2719,7720.0,734.0,0.095,1420.0,1361.0,,659.0,5.0,54700.0,52297.0,2868.0,905.0,12419.0,1837.0,4040.0,1098.0,8380.0,738.0,6364.91,7473.0,691.0,712.0 +2015-02-02 13:00:00+00:00,5024.99,5011.7,491.0,39.5,4653.0,978.47,1264.0,559.05,3389.0,419.42,3045.76,3035.3,32.0,287.77,1979.23,1976.4,32.0,12620.0,12463.0,571.26,432.6,224.59,208.01,68979.0,66657.0,37363.0,2841.0,0.076,28171.0,9185.0,0.326,736.0,301.0,0.4092,27435.0,8884.0,0.3238,10317.0,9046.0,924.0,4416.0,33.0,4383.0,,,,,,,,26953.0,26304.0,573.0,1656.0,22263.0,21597.0,1044.0,2976.0,268.0,2707.0,9446.0,9710.0,300.0,138.0,53122.0,50747.0,42.0,2868.0,945.0,0.3295,11760.0,2245.0,0.1909,4040.0,1189.0,0.2943,7720.0,1056.0,0.1368,1415.0,1328.0,,659.0,4.0,54536.0,52075.0,2868.0,945.0,12419.0,2249.0,4040.0,1189.0,8380.0,1060.0,6542.4,7341.0,459.0,677.0 +2015-02-02 14:00:00+00:00,4901.51,4877.8,491.0,19.81,4653.0,926.43,1264.0,555.12,3389.0,371.31,2939.61,2935.5,31.12,262.22,1961.9,1942.3,36.54,12568.0,12385.0,365.94,422.97,225.65,197.32,67991.0,67400.0,37363.0,1624.0,0.0434,28171.0,8408.0,0.2984,736.0,308.0,0.418,27435.0,8100.0,0.2952,10074.0,10213.0,406.0,4258.0,39.0,4219.0,,,,,,,,26624.0,26091.0,388.0,1658.0,21948.0,21442.0,596.0,2353.0,269.0,2085.0,9346.0,9655.0,234.0,139.0,52798.0,50423.0,37.5,2868.0,801.0,0.2793,11760.0,1953.0,0.166,4040.0,1107.0,0.274,7720.0,846.0,0.1096,1372.0,1301.0,,659.0,5.0,54170.0,51724.0,2868.0,801.0,12419.0,1958.0,4040.0,1107.0,8380.0,851.0,6531.21,7005.0,284.0,677.0 +2015-02-02 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01:00:00+00:00,3275.66,3282.2,491.0,0.0,4653.0,790.64,1264.0,440.59,3389.0,350.05,2017.29,2006.9,29.12,285.68,1258.37,1275.3,29.12,10554.0,10249.0,0.0,218.29,36.48,181.81,49351.0,47625.0,37365.0,0.0,0.0,28210.0,2096.0,0.0743,752.0,84.0,0.1123,27458.0,2011.0,0.0733,7201.0,7006.0,0.0,928.0,16.0,912.0,,,,,,,,19226.0,18745.0,0.0,528.0,15965.0,15276.0,0.0,602.0,69.0,533.0,6959.0,6598.0,0.0,38.0,36871.0,34072.0,33.39,2889.0,0.0,0.0,11760.0,2212.0,0.1881,4040.0,1026.0,0.254,7720.0,1186.0,0.1536,890.0,844.0,,659.0,43.0,37761.0,34916.0,2889.0,0.0,12419.0,2255.0,4040.0,1026.0,8380.0,1229.0,4633.71,4640.0,0.0,368.0 +2015-02-04 02:00:00+00:00,3290.58,3287.0,491.0,0.0,4653.0,822.33,1264.0,452.77,3389.0,369.56,2030.75,2018.3,29.18,308.11,1259.83,1268.7,29.18,10366.0,9963.0,0.0,360.76,168.57,192.19,49460.0,47830.0,37365.0,0.0,0.0,28210.0,2109.0,0.0748,752.0,52.0,0.0695,27458.0,2057.0,0.0749,7318.0,7270.0,0.0,866.0,10.0,856.0,,,,,,,,19345.0,18738.0,0.0,605.0,15889.0,15233.0,0.0,605.0,43.0,562.0,6908.0,6588.0,0.0,34.0,35503.0,33638.0,32.59,2889.0,0.0,0.0,11760.0,2400.0,0.2042,4040.0,1269.0,0.3142,7720.0,1132.0,0.1466,857.0,832.0,,659.0,52.0,36360.0,34470.0,2889.0,0.0,12419.0,2453.0,4040.0,1269.0,8380.0,1184.0,4535.89,4540.0,0.0,396.0 +2015-02-04 03:00:00+00:00,3395.29,3393.0,491.0,0.01,4653.0,919.81,1264.0,501.7,3389.0,418.11,2098.44,2073.0,29.76,351.32,1296.85,1320.0,29.76,10357.0,9898.0,0.0,375.89,161.6,214.29,50406.0,48933.0,37365.0,0.0,0.0,28210.0,2172.0,0.077,752.0,81.0,0.1072,27458.0,2091.0,0.0762,7554.0,7552.0,0.0,828.0,16.0,812.0,,,,,,,,19542.0,19031.0,0.0,708.0,16254.0,15548.0,0.0,602.0,64.0,538.0,7057.0,6802.0,0.0,33.0,34995.0,32834.0,31.83,2889.0,0.0,0.0,11760.0,2272.0,0.1932,4040.0,1136.0,0.2812,7720.0,1136.0,0.1472,839.0,804.0,,659.0,59.0,35834.0,33638.0,2889.0,0.0,12419.0,2331.0,4040.0,1136.0,8380.0,1195.0,4619.96,4638.0,0.0,406.0 +2015-02-04 04:00:00+00:00,3652.81,3692.9,491.0,0.01,4653.0,973.78,1264.0,505.59,3389.0,468.19,2254.41,2261.4,31.08,404.81,1398.4,1431.5,31.08,10747.0,10157.0,0.0,394.55,207.46,187.09,53350.0,51661.0,37365.0,0.0,0.0,28210.0,2177.0,0.0772,752.0,116.0,0.1544,27458.0,2061.0,0.075,7978.0,7846.0,0.0,797.0,12.0,784.0,,,,,,,,20525.0,19975.0,0.0,720.0,17494.0,16681.0,0.0,615.0,104.0,511.0,7353.0,7160.0,0.0,46.0,33718.0,32533.0,32.43,2889.0,0.0,0.0,11760.0,2180.0,0.1854,4040.0,1058.0,0.2618,7720.0,1122.0,0.1454,816.0,797.0,,659.0,63.0,34533.0,33330.0,2889.0,0.0,12419.0,2243.0,4040.0,1058.0,8380.0,1185.0,5119.46,5087.0,0.0,462.0 +2015-02-04 05:00:00+00:00,4362.04,4384.5,491.0,0.01,4653.0,851.63,1264.0,383.43,3389.0,468.2,2701.87,2706.2,35.82,414.2,1660.17,1678.3,35.82,11862.0,11318.0,0.0,301.64,182.3,119.34,60713.0,58382.0,37365.0,0.0,0.0,28210.0,2151.0,0.0763,752.0,122.0,0.1619,27458.0,2029.0,0.0739,8809.0,8383.0,0.0,798.0,5.0,792.0,,,,,,,,23424.0,22752.0,0.0,666.0,20084.0,19060.0,0.0,639.0,117.0,522.0,8396.0,8187.0,0.0,48.0,32940.0,34046.0,34.88,2889.0,0.0,0.0,11760.0,2160.0,0.1837,4040.0,1141.0,0.2824,7720.0,1020.0,0.1321,802.0,807.0,,659.0,56.0,33742.0,34852.0,2889.0,0.0,12419.0,2217.0,4040.0,1141.0,8380.0,1076.0,5942.09,5895.0,8.0,520.0 +2015-02-04 06:00:00+00:00,5138.77,5163.7,491.0,0.01,4653.0,718.0,1264.0,291.94,3389.0,426.06,3221.86,3221.1,49.7,382.87,1916.91,1942.6,49.7,12747.0,12279.0,0.0,340.91,273.01,67.9,67175.0,65220.0,37365.0,8.0,0.0002,28210.0,2012.0,0.0713,752.0,94.0,0.1249,27458.0,1918.0,0.0698,9942.0,9744.0,2.0,809.0,5.0,804.0,,,,,,,,25550.0,24858.0,1.0,568.0,22418.0,21413.0,3.0,584.0,89.0,495.0,9265.0,9206.0,2.0,50.0,36328.0,40219.0,43.05,2889.0,0.0,0.0,11760.0,2414.0,0.2052,4040.0,1427.0,0.3532,7720.0,986.0,0.1278,822.0,900.0,,659.0,57.0,37151.0,41119.0,2889.0,0.0,12419.0,2471.0,4040.0,1427.0,8380.0,1044.0,6405.12,6194.0,168.0,576.0 +2015-02-04 07:00:00+00:00,5296.78,5327.8,491.0,1.75,4653.0,684.04,1264.0,307.58,3389.0,376.46,3309.55,3311.8,61.76,336.57,1987.23,2016.0,61.76,12973.0,12665.0,15.22,171.62,110.34,61.28,68926.0,67401.0,37365.0,460.0,0.0123,28210.0,1934.0,0.0686,752.0,81.0,0.1078,27458.0,1853.0,0.0675,10238.0,9858.0,68.0,804.0,8.0,796.0,,,,,,,,26217.0,25749.0,87.0,544.0,22981.0,22338.0,221.0,542.0,73.0,470.0,9490.0,9455.0,83.0,44.0,44069.0,45392.0,45.48,2889.0,0.0,0.0,11760.0,2548.0,0.2167,4040.0,1498.0,0.3709,7720.0,1050.0,0.136,931.0,1154.0,,659.0,53.0,45000.0,46546.0,2889.0,0.0,12419.0,2601.0,4040.0,1498.0,8380.0,1103.0,6799.37,6747.0,427.0,627.0 +2015-02-04 08:00:00+00:00,5287.65,5300.8,491.0,17.39,4653.0,597.54,1264.0,240.96,3389.0,356.58,3263.56,3271.1,61.09,319.26,2024.09,2029.7,61.09,13085.0,12774.0,228.43,153.18,79.93,73.25,69575.0,68178.0,37365.0,1850.0,0.0495,28210.0,1586.0,0.0563,752.0,66.0,0.0873,27458.0,1521.0,0.0554,10403.0,10419.0,114.0,656.0,10.0,646.0,,,,,,,,26523.0,25763.0,483.0,467.0,22982.0,22396.0,893.0,434.0,55.0,379.0,9666.0,9600.0,360.0,29.0,48440.0,45464.0,46.1,2889.0,48.0,0.0166,11760.0,2465.0,0.2096,4040.0,1483.0,0.3671,7720.0,982.0,0.1272,1196.0,1354.0,,659.0,34.0,49636.0,46818.0,2889.0,48.0,12419.0,2499.0,4040.0,1483.0,8380.0,1016.0,6881.53,6998.0,668.0,682.0 +2015-02-04 09:00:00+00:00,5366.92,5355.1,491.0,38.82,4653.0,592.64,1264.0,253.96,3389.0,338.68,3297.85,3287.3,59.63,296.26,2069.07,2067.8,59.63,12893.0,12797.0,684.89,154.4,81.72,72.68,70397.0,68552.0,37365.0,3585.0,0.0959,28210.0,1430.0,0.0507,752.0,58.0,0.0772,27458.0,1372.0,0.05,10541.0,10452.0,228.0,478.0,9.0,469.0,,,,,,,,26900.0,26023.0,950.0,584.0,23106.0,22349.0,1780.0,344.0,49.0,294.0,9850.0,9728.0,626.0,25.0,49946.0,45748.0,46.07,2889.0,300.0,0.1037,11760.0,2336.0,0.1986,4040.0,1452.0,0.3596,7720.0,884.0,0.1145,1361.0,1335.0,,659.0,29.0,51306.0,47084.0,2889.0,300.0,12419.0,2366.0,4040.0,1452.0,8380.0,913.0,6912.6,7175.0,949.0,720.0 +2015-02-04 10:00:00+00:00,5304.99,5328.9,491.0,52.31,4653.0,612.36,1264.0,258.27,3389.0,354.09,3246.72,3258.2,54.96,301.04,2058.27,2070.7,54.96,12889.0,12873.0,1159.44,141.35,104.02,37.33,70907.0,69853.0,37365.0,5204.0,0.1393,28210.0,1471.0,0.0522,752.0,64.0,0.0853,27458.0,1407.0,0.0513,10658.0,10601.0,339.0,423.0,9.0,414.0,,,,,,,,27151.0,26430.0,1506.0,673.0,23231.0,23058.0,2594.0,351.0,55.0,296.0,9867.0,9764.0,765.0,24.0,50894.0,46146.0,46.77,2889.0,743.0,0.2572,11760.0,2335.0,0.1986,4040.0,1534.0,0.3796,7720.0,802.0,0.1038,1412.0,1349.0,,659.0,41.0,52306.0,47494.0,2889.0,743.0,12419.0,2376.0,4040.0,1534.0,8380.0,842.0,6835.29,7196.0,987.0,792.0 +2015-02-04 11:00:00+00:00,5211.59,5199.6,491.0,50.03,4653.0,731.54,1264.0,271.31,3389.0,460.23,3168.52,3156.3,52.71,393.45,2043.07,2043.3,52.71,12683.0,12678.0,1326.08,251.32,226.32,25.0,70131.0,69108.0,37365.0,6035.0,0.1616,28210.0,1479.0,0.0524,752.0,69.0,0.092,27458.0,1410.0,0.0514,10377.0,10248.0,452.0,435.0,8.0,427.0,,,,,,,,26995.0,26444.0,1841.0,547.0,22838.0,22675.0,3005.0,475.0,61.0,413.0,9922.0,9740.0,737.0,22.0,50670.0,46492.0,41.87,2889.0,1146.0,0.3969,11760.0,2129.0,0.181,4040.0,1357.0,0.3359,7720.0,772.0,0.1,1429.0,1368.0,,659.0,25.0,52099.0,47861.0,2889.0,1146.0,12419.0,2154.0,4040.0,1357.0,8380.0,797.0,6787.85,7216.0,966.0,842.0 +2015-02-04 12:00:00+00:00,5243.42,5208.9,491.0,39.23,4653.0,747.7,1264.0,261.05,3389.0,486.65,3194.61,3187.3,52.25,425.47,2048.81,2021.6,52.25,12682.0,12696.0,1173.26,275.84,231.18,44.66,69435.0,67852.0,37365.0,5982.0,0.1601,28210.0,1663.0,0.059,752.0,78.0,0.1032,27458.0,1585.0,0.0577,10137.0,9573.0,497.0,492.0,7.0,485.0,,,,,,,,26881.0,26281.0,1935.0,464.0,22562.0,22310.0,2944.0,685.0,71.0,615.0,9855.0,9688.0,605.0,22.0,50978.0,46277.0,39.42,2889.0,1400.0,0.4844,11760.0,2176.0,0.1851,4040.0,1398.0,0.3462,7720.0,778.0,0.1008,1417.0,1365.0,,659.0,23.0,52396.0,47642.0,2889.0,1400.0,12419.0,2200.0,4040.0,1398.0,8380.0,801.0,6530.78,6928.0,887.0,877.0 +2015-02-04 13:00:00+00:00,5167.64,5153.1,491.0,28.49,4653.0,730.72,1264.0,277.13,3389.0,453.59,3143.32,3134.5,46.22,405.22,2024.32,2018.6,49.52,12515.0,12592.0,859.88,389.95,327.28,62.67,68297.0,65324.0,37365.0,4898.0,0.1311,28210.0,1970.0,0.0698,752.0,74.0,0.0981,27458.0,1896.0,0.0691,10091.0,9081.0,388.0,544.0,6.0,538.0,,,,,,,,26380.0,25893.0,1601.0,501.0,22184.0,20949.0,2463.0,907.0,68.0,840.0,9643.0,9400.0,446.0,18.0,50542.0,46013.0,37.89,2889.0,1414.0,0.4894,11760.0,2426.0,0.2063,4040.0,1608.0,0.3979,7720.0,818.0,0.106,1402.0,1328.0,,659.0,34.0,51944.0,47341.0,2889.0,1414.0,12419.0,2460.0,4040.0,1608.0,8380.0,853.0,6587.07,6927.0,735.0,868.0 +2015-02-04 14:00:00+00:00,5017.26,5024.8,491.0,16.24,4653.0,689.99,1264.0,272.84,3389.0,417.15,3033.96,3036.7,48.36,368.22,1983.3,1988.1,48.36,12532.0,12580.0,566.24,331.97,255.73,76.24,67418.0,66421.0,37365.0,2971.0,0.0795,28210.0,1961.0,0.0695,752.0,63.0,0.0839,27458.0,1898.0,0.0692,9869.0,9813.0,200.0,493.0,9.0,484.0,,,,,,,,26051.0,25716.0,1025.0,485.0,21929.0,21491.0,1500.0,963.0,54.0,909.0,9569.0,9401.0,245.0,21.0,50454.0,46298.0,36.73,2889.0,1178.0,0.4079,11760.0,2460.0,0.2092,4040.0,1588.0,0.3932,7720.0,872.0,0.113,1357.0,1316.0,,659.0,30.0,51810.0,47614.0,2889.0,1178.0,12419.0,2491.0,4040.0,1588.0,8380.0,902.0,6467.13,6728.0,374.0,839.0 +2015-02-04 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12:00:00+00:00,5007.95,5216.3,491.0,116.95,4653.0,200.31,1264.0,105.71,3389.0,94.6,3086.4,3115.4,46.3,74.9,1921.55,2100.9,46.3,12899.0,12740.0,746.28,661.77,342.02,319.75,69759.0,68045.0,37366.0,5524.0,0.1478,28228.0,4627.0,0.1639,759.0,160.0,0.2108,27468.0,4467.0,0.1626,9506.0,9610.0,1043.0,1021.0,9.0,1012.0,,,,,,,,27519.0,26833.0,1716.0,1676.0,22899.0,22158.0,2133.0,1619.0,151.0,1468.0,9835.0,9444.0,631.0,311.0,52862.0,48046.0,44.85,2889.0,713.0,0.2468,11760.0,1488.0,0.1265,4040.0,1184.0,0.2931,7720.0,304.0,0.0393,1384.0,1342.0,,659.0,28.0,54246.0,49389.0,2889.0,713.0,12419.0,1516.0,4040.0,1184.0,8380.0,332.0,6083.14,6437.0,1230.0,788.0 +2015-02-05 13:00:00+00:00,4891.1,5113.0,491.0,96.67,4653.0,192.48,1264.0,107.9,3389.0,84.58,2986.24,3050.7,36.02,62.38,1904.86,2062.3,43.43,12844.0,12583.0,583.62,841.97,440.47,401.5,68360.0,65965.0,37366.0,4084.0,0.1093,28228.0,5459.0,0.1934,759.0,153.0,0.2015,27468.0,5306.0,0.1932,9504.0,9338.0,916.0,1235.0,14.0,1221.0,,,,,,,,26691.0,26446.0,1176.0,2019.0,22478.0,20818.0,1668.0,1888.0,139.0,1749.0,9687.0,9364.0,325.0,316.0,52510.0,47526.0,38.59,2889.0,712.0,0.2466,11760.0,1574.0,0.1338,4040.0,1289.0,0.3191,7720.0,285.0,0.0369,1376.0,1307.0,,659.0,35.0,53886.0,48833.0,2889.0,712.0,12419.0,1609.0,4040.0,1289.0,8380.0,320.0,6184.47,6378.0,884.0,769.0 +2015-02-05 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15:00:00+00:00,4839.79,4942.7,491.0,13.23,4653.0,176.38,1264.0,112.13,3389.0,64.25,2924.08,2912.6,39.71,33.85,1915.71,2030.1,42.98,12823.0,12387.0,157.41,1119.34,562.93,556.41,67284.0,65770.0,37366.0,709.0,0.019,28228.0,6585.0,0.2333,759.0,141.0,0.1854,27468.0,6444.0,0.2346,9682.0,9502.0,141.0,1552.0,14.0,1538.0,,,,,,,,26126.0,25840.0,208.0,2571.0,22071.0,21419.0,334.0,2166.0,127.0,2039.0,9405.0,9009.0,26.0,297.0,51751.0,48826.0,35.53,2889.0,486.0,0.1684,11760.0,1850.0,0.1572,4040.0,1539.0,0.381,7720.0,310.0,0.0402,1320.0,1308.0,,659.0,33.0,53072.0,50134.0,2889.0,486.0,12419.0,1882.0,4040.0,1539.0,8380.0,343.0,6126.76,6575.0,59.0,755.0 +2015-02-05 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06:00:00+00:00,4714.11,4700.2,491.0,0.0,4653.0,363.32,1264.0,124.01,3389.0,239.31,2889.33,2873.4,43.93,219.3,1824.78,1826.8,43.93,12783.0,12348.0,0.0,,,44.79,66284.0,64899.0,37370.0,34.0,0.0009,28297.0,844.0,0.0298,794.0,13.0,0.0165,27503.0,831.0,0.0302,9768.0,9684.0,13.0,557.0,3.0,554.0,,,,,,,,25648.0,25018.0,4.0,167.0,21832.0,21066.0,4.0,113.0,10.0,103.0,9035.0,9130.0,13.0,7.0,36452.0,41200.0,46.06,2890.0,0.0,0.0,11760.0,606.0,0.0516,4040.0,110.0,0.0271,7720.0,497.0,0.0643,772.0,849.0,,659.0,42.0,37224.0,42049.0,2890.0,0.0,12419.0,648.0,4040.0,110.0,8380.0,539.0,7370.48,7208.0,278.0,565.0 +2015-02-12 07:00:00+00:00,4967.91,4976.2,491.0,0.45,4653.0,314.92,1264.0,93.05,3389.0,221.87,3038.35,3020.2,55.57,206.84,1929.56,1956.0,55.57,12778.0,12504.0,34.55,,,40.4,68495.0,66466.0,37370.0,688.0,0.0184,28297.0,877.0,0.031,794.0,11.0,0.014,27503.0,866.0,0.0315,10084.0,9335.0,254.0,609.0,2.0,607.0,,,,,,,,26470.0,25714.0,128.0,162.0,22675.0,21923.0,123.0,103.0,9.0,94.0,9266.0,9495.0,182.0,3.0,44270.0,46466.0,50.99,2890.0,0.0,0.0,11760.0,642.0,0.0546,4040.0,136.0,0.0338,7720.0,505.0,0.0654,871.0,1106.0,,659.0,44.0,45142.0,47572.0,2890.0,0.0,12419.0,686.0,4040.0,136.0,8380.0,549.0,7968.83,7880.0,729.0,565.0 +2015-02-12 08:00:00+00:00,5084.19,5083.7,491.0,8.05,4653.0,290.96,1264.0,86.58,3389.0,204.38,3090.52,3065.3,54.9,190.54,1993.67,2018.4,54.9,12921.0,12632.0,269.77,,,34.44,69053.0,67333.0,37370.0,2526.0,0.0676,28297.0,824.0,0.0291,794.0,9.0,0.0107,27503.0,815.0,0.0296,10244.0,10017.0,980.0,566.0,1.0,565.0,,,,,,,,26505.0,25680.0,531.0,160.0,22877.0,21945.0,412.0,94.0,8.0,87.0,9427.0,9691.0,602.0,3.0,48220.0,47733.0,54.68,2890.0,18.0,0.006,11760.0,678.0,0.0577,4040.0,170.0,0.042,7720.0,509.0,0.066,1118.0,1287.0,,659.0,49.0,49338.0,49020.0,2890.0,18.0,12419.0,727.0,4040.0,170.0,8380.0,558.0,8143.8,8128.0,1053.0,565.0 +2015-02-12 09:00:00+00:00,5156.89,5176.3,491.0,17.09,4653.0,271.77,1264.0,75.92,3389.0,195.85,3143.17,3124.9,52.52,183.29,2013.72,2051.4,52.52,12837.0,12660.0,630.7,21.3,0.66,20.64,69854.0,67829.0,37370.0,4688.0,0.1254,28297.0,614.0,0.0217,794.0,9.0,0.0114,27503.0,605.0,0.022,10306.0,9960.0,1694.0,401.0,1.0,400.0,,,,,,,,26815.0,25914.0,1191.0,129.0,23194.0,22202.0,736.0,81.0,8.0,74.0,9539.0,9753.0,1066.0,2.0,50372.0,48747.0,59.93,2890.0,95.0,0.0329,11760.0,645.0,0.0548,4040.0,206.0,0.0508,7720.0,440.0,0.057,1280.0,1323.0,,659.0,57.0,51652.0,50070.0,2890.0,95.0,12419.0,702.0,4040.0,206.0,8380.0,497.0,8209.07,8291.0,1312.0,575.0 +2015-02-12 10:00:00+00:00,5166.87,5146.8,491.0,24.65,4653.0,262.01,1264.0,79.16,3389.0,182.85,3151.3,3115.5,48.76,173.32,2015.57,2031.3,48.76,12927.0,12722.0,952.82,15.89,2.41,13.48,70431.0,68693.0,37370.0,6339.0,0.1697,28297.0,505.0,0.0179,794.0,5.0,0.0065,27503.0,500.0,0.0182,10458.0,10360.0,2195.0,343.0,1.0,342.0,,,,,,,,26965.0,26202.0,1754.0,93.0,23440.0,22468.0,1001.0,68.0,4.0,64.0,9568.0,9664.0,1390.0,1.0,51326.0,48978.0,43.6,2890.0,236.0,0.0814,11760.0,645.0,0.0548,4040.0,224.0,0.0554,7720.0,421.0,0.0545,1332.0,1339.0,,659.0,69.0,52658.0,50317.0,2890.0,236.0,12419.0,714.0,4040.0,224.0,8380.0,490.0,8289.62,8354.0,1491.0,560.0 +2015-02-12 11:00:00+00:00,5107.8,5039.7,491.0,29.54,4653.0,272.86,1264.0,90.75,3389.0,182.11,3095.88,3024.2,44.63,172.57,2011.92,2015.5,44.63,12566.0,12468.0,1160.84,16.64,6.06,10.58,69422.0,67444.0,37370.0,7222.0,0.1933,28297.0,478.0,0.0169,794.0,5.0,0.0059,27503.0,473.0,0.0172,10144.0,9350.0,2384.0,315.0,0.0,315.0,,,,,,,,26721.0,26115.0,2166.0,80.0,23162.0,22329.0,1150.0,82.0,4.0,78.0,9396.0,9649.0,1523.0,0.0,51504.0,49230.0,49.7,2890.0,402.0,0.1391,11760.0,659.0,0.056,4040.0,254.0,0.0628,7720.0,405.0,0.0524,1350.0,1331.0,,659.0,61.0,52854.0,50562.0,2890.0,402.0,12419.0,720.0,4040.0,254.0,8380.0,466.0,8209.0,8272.0,1455.0,503.0 +2015-02-12 12:00:00+00:00,5068.54,5005.9,491.0,27.81,4653.0,300.14,1264.0,128.53,3389.0,171.61,3060.88,3006.1,43.9,161.95,2007.66,1999.8,43.9,12448.0,12503.0,1201.14,11.94,0.02,11.92,68081.0,67707.0,37370.0,7158.0,0.1916,28297.0,417.0,0.0147,794.0,4.0,0.0053,27503.0,413.0,0.015,9857.0,9919.0,2263.0,295.0,0.0,294.0,,,,,,,,26097.0,25913.0,2254.0,57.0,22905.0,22040.0,1149.0,65.0,4.0,61.0,9222.0,9835.0,1493.0,0.0,51654.0,49273.0,43.9,2890.0,516.0,0.1784,11760.0,610.0,0.0518,4040.0,206.0,0.0509,7720.0,404.0,0.0524,1348.0,1330.0,,659.0,81.0,53002.0,50603.0,2890.0,516.0,12419.0,691.0,4040.0,206.0,8380.0,485.0,7846.26,7925.0,1284.0,521.0 +2015-02-12 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08:00:00+00:00,5013.89,5127.7,492.0,89.48,4653.0,3353.7,1264.0,1026.76,3389.0,2326.94,3049.36,3082.5,28.41,2032.64,1964.53,2045.2,32.22,12557.0,12565.0,244.65,107.66,93.57,14.09,68986.0,66888.0,37374.0,1824.0,0.0488,28376.0,3642.0,0.1284,854.0,669.0,0.7832,27523.0,2973.0,0.108,10682.0,9822.0,501.0,1245.0,43.0,1202.0,,,,,,,,25994.0,25399.0,439.0,118.0,22772.0,21946.0,641.0,2273.0,626.0,1648.0,9538.0,9720.0,243.0,5.0,44688.0,46143.0,42.19,2903.0,130.0,0.0449,11771.0,5030.0,0.4274,4040.0,1996.0,0.494,7731.0,3034.0,0.3925,1063.0,1214.0,,659.0,413.0,45750.0,47357.0,2903.0,130.0,12430.0,5444.0,4040.0,1996.0,8390.0,3448.0,8033.79,8306.0,1147.0,973.0 +2015-02-18 09:00:00+00:00,5093.14,5175.3,492.0,143.26,4653.0,3557.6,1264.0,1026.43,3389.0,2531.17,3118.7,3109.8,28.14,2223.93,1974.44,2065.5,32.13,12691.0,12568.0,487.16,142.1,124.5,17.6,69892.0,67918.0,37374.0,3588.0,0.096,28376.0,3810.0,0.1343,854.0,669.0,0.7835,27523.0,3141.0,0.1141,10440.0,10121.0,980.0,1147.0,42.0,1105.0,,,,,,,,26625.0,25722.0,892.0,137.0,23098.0,22207.0,1271.0,2521.0,627.0,1894.0,9729.0,9867.0,446.0,4.0,47152.0,47522.0,44.98,2903.0,536.0,0.1846,11771.0,5178.0,0.4399,4040.0,2061.0,0.5102,7731.0,3118.0,0.4032,1200.0,1258.0,,659.0,372.0,48352.0,48781.0,2903.0,536.0,12430.0,5550.0,4040.0,2061.0,8390.0,3489.0,8122.34,8450.0,1419.0,981.0 +2015-02-18 10:00:00+00:00,5063.11,5150.7,492.0,158.13,4653.0,3651.54,1264.0,1004.27,3389.0,2647.27,3106.83,3100.4,27.96,2344.63,1956.28,2050.3,29.53,12798.0,12645.0,641.73,126.08,114.11,11.97,70411.0,68839.0,37374.0,5578.0,0.1493,28376.0,3723.0,0.1312,854.0,675.0,0.7899,27523.0,3049.0,0.1108,10361.0,10325.0,1704.0,1133.0,46.0,1088.0,,,,,,,,26917.0,26064.0,1272.0,181.0,23267.0,22618.0,1997.0,2408.0,629.0,1779.0,9867.0,9832.0,605.0,2.0,47748.0,47586.0,44.98,2903.0,1046.0,0.3601,11771.0,5031.0,0.4274,4040.0,1957.0,0.4844,7731.0,3074.0,0.3976,1278.0,1273.0,,659.0,422.0,49026.0,48859.0,2903.0,1046.0,12430.0,5453.0,4040.0,1957.0,8390.0,3496.0,8198.0,8409.0,1570.0,988.0 +2015-02-18 11:00:00+00:00,4972.34,5058.1,492.0,148.75,4653.0,3802.32,1264.0,1122.84,3389.0,2679.48,3039.8,3023.1,27.87,2355.37,1932.54,2035.0,28.44,12588.0,12415.0,704.48,141.96,131.96,10.0,69571.0,67938.0,37374.0,7097.0,0.1899,28376.0,4307.0,0.1518,854.0,679.0,0.7949,27523.0,3628.0,0.1318,10089.0,10156.0,2350.0,1239.0,47.0,1192.0,,,,,,,,26763.0,26002.0,1558.0,214.0,22938.0,22168.0,2493.0,2854.0,632.0,2221.0,9780.0,9613.0,697.0,1.0,47380.0,47886.0,51.32,2903.0,1498.0,0.5158,11771.0,5185.0,0.4405,4040.0,2038.0,0.5046,7731.0,3146.0,0.407,1302.0,1276.0,,659.0,386.0,48682.0,49163.0,2903.0,1498.0,12430.0,5571.0,4040.0,2038.0,8390.0,3532.0,8180.1,8236.0,1611.0,974.0 +2015-02-18 12:00:00+00:00,4981.97,5037.1,492.0,111.79,4653.0,3835.89,1264.0,1165.51,3389.0,2670.38,3068.28,3050.0,27.81,2327.81,1913.69,1987.1,27.97,12465.0,12385.0,785.96,189.11,180.43,8.68,68638.0,67303.0,37374.0,7617.0,0.2038,28376.0,4845.0,0.1708,854.0,678.0,0.7935,27523.0,4168.0,0.1514,9823.0,9997.0,2552.0,1327.0,48.0,1279.0,,,,,,,,26510.0,25844.0,1765.0,236.0,22545.0,21876.0,2531.0,3280.0,630.0,2650.0,9760.0,9586.0,768.0,2.0,47271.0,47808.0,43.08,2903.0,1713.0,0.5901,11771.0,5768.0,0.49,4040.0,2346.0,0.5806,7731.0,3422.0,0.4426,1326.0,1272.0,,659.0,364.0,48598.0,49080.0,2903.0,1713.0,12430.0,6132.0,4040.0,2346.0,8390.0,3786.0,7943.92,7883.0,1530.0,957.0 +2015-02-18 13:00:00+00:00,4868.32,4907.0,492.0,74.33,4653.0,3795.99,1264.0,1174.24,3389.0,2621.75,2995.14,3008.8,27.78,2278.38,1873.18,1898.2,27.78,12244.0,12239.0,841.41,208.87,201.5,7.37,67572.0,65955.0,37374.0,6832.0,0.1828,28376.0,5310.0,0.1871,854.0,682.0,0.7982,27523.0,4629.0,0.1682,9755.0,9821.0,2289.0,1671.0,48.0,1623.0,,,,,,,,26155.0,25409.0,1645.0,176.0,22098.0,21419.0,2185.0,3460.0,634.0,2826.0,9564.0,9305.0,712.0,3.0,46864.0,47308.0,38.66,2903.0,1630.0,0.5614,11771.0,5748.0,0.4883,4040.0,2401.0,0.5943,7731.0,3347.0,0.4329,1330.0,1247.0,,659.0,360.0,48193.0,48556.0,2903.0,1630.0,12430.0,6108.0,4040.0,2401.0,8390.0,3707.0,7745.0,7923.0,1253.0,941.0 +2015-02-18 14:00:00+00:00,4725.17,4748.3,492.0,41.99,4653.0,3724.56,1264.0,1157.42,3389.0,2567.14,2877.63,2877.9,27.71,2215.22,1847.54,1870.4,27.71,12113.0,12184.0,738.68,210.92,202.38,8.54,66351.0,64934.0,37374.0,4676.0,0.1251,28376.0,5456.0,0.1923,854.0,684.0,0.8004,27523.0,4773.0,0.1734,9497.0,9541.0,1496.0,1896.0,48.0,1848.0,,,,,,,,25677.0,25043.0,1131.0,222.0,21794.0,21122.0,1537.0,3337.0,636.0,2701.0,9382.0,9228.0,512.0,2.0,46426.0,46872.0,40.0,2903.0,1400.0,0.4821,11771.0,5778.0,0.4909,4040.0,2506.0,0.6202,7731.0,3272.0,0.4233,1302.0,1220.0,,659.0,389.0,47728.0,48092.0,2903.0,1400.0,12430.0,6167.0,4040.0,2506.0,8390.0,3662.0,7879.29,7837.0,792.0,916.0 +2015-02-18 15:00:00+00:00,4766.4,4802.3,492.0,12.83,4653.0,3580.96,1264.0,1101.99,3389.0,2478.97,2893.07,2873.9,27.75,2129.03,1873.33,1928.4,27.75,11895.0,12092.0,466.98,204.51,193.03,11.48,65321.0,64042.0,37374.0,1988.0,0.0532,28376.0,5398.0,0.1902,854.0,684.0,0.801,27523.0,4714.0,0.1713,9411.0,9378.0,540.0,1923.0,48.0,1875.0,,,,,,,,25236.0,24599.0,535.0,274.0,21574.0,21023.0,690.0,3201.0,636.0,2565.0,9099.0,9043.0,223.0,2.0,46288.0,46869.0,36.97,2903.0,1056.0,0.3639,11771.0,6150.0,0.5224,4040.0,2722.0,0.6739,7731.0,3427.0,0.4432,1278.0,1223.0,,659.0,400.0,47566.0,48092.0,2903.0,1056.0,12430.0,6550.0,4040.0,2722.0,8390.0,3827.0,7614.51,7893.0,217.0,851.0 +2015-02-18 16:00:00+00:00,5214.98,5238.9,492.0,1.15,4653.0,3514.35,1264.0,1148.56,3389.0,2365.79,3129.88,3132.7,27.91,2017.07,2085.1,2106.2,28.53,11988.0,12307.0,132.27,191.23,177.94,13.29,66769.0,65474.0,37374.0,243.0,0.0065,28376.0,6300.0,0.222,854.0,683.0,0.7999,27523.0,5617.0,0.2041,9842.0,9715.0,39.0,2388.0,48.0,2340.0,,,,,,,,25588.0,25003.0,82.0,324.0,22323.0,21722.0,106.0,3587.0,635.0,2952.0,9017.0,9035.0,16.0,2.0,47031.0,48430.0,37.1,2903.0,658.0,0.2265,11771.0,6136.0,0.5213,4040.0,2652.0,0.6564,7731.0,3484.0,0.4507,1277.0,1248.0,,659.0,444.0,48308.0,49677.0,2903.0,658.0,12430.0,6581.0,4040.0,2652.0,8390.0,3929.0,8312.53,8515.0,0.0,802.0 +2015-02-18 17:00:00+00:00,5264.22,5285.8,492.0,0.01,4653.0,3458.88,1264.0,1170.03,3389.0,2288.85,3141.92,3120.2,27.92,1947.52,2122.3,2165.6,28.53,12618.0,12841.0,0.92,217.76,196.64,21.12,70094.0,68179.0,37374.0,0.0,0.0,28376.0,7842.0,0.2764,854.0,687.0,0.8048,27523.0,7154.0,0.26,10401.0,10233.0,0.0,3349.0,48.0,3301.0,,,,,,,,26846.0,25930.0,0.0,450.0,23514.0,22505.0,0.0,4042.0,639.0,3403.0,9333.0,9510.0,0.0,0.0,49378.0,51678.0,57.97,2903.0,170.0,0.0584,11771.0,6111.0,0.5192,4040.0,2630.0,0.651,7731.0,3481.0,0.4502,1308.0,1376.0,,659.0,467.0,50685.0,53054.0,2903.0,170.0,12430.0,6578.0,4040.0,2630.0,8390.0,3948.0,8741.8,8765.0,0.0,712.0 +2015-02-18 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09:00:00+00:00,5064.88,5189.4,493.0,143.16,4653.0,1385.28,1264.0,671.67,3389.0,713.61,3159.87,3142.9,26.99,534.52,1905.01,2046.5,26.99,12325.0,12262.0,1139.14,915.0,617.82,297.18,69620.0,68772.0,37437.0,10026.0,0.2678,28489.0,16400.0,0.5757,886.0,807.0,0.9111,27603.0,15593.0,0.5649,10394.0,11324.0,3073.0,6098.0,46.0,6052.0,,,,,,,,27109.0,26412.0,2268.0,2377.0,22713.0,23935.0,3066.0,7832.0,761.0,7071.0,9404.0,7101.0,1618.0,93.0,48056.0,47328.0,40.51,2980.0,996.0,0.3342,11849.0,5528.0,0.4666,4040.0,3084.0,0.7635,7809.0,2444.0,0.313,1258.0,1302.0,,659.0,134.0,49314.0,48631.0,2980.0,996.0,12508.0,5663.0,4040.0,3084.0,8468.0,2578.0,6316.81,6633.0,1724.0,161.0 +2015-03-04 10:00:00+00:00,5063.95,5139.0,493.0,127.63,4653.0,1240.23,1264.0,553.69,3389.0,686.54,3187.18,3121.1,26.83,520.41,1876.77,2017.9,26.83,12363.0,12388.0,1066.92,938.22,623.13,315.09,70142.0,70267.0,37437.0,10813.0,0.2888,28489.0,17425.0,0.6116,886.0,810.0,0.914,27603.0,16615.0,0.6019,10394.0,11588.0,2776.0,6910.0,48.0,6862.0,,,,,,,,27331.0,26877.0,2475.0,2552.0,22994.0,24435.0,3618.0,7848.0,762.0,7086.0,9423.0,7366.0,1944.0,115.0,48377.0,47414.0,39.27,2980.0,1562.0,0.524,11849.0,5552.0,0.4686,4040.0,3000.0,0.7427,7809.0,2552.0,0.3267,1316.0,1323.0,,659.0,114.0,49692.0,48737.0,2980.0,1562.0,12508.0,5666.0,4040.0,3000.0,8468.0,2665.0,6285.67,6653.0,1782.0,116.0 +2015-03-04 11:00:00+00:00,5032.76,5080.3,493.0,108.63,4653.0,1358.37,1264.0,650.66,3389.0,707.71,3135.05,3089.0,26.78,520.01,1897.71,1991.3,26.78,12286.0,12181.0,1031.61,976.1,609.61,366.49,70113.0,69387.0,37437.0,10637.0,0.2842,28489.0,18452.0,0.6477,886.0,810.0,0.914,27603.0,17642.0,0.6391,10394.0,11343.0,2019.0,7274.0,47.0,7227.0,,,,,,,,27468.0,26823.0,2554.0,2602.0,22712.0,23911.0,3802.0,8458.0,763.0,7696.0,9538.0,7312.0,2262.0,118.0,48170.0,47982.0,39.05,2980.0,1946.0,0.653,11849.0,5431.0,0.4584,4040.0,2806.0,0.6947,7809.0,2624.0,0.3361,1321.0,1329.0,,659.0,110.0,49491.0,49310.0,2980.0,1946.0,12508.0,5541.0,4040.0,2806.0,8468.0,2734.0,6263.37,6606.0,1737.0,101.0 +2015-03-04 12:00:00+00:00,5083.58,5094.5,493.0,112.52,4653.0,1253.33,1264.0,600.98,3389.0,652.35,3171.24,3114.7,26.72,501.32,1912.34,1979.8,26.72,12288.0,12206.0,931.89,1021.13,559.1,462.03,69519.0,66883.0,37437.0,9554.0,0.2552,28489.0,18717.0,0.657,886.0,814.0,0.9182,27603.0,17903.0,0.6486,10394.0,9787.0,1342.0,7009.0,48.0,6961.0,,,,,,,,27232.0,26620.0,2313.0,2706.0,22428.0,23680.0,3701.0,8880.0,766.0,8115.0,9464.0,6796.0,2197.0,121.0,48250.0,48018.0,36.81,2980.0,2223.0,0.746,11849.0,4866.0,0.4106,4040.0,2481.0,0.6141,7809.0,2384.0,0.3054,1301.0,1351.0,,659.0,139.0,49551.0,49369.0,2980.0,2223.0,12508.0,5005.0,4040.0,2481.0,8468.0,2524.0,6012.62,6356.0,1542.0,105.0 +2015-03-04 13:00:00+00:00,4943.33,4987.7,493.0,107.33,4653.0,1128.54,1264.0,594.87,3389.0,533.67,3046.05,3030.8,26.66,408.21,1897.28,1956.9,26.66,12317.0,12078.0,798.4,976.34,447.46,528.88,67865.0,66475.0,37437.0,7778.0,0.2078,28489.0,18047.0,0.6334,886.0,821.0,0.9272,27603.0,17225.0,0.624,10394.0,10749.0,1049.0,6277.0,48.0,6229.0,,,,,,,,26095.0,26094.0,1977.0,2731.0,22125.0,23210.0,2941.0,8909.0,774.0,8135.0,9251.0,6422.0,1811.0,130.0,47832.0,47414.0,38.19,2980.0,2226.0,0.7472,11849.0,4618.0,0.3898,4040.0,2340.0,0.5793,7809.0,2278.0,0.2917,1295.0,1320.0,,659.0,148.0,49128.0,48734.0,2980.0,2226.0,12508.0,4766.0,4040.0,2340.0,8468.0,2425.0,6042.86,6354.0,1270.0,105.0 +2015-03-04 14:00:00+00:00,4710.39,4762.9,493.0,94.04,4653.0,1208.13,1264.0,593.81,3389.0,614.32,2882.7,2883.7,26.51,513.33,1827.69,1879.2,26.51,12246.0,11989.0,635.85,850.6,318.24,532.36,67230.0,65468.0,37437.0,5760.0,0.1538,28489.0,17617.0,0.6184,886.0,821.0,0.9268,27603.0,16796.0,0.6085,10394.0,10633.0,834.0,6139.0,47.0,6092.0,,,,,,,,25823.0,25649.0,1621.0,2799.0,21926.0,22806.0,2007.0,8535.0,774.0,7761.0,9087.0,6381.0,1298.0,144.0,47396.0,47050.0,37.23,2980.0,2114.0,0.7094,11849.0,4049.0,0.3417,4040.0,2039.0,0.5047,7809.0,2010.0,0.2574,1254.0,1295.0,,659.0,153.0,48650.0,48345.0,2980.0,2114.0,12508.0,4202.0,4040.0,2039.0,8468.0,2163.0,5904.21,6176.0,795.0,111.0 +2015-03-04 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05:00:00+00:00,3002.47,3021.3,501.0,28.49,4669.0,2865.5,1264.0,1022.28,3405.0,1843.22,1829.36,1815.5,9.96,1636.43,1173.11,1205.8,12.94,7899.0,7662.0,246.62,90.14,75.53,14.61,38168.0,39033.0,37628.0,3599.0,0.0956,29342.0,15842.0,0.5399,1403.0,1284.0,0.9149,27940.0,14559.0,0.5211,6587.0,6364.0,869.0,7312.0,92.0,7221.0,,,,,,,,14965.0,14929.0,963.0,736.0,12316.0,13478.0,1203.0,7724.0,1192.0,6533.0,4299.0,4263.0,564.0,70.0,23605.0,23089.0,24.99,4975.0,105.0,0.0211,11926.0,2818.0,0.2363,4040.0,807.0,0.1997,7886.0,2011.0,0.255,590.0,620.0,,664.0,223.0,24194.0,23709.0,4975.0,105.0,12589.0,3041.0,4040.0,807.0,8550.0,2234.0,4221.0,4585.0,408.0,683.0 +2015-05-10 06:00:00+00:00,3332.93,3313.9,501.0,73.46,4669.0,3062.89,1264.0,1017.86,3405.0,2045.03,2018.68,1993.8,9.99,1797.03,1314.25,1320.1,16.85,8567.0,8135.0,558.05,96.28,78.91,17.37,42019.0,42560.0,37628.0,7113.0,0.189,29342.0,15977.0,0.5446,1403.0,1114.0,0.7941,27940.0,14863.0,0.5319,7304.0,7072.0,1450.0,8049.0,91.0,7958.0,,,,,,,,16365.0,16253.0,2081.0,577.0,13605.0,14611.0,2405.0,7302.0,1023.0,6279.0,4745.0,4624.0,1177.0,49.0,26908.0,25187.0,23.67,4975.0,409.0,0.0822,11926.0,3382.0,0.2836,4040.0,1104.0,0.2731,7886.0,2279.0,0.289,636.0,601.0,,664.0,248.0,27544.0,25788.0,4975.0,409.0,12589.0,3630.0,4040.0,1104.0,8550.0,2527.0,4740.0,5067.0,737.0,718.0 +2015-05-10 07:00:00+00:00,3511.71,3507.2,501.0,165.68,4669.0,3216.42,1264.0,996.24,3405.0,2220.18,2094.52,2102.4,9.95,1917.09,1417.19,1404.8,18.98,9056.0,8684.0,891.6,91.94,78.18,13.76,45718.0,45653.0,37628.0,11328.0,0.301,29342.0,15919.0,0.5426,1403.0,974.0,0.694,27940.0,14946.0,0.5349,8187.0,7653.0,2056.0,8631.0,89.0,8542.0,,,,,,,,17740.0,17611.0,3496.0,448.0,14666.0,15344.0,3858.0,6790.0,885.0,5905.0,5125.0,5045.0,1918.0,51.0,30473.0,28179.0,36.81,4975.0,1118.0,0.2248,11926.0,3917.0,0.3284,4040.0,1162.0,0.2878,7886.0,2754.0,0.3493,726.0,631.0,,664.0,271.0,31200.0,28810.0,4975.0,1118.0,12589.0,4188.0,4040.0,1162.0,8550.0,3026.0,5189.0,5434.0,1052.0,758.0 +2015-05-10 08:00:00+00:00,3651.92,3592.9,501.0,264.51,4669.0,3410.53,1264.0,1001.39,3405.0,2409.14,2157.36,2133.9,2.78,2056.44,1494.56,1459.0,20.14,9284.0,9020.0,1232.88,92.87,85.43,7.44,48380.0,48422.0,37628.0,15684.0,0.4168,29342.0,14675.0,0.5001,1403.0,838.0,0.5976,27940.0,13836.0,0.4952,9003.0,8162.0,2770.0,8231.0,91.0,8140.0,,,,,,,,18656.0,18590.0,4802.0,313.0,15288.0,16338.0,5530.0,6070.0,748.0,5322.0,5432.0,5332.0,2582.0,60.0,32870.0,31066.0,39.54,4975.0,2000.0,0.4019,11926.0,4861.0,0.4076,4040.0,1586.0,0.3926,7886.0,3275.0,0.4152,834.0,715.0,,664.0,271.0,33703.0,31780.0,4975.0,2000.0,12589.0,5132.0,4040.0,1586.0,8550.0,3546.0,5483.0,5695.0,1279.0,816.0 +2015-05-10 09:00:00+00:00,3668.51,3665.1,501.0,347.06,4669.0,3399.32,1264.0,984.73,3405.0,2414.59,2153.85,2167.4,2.76,2069.82,1514.66,1497.7,20.18,9419.0,9347.0,1551.33,120.06,113.82,6.24,50426.0,51227.0,37628.0,18825.0,0.5003,29342.0,13431.0,0.4578,1403.0,615.0,0.4382,27940.0,12816.0,0.4587,9712.0,8779.0,3389.0,7868.0,91.0,7776.0,,,,,,,,19247.0,19459.0,5508.0,273.0,15801.0,17236.0,6930.0,5213.0,524.0,4689.0,5666.0,5752.0,2998.0,77.0,35424.0,33092.0,47.04,4975.0,1972.0,0.3964,11926.0,5014.0,0.4204,4040.0,1401.0,0.3468,7886.0,3613.0,0.4582,926.0,821.0,,664.0,270.0,36350.0,33912.0,4975.0,1972.0,12589.0,5284.0,4040.0,1401.0,8550.0,3883.0,5567.0,5667.0,1411.0,835.0 +2015-05-10 10:00:00+00:00,3586.61,3615.4,501.0,397.28,4669.0,3261.29,1264.0,919.17,3405.0,2342.12,2094.52,2119.3,19.65,2034.8,1492.09,1496.1,19.65,9263.0,9451.0,1808.28,82.41,72.16,10.25,49104.0,51098.0,37628.0,20749.0,0.5514,29342.0,11942.0,0.407,1403.0,424.0,0.3025,27940.0,11518.0,0.4122,8896.0,8386.0,3768.0,7178.0,93.0,7085.0,,,,,,,,19111.0,19523.0,5907.0,230.0,15425.0,17287.0,7820.0,4453.0,332.0,4121.0,5673.0,5903.0,3254.0,82.0,34451.0,34082.0,50.71,4975.0,3227.0,0.6486,11926.0,6678.0,0.56,4040.0,2332.0,0.5774,7886.0,4346.0,0.5511,960.0,937.0,,664.0,270.0,35411.0,35019.0,4975.0,3227.0,12589.0,6948.0,4040.0,2332.0,8550.0,4616.0,5326.0,5472.0,1493.0,856.0 +2015-05-10 11:00:00+00:00,3508.91,3544.4,501.0,450.4,4669.0,3089.74,1264.0,861.68,3405.0,2228.06,2052.37,2067.3,19.16,1914.38,1456.54,1477.1,19.16,8916.0,9226.0,1947.68,28.26,10.29,17.97,46899.0,49517.0,37628.0,21659.0,0.5756,29342.0,10346.0,0.3526,1403.0,273.0,0.1948,27940.0,10073.0,0.3605,8339.0,7845.0,3981.0,6424.0,92.0,6332.0,,,,,,,,18312.0,18919.0,5997.0,207.0,14709.0,17166.0,8292.0,3640.0,182.0,3459.0,5540.0,5586.0,3389.0,75.0,37010.0,34708.0,42.35,4975.0,3292.0,0.6617,11926.0,6435.0,0.5396,4040.0,2094.0,0.5182,7886.0,4342.0,0.5506,1019.0,1014.0,,664.0,274.0,38029.0,35722.0,4975.0,3292.0,12589.0,6709.0,4040.0,2094.0,8550.0,4615.0,4838.0,5198.0,1444.0,885.0 +2015-05-10 12:00:00+00:00,3395.62,3458.7,501.0,465.32,4669.0,2939.46,1264.0,807.98,3405.0,2131.48,1992.99,2014.9,19.09,1785.52,1402.63,1443.8,19.09,8633.0,8979.0,1917.02,45.43,20.76,24.67,44599.0,47721.0,37628.0,21557.0,0.5729,29342.0,8985.0,0.3062,1403.0,202.0,0.1438,27940.0,8784.0,0.3144,7789.0,7537.0,4128.0,5923.0,79.0,5845.0,,,,,,,,17449.0,18366.0,5957.0,150.0,14023.0,16501.0,8108.0,2817.0,123.0,2694.0,5338.0,5316.0,3364.0,95.0,36914.0,34188.0,37.52,4975.0,1158.0,0.2327,11926.0,6670.0,0.5593,4040.0,2110.0,0.5222,7886.0,4560.0,0.5783,1044.0,1082.0,,664.0,262.0,37958.0,35271.0,4975.0,1158.0,12589.0,6932.0,4040.0,2110.0,8550.0,4822.0,4464.0,4954.0,1317.0,862.0 +2015-05-10 13:00:00+00:00,3331.46,3393.7,501.0,440.9,4669.0,2708.67,1264.0,801.23,3405.0,1907.44,1962.57,1980.2,18.6,1546.98,1368.89,1413.5,18.6,8543.0,8815.0,1841.1,44.73,15.64,29.09,43570.0,46899.0,37628.0,20207.0,0.537,29342.0,7594.0,0.2588,1403.0,155.0,0.1105,27940.0,7439.0,0.2662,7671.0,7373.0,4024.0,5239.0,53.0,5185.0,,,,,,,,17147.0,17974.0,5560.0,86.0,13646.0,16290.0,7535.0,2190.0,102.0,2088.0,5106.0,5261.0,3088.0,79.0,36062.0,33290.0,35.58,4975.0,3055.0,0.614,11926.0,5935.0,0.4976,4040.0,2160.0,0.5346,7886.0,3775.0,0.4787,987.0,1125.0,,664.0,308.0,37048.0,34415.0,4975.0,3055.0,12589.0,6243.0,4040.0,2160.0,8550.0,4083.0,4371.0,4731.0,1037.0,839.0 +2015-05-10 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09:00:00+00:00,3561.77,3557.1,503.0,264.75,4669.0,480.52,1264.0,75.99,3405.0,404.53,2059.41,2066.5,25.01,356.24,1502.36,1490.6,25.01,9322.0,9280.0,1303.92,88.75,36.48,52.27,47533.0,48595.0,37649.0,13239.0,0.3516,29537.0,2192.0,0.0742,1523.0,57.0,0.0378,28014.0,2135.0,0.0762,8349.0,8350.0,3416.0,655.0,0.0,655.0,,,,,,,,18237.0,17997.0,2666.0,230.0,15503.0,16881.0,4706.0,1302.0,57.0,1245.0,5443.0,5368.0,2450.0,5.0,35575.0,34117.0,43.93,4975.0,2682.0,0.539,11926.0,2470.0,0.2072,4040.0,638.0,0.1579,7886.0,1832.0,0.2324,1100.0,1043.0,,664.0,38.0,36675.0,35160.0,4975.0,2682.0,12589.0,2509.0,4040.0,638.0,8550.0,1871.0,6125.0,6428.0,1708.0,234.0 +2015-05-25 10:00:00+00:00,3536.63,3516.3,503.0,269.51,4669.0,586.17,1264.0,94.08,3405.0,492.09,2027.72,2025.9,25.0,438.73,1508.91,1490.4,25.0,9456.0,9323.0,1405.81,223.57,140.86,82.71,47970.0,48287.0,37649.0,14548.0,0.3864,29537.0,2638.0,0.0894,1523.0,72.0,0.0476,28014.0,2566.0,0.0916,8301.0,8120.0,3516.0,890.0,2.0,888.0,,,,,,,,18627.0,18273.0,3359.0,292.0,15333.0,16647.0,5131.0,1445.0,70.0,1374.0,5708.0,5248.0,2542.0,11.0,35624.0,34715.0,47.09,4975.0,3329.0,0.6692,11926.0,2414.0,0.2024,4040.0,368.0,0.091,7886.0,2046.0,0.2596,1128.0,1103.0,,664.0,35.0,36752.0,35818.0,4975.0,3329.0,12589.0,2449.0,4040.0,368.0,8550.0,2081.0,6126.0,6474.0,1726.0,261.0 +2015-05-25 11:00:00+00:00,3489.82,3481.4,503.0,242.81,4669.0,699.77,1264.0,128.81,3405.0,570.96,1991.41,1996.7,22.81,522.09,1498.41,1484.7,22.81,9141.0,9129.0,1371.02,323.73,172.98,150.75,46195.0,46537.0,37649.0,13913.0,0.3696,29537.0,3502.0,0.1186,1523.0,97.0,0.0634,28014.0,3406.0,0.1216,7877.0,7633.0,3207.0,1234.0,4.0,1229.0,,,,,,,,17963.0,17668.0,3494.0,432.0,14708.0,16095.0,5104.0,1802.0,92.0,1710.0,5646.0,5142.0,2108.0,35.0,36442.0,34747.0,45.03,4975.0,3362.0,0.6758,11926.0,2179.0,0.1827,4040.0,394.0,0.0976,7886.0,1785.0,0.2263,1127.0,1146.0,,664.0,58.0,37568.0,35893.0,4975.0,3362.0,12589.0,2237.0,4040.0,394.0,8550.0,1843.0,5895.0,6285.0,1660.0,280.0 +2015-05-25 12:00:00+00:00,3463.49,3448.8,503.0,230.18,4669.0,718.05,1264.0,109.9,3405.0,608.15,1980.1,1974.3,22.46,573.55,1483.39,1474.5,22.46,8893.0,8920.0,1226.71,347.33,110.24,237.09,44362.0,45160.0,37649.0,12365.0,0.3284,29537.0,4010.0,0.1358,1523.0,140.0,0.0918,28014.0,3871.0,0.1382,7574.0,7384.0,2709.0,1428.0,8.0,1421.0,,,,,,,,17523.0,17257.0,3447.0,596.0,14159.0,15522.0,4751.0,1934.0,132.0,1801.0,5106.0,4997.0,1458.0,52.0,35990.0,34410.0,42.08,4975.0,2766.0,0.556,11926.0,2152.0,0.1804,4040.0,352.0,0.0872,7886.0,1799.0,0.2282,1090.0,1157.0,,664.0,77.0,37080.0,35566.0,4975.0,2766.0,12589.0,2229.0,4040.0,352.0,8550.0,1876.0,5555.0,5899.0,1511.0,317.0 +2015-05-25 13:00:00+00:00,3422.07,3414.7,503.0,192.86,4669.0,746.12,1264.0,113.21,3405.0,632.91,1965.64,1953.4,22.07,616.33,1456.43,1461.3,22.07,8816.0,8711.0,1182.17,386.56,78.53,308.03,43616.0,44160.0,37649.0,10697.0,0.2842,29537.0,4446.0,0.1505,1523.0,195.0,0.1282,28014.0,4251.0,0.1518,7301.0,7189.0,2245.0,1656.0,16.0,1640.0,,,,,,,,17372.0,16722.0,3073.0,636.0,13967.0,15316.0,4197.0,2106.0,180.0,1926.0,4977.0,4932.0,1182.0,49.0,35314.0,33791.0,39.99,4975.0,2787.0,0.5602,11926.0,2504.0,0.2099,4040.0,294.0,0.0726,7886.0,2210.0,0.2802,1060.0,1125.0,,664.0,77.0,36374.0,34916.0,4975.0,2787.0,12589.0,2580.0,4040.0,294.0,8550.0,2287.0,5361.0,5678.0,1257.0,312.0 +2015-05-25 14:00:00+00:00,3457.13,3491.8,503.0,173.8,4669.0,712.68,1264.0,81.67,3405.0,631.01,1994.54,2005.6,21.98,620.79,1462.59,1486.2,21.98,8709.0,8519.0,1070.28,344.88,46.17,298.71,43153.0,43861.0,37649.0,8396.0,0.223,29537.0,4713.0,0.1596,1523.0,211.0,0.1384,28014.0,4503.0,0.1607,7388.0,7184.0,1885.0,1738.0,13.0,1724.0,,,,,,,,16925.0,16350.0,2189.0,597.0,13917.0,15570.0,3438.0,2346.0,198.0,2148.0,4924.0,4757.0,885.0,33.0,35032.0,33446.0,36.09,4975.0,2484.0,0.4993,11926.0,2565.0,0.215,4040.0,324.0,0.0802,7886.0,2241.0,0.2842,1048.0,1100.0,,664.0,86.0,36080.0,34545.0,4975.0,2484.0,12589.0,2651.0,4040.0,324.0,8550.0,2327.0,5395.0,5648.0,913.0,335.0 +2015-05-25 15:00:00+00:00,3737.46,3741.6,503.0,143.44,4669.0,772.04,1264.0,100.02,3405.0,672.02,2194.74,2186.3,22.53,664.73,1542.72,1555.3,22.53,9016.0,8535.0,893.19,397.59,50.24,347.35,44667.0,44451.0,37649.0,6048.0,0.1607,29537.0,4486.0,0.1519,1523.0,241.0,0.1581,28014.0,4246.0,0.1516,7924.0,7324.0,1447.0,1690.0,5.0,1685.0,,,,,,,,17215.0,16456.0,1458.0,545.0,14409.0,15805.0,2559.0,2217.0,236.0,1981.0,5119.0,4866.0,583.0,35.0,35685.0,34068.0,47.03,4975.0,2018.0,0.4055,11926.0,2612.0,0.219,4040.0,450.0,0.1114,7886.0,2162.0,0.2742,1090.0,1089.0,,664.0,78.0,36775.0,35158.0,4975.0,2018.0,12589.0,2691.0,4040.0,450.0,8550.0,2241.0,5452.0,5702.0,494.0,374.0 +2015-05-25 16:00:00+00:00,3740.11,3758.5,503.0,98.3,4669.0,778.42,1264.0,140.36,3405.0,638.06,2200.6,2194.0,24.2,623.61,1539.51,1564.5,26.58,9150.0,8863.0,667.88,345.05,17.56,327.49,45797.0,45578.0,37649.0,3763.0,0.1,29537.0,4446.0,0.1505,1523.0,267.0,0.1754,28014.0,4179.0,0.1492,7903.0,7690.0,923.0,1830.0,0.0,1830.0,,,,,,,,17790.0,16786.0,960.0,538.0,14895.0,16155.0,1589.0,2057.0,267.0,1790.0,5208.0,4947.0,291.0,21.0,36955.0,35373.0,53.05,4975.0,1670.0,0.3356,11926.0,2573.0,0.2157,4040.0,476.0,0.1177,7886.0,2098.0,0.266,1174.0,1119.0,,664.0,75.0,38129.0,36492.0,4975.0,1670.0,12589.0,2648.0,4040.0,476.0,8550.0,2173.0,5584.0,5819.0,140.0,393.0 +2015-05-25 17:00:00+00:00,3542.81,3592.8,503.0,51.46,4669.0,745.11,1264.0,152.66,3405.0,592.45,2085.13,2088.3,23.98,567.62,1457.68,1504.5,30.81,9077.0,8989.0,329.26,316.8,6.96,309.84,46108.0,45899.0,37649.0,1736.0,0.0461,29537.0,4107.0,0.139,1523.0,317.0,0.2081,28014.0,3790.0,0.1353,8135.0,7808.0,458.0,1758.0,0.0,1758.0,,,,,,,,17968.0,16877.0,513.0,509.0,14824.0,16154.0,666.0,1804.0,317.0,1487.0,5180.0,5060.0,100.0,36.0,37654.0,35629.0,55.01,4975.0,1153.0,0.2318,11926.0,2266.0,0.19,4040.0,567.0,0.1402,7886.0,1699.0,0.2155,1137.0,1195.0,,664.0,71.0,38790.0,36824.0,4975.0,1153.0,12589.0,2337.0,4040.0,567.0,8550.0,1770.0,5945.0,6132.0,12.0,374.0 +2015-05-25 18:00:00+00:00,3354.09,3452.7,503.0,17.65,4669.0,736.42,1264.0,195.57,3405.0,540.85,1968.06,2008.0,23.36,512.84,1386.03,1444.7,32.89,8904.0,8909.0,117.39,332.48,17.89,314.59,44784.0,45082.0,37649.0,376.0,0.01,29537.0,3584.0,0.1213,1523.0,324.0,0.2127,28014.0,3260.0,0.1164,7856.0,7665.0,100.0,1538.0,0.0,1538.0,,,,,,,,17257.0,16706.0,135.0,552.0,14542.0,15674.0,139.0,1454.0,324.0,1130.0,5129.0,5038.0,2.0,40.0,36922.0,35022.0,50.56,4975.0,1055.0,0.2121,11926.0,2094.0,0.1755,4040.0,470.0,0.1162,7886.0,1624.0,0.206,1072.0,1159.0,,664.0,73.0,37994.0,36181.0,4975.0,1055.0,12589.0,2167.0,4040.0,470.0,8550.0,1697.0,6387.0,6530.0,0.0,366.0 +2015-05-25 19:00:00+00:00,3247.07,3339.4,503.0,2.03,4669.0,772.01,1264.0,278.02,3405.0,493.99,1901.2,1940.3,25.44,468.03,1345.87,1399.1,32.94,8905.0,8954.0,9.41,295.19,33.09,262.1,44556.0,44972.0,37649.0,18.0,0.0005,29537.0,3662.0,0.124,1523.0,460.0,0.3021,28014.0,3202.0,0.1143,7782.0,7523.0,5.0,1480.0,1.0,1479.0,,,,,,,,17008.0,16601.0,6.0,716.0,14492.0,15509.0,6.0,1445.0,459.0,986.0,5274.0,5340.0,0.0,20.0,35990.0,33808.0,45.96,4975.0,607.0,0.122,11926.0,1824.0,0.153,4040.0,482.0,0.1194,7886.0,1342.0,0.1702,1022.0,1066.0,,664.0,59.0,37012.0,34873.0,4975.0,607.0,12589.0,1884.0,4040.0,482.0,8550.0,1401.0,5794.0,5931.0,0.0,334.0 +2015-05-25 20:00:00+00:00,3175.22,3261.5,503.0,0.03,4669.0,802.23,1264.0,329.19,3405.0,473.04,1875.34,1911.7,23.93,434.18,1299.88,1349.8,33.64,9324.0,9202.0,0.0,239.66,32.62,207.04,44507.0,44675.0,37649.0,0.0,0.0,29537.0,3859.0,0.1306,1523.0,519.0,0.3406,28014.0,3340.0,0.1192,7669.0,7315.0,0.0,1548.0,3.0,1544.0,,,,,,,,16913.0,16534.0,0.0,764.0,14583.0,15385.0,0.0,1531.0,515.0,1016.0,5342.0,5440.0,0.0,16.0,35174.0,32955.0,50.0,4975.0,126.0,0.0252,11926.0,1658.0,0.139,4040.0,498.0,0.1232,7886.0,1160.0,0.1471,1008.0,1023.0,,664.0,51.0,36183.0,33978.0,4975.0,126.0,12589.0,1709.0,4040.0,498.0,8550.0,1211.0,5293.0,5431.0,0.0,297.0 +2015-05-25 21:00:00+00:00,2894.13,3021.0,503.0,0.0,4669.0,793.52,1264.0,351.18,3405.0,442.34,1734.68,1788.5,22.19,409.45,1159.45,1232.5,29.46,8962.0,8812.0,0.0,185.76,20.31,165.45,41200.0,41666.0,37649.0,0.0,0.0,29537.0,4116.0,0.1394,1523.0,538.0,0.3535,28014.0,3577.0,0.1277,7093.0,6900.0,0.0,1606.0,4.0,1602.0,,,,,,,,15701.0,15530.0,0.0,795.0,13422.0,14176.0,0.0,1690.0,534.0,1156.0,4983.0,5060.0,0.0,24.0,34696.0,31870.0,44.01,4975.0,7.0,0.0014,11926.0,1494.0,0.1252,4040.0,464.0,0.1147,7886.0,1030.0,0.1306,991.0,990.0,,664.0,57.0,35686.0,32860.0,4975.0,7.0,12589.0,1551.0,4040.0,464.0,8550.0,1088.0,4834.0,4860.0,0.0,264.0 +2015-05-25 22:00:00+00:00,2710.76,2728.4,503.0,0.0,4669.0,867.88,1264.0,407.23,3405.0,460.65,1643.29,1666.8,22.29,429.77,1067.47,1061.6,22.29,8305.0,8160.0,0.0,177.06,26.75,150.31,38485.0,39547.0,37651.0,0.0,0.0,29544.0,4044.0,0.1369,1527.0,595.0,0.3894,28017.0,3449.0,0.1231,6684.0,6428.0,0.0,1470.0,2.0,1468.0,,,,,,,,14772.0,15101.0,0.0,762.0,12420.0,12280.0,0.0,1781.0,592.0,1189.0,4609.0,5738.0,0.0,30.0,31328.0,28644.0,42.33,4975.0,0.0,0.0,11926.0,1372.0,0.1151,4040.0,418.0,0.1036,7886.0,954.0,0.121,870.0,1011.0,,664.0,80.0,32198.0,29656.0,4975.0,0.0,12589.0,1452.0,4040.0,418.0,8550.0,1034.0,4500.0,4509.0,0.0,228.0 +2015-05-25 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13:00:00+00:00,3305.05,3317.0,503.0,217.18,4669.0,2032.51,1264.0,753.13,3405.0,1279.38,1962.65,1960.6,10.36,1129.44,1342.4,1356.4,10.36,8995.0,8446.0,423.05,1436.27,655.5,780.77,44526.0,43304.0,37712.0,13895.0,0.3685,29608.0,14345.0,0.4845,1558.0,1335.0,0.8566,28050.0,13011.0,0.4638,7626.0,7402.0,4023.0,4602.0,21.0,4581.0,,,,,,,,18044.0,17396.0,2532.0,3738.0,13859.0,13765.0,4685.0,5968.0,1313.0,4654.0,4997.0,4741.0,2656.0,37.0,36803.0,33204.0,36.26,4975.0,2658.0,0.5344,12355.0,7492.0,0.6064,4468.0,2962.0,0.663,7886.0,4530.0,0.5744,932.0,1003.0,4.0,664.0,407.0,37736.0,34208.0,4979.0,2658.0,13018.0,7900.0,4468.0,2962.0,8550.0,4937.0,4657.0,4774.0,1209.0,159.0 +2015-05-31 14:00:00+00:00,3404.5,3345.5,503.0,140.23,4669.0,2015.24,1264.0,807.69,3405.0,1207.55,2036.96,1987.1,10.69,1093.88,1367.54,1358.4,10.69,8981.0,8374.0,390.61,1474.6,651.0,823.6,44280.0,43055.0,37712.0,11323.0,0.3002,29608.0,15155.0,0.5119,1558.0,1375.0,0.8826,28050.0,13780.0,0.4913,7545.0,7353.0,3349.0,4609.0,22.0,4587.0,,,,,,,,17920.0,17314.0,1987.0,3843.0,13930.0,13765.0,3767.0,6644.0,1353.0,5291.0,4885.0,4623.0,2221.0,59.0,36165.0,32957.0,35.95,4975.0,3170.0,0.637,12355.0,7262.0,0.5878,4468.0,2932.0,0.6562,7886.0,4330.0,0.549,905.0,939.0,4.0,664.0,411.0,37070.0,33896.0,4979.0,3170.0,13018.0,7673.0,4468.0,2932.0,8550.0,4741.0,4694.0,4794.0,804.0,147.0 +2015-05-31 15:00:00+00:00,3757.37,3645.8,503.0,73.24,4669.0,1990.48,1264.0,874.73,3405.0,1115.75,2255.15,2207.1,13.58,1011.39,1502.22,1438.7,13.58,9159.0,8555.0,289.83,1491.14,659.95,831.19,45539.0,43759.0,37712.0,8294.0,0.2199,29608.0,15535.0,0.5247,1558.0,1517.0,0.9736,28050.0,14018.0,0.4998,7888.0,7461.0,2513.0,4552.0,24.0,4528.0,,,,,,,,18134.0,17731.0,1383.0,3715.0,14514.0,13902.0,2717.0,7186.0,1492.0,5694.0,5003.0,4665.0,1680.0,82.0,36298.0,33409.0,38.55,4975.0,3002.0,0.6034,12355.0,6950.0,0.5625,4468.0,2868.0,0.6418,7886.0,4082.0,0.5177,925.0,904.0,4.0,664.0,397.0,37223.0,34313.0,4979.0,3002.0,13018.0,7347.0,4468.0,2868.0,8550.0,4479.0,4772.0,4780.0,430.0,130.0 +2015-05-31 16:00:00+00:00,3744.83,3696.5,503.0,34.4,4669.0,2078.08,1264.0,1003.42,3405.0,1074.66,2222.86,2224.5,16.22,990.12,1521.97,1472.0,16.22,9386.0,8793.0,168.31,1425.49,649.22,776.27,47252.0,45404.0,37712.0,5189.0,0.1376,29608.0,15907.0,0.5372,1558.0,1531.0,0.9824,28050.0,14376.0,0.5125,8439.0,7843.0,1460.0,4147.0,29.0,4118.0,,,,,,,,18642.0,18126.0,904.0,3704.0,15004.0,14695.0,1745.0,7998.0,1502.0,6496.0,5168.0,4740.0,1078.0,58.0,36977.0,34457.0,40.03,4975.0,2860.0,0.5748,12355.0,6817.0,0.5518,4468.0,2638.0,0.5904,7886.0,4179.0,0.5299,976.0,922.0,4.0,664.0,382.0,37954.0,35379.0,4979.0,2860.0,13018.0,7199.0,4468.0,2638.0,8550.0,4561.0,4844.0,4841.0,139.0,118.0 +2015-05-31 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19:00:00+00:00,3320.51,3305.4,503.0,0.21,4669.0,3115.24,1264.0,1141.22,3405.0,1974.02,1967.66,1963.7,17.31,1765.93,1352.85,1341.7,17.31,9141.0,8941.0,3.22,805.12,127.89,677.23,46397.0,44741.0,37712.0,16.0,0.0004,29608.0,15198.0,0.5133,1558.0,1369.0,0.879,28050.0,13829.0,0.493,8158.0,7574.0,6.0,3824.0,121.0,3704.0,,,,,,,,17930.0,17982.0,5.0,3492.0,14811.0,14279.0,4.0,7796.0,1249.0,6547.0,5498.0,4906.0,0.0,86.0,35277.0,33613.0,46.99,4975.0,1016.0,0.2043,12355.0,5388.0,0.4362,4468.0,1890.0,0.4229,7886.0,3499.0,0.4437,914.0,925.0,4.0,664.0,229.0,36190.0,34538.0,4979.0,1016.0,13018.0,5618.0,4468.0,1890.0,8550.0,3728.0,5365.0,5694.0,0.0,98.0 +2015-05-31 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21:00:00+00:00,2940.73,2994.2,503.0,0.0,4669.0,3135.15,1264.0,1155.95,3405.0,1979.2,1776.32,1809.6,13.64,1681.84,1164.41,1184.6,13.64,8942.0,8841.0,0.0,632.53,116.54,515.99,42936.0,41251.0,37712.0,0.0,0.0,29608.0,16539.0,0.5586,1558.0,1336.0,0.8574,28050.0,15204.0,0.542,7367.0,6659.0,0.0,5624.0,135.0,5489.0,,,,,,,,16333.0,17387.0,0.0,3566.0,13861.0,12693.0,0.0,7268.0,1201.0,6067.0,5376.0,4512.0,0.0,81.0,34260.0,32081.0,43.0,4975.0,18.0,0.0035,12355.0,5240.0,0.4242,4468.0,2284.0,0.5112,7886.0,2956.0,0.3748,893.0,887.0,4.0,664.0,257.0,35152.0,32968.0,4979.0,18.0,13018.0,5497.0,4468.0,2284.0,8550.0,3213.0,4599.61,4674.0,0.0,101.0 +2015-05-31 22:00:00+00:00,2752.33,2703.1,504.0,0.0,4669.0,3079.85,1264.0,1209.94,3405.0,1869.91,1682.22,1660.3,12.97,1533.2,1070.11,1042.8,12.97,8337.0,8147.0,0.0,643.71,204.55,439.16,40131.0,38590.0,37713.0,0.0,0.0,29612.0,16505.0,0.5574,1561.0,1369.0,0.877,28050.0,15136.0,0.5396,6837.0,6663.0,0.0,5924.0,136.0,5788.0,,,,,,,,15383.0,15542.0,0.0,3405.0,12903.0,11412.0,0.0,7076.0,1233.0,5843.0,5007.0,4972.0,0.0,100.0,31030.0,28736.0,45.0,4975.0,0.0,0.0,12355.0,5311.0,0.4299,4468.0,2363.0,0.5288,7886.0,2948.0,0.3738,822.0,918.0,4.0,664.0,286.0,31852.0,29653.0,4979.0,0.0,13018.0,5597.0,4468.0,2363.0,8550.0,3234.0,4228.07,4324.0,0.0,113.0 +2015-05-31 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08:00:00+00:00,4447.55,4437.6,504.0,247.91,4669.0,1334.05,1264.0,540.14,3406.0,793.91,2770.07,2763.1,23.79,709.25,1677.48,1674.5,37.49,11144.0,10932.0,1591.72,598.38,256.56,341.82,64839.0,64135.0,37720.0,11154.0,0.2957,29682.0,5175.0,0.1743,1601.0,498.0,0.3114,28081.0,4677.0,0.1666,10785.0,10480.0,4006.0,1209.0,25.0,1184.0,,,,,,,,23785.0,23867.0,2979.0,1352.0,20900.0,21013.0,3508.0,2454.0,474.0,1980.0,9368.0,8774.0,662.0,160.0,39746.0,38006.0,55.03,5000.0,3615.0,0.723,12355.0,1426.0,0.1154,4468.0,760.0,0.17,7886.0,666.0,0.0846,1109.0,1035.0,5.0,664.0,51.0,40856.0,39042.0,5006.0,3615.0,13018.0,1477.0,4468.0,760.0,8550.0,718.0,6111.05,6036.0,989.0,505.0 +2015-06-08 09:00:00+00:00,4425.47,4444.5,504.0,292.54,4669.0,1275.54,1264.0,458.18,3406.0,817.36,2741.14,2767.3,24.61,725.54,1684.33,1677.2,38.19,11358.0,11093.0,1949.58,619.26,252.87,366.39,66319.0,65644.0,37720.0,13244.0,0.3511,29682.0,5357.0,0.1805,1601.0,546.0,0.3409,28081.0,4811.0,0.1713,10908.0,10787.0,4327.0,1262.0,14.0,1248.0,,,,,,,,24485.0,24254.0,3490.0,1329.0,21384.0,21280.0,4642.0,2674.0,532.0,2142.0,9542.0,9322.0,785.0,92.0,40752.0,38901.0,48.09,5000.0,2642.0,0.5285,12355.0,2033.0,0.1645,4468.0,1350.0,0.302,7886.0,683.0,0.0866,1135.0,1077.0,5.0,664.0,59.0,41887.0,39978.0,5006.0,2642.0,13018.0,2092.0,4468.0,1350.0,8550.0,742.0,6250.43,6147.0,1071.0,414.0 +2015-06-08 10:00:00+00:00,4362.48,4338.1,504.0,286.78,4669.0,1272.68,1264.0,443.76,3406.0,828.92,2689.93,2669.6,23.91,753.12,1672.55,1668.5,32.61,11110.0,10813.0,2049.27,659.68,255.81,403.87,66214.0,65092.0,37720.0,13820.0,0.3664,29682.0,5449.0,0.1836,1601.0,532.0,0.3325,28081.0,4917.0,0.1751,11421.0,10696.0,4093.0,1360.0,7.0,1354.0,,,,,,,,24411.0,24011.0,3761.0,1332.0,21145.0,21228.0,5067.0,2715.0,526.0,2189.0,9237.0,9158.0,899.0,43.0,41134.0,39441.0,47.04,5000.0,4784.0,0.9567,12355.0,616.0,0.0498,4468.0,367.0,0.0821,7886.0,249.0,0.0316,1140.0,1088.0,5.0,664.0,69.0,42274.0,40529.0,5006.0,4784.0,13018.0,685.0,4468.0,367.0,8550.0,318.0,6234.89,6168.0,1017.0,403.0 +2015-06-08 11:00:00+00:00,4360.37,4368.1,504.0,250.22,4669.0,1210.51,1264.0,407.35,3406.0,803.16,2694.22,2705.8,24.92,743.23,1666.15,1662.3,31.98,11032.0,10803.0,1978.93,716.65,314.41,402.24,65284.0,64380.0,37720.0,13079.0,0.3467,29682.0,5564.0,0.1874,1601.0,506.0,0.3159,28081.0,5058.0,0.1801,11370.0,10521.0,3378.0,1409.0,3.0,1406.0,,,,,,,,23926.0,23776.0,3918.0,1356.0,20832.0,21047.0,4793.0,2762.0,503.0,2259.0,9156.0,9036.0,989.0,37.0,41397.0,39655.0,47.5,5000.0,4712.0,0.9423,12355.0,1391.0,0.1126,4468.0,849.0,0.19,7886.0,541.0,0.0686,1136.0,1117.0,5.0,664.0,69.0,42533.0,40772.0,5006.0,4712.0,13018.0,1460.0,4468.0,849.0,8550.0,611.0,5992.35,5904.0,983.0,426.0 +2015-06-08 12:00:00+00:00,4245.19,4261.5,504.0,226.77,4669.0,1176.11,1264.0,402.22,3406.0,773.89,2606.3,2628.9,28.25,730.61,1638.89,1632.6,30.62,10981.0,10682.0,1860.85,834.34,416.48,417.86,63958.0,63073.0,37720.0,12486.0,0.331,29682.0,6449.0,0.2173,1601.0,622.0,0.3882,28081.0,5828.0,0.2076,11079.0,10348.0,2935.0,1817.0,5.0,1812.0,,,,,,,,23487.0,23436.0,3988.0,1408.0,20427.0,20369.0,4415.0,3171.0,617.0,2554.0,8965.0,8920.0,1148.0,54.0,40438.0,39276.0,54.91,5000.0,4600.0,0.92,12355.0,2059.0,0.1666,4468.0,1240.0,0.2776,7886.0,818.0,0.1038,1090.0,1135.0,5.0,664.0,71.0,41528.0,40410.0,5006.0,4600.0,13018.0,2129.0,4468.0,1240.0,8550.0,889.0,5642.09,5617.0,906.0,396.0 +2015-06-08 13:00:00+00:00,4107.36,4078.5,504.0,227.24,4669.0,1210.64,1264.0,442.35,3406.0,768.29,2525.71,2487.6,23.87,720.71,1581.65,1590.9,30.28,10863.0,10563.0,1716.17,988.73,552.83,435.9,63155.0,61654.0,37720.0,11241.0,0.298,29682.0,7324.0,0.2468,1601.0,704.0,0.4396,28081.0,6620.0,0.2358,11046.0,10132.0,2357.0,2025.0,3.0,2022.0,,,,,,,,23070.0,23044.0,3695.0,1484.0,20180.0,19853.0,4080.0,3727.0,701.0,3026.0,8859.0,8626.0,1108.0,88.0,40380.0,38986.0,45.95,5000.0,4310.0,0.862,12355.0,2278.0,0.1844,4468.0,1534.0,0.3433,7886.0,744.0,0.0943,1058.0,1115.0,5.0,664.0,84.0,41438.0,40102.0,5006.0,4310.0,13018.0,2362.0,4468.0,1534.0,8550.0,828.0,5426.37,5489.0,783.0,397.0 +2015-06-08 14:00:00+00:00,4039.97,4030.2,504.0,212.97,4669.0,1289.84,1264.0,492.87,3406.0,796.97,2475.67,2465.4,21.41,748.26,1564.3,1564.8,29.98,10808.0,10503.0,1467.35,1084.31,611.25,473.06,61740.0,59826.0,37720.0,9105.0,0.2414,29682.0,8063.0,0.2716,1601.0,792.0,0.495,28081.0,7270.0,0.2589,10891.0,9898.0,1673.0,1977.0,0.0,1977.0,,,,,,,,22657.0,22444.0,3206.0,1648.0,19763.0,19281.0,3363.0,4322.0,792.0,3529.0,8429.0,8203.0,863.0,116.0,39884.0,38827.0,40.05,5000.0,3905.0,0.781,12355.0,1739.0,0.1407,4468.0,903.0,0.2022,7886.0,836.0,0.106,1046.0,1072.0,5.0,664.0,81.0,40930.0,39899.0,5006.0,3905.0,13018.0,1820.0,4468.0,903.0,8550.0,917.0,5507.44,5492.0,621.0,353.0 +2015-06-08 15:00:00+00:00,4239.91,4186.8,504.0,185.7,4669.0,1301.25,1264.0,476.97,3406.0,824.28,2583.32,2559.4,21.43,777.91,1656.59,1627.4,32.92,10686.0,10575.0,1153.73,1131.03,629.94,501.09,61443.0,59301.0,37720.0,6682.0,0.1772,29682.0,8686.0,0.2926,1601.0,862.0,0.5386,28081.0,7823.0,0.2786,11076.0,9805.0,1171.0,1966.0,0.0,1966.0,,,,,,,,22632.0,22161.0,2435.0,1818.0,19610.0,19251.0,2381.0,4784.0,862.0,3921.0,8125.0,8084.0,694.0,118.0,40304.0,39700.0,54.93,5000.0,3415.0,0.683,12355.0,1677.0,0.1358,4468.0,1010.0,0.226,7886.0,667.0,0.0846,,1053.0,5.0,664.0,89.0,,40753.0,5006.0,3415.0,13018.0,1766.0,4468.0,1010.0,8550.0,756.0,5582.47,5735.0,347.0,288.0 +2015-06-08 16:00:00+00:00,4154.59,4118.3,504.0,139.22,4669.0,1257.23,1264.0,434.76,3406.0,822.47,2524.67,2508.0,21.46,785.55,1629.92,1610.3,34.73,10576.0,10549.0,833.1,1187.12,641.17,545.95,61241.0,58779.0,37720.0,4339.0,0.1151,29682.0,9091.0,0.3063,1601.0,945.0,0.5904,28081.0,8146.0,0.2901,11138.0,9911.0,756.0,1978.0,0.0,1978.0,,,,,,,,22538.0,21999.0,1536.0,1974.0,19594.0,18948.0,1497.0,4998.0,945.0,4053.0,7972.0,7920.0,550.0,141.0,41508.0,40472.0,57.93,5000.0,2690.0,0.538,12355.0,2936.0,0.2376,4468.0,2019.0,0.4519,7886.0,918.0,0.1164,,1095.0,5.0,664.0,90.0,,41566.0,5006.0,2690.0,13018.0,3026.0,4468.0,2019.0,8550.0,1007.0,5658.22,5985.0,138.0,227.0 +2015-06-08 17:00:00+00:00,3926.7,3911.7,504.0,79.1,4669.0,1079.56,1264.0,402.82,3406.0,676.74,2395.27,2371.6,20.84,646.71,1531.43,1540.1,36.97,10268.0,10249.0,470.81,1213.02,645.17,567.85,60508.0,58001.0,37720.0,2187.0,0.058,29682.0,9052.0,0.305,1601.0,931.0,0.5817,28081.0,8121.0,0.2892,11149.0,10019.0,385.0,2008.0,1.0,2008.0,,,,,,,,22326.0,21726.0,806.0,1996.0,19190.0,18612.0,726.0,4900.0,931.0,3969.0,7844.0,7644.0,270.0,148.0,41066.0,39860.0,55.18,5000.0,1998.0,0.3995,12355.0,1926.0,0.156,4468.0,915.0,0.2048,7886.0,1011.0,0.1282,,1198.0,5.0,664.0,83.0,,41057.0,5006.0,1998.0,13018.0,2009.0,4468.0,915.0,8550.0,1094.0,5918.76,6158.0,20.0,167.0 +2015-06-08 18:00:00+00:00,3692.25,3699.6,504.0,31.1,4669.0,855.93,1264.0,383.67,3406.0,472.26,2255.82,2257.8,18.6,450.35,1436.43,1441.8,34.97,9878.0,9923.0,159.54,1159.29,641.11,518.18,57991.0,55898.0,37720.0,687.0,0.0182,29682.0,8531.0,0.2874,1601.0,963.0,0.6017,28081.0,7567.0,0.2695,10686.0,9605.0,143.0,2052.0,3.0,2049.0,,,,,,,,21669.0,20937.0,296.0,1957.0,18167.0,18018.0,226.0,4383.0,960.0,3423.0,7469.0,7338.0,22.0,139.0,39916.0,38799.0,54.35,5000.0,1334.0,0.2669,12355.0,2708.0,0.2192,4468.0,1862.0,0.4166,7886.0,846.0,0.1072,,1149.0,5.0,664.0,64.0,,39948.0,5006.0,1334.0,13018.0,2772.0,4468.0,1862.0,8550.0,910.0,6276.69,6517.0,0.0,160.0 +2015-06-08 19:00:00+00:00,3505.51,3491.9,504.0,6.29,4669.0,662.53,1264.0,358.78,3406.0,303.75,2137.1,2127.5,16.47,284.92,1368.41,1364.4,33.99,9509.0,9682.0,23.01,1103.17,634.87,468.3,55430.0,53908.0,37720.0,62.0,0.0016,29682.0,7673.0,0.2585,1601.0,929.0,0.58,28081.0,6745.0,0.2402,10080.0,9198.0,15.0,2006.0,4.0,2001.0,,,,,,,,20776.0,20139.0,25.0,1953.0,17298.0,17355.0,21.0,3589.0,924.0,2665.0,7275.0,7216.0,0.0,126.0,38372.0,37536.0,48.09,5000.0,540.0,0.1081,12355.0,2736.0,0.2214,4468.0,2019.0,0.4518,7886.0,717.0,0.0909,,1036.0,5.0,664.0,54.0,,38572.0,5006.0,540.0,13018.0,2790.0,4468.0,2019.0,8550.0,771.0,5861.55,6018.0,0.0,154.0 +2015-06-08 20:00:00+00:00,3392.46,3339.2,504.0,0.15,4669.0,526.66,1264.0,321.15,3406.0,205.51,2063.51,2027.3,16.46,191.44,1328.95,1311.9,32.12,9815.0,9871.0,0.0,1088.59,609.37,479.22,53403.0,51424.0,37720.0,0.0,0.0,29682.0,7602.0,0.2561,1601.0,869.0,0.5427,28081.0,6733.0,0.2398,9781.0,8652.0,0.0,2088.0,3.0,2085.0,,,,,,,,20153.0,19175.0,0.0,1968.0,16574.0,16679.0,0.0,3407.0,866.0,2541.0,6895.0,6917.0,0.0,140.0,36816.0,35978.0,46.06,5000.0,60.0,0.0119,12355.0,2770.0,0.2242,4468.0,1972.0,0.4414,7886.0,798.0,0.1012,,979.0,5.0,664.0,52.0,,36957.0,5006.0,60.0,13018.0,2821.0,4468.0,1972.0,8550.0,849.0,5403.16,5518.0,0.0,131.0 +2015-06-08 21:00:00+00:00,3156.83,3083.3,504.0,0.0,4669.0,452.1,1264.0,296.26,3406.0,155.84,1938.76,1881.1,14.47,144.79,1218.07,1202.2,25.25,9445.0,9476.0,0.0,1036.33,572.61,463.72,48578.0,46858.0,37720.0,0.0,0.0,29682.0,7633.0,0.2572,1601.0,871.0,0.544,28081.0,6762.0,0.2408,8844.0,7892.0,0.0,2183.0,1.0,2182.0,,,,,,,,18482.0,17698.0,0.0,2026.0,15069.0,14994.0,0.0,3292.0,870.0,2422.0,6183.0,6274.0,0.0,131.0,35398.0,34474.0,39.51,5000.0,20.0,0.004,12355.0,2575.0,0.2084,4468.0,1849.0,0.414,7886.0,726.0,0.092,955.0,954.0,5.0,664.0,43.0,36352.0,35428.0,5006.0,20.0,13018.0,2618.0,4468.0,1849.0,8550.0,768.0,4904.76,4859.0,0.0,100.0 +2015-06-08 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11:00:00+00:00,3748.86,3778.5,509.0,276.61,4731.0,975.73,1264.0,175.64,3467.0,800.09,2253.99,2265.7,9.68,630.91,1494.87,1512.8,9.68,9635.0,9447.0,752.04,199.64,33.45,166.19,66401.0,66066.0,37839.0,15908.0,0.4204,30333.0,4685.0,0.1544,1990.0,150.0,0.0754,28343.0,4535.0,0.16,11261.0,11261.0,4382.0,2760.0,93.0,2667.0,,,,,,,,24671.0,24098.0,3383.0,552.0,21000.0,20999.0,5618.0,1328.0,57.0,1271.0,9470.0,9709.0,2524.0,46.0,40901.0,39191.0,49.8,5049.0,2597.0,0.5144,13014.0,4980.0,0.3827,5044.0,2105.0,0.4174,7970.0,2874.0,0.3607,1098.0,1068.0,5.0,664.0,223.0,41999.0,40259.0,5054.0,2597.0,13678.0,5202.0,5044.0,2105.0,8634.0,3097.0,8289.17,8221.0,1734.0,654.0 +2015-07-20 12:00:00+00:00,3684.11,3705.7,509.0,281.91,4731.0,961.8,1264.0,212.32,3467.0,749.48,2207.13,2226.9,9.51,579.85,1476.98,1478.8,9.51,9506.0,9300.0,724.55,378.67,185.24,193.43,64645.0,64376.0,37839.0,15021.0,0.397,30333.0,4277.0,0.141,1990.0,121.0,0.061,28343.0,4155.0,0.1466,10689.0,10689.0,3972.0,2374.0,82.0,2292.0,,,,,,,,23939.0,23665.0,3054.0,669.0,20612.0,20468.0,5518.0,1188.0,39.0,1149.0,9405.0,9555.0,2478.0,46.0,41092.0,38879.0,41.09,5049.0,2966.0,0.5874,13014.0,4670.0,0.3589,5044.0,1716.0,0.3402,7970.0,2954.0,0.3707,1088.0,1075.0,5.0,664.0,234.0,42180.0,39954.0,5054.0,2966.0,13678.0,4904.0,5044.0,1716.0,8634.0,3188.0,8093.4,7812.0,1590.0,643.0 +2015-07-20 13:00:00+00:00,3573.32,3583.3,509.0,276.53,4731.0,899.48,1264.0,211.28,3467.0,688.2,2142.64,2139.8,9.38,554.89,1430.68,1443.5,9.38,9407.0,9181.0,743.65,617.23,410.96,206.27,63567.0,63080.0,37839.0,14124.0,0.3733,30333.0,3739.0,0.1233,1990.0,81.0,0.0408,28343.0,3658.0,0.1291,10463.0,10463.0,3774.0,2128.0,61.0,2067.0,,,,,,,,23614.0,23216.0,2661.0,651.0,20196.0,20075.0,5324.0,922.0,20.0,902.0,9294.0,9327.0,2366.0,39.0,41339.0,38528.0,36.56,5049.0,2858.0,0.5659,13014.0,2847.0,0.2188,5044.0,790.0,0.1567,7970.0,2056.0,0.258,,1062.0,5.0,664.0,261.0,41339.0,39590.0,5054.0,2858.0,13678.0,3107.0,5044.0,790.0,8634.0,2317.0,7877.84,7552.0,1338.0,634.0 +2015-07-20 14:00:00+00:00,3516.95,3523.6,509.0,240.74,4731.0,794.5,1264.0,137.06,3467.0,657.44,2102.59,2101.8,9.34,544.08,1414.36,1421.8,9.34,9411.0,9155.0,832.63,800.78,565.27,235.51,62430.0,61591.0,37839.0,12378.0,0.3271,30333.0,3205.0,0.1056,1990.0,34.0,0.0172,28343.0,3170.0,0.1118,10541.0,10541.0,3398.0,1751.0,26.0,1724.0,,,,,,,,23169.0,22613.0,2351.0,663.0,19867.0,19462.0,4571.0,761.0,8.0,753.0,8853.0,8974.0,2058.0,30.0,41390.0,38488.0,36.61,5049.0,2876.0,0.5695,13014.0,4891.0,0.3758,5044.0,1749.0,0.3468,7970.0,3142.0,0.3942,,1040.0,5.0,664.0,305.0,41390.0,39528.0,5054.0,2876.0,13678.0,5196.0,5044.0,1749.0,8634.0,3447.0,7797.49,7407.0,983.0,614.0 +2015-07-20 15:00:00+00:00,3679.48,3652.1,509.0,189.37,4731.0,661.41,1264.0,75.19,3467.0,586.22,2210.01,2191.6,10.2,488.6,1469.47,1460.5,10.2,9366.0,9171.0,714.92,868.81,645.52,223.29,61826.0,61153.0,37839.0,9476.0,0.2504,30333.0,2740.0,0.0903,1990.0,70.0,0.0354,28343.0,2669.0,0.0942,10567.0,10567.0,2540.0,1366.0,66.0,1300.0,,,,,,,,22888.0,22334.0,1782.0,714.0,19734.0,19587.0,3559.0,635.0,4.0,631.0,8637.0,8664.0,1595.0,25.0,41448.0,39510.0,39.0,5049.0,2274.0,0.4504,13014.0,5810.0,0.4464,5044.0,2776.0,0.5504,7970.0,3033.0,0.3806,,1036.0,5.0,664.0,285.0,41448.0,40546.0,5054.0,2274.0,13678.0,6094.0,5044.0,2776.0,8634.0,3318.0,7731.89,7363.0,549.0,544.0 +2015-07-20 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19:00:00+00:00,3139.52,3145.0,509.0,4.33,4731.0,216.41,1264.0,114.3,3467.0,102.11,1872.51,1892.0,19.94,86.61,1267.01,1253.0,19.94,8484.0,8585.0,10.25,714.29,560.08,154.21,55649.0,54798.0,37839.0,29.0,0.0008,30333.0,1934.0,0.0637,1990.0,442.0,0.2221,28343.0,1492.0,0.0526,9724.0,9724.0,8.0,416.0,26.0,390.0,,,,,,,,20563.0,20080.0,8.0,624.0,17778.0,17294.0,9.0,890.0,416.0,474.0,7585.0,7701.0,4.0,4.0,37978.0,36391.0,43.08,5049.0,740.0,0.1466,13014.0,5536.0,0.4254,5044.0,2744.0,0.5439,7970.0,2793.0,0.3505,,993.0,5.0,664.0,282.0,37978.0,37384.0,5054.0,740.0,13678.0,5819.0,5044.0,2744.0,8634.0,3075.0,7680.62,7430.0,0.0,487.0 +2015-07-20 20:00:00+00:00,3092.64,3110.8,509.0,0.02,4731.0,159.51,1264.0,81.17,3467.0,78.34,1843.54,1892.3,19.95,67.58,1249.1,1218.5,19.95,8852.0,8788.0,0.0,744.62,584.43,160.19,53401.0,52207.0,37839.0,0.0,0.0,30333.0,2640.0,0.087,1990.0,648.0,0.3256,28343.0,1992.0,0.0703,9188.0,9188.0,0.0,675.0,10.0,665.0,,,,,,,,19773.0,19067.0,0.0,716.0,17196.0,16549.0,0.0,1242.0,638.0,604.0,7244.0,7403.0,0.0,7.0,36470.0,35575.0,40.0,5049.0,270.0,0.0534,13014.0,5736.0,0.4408,5044.0,3086.0,0.6118,7970.0,2650.0,0.3326,,939.0,5.0,664.0,274.0,36470.0,36514.0,5054.0,270.0,13678.0,6010.0,5044.0,3086.0,8634.0,2924.0,7405.37,7101.0,0.0,519.0 +2015-07-20 21:00:00+00:00,2835.23,2890.8,509.0,0.0,4731.0,212.8,1264.0,109.46,3467.0,103.34,1695.01,1764.0,10.36,97.03,1140.22,1126.8,10.36,8670.0,8493.0,0.0,669.74,446.0,223.74,49015.0,47704.0,37839.0,0.0,0.0,30333.0,3546.0,0.1169,1990.0,873.0,0.4388,28343.0,2673.0,0.0943,8475.0,8475.0,0.0,703.0,8.0,695.0,,,,,,,,18183.0,17626.0,0.0,942.0,15687.0,14839.0,0.0,1887.0,866.0,1021.0,6670.0,6764.0,0.0,15.0,35366.0,34277.0,40.0,5049.0,18.0,0.0035,13014.0,5694.0,0.4376,5044.0,3125.0,0.6196,7970.0,2570.0,0.3224,,917.0,5.0,664.0,261.0,35366.0,35194.0,5054.0,18.0,13678.0,5956.0,5044.0,3125.0,8634.0,2831.0,6902.67,6690.0,0.0,525.0 +2015-07-20 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07:00:00+00:00,3865.49,3851.8,509.0,105.6,4731.0,2506.57,1264.0,1054.92,3467.0,1451.65,2317.68,2324.0,9.61,1223.58,1547.81,1527.8,9.61,8378.0,8197.0,795.86,204.46,133.97,70.49,65681.0,63997.0,37843.0,11410.0,0.3015,30358.0,10319.0,0.3399,2001.0,2090.0,1.0444,28357.0,8230.0,0.2902,11136.0,11136.0,2528.0,1724.0,176.0,1548.0,,,,,,,,23735.0,23410.0,2349.0,1385.0,21426.0,20145.0,4448.0,7187.0,1914.0,5273.0,9383.0,9306.0,2084.0,23.0,37520.0,35378.0,41.91,5049.0,1724.0,0.3416,13014.0,4958.0,0.381,5044.0,1771.0,0.3512,7970.0,3187.0,0.3999,,793.0,5.0,664.0,31.0,37520.0,36171.0,5054.0,1724.0,13678.0,4989.0,5044.0,1771.0,8634.0,3218.0,7997.45,8038.0,1286.0,957.0 +2015-07-21 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11:00:00+00:00,3813.14,3819.8,509.0,354.97,4731.0,2967.43,1264.0,901.67,3467.0,2065.76,2232.27,2245.2,8.88,1706.99,1580.87,1574.6,8.88,8698.0,8383.0,1256.36,269.52,173.8,95.72,68150.0,66606.0,37843.0,19940.0,0.5269,30358.0,13562.0,0.4468,2001.0,1634.0,0.8165,28357.0,11928.0,0.4206,11715.0,11715.0,3826.0,4770.0,38.0,4732.0,,,,,,,,24906.0,24517.0,5094.0,1359.0,21707.0,20717.0,7603.0,7390.0,1596.0,5794.0,9823.0,9658.0,3416.0,43.0,40702.0,39548.0,41.14,5049.0,3369.0,0.6672,13014.0,3180.0,0.2444,5044.0,887.0,0.1758,7970.0,2293.0,0.2877,,1089.0,5.0,664.0,45.0,40702.0,40637.0,5054.0,3369.0,13678.0,3224.0,5044.0,887.0,8634.0,2337.0,8873.56,8494.0,1691.0,955.0 +2015-07-21 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14:00:00+00:00,3583.54,3568.5,509.0,288.2,4731.0,2618.88,1264.0,769.12,3467.0,1849.76,2106.13,2098.9,8.46,1473.73,1477.41,1469.6,8.46,8358.0,8008.0,1157.45,503.69,354.91,148.78,64970.0,62494.0,37843.0,15955.0,0.4216,30358.0,11529.0,0.3798,2001.0,1209.0,0.6044,28357.0,10320.0,0.3639,11638.0,11638.0,3486.0,5590.0,48.0,5543.0,,,,,,,,23885.0,22991.0,3704.0,1102.0,20294.0,19178.0,6063.0,4816.0,1162.0,3655.0,9153.0,8688.0,2702.0,20.0,39064.0,39067.0,42.52,5049.0,4046.0,0.8012,13014.0,4993.0,0.3836,5044.0,2471.0,0.4899,7970.0,2522.0,0.3164,,1057.0,5.0,664.0,77.0,39064.0,40124.0,5054.0,4046.0,13678.0,5070.0,5044.0,2471.0,8634.0,2599.0,8344.77,7846.0,941.0,903.0 +2015-07-21 15:00:00+00:00,3707.3,3687.9,509.0,229.34,4731.0,2372.22,1264.0,679.91,3467.0,1692.31,2191.17,2177.5,8.76,1347.14,1516.13,1510.4,8.76,8497.0,8056.0,1066.19,570.55,418.41,152.14,63826.0,61702.0,37843.0,12220.0,0.3229,30358.0,10262.0,0.338,2001.0,1044.0,0.5218,28357.0,9218.0,0.3251,11279.0,11279.0,2712.0,4994.0,48.0,4946.0,,,,,,,,23580.0,22716.0,2827.0,1006.0,20151.0,19367.0,4678.0,4251.0,996.0,3255.0,8816.0,8339.0,2003.0,12.0,39874.0,39422.0,49.7,5049.0,1780.0,0.3526,13014.0,4320.0,0.332,5044.0,2350.0,0.466,7970.0,1970.0,0.2472,,1055.0,5.0,664.0,64.0,39874.0,40477.0,5054.0,1780.0,13678.0,4384.0,5044.0,2350.0,8634.0,2034.0,8315.31,7853.0,521.0,898.0 +2015-07-21 16:00:00+00:00,3671.68,3642.6,509.0,142.16,4731.0,2238.48,1264.0,800.99,3467.0,1437.49,2179.01,2162.8,9.05,1124.64,1492.67,1479.8,9.05,8543.0,8029.0,804.88,651.62,514.83,136.79,62597.0,60672.0,37843.0,7557.0,0.1997,30358.0,8628.0,0.2842,2001.0,1010.0,0.5045,28357.0,7618.0,0.2687,11207.0,11207.0,1742.0,4134.0,48.0,4086.0,,,,,,,,23087.0,22312.0,1745.0,865.0,19928.0,19062.0,2925.0,3624.0,962.0,2663.0,8375.0,8090.0,1146.0,5.0,40326.0,39984.0,52.01,5049.0,1990.0,0.3942,13014.0,4056.0,0.3116,5044.0,2212.0,0.4384,7970.0,1844.0,0.2314,,1082.0,5.0,664.0,48.0,40326.0,41066.0,5054.0,1990.0,13678.0,4103.0,5044.0,2212.0,8634.0,1892.0,8130.11,7776.0,165.0,934.0 +2015-07-21 17:00:00+00:00,3450.1,3454.5,509.0,64.84,4731.0,1865.62,1264.0,700.1,3467.0,1165.52,2036.46,2051.5,9.1,917.96,1413.64,1403.0,9.1,8448.0,8174.0,427.3,660.54,553.13,107.41,61341.0,59699.0,37843.0,3372.0,0.0891,30358.0,6167.0,0.2032,2001.0,963.0,0.4815,28357.0,5203.0,0.1835,11194.0,11194.0,806.0,2775.0,47.0,2728.0,,,,,,,,22652.0,21929.0,832.0,700.0,19330.0,18713.0,1292.0,2689.0,916.0,1773.0,8165.0,7862.0,442.0,3.0,40376.0,39414.0,52.01,5049.0,1963.0,0.3888,13014.0,4799.0,0.3688,5044.0,2427.0,0.4812,7970.0,2372.0,0.2976,,1132.0,5.0,664.0,44.0,40376.0,40546.0,5054.0,1963.0,13678.0,4843.0,5044.0,2427.0,8634.0,2416.0,8039.57,7738.0,18.0,924.0 +2015-07-21 18:00:00+00:00,3270.34,3297.2,509.0,20.78,4731.0,1443.92,1264.0,628.88,3467.0,815.04,1936.7,1967.8,9.29,653.07,1333.64,1329.4,9.29,8285.0,8119.0,143.4,703.92,615.22,88.7,58674.0,56667.0,37843.0,911.0,0.0241,30358.0,3665.0,0.1207,2001.0,981.0,0.4903,28357.0,2684.0,0.0947,10426.0,10426.0,210.0,1422.0,38.0,1384.0,,,,,,,,21899.0,21143.0,273.0,426.0,18415.0,17707.0,317.0,1816.0,943.0,873.0,7934.0,7390.0,111.0,2.0,39276.0,38147.0,51.24,5049.0,1564.0,0.3096,13014.0,4238.0,0.3256,5044.0,2240.0,0.4441,7970.0,1998.0,0.2506,,1076.0,5.0,664.0,32.0,39276.0,39223.0,5054.0,1564.0,13678.0,4270.0,5044.0,2240.0,8634.0,2030.0,8212.14,7919.0,0.0,934.0 +2015-07-21 19:00:00+00:00,3153.99,3167.4,509.0,2.35,4731.0,1177.32,1264.0,526.36,3467.0,650.96,1870.56,1885.3,9.18,528.93,1283.43,1282.1,9.18,8221.0,8039.0,15.61,696.1,606.5,89.6,56707.0,54630.0,37843.0,43.0,0.0012,30358.0,2574.0,0.0848,2001.0,890.0,0.4446,28357.0,1684.0,0.0594,9937.0,9937.0,9.0,992.0,28.0,964.0,,,,,,,,21162.0,20467.0,16.0,298.0,17857.0,17049.0,15.0,1280.0,861.0,419.0,7753.0,7178.0,4.0,4.0,38056.0,36514.0,44.97,5049.0,518.0,0.1026,13014.0,2669.0,0.2051,5044.0,1140.0,0.2262,7970.0,1529.0,0.1918,,987.0,5.0,664.0,24.0,38056.0,37501.0,5054.0,518.0,13678.0,2693.0,5044.0,1140.0,8634.0,1553.0,8028.48,7580.0,0.0,930.0 +2015-07-21 20:00:00+00:00,3118.57,3141.1,509.0,0.02,4731.0,1016.08,1264.0,470.14,3467.0,545.94,1855.56,1889.5,9.39,439.16,1263.01,1251.6,9.39,8714.0,8242.0,0.0,659.31,550.46,108.85,54786.0,52186.0,37843.0,0.0,0.0,30358.0,2133.0,0.0703,2001.0,827.0,0.4132,28357.0,1306.0,0.0461,9553.0,9553.0,0.0,743.0,26.0,717.0,,,,,,,,20520.0,19462.0,0.0,306.0,17286.0,16218.0,0.0,1081.0,801.0,280.0,7427.0,6952.0,0.0,3.0,37082.0,35066.0,43.59,5049.0,228.0,0.0452,13014.0,2015.0,0.1548,5044.0,994.0,0.1972,7970.0,1021.0,0.128,,939.0,5.0,664.0,16.0,37082.0,36005.0,5054.0,228.0,13678.0,2031.0,5044.0,994.0,8634.0,1037.0,7738.18,7250.0,0.0,918.0 +2015-07-21 21:00:00+00:00,2876.38,2931.7,509.0,0.0,4731.0,838.39,1264.0,372.8,3467.0,465.59,1720.77,1770.6,9.11,380.73,1155.61,1161.1,9.11,8468.0,7987.0,0.0,641.76,520.92,120.84,50311.0,47857.0,37843.0,0.0,0.0,30358.0,1871.0,0.0616,2001.0,821.0,0.4104,28357.0,1050.0,0.037,8680.0,8680.0,0.0,503.0,18.0,485.0,,,,,,,,19036.0,18033.0,0.0,288.0,15787.0,14696.0,0.0,1075.0,803.0,272.0,6808.0,6447.0,0.0,5.0,36252.0,33940.0,42.7,5049.0,12.0,0.0023,13014.0,2692.0,0.2068,5044.0,1548.0,0.3069,7970.0,1144.0,0.1435,,918.0,5.0,664.0,15.0,36252.0,34858.0,5054.0,12.0,13678.0,2707.0,5044.0,1548.0,8634.0,1159.0,7258.4,7070.0,0.0,940.0 +2015-07-21 22:00:00+00:00,2635.11,2626.3,509.0,0.0,4731.0,885.77,1264.0,413.84,3467.0,471.93,1580.13,1576.5,8.83,395.61,1054.98,1049.8,8.83,7911.0,7729.0,0.0,520.31,380.72,139.59,47111.0,45826.0,37848.0,0.0,0.0,30374.0,1780.0,0.0586,2011.0,835.0,0.4153,28363.0,945.0,0.0333,8177.0,8177.0,0.0,336.0,16.0,321.0,,,,,,,,17858.0,16739.0,0.0,240.0,14798.0,14802.0,0.0,1194.0,820.0,374.0,6277.0,6108.0,0.0,10.0,32678.0,30602.0,40.94,5049.0,0.0,0.0,13014.0,2468.0,0.1896,5044.0,1381.0,0.2738,7970.0,1087.0,0.1364,,957.0,5.0,664.0,14.0,32678.0,31559.0,5054.0,0.0,13678.0,2482.0,5044.0,1381.0,8634.0,1101.0,6780.33,6590.0,0.0,991.0 +2015-07-21 23:00:00+00:00,2485.08,2483.7,509.0,0.0,4731.0,877.1,1264.0,436.1,3467.0,441.0,1504.97,1500.6,8.3,385.53,980.11,983.1,8.3,7474.0,7499.0,0.0,463.28,330.45,132.83,45007.0,43679.0,37848.0,0.0,0.0,30374.0,1769.0,0.0582,2011.0,745.0,0.3702,28363.0,1025.0,0.0361,7911.0,7911.0,0.0,241.0,8.0,233.0,,,,,,,,17013.0,16094.0,0.0,194.0,14142.0,13906.0,0.0,1320.0,736.0,584.0,5941.0,5768.0,0.0,13.0,28836.0,26788.0,35.92,5049.0,22.0,0.0044,13014.0,1744.0,0.134,5044.0,980.0,0.1942,7970.0,765.0,0.096,,,5.0,664.0,15.0,28836.0,26788.0,5054.0,22.0,13678.0,1760.0,5044.0,980.0,8634.0,780.0,6447.92,6248.0,0.0,1027.0 +2015-07-22 00:00:00+00:00,2421.17,2414.7,509.0,0.0,4731.0,858.8,1264.0,418.55,3467.0,440.25,1468.64,1468.9,7.88,386.56,952.53,945.8,7.88,7243.0,7091.0,0.0,416.83,289.01,127.82,43774.0,42765.0,37848.0,0.0,0.0,30374.0,2133.0,0.0702,2011.0,843.0,0.4193,28363.0,1289.0,0.0455,7707.0,7707.0,0.0,253.0,6.0,247.0,,,,,,,,16584.0,15713.0,0.0,172.0,13725.0,13783.0,0.0,1691.0,837.0,853.0,5758.0,5562.0,0.0,17.0,26412.0,24892.0,35.61,5049.0,0.0,0.0,13014.0,2124.0,0.1632,5044.0,1200.0,0.238,7970.0,924.0,0.1158,,,5.0,664.0,16.0,26412.0,24892.0,5054.0,0.0,13678.0,2140.0,5044.0,1200.0,8634.0,939.0,6262.42,6048.0,0.0,1037.0 +2015-07-22 01:00:00+00:00,2410.17,2398.9,509.0,0.0,4731.0,923.08,1264.0,483.41,3467.0,439.67,1458.79,1458.1,7.58,381.07,951.38,940.8,7.58,7142.0,6885.0,0.0,400.33,275.71,124.62,44021.0,42937.0,37848.0,0.0,0.0,30374.0,2449.0,0.0806,2011.0,841.0,0.4184,28363.0,1608.0,0.0567,7783.0,7783.0,0.0,341.0,5.0,336.0,,,,,,,,16780.0,15725.0,0.0,256.0,13713.0,13862.0,0.0,1830.0,836.0,994.0,5746.0,5567.0,0.0,22.0,25229.0,24118.0,34.94,5049.0,0.0,0.0,13014.0,1824.0,0.1402,5044.0,962.0,0.1908,7970.0,862.0,0.1082,,,5.0,664.0,19.0,25229.0,24118.0,5054.0,0.0,13678.0,1843.0,5044.0,962.0,8634.0,881.0,6132.66,5939.0,0.0,1037.0 +2015-07-22 02:00:00+00:00,2412.79,2415.7,509.0,0.0,4731.0,823.19,1264.0,368.23,3467.0,454.96,1474.85,1473.0,7.18,390.62,937.94,942.7,7.18,7184.0,6916.0,0.0,397.6,280.13,117.47,44786.0,44076.0,37848.0,0.0,0.0,30374.0,2394.0,0.0788,2011.0,666.0,0.3311,28363.0,1728.0,0.0609,8026.0,8026.0,0.0,451.0,6.0,445.0,,,,,,,,16938.0,15998.0,0.0,287.0,13944.0,14354.0,0.0,1631.0,659.0,972.0,5877.0,5697.0,0.0,24.0,24432.0,23634.0,32.16,5049.0,0.0,0.0,13014.0,1768.0,0.1358,5044.0,986.0,0.1956,7970.0,781.0,0.098,,,5.0,664.0,23.0,24432.0,23634.0,5054.0,0.0,13678.0,1791.0,5044.0,986.0,8634.0,804.0,6082.18,5918.0,0.0,1040.0 +2015-07-22 03:00:00+00:00,2479.6,2486.5,509.0,3.18,4731.0,714.07,1264.0,312.8,3467.0,401.27,1510.48,1515.0,7.87,328.91,969.12,971.5,7.87,7418.0,7013.0,0.0,335.6,222.64,112.96,47608.0,47327.0,37848.0,65.0,0.0017,30374.0,2670.0,0.0879,2011.0,622.0,0.309,28363.0,2048.0,0.0722,8442.0,8442.0,35.0,579.0,6.0,572.0,,,,,,,,18027.0,16955.0,5.0,312.0,14799.0,15796.0,20.0,1760.0,615.0,1145.0,6340.0,6133.0,5.0,19.0,24276.0,23442.0,32.51,5049.0,0.0,0.0,13014.0,1656.0,0.1273,5044.0,962.0,0.1909,7970.0,694.0,0.087,,,5.0,664.0,27.0,24276.0,23442.0,5054.0,0.0,13678.0,1684.0,5044.0,962.0,8634.0,721.0,6017.55,5948.0,8.0,1047.0 +2015-07-22 04:00:00+00:00,2802.02,2808.0,509.0,27.16,4731.0,693.15,1264.0,339.01,3467.0,354.14,1694.26,1705.7,9.3,281.25,1107.76,1102.3,9.3,7849.0,7689.0,29.43,248.28,167.27,81.01,54971.0,54235.0,37848.0,1168.0,0.0309,30374.0,2861.0,0.0942,2011.0,563.0,0.2798,28363.0,2298.0,0.0811,9481.0,9481.0,370.0,688.0,4.0,684.0,,,,,,,,20798.0,19526.0,177.0,287.0,17251.0,17980.0,469.0,1868.0,559.0,1309.0,7440.0,7248.0,152.0,18.0,24242.0,23396.0,35.07,5049.0,12.0,0.0023,13014.0,1500.0,0.1153,5044.0,882.0,0.1748,7970.0,618.0,0.0776,,,5.0,664.0,25.0,24242.0,23396.0,5054.0,12.0,13678.0,1525.0,5044.0,882.0,8634.0,643.0,6493.01,6548.0,129.0,1051.0 +2015-07-22 05:00:00+00:00,3262.1,3251.7,509.0,74.28,4731.0,640.9,1264.0,335.07,3467.0,305.83,1975.56,1971.0,9.94,246.42,1286.54,1280.7,9.94,8583.0,8854.0,170.35,237.37,165.52,71.85,61479.0,59450.0,37848.0,3970.0,0.1049,30374.0,2480.0,0.0816,2011.0,482.0,0.2396,28363.0,1998.0,0.0704,10572.0,10572.0,1069.0,692.0,6.0,685.0,,,,,,,,22976.0,21522.0,705.0,230.0,19406.0,19053.0,1570.0,1533.0,475.0,1058.0,8524.0,8303.0,626.0,24.0,27340.0,26090.0,43.59,5049.0,162.0,0.0321,13014.0,1269.0,0.0975,5044.0,687.0,0.1362,7970.0,582.0,0.073,,,5.0,664.0,32.0,27340.0,26090.0,5054.0,162.0,13678.0,1301.0,5044.0,687.0,8634.0,614.0,7163.63,7383.0,440.0,1039.0 +2015-07-22 06:00:00+00:00,3597.58,3605.3,509.0,135.92,4731.0,626.97,1264.0,312.91,3467.0,314.06,2175.86,2176.4,10.44,258.49,1421.72,1428.9,10.44,9252.0,9781.0,483.63,149.16,97.08,52.08,64896.0,62452.0,37848.0,8147.0,0.2153,30374.0,1577.0,0.052,2011.0,380.0,0.189,28363.0,1197.0,0.0422,11030.0,11030.0,2063.0,368.0,4.0,364.0,,,,,,,,24130.0,22727.0,1463.0,243.0,20627.0,19932.0,3269.0,955.0,376.0,578.0,9109.0,8763.0,1352.0,12.0,33323.0,31430.0,42.53,5049.0,566.0,0.1121,13014.0,1000.0,0.0768,5044.0,476.0,0.0944,7970.0,524.0,0.0657,,,5.0,664.0,39.0,33323.0,31430.0,5054.0,566.0,13678.0,1039.0,5044.0,476.0,8634.0,563.0,7801.3,8203.0,852.0,1084.0 +2015-07-22 07:00:00+00:00,3772.95,3773.9,509.0,216.81,4731.0,506.89,1264.0,172.9,3467.0,333.99,2267.37,2271.4,10.65,275.57,1505.58,1502.5,10.65,9786.0,10191.0,1055.54,77.41,38.27,39.14,66319.0,63663.0,37848.0,12427.0,0.3283,30374.0,1380.0,0.0454,2011.0,286.0,0.1422,28363.0,1094.0,0.0386,11152.0,11152.0,2983.0,215.0,1.0,214.0,,,,,,,,24598.0,23161.0,2337.0,265.0,21141.0,20434.0,5027.0,898.0,285.0,613.0,9427.0,8916.0,2079.0,2.0,37740.0,35530.0,55.8,5049.0,1226.0,0.2427,13014.0,964.0,0.0741,5044.0,400.0,0.0793,7970.0,564.0,0.0708,,,5.0,664.0,84.0,37740.0,35530.0,5054.0,1226.0,13678.0,1048.0,5044.0,400.0,8634.0,648.0,8178.3,8619.0,1236.0,1098.0 +2015-07-22 08:00:00+00:00,3905.19,3897.4,509.0,286.47,4731.0,493.44,1264.0,107.05,3467.0,386.39,2346.6,2338.9,10.95,310.84,1558.59,1558.5,10.95,9940.0,10417.0,1495.58,118.69,72.66,46.03,67991.0,65241.0,37848.0,16068.0,0.4246,30374.0,1811.0,0.0596,2011.0,246.0,0.1223,28363.0,1565.0,0.0552,11433.0,11433.0,3866.0,342.0,1.0,342.0,,,,,,,,24985.0,23797.0,3231.0,256.0,21695.0,20540.0,6348.0,1210.0,245.0,965.0,9878.0,9470.0,2623.0,4.0,39891.0,37689.0,55.77,5049.0,2039.0,0.4039,13014.0,1031.0,0.0792,5044.0,366.0,0.0724,7970.0,666.0,0.0835,,,5.0,664.0,103.0,39891.0,37689.0,5054.0,2039.0,13678.0,1134.0,5044.0,366.0,8634.0,769.0,8458.18,8928.0,1527.0,1088.0 +2015-07-22 09:00:00+00:00,3905.18,3909.8,509.0,300.59,4731.0,461.85,1264.0,79.65,3467.0,382.2,2343.21,2348.5,10.66,311.81,1561.97,1561.3,10.66,10099.0,10667.0,1683.08,160.02,99.92,60.1,69426.0,67021.0,37848.0,18840.0,0.4978,30374.0,2417.0,0.0796,2011.0,273.0,0.1356,28363.0,2144.0,0.0756,11806.0,11806.0,4336.0,622.0,0.0,622.0,,,,,,,,25482.0,24402.0,4263.0,293.0,22086.0,20968.0,7283.0,1499.0,273.0,1226.0,10052.0,9844.0,2958.0,3.0,41060.0,38262.0,55.0,5049.0,2586.0,0.512,13014.0,1202.0,0.0924,5044.0,455.0,0.0902,7970.0,748.0,0.0938,,,5.0,664.0,108.0,41060.0,38262.0,5054.0,2586.0,13678.0,1310.0,5044.0,455.0,8634.0,855.0,8696.08,9128.0,1675.0,1064.0 +2015-07-22 10:00:00+00:00,3854.45,3862.3,509.0,306.37,4731.0,453.4,1264.0,60.65,3467.0,392.75,2298.72,2305.8,10.5,331.94,1555.73,1556.5,10.5,10036.0,10540.0,1799.33,194.34,120.97,73.37,68777.0,66349.0,37848.0,20347.0,0.5376,30374.0,2965.0,0.0976,2011.0,380.0,0.1887,28363.0,2586.0,0.0912,11617.0,11617.0,4476.0,814.0,0.0,814.0,,,,,,,,25327.0,24390.0,4857.0,376.0,21871.0,20750.0,7803.0,1766.0,380.0,1386.0,9962.0,9592.0,3211.0,9.0,40939.0,38634.0,54.7,5049.0,2434.0,0.4822,13014.0,1198.0,0.092,5044.0,320.0,0.0636,7970.0,877.0,0.11,,,5.0,664.0,107.0,40939.0,38634.0,5054.0,2434.0,13678.0,1304.0,5044.0,320.0,8634.0,984.0,8847.88,9186.0,1758.0,1079.0 +2015-07-22 11:00:00+00:00,3853.12,3833.3,509.0,279.05,4731.0,548.47,1264.0,74.99,3467.0,473.48,2300.09,2287.3,10.39,417.29,1553.03,1546.0,10.39,9888.0,10476.0,1697.93,213.14,101.95,111.19,68051.0,65536.0,37848.0,20225.0,0.5344,30374.0,3558.0,0.1171,2011.0,472.0,0.2349,28363.0,3085.0,0.1088,11350.0,11350.0,4447.0,895.0,0.0,895.0,,,,,,,,25151.0,24097.0,4864.0,524.0,21698.0,20538.0,7827.0,2130.0,472.0,1658.0,9852.0,9551.0,3086.0,9.0,40958.0,39162.0,50.0,5049.0,2481.0,0.4914,13014.0,1554.0,0.1194,5044.0,473.0,0.0938,7970.0,1080.0,0.1356,,,5.0,664.0,133.0,40958.0,39162.0,5054.0,2481.0,13678.0,1686.0,5044.0,473.0,8634.0,1213.0,8771.4,8984.0,1734.0,1103.0 +2015-07-22 12:00:00+00:00,3788.76,3788.9,509.0,270.54,4731.0,541.02,1264.0,71.96,3467.0,469.06,2246.3,2254.3,10.28,412.61,1542.46,1534.6,10.28,9732.0,10298.0,1537.71,390.73,225.45,165.28,66955.0,63823.0,37848.0,18699.0,0.494,30374.0,4063.0,0.1337,2011.0,567.0,0.2818,28363.0,3496.0,0.1232,11146.0,11146.0,4218.0,928.0,2.0,926.0,,,,,,,,24680.0,23648.0,4376.0,695.0,21262.0,19603.0,7267.0,2428.0,565.0,1863.0,9867.0,9425.0,2838.0,12.0,40654.0,38940.0,37.06,5049.0,3186.0,0.631,13014.0,1881.0,0.1446,5044.0,688.0,0.1364,7970.0,1193.0,0.1497,,,5.0,664.0,167.0,40654.0,38940.0,5054.0,3186.0,13678.0,2048.0,5044.0,688.0,8634.0,1360.0,8506.78,8665.0,1589.0,1073.0 +2015-07-22 13:00:00+00:00,3677.16,3683.4,509.0,277.13,4731.0,504.13,1264.0,62.67,3467.0,441.46,2171.01,2177.4,10.2,392.31,1506.15,1506.0,10.2,9657.0,10212.0,1305.48,563.5,381.93,181.57,65866.0,62974.0,37848.0,16204.0,0.4282,30374.0,4318.0,0.1422,2011.0,616.0,0.3062,28363.0,3702.0,0.1305,11126.0,11126.0,3706.0,929.0,0.0,929.0,,,,,,,,24343.0,23228.0,3891.0,854.0,20868.0,19371.0,6304.0,2505.0,616.0,1890.0,9529.0,9250.0,2303.0,30.0,40443.0,38400.0,36.54,5049.0,3181.0,0.63,13014.0,2283.0,0.1754,5044.0,808.0,0.1602,7970.0,1475.0,0.1851,,,5.0,664.0,176.0,40443.0,38400.0,5054.0,3181.0,13678.0,2459.0,5044.0,808.0,8634.0,1651.0,8208.86,8291.0,1348.0,1010.0 +2015-07-22 14:00:00+00:00,3597.71,3597.5,509.0,246.44,4731.0,558.46,1264.0,76.44,3467.0,482.02,2113.6,2123.0,10.24,433.72,1484.11,1474.5,10.24,9624.0,10100.0,1081.11,662.42,479.27,183.15,63969.0,61605.0,37848.0,12742.0,0.3367,30374.0,4777.0,0.1573,2011.0,791.0,0.3933,28363.0,3986.0,0.1406,10742.0,10742.0,3061.0,967.0,0.0,967.0,,,,,,,,23419.0,22663.0,3042.0,1024.0,20504.0,19282.0,4921.0,2766.0,791.0,1975.0,9303.0,8918.0,1718.0,21.0,40284.0,38056.0,36.59,5049.0,3222.0,0.6382,13014.0,2701.0,0.2076,5044.0,1050.0,0.2082,7970.0,1651.0,0.2072,1027.0,,5.0,664.0,179.0,41311.0,38056.0,5054.0,3222.0,13678.0,2880.0,5044.0,1050.0,8634.0,1830.0,8062.76,7999.0,999.0,977.0 +2015-07-22 15:00:00+00:00,3721.89,3676.9,509.0,189.08,4731.0,605.72,1264.0,121.44,3467.0,484.28,2204.67,2179.8,11.47,440.1,1517.22,1497.1,11.47,9669.0,10079.0,817.52,690.6,512.78,177.82,62855.0,60984.0,37848.0,8586.0,0.2268,30374.0,5424.0,0.1786,2011.0,953.0,0.4737,28363.0,4472.0,0.1577,10834.0,10834.0,2303.0,991.0,0.0,991.0,,,,,,,,22729.0,22377.0,2139.0,1111.0,20401.0,19257.0,3156.0,3208.0,953.0,2255.0,8891.0,8516.0,988.0,115.0,40792.0,38871.0,46.65,5049.0,2990.0,0.5921,13014.0,2786.0,0.2141,5044.0,1052.0,0.2086,7970.0,1734.0,0.2176,1053.0,,5.0,664.0,174.0,41844.0,38871.0,5054.0,2990.0,13678.0,2961.0,5044.0,1052.0,8634.0,1908.0,7878.99,7835.0,558.0,943.0 +2015-07-22 16:00:00+00:00,3646.82,3625.7,509.0,114.44,4731.0,715.02,1264.0,192.78,3467.0,522.24,2166.83,2159.6,14.95,479.77,1479.99,1466.1,14.95,9526.0,9871.0,551.54,702.01,513.35,188.66,62058.0,60269.0,37848.0,4988.0,0.1318,30374.0,5985.0,0.197,2011.0,963.0,0.4788,28363.0,5023.0,0.1771,10956.0,10956.0,1434.0,1094.0,0.0,1094.0,,,,,,,,22480.0,22061.0,1405.0,1052.0,20100.0,19032.0,1608.0,3638.0,963.0,2676.0,8523.0,8221.0,541.0,201.0,41072.0,39424.0,52.21,5049.0,3007.0,0.5956,13014.0,2812.0,0.216,5044.0,1152.0,0.2284,7970.0,1660.0,0.2083,1114.0,,5.0,664.0,187.0,42186.0,39424.0,5054.0,3007.0,13678.0,2999.0,5044.0,1152.0,8634.0,1847.0,7684.51,7666.0,164.0,924.0 +2015-07-22 17:00:00+00:00,3494.52,3446.7,509.0,56.78,4731.0,769.39,1264.0,212.18,3467.0,557.21,2084.57,2061.8,29.86,513.8,1409.95,1384.9,29.86,9148.0,9494.0,289.29,758.47,577.06,181.41,61011.0,59794.0,37848.0,2447.0,0.0647,30374.0,6164.0,0.203,2011.0,912.0,0.4534,28363.0,5253.0,0.1852,11123.0,11123.0,660.0,1362.0,0.0,1362.0,,,,,,,,22054.0,21707.0,711.0,1055.0,19545.0,18837.0,747.0,3654.0,912.0,2742.0,8288.0,8126.0,330.0,93.0,40516.0,38541.0,42.8,5049.0,2397.0,0.4748,13014.0,3180.0,0.2444,5044.0,1341.0,0.2659,7970.0,1840.0,0.2308,1068.0,,5.0,664.0,160.0,41584.0,38541.0,5054.0,2397.0,13678.0,3341.0,5044.0,1341.0,8634.0,2000.0,7642.31,7648.0,16.0,896.0 +2015-07-22 18:00:00+00:00,3311.8,3284.5,509.0,17.24,4731.0,834.07,1264.0,292.49,3467.0,541.58,1972.44,1975.6,29.4,486.21,1339.36,1308.9,29.4,8823.0,9234.0,112.11,778.11,605.93,172.18,57908.0,57174.0,37848.0,641.0,0.0169,30374.0,5577.0,0.1836,2011.0,667.0,0.3315,28363.0,4911.0,0.1731,10492.0,10492.0,138.0,1488.0,0.0,1488.0,,,,,,,,20944.0,20918.0,228.0,922.0,18646.0,18002.0,211.0,3106.0,666.0,2439.0,7825.0,7762.0,64.0,61.0,39362.0,37640.0,48.37,5049.0,1961.0,0.3884,13014.0,2732.0,0.21,5044.0,947.0,0.1878,7970.0,1786.0,0.224,987.0,,5.0,664.0,150.0,40350.0,37640.0,5054.0,1961.0,13678.0,2882.0,5044.0,947.0,8634.0,1935.0,7906.38,7960.0,0.0,901.0 +2015-07-22 19:00:00+00:00,3232.62,3172.6,509.0,1.18,4731.0,945.18,1264.0,296.75,3467.0,648.43,1919.46,1909.6,19.99,566.88,1313.16,1263.0,19.99,8687.0,9102.0,7.5,528.45,343.0,185.45,56118.0,55793.0,37848.0,35.0,0.0009,30374.0,5238.0,0.1724,2011.0,441.0,0.2194,28363.0,4797.0,0.1691,10177.0,10177.0,7.0,1916.0,6.0,1910.0,,,,,,,,20279.0,20279.0,14.0,888.0,17960.0,17784.0,12.0,2366.0,435.0,1932.0,7702.0,7553.0,2.0,68.0,37770.0,36408.0,44.52,5049.0,1110.0,0.22,13014.0,2606.0,0.2002,5044.0,909.0,0.1802,7970.0,1697.0,0.2129,953.0,,5.0,664.0,109.0,38724.0,36408.0,5054.0,1110.0,13678.0,2714.0,5044.0,909.0,8634.0,1805.0,7675.97,7599.0,0.0,933.0 +2015-07-22 20:00:00+00:00,3161.64,3139.6,509.0,0.01,4731.0,1052.84,1264.0,362.65,3467.0,690.19,1885.6,1908.6,23.43,588.74,1276.04,1231.0,23.43,9069.0,9342.0,0.0,302.36,126.02,176.34,53854.0,53310.0,37848.0,0.0,0.0,30374.0,4834.0,0.1591,2011.0,425.0,0.2112,28363.0,4409.0,0.1554,9703.0,9703.0,0.0,1997.0,20.0,1977.0,,,,,,,,19656.0,19309.0,0.0,829.0,17149.0,17029.0,0.0,1926.0,405.0,1521.0,7347.0,7270.0,0.0,82.0,36374.0,34870.0,43.5,5049.0,320.0,0.0635,13014.0,2216.0,0.1703,5044.0,668.0,0.1325,7970.0,1547.0,0.1941,934.0,,5.0,664.0,101.0,37307.0,34870.0,5054.0,320.0,13678.0,2316.0,5044.0,668.0,8634.0,1648.0,7382.84,7261.0,0.0,942.0 +2015-07-22 21:00:00+00:00,2913.36,2922.8,509.0,0.0,4731.0,1378.21,1264.0,556.41,3467.0,821.8,1749.47,1780.2,9.99,699.62,1163.89,1142.6,9.99,8642.0,9041.0,0.0,285.96,96.64,189.32,49448.0,48971.0,37848.0,0.0,0.0,30374.0,4294.0,0.1414,2011.0,434.0,0.216,28363.0,3860.0,0.1361,8873.0,8873.0,0.0,1884.0,23.0,1861.0,,,,,,,,18131.0,17881.0,0.0,712.0,15715.0,15603.0,0.0,1645.0,411.0,1233.0,6729.0,6614.0,0.0,53.0,35800.0,33258.0,42.01,5049.0,9.0,0.0018,13014.0,2338.0,0.1796,5044.0,789.0,0.1564,7970.0,1549.0,0.1944,940.0,,5.0,664.0,133.0,36740.0,33258.0,5054.0,9.0,13678.0,2471.0,5044.0,789.0,8634.0,1682.0,6985.79,6990.0,0.0,942.0 +2015-07-22 22:00:00+00:00,2656.06,2658.7,509.0,0.0,4733.0,1440.07,1264.0,468.63,3469.0,971.44,1599.95,1592.2,-6.79,852.4,1056.11,1066.5,9.09,8045.0,8410.0,0.0,260.64,74.45,186.19,46179.0,46339.0,37850.0,0.0,0.0,30385.0,4190.0,0.1378,2020.0,506.0,0.2505,28365.0,3683.0,0.1299,8412.0,8412.0,0.0,1964.0,28.0,1936.0,,,,,,,,16923.0,16714.0,0.0,752.0,14621.0,15106.0,0.0,1417.0,478.0,939.0,6223.0,6106.0,0.0,56.0,32294.0,29314.0,43.0,5049.0,0.0,0.0,13014.0,2034.0,0.1563,5044.0,412.0,0.0816,7970.0,1622.0,0.2035,848.0,,5.0,664.0,130.0,33142.0,29314.0,5054.0,0.0,13678.0,2164.0,5044.0,412.0,8634.0,1752.0,6602.49,6549.0,0.0,929.0 +2015-07-22 23:00:00+00:00,2516.15,2514.4,509.0,0.0,4733.0,1873.39,1264.0,787.93,3469.0,1085.46,1513.02,1514.4,0.06,953.84,1003.13,1000.0,8.54,7746.0,8070.0,0.0,245.95,48.88,197.07,44362.0,44094.0,37850.0,0.0,0.0,30385.0,3532.0,0.1162,2020.0,472.0,0.2338,28365.0,3060.0,0.1079,8047.0,8047.0,0.0,1652.0,35.0,1616.0,,,,,,,,16355.0,16093.0,0.0,673.0,14064.0,14199.0,0.0,1117.0,437.0,680.0,5895.0,5755.0,0.0,90.0,28516.0,26058.0,37.96,5049.0,0.0,0.0,13014.0,2406.0,0.1849,5044.0,672.0,0.1332,7970.0,1734.0,0.2176,726.0,,5.0,664.0,123.0,29242.0,26058.0,5054.0,0.0,13678.0,2529.0,5044.0,672.0,8634.0,1857.0,6261.75,6218.0,0.0,912.0 +2015-07-23 00:00:00+00:00,2451.92,2447.1,509.0,0.0,4733.0,2017.01,1264.0,910.61,3469.0,1106.4,1481.95,1480.8,7.8,952.57,969.97,966.3,7.8,7507.0,7801.0,0.0,248.05,53.99,194.06,43259.0,43133.0,37850.0,0.0,0.0,30385.0,3299.0,0.1086,2020.0,429.0,0.2122,28365.0,2871.0,0.1012,7855.0,7855.0,0.0,1589.0,23.0,1566.0,,,,,,,,15908.0,15758.0,0.0,572.0,13783.0,13963.0,0.0,1029.0,406.0,623.0,5714.0,5557.0,0.0,109.0,25601.0,24360.0,37.9,5049.0,0.0,0.0,13014.0,1565.0,0.1202,5044.0,379.0,0.0752,7970.0,1186.0,0.1488,658.0,,5.0,664.0,119.0,26259.0,24360.0,5054.0,0.0,13678.0,1684.0,5044.0,379.0,8634.0,1304.0,6047.92,5978.0,0.0,899.0 +2015-07-23 01:00:00+00:00,2431.59,2425.7,509.0,0.0,4733.0,1994.78,1264.0,912.6,3469.0,1082.18,1482.65,1471.4,7.19,945.5,948.94,954.3,7.19,7384.0,7685.0,0.0,250.17,79.22,170.95,43303.0,43288.0,37850.0,0.0,0.0,30385.0,3282.0,0.108,2020.0,418.0,0.2072,28365.0,2863.0,0.1009,7852.0,7852.0,0.0,1629.0,33.0,1597.0,,,,,,,,15938.0,15823.0,0.0,605.0,13802.0,14053.0,0.0,965.0,386.0,579.0,5712.0,5560.0,0.0,83.0,24447.0,23909.0,34.92,5049.0,0.0,0.0,13014.0,2318.0,0.1781,5044.0,534.0,0.1058,7970.0,1784.0,0.2239,628.0,,5.0,664.0,107.0,25075.0,23909.0,5054.0,0.0,13678.0,2425.0,5044.0,534.0,8634.0,1892.0,5900.88,5859.0,0.0,899.0 +2015-07-23 02:00:00+00:00,2432.91,2434.5,509.0,0.0,4733.0,1896.97,1264.0,851.67,3469.0,1045.3,1486.29,1484.9,7.04,902.46,946.62,949.6,7.04,7450.0,7693.0,0.0,178.58,51.01,127.57,44290.0,44018.0,37850.0,0.0,0.0,30385.0,3682.0,0.1212,2020.0,451.0,0.2232,28365.0,3231.0,0.1139,8082.0,8082.0,0.0,1688.0,24.0,1664.0,,,,,,,,16248.0,16062.0,0.0,678.0,14174.0,14187.0,0.0,1224.0,427.0,796.0,5787.0,5687.0,0.0,94.0,23844.0,23644.0,32.91,5049.0,0.0,0.0,13014.0,2378.0,0.1828,5044.0,508.0,0.1006,7970.0,1871.0,0.2348,609.0,,5.0,664.0,99.0,24452.0,23644.0,5054.0,0.0,13678.0,2478.0,5044.0,508.0,8634.0,1970.0,5810.83,5868.0,0.0,871.0 +2015-07-23 03:00:00+00:00,2469.75,2471.2,509.0,4.11,4733.0,1732.72,1264.0,840.34,3469.0,892.38,1494.07,1496.5,7.86,780.2,975.68,974.7,7.86,7622.0,7798.0,0.0,166.74,76.83,89.91,47214.0,46595.0,37850.0,44.0,0.0012,30385.0,3843.0,0.1265,2020.0,428.0,0.2119,28365.0,3415.0,0.1204,8367.0,8367.0,30.0,1606.0,27.0,1579.0,,,,,,,,17412.0,17004.0,4.0,653.0,15109.0,15121.0,8.0,1464.0,401.0,1063.0,6325.0,6103.0,2.0,120.0,23672.0,23734.0,32.73,5049.0,0.0,0.0,13014.0,2506.0,0.1926,5044.0,575.0,0.114,7970.0,1931.0,0.2422,600.0,,5.0,664.0,105.0,24273.0,23734.0,5054.0,0.0,13678.0,2611.0,5044.0,575.0,8634.0,2036.0,5800.24,5867.0,8.0,869.0 +2015-07-23 04:00:00+00:00,2772.86,2777.1,509.0,29.73,4733.0,1564.14,1264.0,791.61,3469.0,772.53,1671.52,1679.2,9.06,659.56,1101.34,1097.9,9.06,7960.0,8351.0,54.97,100.32,44.62,55.7,54406.0,53807.0,37850.0,751.0,0.0198,30385.0,3554.0,0.117,2020.0,383.0,0.1895,28365.0,3171.0,0.1118,9546.0,9546.0,263.0,1293.0,21.0,1272.0,,,,,,,,20046.0,19573.0,153.0,584.0,17389.0,17432.0,254.0,1593.0,362.0,1231.0,7426.0,7257.0,80.0,84.0,23654.0,23863.0,32.73,5049.0,17.0,0.0034,13014.0,2691.0,0.2068,5044.0,643.0,0.1275,7970.0,2048.0,0.257,590.0,,5.0,664.0,95.0,24244.0,23863.0,5054.0,17.0,13678.0,2786.0,5044.0,643.0,8634.0,2143.0,6249.21,6386.0,132.0,828.0 +2015-07-23 05:00:00+00:00,3221.95,3219.7,509.0,84.74,4733.0,1655.11,1264.0,744.89,3469.0,910.22,1939.41,1933.6,9.34,751.04,1282.54,1286.1,9.34,8639.0,9111.0,253.59,42.29,16.1,26.19,60246.0,60017.0,37850.0,2660.0,0.0702,30385.0,2869.0,0.0944,2020.0,384.0,0.19,28365.0,2485.0,0.0876,10330.0,10330.0,850.0,846.0,21.0,825.0,,,,,,,,22015.0,21615.0,649.0,481.0,19451.0,19758.0,841.0,1477.0,363.0,1114.0,8450.0,8314.0,319.0,66.0,26698.0,26986.0,42.01,5049.0,231.0,0.0458,13014.0,2828.0,0.2174,5044.0,625.0,0.1238,7970.0,2204.0,0.2765,640.0,,5.0,664.0,96.0,27338.0,26986.0,5054.0,231.0,13678.0,2924.0,5044.0,625.0,8634.0,2299.0,6859.63,7150.0,511.0,791.0 +2015-07-23 06:00:00+00:00,3571.39,3605.3,509.0,158.5,4733.0,1946.8,1264.0,685.01,3469.0,1261.79,2161.45,2161.0,9.82,1079.14,1409.94,1444.3,9.82,9239.0,9694.0,622.59,,,12.33,63537.0,63462.0,37850.0,5653.0,0.1493,30385.0,2580.0,0.0849,2020.0,275.0,0.1363,28365.0,2305.0,0.0812,10747.0,10747.0,1896.0,712.0,28.0,684.0,,,,,,,,23143.0,22723.0,1374.0,414.0,20689.0,21038.0,1669.0,1418.0,247.0,1171.0,8958.0,8954.0,714.0,36.0,32588.0,32560.0,40.49,5049.0,1582.0,0.3132,13014.0,3074.0,0.2362,5044.0,516.0,0.1024,7970.0,2558.0,0.3209,787.0,,5.0,664.0,88.0,33375.0,32560.0,5054.0,1582.0,13678.0,3162.0,5044.0,516.0,8634.0,2646.0,7516.83,7898.0,977.0,752.0 +2015-07-23 07:00:00+00:00,3745.76,3794.6,509.0,233.32,4733.0,1906.97,1264.0,615.62,3469.0,1291.35,2262.45,2265.3,10.02,1113.57,1483.31,1529.3,10.02,9747.0,10060.0,1012.05,,,9.67,64646.0,64661.0,37850.0,8844.0,0.2337,30385.0,2682.0,0.0883,2020.0,221.0,0.1095,28365.0,2461.0,0.0867,10916.0,10916.0,3073.0,677.0,20.0,657.0,,,,,,,,23567.0,23142.0,2180.0,392.0,21100.0,21446.0,2483.0,1604.0,201.0,1403.0,9063.0,9156.0,1108.0,9.0,37144.0,36298.0,41.8,5049.0,1474.0,0.292,13014.0,3107.0,0.2388,5044.0,541.0,0.1072,7970.0,2566.0,0.322,930.0,,5.0,664.0,119.0,38074.0,36298.0,5054.0,1474.0,13678.0,3226.0,5044.0,541.0,8634.0,2685.0,7870.06,8384.0,1349.0,728.0 +2015-07-23 08:00:00+00:00,3834.87,3871.0,509.0,281.73,4733.0,1811.4,1264.0,584.03,3469.0,1227.37,2311.5,2319.2,10.24,1075.09,1523.37,1551.8,10.24,9888.0,10286.0,1295.89,,,7.66,65842.0,65960.0,37850.0,11889.0,0.3141,30385.0,3098.0,0.102,2020.0,268.0,0.1327,28365.0,2830.0,0.0998,11112.0,11112.0,3925.0,966.0,17.0,949.0,,,,,,,,23921.0,23721.0,2961.0,371.0,21499.0,21622.0,3498.0,1761.0,251.0,1510.0,9311.0,9505.0,1506.0,1.0,39680.0,37790.0,52.8,5049.0,2016.0,0.3993,13014.0,2136.0,0.1641,5044.0,260.0,0.0514,7970.0,1876.0,0.2354,1016.0,,5.0,664.0,153.0,40696.0,37790.0,5054.0,2016.0,13678.0,2289.0,5044.0,260.0,8634.0,2029.0,8220.9,8729.0,1623.0,666.0 +2015-07-23 09:00:00+00:00,3832.23,3846.7,509.0,319.58,4733.0,1820.21,1264.0,571.92,3469.0,1248.29,2308.37,2308.8,10.14,1099.59,1523.86,1537.9,10.14,10013.0,10515.0,1349.91,,,9.02,67868.0,67934.0,37850.0,14397.0,0.3804,30385.0,3509.0,0.1155,2020.0,346.0,0.1712,28365.0,3164.0,0.1115,11640.0,11640.0,4198.0,1224.0,24.0,1200.0,,,,,,,,24492.0,24268.0,3645.0,378.0,21965.0,22193.0,4676.0,1903.0,322.0,1581.0,9772.0,9832.0,1879.0,4.0,40536.0,38646.0,50.01,5049.0,2858.0,0.5661,13014.0,3450.0,0.2651,5044.0,512.0,0.1015,7970.0,2938.0,0.3687,1030.0,,5.0,664.0,161.0,41566.0,38646.0,5054.0,2858.0,13678.0,3611.0,5044.0,512.0,8634.0,3099.0,8451.12,8958.0,1777.0,592.0 +2015-07-23 10:00:00+00:00,3792.22,3812.1,509.0,341.41,4733.0,1928.67,1264.0,527.37,3469.0,1401.3,2268.42,2271.1,10.0,1255.38,1523.8,1541.0,10.0,9921.0,10397.0,1328.54,,,13.02,67636.0,67597.0,37850.0,15147.0,0.4002,30385.0,4127.0,0.1358,2020.0,449.0,0.2223,28365.0,3678.0,0.1297,11746.0,11746.0,3954.0,1529.0,34.0,1495.0,,,,,,,,24543.0,24207.0,3941.0,476.0,21614.0,21937.0,5216.0,2117.0,415.0,1701.0,9733.0,9708.0,2036.0,6.0,40578.0,39028.0,50.09,5049.0,3640.0,0.721,13014.0,3446.0,0.2648,5044.0,425.0,0.0842,7970.0,3021.0,0.379,1046.0,,5.0,664.0,203.0,41624.0,39028.0,5054.0,3640.0,13678.0,3649.0,5044.0,425.0,8634.0,3224.0,8601.29,9045.0,1828.0,543.0 +2015-07-23 11:00:00+00:00,3749.49,3786.4,509.0,337.94,4733.0,2094.37,1264.0,578.08,3469.0,1516.29,2237.58,2252.0,9.82,1375.31,1511.91,1534.4,9.82,9846.0,10319.0,1378.58,,,18.01,66165.0,66861.0,37850.0,14616.0,0.3862,30385.0,4500.0,0.1481,2020.0,543.0,0.2688,28365.0,3957.0,0.1395,11434.0,11434.0,3549.0,1564.0,31.0,1533.0,,,,,,,,23859.0,23902.0,3781.0,542.0,21360.0,21816.0,5304.0,2392.0,512.0,1880.0,9512.0,9709.0,1983.0,3.0,40593.0,39658.0,42.05,5049.0,4242.0,0.8402,13014.0,3572.0,0.2744,5044.0,546.0,0.1084,7970.0,3025.0,0.3796,1052.0,,5.0,664.0,182.0,41645.0,39658.0,5054.0,4242.0,13678.0,3754.0,5044.0,546.0,8634.0,3207.0,8492.2,8865.0,1778.0,509.0 +2015-07-23 12:00:00+00:00,3663.73,3692.6,509.0,314.44,4733.0,2187.24,1264.0,545.39,3469.0,1641.85,2182.02,2206.1,9.6,1470.1,1481.71,1486.5,9.6,9743.0,10226.0,1431.42,,,23.89,64720.0,65461.0,37850.0,14356.0,0.3793,30385.0,4542.0,0.1495,2020.0,637.0,0.3155,28365.0,3905.0,0.1377,11385.0,11385.0,3136.0,1537.0,25.0,1512.0,,,,,,,,23222.0,23439.0,3744.0,557.0,20909.0,21082.0,5399.0,2445.0,613.0,1832.0,9204.0,9555.0,2076.0,3.0,40286.0,39654.0,39.92,5049.0,4080.0,0.8081,13014.0,3759.0,0.2888,5044.0,614.0,0.1218,7970.0,3145.0,0.3946,1018.0,,5.0,664.0,173.0,41303.0,39654.0,5054.0,4080.0,13678.0,3932.0,5044.0,614.0,8634.0,3318.0,8249.55,8610.0,1632.0,452.0 +2015-07-23 13:00:00+00:00,3543.86,3581.2,509.0,300.69,4733.0,2304.68,1264.0,634.88,3469.0,1669.8,2099.04,2133.0,9.44,1480.73,1444.82,1448.2,9.44,9694.0,10111.0,1186.69,,,25.37,63485.0,63721.0,37850.0,13692.0,0.3618,30385.0,4569.0,0.1504,2020.0,730.0,0.3612,28365.0,3839.0,0.1354,11022.0,11022.0,2724.0,1514.0,29.0,1485.0,,,,,,,,22930.0,22951.0,3700.0,509.0,20524.0,20421.0,5176.0,2542.0,700.0,1842.0,9009.0,9327.0,2092.0,4.0,39764.0,39516.0,37.6,5049.0,4000.0,0.7922,13014.0,3762.0,0.2891,5044.0,648.0,0.1284,7970.0,3115.0,0.3909,992.0,,5.0,664.0,149.0,40755.0,39516.0,5054.0,4000.0,13678.0,3911.0,5044.0,648.0,8634.0,3264.0,8072.24,8321.0,1387.0,402.0 +2015-07-23 14:00:00+00:00,3491.81,3484.3,509.0,261.13,4733.0,2242.23,1264.0,610.58,3469.0,1631.65,2075.21,2062.2,9.46,1467.75,1416.6,1422.1,9.46,9573.0,10007.0,1014.75,33.78,10.53,23.25,62419.0,62403.0,37850.0,12220.0,0.3228,30385.0,4379.0,0.1441,2020.0,596.0,0.2949,28365.0,3784.0,0.1334,10834.0,10834.0,2295.0,1725.0,21.0,1704.0,,,,,,,,22719.0,22399.0,3296.0,403.0,20066.0,20196.0,4733.0,2245.0,575.0,1670.0,8799.0,8974.0,1896.0,6.0,39570.0,39300.0,37.51,5049.0,3434.0,0.68,13014.0,3618.0,0.278,5044.0,618.0,0.1226,7970.0,2999.0,0.3763,998.0,,5.0,664.0,123.0,40568.0,39300.0,5054.0,3434.0,13678.0,3740.0,5044.0,618.0,8634.0,3122.0,7980.62,8101.0,1022.0,364.0 +2015-07-23 15:00:00+00:00,3652.86,3599.6,509.0,206.59,4733.0,1999.89,1264.0,542.66,3469.0,1457.23,2168.11,2158.4,9.6,1282.3,1484.75,1441.2,9.6,9578.0,9964.0,868.81,50.03,26.13,23.9,61649.0,61974.0,37850.0,9344.0,0.2469,30385.0,4118.0,0.1355,2020.0,595.0,0.2948,28365.0,3523.0,0.1242,10829.0,10829.0,1804.0,1750.0,23.0,1727.0,,,,,,,,22454.0,22114.0,2332.0,292.0,19908.0,20367.0,3794.0,2070.0,572.0,1497.0,8457.0,8664.0,1414.0,6.0,39852.0,39852.0,52.7,5049.0,3194.0,0.6325,13014.0,3348.0,0.2572,5044.0,704.0,0.1395,7970.0,2644.0,0.3318,1021.0,,5.0,664.0,110.0,40874.0,39852.0,5054.0,3194.0,13678.0,3458.0,5044.0,704.0,8634.0,2754.0,7945.24,7959.0,575.0,350.0 +2015-07-23 16:00:00+00:00,3591.51,3594.7,509.0,138.24,4733.0,1780.87,1264.0,567.14,3469.0,1213.73,2129.19,2142.8,9.77,1064.25,1462.32,1451.9,9.77,9396.0,9788.0,650.66,56.61,35.55,21.06,60777.0,61212.0,37850.0,5798.0,0.1532,30385.0,3716.0,0.1223,2020.0,555.0,0.2748,28365.0,3161.0,0.1114,10872.0,10872.0,1147.0,1603.0,22.0,1580.0,,,,,,,,22104.0,21767.0,1454.0,228.0,19642.0,20090.0,2350.0,1881.0,533.0,1348.0,8159.0,8483.0,847.0,4.0,40598.0,40334.0,52.81,5049.0,2456.0,0.4865,13014.0,3194.0,0.2454,5044.0,760.0,0.1506,7970.0,2434.0,0.3054,1076.0,,5.0,664.0,83.0,41674.0,40334.0,5054.0,2456.0,13678.0,3276.0,5044.0,760.0,8634.0,2516.0,7808.59,7733.0,173.0,293.0 +2015-07-23 17:00:00+00:00,3391.02,3397.2,509.0,78.24,4733.0,1605.3,1264.0,633.62,3469.0,971.68,2007.11,2015.6,9.81,862.9,1383.91,1381.6,9.81,9033.0,9450.0,405.34,71.18,57.33,13.85,59858.0,60369.0,37850.0,2782.0,0.0735,30385.0,2752.0,0.0906,2020.0,451.0,0.2232,28365.0,2301.0,0.0811,10906.0,10906.0,564.0,1246.0,18.0,1228.0,,,,,,,,21924.0,21395.0,747.0,138.0,19094.0,19801.0,1099.0,1365.0,432.0,933.0,7934.0,8268.0,373.0,2.0,40328.0,39130.0,49.0,5049.0,1876.0,0.3714,13014.0,2911.0,0.2237,5044.0,774.0,0.1533,7970.0,2138.0,0.2682,1049.0,,5.0,664.0,56.0,41376.0,39130.0,5054.0,1876.0,13678.0,2967.0,5044.0,774.0,8634.0,2194.0,7732.7,7698.0,18.0,269.0 +2015-07-23 18:00:00+00:00,3233.22,3240.8,509.0,28.31,4733.0,1231.46,1264.0,535.04,3469.0,696.42,1928.0,1928.3,9.72,623.53,1305.22,1312.5,9.72,8783.0,9132.0,162.78,76.8,56.27,20.53,57009.0,57418.0,37850.0,704.0,0.0186,30385.0,1899.0,0.0625,2020.0,368.0,0.1821,28365.0,1531.0,0.054,10283.0,10283.0,131.0,896.0,16.0,880.0,,,,,,,,21015.0,20624.0,241.0,105.0,18100.0,18624.0,269.0,897.0,352.0,545.0,7611.0,7887.0,62.0,1.0,39364.0,37782.0,47.45,5049.0,1170.0,0.2316,13014.0,2529.0,0.1944,5044.0,648.0,0.1286,7970.0,1880.0,0.236,987.0,,5.0,664.0,26.0,40350.0,37782.0,5054.0,1170.0,13678.0,2555.0,5044.0,648.0,8634.0,1906.0,7920.13,7950.0,0.0,280.0 +2015-07-23 19:00:00+00:00,3098.94,3134.8,509.0,3.5,4733.0,903.56,1264.0,434.31,3469.0,469.25,1840.32,1865.3,9.83,414.34,1258.62,1269.5,9.83,8616.0,8971.0,20.23,87.32,56.39,30.93,55369.0,55655.0,37850.0,33.0,0.0008,30385.0,1582.0,0.0521,2020.0,305.0,0.1508,28365.0,1277.0,0.045,9963.0,9963.0,7.0,774.0,13.0,761.0,,,,,,,,20325.0,20012.0,13.0,122.0,17516.0,17979.0,13.0,678.0,292.0,386.0,7565.0,7701.0,0.0,9.0,37822.0,36440.0,42.0,5049.0,572.0,0.1134,13014.0,2055.0,0.1579,5044.0,554.0,0.1098,7970.0,1501.0,0.1884,954.0,,5.0,664.0,13.0,38776.0,36440.0,5054.0,572.0,13678.0,2068.0,5044.0,554.0,8634.0,1514.0,7692.48,7619.0,0.0,275.0 +2015-07-23 20:00:00+00:00,3069.77,3104.1,509.0,0.02,4733.0,791.74,1264.0,362.45,3469.0,429.29,1823.7,1865.1,9.84,372.94,1246.07,1239.0,9.84,9110.0,9265.0,0.0,61.17,34.29,26.88,53381.0,53193.0,37850.0,0.0,0.0,30385.0,1482.0,0.0488,2020.0,269.0,0.1332,28365.0,1213.0,0.0428,9510.0,9510.0,0.0,679.0,12.0,667.0,,,,,,,,19775.0,19046.0,0.0,179.0,16901.0,17234.0,0.0,609.0,257.0,353.0,7194.0,7403.0,0.0,14.0,36760.0,34736.0,43.54,5049.0,172.0,0.034,13014.0,1712.0,0.1315,5044.0,384.0,0.0761,7970.0,1328.0,0.1666,944.0,,5.0,664.0,12.0,37704.0,34736.0,5054.0,172.0,13678.0,1724.0,5044.0,384.0,8634.0,1340.0,7375.29,7311.0,0.0,248.0 +2015-07-23 21:00:00+00:00,2838.62,2886.6,509.0,0.0,4733.0,791.3,1264.0,387.62,3469.0,403.68,1693.5,1739.5,9.44,343.96,1145.12,1147.1,9.44,8753.0,9020.0,0.0,54.13,14.19,39.94,48985.0,48952.0,37850.0,0.0,0.0,30385.0,1475.0,0.0485,2020.0,250.0,0.1236,28365.0,1225.0,0.0432,8726.0,8726.0,0.0,619.0,7.0,612.0,,,,,,,,18197.0,17645.0,0.0,256.0,15495.0,15816.0,0.0,579.0,243.0,336.0,6568.0,6764.0,0.0,20.0,35809.0,33192.0,42.55,5049.0,8.0,0.0017,13014.0,1413.0,0.1086,5044.0,230.0,0.0456,7970.0,1183.0,0.1484,950.0,,5.0,664.0,3.0,36759.0,33192.0,5054.0,8.0,13678.0,1416.0,5044.0,230.0,8634.0,1186.0,6961.23,6779.0,0.0,215.0 +2015-07-23 22:00:00+00:00,2579.03,2624.2,509.0,0.0,4736.0,756.76,1264.0,392.02,3473.0,364.74,1546.44,1571.0,9.16,322.16,1032.59,1053.2,9.16,8170.0,8352.0,0.0,51.92,0.37,51.55,45743.0,45275.0,37861.0,0.0,0.0,30402.0,1575.0,0.0518,2024.0,235.0,0.1161,28378.0,1340.0,0.0472,8198.0,8198.0,0.0,628.0,5.0,623.0,,,,,,,,17028.0,16565.0,0.0,334.0,14426.0,14263.0,0.0,593.0,230.0,364.0,6090.0,6248.0,0.0,20.0,32396.0,29462.0,40.92,5049.0,0.0,0.0,13014.0,1177.0,0.0904,5044.0,156.0,0.0308,7970.0,1022.0,0.1281,853.0,,5.0,664.0,3.0,33249.0,29462.0,5054.0,0.0,13678.0,1180.0,5044.0,156.0,8634.0,1025.0,6543.26,6379.0,0.0,207.0 +2015-07-23 23:00:00+00:00,2447.04,2446.7,509.0,0.0,4736.0,730.87,1264.0,368.39,3473.0,362.48,1470.04,1471.5,8.7,322.18,977.0,975.2,8.7,7850.0,7979.0,0.0,,,41.82,43697.0,43068.0,37861.0,0.0,0.0,30402.0,1726.0,0.0568,2024.0,190.0,0.0938,28378.0,1536.0,0.0541,7760.0,7760.0,0.0,633.0,3.0,630.0,,,,,,,,16328.0,15918.0,0.0,424.0,13855.0,13479.0,0.0,660.0,187.0,472.0,5754.0,5912.0,0.0,9.0,28858.0,26233.0,37.97,5049.0,0.0,0.0,13014.0,978.0,0.0751,5044.0,112.0,0.0221,7970.0,866.0,0.1086,734.0,840.0,5.0,664.0,3.0,29592.0,27073.0,5054.0,0.0,13678.0,981.0,5044.0,112.0,8634.0,869.0,6212.59,6108.0,0.0,232.0 +2015-07-24 00:00:00+00:00,2384.8,2384.9,509.0,0.0,4736.0,661.58,1264.0,299.2,3473.0,362.38,1439.82,1440.5,8.28,331.49,944.98,944.4,8.28,7614.0,7744.0,0.0,,,45.48,42589.0,42208.0,37861.0,0.0,0.0,30402.0,1910.0,0.0628,2024.0,168.0,0.0828,28378.0,1742.0,0.0614,7558.0,7558.0,0.0,686.0,4.0,682.0,,,,,,,,15899.0,15547.0,0.0,431.0,13523.0,13396.0,0.0,786.0,164.0,622.0,5609.0,5708.0,0.0,8.0,25428.0,24774.0,37.9,5049.0,0.0,0.0,13014.0,878.0,0.0675,5044.0,72.0,0.0143,7970.0,806.0,0.1011,660.0,764.0,5.0,664.0,3.0,26088.0,25538.0,5054.0,0.0,13678.0,881.0,5044.0,72.0,8634.0,809.0,6025.29,5929.0,0.0,266.0 +2015-07-24 01:00:00+00:00,2369.67,2367.9,509.0,0.0,4736.0,545.04,1264.0,224.41,3473.0,320.63,1433.43,1433.8,8.08,293.97,936.24,934.1,8.08,7480.0,7653.0,0.0,,,37.51,42620.0,42413.0,37861.0,0.0,0.0,30402.0,2010.0,0.0661,2024.0,165.0,0.0816,28378.0,1844.0,0.065,7645.0,7645.0,0.0,659.0,2.0,657.0,,,,,,,,15917.0,15587.0,0.0,417.0,13470.0,13488.0,0.0,926.0,163.0,764.0,5588.0,5694.0,0.0,7.0,24628.0,24010.0,34.92,5049.0,0.0,0.0,13014.0,828.0,0.0636,5044.0,50.0,0.01,7970.0,778.0,0.0976,628.0,695.0,5.0,664.0,2.0,25256.0,24704.0,5054.0,0.0,13678.0,830.0,5044.0,50.0,8634.0,780.0,5885.72,5800.0,0.0,287.0 +2015-07-24 02:00:00+00:00,2383.9,2378.0,509.0,0.01,4736.0,466.73,1264.0,185.64,3473.0,281.09,1451.35,1445.0,8.0,255.59,932.55,933.0,8.0,7511.0,7653.0,0.0,,,24.32,43546.0,43249.0,37861.0,0.0,0.0,30402.0,2017.0,0.0663,2024.0,141.0,0.0696,28378.0,1876.0,0.0661,7870.0,7870.0,0.0,655.0,1.0,655.0,,,,,,,,16260.0,15821.0,0.0,367.0,13754.0,13758.0,0.0,983.0,140.0,843.0,5663.0,5800.0,0.0,12.0,23950.0,23615.0,34.39,5049.0,0.0,0.0,13014.0,800.0,0.0614,5044.0,82.0,0.0161,7970.0,718.0,0.0901,614.0,635.0,5.0,664.0,2.0,24564.0,24250.0,5054.0,0.0,13678.0,801.0,5044.0,82.0,8634.0,720.0,5891.6,5779.0,0.0,299.0 +2015-07-24 03:00:00+00:00,2436.9,2432.3,509.0,3.81,4736.0,393.9,1264.0,160.34,3473.0,233.56,1476.75,1472.5,8.25,208.89,960.15,959.8,8.25,7719.0,7740.0,0.0,,,20.96,46306.0,46084.0,37861.0,53.0,0.0014,30402.0,2108.0,0.0693,2024.0,122.0,0.0605,28378.0,1986.0,0.07,8333.0,8333.0,32.0,665.0,0.0,665.0,,,,,,,,17233.0,16744.0,4.0,370.0,14623.0,14756.0,14.0,1053.0,122.0,930.0,6117.0,6251.0,4.0,20.0,23946.0,23666.0,33.91,5049.0,84.0,0.0168,13014.0,610.0,0.0468,5044.0,106.0,0.021,7970.0,504.0,0.0632,605.0,595.0,5.0,664.0,2.0,24550.0,24260.0,5054.0,84.0,13678.0,612.0,5044.0,106.0,8634.0,506.0,5837.87,5847.0,8.0,315.0 +2015-07-24 04:00:00+00:00,2778.88,2742.5,509.0,26.91,4736.0,359.94,1264.0,159.57,3473.0,200.37,1674.97,1659.7,9.24,181.96,1103.91,1082.8,9.24,8152.0,8276.0,35.84,,,30.12,53320.0,53143.0,37861.0,1018.0,0.0269,30402.0,2073.0,0.0682,2024.0,81.0,0.0398,28378.0,1993.0,0.0702,9464.0,9464.0,330.0,667.0,0.0,667.0,,,,,,,,19845.0,19260.0,159.0,402.0,16813.0,17074.0,392.0,978.0,81.0,897.0,7198.0,7345.0,136.0,27.0,24098.0,23707.0,34.39,5049.0,250.0,0.0494,13014.0,535.0,0.0411,5044.0,108.0,0.0215,7970.0,426.0,0.0535,594.0,586.0,5.0,664.0,1.0,24693.0,24293.0,5054.0,250.0,13678.0,536.0,5044.0,108.0,8634.0,428.0,6221.46,6376.0,134.0,284.0 +2015-07-24 05:00:00+00:00,3233.32,3206.4,509.0,76.58,4736.0,308.05,1264.0,140.24,3473.0,167.81,1951.45,1935.9,23.57,145.87,1281.87,1270.5,23.57,8756.0,9064.0,213.08,,,23.92,59075.0,58958.0,37861.0,3657.0,0.0966,30402.0,1759.0,0.0578,2024.0,72.0,0.0358,28378.0,1687.0,0.0594,10304.0,10304.0,1026.0,521.0,0.0,521.0,,,,,,,,21813.0,21279.0,722.0,323.0,18763.0,18998.0,1329.0,877.0,72.0,804.0,8195.0,8378.0,581.0,38.0,26778.0,26693.0,41.67,5049.0,423.0,0.0837,13014.0,474.0,0.0364,5044.0,96.0,0.0192,7970.0,378.0,0.0474,646.0,566.0,5.0,664.0,1.0,27425.0,27259.0,5054.0,423.0,13678.0,475.0,5044.0,96.0,8634.0,379.0,6907.86,6977.0,496.0,252.0 +2015-07-24 06:00:00+00:00,3571.58,3567.6,509.0,153.92,4736.0,300.3,1264.0,100.2,3473.0,200.1,2159.64,2151.9,36.21,175.7,1411.94,1415.7,36.21,9317.0,9622.0,440.14,,,8.74,62299.0,62216.0,37861.0,7583.0,0.2003,30402.0,1081.0,0.0356,2024.0,57.0,0.0284,28378.0,1023.0,0.036,10583.0,10583.0,1977.0,339.0,0.0,339.0,,,,,,,,22936.0,22463.0,1490.0,170.0,19981.0,20163.0,2772.0,538.0,57.0,481.0,8800.0,9008.0,1344.0,33.0,32175.0,32484.0,39.76,5049.0,312.0,0.0619,13014.0,357.0,0.0274,5044.0,69.0,0.0138,7970.0,288.0,0.0361,783.0,630.0,5.0,664.0,1.0,32958.0,33114.0,5054.0,312.0,13678.0,358.0,5044.0,69.0,8634.0,289.0,7469.06,7606.0,941.0,222.0 +2015-07-24 07:00:00+00:00,3769.54,3774.1,509.0,208.24,4736.0,368.8,1264.0,69.46,3473.0,299.34,2269.71,2273.9,39.67,270.27,1499.83,1500.2,39.67,9704.0,9980.0,625.61,,,2.07,63582.0,63372.0,37861.0,12200.0,0.3223,30402.0,951.0,0.0313,2024.0,36.0,0.018,28378.0,915.0,0.0322,10700.0,10700.0,3089.0,464.0,0.0,464.0,,,,,,,,23451.0,22893.0,2561.0,87.0,20396.0,20473.0,4398.0,376.0,36.0,339.0,9035.0,9306.0,2152.0,25.0,36782.0,36520.0,46.97,5049.0,634.0,0.1254,13014.0,465.0,0.0358,5044.0,122.0,0.0242,7970.0,343.0,0.043,935.0,790.0,5.0,664.0,1.0,37716.0,37310.0,5054.0,634.0,13678.0,466.0,5044.0,122.0,8634.0,344.0,7863.64,7954.0,1320.0,212.0 +2015-07-24 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06:00:00+00:00,3041.59,3027.8,511.0,64.89,4746.0,470.37,1264.0,333.07,3482.0,137.3,1818.42,1818.3,11.19,106.69,1223.17,1209.5,28.05,7648.0,7618.0,377.05,111.16,45.02,66.14,41725.0,41550.0,38006.0,1993.0,0.0524,30622.0,2113.0,0.069,2091.0,426.0,0.2039,28531.0,1686.0,0.0591,7953.0,7953.0,1050.0,178.0,22.0,156.0,,,,,,,,16265.0,15273.0,198.0,766.0,12912.0,13384.0,532.0,1136.0,404.0,732.0,4595.0,4939.0,212.0,33.0,23712.0,23058.0,35.43,5075.0,372.0,0.0733,13037.0,341.0,0.0262,5044.0,106.0,0.0209,7992.0,236.0,0.0294,577.0,557.0,5.0,664.0,13.0,24290.0,23615.0,5080.0,372.0,13701.0,354.0,5044.0,106.0,8656.0,249.0,5309.28,5259.0,859.0,13.0 +2015-08-16 07:00:00+00:00,3285.08,3275.5,511.0,103.98,4746.0,501.28,1264.0,361.83,3482.0,139.45,1961.77,1952.5,12.08,106.53,1323.31,1323.0,30.08,8120.0,8017.0,786.5,99.07,45.38,53.69,44113.0,44369.0,38006.0,3153.0,0.083,30622.0,2212.0,0.0723,2091.0,464.0,0.222,28531.0,1748.0,0.0613,8039.0,8039.0,1532.0,150.0,26.0,124.0,,,,,,,,17348.0,16622.0,395.0,833.0,13766.0,14436.0,866.0,1200.0,438.0,761.0,4960.0,5272.0,360.0,29.0,27373.0,25976.0,36.95,5075.0,1184.0,0.2333,13037.0,307.0,0.0236,5044.0,89.0,0.0176,7992.0,219.0,0.0274,666.0,613.0,5.0,664.0,13.0,28038.0,26590.0,5080.0,1184.0,13701.0,321.0,5044.0,89.0,8656.0,232.0,5691.4,5561.0,1235.0,14.0 +2015-08-16 08:00:00+00:00,3409.57,3417.5,511.0,140.82,4746.0,500.16,1264.0,328.75,3482.0,171.41,2025.51,2036.2,32.7,135.35,1384.06,1381.3,32.7,8467.0,8377.0,1179.91,107.52,34.4,73.12,46392.0,46866.0,38006.0,4613.0,0.1214,30622.0,2189.0,0.0715,2091.0,456.0,0.2182,28531.0,1733.0,0.0608,8400.0,8400.0,2286.0,159.0,26.0,133.0,,,,,,,,18313.0,17698.0,630.0,810.0,14474.0,15243.0,1180.0,1191.0,430.0,760.0,5206.0,5525.0,517.0,30.0,30632.0,29294.0,37.98,5075.0,2310.0,0.4551,13037.0,304.0,0.0233,5044.0,59.0,0.0117,7992.0,245.0,0.0306,754.0,709.0,5.0,664.0,28.0,31387.0,30002.0,5080.0,2310.0,13701.0,332.0,5044.0,59.0,8656.0,273.0,5981.65,5845.0,1502.0,19.0 +2015-08-16 09:00:00+00:00,3462.98,3478.5,511.0,162.78,4746.0,448.38,1264.0,243.84,3482.0,204.54,2033.72,2064.0,37.98,164.38,1429.26,1414.5,37.98,8690.0,8544.0,1492.67,108.06,19.27,88.79,48730.0,49321.0,38006.0,6018.0,0.1584,30622.0,2545.0,0.0831,2091.0,571.0,0.2732,28531.0,1974.0,0.0692,8912.0,8912.0,2898.0,284.0,81.0,203.0,,,,,,,,19136.0,18655.0,842.0,886.0,15181.0,15895.0,1555.0,1337.0,490.0,846.0,5501.0,5860.0,723.0,38.0,33660.0,32078.0,39.07,5075.0,3304.0,0.6512,13037.0,327.0,0.025,5044.0,61.0,0.0121,7992.0,266.0,0.0333,828.0,811.0,5.0,664.0,50.0,34489.0,32888.0,5080.0,3304.0,13701.0,377.0,5044.0,61.0,8656.0,316.0,6233.98,5980.0,1657.0,41.0 +2015-08-16 10:00:00+00:00,3439.22,3486.9,511.0,184.71,4746.0,374.73,1264.0,160.45,3482.0,214.28,2009.84,2052.9,26.37,173.16,1429.38,1434.0,37.85,8657.0,8579.0,1591.55,106.84,11.57,95.27,48583.0,49154.0,38006.0,7118.0,0.1873,30622.0,3046.0,0.0994,2091.0,719.0,0.3438,28531.0,2327.0,0.0816,8713.0,8713.0,3295.0,433.0,135.0,299.0,,,,,,,,19357.0,18801.0,986.0,955.0,15034.0,15805.0,1929.0,1615.0,584.0,1031.0,5479.0,5834.0,908.0,43.0,35040.0,33512.0,43.0,5075.0,4151.0,0.8179,13037.0,267.0,0.0204,5044.0,30.0,0.006,7992.0,237.0,0.0296,865.0,879.0,5.0,664.0,44.0,35906.0,34390.0,5080.0,4151.0,13701.0,311.0,5044.0,30.0,8656.0,281.0,6389.07,6106.0,1690.0,74.0 +2015-08-16 11:00:00+00:00,3436.48,3424.4,511.0,186.25,4746.0,426.22,1264.0,201.63,3482.0,224.59,2017.34,1998.8,14.95,177.48,1419.14,1425.6,31.03,8463.0,8208.0,1473.47,121.92,10.53,111.39,46851.0,47489.0,38006.0,7419.0,0.1952,30622.0,3406.0,0.1112,2091.0,767.0,0.3668,28531.0,2639.0,0.0925,8413.0,8413.0,3117.0,585.0,204.0,381.0,,,,,,,,18730.0,18189.0,1044.0,998.0,14460.0,15284.0,2217.0,1776.0,564.0,1213.0,5247.0,5603.0,1042.0,47.0,35541.0,34234.0,43.19,5075.0,4306.0,0.8486,13037.0,442.0,0.0339,5044.0,46.0,0.009,7992.0,396.0,0.0495,909.0,915.0,5.0,664.0,34.0,36450.0,35148.0,5080.0,4306.0,13701.0,475.0,5044.0,46.0,8656.0,430.0,6319.65,5945.0,1634.0,133.0 +2015-08-16 12:00:00+00:00,3398.97,3404.8,511.0,194.13,4746.0,612.04,1264.0,348.88,3482.0,263.16,1989.26,1998.3,11.48,178.63,1409.71,1406.5,29.68,8256.0,7881.0,1425.61,134.64,10.31,124.33,45456.0,46414.0,38006.0,7017.0,0.1846,30622.0,4018.0,0.1312,2091.0,840.0,0.4017,28531.0,3178.0,0.1114,8248.0,8248.0,2518.0,733.0,225.0,508.0,,,,,,,,18042.0,17693.0,1098.0,1054.0,14061.0,14879.0,2224.0,2173.0,615.0,1558.0,5104.0,5594.0,1176.0,58.0,35892.0,34182.0,39.08,5075.0,3952.0,0.7787,13037.0,500.0,0.0383,5044.0,62.0,0.0123,7992.0,438.0,0.0548,923.0,969.0,5.0,664.0,36.0,36814.0,35152.0,5080.0,3952.0,13701.0,536.0,5044.0,62.0,8656.0,474.0,6235.69,5834.0,1440.0,190.0 +2015-08-16 13:00:00+00:00,3346.08,3366.5,511.0,183.62,4746.0,722.87,1264.0,423.6,3482.0,299.27,1963.51,1974.3,11.11,204.9,1382.57,1392.2,28.46,8083.0,7752.0,1239.6,150.49,18.01,132.48,44865.0,45699.0,38006.0,6482.0,0.1706,30622.0,4478.0,0.1462,2091.0,830.0,0.3971,28531.0,3648.0,0.1278,8019.0,8019.0,2029.0,869.0,204.0,665.0,,,,,,,,17880.0,17325.0,1153.0,1117.0,13935.0,14733.0,2147.0,2425.0,626.0,1799.0,5031.0,5622.0,1154.0,67.0,34888.0,33264.0,37.0,5075.0,2248.0,0.443,13037.0,448.0,0.0344,5044.0,62.0,0.0123,7992.0,386.0,0.0483,866.0,987.0,5.0,664.0,29.0,35755.0,34251.0,5080.0,2248.0,13701.0,477.0,5044.0,62.0,8656.0,415.0,6168.89,5746.0,1151.0,238.0 +2015-08-16 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07:00:00+00:00,3569.56,3555.3,514.0,95.52,4777.0,1606.88,1264.0,705.15,3513.0,901.73,2154.78,2131.9,22.41,840.51,1414.78,1423.4,40.09,9574.0,9348.0,373.58,659.78,304.9,354.88,52705.0,53565.0,38304.0,7203.0,0.188,30915.0,3860.0,0.1249,2128.0,992.0,0.466,28787.0,2868.0,0.0996,9398.0,9398.0,2039.0,660.0,94.0,566.0,,,,,,,,20430.0,19910.0,1098.0,851.0,16518.0,18017.0,2788.0,2307.0,897.0,1410.0,6358.0,6240.0,1279.0,41.0,32294.0,30264.0,33.0,5098.0,340.0,0.0667,13067.0,7377.0,0.5646,5044.0,2904.0,0.5756,8022.0,4474.0,0.5576,852.0,677.0,5.0,664.0,348.0,33146.0,30941.0,5103.0,340.0,13730.0,7725.0,5044.0,2904.0,8686.0,4821.0,5639.01,6126.0,1304.0,361.0 +2015-09-12 08:00:00+00:00,3634.31,3667.2,514.0,124.29,4777.0,1726.11,1264.0,772.59,3513.0,953.52,2179.03,2189.2,22.57,882.39,1455.28,1478.0,34.15,9716.0,9572.0,692.03,527.58,286.79,240.79,54418.0,55540.0,38304.0,11930.0,0.3114,30915.0,4244.0,0.1373,2128.0,1172.0,0.5507,28787.0,3072.0,0.1067,9700.0,9700.0,3194.0,637.0,132.0,505.0,,,,,,,,20953.0,20580.0,2229.0,776.0,17023.0,18681.0,4264.0,2791.0,1040.0,1751.0,6742.0,6580.0,2243.0,39.0,35571.0,33574.0,36.52,5098.0,852.0,0.167,13067.0,7726.0,0.5913,5044.0,2814.0,0.5579,8022.0,4912.0,0.6123,976.0,804.0,5.0,664.0,395.0,36546.0,34378.0,5103.0,852.0,13730.0,8121.0,5044.0,2814.0,8686.0,5307.0,5900.83,6346.0,1559.0,380.0 +2015-09-12 09:00:00+00:00,3582.92,3654.8,514.0,163.34,4777.0,1791.12,1264.0,754.44,3513.0,1036.68,2117.33,2173.7,23.91,945.4,1465.59,1481.1,27.95,9980.0,9781.0,1098.09,383.99,186.94,197.05,55620.0,56557.0,38304.0,16142.0,0.4214,30915.0,4354.0,0.1408,2128.0,1191.0,0.5598,28787.0,3163.0,0.1099,9701.0,9701.0,4011.0,654.0,135.0,519.0,,,,,,,,21460.0,20905.0,3261.0,758.0,17401.0,19113.0,5968.0,2894.0,1056.0,1838.0,7058.0,6838.0,2903.0,48.0,37093.0,35086.0,51.68,5098.0,760.0,0.1491,13067.0,5761.0,0.4408,5044.0,2292.0,0.4545,8022.0,3468.0,0.4324,998.0,912.0,5.0,664.0,408.0,38090.0,35998.0,5103.0,760.0,13730.0,6169.0,5044.0,2292.0,8686.0,3877.0,6112.49,6554.0,1698.0,342.0 +2015-09-12 10:00:00+00:00,3489.2,3508.9,514.0,180.88,4777.0,1891.44,1264.0,671.28,3513.0,1220.16,2036.9,2047.5,9.54,1096.95,1452.3,1461.4,24.97,9994.0,9712.0,915.96,464.7,142.35,322.35,54537.0,55596.0,38304.0,18842.0,0.4919,30915.0,4703.0,0.1521,2128.0,1203.0,0.5651,28787.0,3500.0,0.1216,9404.0,9404.0,4630.0,773.0,129.0,644.0,,,,,,,,21105.0,20681.0,3611.0,863.0,17108.0,18873.0,7446.0,3002.0,1073.0,1929.0,6921.0,6638.0,3155.0,64.0,37380.0,35701.0,46.08,5098.0,2660.0,0.5218,13067.0,7203.0,0.5512,5044.0,2444.0,0.4845,8022.0,4759.0,0.5933,1000.0,952.0,5.0,664.0,396.0,38380.0,36653.0,5103.0,2660.0,13730.0,7599.0,5044.0,2444.0,8686.0,5155.0,6138.83,6635.0,1734.0,313.0 +2015-09-12 11:00:00+00:00,3468.52,3452.7,514.0,184.98,4777.0,2026.99,1264.0,688.1,3513.0,1338.89,2031.16,2011.4,12.01,1185.13,1437.36,1441.3,21.86,9766.0,9403.0,596.35,395.31,146.9,248.41,52309.0,53938.0,38304.0,19748.0,0.5156,30915.0,5287.0,0.171,2128.0,1269.0,0.5963,28787.0,4018.0,0.1396,8823.0,8823.0,4791.0,968.0,161.0,808.0,,,,,,,,20288.0,20049.0,3849.0,898.0,16583.0,18580.0,7949.0,3360.0,1108.0,2252.0,6614.0,6486.0,3160.0,60.0,37140.0,35879.0,36.11,5098.0,3690.0,0.7238,13067.0,6944.0,0.5314,5044.0,2370.0,0.4698,8022.0,4574.0,0.5702,992.0,957.0,5.0,664.0,411.0,38133.0,36836.0,5103.0,3690.0,13730.0,7356.0,5044.0,2370.0,8686.0,4986.0,5863.44,6358.0,1621.0,318.0 +2015-09-12 12:00:00+00:00,3427.22,3419.2,514.0,198.93,4777.0,1971.42,1264.0,660.49,3513.0,1310.93,2018.97,1999.1,12.64,1136.08,1408.25,1420.1,19.29,9461.0,9214.0,377.14,316.49,156.29,160.2,51630.0,52414.0,38304.0,18878.0,0.4928,30915.0,5745.0,0.1858,2128.0,1316.0,0.6186,28787.0,4428.0,0.1539,8814.0,8814.0,4694.0,1145.0,184.0,960.0,,,,,,,,20470.0,19420.0,3803.0,853.0,15974.0,17951.0,7471.0,3680.0,1132.0,2548.0,6371.0,6228.0,2910.0,67.0,36530.0,35038.0,32.85,5098.0,4095.0,0.8032,13067.0,6830.0,0.5228,5044.0,2439.0,0.4836,8022.0,4392.0,0.5474,965.0,942.0,5.0,664.0,384.0,37494.0,35980.0,5103.0,4095.0,13730.0,7214.0,5044.0,2439.0,8686.0,4775.0,5572.24,6038.0,1440.0,287.0 +2015-09-12 13:00:00+00:00,3378.35,3376.4,514.0,186.46,4777.0,1993.49,1264.0,670.89,3513.0,1322.6,1979.78,1981.9,12.62,1136.68,1398.57,1394.5,21.19,9465.0,9145.0,375.14,268.32,175.35,92.97,51274.0,51464.0,38304.0,15546.0,0.4058,30915.0,5876.0,0.1901,2128.0,1346.0,0.6327,28787.0,4530.0,0.1574,8908.0,8908.0,4030.0,1242.0,180.0,1063.0,,,,,,,,20528.0,19067.0,2885.0,682.0,15577.0,17592.0,6295.0,3892.0,1167.0,2725.0,6261.0,5896.0,2336.0,60.0,35146.0,33918.0,32.85,5098.0,4340.0,0.8513,13067.0,7052.0,0.5396,5044.0,2799.0,0.5549,8022.0,4252.0,0.5301,922.0,909.0,5.0,664.0,351.0,36069.0,34827.0,5103.0,4340.0,13730.0,7403.0,5044.0,2799.0,8686.0,4604.0,5442.13,5841.0,1167.0,288.0 +2015-09-12 14:00:00+00:00,3437.43,3451.7,514.0,155.82,4777.0,2066.04,1264.0,697.67,3513.0,1368.37,2006.51,2007.7,13.4,1173.24,1430.92,1444.0,21.1,9507.0,9098.0,348.59,327.05,236.28,90.77,50214.0,50491.0,38304.0,10692.0,0.2791,30915.0,6030.0,0.195,2128.0,1388.0,0.6524,28787.0,4642.0,0.1612,8679.0,8679.0,2902.0,1271.0,158.0,1113.0,,,,,,,,19902.0,18741.0,1739.0,590.0,15479.0,17303.0,4483.0,4125.0,1231.0,2895.0,6154.0,5768.0,1568.0,43.0,34377.0,33099.0,32.85,5098.0,3955.0,0.7758,13067.0,7155.0,0.5476,5044.0,3016.0,0.598,8022.0,4138.0,0.5159,900.0,861.0,5.0,664.0,277.0,35278.0,33960.0,5103.0,3955.0,13730.0,7432.0,5044.0,3016.0,8686.0,4415.0,5431.16,5854.0,771.0,275.0 +2015-09-12 15:00:00+00:00,3668.53,3690.8,514.0,88.94,4777.0,2018.4,1264.0,725.19,3513.0,1293.21,2150.78,2162.7,13.65,1110.99,1517.75,1528.1,21.5,9666.0,9108.0,228.2,440.59,312.2,128.39,50594.0,51028.0,38304.0,5727.0,0.1495,30915.0,6299.0,0.2037,2128.0,1529.0,0.7184,28787.0,4770.0,0.1657,8874.0,8874.0,1663.0,1420.0,177.0,1243.0,,,,,,,,20104.0,18763.0,961.0,664.0,15646.0,17600.0,2332.0,4185.0,1352.0,2833.0,5970.0,5791.0,771.0,30.0,34689.0,33130.0,35.06,5098.0,3190.0,0.6258,13067.0,6480.0,0.496,5044.0,2668.0,0.5288,8022.0,3813.0,0.4753,924.0,840.0,5.0,664.0,244.0,35614.0,33969.0,5103.0,3190.0,13730.0,6725.0,5044.0,2668.0,8686.0,4057.0,5449.8,5855.0,301.0,284.0 +2015-09-12 16:00:00+00:00,3717.26,3668.8,514.0,31.95,4777.0,2146.45,1264.0,917.02,3513.0,1229.43,2188.48,2146.8,13.98,1066.43,1528.78,1522.0,21.51,9699.0,9078.0,105.92,491.91,384.14,107.77,51008.0,51460.0,38304.0,1863.0,0.0486,30915.0,6779.0,0.2193,2128.0,1633.0,0.7676,28787.0,5146.0,0.1788,9200.0,9200.0,555.0,1692.0,211.0,1481.0,,,,,,,,19852.0,18852.0,318.0,804.0,15980.0,17580.0,793.0,4250.0,1422.0,2827.0,5976.0,5829.0,196.0,34.0,35730.0,34928.0,40.97,5098.0,2265.0,0.4442,13067.0,5646.0,0.432,5044.0,2236.0,0.4432,8022.0,3410.0,0.4251,981.0,864.0,5.0,664.0,176.0,36712.0,35792.0,5103.0,2265.0,13730.0,5821.0,5044.0,2236.0,8686.0,3586.0,5580.73,5971.0,23.0,298.0 +2015-09-12 17:00:00+00:00,3542.03,3536.6,514.0,3.48,4777.0,2170.17,1264.0,1012.85,3513.0,1157.32,2069.45,2057.9,13.58,1003.3,1472.58,1478.7,21.85,9586.0,8886.0,22.37,543.78,390.82,152.96,51482.0,51117.0,38304.0,140.0,0.0036,30915.0,8312.0,0.2689,2128.0,1599.0,0.7512,28787.0,6714.0,0.2332,9188.0,9188.0,45.0,2646.0,202.0,2444.0,,,,,,,,20081.0,18959.0,26.0,878.0,16188.0,17228.0,69.0,4684.0,1396.0,3288.0,6026.0,5743.0,0.0,104.0,36512.0,35609.0,41.23,5098.0,1246.0,0.2444,13067.0,5146.0,0.3938,5044.0,2142.0,0.4246,8022.0,3004.0,0.3744,987.0,925.0,5.0,664.0,159.0,37499.0,36534.0,5103.0,1246.0,13730.0,5305.0,5044.0,2142.0,8686.0,3163.0,6040.32,6440.0,0.0,323.0 +2015-09-12 18:00:00+00:00,3531.5,3496.0,514.0,0.01,4777.0,2312.06,1264.0,1078.39,3513.0,1233.67,2075.78,2055.2,13.6,1082.53,1455.72,1440.8,21.92,9758.0,8923.0,0.16,583.54,397.13,186.41,51546.0,51096.0,38304.0,0.0,0.0,30915.0,10731.0,0.3471,2128.0,1715.0,0.8058,28787.0,9016.0,0.3132,9552.0,9552.0,0.0,4242.0,232.0,4010.0,,,,,,,,19756.0,18880.0,0.0,1024.0,16183.0,16877.0,0.0,5315.0,1482.0,3833.0,6055.0,5787.0,0.0,150.0,36521.0,35428.0,50.28,5098.0,254.0,0.0497,13067.0,4698.0,0.3595,5044.0,2212.0,0.4384,8022.0,2486.0,0.3099,948.0,943.0,5.0,664.0,166.0,37469.0,36370.0,5103.0,254.0,13730.0,4864.0,5044.0,2212.0,8686.0,2652.0,5794.6,6191.0,0.0,328.0 +2015-09-12 19:00:00+00:00,3341.29,3310.5,514.0,0.0,4777.0,2408.84,1264.0,1107.51,3513.0,1301.33,1964.06,1949.2,13.5,1147.98,1377.23,1361.3,21.25,9283.0,8953.0,0.0,522.1,336.8,185.3,48630.0,48380.0,38304.0,0.0,0.0,30915.0,11574.0,0.3744,2128.0,1622.0,0.7622,28787.0,9952.0,0.3457,8950.0,8950.0,0.0,5017.0,240.0,4777.0,,,,,,,,18757.0,17845.0,0.0,1127.0,15168.0,16073.0,0.0,5219.0,1382.0,3837.0,5755.0,5512.0,0.0,211.0,37622.0,35832.0,67.34,5098.0,0.0,0.0,13067.0,3276.0,0.2507,5044.0,1159.0,0.2297,8022.0,2118.0,0.264,996.0,919.0,5.0,664.0,171.0,38618.0,36751.0,5103.0,0.0,13730.0,3447.0,5044.0,1159.0,8686.0,2289.0,5345.35,5710.0,0.0,333.0 +2015-09-12 20:00:00+00:00,3098.82,3069.9,514.0,0.0,4777.0,2402.3,1264.0,1138.86,3513.0,1263.44,1823.37,1809.0,12.39,1103.64,1275.45,1260.9,20.56,9043.0,8713.0,0.0,502.13,351.8,150.33,45972.0,46028.0,38304.0,0.0,0.0,30915.0,11113.0,0.3595,2128.0,1435.0,0.6742,28787.0,9678.0,0.3362,8476.0,8476.0,0.0,5249.0,242.0,5007.0,,,,,,,,17754.0,16973.0,0.0,1108.0,14265.0,15308.0,0.0,4547.0,1193.0,3354.0,5477.0,5271.0,0.0,209.0,35937.0,35070.0,42.56,5098.0,0.0,0.0,13067.0,4196.0,0.3212,5044.0,2178.0,0.4317,8022.0,2019.0,0.2517,966.0,929.0,5.0,664.0,168.0,36902.0,35999.0,5103.0,0.0,13730.0,4365.0,5044.0,2178.0,8686.0,2187.0,5025.38,5391.0,0.0,339.0 +2015-09-12 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10:00:00+00:00,4685.98,4638.4,516.0,92.79,4794.0,1230.74,1264.0,321.29,3530.0,909.45,2897.47,2868.5,26.54,856.27,1788.51,1769.9,45.26,11543.0,11473.0,584.12,553.52,457.9,95.62,67704.0,66180.0,38385.0,6864.0,0.1788,31321.0,4693.0,0.1498,2162.0,2195.0,1.0154,29158.0,2498.0,0.0857,11319.0,10096.0,1216.0,534.0,21.0,513.0,,,,,,,,24850.0,25083.0,1859.0,284.0,22149.0,21594.0,2430.0,3874.0,2174.0,1700.0,9386.0,9406.0,1359.0,2.0,44963.0,43723.0,39.23,5245.0,592.0,0.1128,13123.0,5304.0,0.4042,5094.0,2019.0,0.3964,8029.0,3285.0,0.4092,1194.0,1150.0,5.0,664.0,294.0,46157.0,44873.0,5250.0,592.0,13786.0,5598.0,5094.0,2019.0,8692.0,3579.0,5987.63,6407.0,864.0,174.0 +2015-10-21 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18:00:00+00:00,4335.79,4301.7,521.0,0.0,4795.0,1850.55,1264.0,588.86,3531.0,1261.69,2452.28,2449.7,25.91,898.26,1883.51,1852.0,25.91,10734.0,10477.0,0.0,547.95,407.26,140.69,56565.0,55778.0,38462.0,0.0,0.0,31731.0,13671.0,0.4308,2162.0,2586.0,1.1959,29568.0,11086.0,0.3749,10478.0,9704.0,0.0,2011.0,287.0,1724.0,,,,,,,,21275.0,21334.0,0.0,2448.0,18137.0,18233.0,0.0,9030.0,2299.0,6731.0,6674.0,6508.0,0.0,181.0,49621.0,47042.0,47.01,5316.0,0.0,0.0,13135.0,2723.0,0.2074,5094.0,1813.0,0.356,8041.0,910.0,0.1132,1393.0,1352.0,5.0,676.0,1.0,51014.0,48394.0,5321.0,0.0,13811.0,2724.0,5094.0,1813.0,8717.0,911.0,5659.88,5976.0,0.0,1181.0 +2015-11-22 19:00:00+00:00,4078.43,4027.9,521.0,0.0,4795.0,1655.19,1264.0,607.21,3531.0,1047.98,2313.05,2302.7,25.39,704.08,1765.38,1725.2,25.39,10385.0,10165.0,0.0,414.13,276.39,137.74,54291.0,53451.0,38462.0,0.0,0.0,31731.0,12744.0,0.4016,2162.0,2589.0,1.1976,29568.0,10154.0,0.3434,9833.0,9074.0,0.0,2014.0,286.0,1729.0,,,,,,,,20777.0,20540.0,0.0,2168.0,17173.0,17515.0,0.0,8405.0,2303.0,6102.0,6508.0,6322.0,0.0,156.0,47258.0,45028.0,39.9,5316.0,0.0,0.0,13135.0,2650.0,0.2018,5094.0,1755.0,0.3446,8041.0,895.0,0.1113,1378.0,1281.0,5.0,676.0,2.0,48635.0,46308.0,5321.0,0.0,13811.0,2652.0,5094.0,1755.0,8717.0,897.0,5408.16,5696.0,0.0,1188.0 +2015-11-22 20:00:00+00:00,3840.43,3821.0,521.0,0.0,4795.0,1542.57,1264.0,659.82,3531.0,882.75,2203.21,2181.6,25.18,585.52,1637.22,1639.4,25.18,9959.0,9747.0,0.0,401.21,284.26,116.95,52728.0,49453.0,38462.0,0.0,0.0,31731.0,12132.0,0.3824,2162.0,2575.0,1.1908,29568.0,9558.0,0.3233,9352.0,8644.0,0.0,2045.0,282.0,1763.0,,,,,,,,20379.0,20035.0,0.0,2029.0,16553.0,16703.0,0.0,7923.0,2292.0,5631.0,6445.0,6224.0,0.0,135.0,44798.0,42581.0,36.58,5316.0,0.0,0.0,13135.0,2262.0,0.1723,5094.0,1454.0,0.2854,8041.0,808.0,0.1006,1296.0,1199.0,5.0,676.0,3.0,46094.0,43780.0,5321.0,0.0,13811.0,2266.0,5094.0,1454.0,8717.0,812.0,4956.76,5277.0,0.0,1171.0 +2015-11-22 21:00:00+00:00,3568.99,3510.4,521.0,0.0,4795.0,1263.31,1264.0,567.84,3531.0,695.47,2060.63,2013.9,24.93,447.57,1508.36,1496.5,24.93,9982.0,9672.0,0.0,426.93,314.9,112.03,52187.0,51164.0,38462.0,0.0,0.0,31731.0,11437.0,0.3604,2162.0,2520.0,1.1654,29568.0,8917.0,0.3016,9252.0,8624.0,0.0,2090.0,287.0,1802.0,,,,,,,,19902.0,19771.0,0.0,1906.0,16504.0,16584.0,0.0,7325.0,2232.0,5093.0,6529.0,6185.0,0.0,117.0,41758.0,39214.0,37.19,5316.0,0.0,0.0,13135.0,2006.0,0.1527,5094.0,1076.0,0.2112,8041.0,930.0,0.1156,1218.0,1119.0,5.0,676.0,7.0,42976.0,40332.0,5321.0,0.0,13811.0,2012.0,5094.0,1076.0,8717.0,936.0,4570.5,4905.0,0.0,1155.0 +2015-11-22 22:00:00+00:00,3292.5,3209.6,521.0,0.0,4795.0,988.16,1264.0,455.83,3531.0,532.33,1921.71,1852.3,24.57,322.16,1370.79,1357.3,24.57,9924.0,9451.0,0.0,492.35,353.97,138.38,48991.0,48006.0,38462.0,0.0,0.0,31731.0,10776.0,0.3396,2162.0,2397.0,1.1089,29568.0,8379.0,0.2834,8737.0,8262.0,0.0,1939.0,298.0,1641.0,,,,,,,,18523.0,18510.0,0.0,1804.0,15484.0,15352.0,0.0,6926.0,2100.0,4826.0,6246.0,5881.0,0.0,107.0,38278.0,35640.0,34.32,5316.0,0.0,0.0,13135.0,1882.0,0.1432,5094.0,1073.0,0.2106,8041.0,810.0,0.1007,1138.0,1013.0,5.0,676.0,18.0,39416.0,36653.0,5321.0,0.0,13811.0,1900.0,5094.0,1073.0,8717.0,828.0,4171.45,4446.0,0.0,1111.0 +2015-11-22 23:00:00+00:00,3132.97,3079.2,522.0,0.0,4795.0,832.32,1264.0,396.41,3531.0,435.91,1854.05,1833.2,23.65,241.48,1278.92,1246.0,24.0,9355.0,8994.0,0.0,465.07,337.85,127.22,46248.0,45060.0,38464.0,0.0,0.0,31746.0,9515.0,0.2997,2162.0,2041.0,0.9439,29583.0,7474.0,0.2526,8306.0,7850.0,0.0,1685.0,290.0,1395.0,,,,,,,,17483.0,17318.0,0.0,1661.0,14621.0,14443.0,0.0,6047.0,1751.0,4296.0,5838.0,5450.0,0.0,123.0,34573.0,31934.0,34.0,5316.0,0.0,0.0,13135.0,1894.0,0.1442,5094.0,970.0,0.1904,8041.0,924.0,0.115,1028.0,886.0,5.0,676.0,36.0,35601.0,32820.0,5321.0,0.0,13811.0,1930.0,5094.0,970.0,8717.0,960.0,3868.97,4126.0,0.0,1060.0 +2015-11-23 00:00:00+00:00,3049.79,2981.8,522.0,0.0,4795.0,761.11,1264.0,393.73,3531.0,367.38,1816.78,1800.1,24.52,186.19,1233.01,1181.7,24.52,8946.0,8500.0,0.0,183.39,65.71,117.68,44679.0,43673.0,38464.0,0.0,0.0,31746.0,8534.0,0.2688,2162.0,1491.0,0.6896,29583.0,7043.0,0.238,8071.0,7734.0,0.0,1551.0,246.0,1304.0,,,,,,,,17027.0,16790.0,0.0,1599.0,13986.0,13936.0,0.0,5243.0,1245.0,3999.0,5595.0,5214.0,0.0,141.0,33810.0,30473.0,32.44,5321.0,0.0,0.0,13148.0,2048.0,0.1558,5094.0,900.0,0.1765,8054.0,1148.0,0.1426,873.0,782.0,5.0,676.0,52.0,34683.0,31255.0,5326.0,0.0,13823.0,2099.0,5094.0,900.0,8729.0,1200.0,3792.39,4016.0,0.0,1002.0 +2015-11-23 01:00:00+00:00,3037.02,2930.1,522.0,0.0,4795.0,696.7,1264.0,397.08,3531.0,299.62,1809.4,1781.8,24.73,138.21,1227.62,1148.3,24.73,8549.0,8207.0,0.0,160.48,56.31,104.17,44190.0,43406.0,38464.0,0.0,0.0,31746.0,7705.0,0.2427,2162.0,1105.0,0.511,29583.0,6600.0,0.2231,8084.0,7702.0,0.0,1607.0,237.0,1370.0,,,,,,,,16925.0,16700.0,0.0,1478.0,13618.0,13810.0,0.0,4501.0,868.0,3633.0,5563.0,5194.0,0.0,119.0,32778.0,30576.0,30.0,5321.0,0.0,0.0,13148.0,2476.0,0.1882,5094.0,1207.0,0.237,8054.0,1268.0,0.1575,761.0,734.0,5.0,676.0,55.0,33540.0,31309.0,5326.0,0.0,13823.0,2530.0,5094.0,1207.0,8729.0,1323.0,3670.48,3916.0,0.0,942.0 +2015-11-23 02:00:00+00:00,3072.77,2971.6,522.0,0.0,4795.0,575.29,1264.0,321.06,3531.0,254.23,1840.19,1818.3,25.02,125.28,1232.58,1153.3,25.02,8413.0,8247.0,0.0,190.47,72.03,118.44,44681.0,43904.0,38464.0,0.0,0.0,31746.0,7175.0,0.226,2162.0,833.0,0.3851,29583.0,6342.0,0.2144,8196.0,7792.0,0.0,1685.0,222.0,1463.0,,,,,,,,17084.0,16867.0,0.0,1353.0,13704.0,13969.0,0.0,4029.0,611.0,3418.0,5697.0,5276.0,0.0,109.0,31658.0,31324.0,24.91,5321.0,1216.0,0.2284,13148.0,2690.0,0.2046,5094.0,741.0,0.1454,8054.0,1948.0,0.242,725.0,704.0,5.0,676.0,70.0,32384.0,32028.0,5326.0,1216.0,13823.0,2759.0,5094.0,741.0,8729.0,2018.0,3645.99,3876.0,0.0,885.0 +2015-11-23 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04:00:00+00:00,3462.98,3457.6,522.0,0.0,4795.0,407.71,1264.0,220.66,3531.0,187.05,2100.65,2095.7,26.44,114.85,1362.33,1361.9,26.44,9012.0,9061.0,0.0,115.66,47.27,68.39,50790.0,49596.0,38464.0,0.0,0.0,31746.0,5642.0,0.1777,2162.0,425.0,0.1968,29583.0,5216.0,0.1763,9384.0,8603.0,0.0,1737.0,187.0,1551.0,,,,,,,,19173.0,18553.0,0.0,944.0,15701.0,16117.0,0.0,2845.0,239.0,2606.0,6532.0,6322.0,0.0,115.0,29191.0,30927.0,27.42,5321.0,0.0,0.0,13148.0,2614.0,0.1988,5094.0,634.0,0.1245,8054.0,1980.0,0.2458,682.0,680.0,5.0,676.0,103.0,29873.0,31607.0,5326.0,0.0,13823.0,2717.0,5094.0,634.0,8729.0,2083.0,4218.47,4435.0,0.0,680.0 +2015-11-23 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21:00:00+00:00,3822.97,3728.9,539.0,0.0,4888.0,698.65,1264.0,373.24,3625.0,325.41,2297.67,2230.8,18.33,154.82,1525.3,1498.1,17.82,11040.0,10822.0,0.0,1342.9,638.06,704.84,59184.0,56563.0,38699.0,0.0,0.0,33006.0,26535.0,0.8039,2162.0,1176.0,0.544,30843.0,25358.0,0.8222,10375.0,8931.0,0.0,11530.0,329.0,11201.0,,,,,,,,22319.0,22540.0,0.0,4318.0,18846.0,17474.0,0.0,10142.0,847.0,9295.0,7644.0,7618.0,0.0,544.0,45382.0,40964.0,31.58,5862.0,0.0,0.0,13418.0,7136.0,0.5318,5094.0,3174.0,0.623,8324.0,3962.0,0.476,1362.0,1246.0,5.0,677.0,155.0,46744.0,42210.0,5867.0,0.0,14094.0,7291.0,5094.0,3174.0,9000.0,4118.0,6012.03,6346.0,0.0,564.0 +2016-02-09 22:00:00+00:00,3523.83,3385.5,539.0,0.0,4888.0,642.92,1264.0,323.02,3625.0,319.9,2124.13,2020.6,15.99,112.44,1399.7,1364.9,16.5,10946.0,10775.0,0.0,1290.39,630.15,660.24,55249.0,51798.0,38699.0,0.0,0.0,33006.0,26272.0,0.796,2162.0,1237.0,0.5723,30843.0,25035.0,0.8117,9635.0,8024.0,0.0,11722.0,330.0,11392.0,,,,,,,,20985.0,20792.0,0.0,3832.0,17486.0,15947.0,0.0,10201.0,907.0,9294.0,7145.0,7035.0,0.0,517.0,41174.0,36375.0,31.0,5862.0,0.0,0.0,13418.0,7267.0,0.5416,5094.0,3247.0,0.6374,8324.0,4020.0,0.4829,1262.0,1099.0,5.0,677.0,137.0,42436.0,37474.0,5867.0,0.0,14094.0,7404.0,5094.0,3247.0,9000.0,4157.0,5370.95,5505.0,0.0,617.0 +2016-02-09 23:00:00+00:00,3290.24,3310.3,539.0,0.0,4888.0,590.77,1264.0,299.27,3625.0,291.5,1989.57,2017.7,16.39,87.3,1300.67,1292.6,16.39,10327.0,10098.0,0.0,1289.09,639.16,649.93,51681.0,48923.0,38702.0,0.0,0.0,33019.0,25460.0,0.771,2162.0,1470.0,0.6799,30857.0,23990.0,0.7775,9060.0,7498.0,0.0,11829.0,321.0,11508.0,,,,,,,,19507.0,19535.0,0.0,3333.0,16462.0,15436.0,0.0,9811.0,1149.0,8662.0,6653.0,6454.0,0.0,487.0,37910.0,32736.0,28.56,5862.0,0.0,0.0,13418.0,7208.0,0.5372,5094.0,3262.0,0.6402,8324.0,3946.0,0.4741,1118.0,930.0,5.0,677.0,124.0,39028.0,33665.0,5867.0,0.0,14094.0,7332.0,5094.0,3262.0,9000.0,4071.0,4870.58,5034.0,0.0,737.0 +2016-02-10 00:00:00+00:00,3218.98,3218.0,539.0,0.0,4888.0,707.08,1264.0,350.1,3625.0,356.98,1948.73,1972.4,16.03,111.46,1270.25,1245.6,16.03,9856.0,9547.0,0.0,1298.22,633.09,665.13,50146.0,47018.0,38702.0,0.0,0.0,33019.0,24426.0,0.7397,2162.0,1446.0,0.6689,30857.0,22980.0,0.7447,8870.0,7149.0,0.0,11856.0,305.0,11550.0,,,,,,,,19219.0,18953.0,0.0,3144.0,15760.0,14833.0,0.0,8965.0,1141.0,7824.0,6297.0,6084.0,0.0,461.0,35781.0,32808.0,28.61,5862.0,0.0,0.0,13418.0,6542.0,0.4876,5094.0,2818.0,0.5531,8324.0,3725.0,0.4475,939.0,805.0,5.0,677.0,85.0,36720.0,33612.0,5867.0,0.0,14094.0,6628.0,5094.0,2818.0,9000.0,3810.0,4832.78,5004.0,0.0,815.0 +2016-02-10 01:00:00+00:00,3181.1,3175.7,539.0,0.0,4888.0,820.57,1264.0,402.29,3625.0,418.28,1938.1,1943.8,16.03,117.97,1243.0,1231.9,16.03,9552.0,9184.0,0.0,1340.44,619.87,720.57,49319.0,46163.0,38702.0,0.0,0.0,33019.0,22916.0,0.694,2162.0,1873.0,0.8664,30857.0,21043.0,0.682,8856.0,7039.0,0.0,11147.0,300.0,10847.0,,,,,,,,19044.0,18786.0,0.0,2950.0,15238.0,14330.0,0.0,8398.0,1573.0,6824.0,6180.0,6008.0,0.0,422.0,34779.0,32278.0,28.54,5862.0,0.0,0.0,13418.0,6302.0,0.4697,5094.0,2758.0,0.5413,8324.0,3545.0,0.4259,808.0,757.0,5.0,677.0,87.0,35587.0,33034.0,5867.0,0.0,14094.0,6389.0,5094.0,2758.0,9000.0,3632.0,4683.13,4814.0,0.0,899.0 +2016-02-10 02:00:00+00:00,3202.41,3165.7,539.0,0.0,4888.0,1001.64,1264.0,521.6,3625.0,480.04,1935.48,1930.0,16.02,133.49,1266.93,1235.7,16.02,9246.0,9138.0,0.0,1433.61,630.43,803.18,49953.0,46609.0,38702.0,0.0,0.0,33019.0,21843.0,0.6615,2162.0,1983.0,0.9171,30857.0,19861.0,0.6437,9209.0,7088.0,0.0,10138.0,312.0,9827.0,,,,,,,,19295.0,19045.0,0.0,3222.0,15222.0,14361.0,0.0,8137.0,1671.0,6465.0,6227.0,6115.0,0.0,347.0,33232.0,31702.0,27.12,5862.0,0.0,0.0,13418.0,6163.0,0.4593,5094.0,2790.0,0.5477,8324.0,3373.0,0.4052,776.0,736.0,5.0,677.0,69.0,34008.0,32438.0,5867.0,0.0,14094.0,6232.0,5094.0,2790.0,9000.0,3442.0,4568.97,4703.0,0.0,1303.0 +2016-02-10 03:00:00+00:00,3295.53,3235.7,539.0,0.0,4888.0,1032.69,1264.0,506.07,3625.0,526.62,1981.79,1972.7,16.15,147.1,1313.74,1263.0,16.15,9297.0,9259.0,0.0,1396.29,625.61,770.68,51102.0,47831.0,38702.0,0.0,0.0,33019.0,19613.0,0.594,2162.0,1570.0,0.7261,30857.0,18043.0,0.5848,9600.0,7153.0,0.0,8653.0,271.0,8382.0,,,,,,,,19447.0,19463.0,0.0,3267.0,15583.0,14767.0,0.0,7397.0,1299.0,6098.0,6471.0,6447.0,0.0,297.0,31210.0,30390.0,21.62,5862.0,0.0,0.0,13418.0,5941.0,0.4428,5094.0,2638.0,0.518,8324.0,3302.0,0.3968,755.0,726.0,5.0,677.0,59.0,31964.0,31116.0,5867.0,0.0,14094.0,6000.0,5094.0,2638.0,9000.0,3361.0,4627.74,4782.0,245.0,1223.0 +2016-02-10 04:00:00+00:00,3530.93,3512.1,539.0,0.0,4888.0,972.03,1264.0,496.52,3625.0,475.51,2147.77,2139.8,16.2,127.63,1383.16,1372.3,16.81,9742.0,9604.0,0.0,1358.95,629.72,729.23,53914.0,50253.0,38702.0,0.0,0.0,33019.0,18177.0,0.5505,2162.0,1587.0,0.734,30857.0,16590.0,0.5376,10050.0,7151.0,0.0,7727.0,322.0,7405.0,,,,,,,,20346.0,20526.0,0.0,3461.0,16742.0,15686.0,0.0,6741.0,1265.0,5476.0,6776.0,6890.0,0.0,248.0,31080.0,29701.0,19.13,5862.0,0.0,0.0,13418.0,5852.0,0.4362,5094.0,2610.0,0.5125,8324.0,3242.0,0.3895,742.0,721.0,5.0,677.0,57.0,31822.0,30422.0,5867.0,0.0,14094.0,5910.0,5094.0,2610.0,9000.0,3299.0,5138.36,5242.0,6.0,1158.0 +2016-02-10 05:00:00+00:00,4210.49,4158.4,539.0,0.0,4888.0,911.38,1264.0,503.49,3625.0,407.89,2555.7,2538.9,18.85,105.77,1654.79,1619.5,18.25,10568.0,10659.0,0.0,1291.29,627.24,664.05,61038.0,55862.0,38702.0,0.0,0.0,33019.0,17941.0,0.5434,2162.0,1792.0,0.8287,30857.0,16149.0,0.5234,11045.0,6751.0,0.0,7046.0,328.0,6718.0,,,,,,,,23044.0,23304.0,0.0,3621.0,19286.0,17759.0,0.0,7009.0,1464.0,5545.0,7663.0,8049.0,0.0,266.0,33480.0,31667.0,26.09,5862.0,0.0,0.0,13418.0,5481.0,0.4085,5094.0,2436.0,0.4782,8324.0,3045.0,0.3658,734.0,739.0,5.0,677.0,41.0,34214.0,32406.0,5867.0,0.0,14094.0,5522.0,5094.0,2436.0,9000.0,3086.0,5910.6,6034.0,156.0,1173.0 +2016-02-10 06:00:00+00:00,4926.25,4931.2,539.0,0.06,4888.0,872.22,1264.0,497.69,3625.0,374.53,3016.75,3050.9,19.64,141.62,1909.5,1880.3,19.64,11314.0,11600.0,0.0,1258.69,635.16,623.53,67821.0,63635.0,38702.0,33.0,0.0008,33019.0,17458.0,0.5287,2162.0,1643.0,0.7598,30857.0,15815.0,0.5125,12235.0,9618.0,20.0,6759.0,328.0,6431.0,,,,,,,,25376.0,25293.0,4.0,3611.0,21607.0,19673.0,3.0,6767.0,1315.0,5452.0,8603.0,9050.0,5.0,321.0,40857.0,37948.0,32.06,5862.0,0.0,0.0,13418.0,5402.0,0.4026,5094.0,2462.0,0.4834,8324.0,2940.0,0.3531,758.0,830.0,5.0,677.0,54.0,41616.0,38777.0,5867.0,0.0,14094.0,5456.0,5094.0,2462.0,9000.0,2993.0,6333.03,6448.0,59.0,1129.0 +2016-02-10 07:00:00+00:00,5078.6,5101.0,539.0,8.86,4888.0,944.5,1264.0,566.99,3625.0,377.51,3097.01,3115.1,19.99,129.64,1981.59,1985.9,19.99,11805.0,11994.0,6.54,1158.77,601.14,557.63,70153.0,68021.0,38702.0,759.0,0.0196,33019.0,16714.0,0.5062,2162.0,1607.0,0.7432,30857.0,15107.0,0.4896,12752.0,12273.0,369.0,6207.0,327.0,5880.0,,,,,,,,26075.0,26035.0,101.0,3640.0,22373.0,20330.0,161.0,6556.0,1280.0,5276.0,8954.0,9384.0,128.0,311.0,47268.0,43400.0,32.02,5862.0,13.0,0.0022,13418.0,5296.0,0.3948,5094.0,2539.0,0.4984,8324.0,2758.0,0.3312,868.0,1083.0,5.0,677.0,55.0,48135.0,44483.0,5867.0,13.0,14094.0,5352.0,5094.0,2539.0,9000.0,2813.0,6726.32,6755.0,0.0,1301.0 +2016-02-10 08:00:00+00:00,5097.14,5092.7,539.0,38.65,4888.0,871.24,1264.0,502.85,3625.0,368.39,3079.99,3070.3,20.1,149.31,2017.15,2022.4,20.1,12163.0,12150.0,84.11,1125.3,594.16,531.14,69963.0,68945.0,38702.0,2196.0,0.0567,33019.0,16135.0,0.4886,2162.0,1488.0,0.6885,30857.0,14646.0,0.4746,12463.0,12736.0,892.0,5906.0,326.0,5580.0,,,,,,,,25893.0,26139.0,322.0,3561.0,22566.0,20537.0,631.0,6290.0,1162.0,5127.0,9039.0,9533.0,351.0,378.0,48724.0,44700.0,34.14,5862.0,146.0,0.0248,13418.0,5016.0,0.3738,5094.0,2464.0,0.4838,8324.0,2552.0,0.3066,1114.0,1299.0,5.0,677.0,104.0,49838.0,45999.0,5867.0,146.0,14094.0,5120.0,5094.0,2464.0,9000.0,2656.0,6872.76,6808.0,0.0,1231.0 +2016-02-10 09:00:00+00:00,5107.43,5108.3,539.0,67.79,4888.0,848.65,1264.0,459.4,3625.0,389.25,3084.4,3077.7,21.75,163.74,2023.03,2030.6,21.75,12410.0,12171.0,195.48,1144.31,552.49,591.82,70870.0,70150.0,38702.0,4215.0,0.1089,33019.0,15844.0,0.4798,2162.0,1397.0,0.646,30857.0,14448.0,0.4682,12267.0,12828.0,1662.0,5861.0,325.0,5535.0,,,,,,,,26372.0,26496.0,697.0,3377.0,22922.0,21035.0,1171.0,6148.0,1071.0,5077.0,9309.0,9792.0,686.0,458.0,49234.0,45530.0,39.34,5862.0,589.0,0.1005,13418.0,4808.0,0.3583,5094.0,2336.0,0.4586,8324.0,2472.0,0.297,1273.0,1334.0,5.0,677.0,31.0,50508.0,46863.0,5867.0,589.0,14094.0,4838.0,5094.0,2336.0,9000.0,2502.0,6969.2,6763.0,561.0,1229.0 +2016-02-10 10:00:00+00:00,5070.42,5064.9,539.0,106.13,4888.0,801.52,1264.0,408.71,3625.0,392.81,3070.17,3063.3,21.21,184.86,2000.25,2001.6,21.21,12594.0,12206.0,331.83,1143.21,561.96,581.25,72136.0,71036.0,38702.0,5438.0,0.1405,33019.0,16207.0,0.4908,2162.0,1263.0,0.5842,30857.0,14944.0,0.4843,12438.0,12730.0,2207.0,6397.0,327.0,6071.0,,,,,,,,26973.0,26882.0,942.0,3351.0,23228.0,21612.0,1479.0,5992.0,936.0,5055.0,9498.0,9812.0,810.0,467.0,48916.0,45672.0,34.54,5862.0,1222.0,0.2085,13418.0,4485.0,0.3342,5094.0,2110.0,0.4141,8324.0,2376.0,0.2854,1359.0,1330.0,5.0,677.0,28.0,50274.0,47002.0,5867.0,1222.0,14094.0,4513.0,5094.0,2110.0,9000.0,2403.0,7046.15,6716.0,603.0,1204.0 +2016-02-10 11:00:00+00:00,4958.87,4946.6,539.0,114.11,4888.0,658.72,1264.0,272.34,3625.0,386.38,2981.5,2967.3,19.85,194.65,1977.37,1979.3,19.85,12291.0,11907.0,395.36,1070.6,508.39,562.21,71850.0,70341.0,38702.0,5098.0,0.1317,33019.0,15931.0,0.4825,2162.0,1062.0,0.4912,30857.0,14869.0,0.4819,12688.0,12744.0,2190.0,6496.0,326.0,6170.0,,,,,,,,26685.0,26838.0,962.0,3321.0,22960.0,21118.0,1487.0,5602.0,736.0,4866.0,9517.0,9641.0,713.0,512.0,49114.0,45868.0,31.0,5862.0,1848.0,0.3154,13418.0,4460.0,0.3324,5094.0,1940.0,0.381,8324.0,2520.0,0.3026,1300.0,1307.0,5.0,677.0,40.0,50414.0,47176.0,5867.0,1848.0,14094.0,4500.0,5094.0,1940.0,9000.0,2559.0,7022.8,6656.0,583.0,1208.0 +2016-02-10 12:00:00+00:00,4961.13,4953.2,539.0,104.89,4888.0,581.98,1264.0,222.38,3625.0,359.6,2996.95,2990.5,19.7,180.89,1964.18,1962.7,19.7,12280.0,11934.0,482.27,870.64,350.86,519.78,71127.0,69553.0,38702.0,4529.0,0.1171,33019.0,15596.0,0.4723,2162.0,954.0,0.4412,30857.0,14642.0,0.4745,12552.0,12570.0,1665.0,6298.0,297.0,6001.0,,,,,,,,26486.0,26573.0,926.0,3318.0,22709.0,20770.0,1368.0,5450.0,657.0,4793.0,9381.0,9639.0,570.0,530.0,49280.0,45698.0,27.8,5862.0,2428.0,0.4142,13418.0,4393.0,0.3274,5094.0,1765.0,0.3465,8324.0,2628.0,0.3157,1281.0,1298.0,5.0,677.0,58.0,50561.0,46996.0,5867.0,2428.0,14094.0,4451.0,5094.0,1765.0,9000.0,2686.0,6867.63,6486.0,502.0,1237.0 +2016-02-10 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06:00:00+00:00,3082.59,3059.1,543.0,250.33,4916.0,162.03,1264.0,115.22,3652.0,46.81,1883.21,1880.1,20.15,34.53,1199.38,1179.0,24.86,8049.0,8022.0,315.18,317.58,268.14,49.44,42630.0,41841.0,38996.0,7777.0,0.1994,34271.0,604.0,0.0176,2275.0,383.0,0.1685,31996.0,220.0,0.0069,8490.0,8727.0,3078.0,92.0,72.0,20.0,,,,,,,,16221.0,15305.0,1581.0,29.0,13064.0,13192.0,2180.0,470.0,311.0,159.0,4855.0,4616.0,937.0,13.0,26489.0,24604.0,14.45,7157.0,1265.0,0.1768,13727.0,2044.0,0.149,5094.0,1490.0,0.2926,8633.0,554.0,0.0642,590.0,588.0,10.0,677.0,26.0,27080.0,25193.0,7167.0,1265.0,14404.0,2070.0,5094.0,1490.0,9310.0,580.0,5049.0,5028.0,1016.0,30.0 +2016-06-05 07:00:00+00:00,3281.95,3278.0,543.0,371.23,4916.0,142.2,1264.0,83.11,3652.0,59.09,1975.69,1992.0,21.52,50.64,1306.26,1286.0,26.8,8578.0,8550.0,578.01,334.4,288.07,46.33,45796.0,44807.0,38996.0,11324.0,0.2904,34271.0,605.0,0.0176,2275.0,430.0,0.189,31996.0,175.0,0.0054,8934.0,9247.0,4316.0,83.0,61.0,22.0,,,,,,,,17552.0,16492.0,2464.0,24.0,13971.0,14174.0,3257.0,492.0,369.0,123.0,5339.0,4894.0,1286.0,6.0,30986.0,27422.0,16.82,7157.0,2210.0,0.3088,13727.0,1976.0,0.144,5094.0,1451.0,0.2848,8633.0,525.0,0.0608,674.0,626.0,10.0,677.0,24.0,31660.0,28048.0,7167.0,2210.0,14404.0,2000.0,5094.0,1451.0,9310.0,549.0,5413.0,5388.0,1376.0,24.0 +2016-06-05 08:00:00+00:00,3385.69,3381.6,543.0,471.11,4916.0,136.28,1264.0,67.54,3652.0,68.74,2009.64,2030.8,21.77,59.21,1376.05,1350.8,28.04,8967.0,8901.0,944.55,358.39,313.33,45.06,48078.0,47626.0,38996.0,14671.0,0.3762,34271.0,717.0,0.0209,2275.0,455.0,0.1998,31996.0,263.0,0.0082,9289.0,9603.0,5226.0,90.0,52.0,39.0,,,,,,,,18425.0,17446.0,3428.0,38.0,14658.0,15404.0,4455.0,586.0,403.0,184.0,5707.0,5173.0,1562.0,2.0,34720.0,30164.0,27.78,7157.0,3565.0,0.4981,13727.0,1935.0,0.141,5094.0,1371.0,0.2691,8633.0,565.0,0.0654,777.0,712.0,10.0,677.0,21.0,35497.0,30875.0,7167.0,3565.0,14404.0,1956.0,5094.0,1371.0,9310.0,585.0,5767.0,5670.0,1607.0,26.0 +2016-06-05 09:00:00+00:00,3392.57,3413.7,543.0,540.97,4916.0,153.65,1264.0,70.97,3652.0,82.68,2014.36,2037.3,21.21,72.77,1378.21,1376.4,28.18,9202.0,9283.0,1417.15,379.33,339.81,39.52,50129.0,49674.0,38996.0,17272.0,0.4429,34271.0,894.0,0.0261,2275.0,502.0,0.2206,31996.0,392.0,0.0123,9955.0,9957.0,5712.0,130.0,77.0,53.0,,,,,,,,18978.0,18183.0,4310.0,93.0,15249.0,15773.0,5593.0,669.0,425.0,245.0,5947.0,5762.0,1656.0,2.0,35835.0,31982.0,31.53,7157.0,5105.0,0.7133,13727.0,1907.0,0.1389,5094.0,1297.0,0.2546,8633.0,610.0,0.0706,852.0,788.0,10.0,677.0,33.0,36687.0,32770.0,7167.0,5105.0,14404.0,1940.0,5094.0,1297.0,9310.0,643.0,5896.0,5844.0,1697.0,41.0 +2016-06-05 10:00:00+00:00,3362.54,3364.4,543.0,570.23,4916.0,194.33,1264.0,90.2,3652.0,104.13,2000.95,1996.1,21.53,88.02,1361.59,1368.3,28.16,9301.0,9331.0,1820.91,379.02,325.47,53.55,49863.0,49378.0,38996.0,19155.0,0.4912,34271.0,1144.0,0.0334,2275.0,575.0,0.2529,31996.0,569.0,0.0178,9673.0,9591.0,5857.0,184.0,96.0,88.0,,,,,,,,19033.0,18225.0,4958.0,150.0,15139.0,15714.0,6389.0,808.0,480.0,328.0,6018.0,5848.0,1951.0,2.0,35748.0,32721.0,37.78,7157.0,6475.0,0.9047,13727.0,1773.0,0.1291,5094.0,1133.0,0.2224,8633.0,639.0,0.074,884.0,851.0,10.0,677.0,42.0,36631.0,33572.0,7167.0,6475.0,14404.0,1815.0,5094.0,1133.0,9310.0,681.0,5798.0,5790.0,1664.0,70.0 +2016-06-05 11:00:00+00:00,3306.54,3332.8,543.0,587.69,4916.0,228.57,1264.0,104.26,3652.0,124.31,1967.76,1978.5,21.5,106.89,1338.78,1354.3,27.6,9002.0,8947.0,2025.15,360.69,299.06,61.63,48166.0,47664.0,38996.0,19753.0,0.5065,34271.0,1478.0,0.0431,2275.0,667.0,0.2932,31996.0,811.0,0.0254,9162.0,8977.0,5566.0,253.0,124.0,129.0,,,,,,,,18720.0,17696.0,5311.0,209.0,14560.0,15414.0,6865.0,1008.0,543.0,464.0,5724.0,5577.0,2012.0,9.0,35484.0,33010.0,33.9,7157.0,6713.0,0.938,13727.0,1616.0,0.1177,5094.0,959.0,0.1881,8633.0,657.0,0.0761,904.0,880.0,10.0,677.0,43.0,36388.0,33890.0,7167.0,6713.0,14404.0,1659.0,5094.0,959.0,9310.0,700.0,5476.0,5414.0,1551.0,97.0 +2016-06-05 12:00:00+00:00,3251.96,3294.1,543.0,562.07,4916.0,247.64,1264.0,103.58,3652.0,144.06,1937.64,1956.5,21.54,123.03,1314.32,1337.6,26.65,8659.0,8655.0,1933.05,359.25,294.17,65.08,46677.0,46273.0,38996.0,19014.0,0.4876,34271.0,1814.0,0.0529,2275.0,570.0,0.2506,31996.0,1244.0,0.0389,8738.0,8671.0,5161.0,394.0,121.0,273.0,,,,,,,,18367.0,17199.0,5061.0,228.0,14216.0,15019.0,6665.0,1187.0,449.0,738.0,5356.0,5384.0,2126.0,4.0,34594.0,32482.0,30.07,7157.0,7320.0,1.0228,13727.0,1518.0,0.1106,5094.0,833.0,0.1636,8633.0,685.0,0.0794,890.0,894.0,10.0,677.0,46.0,35484.0,33376.0,7167.0,7320.0,14404.0,1564.0,5094.0,833.0,9310.0,731.0,5181.0,5035.0,1346.0,140.0 +2016-06-05 13:00:00+00:00,3240.89,3246.0,543.0,512.67,4916.0,277.59,1264.0,127.12,3652.0,150.47,1951.55,1940.9,21.53,127.87,1289.34,1305.1,26.08,8477.0,8518.0,1606.0,368.48,308.01,60.47,45844.0,45701.0,38996.0,17406.0,0.4464,34271.0,2452.0,0.0716,2275.0,538.0,0.2367,31996.0,1914.0,0.0598,8649.0,8785.0,4530.0,772.0,143.0,629.0,,,,,,,,18220.0,16931.0,4455.0,246.0,13940.0,14664.0,6324.0,1426.0,395.0,1030.0,5035.0,5322.0,2097.0,8.0,33780.0,31724.0,25.45,7157.0,6930.0,0.9682,13727.0,1508.0,0.1099,5094.0,811.0,0.1592,8633.0,697.0,0.0807,838.0,908.0,10.0,677.0,65.0,34618.0,32631.0,7167.0,6930.0,14404.0,1573.0,5094.0,811.0,9310.0,762.0,5145.0,4942.0,1067.0,204.0 +2016-06-05 14:00:00+00:00,3293.2,3309.5,543.0,433.39,4916.0,310.66,1264.0,149.78,3652.0,160.88,1994.78,1993.5,21.32,123.47,1298.42,1316.0,25.99,8409.0,8518.0,1173.71,420.18,360.24,59.94,45845.0,45786.0,38996.0,15126.0,0.3879,34271.0,3077.0,0.0898,2275.0,570.0,0.2507,31996.0,2507.0,0.0784,8727.0,8827.0,3882.0,1013.0,154.0,860.0,,,,,,,,17924.0,16805.0,3625.0,353.0,13985.0,14588.0,5664.0,1700.0,417.0,1284.0,5209.0,5567.0,1955.0,10.0,33624.0,31630.0,26.83,7157.0,6100.0,0.8523,13727.0,1471.0,0.1072,5094.0,780.0,0.1532,8633.0,691.0,0.08,804.0,890.0,10.0,677.0,71.0,34428.0,32520.0,7167.0,6100.0,14404.0,1542.0,5094.0,780.0,9310.0,761.0,5210.0,4967.0,705.0,259.0 +2016-06-05 15:00:00+00:00,3521.04,3517.2,543.0,339.36,4916.0,366.39,1264.0,169.24,3652.0,197.15,2152.47,2151.4,21.81,150.2,1368.57,1365.8,26.56,8549.0,8674.0,837.2,415.04,357.18,57.86,46991.0,46241.0,38996.0,11061.0,0.2836,34271.0,3559.0,0.1038,2275.0,532.0,0.2341,31996.0,3027.0,0.0946,9143.0,8816.0,2863.0,1220.0,149.0,1071.0,,,,,,,,18084.0,17002.0,2639.0,402.0,14360.0,14864.0,4039.0,1915.0,384.0,1532.0,5404.0,5559.0,1520.0,22.0,34638.0,32079.0,30.35,7157.0,4925.0,0.6881,13727.0,1400.0,0.102,5094.0,737.0,0.1448,8633.0,663.0,0.0768,809.0,835.0,10.0,677.0,75.0,35447.0,32914.0,7167.0,4925.0,14404.0,1475.0,5094.0,737.0,9310.0,738.0,5312.0,5071.0,335.0,283.0 +2016-06-05 16:00:00+00:00,3527.91,3521.2,543.0,217.61,4916.0,386.76,1264.0,169.0,3652.0,217.76,2159.06,2138.7,23.91,168.51,1368.85,1382.5,26.26,8737.0,8949.0,536.74,384.22,314.63,69.59,47706.0,47857.0,38996.0,6590.0,0.169,34271.0,3525.0,0.1028,2275.0,535.0,0.2353,31996.0,2990.0,0.0934,9321.0,9247.0,1565.0,1311.0,148.0,1163.0,,,,,,,,18186.0,17394.0,1675.0,336.0,14850.0,15469.0,2443.0,1774.0,387.0,1386.0,5349.0,5747.0,907.0,105.0,36142.0,32473.0,38.0,7157.0,3510.0,0.4904,13727.0,1371.0,0.0998,5094.0,752.0,0.1475,8633.0,619.0,0.0717,856.0,828.0,10.0,677.0,70.0,36998.0,33300.0,7167.0,3510.0,14404.0,1441.0,5094.0,752.0,9310.0,689.0,5300.0,5255.0,116.0,267.0 +2016-06-05 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03:00:00+00:00,2477.64,2483.8,546.0,0.0,5004.0,1195.8,1264.0,653.03,3740.0,542.77,1488.51,1491.0,22.91,378.44,989.13,992.8,22.91,7731.0,7359.0,0.0,687.93,529.39,158.54,40416.0,40676.0,39309.0,0.0,0.0,35433.0,3806.0,0.1074,2432.0,379.0,0.156,33001.0,3427.0,0.1038,7766.0,6576.0,0.0,2427.0,286.0,2141.0,,,,,,,,14909.0,15950.0,0.0,242.0,12912.0,13294.0,0.0,972.0,93.0,879.0,4828.0,4856.0,0.0,166.0,20230.0,21964.0,20.1,7339.0,0.0,0.0,14052.0,3149.0,0.2241,5094.0,1833.0,0.3599,8958.0,1315.0,0.1468,568.0,564.0,15.0,702.0,16.0,20797.0,22528.0,7354.0,0.0,14754.0,3165.0,5094.0,1833.0,9660.0,1332.0,4570.0,4473.0,0.0,89.0 +2016-09-17 04:00:00+00:00,2628.21,2633.4,546.0,0.1,5004.0,1146.07,1264.0,641.66,3740.0,504.41,1598.86,1596.1,24.91,347.61,1029.35,1037.3,24.91,8107.0,7620.0,0.0,578.74,428.16,150.58,43196.0,42840.0,39309.0,6.0,0.0002,35433.0,4209.0,0.1188,2432.0,351.0,0.1441,33001.0,3859.0,0.1169,8430.0,6736.0,4.0,2684.0,252.0,2433.0,,,,,,,,15766.0,17018.0,0.0,221.0,13840.0,13885.0,0.0,1115.0,99.0,1017.0,5159.0,5201.0,2.0,189.0,21220.0,22851.0,21.86,7339.0,0.0,0.0,14052.0,3039.0,0.2162,5094.0,1821.0,0.3574,8958.0,1218.0,0.136,576.0,546.0,15.0,702.0,19.0,21796.0,23397.0,7354.0,0.0,14754.0,3058.0,5094.0,1821.0,9660.0,1237.0,4628.0,4636.0,63.0,84.0 +2016-09-17 05:00:00+00:00,2858.81,2871.8,546.0,13.62,5004.0,1110.01,1264.0,633.6,3740.0,476.41,1734.84,1742.5,26.01,330.83,1123.97,1129.3,26.01,8492.0,7933.0,12.99,407.2,272.73,134.47,45565.0,45764.0,39309.0,260.0,0.0066,35433.0,4244.0,0.1198,2432.0,354.0,0.1457,33001.0,3890.0,0.1179,9003.0,7584.0,112.0,2488.0,243.0,2245.0,,,,,,,,16436.0,18053.0,63.0,231.0,14655.0,14612.0,49.0,1320.0,112.0,1208.0,5471.0,5515.0,35.0,206.0,24112.0,24939.0,30.57,7339.0,4.0,0.0006,14052.0,2927.0,0.2084,5094.0,1764.0,0.3462,8958.0,1164.0,0.1299,626.0,546.0,15.0,702.0,30.0,24738.0,25485.0,7354.0,4.0,14754.0,2958.0,5094.0,1764.0,9660.0,1194.0,5177.0,5106.0,393.0,91.0 +2016-09-17 06:00:00+00:00,3177.82,3178.2,546.0,61.5,5004.0,1062.45,1264.0,609.34,3740.0,453.11,1912.25,1913.8,27.44,310.37,1265.57,1264.4,27.44,8983.0,8524.0,152.15,204.23,91.81,112.42,48921.0,49509.0,39309.0,1296.0,0.033,35433.0,4564.0,0.1288,2432.0,383.0,0.1576,33001.0,4181.0,0.1267,9699.0,8559.0,331.0,2566.0,240.0,2326.0,,,,,,,,17549.0,19456.0,426.0,176.0,15839.0,15534.0,306.0,1640.0,144.0,1496.0,5834.0,5960.0,233.0,183.0,27945.0,28328.0,27.36,7339.0,335.0,0.0457,14052.0,2813.0,0.2002,5094.0,1722.0,0.338,8958.0,1091.0,0.1218,680.0,576.0,15.0,702.0,24.0,28624.0,28904.0,7354.0,335.0,14754.0,2837.0,5094.0,1722.0,9660.0,1115.0,5666.0,5510.0,817.0,59.0 +2016-09-17 07:00:00+00:00,3383.36,3387.0,546.0,129.64,5004.0,1084.0,1264.0,601.42,3740.0,482.58,2018.53,2012.8,28.98,328.44,1364.83,1374.2,28.98,9499.0,9075.0,340.38,105.16,4.81,100.35,51638.0,52179.0,39309.0,2992.0,0.0761,35433.0,4856.0,0.1371,2432.0,451.0,0.1856,33001.0,4405.0,0.1335,10212.0,9060.0,634.0,2573.0,224.0,2350.0,,,,,,,,18572.0,20547.0,1110.0,145.0,16729.0,16226.0,667.0,1986.0,228.0,1758.0,6125.0,6346.0,582.0,152.0,32602.0,31650.0,32.17,7339.0,1097.0,0.1495,14052.0,2642.0,0.188,5094.0,1651.0,0.3242,8958.0,991.0,0.1106,811.0,628.0,15.0,702.0,15.0,33413.0,32278.0,7354.0,1097.0,14754.0,2656.0,5094.0,1651.0,9660.0,1005.0,6030.0,5815.0,1212.0,50.0 +2016-09-17 08:00:00+00:00,3483.32,3490.1,546.0,197.45,5004.0,1110.23,1264.0,597.77,3740.0,512.46,2058.72,2059.2,28.94,367.97,1424.6,1430.9,29.59,9765.0,9375.0,511.34,89.705,6.365,83.34,53196.0,53728.0,39309.0,4805.0,0.1222,35433.0,5325.0,0.1503,2432.0,572.0,0.2352,33001.0,4753.0,0.144,10609.0,9413.0,1061.0,2680.0,210.0,2470.0,,,,,,,,19043.0,21173.0,1614.0,134.0,17321.0,16569.0,1148.0,2387.0,362.0,2025.0,6224.0,6573.0,982.0,124.0,36104.0,33658.0,36.0,7339.0,1905.0,0.2596,14052.0,2567.0,0.1826,5094.0,1618.0,0.3176,8958.0,949.0,0.1059,922.0,726.0,15.0,702.0,14.0,37026.0,34384.0,7354.0,1905.0,14754.0,2581.0,5094.0,1618.0,9660.0,963.0,6194.0,6020.0,1496.0,79.0 +2016-09-17 09:00:00+00:00,3438.71,3460.5,546.0,240.83,5004.0,1085.77,1264.0,544.01,3740.0,541.76,2031.95,2037.9,28.93,407.51,1406.76,1422.6,29.64,9902.0,9512.0,702.75,64.25,7.92,56.33,53751.0,54750.0,39309.0,6425.0,0.1635,35433.0,6014.0,0.1697,2432.0,772.0,0.3175,33001.0,5242.0,0.1588,10876.0,9790.0,1415.0,2947.0,245.0,2703.0,,,,,,,,19033.0,21427.0,2121.0,118.0,17658.0,16833.0,1530.0,2827.0,528.0,2300.0,6184.0,6701.0,1359.0,121.0,36848.0,34021.0,32.42,7339.0,2550.0,0.3475,14052.0,2511.0,0.1786,5094.0,1571.0,0.3083,8958.0,940.0,0.105,953.0,863.0,15.0,702.0,33.0,37802.0,34884.0,7354.0,2550.0,14754.0,2544.0,5094.0,1571.0,9660.0,973.0,6449.0,6190.0,1655.0,99.0 +2016-09-17 10:00:00+00:00,3384.88,3379.7,546.0,273.86,5004.0,1108.68,1264.0,502.34,3740.0,606.34,1987.11,1984.9,28.7,467.24,1397.77,1394.8,28.7,9829.0,9432.0,838.06,48.26,5.06,43.2,53113.0,53862.0,39309.0,7834.0,0.1993,35433.0,6611.0,0.1866,2432.0,833.0,0.3426,33001.0,5778.0,0.1751,10627.0,9396.0,1871.0,3202.0,262.0,2940.0,,,,,,,,18826.0,21245.0,2516.0,118.0,17390.0,16629.0,1752.0,3142.0,571.0,2571.0,6272.0,6591.0,1696.0,149.0,36776.0,33601.0,29.96,7339.0,3055.0,0.4162,14052.0,2463.0,0.1753,5094.0,1550.0,0.3043,8958.0,913.0,0.1019,950.0,932.0,15.0,702.0,46.0,37726.0,34533.0,7354.0,3055.0,14754.0,2509.0,5094.0,1550.0,9660.0,959.0,6475.0,6234.0,1688.0,143.0 +2016-09-17 11:00:00+00:00,3312.05,3314.4,546.0,271.59,5004.0,1100.84,1264.0,458.26,3740.0,642.58,1933.14,1938.8,25.1,511.26,1378.91,1375.6,26.93,9604.0,9158.0,1041.34,46.68,11.05,35.63,51584.0,51919.0,39309.0,7743.0,0.197,35433.0,7533.0,0.2126,2432.0,1003.0,0.4122,33001.0,6530.0,0.1979,10163.0,8781.0,1839.0,3552.0,292.0,3260.0,,,,,,,,18343.0,20672.0,2406.0,146.0,16907.0,16172.0,1858.0,3667.0,710.0,2956.0,6171.0,6294.0,1640.0,168.0,36412.0,33126.0,28.43,7339.0,3595.0,0.4898,14052.0,2366.0,0.1684,5094.0,1487.0,0.2918,8958.0,880.0,0.0982,935.0,949.0,15.0,702.0,63.0,37348.0,34075.0,7354.0,3595.0,14754.0,2429.0,5094.0,1487.0,9660.0,943.0,6196.0,6033.0,1607.0,180.0 +2016-09-17 12:00:00+00:00,3241.57,3259.7,546.0,268.34,5004.0,1031.65,1264.0,417.19,3740.0,614.46,1857.94,1900.1,24.79,465.68,1383.63,1359.6,25.85,9339.0,8992.0,1134.85,51.59,18.09,33.5,50443.0,50361.0,39309.0,6870.0,0.1748,35433.0,8257.0,0.233,2432.0,1053.0,0.4329,33001.0,7204.0,0.2183,9914.0,8465.0,1773.0,3821.0,296.0,3524.0,,,,,,,,18090.0,19988.0,1916.0,233.0,16310.0,15795.0,1776.0,4013.0,756.0,3257.0,6129.0,6114.0,1405.0,190.0,35268.0,32342.0,27.29,7339.0,3560.0,0.4851,14052.0,2314.0,0.1647,5094.0,1446.0,0.2839,8958.0,868.0,0.0969,898.0,948.0,15.0,702.0,89.0,36166.0,33291.0,7354.0,3560.0,14754.0,2403.0,5094.0,1446.0,9660.0,957.0,5856.0,5722.0,1415.0,234.0 +2016-09-17 13:00:00+00:00,3201.27,3214.4,546.0,255.99,5004.0,928.35,1264.0,353.59,3740.0,574.76,1842.95,1862.0,24.14,417.83,1358.32,1352.4,25.83,9173.0,8867.0,1007.4,48.78,5.35,43.43,49915.0,49676.0,39309.0,5305.0,0.135,35433.0,8612.0,0.243,2432.0,1005.0,0.4132,33001.0,7607.0,0.2305,9727.0,8566.0,1425.0,3952.0,311.0,3641.0,,,,,,,,17978.0,19625.0,1358.0,320.0,16065.0,15550.0,1483.0,4122.0,694.0,3428.0,6146.0,5934.0,1039.0,217.0,34800.0,31707.0,27.28,7339.0,3100.0,0.4224,14052.0,2268.0,0.1614,5094.0,1394.0,0.2737,8958.0,873.0,0.0975,858.0,932.0,15.0,702.0,91.0,35658.0,32639.0,7354.0,3100.0,14754.0,2358.0,5094.0,1394.0,9660.0,964.0,5737.0,5604.0,1109.0,265.0 +2016-09-17 14:00:00+00:00,3239.42,3260.2,546.0,216.88,5004.0,1017.31,1264.0,494.44,3740.0,522.87,1877.66,1887.0,24.93,356.37,1361.76,1373.2,25.94,9129.0,8867.0,864.11,,,49.74,49456.0,49528.0,39309.0,3546.0,0.0902,35433.0,8918.0,0.2517,2432.0,1014.0,0.417,33001.0,7904.0,0.2395,9511.0,8682.0,959.0,4238.0,312.0,3926.0,,,,,,,,17981.0,19333.0,893.0,455.0,15975.0,15654.0,1077.0,4007.0,702.0,3305.0,5989.0,5860.0,617.0,218.0,34976.0,31536.0,27.63,7339.0,2305.0,0.314,14052.0,2217.0,0.1578,5094.0,1296.0,0.2544,8958.0,921.0,0.1028,844.0,893.0,15.0,702.0,113.0,35820.0,32429.0,7354.0,2305.0,14754.0,2330.0,5094.0,1296.0,9660.0,1034.0,5766.0,5631.0,695.0,270.0 +2016-09-17 15:00:00+00:00,3446.85,3459.7,546.0,130.31,5004.0,1023.07,1264.0,521.41,3740.0,501.66,2020.4,2031.4,27.85,317.65,1426.45,1428.3,27.85,9210.0,8905.0,626.51,,,55.68,50009.0,49776.0,39309.0,1838.0,0.0468,35433.0,9578.0,0.2704,2432.0,1040.0,0.4276,33001.0,8539.0,0.2587,9706.0,8678.0,489.0,4804.0,300.0,4504.0,,,,,,,,17990.0,19332.0,465.0,556.0,16326.0,15917.0,619.0,4005.0,740.0,3265.0,5988.0,5849.0,266.0,214.0,35816.0,32667.0,29.05,7339.0,1670.0,0.2276,14052.0,2099.0,0.1494,5094.0,1187.0,0.233,8958.0,912.0,0.1018,868.0,868.0,15.0,702.0,107.0,36684.0,33535.0,7354.0,1670.0,14754.0,2206.0,5094.0,1187.0,9660.0,1019.0,5792.0,5729.0,291.0,292.0 +2016-09-17 16:00:00+00:00,3480.37,3489.7,546.0,48.37,5004.0,947.31,1264.0,520.51,3740.0,426.8,2018.15,2028.2,27.03,250.06,1462.22,1461.5,28.08,9217.0,8914.0,293.01,,,55.7,50540.0,50647.0,39309.0,510.0,0.013,35433.0,10056.0,0.2838,2432.0,1080.0,0.4442,33001.0,8976.0,0.272,9812.0,9089.0,126.0,5250.0,311.0,4939.0,,,,,,,,18148.0,19391.0,168.0,688.0,16612.0,16287.0,199.0,3885.0,769.0,3116.0,5969.0,5880.0,16.0,232.0,37085.0,33866.0,39.59,7339.0,1083.0,0.1476,14052.0,1710.0,0.1216,5094.0,835.0,0.1639,8958.0,875.0,0.0976,945.0,859.0,15.0,702.0,98.0,38030.0,34725.0,7354.0,1083.0,14754.0,1808.0,5094.0,835.0,9660.0,973.0,5912.0,6029.0,34.0,377.0 +2016-09-17 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06:00:00+00:00,3228.9,3193.4,546.0,1.31,5005.0,1263.1,1264.0,723.87,3741.0,539.23,1881.77,1865.9,31.44,451.28,1347.13,1327.5,36.63,8404.0,8405.0,53.99,694.74,299.16,395.58,43591.0,45049.0,39408.0,1414.0,0.0359,35875.0,2880.0,0.0803,2474.0,880.0,0.3557,33401.0,2000.0,0.0599,8383.0,8533.0,190.0,563.0,179.0,384.0,,,,,,,,16478.0,16620.0,459.0,680.0,13507.0,14504.0,524.0,1628.0,701.0,927.0,5223.0,5391.0,241.0,9.0,28001.0,28058.0,33.95,7354.0,20.0,0.0026,14137.0,4967.0,0.3514,5094.0,2045.0,0.4014,9043.0,2922.0,0.3232,644.0,599.0,15.0,702.0,262.0,28646.0,28657.0,7369.0,20.0,14840.0,5229.0,5094.0,2045.0,9746.0,3184.0,4951.0,4689.0,587.0,129.0 +2016-10-16 07:00:00+00:00,3468.05,3456.3,546.0,9.73,5005.0,1241.11,1264.0,721.72,3741.0,519.39,2006.13,1997.0,32.38,434.52,1461.92,1459.3,38.48,8850.0,8864.0,414.18,714.6,334.23,380.37,46616.0,48158.0,39408.0,4929.0,0.125,35875.0,3102.0,0.0865,2474.0,1098.0,0.4436,33401.0,2005.0,0.06,8915.0,9104.0,826.0,505.0,168.0,336.0,,,,,,,,17640.0,17674.0,1452.0,689.0,14558.0,15475.0,1903.0,1900.0,929.0,970.0,5502.0,5906.0,747.0,9.0,30810.0,31265.0,35.51,7354.0,214.0,0.0291,14137.0,5626.0,0.398,5094.0,2451.0,0.481,9043.0,3176.0,0.3512,710.0,661.0,15.0,702.0,268.0,31520.0,31926.0,7369.0,214.0,14840.0,5895.0,5094.0,2451.0,9746.0,3444.0,5432.0,5055.0,944.0,152.0 +2016-10-16 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09:00:00+00:00,3648.17,3645.6,546.0,30.09,5005.0,1227.66,1264.0,676.26,3741.0,551.4,2084.68,2078.3,33.58,462.53,1563.49,1567.3,34.57,9369.0,9342.0,1355.06,619.42,395.25,224.17,50948.0,52204.0,39408.0,13144.0,0.3335,35875.0,2134.0,0.0594,2474.0,1034.0,0.4182,33401.0,1099.0,0.0329,10263.0,9886.0,2343.0,444.0,158.0,286.0,,,,,,,,19101.0,19257.0,3643.0,333.0,15759.0,16668.0,5191.0,1349.0,876.0,473.0,5825.0,6393.0,1967.0,7.0,36700.0,34883.0,37.56,7354.0,1815.0,0.2468,14137.0,6277.0,0.444,5094.0,2775.0,0.5448,9043.0,3502.0,0.3873,945.0,822.0,15.0,702.0,321.0,37646.0,35705.0,7369.0,1815.0,14840.0,6598.0,5094.0,2775.0,9746.0,3823.0,6022.0,5484.0,1381.0,144.0 +2016-10-16 10:00:00+00:00,3620.68,3624.4,546.0,34.64,5005.0,1181.58,1264.0,624.98,3741.0,556.6,2057.67,2049.3,32.72,463.34,1563.01,1575.1,32.72,9306.0,9410.0,1585.59,695.38,376.74,318.64,50375.0,51726.0,39408.0,15506.0,0.3935,35875.0,2411.0,0.0672,2474.0,1124.0,0.4545,33401.0,1286.0,0.0385,9926.0,9528.0,2734.0,473.0,174.0,299.0,,,,,,,,19127.0,19327.0,4218.0,323.0,15588.0,16442.0,6234.0,1606.0,950.0,656.0,5734.0,6429.0,2320.0,8.0,36849.0,35840.0,37.56,7354.0,2995.0,0.4072,14137.0,6369.0,0.4506,5094.0,2786.0,0.547,9043.0,3583.0,0.3962,984.0,899.0,15.0,702.0,398.0,37833.0,36740.0,7369.0,2995.0,14840.0,6768.0,5094.0,2786.0,9746.0,3981.0,5904.0,5405.0,1402.0,160.0 +2016-10-16 11:00:00+00:00,3591.49,3601.4,546.0,39.17,5005.0,1259.98,1264.0,642.65,3741.0,617.33,2020.13,2035.3,31.54,523.72,1571.36,1566.1,31.92,8952.0,9091.0,1620.5,910.48,494.73,415.75,47771.0,49657.0,39408.0,15889.0,0.4032,35875.0,2916.0,0.0813,2474.0,1207.0,0.4878,33401.0,1709.0,0.0511,9174.0,8930.0,2603.0,536.0,170.0,366.0,,,,,,,,18295.0,18763.0,4483.0,382.0,14930.0,15822.0,6376.0,1983.0,1036.0,946.0,5372.0,6141.0,2427.0,16.0,37254.0,36292.0,36.8,7354.0,3920.0,0.533,14137.0,7089.0,0.5014,5094.0,3004.0,0.5896,9043.0,4086.0,0.4518,1011.0,945.0,15.0,702.0,429.0,38264.0,37238.0,7369.0,3920.0,14840.0,7518.0,5094.0,3004.0,9746.0,4514.0,5446.0,4993.0,1299.0,202.0 +2016-10-16 12:00:00+00:00,3551.67,3545.0,546.0,36.97,5005.0,1268.83,1264.0,647.71,3741.0,621.12,1991.47,1988.2,30.08,532.98,1560.2,1556.8,30.82,8588.0,8842.0,1483.21,759.32,372.3,387.02,45891.0,47884.0,39408.0,14714.0,0.3734,35875.0,3295.0,0.0918,2474.0,1318.0,0.5327,33401.0,1977.0,0.0592,8722.0,8586.0,2326.0,593.0,162.0,431.0,,,,,,,,17549.0,18177.0,4211.0,422.0,14418.0,15333.0,5786.0,2261.0,1156.0,1105.0,5201.0,5789.0,2391.0,19.0,36278.0,35328.0,35.11,7354.0,4220.0,0.5738,14137.0,7241.0,0.5122,5094.0,2922.0,0.5737,9043.0,4318.0,0.4775,1004.0,974.0,15.0,702.0,421.0,37282.0,36303.0,7369.0,4220.0,14840.0,7661.0,5094.0,2922.0,9746.0,4739.0,5085.0,4685.0,1070.0,241.0 +2016-10-16 13:00:00+00:00,3572.92,3552.0,546.0,27.59,5005.0,1225.17,1264.0,630.25,3741.0,594.92,2018.03,1993.7,30.44,516.05,1554.89,1558.3,31.19,8322.0,8679.0,1170.67,652.94,328.43,324.51,45245.0,47397.0,39408.0,11975.0,0.3039,35875.0,3640.0,0.1014,2474.0,1417.0,0.5726,33401.0,2223.0,0.0666,8672.0,8698.0,1916.0,765.0,167.0,598.0,,,,,,,,17332.0,17855.0,3413.0,382.0,14119.0,15213.0,4663.0,2476.0,1249.0,1227.0,5121.0,5632.0,1983.0,17.0,36100.0,34926.0,34.0,7354.0,3820.0,0.5194,14137.0,7356.0,0.5204,5094.0,2879.0,0.5652,9043.0,4477.0,0.495,956.0,1015.0,15.0,702.0,376.0,37056.0,35942.0,7369.0,3820.0,14840.0,7732.0,5094.0,2879.0,9746.0,4852.0,4881.0,4584.0,738.0,259.0 +2016-10-16 14:00:00+00:00,3658.19,3663.4,546.0,14.96,5005.0,1247.9,1264.0,635.06,3741.0,612.84,2071.9,2062.4,31.05,539.94,1586.29,1601.0,31.81,8359.0,8633.0,726.28,405.72,207.29,198.43,45508.0,47554.0,39408.0,7504.0,0.1904,35875.0,4347.0,0.1212,2474.0,1719.0,0.6948,33401.0,2628.0,0.0787,8760.0,8782.0,1136.0,998.0,177.0,822.0,,,,,,,,17254.0,17694.0,2181.0,344.0,14322.0,15419.0,2929.0,2991.0,1542.0,1448.0,5173.0,5658.0,1258.0,14.0,36522.0,35430.0,31.92,7354.0,2885.0,0.3923,14137.0,7011.0,0.496,5094.0,2713.0,0.5327,9043.0,4298.0,0.4753,924.0,978.0,15.0,702.0,388.0,37446.0,36408.0,7369.0,2885.0,14840.0,7399.0,5094.0,2713.0,9746.0,4686.0,4790.0,4667.0,370.0,309.0 +2016-10-16 15:00:00+00:00,3987.97,3969.3,546.0,3.65,5005.0,1267.25,1264.0,672.47,3741.0,594.78,2294.58,2274.4,34.43,523.91,1693.39,1694.9,35.29,8718.0,8864.0,262.2,311.04,167.28,143.76,47769.0,49232.0,39408.0,2573.0,0.0653,35875.0,5330.0,0.1486,2474.0,2120.0,0.8568,33401.0,3210.0,0.0961,9170.0,8958.0,342.0,1271.0,165.0,1106.0,,,,,,,,17872.0,18232.0,817.0,387.0,15167.0,16212.0,941.0,3644.0,1955.0,1689.0,5560.0,5829.0,473.0,28.0,37069.0,37030.0,36.88,7354.0,1660.0,0.2257,14137.0,6470.0,0.4576,5094.0,2416.0,0.4743,9043.0,4054.0,0.4482,946.0,928.0,15.0,702.0,352.0,38015.0,37958.0,7369.0,1660.0,14840.0,6822.0,5094.0,2416.0,9746.0,4406.0,4981.0,4910.0,92.0,276.0 +2016-10-16 16:00:00+00:00,4141.15,4160.1,546.0,0.05,5005.0,1314.37,1264.0,728.72,3741.0,585.65,2373.75,2388.6,35.79,509.08,1767.4,1771.5,45.85,9292.0,9200.0,21.5,313.26,113.74,199.52,51477.0,51905.0,39408.0,106.0,0.0027,35875.0,7203.0,0.2008,2474.0,2421.0,0.9785,33401.0,4783.0,0.1432,9990.0,9391.0,12.0,1786.0,175.0,1611.0,,,,,,,,18839.0,19287.0,43.0,676.0,16580.0,17036.0,39.0,4689.0,2246.0,2443.0,6067.0,6190.0,12.0,53.0,38933.0,38290.0,47.46,7354.0,408.0,0.0556,14137.0,6170.0,0.4364,5094.0,2166.0,0.4251,9043.0,4004.0,0.4428,1022.0,927.0,15.0,702.0,298.0,39954.0,39218.0,7369.0,408.0,14840.0,6468.0,5094.0,2166.0,9746.0,4303.0,5625.0,5437.0,0.0,202.0 +2016-10-16 17:00:00+00:00,3909.55,3951.3,546.0,0.01,5005.0,1429.46,1264.0,784.2,3741.0,645.26,2235.26,2275.8,33.76,561.27,1674.29,1675.5,48.93,9990.0,9664.0,0.0,417.31,130.71,286.6,53842.0,53677.0,39408.0,0.0,0.0,35875.0,9207.0,0.2566,2474.0,2634.0,1.0645,33401.0,6574.0,0.1968,10303.0,9525.0,0.0,2339.0,186.0,2153.0,,,,,,,,19862.0,20209.0,0.0,1120.0,17217.0,17295.0,0.0,5648.0,2448.0,3201.0,6460.0,6648.0,0.0,100.0,42820.0,40994.0,99.94,7354.0,2.0,0.0002,14137.0,6490.0,0.459,5094.0,2578.0,0.5062,9043.0,3911.0,0.4326,1060.0,978.0,15.0,702.0,300.0,43880.0,41972.0,7369.0,2.0,14840.0,6790.0,5094.0,2578.0,9746.0,4211.0,5695.0,5707.0,0.0,142.0 +2016-10-16 18:00:00+00:00,3624.53,3630.9,546.0,0.0,5005.0,1549.06,1264.0,814.25,3741.0,734.81,2083.13,2084.7,32.15,649.86,1541.4,1546.2,33.67,9744.0,9702.0,0.0,463.84,88.51,375.33,51452.0,51685.0,39408.0,0.0,0.0,35875.0,10366.0,0.289,2474.0,2666.0,1.0776,33401.0,7700.0,0.2305,9661.0,9128.0,0.0,2554.0,189.0,2365.0,,,,,,,,19164.0,19459.0,0.0,1465.0,16391.0,16585.0,0.0,6189.0,2477.0,3712.0,6236.0,6513.0,0.0,158.0,41949.0,39527.0,89.92,7354.0,0.0,0.0,14137.0,6298.0,0.4456,5094.0,2637.0,0.5176,9043.0,3662.0,0.405,1138.0,1037.0,15.0,702.0,295.0,43088.0,40564.0,7369.0,0.0,14840.0,6593.0,5094.0,2637.0,9746.0,3956.0,5456.0,5575.0,0.0,144.0 +2016-10-16 19:00:00+00:00,3358.64,3393.9,546.0,0.0,5005.0,1555.74,1264.0,812.84,3741.0,742.9,1919.18,1960.4,31.06,668.65,1439.46,1433.5,31.54,9311.0,9312.0,0.0,497.24,67.09,430.15,49508.0,49720.0,39408.0,0.0,0.0,35875.0,10950.0,0.3052,2474.0,2623.0,1.0602,33401.0,8327.0,0.2493,9169.0,8730.0,0.0,2540.0,176.0,2364.0,,,,,,,,18536.0,18649.0,0.0,1588.0,15702.0,15978.0,0.0,6557.0,2447.0,4110.0,6102.0,6363.0,0.0,266.0,37730.0,36888.0,46.28,7354.0,0.0,0.0,14137.0,5803.0,0.4104,5094.0,2327.0,0.4568,9043.0,3476.0,0.3843,1076.0,1084.0,15.0,702.0,292.0,38806.0,37973.0,7369.0,0.0,14840.0,6095.0,5094.0,2327.0,9746.0,3768.0,5065.0,5137.0,0.0,174.0 +2016-10-16 20:00:00+00:00,3105.45,3134.3,546.0,0.0,5005.0,1563.73,1264.0,805.94,3741.0,757.79,1777.95,1821.9,30.57,689.22,1327.5,1312.4,30.57,9212.0,9029.0,0.0,516.52,84.96,431.56,49005.0,48950.0,39408.0,0.0,0.0,35875.0,10829.0,0.3019,2474.0,2527.0,1.0216,33401.0,8302.0,0.2485,8876.0,8800.0,0.0,2458.0,178.0,2280.0,,,,,,,,18390.0,18092.0,0.0,1510.0,15530.0,15659.0,0.0,6554.0,2350.0,4205.0,6209.0,6399.0,0.0,306.0,34602.0,33786.0,40.0,7354.0,0.0,0.0,14137.0,5793.0,0.4098,5094.0,2203.0,0.4324,9043.0,3590.0,0.397,994.0,1130.0,15.0,702.0,250.0,35596.0,34916.0,7369.0,0.0,14840.0,6043.0,5094.0,2203.0,9746.0,3840.0,4754.0,4766.0,0.0,192.0 +2016-10-16 21:00:00+00:00,2888.73,2879.7,546.0,0.0,5005.0,1590.98,1264.0,802.37,3741.0,788.61,1671.33,1684.5,28.84,715.96,1217.4,1195.2,28.84,8974.0,8694.0,0.0,536.63,150.22,386.41,45521.0,45138.0,39408.0,0.0,0.0,35875.0,10609.0,0.2958,2474.0,2269.0,0.917,33401.0,8341.0,0.2497,8379.0,7758.0,0.0,2360.0,179.0,2181.0,,,,,,,,16979.0,16864.0,0.0,1466.0,14295.0,14456.0,0.0,6487.0,2090.0,4398.0,5869.0,6060.0,0.0,296.0,31151.0,29990.0,36.02,7354.0,0.0,0.0,14137.0,5800.0,0.4103,5094.0,2159.0,0.424,9043.0,3641.0,0.4026,888.0,1072.0,15.0,702.0,243.0,32039.0,31062.0,7369.0,0.0,14840.0,6043.0,5094.0,2159.0,9746.0,3883.0,4333.0,4403.0,0.0,219.0 +2016-10-16 22:00:00+00:00,2701.87,2678.4,547.0,0.0,5005.0,1537.33,1264.0,792.6,3741.0,744.73,1580.92,1578.3,30.01,675.24,1120.95,1100.1,30.01,8387.0,7968.0,0.0,480.16,238.51,241.65,42799.0,42288.0,39418.0,0.0,0.0,35879.0,10206.0,0.2844,2478.0,2083.0,0.8408,33401.0,8123.0,0.2432,7891.0,7029.0,0.0,2271.0,150.0,2121.0,,,,,,,,15905.0,15802.0,0.0,1462.0,13440.0,13724.0,0.0,6199.0,1933.0,4266.0,5563.0,5734.0,0.0,274.0,27781.0,26380.0,36.0,7354.0,0.0,0.0,14137.0,5750.0,0.4067,5094.0,2102.0,0.4127,9043.0,3647.0,0.4034,753.0,968.0,15.0,702.0,218.0,28534.0,27349.0,7369.0,0.0,14840.0,5967.0,5094.0,2102.0,9746.0,3865.0,4027.0,4093.0,0.0,237.0 +2016-10-16 23:00:00+00:00,2598.53,2611.8,547.0,0.0,5005.0,1498.88,1264.0,770.95,3741.0,727.93,1533.23,1546.7,29.56,658.8,1065.3,1065.1,29.56,8040.0,7645.0,0.0,413.66,193.72,219.94,41233.0,40731.0,39418.0,0.0,0.0,35879.0,9978.0,0.2781,2478.0,2013.0,0.8122,33401.0,7965.0,0.2384,7661.0,6739.0,0.0,2203.0,126.0,2077.0,,,,,,,,15238.0,15308.0,0.0,1452.0,13037.0,13235.0,0.0,6085.0,1886.0,4199.0,5298.0,5448.0,0.0,238.0,23414.0,24653.0,27.28,7354.0,0.0,0.0,14137.0,5374.0,0.3802,5094.0,2151.0,0.4224,9043.0,3222.0,0.3564,658.0,852.0,15.0,702.0,215.0,24072.0,25505.0,7369.0,0.0,14840.0,5589.0,5094.0,2151.0,9746.0,3438.0,3830.0,3932.0,0.0,319.0 +2016-10-17 00:00:00+00:00,2528.25,2565.9,547.0,0.0,5005.0,1465.98,1264.0,744.71,3741.0,721.27,1496.83,1524.5,29.17,647.13,1031.42,1041.4,29.17,7836.0,7471.0,0.0,438.32,216.02,222.3,40690.0,40167.0,39418.0,0.0,0.0,35879.0,9863.0,0.2749,2478.0,2111.0,0.8521,33401.0,7751.0,0.2321,7600.0,6663.0,0.0,2123.0,116.0,2007.0,,,,,,,,15089.0,15129.0,0.0,1358.0,12794.0,12967.0,0.0,6193.0,1995.0,4198.0,5207.0,5408.0,0.0,188.0,23286.0,25203.0,26.92,7354.0,0.0,0.0,14137.0,5189.0,0.367,5094.0,2202.0,0.4324,9043.0,2986.0,0.3302,608.0,727.0,15.0,702.0,200.0,23894.0,25930.0,7369.0,0.0,14840.0,5389.0,5094.0,2202.0,9746.0,3186.0,3745.0,3842.0,0.0,408.0 +2016-10-17 01:00:00+00:00,2562.94,2551.6,547.0,0.0,5005.0,1451.63,1264.0,720.72,3741.0,730.91,1519.74,1505.3,28.44,643.81,1043.2,1046.3,28.44,7727.0,7433.0,0.0,417.66,188.84,228.82,41577.0,40569.0,39418.0,0.0,0.0,35879.0,9758.0,0.272,2478.0,2160.0,0.8716,33401.0,7599.0,0.2275,7758.0,6687.0,0.0,2037.0,117.0,1920.0,,,,,,,,15638.0,15353.0,0.0,1322.0,12912.0,13039.0,0.0,6241.0,2043.0,4199.0,5269.0,5489.0,0.0,159.0,22252.0,24710.0,26.18,7354.0,0.0,0.0,14137.0,5555.0,0.393,5094.0,2183.0,0.4286,9043.0,3372.0,0.3728,598.0,650.0,15.0,702.0,231.0,22851.0,25360.0,7369.0,0.0,14840.0,5786.0,5094.0,2183.0,9746.0,3603.0,3702.0,3842.0,0.0,510.0 +2016-10-17 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15:00:00+00:00,4609.33,4620.0,547.0,0.03,5093.0,2771.14,1264.0,915.59,3829.0,1855.55,2638.71,2613.8,30.01,1660.86,1970.62,2006.2,55.34,12059.0,11314.0,2.35,118.86,68.18,50.68,66870.0,65278.0,39880.0,27.0,0.0007,36905.0,8993.0,0.2437,2583.0,3005.0,1.1634,34322.0,5988.0,0.1744,12038.0,11662.0,0.0,2438.0,145.0,2293.0,,,,,,,,24690.0,23825.0,8.0,378.0,21851.0,21589.0,12.0,6073.0,2860.0,3213.0,8291.0,8202.0,7.0,103.0,44542.0,44278.0,55.01,7493.0,254.0,0.0338,14424.0,7682.0,0.5326,5094.0,2390.0,0.4692,9330.0,5293.0,0.5672,1178.0,1268.0,15.0,761.0,356.0,45720.0,45545.0,7507.0,254.0,15185.0,8039.0,5094.0,2390.0,10091.0,5649.0,8094.0,8334.0,5.0,1144.0 +2016-12-22 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18:00:00+00:00,4294.86,4351.0,547.0,0.0,5093.0,3175.82,1264.0,1066.57,3829.0,2109.25,2406.19,2433.0,29.76,1839.97,1888.67,1918.0,29.87,11857.0,11508.0,0.0,256.72,176.89,79.83,66556.0,65474.0,39880.0,0.0,0.0,36905.0,10767.0,0.2918,2583.0,3237.0,1.2532,34322.0,7530.0,0.2194,12070.0,12410.0,0.0,2832.0,237.0,2595.0,,,,,,,,24662.0,23755.0,0.0,538.0,21461.0,21177.0,0.0,7227.0,3000.0,4226.0,8363.0,8132.0,0.0,171.0,49197.0,47575.0,64.38,7493.0,0.0,0.0,14424.0,7586.0,0.5259,5094.0,2813.0,0.5522,9330.0,4773.0,0.5115,1454.0,1545.0,15.0,761.0,261.0,50650.0,49120.0,7507.0,0.0,15185.0,7846.0,5094.0,2813.0,10091.0,5033.0,8245.0,8600.0,0.0,1230.0 +2016-12-22 19:00:00+00:00,4057.16,4111.8,547.0,0.0,5093.0,3072.36,1264.0,1058.27,3829.0,2014.09,2296.68,2333.3,29.5,1725.31,1760.48,1778.5,29.5,11287.0,10976.0,0.0,359.2,260.51,98.69,62288.0,62025.0,39880.0,0.0,0.0,36905.0,11763.0,0.3188,2583.0,3272.0,1.2668,34322.0,8491.0,0.2474,11243.0,11587.0,0.0,2951.0,262.0,2689.0,,,,,,,,23065.0,22532.0,0.0,693.0,20069.0,20209.0,0.0,7945.0,3010.0,4935.0,7911.0,7696.0,0.0,173.0,47171.0,45073.0,60.0,7493.0,0.0,0.0,14424.0,7624.0,0.5286,5094.0,2788.0,0.5472,9330.0,4836.0,0.5184,1435.0,1450.0,15.0,761.0,238.0,48606.0,46523.0,7507.0,0.0,15185.0,7862.0,5094.0,2788.0,10091.0,5074.0,7760.0,8150.0,0.0,1241.0 +2016-12-22 20:00:00+00:00,3902.71,3875.9,547.0,0.0,5093.0,2991.85,1264.0,987.21,3829.0,2004.64,2258.81,2231.1,29.22,1685.35,1643.9,1644.8,29.22,10709.0,10483.0,0.0,452.23,345.12,107.11,59093.0,58801.0,39880.0,0.0,0.0,36905.0,12826.0,0.3476,2583.0,3308.0,1.2809,34322.0,9518.0,0.2773,10357.0,10806.0,0.0,3102.0,294.0,2808.0,,,,,,,,22331.0,21770.0,0.0,790.0,18845.0,18885.0,0.0,8704.0,3015.0,5690.0,7560.0,7339.0,0.0,229.0,43871.0,42500.0,50.46,7493.0,0.0,0.0,14424.0,7725.0,0.5356,5094.0,2826.0,0.5549,9330.0,4898.0,0.525,1370.0,1380.0,15.0,761.0,255.0,45241.0,43880.0,7507.0,0.0,15185.0,7980.0,5094.0,2826.0,10091.0,5153.0,7143.0,7460.0,0.0,1248.0 +2016-12-22 21:00:00+00:00,3599.71,3650.9,547.0,0.0,5093.0,3062.87,1264.0,1048.37,3829.0,2014.5,2087.76,2143.3,28.62,1674.5,1511.95,1507.6,28.62,10710.0,10593.0,0.0,476.4,360.72,115.68,56847.0,56582.0,39880.0,0.0,0.0,36905.0,12943.0,0.3507,2583.0,3321.0,1.2858,34322.0,9622.0,0.2804,9822.0,10647.0,0.0,3258.0,313.0,2946.0,,,,,,,,21380.0,20803.0,0.0,842.0,18353.0,18222.0,0.0,8625.0,3008.0,5617.0,7292.0,6911.0,0.0,217.0,40218.0,38771.0,47.08,7493.0,0.0,0.0,14424.0,7698.0,0.5337,5094.0,2849.0,0.5594,9330.0,4848.0,0.5196,1286.0,1310.0,15.0,761.0,251.0,41504.0,40081.0,7507.0,0.0,15185.0,7949.0,5094.0,2849.0,10091.0,5100.0,6604.0,6810.0,0.0,1268.0 +2016-12-22 22:00:00+00:00,3329.39,3288.6,547.0,0.0,5093.0,2926.74,1264.0,1048.27,3829.0,1878.47,1956.1,1923.5,27.36,1508.24,1373.29,1365.1,27.36,10578.0,10480.0,0.0,552.41,419.97,132.44,52654.0,51870.0,39880.0,0.0,0.0,36905.0,13382.0,0.3626,2583.0,3318.0,1.2843,34322.0,10065.0,0.2932,9012.0,9485.0,0.0,3519.0,321.0,3197.0,,,,,,,,19817.0,19214.0,0.0,936.0,17029.0,16895.0,0.0,8711.0,2996.0,5715.0,6797.0,6275.0,0.0,216.0,36740.0,35116.0,38.92,7493.0,0.0,0.0,14424.0,7220.0,0.5006,5094.0,2791.0,0.5479,9330.0,4428.0,0.4746,1206.0,1204.0,15.0,761.0,311.0,37946.0,36319.0,7507.0,0.0,15185.0,7531.0,5094.0,2791.0,10091.0,4739.0,6125.0,5840.0,0.0,1220.0 +2016-12-22 23:00:00+00:00,2949.94,2997.5,547.0,0.0,5093.0,3117.85,1264.0,1113.63,3829.0,2004.22,1684.03,1751.5,24.45,1599.19,1265.91,1246.0,24.45,10003.0,9859.0,0.0,557.23,427.67,129.56,48657.0,47682.0,39942.0,0.0,0.0,36955.0,13613.0,0.3684,2583.0,3321.0,1.2858,34372.0,10292.0,0.2994,8260.0,8581.0,0.0,3684.0,323.0,3361.0,,,,,,,,18137.0,17421.0,0.0,972.0,15970.0,15890.0,0.0,8779.0,2998.0,5781.0,6289.0,5790.0,0.0,178.0,34660.0,31640.0,50.31,7493.0,0.0,0.0,14424.0,6893.0,0.4779,5094.0,2758.0,0.5414,9330.0,4135.0,0.4432,1094.0,1043.0,15.0,761.0,376.0,35754.0,32683.0,7507.0,0.0,15185.0,7270.0,5094.0,2758.0,10091.0,4512.0,5179.0,5250.0,0.0,1203.0 +2016-12-23 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11:00:00+00:00,5331.19,5420.7,547.0,215.12,5653.0,3562.76,1294.0,942.37,4359.0,2620.39,3282.3,3302.1,32.84,2333.01,2048.89,2118.6,44.97,12493.0,12452.0,2102.02,1894.87,811.39,1083.48,73082.0,74491.0,42598.0,16910.0,0.397,43495.0,22945.0,0.5276,3483.0,4324.0,1.2416,40012.0,18621.0,0.4654,12478.0,13376.0,4146.0,4791.0,501.0,4290.0,,,,,,,,27834.0,28039.0,3672.0,4755.0,23143.0,23605.0,6540.0,12897.0,3824.0,9073.0,9626.0,9471.0,2553.0,503.0,49220.0,49160.0,52.9,8172.0,3480.0,0.4258,18164.0,9541.0,0.5253,6798.0,3504.0,0.5155,11365.0,6037.0,0.5312,1312.0,1262.0,190.0,1045.0,779.0,50532.0,50422.0,8362.0,3480.0,19209.0,10320.0,6798.0,3504.0,12410.0,6816.0,6811.33,7350.0,1575.0,897.0 +2018-02-28 12:00:00+00:00,5321.93,5369.9,547.0,191.37,5653.0,3880.79,1294.0,1071.64,4359.0,2809.15,3285.97,3271.7,32.06,2510.16,2035.96,2098.2,43.47,12267.0,12364.0,2064.46,1752.56,807.82,944.74,72286.0,73862.0,42598.0,16907.0,0.3969,43495.0,23115.0,0.5314,3483.0,4313.0,1.2382,40012.0,18803.0,0.47,11989.0,13295.0,4314.0,4590.0,505.0,4084.0,,,,,,,,27778.0,27767.0,3628.0,4880.0,22909.0,23413.0,6504.0,13105.0,3807.0,9297.0,9611.0,9387.0,2462.0,542.0,49424.0,49482.0,52.51,8172.0,3610.0,0.4418,18164.0,9768.0,0.5378,6798.0,3448.0,0.5072,11365.0,6320.0,0.5561,1344.0,1237.0,190.0,1045.0,779.0,50768.0,50719.0,8362.0,3610.0,19209.0,10547.0,6798.0,3448.0,12410.0,7099.0,6636.9,7151.0,1426.0,915.0 +2018-02-28 13:00:00+00:00,5301.28,5239.9,547.0,160.2,5653.0,3938.27,1294.0,1083.43,4359.0,2854.84,3264.17,3224.2,32.51,2542.25,2037.11,2015.7,42.89,12085.0,12220.0,1809.83,1851.33,824.74,1026.59,71772.0,72588.0,42598.0,14737.0,0.346,43495.0,23994.0,0.5516,3483.0,4337.0,1.2453,40012.0,19656.0,0.4913,11972.0,13315.0,3786.0,4773.0,498.0,4274.0,,,,,,,,27900.0,27200.0,3212.0,5219.0,22470.0,22899.0,5613.0,13441.0,3839.0,9602.0,9430.0,9174.0,2126.0,562.0,48146.0,49383.0,51.5,8172.0,4448.0,0.5443,18164.0,9605.0,0.5288,6798.0,3497.0,0.5144,11365.0,6108.0,0.5374,1351.0,1231.0,190.0,1045.0,740.0,49497.0,50614.0,8362.0,4448.0,19209.0,10344.0,6798.0,3497.0,12410.0,6848.0,6717.57,7252.0,1186.0,893.0 +2018-02-28 14:00:00+00:00,5165.28,5190.5,547.0,113.99,5653.0,3919.2,1294.0,1111.65,4359.0,2807.55,3155.16,3180.2,32.01,2501.99,2010.12,2010.3,42.82,12117.0,12153.0,1357.98,1930.03,833.73,1096.3,71183.0,71387.0,42598.0,10387.0,0.2438,43495.0,25659.0,0.5899,3483.0,4470.0,1.2834,40012.0,21189.0,0.5296,11739.0,12832.0,2590.0,5184.0,511.0,4674.0,,,,,,,,27821.0,26852.0,2359.0,5824.0,22286.0,22658.0,3996.0,13995.0,3959.0,10035.0,9338.0,9045.0,1442.0,656.0,46990.0,49284.0,50.81,8172.0,3020.0,0.3696,18164.0,9437.0,0.5196,6798.0,3453.0,0.5079,11365.0,5985.0,0.5266,1326.0,1188.0,190.0,1045.0,597.0,48316.0,50472.0,8362.0,3020.0,19209.0,10034.0,6798.0,3453.0,12410.0,6581.0,6609.18,7163.0,760.0,849.0 +2018-02-28 15:00:00+00:00,5162.53,5197.3,547.0,55.2,5653.0,3873.74,1294.0,1120.31,4359.0,2753.43,3156.42,3157.0,35.1,2424.55,2006.11,2040.3,44.96,12034.0,12055.0,717.59,1971.46,836.83,1134.63,70920.0,70233.0,42598.0,4985.0,0.117,43495.0,26564.0,0.6108,3483.0,4475.0,1.2847,40012.0,22089.0,0.552,11582.0,12676.0,1166.0,5639.0,514.0,5125.0,,,,,,,,27847.0,26206.0,1189.0,6078.0,22141.0,22548.0,1921.0,14105.0,3960.0,10144.0,9350.0,8803.0,710.0,742.0,47452.0,49373.0,55.6,8172.0,2105.0,0.2576,18164.0,9229.0,0.5081,6798.0,3478.0,0.5116,11365.0,5751.0,0.506,1282.0,1199.0,190.0,1045.0,603.0,48734.0,50572.0,8362.0,2105.0,19209.0,9832.0,6798.0,3478.0,12410.0,6354.0,6544.04,6856.0,247.0,724.0 +2018-02-28 16:00:00+00:00,5480.13,5486.2,547.0,9.17,5653.0,3880.48,1294.0,1143.15,4359.0,2737.33,3322.61,3345.3,42.99,2366.7,2157.52,2140.9,67.94,12249.0,12227.0,179.03,2035.75,834.2,1201.55,72142.0,71226.0,42598.0,823.0,0.0194,43495.0,27118.0,0.6235,3483.0,4460.0,1.2806,40012.0,22657.0,0.5662,12267.0,13299.0,142.0,6000.0,511.0,5489.0,,,,,,,,27894.0,26322.0,260.0,6492.0,22815.0,22875.0,291.0,13839.0,3949.0,9890.0,9166.0,8731.0,130.0,786.0,47963.0,50734.0,59.55,8172.0,944.0,0.1155,18164.0,8983.0,0.4946,6798.0,3448.0,0.5073,11365.0,5534.0,0.487,1294.0,1235.0,190.0,1045.0,535.0,49256.0,51968.0,8362.0,944.0,19209.0,9517.0,6798.0,3448.0,12410.0,6069.0,7173.3,7435.0,3.0,694.0 +2018-02-28 17:00:00+00:00,5654.01,5648.1,547.0,0.11,5653.0,3828.63,1294.0,1123.31,4359.0,2705.32,3401.09,3393.5,42.9,2321.78,2252.92,2254.6,72.91,12859.0,12769.0,4.51,2082.09,839.31,1242.78,75696.0,73216.0,42598.0,1.0,0.0,43495.0,28578.0,0.657,3483.0,4455.0,1.279,40012.0,24123.0,0.6029,12869.0,13187.0,0.0,6588.0,511.0,6078.0,,,,,,,,29092.0,27442.0,1.0,6929.0,24189.0,23490.0,0.0,14137.0,3944.0,10193.0,9545.0,9097.0,0.0,924.0,47638.0,51975.0,80.0,8172.0,99.0,0.0122,18164.0,8775.0,0.4831,6798.0,3459.0,0.5088,11365.0,5316.0,0.4677,1346.0,1337.0,190.0,1045.0,495.0,48985.0,53312.0,8362.0,99.0,19209.0,9269.0,6798.0,3459.0,12410.0,5810.0,7751.2,7964.0,0.0,560.0 +2018-02-28 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04:00:00+00:00,3683.02,3703.4,547.0,0.01,5654.0,1975.16,1294.0,791.74,4359.0,1183.42,2243.24,2289.0,30.0,881.29,1439.78,1414.4,30.0,9545.0,9710.0,0.0,1243.94,729.71,514.23,57288.0,55605.0,42787.0,0.0,0.0,44164.0,24939.0,0.5647,3483.0,3465.0,0.9948,40682.0,21474.0,0.5279,10738.0,10319.0,0.0,8116.0,517.0,7599.0,,,,,,,,21914.0,21290.0,0.0,3550.0,17478.0,17256.0,0.0,13032.0,2948.0,10084.0,7159.0,6741.0,0.0,242.0,28286.0,28800.0,46.34,8172.0,1.0,0.0001,18164.0,7043.0,0.3878,6798.0,2984.0,0.439,11365.0,4059.0,0.3572,716.0,653.0,190.0,1067.0,273.0,29002.0,29453.0,8362.0,1.0,19231.0,7316.0,6798.0,2984.0,12432.0,4332.0,4671.86,4761.0,2.0,690.0 +2018-03-22 05:00:00+00:00,4247.75,4205.3,547.0,1.04,5654.0,1671.21,1294.0,700.6,4359.0,970.61,2575.44,2564.8,35.7,698.79,1672.31,1640.5,35.7,10628.0,10729.0,0.07,1312.73,776.42,536.31,63458.0,61937.0,42787.0,191.0,0.0045,44164.0,26305.0,0.5956,3483.0,3504.0,1.0062,40682.0,22800.0,0.5604,11662.0,11231.0,34.0,8716.0,543.0,8173.0,,,,,,,,24313.0,23807.0,28.0,4021.0,19578.0,19160.0,111.0,13280.0,2961.0,10319.0,7904.0,7739.0,19.0,287.0,27650.0,30496.0,51.98,8172.0,2.0,0.0003,18164.0,6578.0,0.3622,6798.0,2917.0,0.429,11365.0,3662.0,0.3222,707.0,657.0,190.0,1067.0,336.0,28358.0,31153.0,8362.0,2.0,19231.0,6914.0,6798.0,2917.0,12432.0,3997.0,5422.08,5454.0,80.0,719.0 +2018-03-22 06:00:00+00:00,4810.39,4765.8,547.0,15.14,5654.0,1390.95,1294.0,631.85,4359.0,759.1,2898.84,2871.2,39.69,532.42,1911.55,1894.6,39.69,11345.0,11410.0,15.82,1284.99,745.3,539.69,67996.0,67233.0,42787.0,1844.0,0.0431,44164.0,26308.0,0.5957,3483.0,3495.0,1.0035,40682.0,22813.0,0.5608,12669.0,12266.0,302.0,9197.0,533.0,8664.0,,,,,,,,25712.0,25296.0,329.0,4219.0,21244.0,21306.0,894.0,12565.0,2962.0,9603.0,8371.0,8366.0,319.0,327.0,32844.0,36056.0,59.99,8172.0,97.0,0.0119,18164.0,6365.0,0.3504,6798.0,2774.0,0.408,11365.0,3591.0,0.316,720.0,704.0,190.0,1067.0,353.0,33564.0,36760.0,8362.0,97.0,19231.0,6718.0,6798.0,2774.0,12432.0,3944.0,5866.31,5851.0,207.0,669.0 +2018-03-22 07:00:00+00:00,5041.16,5034.2,547.0,43.82,5654.0,1246.1,1294.0,625.61,4359.0,620.49,3023.46,3014.7,40.11,428.91,2017.7,2019.5,40.11,11784.0,11730.0,71.82,1316.73,720.09,596.64,71141.0,70447.0,42787.0,4331.0,0.1012,44164.0,25956.0,0.5877,3483.0,3573.0,1.0258,40682.0,22384.0,0.5502,13496.0,12597.0,636.0,9417.0,460.0,8957.0,,,,,,,,26776.0,26342.0,745.0,4414.0,22087.0,22627.0,2038.0,11822.0,3113.0,8709.0,8783.0,8880.0,912.0,304.0,38178.0,40998.0,67.94,8172.0,744.0,0.091,18164.0,6096.0,0.3356,6798.0,2617.0,0.385,11365.0,3479.0,0.3061,814.0,843.0,190.0,1067.0,377.0,38992.0,41841.0,8362.0,744.0,19231.0,6473.0,6798.0,2617.0,12432.0,3856.0,6211.3,6173.0,302.0,559.0 +2018-03-22 08:00:00+00:00,7455.77,5090.6,547.0,74.68,5654.0,3395.83,1294.0,579.17,4359.0,2816.66,5403.59,3034.2,40.33,2696.72,2052.18,2056.4,40.33,12131.0,11944.0,154.0,1107.75,585.47,522.28,71905.0,71125.0,42787.0,6451.0,0.1508,44164.0,25764.0,0.5834,3483.0,3729.0,1.0705,40682.0,22035.0,0.5416,13694.0,12578.0,916.0,9478.0,300.0,9177.0,,,,,,,,27020.0,26613.0,1096.0,4281.0,22222.0,22897.0,3074.0,11753.0,3428.0,8324.0,8968.0,9036.0,1364.0,252.0,40202.0,42439.0,67.92,8172.0,1930.0,0.2362,18164.0,5999.0,0.3302,6798.0,2451.0,0.3605,11365.0,3548.0,0.3122,997.0,1043.0,190.0,1067.0,422.0,41198.0,43482.0,8362.0,1930.0,19231.0,6421.0,6798.0,2451.0,12432.0,3970.0,6362.39,6296.0,358.0,503.0 +2018-03-22 09:00:00+00:00,5113.48,5169.9,547.0,95.92,5654.0,772.0,1294.0,446.85,4359.0,325.15,3070.53,3107.9,40.42,246.64,2042.95,2062.0,41.75,12130.0,11968.0,228.3,997.47,558.32,439.15,72585.0,72030.0,42787.0,7824.0,0.1829,44164.0,24936.0,0.5646,3483.0,3619.0,1.0389,40682.0,21317.0,0.524,13610.0,12749.0,1050.0,9002.0,152.0,8850.0,,,,,,,,27470.0,26913.0,1315.0,4120.0,22458.0,23314.0,3907.0,11518.0,3467.0,8051.0,9047.0,9054.0,1553.0,296.0,40764.0,42392.0,60.54,8172.0,2955.0,0.3616,18164.0,6359.0,0.3501,6798.0,2380.0,0.3501,11365.0,3978.0,0.3501,1146.0,1131.0,190.0,1067.0,493.0,41910.0,43523.0,8362.0,2955.0,19231.0,6852.0,6798.0,2380.0,12432.0,4471.0,6528.76,6341.0,429.0,522.0 +2018-03-22 10:00:00+00:00,5081.38,5076.9,547.0,120.71,5654.0,513.91,1294.0,253.39,4359.0,260.52,3061.02,3053.9,40.24,202.99,2020.36,2023.0,41.25,12283.0,12037.0,298.7,820.91,436.79,384.12,73172.0,72651.0,42787.0,8566.0,0.2002,44164.0,23870.0,0.5404,3483.0,3545.0,1.0178,40682.0,20324.0,0.4996,13712.0,12800.0,1165.0,8294.0,76.0,8218.0,,,,,,,,27665.0,27181.0,1458.0,3838.0,22651.0,23605.0,4320.0,11391.0,3469.0,7922.0,9143.0,9066.0,1623.0,347.0,40290.0,42672.0,54.47,8172.0,3445.0,0.4216,18164.0,6578.0,0.3621,6798.0,2034.0,0.2992,11365.0,4544.0,0.3998,1208.0,1164.0,190.0,1067.0,595.0,41498.0,43836.0,8362.0,3445.0,19231.0,7172.0,6798.0,2034.0,12432.0,5139.0,6598.4,6365.0,472.0,483.0 +2018-03-22 11:00:00+00:00,4991.2,4974.0,547.0,146.79,5654.0,382.51,1294.0,202.4,4359.0,180.11,2979.25,2984.2,40.12,140.94,2011.95,1989.8,40.87,12037.0,11795.0,316.21,675.99,299.77,376.22,72915.0,71958.0,42787.0,8240.0,0.1926,44164.0,22146.0,0.5014,3483.0,3273.0,0.9397,40682.0,18873.0,0.4639,13907.0,12632.0,1197.0,7427.0,45.0,7382.0,,,,,,,,27690.0,27200.0,1530.0,3423.0,22402.0,23361.0,4149.0,10828.0,3228.0,7600.0,8915.0,8765.0,1364.0,468.0,40684.0,43418.0,51.0,8172.0,3270.0,0.4002,18164.0,6647.0,0.366,6798.0,1907.0,0.2805,11365.0,4740.0,0.4171,1199.0,1155.0,190.0,1067.0,603.0,41883.0,44573.0,8362.0,3270.0,19231.0,7250.0,6798.0,1907.0,12432.0,5343.0,6498.23,6292.0,407.0,443.0 +2018-03-22 12:00:00+00:00,4979.46,4977.2,547.0,150.4,5654.0,392.22,1294.0,225.64,4359.0,166.58,2978.64,2992.8,39.97,111.51,2000.82,1984.4,40.62,12022.0,11768.0,315.35,578.03,211.67,366.36,72673.0,71304.0,42787.0,7189.0,0.168,44164.0,18955.0,0.4292,3483.0,2691.0,0.7725,40682.0,16264.0,0.3998,13810.0,12553.0,1086.0,6277.0,16.0,6261.0,,,,,,,,27611.0,27017.0,1425.0,3064.0,22126.0,23076.0,3596.0,9106.0,2674.0,6432.0,9126.0,8657.0,1082.0,508.0,41042.0,43503.0,50.07,8172.0,2975.0,0.364,18164.0,6438.0,0.3545,6798.0,1445.0,0.2126,11365.0,4993.0,0.4393,1206.0,1144.0,190.0,1067.0,571.0,42249.0,44647.0,8362.0,2975.0,19231.0,7009.0,6798.0,1445.0,12432.0,5564.0,6367.72,6031.0,360.0,409.0 +2018-03-22 13:00:00+00:00,4880.49,4861.9,547.0,155.17,5654.0,316.94,1294.0,192.29,4359.0,124.65,2905.09,2912.1,41.22,76.31,1975.4,1949.8,41.22,12018.0,11668.0,328.03,505.65,169.01,336.64,72071.0,70263.0,42787.0,5481.0,0.1281,44164.0,15771.0,0.3571,3483.0,1972.0,0.5661,40682.0,13799.0,0.3392,14023.0,12379.0,877.0,5366.0,14.0,5352.0,,,,,,,,27244.0,26606.0,1161.0,2782.0,21681.0,22436.0,2676.0,7160.0,1957.0,5203.0,9123.0,8842.0,766.0,462.0,42070.0,42731.0,45.5,8172.0,4137.0,0.5062,18164.0,6510.0,0.3584,6798.0,1457.0,0.2143,11365.0,5053.0,0.4446,1202.0,1133.0,190.0,1067.0,559.0,43272.0,43864.0,8362.0,4137.0,19231.0,7069.0,6798.0,1457.0,12432.0,5612.0,6354.38,5977.0,295.0,434.0 +2018-03-22 14:00:00+00:00,4748.88,4697.2,547.0,136.88,5654.0,229.04,1294.0,127.53,4359.0,101.51,2817.63,2798.9,42.99,45.36,1931.25,1898.3,42.99,11974.0,11582.0,271.88,436.34,122.12,314.22,71367.0,69072.0,42787.0,3589.0,0.0838,44164.0,13341.0,0.3021,3483.0,1491.0,0.4282,40682.0,11849.0,0.2912,13944.0,12137.0,606.0,4748.0,21.0,4726.0,,,,,,,,26914.0,26236.0,785.0,2384.0,21400.0,21942.0,1662.0,5789.0,1470.0,4319.0,9110.0,8757.0,536.0,419.0,41211.0,41652.0,44.77,8172.0,2615.0,0.32,18164.0,6860.0,0.3777,6798.0,1715.0,0.2523,11365.0,5145.0,0.4527,1176.0,1099.0,190.0,1067.0,582.0,42387.0,42751.0,8362.0,2615.0,19231.0,7442.0,6798.0,1715.0,12432.0,5727.0,6199.08,5836.0,167.0,421.0 +2018-03-22 15:00:00+00:00,4761.75,4671.4,547.0,90.94,5654.0,192.64,1294.0,110.57,4359.0,82.07,2792.75,2782.3,47.98,32.19,1969.0,1889.1,47.98,11772.0,11438.0,183.76,412.07,108.51,303.56,70439.0,67576.0,42787.0,1938.0,0.0453,44164.0,11618.0,0.263,3483.0,1327.0,0.3809,40682.0,10292.0,0.253,13983.0,12153.0,317.0,4222.0,16.0,4205.0,,,,,,,,26215.0,25543.0,459.0,2123.0,21190.0,21474.0,841.0,4804.0,1310.0,3494.0,9051.0,8406.0,322.0,470.0,40044.0,41446.0,45.34,8172.0,1765.0,0.216,18164.0,7511.0,0.4135,6798.0,2148.0,0.316,11365.0,5363.0,0.4718,1163.0,1118.0,190.0,1067.0,597.0,41206.0,42564.0,8362.0,1765.0,19231.0,8108.0,6798.0,2148.0,12432.0,5960.0,6148.43,5805.0,48.0,403.0 +2018-03-22 16:00:00+00:00,5000.82,4928.1,547.0,37.13,5654.0,172.86,1294.0,84.63,4359.0,88.23,2916.15,2926.5,57.92,51.12,2084.67,2001.6,57.92,11779.0,11493.0,84.88,384.06,106.79,277.27,70343.0,68204.0,42787.0,630.0,0.0147,44164.0,9842.0,0.2228,3483.0,978.0,0.2808,40682.0,8864.0,0.2179,14106.0,12852.0,110.0,3549.0,4.0,3546.0,,,,,,,,25942.0,25242.0,176.0,1916.0,21399.0,21723.0,255.0,3917.0,975.0,2943.0,8897.0,8386.0,88.0,460.0,40902.0,42723.0,49.59,8172.0,829.0,0.1014,18164.0,8156.0,0.449,6798.0,2812.0,0.4136,11365.0,5344.0,0.4702,1172.0,1154.0,190.0,1067.0,553.0,42074.0,43877.0,8362.0,829.0,19231.0,8709.0,6798.0,2812.0,12432.0,5897.0,6476.58,6192.0,3.0,393.0 +2018-03-22 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