Genus 2 curve data - 450.a.2700.1

Formats: - HTML - YAML - JSON - 2026-02-15T15:12:00.855490

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '898745104469034071', 'abs_disc': 2700, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[2,[1,2,3,2]],[3,[1,0,-1]],[5,[1,0,-1]]]', 'bad_primes': [2, 3, 5], 'class': '450.a', 'cond': 450, 'disc_sign': -1, 'end_alg': 'Q x Q', 'eqn': '[[0,1,3,3,3,1],[1,0,0,1]]', 'g2_inv': "['6240321451/2700','8289281/150','0']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['364','3529','393211','345600']", 'igusa_inv': "['91','198','0','-9801','2700']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '450.a.2700.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1956145387347962213478497730349625509052434223455857702', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.180.4', '3.720.4'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 6, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '18.778995718540437249393578211', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'SU(2)xSU(2)', 'st_label': '1.4.B.1.1a', 'st_label_components': [1, 4, 1, 1, 1, 0], 'tamagawa_product': 6, 'torsion_order': 24, 'torsion_subgroup': '[24]', 'two_selmer_rank': 1, 'two_torsion_field': ['4.0.225.1', [1, 1, 2, -1, 1], [4, 2], True]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '450.a.2700.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[1], [-161]], [[-71], [-1837]]], 'spl_facs_condnorms': [15, 30], 'spl_facs_labels': ['15.a7', '30.a8'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'G_{3,3}', 'st_group_geom': 'G_{3,3}'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '450.a.2700.1', 'mw_gens': [[[[1, 2], [1, 2], [1, 1]], [[-1, 2], [-1, 2], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [24], 'num_rat_pts': 6, 'rat_pts': [[-1, -1, 1], [-1, 1, 1], [0, -1, 1], [0, 0, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 43
    {'conductor': 450, 'lmfdb_label': '450.a.2700.1', 'modell_image': '2.180.4', 'prime': 2}
  • id: 44
    {'conductor': 450, 'lmfdb_label': '450.a.2700.1', 'modell_image': '3.720.4', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 97152
    {'label': '450.a.2700.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 2}
  • id: 97153
    {'cluster_label': 'cc2_1~2c2_1~2c2_1~2_0', 'label': '450.a.2700.1', 'local_root_number': -1, 'p': 3, 'tamagawa_number': 3}
  • id: 97154
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '450.a.2700.1', 'local_root_number': -1, 'p': 5, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '450.a.2700.1', 'plot': 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IQwBEpXvwQWnIEKmwULr0UmnhQqcrAgAAJREAUel8Phv%2B7dlT2rVLOu88O0MYAABEBgIgqkT16tJbb0nt2knZ2VK/frZXIAAg8mVlZSkjI0OZmZlOl4IqwipgVKkNG6SuXe22WzfbIzAx0emqAAAVwSpg96IHEFWqaVNp1izbI/C//5WuvNLmBgIAAOcQAFHlTj7ZTgupXl2aOVMaMYKNogEAcBIBEGFx5pnSq6/aApFnnpHGjnW6IgAAvIsAiLC55BJp4kS7fuCB0KbRAAAgvAiACKvhw6W//tWub7pJmjHD2XoAAPAiAiDC7qGHpOuvl4qKpCuukL780umKAADwFgIgws7nk55%2BWho0SPL7pfPPl77/3umqAADwDgIgHBEXJ732mnT22dLOndK550qrVjldFQAA3kAAhGMSEqR33pFOOUXavFnq08c2jAYAOIuTQNyPk0DguJwcqXt36wFs00aaO1eqX9/pqgAAnATiXvQAwnENG9oRcU2bSitW2HBwXp7TVQEA4F4EQESEZs0sBNavL333nTRwoLR7t9NVAQDgTgRARIw2baSPPpKSk6U5c6SLL5b27nW6KgAA3IcAiIhyyinS%2B%2B9LiYnSrFnSVVdJ%2B/c7XRUAAO5CAETEOeMMOyGkenXpzTel666zTaMBAEDlIAAiIvXtK02fLsXGSlOnSiNGSKxXBwCgchAAEbEGDbLwFzw55M47CYEAAFQGAiAi2pVXSpMn2/UTT0h/%2B5uz9QAA4AYEQES8666TJk6063/8Q/r7352tBwCAaEcARFQYMUJ67DG7fuAB6ZFHnK0HANyMo%2BDcj6PgEFXGj5fuu8%2BuH39cuv12Z%2BsBADfjKDj3ogcQUWXUKGnMGLu%2B4w7p//7P0XIAAIhKBEBEnQcekP76V7u%2B7bbQ/EAAAFAxBEBEHZ9Peugh6w2UpFtvlSZNcrYmAACiCQEQUcnnsxXB99xj92%2B5hRAIAEBFEQARtXw%2BWxRSMgQyHAwAwOERABHVgiHw3nvt/q23Sv/8p7M1AQAQ6QiAiHo%2BnzRuXGh7mNtvD%2B0ZCAAADkQAhCv4fNLYsbZCWJLuustCIQAAOBABEK7h80kPPhg6Ku7%2B%2B6XRoyW2OgeAI8NJIO7HSSBwpQkTQotD7r5bevhhC4gAgIrjJBD3ogcQrnT33dKTT9r1hAnSyJFSUZGzNQEAECkIgHCtkSOlZ56x64kTpT//WSosdLYmAAAiAQEQrnbDDdJLL0kxMdK//iUNHizt2%2Bd0VQAAOIsACNe7%2Bmrp9deluDhp%2BnTp4oulPXucrgoAAOcQAOEJl1wivfOOlJAgvfee1L%2B/tHOn01UBQNUZP368MjMzlZSUpIYNG%2BrCCy/UypUrnS4LEYIACM847zzpww%2BlWrWkzz%2BXevWStm51uioAqBqzZ8/W8OHDNX/%2BfH3yySfav3%2B/%2BvTpo/z8fKdLQwRgGxh4zoIFUr9%2BFv4yMqSPP5aaNHG6KgCoWps3b1bDhg01e/ZsnXnmmRV6DdvAuBc9gPCczExpzhypcWNp2TLpjDOkn35yuioAqFq5ubmSpLp16zpcCSIBPYDwrLVrpd69Lfw1bGjDw506OV0VAFS%2BQCCgCy64QNu3b9fcuXMP%2Bjy/3y%2B/3198Py8vT2lpafQAuhA9gPCs44%2BXvvpK6thRysmRzjpL%2BvJLp6sCgMo3YsQI/fjjj5o2bdohnzd%2B/HilpKQUt7S0tDBViHCjBxCel5srnX%2B%2BDQvHx0uvvSZddJHTVQFA5bjllls0c%2BZMzZkzR%2Bnp6Yd8Lj2A3kEPIDwvJUX66CNp0CDJ77ctY4IniABAtAoEAhoxYoTefvttff7554cNf5IUHx%2Bv5OTkUg3uRAAEZPsDvvGGNGyYFAhIN90kPfCAXQNANBo%2BfLheeeUVvfbaa0pKSlJ2drays7O1e/dup0tDBGAIGCghEJAefNCaJF17rfTss3aKCABEE5/PV%2B7jL774ov70pz9V6HOwDYx7EQCBcjz3nPUCFhXZBtKvv24bSAOAlxAA3YshYKAcw4ZJM2ZIiYnSBx9IPXpI2dlOVwUAQOUgAAIHcf75dmRc/frSt99KXbtKy5c7XRUAAMeOAAgcQpcu0rx50gkn2MbRp58uzZ7tdFUAABwbAiBwGCecYCGwa1dpxw6pTx/p1VedrgoAgKNHAAQqoH596bPPpIsvlvbulQYPlh56iG1iALhTVlaWMjIylJmZ6XQpqCKsAgaOQFGRdO%2B90qOP2v3Bg6Xnn7cTRADAbVgF7F70AAJHICZGmjDB9gaMjZVeeUXq3VvassXpygAAqDgCIHAUhg2TZs2yY%2BTmzpU6d5aWLXO6KgAAKoYACByl3r2lr7%2BW0tOlNWtskciHHzpdFQAAh0cABI5BRob0zTfSGWdIeXlS//7SU0%2BxOAQAENkIgMAxatBA%2BvRTaehQWyQycqR0ww22WhgAgEhEAAQqQXy89MIL0mOPST6fNHmy1KuXlJPjdGUAAByIAAhUEp9PuuMO6T//kZKTbXFIZqa0cKHTlQEAUBoBEKhk551n8wJbtZLWrZO6dZOmT3e6KgAAQgiAQBVo08ZC4LnnSrt3S1dcId1zj1RY6HRlAAAQAIEqU6eODQffc4/dnzBB6tdP2rrV2boA4HA4Cs79OAoOCIPXX5euvVYqKJCOP16aMUPq2NHpqgDg0DgKzr3oAQTC4I9/lObNk1q0kNautU2jp051uioAgFcRAIEwad9e%2BvZbWySyZ490zTXSzTdLfr/TlQEAvIYACIRRnTrSe%2B9Jo0fbtjFPPy2ddZatFgYAIFwIgECYxcRIY8bYApE6dWy18CmnSB995HRlAACvIAACDjnvPOm77yz8bd1qK4RHj2arGABA1SMAAg5KT5f%2B%2B187OzgQkB56SOrbV9q0yenKAABuRgAEHJaQID3zjPTyy1KNGtJnn9kWMV9%2B6XRlAAC3IgACEWLwYGnBAikjQ8rOlnr2tB5BhoQBAJWNAAhEkIwM6f/9P2noUKmoyOYE9u4tbdzodGUAADchAAIRpmZN6V//so2ia9aUvvhC6tBB%2BuADpysD4BUcBed%2BHAUHRLCVK%2B0UkR9%2BsPt/%2BYs0frwUH%2B9sXQC8gaPg3IseQCCCtW4tzZ8v3XKL3f/nP%2B0YuZUrna0LABDdCIBAhEtIkJ56Snr3XalePWnhQts7cPJk2zoGAIAjRQAEosTAgdKPP0rnnCMVFEjDhkkXXSRt2eJ0ZQCAaEMABKJI48bSJ59IEyZI1apJM2dK7dpxjBwA4MgQAIEoExMj3XWXnSHctq3tGXjuudKIEdYzCADA4RAAgSjVqZOdJTxihN3PyrK5gQsWOFsXACDyEQCBKJaYKE2cKH34oXTccbY6uGtX6YEHpL17na4OABCpCICAC/TtKy1ZIl1%2BuR0d9/e/S126SIsXO10ZACASEQABl6hbV5o2TXr9dbteuFD6wx9s4%2Bj9%2B52uDkA04SQQ9%2BMkEMCFsrOlG26wvQMlKTPTjpc7%2BWRn6wIQXTgJxL3oAQRcKDXVtoh56SWpdm1bGHLqqdI//iHt2%2Bd0dQAApxEAAZfy%2BaSrr5aWLpUGDLBFIX/9q9S5sw0PAwC8iwAIuFzjxjYU/PLLUp06Fv4yM6X77pP27HG6OgCAEwiAgAf4fNLgwdLy5dIll9hK4fHjpQ4dpDlznK4OABBuBEDAQxo1kt54Q3r7bds3cNUq6ayz7Fzh7dv/96TPP5euuUY67zzpjjvsSQCi0pw5czRw4EA1btxYPp9PM2fOdLokRAgCIOBBgwZJy5ZZ8JOkyZOljDZFWtt9iNSzpzR1qjRrlvTEE3be3PPPO1swgKOSn5%2BvDh06aNKkSU6XggjDNjCAx82da0HwnBVZytKI8p8UE2O7SmdkhLc4AJXG5/NpxowZuvDCCyv8GraBcS96AAGP695dWrRIGtMg6%2BBPKiqSnnkmfEUBAKpUnNMFAHBefLUiNdi8/NBPWrIkPMUAcIzf75ff7y%2B%2Bn5eX52A1qEr0AAKwId66dQ/5lIKa9cNUDACnjB8/XikpKcUtLS3N6ZJQRQiAAMyQIYf88NWfXK0JE2xDaQDuNGrUKOXm5ha39evXO10SqggBEIAZNUpKTy/3Q7PrXKi3/efpnnukdu2kjz4Kc20AwiI%2BPl7JycmlGtyJAAjANGokff21NHy4FPyhf/zx0iOPqHv2G3pxSowaNbJtAc89Vzr/fOmnnxytGMBh7Nq1S4sWLdKiRYskSb/88osWLVqkdevWOVwZnMY2MAAOFAjYOXGJiaUezs2VHnpIeuopaf9%2BqXp1aeRIO2OYjgIg8nz55Zfq0aPHAY9fc801mjJlymFfzzYw7kUABHDEli%2BX/vKX0FBww4bS2LHStddKsbHO1gag8hAA3YshYABHrG1bOyjkvfekE0%2BUcnJsM%2BlTTpE%2B/dTp6gAAh0MABHBUfD5pwAA7IOSf/5Rq15Z%2B/FHq3Vvq319autTpCgEAB0MABHBMqleXbrvNFoTceqsUFyd98IHUvr305z9LGzc6XSEAoCwCIIBKUa%2Be9H//Jy1bJg0aZKfHPf%2B81KqVLRLhQAEAiBwEQACVqlUr6e23pa%2B%2Bkrp2lQoKpH/8Q2rRQnrySanEKVMAIlRWVpYyMjKUmZnpdCmoIqwCBlBlAgFpxgzpvvuklSvtsWbNpDFj7OCROE4jByIaq4Ddix5AAFXG55MuukhaskSaPFlq0kRat862i2nXTnrjDRsqBgCEFwEQQJWLi5Ouv15avVp69FGbL7hihXTZZdKpp9p2MoxFAED4EAABhE1ionTnndKaNTYMnJQkLVpkx8p17mx7CxIEAaDqEQABhF1ysjR6tPTLL9I990g1akgLFkjnnSedfrr04YcEQQCoSgRAAI6pV096%2BGELgnfcYT2E8%2BdL/frZCuL33ycIAkBVIAACcFzDhtJjj9nQ8O23WxD85hs7aeQPf7CVxCwWAYDKQwAEEDFSU6XHH7cewTvvtKHh77%2B3lcTt20uvvCLt3%2B90lQAQ/QiAACJOo0a2WvjXX6X777c5g0uX2t6BJ54oPf20tGeP01UCQPRiI2gAEW/HDikry04S2bLFHmvYUBo5UrrpJqlOHWfrA9yKjaDdix5AABGvdm3rCfz1V%2Bmpp%2Bw0kZwce6xZM5s3%2BOuvTlcJuAdHwbkfPYAAos6%2BfdLrr0sTJkiLF9tjsbHSpZfaauI//MHZ%2BgC3oAfQvegBBBB1qlWTBg%2BWfvjBNo/u2VMqLJSmT5cyM6Uzz7SVw4WFTlcKAJGJAAggavl80rnnSp9%2BaquFBw%2B2Y%2BfmzrWVw61aSU88YXMIAQAhDAEDcJXffrMFI88%2BK23bZo/VrCldfbV0yy1S27bO1gdEE4aA3YsACMCVCgps38CnnrItZIJ69pRuvtnOH46Lc64%2BIBoQAN2LAAjA1QIB6fPPpUmTpHffDZ0o0qSJNGyYdP31UuPGztYIRCoCoHsRAAF4xrp10jPPSM8/L23ebI/Fxlpv4I03Sr16STHMjAaKEQDdiwAIwHP8fumtt%2BxEka%2B%2BCj2enm49gn/6E72CgEQAdDMCIABPW7LEFoy8/LKUm2uPxcZK550nXXed3Var5myNgFMIgO5FAAQA2aKRN96w4eGSvYKNGtkZxEOHShkZztUHhFNWVpaysrJUWFioVatWEQBdiAAIAGWsWCG98II0daodORf0hz/Y8PDll0v16jlWHhA29AC6FwEQAA5i3z7pgw%2BkF1%2BU3n9f2r/fHq9WTerf3zaeHjBAio93tk6gqhAA3YsACAAVkJMjvfaa9QouXBh6PCXFziC%2B6iqpe3ebPwi4BQHQvQiAAHCEliyxRSOvvSZt2BB6vHFj6Y9/tCHizEw7qg6IZgRA9yIAAsBRKiqS5syxE0feeqv0mcMtWkiXXWatY0fCIKITAdC9CIAAUAn8fumjj6Rp0%2BzEkYKC0MdOOEG65BJrp5xCGET0IAC6FwEQACpZfr4tGnn9dVtEsmdP6GPHHy9ddJE0aJDUtStzBhHZCIDuRQAEgCq0a5eFwTfftNvdu0Mfa9TIjqG74AKpZ08pIcG5OoHyRFMALCiQPvzQtmtq1szpaiIfARAAwiT4H9SMGdJ774VOHpGkGjWkvn2lgQNti5mGDZ2rEwiK9ACYm2u/WL39tvW2794tjRsnjRrldGWRjwAIAA7Yu1eaPVuaOdPmDJZcTezzSaedZkFwwAAWkcA5kRgAN260fzMzZ0qff277dQYdf7x0xx3SiBGOlRc1CIAA4LBAQPr%2Be%2BsVfO89uy4pNVXq108691wyZjoEAAAX9UlEQVSpVy%2Bpbl1n6oR3RNJRcIGAtHix/dt45x1pwYLSH2/b1ubUXnQRi6yOBAEQACLMb7/ZcNb770uffmqLSoJiYqx3sG9fqU8fu46Lc65WuJtTPYC7d0tffGH/Dt57T1q3rvTHO3eWLrzQWps2YSvLVQiAABDB/H5p7lxp1iybP7hsWemPJydLZ59tPYM9e1pvCD0gqCzhDIA//2zv8Q8%2BsPBXcsFUYqK9v88/36ZFHHdclZbiCQRAAIgi69ZJH38sffKJ9Q5u21b646mp0jnnSD16WDBs2ZJAiKNXlQFw1y4Leh9/bHtorl5d%2BuNNm4bmwZ5zji2UQuUhAAJAlCoslBYtCoXB//639J6DktSkiXTWWda6d7fhMgIhKqoyA%2BD%2B/TZ/79NPrc2bV3oBR1yc1K2bzXft109q1473alUiAAKAS/j99p/qF19Ymz%2B/9H%2BwklS/vnTGGfYfbbdu0qmnStWrO1MvIt%2BxBMDCQunHH%2B29%2BPnndmzizp2ln5OebvNZ%2B/a1Xr4IWWjsCQRAAHCpggILgbNn23%2B%2B8%2Bcf2EMYH28b53btaq1LF6lxY2fqRQSZN096/HHlffGFUrZtU%2B511yl51CibU3AQ%2B/dLCxfae232bJu7WvJ8bMlWsPfoYXNWe/c%2B5KdDFSMAAoBH7N0rffed/cf83/9KX38tbdly4POaNrXVxaedJmVmWi9hSkr464VDpk2ThgyRCguVJylFUq6k5Nq1rSuvUydJUl6e9M039l766iv7BaPkinVJSkqyHucePWwRR8eOtpIdziMAAoBHBQI28X7evFBbskQqKjrwuSeeaEHw1FNtr7WOHaU6dcJfM6rYrl02cTQvT5JKB0BJOS0662%2B95mv%2B/PLfK7VrW%2BA780xbhNSpE9sURSoC4DEIBALaWXZCAwBEsV27bGHJt9/ahtTffy%2BtX1/%2Bc5s1s4n67dpJJ58snXSSncRAD0/02jdliqqNHFl8P09SmqT1sgAoSadpvlaqrSR7D3TubFMHuna1bYii6fuflJQkn0dXmhAAj0FwciwAAIg%2BkXTEXbgRAI9ByR7AzMxMLSh7Pk0VCMfXycvLU1pamtavX1/l/zDc9Pfmpq/De4CvIx36fbB9ux3PtWRJ6HbFCptnWJ6aNW0YuVWrUDvhBKlFC%2Bnss93zdxapX8fvt42WV66079Py5dZ%2B%2Bqn0MO4gvaUpurb4fnk9gFq40L5xlcyJvzcv9wAyMn8MfD5f8Q/F2NjYsPwWEa6vI0nJyclV/rXc9vfmtq/De4CvI5X/PkhOlpo3t016g/bvtzmFS5ZYW7rUTi5ZvdoWByxcaK2suLi5GjAguTgQpqeHWqNGlTek6LbvTdmvs3%2B/Ddf//LP9na9eLa1aZaFvzZry53ZKtjK3fXub13nKSVeqxt33KG775lLPSf5fU69e9sQw/HmqSjj/7UQyAmAlGT58uKu%2BTri47e/NbV8nHNz2d%2Ba2r3Mk4uJsDljbttKll4Ye37fPepqWL7cwEmyrVtlJJvv3N9TcubY6uaz4eJtn1qyZlJZmK5SDrUkTOxKsQYOKhcRo/97k59s50Rs2WNBr0eJFXX%2B9tHattV9/tRB4MElJ9r056aRQa9fOtv0JdYLFS%2BnT7My1goLSnyAtTXruuSr5s0nR//2JNgwB4wBOHf6NyMF7AFJ43gfbtlk4/PnnUFuzxgLNhg0H77UqKTbWQmCjRtYaNrT7DRpI9epZq1vXVi3Xrm1b2iQn2%2BucUlRkC2527LC2bZu0datty7N5s7VNm6TsbLvduLF4Ye4hxcdbz%2BkJJ1hr3dqG3lu3Lhv0DuPnn6WsLG3/%2BGPVXbpUm%2B69Vw3vvNP%2BMuEK9ADiAPHx8Ro9erTi4%2BOdLgUO4T0AKTzvg7p1Q3sOlrVvn/V4/fqrtfXrLRQGe8E2bpRycuzEiexsa0eiRg0LgrVq2RzFGjWsJSZKCQkWpqpXl6pVs97N2FhrPl%2BoBQIW5goLrd5g8/ut7d5tLT/f2q5ddhpGXp699kjVrGm9n8Ee0ebNrQWHzJs0qaQh85YtpSee0P7Nm6WGDeW7/XbCn8vQAwgAiFr79lkIDPaU5eSEetA2b7Zeta1brYdt%2B3brbfP7na46pFo165WsWzfUW9mggfViNmxoPZqpqTbU3bixBdZwrllgNMC96AEEAEStatWs16tJk4q/xu%2B3Hri8vFCPXH6%2BTXkrKLDj8nbvthXNe/eGevUKC60FAqHeO5/PetyCvYPBHsP4eGuJidZq1rRWq5aFuKQkC34JCeENdEAQARAA4Cnx8aE5goBXRdF%2B3QAAAKgMBEAAAACPIQB61Ntvv62%2Bffuqfv368vl8WrRo0WFfM2XKFPl8vgPanj17wlAxwulo3h%2BIToFAQGPGjFHjxo2VmJios88%2BW0uXLj3ka8aMGXPAz4HU1NQwVYxwyMrKUkZGhjIzM50uBVWEAOhR%2Bfn56tatmx5%2B%2BOEjel1ycrJ%2B//33Ui0hIaGKqoRTjvb9gegzYcIEPfHEE5o0aZIWLFig1NRU9e7du/iYy4M56aSTSv0cWLx4cZgqRjgMHz5cy5YtC8vRbHAGi0A8asiQIZKktWvXHtHr%2BE3fG472/YHoEggE9OSTT%2Br%2B%2B%2B/XRRddJEl66aWX1KhRI7322mu64YYbDvrauLg4fhYAUYweQByRXbt2qXnz5mratKkGDBigheUd7AkgKvzyyy/Kzs5Wnz59ih%2BLj4/XWWedpa%2B//vqQr129erUaN26s9PR0XX755VqzZk1VlwugEhEAUWFt2rTRlClT9O6772ratGlKSEhQt27dtHr1aqdLA3AUsv93dEajRo1KPd6oUaPij5Wnc%2BfOmjp1qj766CNNnjxZ2dnZOv3007V169YqrRdA5SEAesCrr76qWrVqFbe55Z24XgFdunTR4MGD1aFDB3Xv3l3//ve/deKJJ2rixImVXDHCqbLeH4h8Zb/X%2B/btk2RTO0oKBAIHPFZSv379dPHFF6tdu3bq1auX3n//fUk2fAwgOjAH0APOP/98de7cufh%2BkyPZMv8QYmJilJmZSQ9glKuq9wciT9nvtf9/Z6JlZ2fruOOOK348JyfngF7BQ6lZs6batWvHzwIgihAAPSApKUlJSUmV/nkDgYAWLVqkdu3aVfrnRvhU1fsDkafs9zoQCCg1NVWffPKJOnXqJEnau3evZs%2BerUceeaTCn9fv92v58uXq3r17pdcMoGoQAD1q27ZtWrdunTZu3ChJWrlypSQpNTW1eGXf1VdfrSZNmmj8%2BPGSpAcffFBdunRRq1atlJeXp6eeekqLFi1SVlaWM38IVJmKvD8Q/Xw%2Bn2677TaNGzdOrVq1UqtWrTRu3DjVqFFDV155ZfHzevbsqUGDBmnEiBGSpDvvvFMDBw5Us2bNlJOTo7FjxyovL0/XXHONU38UAEeIAOhR7777roYOHVp8//LLL5ckjR49WmPGjJEkrVu3TjExoWmiO3bs0LBhw5Sdna2UlBR16tRJc%2BbM0WmnnRbW2lH1KvL%2BgDvcfffd2r17t26%2B%2BWZt375dnTt31scff1yqp/Dnn3/Wli1biu9v2LBBV1xxhbZs2aIGDRqoS5cumj9/vpo3b%2B7EHwHAUfAFAoGA00UAAIDIk5eXp5SUFOXm5io5OdnpclCJWAUMAABK4Sg496MHEAAAlIseQPeiBxAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAJTCSSDux0kgAACgXJwE4l70AAIAAHgMARAAAMBj4pwuwAuWLJHmzpVOPVVq315KSHC6IgAA4GUEwDB47z3pvvvsOi5OysiQTjnFWqdOUocOUlKSszUCAADvYBFIGEyfLk2ZIn33nbRlS/nPOeEEqWNHC4MdOth106aSzxfWUgEAKMYiEPciAIZRICCtXy8tXCh9/721hQul334r//l16tiQcfv2Urt21k46id5CAEB4EADdiwAYATZvlhYtsvbDD9ZWrJD27y//%2BccfHwqDJ59st61bS4mJYS0bAOByBED3IgBGKL9fWrZM%2BvFHafHiUPv99/Kf7/NJLVrY/MK2bUs3/s0CAI4GAdC9CIBRZutWW1UcbEuXWtu27eCvadzYeghbt5batLHbE0%2BUmjeXYmPDVzsAILoQAN2LAOgCgYCUk2NBcNkyafnyUMvOPvjrqlWTWra0MNiqlS1ECba0NMIhAHhVVlaWsrKyVFhYqFWrVhEAXYgA6HI7dkgrV1pbscJuV62SVq%2B2YeaDqVZNSk%2B3gNiiRei2RQt7vFat8P0ZAADOoAfQvQiAHlVYaCuSV6%2B2tmqV9NNP1taskfbtO/Tr69e3xSjp6XbbvHnotlkz5h0COHKBgLRnj5SfLxUUSLt3h25377aPlW1%2Bv7R3b%2Bh2377Q7f79dltYGGpFRaFWls8nxcSEWlycjYRUq2atenVr8fHWEhNtY//ERKlGDalmTWu1allLTrZWs6Z9vmhEAHQvAiAOEAyHP/9sYTB4G2zbtx/%2Bc6SkWBBs1syGk4OtaVNrTZrYD0wA0SkQsNCVl2dt505rJa937Sp9m59v12XvFxTY7e7d9nndxuezn4m1a9v2XnXrWqtXz36Zrl9fatgw1FJT7WOREBoJgO5FAMQRy82V1q6VfvnFbteulX79NdQOtSClpDp1LAg2aWILVUq2446zH4KpqfabNoDKEQhY0MrNDYW34HXJx8p%2BrGTQC94ebqTgWFSvbr8kJiaWbgkJoRbsiYuPD/XOVa8e6rGLiwvdxsaW7t2LjS290X4gYC3YOxjsMQz2IgZbsKcx2AMZ7J0sKLBAGwy1wRBcWHh0f/64OPv5F/yZ2KSJ/fKclhb65bpJE3teVSIAuhcBEJVu1y5p3Tpr69eXbhs2WMvPr/jnq1PHfhA2ahS6bdSo9G/MDRtKDRrYUAunp1SCrVutNW1KV22ECAa3sgHtUGEtN7f8x442lBxMrVq2QX1Skg15Bq%2BTkkLDoSWvgy04XBocOg0Oo9ao4Y5FaCW/Zzt2WNu%2B3X5JDv4T27LFWk6OtU2b7PGKiI21f6Lp6aG52i1b2kK%2BVq0qYSpOYaHyFi9WSqdOBEAXIgAi7AIB%2B0/ot99Kt40bbZ/D33%2B36%2BzsI%2B9hSEiwIBgcVqlf34ZSgi047BIcgqlTx4Zmqvq36KixYoV0993S%2B%2B9bN0hSknT11dLDD7Py5ygUFh7YI3SoVnL4NHi/ZKvM4ObzheaoJSfbv4OSt8HrpKTQYyUDXvCxWrUiY6jSTfbtsyAY/FkY/BkZ/EX611/tF%2BnD/XysXXuPfL5VKihYJL9/kSZOHK6BA1uqWbMK/KL81FPS448rb906pUjK7dBByWPHSgMGVNYfEw4jACJiBQL2m3J2trVNm0K3wd%2BUc3LsJJWcHBuOOVpJSRYGa9cOtZSUA1vwP8aSPRzBlpAQ5b2Pa9ZInTuXf2D16adLX35p42kuU1QUGsIr2YILEYLDeiXbrl0H3pbXCgoqv16fLxTEyray4e1Q17VqRfn71eOKiiwgrl1beo52cDFfTs7BX1uzph0acPLJoaNG27e3X54lSffdJ40fL0nKkywASkqOiZFef1265JKq/cMhLAiAcI38fAuDwbZlS2iIZevW0LDLtm2htmtX5X39mJjSKwCDw1rBIa1gC64YLDunKXgdnNMUvC656jC4ErHkbbVqB85nOirXXy%2B98MLBPz5tmnT55cf4RSzYl51XtX//gSs49%2B4NteC8q%2BD1nj2lr4OrQctbJVpQcOBcrZLXh9oOqbLExBz4C0NwlWjZx8v2tJUNedG8ohThk5truzusWiXNn79DkyZ9ohYtBmj9%2BsSD9hw2biyd0/Z3TfmimWKL7CzSUgFQsrHmn37itwcXIADC0/btC83LKXmbmxu6LTuPquxQ3ZHMZ6xKcXGhFhsbug224MT3YFiMiSl9%2B83yJNUsOngi/izpAt2aNrN4snxwwnwgUHqLjZK3wUn0%2B/eHrsvbfiNSBIN42flotWoduM1H2duDNVf0DiOqrV27Vunp6Vq4cKFOOqmjfvrJDg5YvDh03OjPP9tzR2iiJurW4tceEAAlaf58Gy1AVGPmEzytWjUb9ige%2BjgKRUUHDgWWHSosObRYshcq2DNVdl%2Bz4G3JFuwB27ev/BAVDFpHw6ciJeowSXbnTi1bdnSf/7Bf31d%2B72bJ1Z1le0NLtpKrQkv2pJbsZS3b8xq8X7Jnlp41uF21aqFz4kuO5O7caWFw9bXfSqsO/TkGX7BThT2krl1tdkiHDq6cHeJ6BEDgGMXEhIbnwqWwsPQQanAYtWQrufltsFcu2AMX7LkL3gYCMdp106lKXvXtQb9myz9m6rNhpXsNg9clexhL9jQGW8meybLbcwSHsAEcvVdffVU33HBD8f1Zs2ape/fuFX59UpLUrZuU%2Bc8/Sv2nHvR5flXXR5s6aMt0afp0e6xGDesQPOMMqXt3C4asGYt8DAEDMK%2B%2BKg0eXP7HEhLscOnjjw9rSQAqZufOndq0aVPx/SZNmigxMVFS6SHgjh07HvoTBQK2ImTJEkkHDgHvvepPmvOnFzVvnjRvno0Glz0cIDZWOvVU6eyzpR49LFgmJVXWnxSVhQAIIGTUKOmRR0ofx1Czpi0AGTjQuboAHLUjCoCSLSfu21f66afSAfCcc6R33inVvVdUZL8bfvWVtTlzbA/YkmJjrYfwnHOknj2th5AN/p1HAARQ2urV0tSpto9E27bSNdfYHjkAosq2bdu0bt06bdy4Uf3799f06dPVunVrpaamKjU19dAv3rdPmjFDeR9/rJQXXlDuf/6j5P79K/R11661IPjFF7Z71Nq1pT9eo4Z01llSnz6WM9u0YZGUEwiAAAC40JQpUzR06NADHh89erTGjBlToc9RGUfB/fKL9Pnn0mefWSu7R2GzZtK550r9%2BlkPIcPF4UEABAAA5arss4CLimx64ccfSx99JM2dW3ovzmrVrHdwwABrLVse85fEQRAAAQBAuSo7AJZVUGDDxLNmWQvuRxiUkSGdf761zp3ZqqkyEQABAEC5qjoAlrVypR1F/p//2DzCkudfp6ZKF1wgDRpkq4urV6/yclyNAAgAAMoV7gBY0o4d1iv4zjt2m5cX%2Bljt2rYxwaWXSr17205VODIEQAAAUC4nA2BJe/faquIZM6SZM6USWx4qOdmGiC%2B7zFYWs8VMxRAAAQBAKVlZWcrKylJhYaFWrVrleAAsqbBQ%2Bvpr6c03pbfekn77LfSx2rWlu%2B6S7rvPufqiBQEQAACUK1J6AA%2BmqMhOI3n9demNN6Tff5cefVS6806nK4t8BEAAAFCuSA%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%3D%3D'}