Genus 2 curve data - 169.a.169.1

Formats: - HTML - YAML - JSON - 2026-03-09T14:52:45.113559

• 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': '1456780685049277288', 'abs_disc': 169, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[6,2]', 'aut_grp_label': '6.2', 'aut_grp_tex': 'C_6', 'bad_lfactors': '[[13,[1,5,13]]]', 'bad_primes': [13], 'class': '169.a', 'cond': 169, 'disc_sign': -1, 'end_alg': 'CM', 'eqn': '[[0,0,0,0,1,1],[1,1,0,1]]', 'g2_inv': "['-1/169','33/169','43/169']", 'geom_aut_grp_id': '[12,4]', 'geom_aut_grp_label': '12.4', 'geom_aut_grp_tex': 'D_6', 'geom_end_alg': 'M_2(Q)', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['4','793','3757','-21632']", 'igusa_inv': "['1','-33','-43','-283','-169']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': False, 'label': '169.a.169.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.0904903908324296291135897572580154122647328643874476536', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.40.3', '3.480.12'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 6, 'num_rat_wpts': 0, 'real_geom_end_alg': 'M_2(R)', 'real_period': {'__RealLiteral__': 0, 'data': '32.667031090507096110005902370', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'E_6', 'st_label': '1.4.E.6.2a', 'st_label_components': [1, 4, 4, 6, 2, 0], 'tamagawa_product': 1, 'torsion_order': 19, 'torsion_subgroup': '[19]', 'two_selmer_rank': 0, 'two_torsion_field': ['6.0.10816.1', [1, 2, 0, -2, -1, 0, 1], [6, 5], False]}
• 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': [['2.0.3.1', [1, -1, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], 1]], 'factorsRR_base': ['CC'], 'factorsRR_geom': ['M_2(RR)'], 'fod_coeffs': [-1, -3, 6, 4, -5, -1, 1], 'fod_label': '6.6.371293.1', 'is_simple_base': True, 'is_simple_geom': False, 'label': '169.a.169.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0, 0, 0]], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_6'], [['2.2.13.1', [-3, -1, 1], [-2, -2, 4, 1, -1, 0]], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_3'], [['3.3.169.1', [-1, -4, -1, 1], [2, -3, -1, 1, 0, 0]], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_2'], [['6.6.371293.1', [-1, -3, 6, 4, -5, -1, 1], [0, 1, 0, 0, 0, 0]], [['1.1.1.1', [0, 1], 1]], ['M_2(RR)'], [3, 1], 'E_1']], 'ring_base': [1, -1], 'ring_geom': [3, 1], 'spl_facs_coeffs': [[[2644, 9457, -8353, -15597, 1720, 3630], [207207, 759759, -759759, -1342341, 160160, 316316]]], 'spl_facs_condnorms': [1], 'spl_fod_coeffs': [-1, -3, 6, 4, -5, -1, 1], 'spl_fod_gen': [0, 1, 0, 0, 0, 0], 'spl_fod_label': '6.6.371293.1', 'st_group_base': 'E_6', 'st_group_geom': 'E_1'}
• 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': '169.a.169.1', 'mw_gens': [[[[0, 1], [0, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [19], 'num_rat_pts': 6, 'rat_pts': [[-1, 0, 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: 0
    {'conductor': 169, 'lmfdb_label': '169.a.169.1', 'modell_image': '2.40.3', 'prime': 2}
  • id: 1
    {'conductor': 169, 'lmfdb_label': '169.a.169.1', 'modell_image': '3.480.12', 'prime': 3}
• g2c_tamagawa •

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

  
  {'cluster_label': 'c3c3_1~3_0', 'label': '169.a.169.1', 'local_root_number': 1, 'p': 13, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '169.a.169.1', 'plot': 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%2BLQaRpP79pe%2B%2Bc7ceAJEtMzNT6enpysjIcLsUwFN8juM4bheB2LJnj9Sjh/T%2B%2B1JamvTNN1L16m5XBSCSBQIB%2Bf1%2BZWVlKTk52e1ygJjHCCBKXbly0pQpUoMG0rp1tlNIfr7bVQEAgCACIMpElSrSW29JiYnSzJnS4MFuVwQAAIIIgCgzJ50UWggydiyLQgAAiBQEQJSpXr2k%2B%2B6z29dfL82f7249AACAAIgwGDZMuvBCKTdX6tlT2rjR7YoAAPA2AiDKXLly0ssv25Twb79JF10k7drldlUAAHgXARBhkZQk/e9/0rHHWluYf/5TogERAADuIAAibOrVk6ZNk8qXl6ZOlYYPd7siAAC8iQCIsDrzTGnCBLs9fLj06qvu1gMAgBcRABF211wj3XGH3e7XT/ryS1fLAQDAcwiAcMXo0dIFF9jK4AsvlNascbsiAAC8gwAIV8TF2XZxLVpIW7dK550n/fGH21UBAOANBEC4pnJl6Z13pNq1peXLpYsvlvLy3K4KAIDYRwCEq2rXlt5/39rEzJpl1wfSHgYAgLJFAITrTj5Zev11mxaePDm0dRwAACgbBEBEhG7dpGeesdsPPRS6DSC2ZWZmKj09XRkZGW6XAniKz3GYcEPkGDpUeuAB2z7urbdspTCA2BcIBOT3%2B5WVlaXk5GS3ywFiHiOAiCjDhtk2cXv2SL170yMQAICyQABERPH5pIkTrS3MX39J3btLy5a5XRUAALGFAIiIU7689NprUqtW0vbtdn3ghg1uVwUAQOwgACIiJSZK774rNW5s4a9rV%2Bn3392uCgCA2EAARMSqXl36%2BONQo%2Bjzz5d27HC7KgAAoh8BEBGtTh0LgdWqSV9/LV10kZST43ZVAABENwIgIl56uvThh7Z13KefSldcIeXnu10VAADRiwCIqJCRIf3vf1JCgvT226FWMQAAoOQIgIganTvb6uC4OOnll6WbbmLfYAAAjgQBEFHlggss/Pl80oQJ0uDBhEAAAEqKAIioc8UVob2CH31Uuv9%2Bd%2BsBACDaEAARla69VnrqKbs9YoQdAACgeAiAiFo33yw9/LDdvu8%2B6T//cbcewKuGDRsmn89X6EhJSXG7LACHUN7tAoCjMWiQlJcn3XuvdM89tkBk8GC3qwK8p2nTpvrkk0/23o%2BLi3OxGgCHQwBE1BsyxPoCDh0q3XmnVK6cdMcdblcFeEv58uUZ9QOiCFPAiAn3328BULJRwUcecbcewGtWrlypWrVqqW7duurdu7dWr159yOfn5uYqEAgUOgCEDwEQMWPYsFAIHDyYawKBcGnVqpVeeuklffTRR3r22We1adMmtW3bVtu2bTvoa0aNGiW/37/3SE1NDWPFAHyOQxc1xJYHHggFwQcflP79b3frAbxm586dql%2B/vu68804NHDiwyOfk5uYqNzd37/1AIKDU1FRlZWUpOTk5XKUCnsU1gIg5998vlS9vC0Puu0/KybEg6PO5XRngDYmJiWrWrJlWrlx50OckJCQoISEhjFUB2BdTwGGwZo0tVNjnj12UsSFDQi1iHnrIrgtkrBsIj9zcXC1btkw1a9Z0uxQAB0EALGOOI/XrJ40aJWVkSD/84HZF3jFoUKhZ9GOPSTfeKO3Z425NQCwaNGiQ5syZozVr1mj%2B/Pm65JJLFAgE1LdvX7dLA3AQBMAy5vNJAwdKxx8vLVpkIXDkSGtbgrJ3883Sc8%2BF9g7u25dzD5S2X375RVdccYUaN26sXr16KT4%2BXl999ZXS0tLcLg3AQbAIJEy2bJH695feftvut24tTZokNW7salme8eqrUp8%2BFv4uuECaOlWqUMHtqgAEBQIB%2Bf1%2BFoEAYcIIYJgcf7w0bZr04otScrL01VdSixbSE08wLRkOvXtLb70lJSRI06dL550nZWe7XRUAAO4gAIaRzyddfbW0eLF09tnSX39JAwZInTvbQhGUre7dpQ8/lCpXlmbNsvO%2BdavbVQEAEH4EQBekpkoffSSNHy9VqiTNmSM1ayY9/TQrVctax44W/qpXl775RjrjDGntWrerAgAgvAiALvH5pBtukH78UTrzTGnnTlulevbZBJKydtpp0uefS3XqSCtWSG3b2n8HAAC8ggDosvr1bURq7FipYkXp009tNHDCBEYDy1LjxtK8eVLTptJvv1kInzPH7aoAAAgPAmAEKFdOuu02G4U64wxpxw4bHezShWsDy1Lt2tJnn9k5z8qSunaVXnvN7aoAACh7BMAI0qCBjUIFRwNnzrTRwHHjWClcVqpWlT7%2BWOrVS8rLky6/3JpGM/oKAIhlBMAIs%2B9oYPDawFtuscULK1a4XV1sqljRRv5uvtnu33GHdOutUkGBu3UBAFBWCIARqkEDuzZw3DgpMdGmKps3l8aMYSeLshAXJz35ZGj/4HHjpJ49LYADABBr2AkkCqxdK11/vTRjht1v2VJ6/nkLhCh9r79uu4bk5kqnniq9845Uq5bbVQGxjZ1AgPBiBDAKnHii9Q3873%2BlKlWkb7%2B1Vib//reUk%2BN2dbHn0ktDvQK/%2B046/XTp%2B%2B/drgoAgNJDAIwSPp/0j39IS5fagoX8fOmhh6RTTrHpYZSuNm2k%2BfOlJk2kjRttpXBwH2cApSczM1Pp6enKyMhwuxTAU5gCjlLTpkk33SRt2mT3r79eGj3aRghRev78U7rsMpt%2B9/ksdN99t90GUHqYAgbCixHAKNWrl7RsmXTddXb/mWek9HTpjTdoYVKaqlSR3n/fwrbjSEOGSFddZfs4AwAQrQiAUaxKFQt%2Bs2dLjRrZjhaXXipdeKG0fr3b1cWO8uVtVfDTT9vtV16R2reXfvnF7coAADgyBMAY0KGD9MMPtijkmGNs1Wp6ujWUpmVM6fnXv2wq%2BNhjQwtxPv/c7aoAACg5AmCMqFBBevBBW63arp31r7v9dqlVK%2Bmbb9yuLnZ07CgtWCCdfLK0ebPUubONDDLtDgCIJgTAGNO0qTR3rjRxok0RB9uY3HKL7XeLo1e3rjRvni0O2b1buvFG6ZpruC4QABA9CIAxqFw5WxW8fLn097/b6NS4cdLf/ia9%2BiqjVaUhMdHO5ejRdr5feMFaxaxd63ZlAAAcHgEwhtWoIU2eLH3yidSwoS0SueIKqWtX9hUuDT6fdOed1qT72GNttLVlS%2BmDD9yuDACAQyMAesBZZ0mLFknDh0sJCRYImzWT7rtP2rXL7eqiX5cuFv4yMqTt26XzzrNzW1DgdmUAABSNAOgRCQnS/fdLS5ZI55wj5eVJI0bYNYPTp7tdXfSrU8d2ZLnhBrs/YoSNtG7e7G5dAAAUhQDoMfXrW2PjN9%2BUUlPtmrULL5S6d5d%2B/tnt6qJbQoI0frw0ZYpdIzhzpm3VN3Om25UBAFAYAdCDfL7QTiJ33229A997z3oH/vvfTAsfrSuvtNY7J51kW/V16SINHcqUMAAgchAAPSwxURo1yq4PPPtsmxZ%2B6CGpSRO2lDtaTZpI8%2BdbexjHkR54QOrUSdqwwe3KAAAgAEJS48a2kvXNN6W0NAspl15qi0cWL3a7uuhVqZL03HM2JVy5sl0j2Ly59PbbblcGAPA6AiAkhaaFly61xSIJCdKsWXYN2y232OpWHJkrr7QdWk47TfrjD6lnT9tWjql2AIBbCIAopFIlaxezfLkFwoICayLdsKGUmcnewkeqQQPpiy%2BkwYPt/sSJ0qmn2p7CAACEGwEQRTrxRJsS/vRTW8ywfbt08802IjhjhtvVRaf4eGnMGOvDWKuW9NNPUuvW0siRLBCBd2VmZio9PV0ZGRlulwJ4is9xuNQfh5afLz3zjDU3Dk4Fd%2B8uPfywLXZAyW3bJvXvbyFbktq2lV580UYKAS8KBALy%2B/3KyspScnKy2%2BUAMY8RQBxW%2BfLSjTdKq1ZJt91m999913YTufVWCzMomWOPlV5/XZo0SUpOlubNswUiTz/N6msAQNkjAKLYqlaVxo61lcHdu9vI4FNP2ajVo49KubluVxhdfD6pb1/pxx%2Bljh1tUciNN0rduknr17tdHQAglhEAUWKNG0vvvGPXsjVvLv35pzRokPS3v0lTpzKCVVJpaXat5dixUoUKdo3lSSdZCxnOJQCgLBAAccTOOstWsT7/vFSzprRmjdS7t9SmjfW8Q/GVK2fT6z/8YOcvO1u67jobDVy71u3qAACxhgCIoxIXJ/3zn9LKldY%2BJjHRdsA480zbY3jpUrcrjC6NGll4fuSRwqOB48ZJe/a4XR0AIFYQAFEqEhOtgfSqVba6NS5Omj7dFopcd520caPbFUaPuDjpjjvs2sD27aWdO60Zd/v2BGoAQOkgAKJUpaRIEybYQpGLLrJRq%2Bees4Uid93FjiIl0bChNHu2jf5VrmwrhU85RRo6VMrJcbs6AEA0ow8gytS8eRb8Pv/c7vv9dv/WW23UEMWzYYOtEH73XbvfqJEF7U6d3K0LKC30AQTCixFAlKm2baW5c23VcLNmUlaWNGSIVL%2B%2BtZChdUzxpKbalPrrr9so64oVUufO0tVXS1u2uF0dACDaEABR5nw%2B6xu4cKE0ebJUr560ebONAjZqJP33v%2BwxXBw%2Bn3TJJdKyZdINN9j9l1%2B2tjxPP812cgCA4iMAImzKlZP%2B/ncLMOPH236469dL11xjPQSnTCHEFEeVKnb%2BvvpKatHC%2BjDeeKPUqpWtwAYA4HAIgAi7%2BHgbwVq1ytqdVK9ut6%2B6yqaJp06l5UlxnH66tGCBTaX7/daTsXVrC9RMCwMADoUACNdUrGjtTlavlh56yLaaW7bMmkmffLJd70YQPLS4OOnmm6WffrJt5SSbUm/YUHrsMSkvz936AACRiVXAiBhZWdITT1hwycqyx5o2le67z659i4tzt75o8OWX1jPw22/tfuPGtk/zeefZNYNApGIVMBBejAAiYvj91kx67Vrrdef3S0uW2IjgSSfZNYIsFjm0Nm3sOsBnn5WOO85GBrt3l845x3ozAgAgEQARgapUkYYNsyA4fLjdX77crhFs0sQaSzO1eXBxcdK119r2fIMGScccI338sdS8ue3SsmmT2xUCANzGFDAiXiBgu2E89pi0bZs9dsIJ0uDBFnQqVXK3vkj388/WfPvNN%2B1%2BYqJ0553SwIG2wwjgpszMTGVmZqqgoEArVqxgChgIEwIgosbOndLEibZy%2BLff7LHq1aXbbpNuuskWkeDgPv/cFt18/bXdr1HDRlqvucZGCQE3cQ0gEF4EQESdnBxp0iRpzBhpzRp7rHJl6frrpQEDbNcMFM1xbHX1kCE2MijZPs0jRkiXXmq9GgE3EACB8OLtHlGnQgXpX/%2By7dCmTLHegTt22BRxvXrWDmXRIrerjEw%2Bn3TZZdLSpdKTT9pCkVWrbKFNy5bS%2B%2B9bSAQAxDZGABH1HEf68ENp9GhpzpzQ49262SKIs86iBcrBZGdLjz9u0%2BrZ2fZYmzY2Iti5s7u1wVsYAQTCiwCImPL11xZm3nwz1ES6WTPp9tulK6%2BUEhLcrS9S/f67Behx42yKXZI6dLBV2B06uFsbvIEACIQXARAxafVqaexY2xVj50577PjjbQu6G26wBRA40G%2B/SSNHSs88E2q107Gj9WXs2NHNyhDrCIBAeBEAEdP%2B%2BMOaIj/1lPTLL/ZYfLx0%2BeXSrbdKp53mbn2R6pdfLAg%2B/3woCLZvb7uydOnClDpKHwEQCC8CIDxh925p2jQbFfzqq9DjrVvbXrqXXML0cFE2bJD%2B85/CzbczMqR775V69GDVMEoPARAILwIgPGfBAlsBO3WqBUPJVsNee63tlJGW5m59kWjjRunhh21q%2BK%2B/7LGmTa2h9BVX0EcQR48ACIQXARCetXmzTQ9PmGABR7KpzfPOs%2BsEzznHtlVDyJYtNoqamWk7tEjWd3HgQGsonZTkbn2IXgRAILwIgPC8/Hxp%2BnRp/Hjp009Dj6emWqi55hrbeg4hWVnS009bGNy82R6rUsX6M958s1S7trv1IfoQAIHwIgAC%2B1ixwrabmzRJ2r7dHitXzkYDr71W6t6d6c595eRIL70kPfqonTvJzs/ll9uoYIsW7taH6EEABMKLAAgUISfHegk%2B%2B2zh5tLHHy/16SP9859Serp79UWaPXukd96xIPjZZ6HH27e3vZovvFAqX/7/Hty82YYPp0%2B34ddOnaRbbrE96eBZBEAgvAiAwGGsWGHtUF58MTTdKdlq2H79bLTr2GNdKy/ifPON7S7y2muW7ySbTr/hBql/x59UrVdHadOmwi%2BqVMkSJNuPeBYBEAgvAiBQTLt32165L7wgvfdeKNwcc4zPz1dYAAAgAElEQVRNDffpYwtIaCdjfv3VrqucONF2GpGkz31nqJ3zRdEvSEmR1q9njt2jCIBAeBEAgSOwZYv0yis2KrhwYejxqlWlSy%2B1befat6dPnmTT6VOnSu8/slRTFzc99JPfeEO6%2BOLwFIaIQgAEwosACBylH3%2BUJk%2BWpkyxUa%2Bg2rWl3r3taNmS3TP03ns2VHoIzpiH5Rs8KEwFIZIQAIHwYnwCOEonnyyNGWOzl59%2BKv3jH5Lfb70FH33UrhVs0EC65x7pu%2B8kz/7JVYzeMIPG1tbDDx94iSBiV2ZmptLT05WRkeF2KYCnMAIIlIGcHOmDD6RXX7W1DcHdMySpXj3beq5XLwuHnpombtnSUnARtqmaTtAvylFFxcVJ554rXX21bTlXoUKY60TYMQIIhBcBEChjO3fa7Odrr9kikn3D4AknSBddZMeZZ3pg/cN330lnnSX9%2BWfhx485RrtemKrJO3vqhRcK79fs90uXXSZddZV0xhkeC8weQgDE0crPty4EaWlSzZpuVxP5CIBAGO3caSHwzTctFO7YEfpclSq2iviCC6zxtN/vXp1l6uefrU/M9Om2tLpzZ%2Bsa3bLl3qcsX24Npl9%2BWfrll9BLU1PtmsorrpBOOYXrKmMJARAltXu3/U05Z440e7b0%2BedSdrb0xBPSrbe6XV3kIwACLsnJkT75RHrrLZsm3ro19Lny5W0V8fnnWyhs0sSbYWfPHntznzzZFggH9x%2BWpIYNrQfjpZdKzZp58/zEEgIgDmfXLmn%2BfAt6c%2BdK8%2BbZY/uqWlW66y47cGgEQCACFBTYtOf//mdhcPnywp8/8US7Ju6cc2zjjKQkV8p01V9/2ejpq69K775rATqoYUPrHtOzp11XSRiMPgRA7G/jRunLL6UvvrDj%2B%2B9D/VeDqla1y2c6dpQ6dJCaN%2BcykeIiAAIRaNUqmyJ%2B/32b2sjLC32ufHmpXTvp7LOlLl1s5nTvNmsekZ1tIXDqVOnDD6Xc3NDnTjjBFo5cdJH9QqAxd3QgAHrbrl0W8ObPtz%2BGv/pK2rDhwOfVrm3XArdvb8dJJxH4jhQBEIhwO3dKs2bZquIPP5RWry78eb/f/vrt3NmO9HRvvSFmZ1tYfust%2B7hzZ%2BhzlStLXbvaVHq3bsXqRAOXEAC9IzdXWrxY%2BvZbW7Tx9dd2v6Cg8PPKlbM2W23a2B%2B97drZAg9G%2BEsHARCIMj//LH38sV0/OHPmgQtqq1cPTYm0b2/Xx8XFuVJq2OXkWC/G4FT6/v0Emze3QNitm/0yob1M5CAAxqYdO6xZ/vffh45Fi2wBx/5SUqRWrexo3dou56hcOfw1ewUBEIhiBQW2Cm7mTDs%2B%2B6xwmxnJRgjbtg39BX366VKlSu7UG0579ti5efddGz1dsKBwE%2B4KFey8dOli11WeeqoUH%2B9evV5HAIxuBQX2x%2BnixRbwfvzRjp9/Lrr5fdWqdvnKaafZcfrpdvkGo3vhQwAEYkhengWdOXNCq%2BSysws/p3x5Gwlr3dqmVk4/3XYqifU33i1bpBkz7Pj4Y%2Bm33wp/vlIlC4Tt29vHVq28udjGLQTA6JCXZ6Fu2bLQsWSJLVzbd2HWvmrWtLZNLVrYH1qnnmoL22L9PSfSEQCBGJafL/3wg7VNCK6k23e/4qCqVW26JSPD/ipv2dJ67sXqG7Tj2C%2BsTz%2B1kdO5c6Vt2wo/p1w5qWlTC8itWtm5SU9nlLCsEAAjR36%2B9d9ctUpaudKOFSvsWL36wGv1gipWlP72N7vs5OST7Q/Nk0%2BWjjsuvPWjeAiAgIc4ju1Z/NVX1l5h/ny7JmffVbRBxx1nf7EH/3I/5RRrt1Iq1xPu2WPJND/ffkO4vFTXcWwUY84cGzX94gtp3boDn3fMMRYKW7SwX25Nm9qRkhK7YTkstmxRYNEi%2Bbt0IQCGwZ49NgK%2Bfr39nK9da8fq1dKaNXZ7/3Yr%2B0pMtN6kf/ub/VGUnm7/H9St653rjWMBARDwuLw8u2bn669tRd6331oYKuoXQIUK9kZ/8sn2V37TptaGoWbNEgSgF16Qhg8PJazq1aXbbpPuvTeiUtSvv9o5mT/fPn733YELboKqVLFfho0aSfXrhz7WrStVqxZR31Zk2bpVuuUWado0BXbvll9SVps2Ss7MtJSNEnEcu%2BRj0yY7fv01dGzcaMeGDTa6d6iAJ9lId716dnlIw4b2M92wodS4sa2m52c6%2BhEAARwgJ8dC4b4r93788cAFJkFVqlgYDI4KBD%2Bmpe03IvDMM1L//kV/kdtuk8aOLfXvpbQ4jmXW776TFi60i92XLLFpsj17Dv66xES7uD011Y7ataVataQaNaTjj7eR1mOPtaDopfY92rXL5tYXL5YkBSQLgJKNAH79taUNj9qzx1bQ/vGH/eHxxx92bNtmx%2B%2B/W34OHlu22HGw/0f3FxdnP4tpaXaceKL9wVKvnh21azOaF%2BsIgACKpaDApoiCq/uCAWjlyoMHoPj40IhY43q7NfT5VFUKbC76yeXL29xTlDXry8mxa6OWL7dzEbxuavXqAxeaHEq5cnYtZrVqdvj9diQn22KUypUtTFaqVPioUMGuvapQwWbSExLsvMfH25T1vkdcnB3lykXACM6zz0rXX7/3bqEAKEn9%2Btloscscx3728/NDH3fvLnzk5dnHnBy7nZtrt3NzLZDl5Fje/esvO3buDB07dtiRnW1bHe57HOoPi0OpXNkuS6hZM/QHR%2B3adgT/EKlZ03sN5FEYAfAoOI6j7P2XWAIek5NjgScYgH76yQLRqlWFdzA5Q3P1nnoc8mt9f9UjKvjHdUpLs5lh10PKUcrJCU29bdxoU2%2B//WbTc1u22CjOtm2F9zgOl3LlCgfCfY/geQ/eDh7SgbeL%2BlgcE7f2Uvu8T/feD0hKlbRBFgCzVVmtam887NfZ/zdY8P6%2BH/e/7TgWrva/HzwKCkIf3XbMMTbC7vfbHwjBPxKOPdaO6tVtFHnfIzHR7aqjR1JSknzR/kZzhAiARyG4ag0AAEQfLy86IgAehZKMAAYCAaWmpmrDhg0l%2BmHLyMjQggULSlRXJL%2BG8xDec3CkryuT1/z%2Bu10cWNQWAP/n1vRP9NH2jAN28DiYpCSbygpOcdWqFZr6Cn48/nipTZvIPXdl/fMQnLLMz5c6dOisGTNm7p3O3He0Kzgitv/H3r1765VXXj1gJG3fj/vfvvrqPnrppZeLbAAcVGPiUNV%2BNXTN5wEjgM3baeXY9w87qhj8t/Z/3sFGLH0%2B6YorLtdrr02Vz1d4lHPfUdDg6GhwhLRLl4767LPZKl/eRuWKc71muH4WjuTfiuTXhPM90ssjgFwBcBR8Pl%2BJ/ydNTk4u0Wvi4uJK/G9E8muCOA/hOQdH%2BroyeU1ystS3r/Tcc0V/vl07Tfr8LEl27dSGDdamIvgxeHvWrJ9VoUJ9BQJ23VR2tk05H4z9Qv9MZ52VrFq1QoExeJxwgoXHolbrxtrPQ3z8LtWtW7LXVKq0Vm3bluw1iYkr1aHDYV5Tf6D0zrOFN2%2BWhb9kSckP3qXaHQ//7xbr39pPpUrrdPrpJT13OapZMzJ/Fo7034rk10jhe4/0KgJghLvpppti6jVHKpK/p3CdhyP9dyLqPDz5pI0Evv124cczMqQ339x7NyHB2k80aHDgl8jM/FA33XSTsrMLX1sXPH77LdT6YtOm4AjX8frmm0OXFh9fOBSmpkrp6RM1bZoFxBNOsFHFw62MjOSfh4h6TWqqNH26dOml0vbtex92jjlGGj1a6nHo60XLvD6XXnOkIvl7iuT/J7yMKeAwocu94TxwDiRJ33%2BvnNdf1yOjRun26dOVWMxf9iVVUGCLLYJB8bffQkewN9ovv1gmLY7y5S23nHiiHXXqhFpo1KtnIbGkrTM8//Pw11/Sa69p69df6/jx4/XLd9%2Bptkd7AHr%2BZ%2BH/cB7CgxHAMElISNDQoUOV4PKOB27jPHAOJEktWsiXnq78%2BHiV79q1zP6ZuDib8q1Z0zacP5icnFDj3H1HFNevLzyymJ9vOyWsWVP014mPDwXC%2BvVDTXQbNrQeaxUqHPgaz/88VKxolwacd540frziTzjB7Ypc4/mfhf/DeQgPRgABoBjy8y0EBrfOWrcudAS3zzrE%2Bhb5fDZ62KiRrYVp1Ch0pKV5rAl0ERj1AcKLAAgApaCgwEYK16yxJtCrV1tfxBUrpJ9/tsUqB1Ohgm16ETyCe6w2blz0qGEsIgAC4UUABIAy5ji2XVewUXawaXbw2Ldh9r58PhsdDG6tF9xur0kTawIcSwiAQHgRAAHARcEt9pYvD20pt2yZtHSp7f16MMcdZ6OEwZHCJk3s9gknROcOKgRAILwIgAAQgYKjhsuX27F0qR3Ll1s/xINJSgpdZxgMhU2a2KKUSL6mngAIhJfHLzsuPbt379Zdd92lZs2aKTExUbVq1dLVV1%2BtX3/99ZCvGzZsmHw%2BX6EjJSUlTFWHz5Gen2g1bdo0devWTdWrV5fP59PChQsP%2B5pJkyYd8LPg8/mUk5MThorD70jOUbRyHEfDhg1TrVq1VLFiRXXs2FFLliw55GuGDx%2BmGjV86tDBp/79fXriCZ9%2B/DFF69dLO3ZICxZIL74o3XOP1LOnTRGXL2/XGn77rTRlinTffdLFF9vUcaVKtiq5e3dp8GDp2WelOXNs9TPDAOEzfvx41a1bVxUqVFDLli312WefHfS5XntPmDt3rnr06KFatWrJ5/Pp7f37haJU0QamlOzatUvfffed7rvvPjVv3lx//PGHBgwYoAsuuEDfHKYDbdOmTfXJJ5/svR9X0kZiUeBozk802rlzp9q1a6dLL71U1113XbFfl5ycrJ9%2B%2BqnQYxVidBXAkZ6jaDRmzBg99thjmjRpkho1aqQRI0bo7LPP1k8//aSkpKSDvu5g7w2JidbWZv/WNnl50qpVoesMgyOHP/1kwfDnn%2B14773Cr/P7bRq5YcPQyuTg/cqVS%2B00eN7UqVM1YMAAjR8/Xu3atdPEiRN17rnnaunSpapTp06Rr/Hae0Lz5s31j3/8QxdffLHb5cQ8AmAp8fv9mjFjRqHHnnrqKZ1%2B%2Bulav379Qf/nlqTy5cvH5Kjfvo7m/ESjPn36SJLWrl1botfF6ghwUY70HEUbx3E0duxY3XvvverVq5ck6cUXX1SNGjX0yiuvqH///gd9bUnfG%2BLjQ9cFFq5B2rw5dH3h8uUWCleutPY1WVnS11/bsb%2BUlFAvw0aN7GP9%2BnaUSjj8/Xdp0iRp0SK7v2qVdOqppfCFI89jjz2ma665Rtdee60kaezYsfroo4/09NNPa9SoUUW%2BxkvvCeeee67OPfdct8vwDAJgGcrKypLP51OVKlUO%2BbyVK1eqVq1aSkhIUKtWrTRy5EjVq1cvTFW6p7jnx0t27NihtLQ0FRQU6JRTTtGDDz6oFh7dFSFWrFmzRps2bVLXfRpeJyQkqEOHDpo3b94hA2BpvTf4fBbkUlKkjh0Lfy4nJ9SuJvhxxQoLiL//blPEmzZJRc1UBsNhgwahUBjcwq9Y/1tPmyZddZXtBhLUsqU0ZIj00EMl/j4jWV5enr799lvdfffdhR7v2rWr5s2bd9DX8Z6AskIALCM5OTm6%2B%2B67deWVVx7yguZWrVrppZdeUqNGjbR582aNGDFCbdu21ZIlS3RsrPV52Edxz4%2BXNGnSRJMmTVKzZs0UCAT0xBNPqF27dvrhhx/UsGFDt8vDEdq0aZMkqUaNGoUer1GjhtatW3fQ14XrvaFCBalZMzv29%2BefFgpXrQoFw1WrbBp527ZDh8Nq1SwQ1qtnRzAk1qtneyuXW71KuuKKonvgjBxpBfXuXWrfp9t%2B//13FRQUFPlzEPwZ2R/vCShTDo7I5MmTncTExL3H3Llz934uLy/PufDCC50WLVo4WVlZJfq6O3bscGrUqOE8%2BuijpV1yWJXV%2BYlEh/pe16xZ40hyvv/%2B%2BxJ/3YKCAqd58%2BbOLbfcUprluqKszlEk2v97nT17tiPJ%2BfXXXws979prr3W6detW7K8bae8N27c7ztdfO86UKY7zwAOO07ev47Rr5zgpKY5jk84HP%2BLjHef5qgMLPZglOfq/j47kOK1bu/0tlqqNGzc6kpx58%2BYVenzEiBFO48aNi/U1Yuk94XAkOW%2B99ZbbZcQ0RgCP0AUXXKBWrVrtvV%2B7dm1Jttr1sssu05o1azRz5swSj24lJiaqWbNmWrlyZanWG25ldX4i0cG%2B16NVrlw5ZWRkRP3PglR25ygS7f%2B95ubmSrKRwJo1a%2B59fMuWLQeMBh1KpL03VK0qZWTYsb/sbOttuGZNaOHJqlX22Lp1NuhXP%2B/Qi7/y53%2Bjf/SxPZSDU8v160s1akRnn8Pq1asrLi7ugNG%2BkvwcxNJ7AtxHADxCSUlJB6zeC4ablStXatasWUc0TZObm6tly5apffv2pVWqK8rq/ESior7X0uA4jhYuXKhmRc3NRZmyOkeRaP/v1XEcpaSkaMaMGXuv3crLy9OcOXM0evToYn/daHpvSEqSmje3Y38FBdbHsHLvJGn%2Bwb9GtlNZkycf%2BHilSjaNvO/1hsHrD%2BvUsVY4kSg%2BPl4tW7bUjBkz1LNnz72Pz5gxQxdeeGGxvkYsvSfAfRH6v0r0yc/P1yWXXKLvvvtO7777rgoKCvb%2BpVetWjXFx8dLks466yz17NlTN998syRp0KBB6tGjh%2BrUqaMtW7ZoxIgRCgQC6tu3r2vfS1ko7vmJFdu3b9f69ev39jkMtnFISUnZu6Lv6quvVu3atfeu/hs%2BfLhat26thg0bKhAI6Mknn9TChQuVmZnpzjdRxopzjmKBz%2BfTgAEDNHLkSDVs2FANGzbUyJEjValSJV155ZV7n%2BeV94a4OOnEEyXddLk0/72DPm/b2b01qrONGgZHEdevl3btkhYvtmN/5ctbOAyuWm7QINTWJjVVKudy59uBAweqT58%2BOu2009SmTRs988wzWr9%2Bvf71r39J4j1hx44dWrVq1d77a9as0cKFC1WtWrWY6xQREVyego4ZweuYijpmzZq193lpaWnO0KFD996//PLLnZo1azrHHHOMU6tWLadXr17OkiVLwv8NlLHinp9Y8cILLxT5ve77375Dhw5O3759994fMGCAU6dOHSc%2BPt457rjjnK5dux5wvVAsKc45ihV79uxxhg4d6qSkpDgJCQnOmWee6SxatKjQczz33pCb6zht2hR9DeDxxzvO2rVFvmTFCsf54APHGTfOcQYMcJzu3R3nb39znISEQ193WLGi4zRr5jiXXOI4997rOJMnO8433zhOdnZ4v%2B3MzEwnLS3NiY%2BPd0499VRnzpw5ez/n9feEWbNmFfmesO85QelhKzgAgDuys63ly4svKpCdLb%2BkrO7dlfz44zZ8VwJ79ki//GKrlvddubxypY0e7t598NemptpuKU2bWg/Fk06yjzTBRiwjAAIA3LVrlwI//ST/qaeWyV7A%2BfnW8DrY33Dfhthbtx78dfXrWxg8%2BWS7nrFFC5u%2BdnsqGSgNBEAAgOsCgYD8fn%2BZBMBD2bbNwuDSpXZd4dKl0pIl1t%2BwKMnJFgZPPdW24mvZ0q4xjMEdPBHjCIAAANe5FQAPZutW251u0SLpxx%2BlhQstIBbVt7pyZQuDp58utWoltWkj7dPxB4hIBEAAgOsiLQAWZfdumzb%2B/nvpm2%2Bkb7%2B1YLhr14HPrVNHattWOuMMqX17u76QUUJEEgIgAMB10RAAi1JQYNPGX39tx1df2Ujhnj2Fn%2Bf3WxDs2NGOU04hEMJdBEAAgOuiNQAWJTvbwuAXX0iffy59%2BaW0Y0fh51StakGwSxepa9cSL3oGjhoBEADgulgKgPvLz7ep4tmz7fjsMykQKPycunWlc86RzjtP6tRJSkx0o1J4CQEQAOC6WA6A%2B8vPt2sIZ86UPv7YRgrz80OfT0iQOneWevSQLrhAiuGts%2BEiAiAAwHVeCoD727FDmjVL%2BuAD6f33pXXrCn8%2BI0Pq2VO6%2BGJrOQOUBgIgAMB1Xg6A%2B3IcW1TyzjvS9Om2qGTf39LNmkmXXSZdfrntdwwcKQIgAMB1BMCibdpkQXDaNOnTTwtPFbdsKV15pXTFFfQdRMkRAAEAriMAHt727dLbb0uvvSZ98om1oJFsa7ouXaS%2BfaWLLpIqVXK3TkQHAiAAwHUEwJLZulV64w1p8mRp3rzQ48nJNiJ4zTW2O4nP516NiGwEQACAazIzM5WZmamCggKtWLGCAHgEfv5ZeuklO9auDT3evLnUv7901VVSUpJr5SFCEQABAK5jBPDo7dljfQaff156800pN9cer1xZ6tNHuukm25IOkAiAAIAIQAAsXdu324jgxIm2f3FQ587SbbdJ3bvbtYPwLgIgAMB1BMCy4TjWYzAz0xaQBPcobtBAuv12qV8/Fo14FQEQAOA6AmDZW7dOGj9eeuYZ6c8/7bFjj5Vuvlm65Ra7De8gAAIAXEcADJ%2BdO6X//lcaO1Zavdoeq1TJFowMGiTVquVufQgPrgAAAMBDEhNtxG/FCmnqVKlFC2nXLunxx6W6daUbb5Q2bHC7SpQ1AiAAAB4UF2fbyn37rfThh9IZZ0h5edLTT0v161sQ3LjR7SpRVgiAAAB4mM8ndesmffaZLRjp1EnavTsUBAcOtMbTiC0EQAAAIEnq2FGaOdOCYPv21kvw8celevWk4cOlHTvcrhClhQAIAAAK6dhRmjPHpoZPPdWC37Bh1j5mwgQpP9/tCnG0CIAAAOAAwanhBQtssUiDBtLmzdINN0gnnyy9/771GUR0IgACAICDKlfOFossWSI99ZT1C1y2TDr/fOncc%2B02og8BEAAAHFZ8vDWNXrVKuuMO6ZhjpI8%2BstHAgQOlrCy3K0RJEAABAECxVakiPfKItHSpdMEFdj3g449LjRtLkyczLRwtCIAAAKDEGjSQ/vc/6YMPpEaN7PrAPn2sjQzTwpGPAAgAAI7YOedIP/4ojRwpVaxoq4ebN5fuv1/KyXG7OhwMARAAAByVhATpnntsWvj8862R9IMPSqecYg2mEXkIgAAA12RmZio9PV0ZGRlul4JScOKJ0jvvSK%2B/LtWsKf30k3TmmbatXHa229VhXz7H4XJNAIC7AoGA/H6/srKylJyc7HY5KAV//CHdeaf03HN2v04d6fnnpS5d3K0LhhFAAABQ6qpWlZ59Vvr0U6luXWn9eunss62RNFvKuY8ACAAAykznzrZI5Kab7P6ECbZI5Isv3K3L6wiAAACgTFWuLI0bZ6OBdepIq1fbtYFDhkh5eW5X500EQAAAEBbB0cC%2BfaU9e6RRo6R27aQVK9yuzHsIgAAAIGz8fmnSJOmNN%2Bw6wW%2B%2BkVq0kF54gV1EwokACAAAwu7ii6VFi2xUcNcu6Z//lP7%2BdykQcLsybyAAAgAAV9SuLX38se0iEhcn/b//J7VsKX3/vduVxT4CIAAAcE1cnO0iMneuLRBZtUpq00aaOJEp4bJEAAQAAK5r29ZG/nr0kHJzpX/9S7r6amnnTrcri00EQAAAEBGqVZP%2B9z9pzBgbGZw8WWrdWlq50u3KYg8BEAAARAyfTxo8WJo5U6pRQ1q8WMrIkN57z%2B3KYgsBEAAARJwzz7Qp4bZtpawsmxp%2B8EHrH4ijRwAEAAARqWZNadYs6cYbbUHI/fdLl17KXsKlgQAIAAAiVny8lJkpPfec3Z42zXYPWbfO7cqiGwEQAABEvGuusdHAGjVsO7mMDGnePLeril4EQAAAEBXatpUWLJBOOUXaulXq1EmaMsXtqqITARAA4JrMzEylp6crIyPD7VIQJVJTpc8/l3r2lPLypKuukoYNo2l0Sfkch1MGAHBXIBCQ3%2B9XVlaWkpOT3S4HUWDPHttBZMwYu9%2BnT%2Bg6QRweI4AAACDqlCsnjR4tPfOMNY1%2B%2BWXpnHOkP/90u7LoQAAEAABR67rrrEl0UpItEunQwbaSw6ERAAEAQFTr1k2aO9f6BvbrJyUkuF1R5CvvdgEAAABH65RTbNu4atXcriQ6MAIIAABiAuGv%2BAiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAI5Kv3795PP5Ch2tW7d2uywAh0AjaADAUTvnnHP0wgsv7L0fHx/vYjUADocACAA4agkJCUpJSXG7DADFxBQwAOCozZ49W8cff7waNWqk6667Tlu2bDnk83NzcxUIBAodAMLH5ziO43YRAIDoNXXqVFWuXFlpaWlas2aN7rvvPuXn5%2Bvbb79VQkJCka8ZNmyYhg8ffsDjWVlZSk5OLuuSAc8jAAIAim3KlCnq37//3vsffPCB2rdvX%2Bg5v/32m9LS0vTqq6%2BqV69eRX6d3Nxc5ebm7r0fCASUmppKAATChGsAAQDFdsEFF6hVq1Z779euXfuA59SsWVNpaWlauXLlQb9OQkLCQUcHAZQ9AiAAoNiSkpKUlJR0yOds27ZNGzZsUM2aNcNUFYCSYhEIAOCI7dixQ4MGDdKXX36ptWvXavbs2erRo4eqV6%2Bunj17ul0egINgBBAAcMTi4uK0aNEivfTSS/rzzz9Vs2ZNderUSVOnTj3sSCEA97AIBADgukAgIL/fzyIQIEyYAgYAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAHBNZmam0tPTlZGR4XYpgKf4HMdx3C4CAOBtgUBAfr9fWVlZSk5OdrscIOYxAggAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHsMqYACA6xzHUXZ2tpKSkuTz%2BdwuB4h5BEAAAACPYQoYAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAMWMOZ4AAADuSURBVOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHvP/AUvF2Eiynr8jAAAAAElFTkSuQmCC'}