Genus 2 curve data - 1270.a.325120.1

Formats: - HTML - YAML - JSON - 2026-10-15T20:42:18.654484

• 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
  sha_proved	boolean
  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': '2268199784340011667', 'abs_disc': 325120, 'analytic_rank': 0, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[2,[1,0,1,2]],[5,[1,2,2,-5]],[127,[1,-21,147,-127]]]', 'bad_primes': [2, 5, 127], 'class': '1270.a', 'cond': 1270, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[3,-32,14,76,17,1],[0,1,1]]', 'g2_inv': "['764790054928595680699001/325120','15368348330455841308623/162560','97860226229056869361/20320']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['239204','126763297','10436094933809','41615360']", 'igusa_inv': "['59801','143724846','437833820176','1381517230655315','325120']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '1270.a.325120.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.4736509421369822779710318478398234651284049699476694597', 'prec': 183}, 'locally_solvable': True, 'modell_images': ['2.60.1', '3.80.2'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 3], 'non_solvable_places': [], 'num_rat_pts': 1, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '1.8946037685479291118841273914', 'prec': 97}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 7}, 'root_number': 1, 'st_group': 'USp(4)', 'st_label': '1.4.A.1.1a', 'st_label_components': [1, 4, 0, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 2, 'torsion_subgroup': '[2]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.6.129032000.1', [-31, -15, 42, 11, -16, -1, 1], [6, 3], 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': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '1270.a.325120.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'USp(4)']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'USp(4)', 'st_group_geom': 'USp(4)'}
• 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': '1270.a.325120.1', 'mw_gens': [[[[-4, 1], [8, 1], [1, 1]], [[-2, 1], [7, 2], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2], 'num_rat_pts': 1, 'rat_pts': [[1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 371
    {'conductor': 1270, 'lmfdb_label': '1270.a.325120.1', 'modell_image': '2.60.1', 'prime': 2}
  • id: 372
    {'conductor': 1270, 'lmfdb_label': '1270.a.325120.1', 'modell_image': '3.80.2', 'prime': 3}
• g2c_tamagawa •

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

  
  
  • id: 11728
    {'label': '1270.a.325120.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 1}
  • id: 11729
    {'cluster_label': 'c3c2_1~2_0', 'label': '1270.a.325120.1', 'local_root_number': -1, 'p': 5, 'tamagawa_number': 1}
  • id: 11730
    {'cluster_label': 'c3c2_1~2_0', 'label': '1270.a.325120.1', 'local_root_number': -1, 'p': 127, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '1270.a.325120.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl8VNXh/vFnIBtLMiySsBgiRUFHaLUwbFKXouCCWFwAwfh1YbFEMIBsAlqrEtDiHhCxSmURa5HdRvEnRBFBCuIGAgoiW8RWnAGUJJD7%2B%2BOU0GiUAEnO3Lmf9%2Bs1r5zMJDPPjLE%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%2B/fL7/dr//79tqMA1lAAAQAAPIYCCAAA4DEUQABARNm1a5duuukm1a1bV9WrV9d5552ntWvXFj/uOI7%2B9Kc/qWHDhqpWrZouvvhiffrppxYTA%2B5DAQQARIx9%2B/bpggsuUGxsrP75z39qw4YNmjRpkmrVqlX8Mw8//LAeffRRPf3001qzZo3q16%2Bvyy67jHP6gBPgc7gMCgAQIUaNGqV3331X77zzTqmPO46jhg0bKjMzUyNHjpQk5efnKyUlRRMnTtSAAQOO%2BxrhcFh%2Bv1%2BhUEhJSUnlmh9wC2YAAQARY%2BHChWrdurVuuOEGJScn6/zzz9e0adOKH9%2B2bZvy8vLUuXPn4vvi4%2BN10UUXaeXKlTYiA65EAQQARIytW7dqypQpOuuss/T666/rjjvu0ODBg/Xiiy9KkvLy8iRJKSkpJX4vJSWl%2BLEfy8/PVzgcLnEDvC7GdgAAAI4qKipS69atNX78eEnS%2Beefr08//VRTpkzRzTffXPxzPp%2BvxO85jvOT%2B47KysrS/fffX3GhARdiBhAAEDEaNGigQCBQ4r5zzjlHX331lSSpfv36kvST2b69e/f%2BZFbwqNGjRysUChXfduzYUQHJAXehAAIAIsYFF1ygTZs2lbhv8%2BbNSktLkyQ1adJE9evX19KlS4sfLygoUG5urjp06FDqc8bHxyspKanEDfA6DgEDACLGkCFD1KFDB40fP149evTQ%2B%2B%2B/r2effVbPPvusJHPoNzMzU%2BPHj9dZZ52ls846S%2BPHj1f16tXVu3dvy%2BkB92AZGABARFm8eLFGjx6tLVu2qEmTJho6dKj69etX/LjjOLr//vs1depU7du3T23btlV2drZatGhRpudnGRiAAggA8BgKIMA5gAAAAJ5DAQQAAPAYCiAAAIDHUAABAAA8hgIIAADgMRRAAAAAj6EAAgAAeAwFEADgCdnZ2QoEAgoGg7ajANaxEDQAwFNYCBpgBhAAAMBzKIAAAAAeQwEEAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI%2BhAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwGAogAACAx1AAAQCekJ2drUAgoGAwaDsKKkh%2Bvu0E7uFzHMexHQIAgMoSDofl9/sVCoWUlJRkOw7KieNIbdtKTZpIkyZJp59uO1FkYwYQAAC43oIF0po10pIlUny87TSRjwIIAABcrahIuvdeM77rLqlePbt53IACCAAAXO2VV6SPP5aSkqRhw2yncQcKIAAAcK3Dh6X77jPjYcOkOnXs5nELCiAAAHCtmTOlTZukunWlzEzbadyDAggAAFypoEC6/34zHjnSHAJG2VAAAQCAK02bJn35pdSggZSRYTuNu1AAAQCA6xw8KD34oBmPHStVr243j9tQAAEAgOs89ZSUl2cWfu7b13Ya96EAAgAAV9m3T5o40Yzvv1%2BKi7Obx40ogAAAwFUeflj67jupRQupd2/badyJAggA8ITs7GwFAgEFg0HbUXAKdu%2BWnnjCjB96SKpa1W4et/I5juPYDgEAQGUJh8Py%2B/0KhUJKYt0Q17njDmnqVKlDB2nFCsnns53InZgBBAAArrBpk/Tcc2Y8YQLl71RQAAEAgCuMGSMdOSJ17Sr97ne207gbBRAAAES81auluXOlKlWkrCzbadyPAggAACKa40gjRpjxzTebq39xaiiAAAAgoi1eLL39tpSQIP35z7bTRAcKIAAAiFiHD0sjR5rxXXdJqal280QLCiAAAIhYzz8vbdwo1akjjRplO030oAACACJWVlaWfD6fMjMzi%2B/Lz8/XoEGDdNppp6lGjRrq1q2bdu7caTElKsqBA9J995nxvfdKtWrZzRNNKIAAgIi0Zs0aPfvss/r1r39d4v7MzEzNmzdPc%2BbM0YoVK3TgwAF17dpVR44csZQUFeWRR6S8PKlpU%2BmPf7SdJrpQAAEAEefAgQPq06ePpk2bptq1axffHwqF9Ne//lWTJk3SpZdeqvPPP18zZ87Uxx9/rDfffNNiYpS3Xbukv/zFjCdMkOLi7OaJNhRAAEDEycjI0FVXXaVLL720xP1r165VYWGhOnfuXHxfw4YN1aJFC61cubKyY6ICjRsnff%2B92fLtuutsp4k%2BMbYDAADwv%2BbMmaN169ZpzZo1P3ksLy9PcXFxJWYFJSklJUV5eXmlPl9%2Bfr7y8/OLvw%2BHw%2BUbGOXugw%2Bk6dPNeNIktnyrCMwAAgAixo4dO3TXXXdp5syZSkhIKPPvOY4j38%2B0hKysLPn9/uJbKuuIRDTHkYYNM1979ZLatbOdKDpRAAEAEWPt2rXau3evWrVqpZiYGMXExCg3N1dPPvmkYmJilJKSooKCAu3bt6/E7%2B3du1cpKSmlPufo0aMVCoWKbzt27KiMt4KTtGCBtGyZFB9vzv1DxeAQMAAgYnTq1Ekff/xxiftuvfVWnX322Ro5cqRSU1MVGxurpUuXqkePHpKkPXv26JNPPtHDDz9c6nPGx8crPj6%2BwrPj1BUUSMOHm/HQoVJamt080YwCCACIGImJiWrxo41ea9Soobp16xbff/vtt2vYsGGqW7eu6tSpo7vvvlstW7b8yQUjcJ%2BnnpI%2B/1xKSZFGj7adJrpRAAEArvLYY48pJiZGPXr00A8//KBOnTpp%2BvTpqlq1qu1oOAV79x7b53f8eCkx0W6eaOdzHMexHQIAgMoSDofl9/sVCoWUlJRkOw7%2Ba8AA6dlnpd/%2BVnr/fYk%2BX7G4CAQAAFi1fr00bZoZP/445a8yUAABAIA1jiMNHmy%2B9uwp/e53thN5AwUQAABY8/LL0jvvSNWqmb1/UTkogAAAwIqDB48t%2BzJqlMQa3ZWHAggAAKzIypJ27pTOOONYEUTloAACAIBK9/nnxw75PvqoOQSMykMBBAAAlS4z0%2Bz80bmz9Ic/2E7jPRRAAIAnZGdnKxAIKBgM2o7ieYsWSUuWSLGx0hNPSD6f7UTew0LQAABPYSFou374QTr3XGnbNmnkSGnCBNuJvIkZQAAAUGkmTDDlr1EjaexY22m8iwIIAAAqxeefSxMnmvFjj0k1a9rN42UUQAAAUOEcR7rzTik/31z4cf31thN5GwUQAABUuH/8Q3r9dSkuTnrqKS78sI0CCAAAKlQ4bJZ9kcyOH82a2c0DCiAAAKhg994r7d4tNW0qjR5tOw0kCiAAAKhAa9eaQ76SNHmylJBgNw8MCiAAAKgQR45IAwZIRUVSr17m4g9EBgogAACoEE89ZWYAa9Uyy74gclAAAQBAufvqq2MLPU%2BYINWvbzcPSqIAAgCAcuU40sCB0sGDUseOUr9%2BthPhxyiAAACgXP3979KSJVJsrDR1qlSFthFx%2BEcCAPCE7OxsBQIBBYNB21Gi2n/%2BIw0ebMb33CMFAnbzoHQ%2Bx3Ec2yEAAKgs4XBYfr9foVBISUlJtuNEnVtukf72N1P81q2T4uNtJ0JpmAEEAADl4o03TPnz%2BaTnnqP8RTIKIAAAOGX790v9%2B5vxoEFS%2B/Z28%2BCXUQABAMApGzVK2r5dOuMM6aGHbKfB8VAAAQDAKcnNNdu8SebQb82advPg%2BCiAAADgpB08KN12mxn36yd16mQ3D8qGAggAAE7aPfdIW7dKqanSI4/YToOyogACAICTkpsrPfmkGU%2BbJvn9dvOg7CiAAADghB04IN16qxn37St16WI3D04MBRAAAJyw4cOlbdukxo2lSZNsp8GJogACAIAT8vrr0jPPmPELL0hsqOI%2BFEAAAFBm%2B/Ydu%2Bp30CDp97%2B3mwcnhwIIAPCE7OxsBQIBBYNB21FcbeBAafduqVkzacIE22lwsnyO4zi2QwAAUFnC4bD8fr9CoZCSOHZ5Ql56SerdW6paVVq5UmrTxnYinCxmAAEAwHHt2GFm/yRp7FjKn9tRAAEAwC8qKpL%2B7/%2Bk774zxW/MGNuJcKoogAAA4Bf95S/SsmVS9erSzJlSbKztRDhVFEAAAPCz1q0zh3wl6YknpLPOspsH5YMCCAAASnXwoHTjjVJhoXTttdLtt9tOhPJCAQQAAKUaPFjavFlq1Mjs9evz2U6E8kIBBAAAP/Hyy9Lzz5vSN3OmVKeO7UQoTxRAAEDEyMrKUjAYVGJiopKTk/WHP/xBmzZtKvEz%2Bfn5GjRokE477TTVqFFD3bp1086dOy0ljk5bt0r9%2B5vxmDHSxRdbjYMKQAEEAESM3NxcZWRkaNWqVVq6dKkOHz6szp076%2BDBg8U/k5mZqXnz5mnOnDlasWKFDhw4oK5du%2BrIkSMWk0ePggKpVy8pHJYuuEC67z7biVAR2AkEABCxvvnmGyUnJys3N1cXXnihQqGQ6tWrpxkzZqhnz56SpN27dys1NVWvvfaaunTpctznZCeQXzZ0qPTYY1Lt2tL69VLjxrYToSIwAwgAiFihUEiSVOe/J6CtXbtWhYWF6ty5c/HPNGzYUC1atNDKlSutZIwmCxea8idJ06dT/qJZjO0AAACUxnEcDR06VB07dlSLFi0kSXl5eYqLi1Pt2rVL/GxKSory8vJKfZ78/Hzl5%2BcXfx8OhysutIt9%2BaXZ7UOSMjOlbt2sxkEFYwYQABCR7rzzTn300Ud66aWXjvuzjuPI9zNrlGRlZcnv9xffUlNTyzuq6%2BXnSz16mK3e2raVJk60nQgVjQIIAIg4gwYN0sKFC7Vs2TKdfvrpxffXr19fBQUF2rdvX4mf37t3r1JSUkp9rtGjRysUChXfduzYUaHZ3WjYMGnNGrPUy8svS3FxthOholEAAQARw3Ec3XnnnXr11Vf11ltvqUmTJiUeb9WqlWJjY7V06dLi%2B/bs2aNPPvlEHTp0KPU54%2BPjlZSUVOKGY2bPlrKzzXjGDCktzW4eVA7OAQQARIyMjAzNnj1bCxYsUGJiYvF5fX6/X9WqVZPf79ftt9%2BuYcOGqW7duqpTp47uvvtutWzZUpdeeqnl9O7zySdSv35mPHasdOWVdvOg8rAMDAAgYvzceXwvvPCCbrnlFknSoUOHNHz4cM2ePVs//PCDOnXqpMmTJ5f53D6WgTFCISkYlLZskS69VMrJkapWtZ0KlYUCCADwFAqgVFQkde9uln1p3Fhau1Y67TTbqVCZOAcQAACPefBBU/7i46W5cyl/XkQBBADAQxYtOra925QpUuvWdvPADgogAAAesXGj1KePGWdkSLfeajcP7KEAAgDgAfv2SddcI%2B3fL1144bEt3%2BBNFEAAAKLc4cNSz57mit/GjaV//EOKjbWdCjZRAAEAiHLDhklLl0rVq5uLP%2BrVs50ItlEAAQCIYlOnSk8%2BacYzZki/%2BY3dPIgMFEAAgCdkZ2crEAgoGAzajlJp3nzTXOwhmaVfrr3Wbh5EDhaCBgB4ilcWgt6wQerQwez40aePmf37mY1W4EHMAAIAEGW%2B/lq66ipT/jp2lJ57jvKHkiiAAABEke%2B/l7p1k778UmraVJo3T0pIsJ0KkYYCCABAlDhyROrdW3r/falOHem119jmDaWjAAIAEAUcRxo8WFqwwOzxu3Ch1KyZ7VSIVBRAAACiQFaWNHmyOddv5kzpggtsJ0IkowACAOByL7wgjRljxo8/Ll1/vd08iHwUQAAAXGzRIqlfPzMeOdIcBgaOhwIIAIBLvf221KOHufjjllvMYWCgLCiAAAC40Lp10tVXS4cOma/TprHWH8qOAggAgMts3Ch16SKFw9JFF0kvvyzFxNhOBTehAAIA4CJbt0qXXir9%2B99Sq1ZmuZdq1WyngttQAAEAcIkdO6ROnaTdu6Vzz5VycqQo3s4YFYgCCADwhOzsbAUCAQWDQdtRTsru3dLvf2%2B2eDvzTGnpUnb5wMnzOY7j2A4BAEBlCYfD8vv9CoVCSnLJ9FlennTxxdKmTdIZZ5irf1NTbaeCmzEDCABABMvLMzN/mzaZ0vfWW5Q/nDoKIAAAEWrPHumSS8xVv6efLi1bJjVpYjsVogEFEACACLRrlzns%2B9lnZsZv%2BXKpaVPbqRAtKIAAAESY7dvN%2Bn6bN0uNG1P%2BUP4ogAAARJDPP5cuvFD64gtzuPftt6Vf/cp2KkQbCiAAABHik0%2Bk3/1O%2BuorqXlzU/7S0mynQjSiAAIAEAFWrzYzf3l50q9/LeXmmgs/gIpAAQQAwLKlS80OH/v2Se3amXP%2BUlJsp0I0owACAGDR3/8uXXWVdPCgdNllpgzWrm07FaIdBRAAAEueekrq1UsqLJRuuEFatEiqWdN2KngBBRAAgEpWVCSNHCkNHiw5jpSRIb30khQfbzsZvIICCADwhOzsbAUCAQWDQas58vOlm2%2BWHn7YfP/gg2YmsGpVq7HgMT7HcRzbIQAAqCzhcFh%2Bv1%2BhUEhJSUmV%2BtrffSd1724u8oiJkaZNk265pVIjAJKkGNsBAACQzKHQL76QPv1U2rrV7IO7b5%2BZMZPM4VG/X0pONlujnXmmdPbZUmKi3dxl9eWX0pVXmn19a9aU5s6VOne2nQpeRQEEAFizf780f760YIGZFfvPf078Oc480yydcuGFZimVSNw1Y80aqWtXae9eqVEjackS6Te/sZ0KXsYhYABApfvqK2niROlvfzPLnxwVHy%2Bde64pdY0ameVQqlUzjx06ZA6hfv212St3yxazaPKPnX22Oczaq5dZUPnHKvsQ8Lx5Up8%2B0g8/mNK3eDELPMM%2BCiAAoNLk50sPPWTKX0GBua9ZM6lnT%2BmKK6RWraS4uLI/37//Lf3rX9LKldKyZdKqVdLhw8ce/81vpH79zEUXRw8VV1YBdBzp8celYcPM%2BIorpJdfds8ha0Q3CiAAoFLs2CH94Q/SunXm%2B0sukcaONV99vvJ5jVBIeu016ZVXzGHWoyUzKUm64w5p6FCpWrWKL4BHjkhDhpireyXpj3%2BUnnzSXPgBRAIKIACgwm3dKl10kbRzp1S3rvTMM9J115Vf8SvNt99KM2dKkydLmzaZ%2B6pXlwYODOsvf6m4AnjokHTTTeYiD0l65BEzC1iR7xU4URRAAECF2r9fatNG%2Buwzc35eTo6UllZ5r19UZGYDH3xQev99SQpL8mvmzJD69CnfArh/v3TNNeZwdFycNGOG1KNHub4EUC5YCBoAUKHGjDHlr1Ej6a23Krf8SVKVKtLVV5vzA199VWrSxNx/001S375mxq48HDhgzvNbtsyc55eTQ/lD5GIGEABQYXbuNIXr8GFp6VLp0kttJ5L27g0rJcUvKSQpSR07mvMGT%2BXijMOHzTIvr78u1aolvfGGZHnDEeAXMQMIAKgwc%2BaYcvS730VG%2BZOkhATzdf58s7D0ihXSjTeaK3VP1r33mvJXvbqZ%2BaP8IdJRAAEAFWblSvP1D3%2Bwm6M0l1xiZiUTEsw5gosWndzzbN58bF/f6dOltm3LLSJQYbgg/RQ4jqP9%2B/fbjgEAEWvnTvM1JUUKh%2B1kyM/PV/7R/eSk4v/dDofDat5cuv12KTvbXLV78cUn/vxTpphlXy67TOrSxd77xIlLTEyUz6OXZ3MO4Ck4upgoAABwn8raDSYSUQBPQVlmAIPBoNasWVNurxkOh5WamqodO3aU%2Bx9teWd1y3NW1GfqhvdeEc/J36h7PtPKeO8jRkhTp5qdOI4uinyqz3mifjwDuGfPHrVp00YbNmzQ1q2N1L27VFgozZ4tXXXViT9/erq0cKFUt%2B5T2rp10Enn/Dn8jVbce/fyDCCHgE%2BBz%2Bc77h961apVK%2BT/XSQlJZX781ZEVrc8p1T%2Bn6lb3rtbPk/JPe/fLZ9pZbz3Hj1MAZw715wnl5Jy6s9ZXubPr6P7709SYeGxvYNPpgt06WIK4P79fVStWpJiY8s3J3%2Bj7njvbsNFIBUsIyPDdoQyq4isbnnOiuCW9%2B6Wz1Nyz/t3y2daGe/9kkuk1q2lgwelAQPMosyn%2Bpyn6tNPzX/67rmnmvLzTfmbNevkd%2BpIT5fq1ZMKCs7Qgw%2BWY9D/4m808p/TjTgE7DKVtYm5l/CZli8%2Bz/Ln9s903TqpfXuzL2%2B/fmZrNht74v7rX9KECdLcuWYnkISE7zR%2BvF933WUWiz4Vc%2BaYpWR8PnNF8RVXlEtk13D736gXVf3Tn/70J9shcGKqVq2qiy%2B%2BWDHsKl5u%2BEzLF59n%2BXPzZ9qggdS0qTRvnrR2rVl37%2BKLzYLJFe3AAXNu38CBZq2%2BjRslKV/SBK1ePVTXXlujXPbobdFC2r3bvL8FC0wBbNDg1J/XTdz8N%2BpFzAACACrFq6%2Bai0EOHpSqVZMGD5buuqv8i9K%2BfWYx5nnzpMWLpR9%2BMPfHxprz/AYODKt9%2B/KfrSookC6/3GwFl5wsvfOO1KxZuT09UK4ogACASrN5szkM/Pbb5vuYGDNbdu21UqdOUmrqiT2f40g7dpiZt/fek3JzzaHe/z3X8KyzpFtukW67Tapfv2IPV4ZC5rzHDz6QTj/dvM%2Bjew8DkYQCCACoVI5jZuYmTDi2U8hRDRqYw6lNm5qyVru22anD55Py880iy//%2Bt7Rrl7RtmymUodBPXyMQkK6%2BWrr%2BeqlVq5IXeFT0%2BWp795pD3Bs3SmecYUpp48bl/jLAKaEAAgCs2bBBevll6Z//NBeLHDly4s8RE2MKX9u2UseOZgbul2YSK%2BOChd27pYsukj7/XPrVr0wJPP30Cnkp4KRQAAEAEeHgQenDD6XPPpO%2B/FL6%2Bmszu3fokJk1jIuTEhOlunWlhg2ltDRzeLdZMyk%2BvuyvU1lXrO7caUrg1q3SmWdKy5dLjRpV2MsBJ8aBq%2Bzfv9/JyMhwGjVq5CQkJDhnn322M3nyZNuxXG/Dhg3O1Vdf7SQlJTk1a9Z02rZt62zfvt12LNfr37%2B/I8l57LHHbEdxrYKCAmfEiBFOixYtnOrVqzsNGjRw0tPTnV27dtmO5lqhUMiR5IRCoQp/re3bHadJE8eRHOessxwnmv6xjR8/3mndurVTs2ZNp169es4111zjfPbZZ7ZjoYxYCNplhgwZopycHM2cOVMbN27UkCFDNGjQIC1YsMB2NNf64osv1LFjR5199tlavny5PvzwQ40bN04JCQm2o7na/PnztXr1ajVs2NB2FFf7/vvvtW7dOo0bN07r1q3Tq6%2B%2Bqs2bN6tbt262o6EMGjc2VwWnpUlbtki//72Ul2c7VfnIzc1VRkaGVq1apaVLl%2Brw4cPq3LmzDh48aDsayoBDwC7TokUL9ezZU%2BPGjSu%2Br1WrVrryyiv1wAMPWEzmXr169VJsbKxmzJhhO0rU2LVrl9q2bavXX39dV111lTIzM5WZmWk7VtRYs2aN2rRpo%2B3bt6sxVxeUWXZ2trKzs3XkyBFt3ry5Uhct3rbNXBjy1VfmfMWjS8VEk2%2B%2B%2BUbJycnKzc3VhRdeaDsOjoMZQJfp2LGjFi5cqF27dslxHC1btkybN29Wly5dbEdzpaKiIi1ZskTNmjVTly5dlJycrLZt22r%2B/Pm2o7lWUVGR0tPTNXz4cJ177rm240SlUCgkn8%2BnWpWxknIUycjI0IYNG7RmzZpKf%2B0mTaS33jLnAG7YIF12mfTtt5Ueo0KF/ns5dp06dSwnQVlQAF3mySefVCAQ0Omnn664uDhdfvnlmjx5sjp27Gg7mivt3btXBw4c0IQJE3T55ZfrjTfeUPfu3XXttdcqNzfXdjxXmjhxomJiYjR48GDbUaLSoUOHNGrUKPXu3Zstt1ymaVNTAuvXlz76SOrSpfQlbNzIcRwNHTpUHTt2VIsWLWzHQRlQACPYrFmzVLNmzeLbO%2B%2B8oyeffFKrVq3SwoULtXbtWk2aNEkDBw7Um2%2B%2BaTuuK/z4M920aZMk6ZprrtGQIUN03nnnadSoUerataueeeYZy2kj348/z9zcXD3xxBOaPn26fOWxv5YHlfbv/VGFhYXq1auXioqKNHnyZIspcbKaNZPefNNcyfyvf0ldu0rff2871am788479dFHH%2Bmll16yHQVlxDmAEWz//v36%2Buuvi79v1KiR/H6/5s2bp6uuuqr4/r59%2B2rnzp3KycmxEdNVfvyZ1qtXT6eddpruu%2B8%2BjR07tvj%2BkSNHasWKFXr33XdtxHSNH3%2Ber7zyisaMGaMqVY79f8sjR46oSpUqSk1N1ZdffmkhpbuU9u99tWrVVFhYqB49emjr1q166623VLduXYsp3a2yloH5JR98YNYrDIXMTOCCBSe2lE0kGTRokObPn6%2B3335bTdj2xDXYsTmCJSYmKjExsfj7cDiswsLCEv9xlcwG3EX/u%2B8RftaPP1NJCgaDxTOBR23evFlpaWmVGc2Vfvx59u/fX1dffXWJn%2BnSpYvS09N16623VnY8Vyrtb/Ro%2BduyZYsY8AEnAAAW00lEQVSWLVtG%2BYsC558vvfaaORfw9delm26S5syRqla1nazsHMfRoEGDNG/ePC1fvpzy5zIUQBdJSkrSRRddpOHDh6tatWpKS0tTbm6uXnzxRT366KO247nW8OHD1bNnT1144YW65JJLlJOTo0WLFmn58uW2o7lO3bp1f1JOYmNjVb9%2BfTVv3txSKnc7fPiwrr/%2Beq1bt06LFy/WkSNHlPffdUTq1KmjuLg4ywlxsjp0kObPN4eB//EPaeBA6ZlnSm5bF8kyMjI0e/ZsLViwQImJicV/l36/X9WqVbOcDsdlcQ1CnIQ9e/Y4t9xyi9OwYUMnISHBad68uTNp0iSnqKjIdjRX%2B%2Btf/%2BqceeaZTkJCgvOb3/zGmT9/vu1IUSMtLY2FoE/Btm3bHEml3pYtW2Y7nitV5kLQZfHKK45TpYpZLHrMGNtpyu7n/i5feOEF29FQBpwDCADwlEg4B/DHnn1WGjDAjJ9%2BWsrIsJsH0Y%2BrgAEAsKx/f%2BnPfzbjQYOkuXPt5kH0owACABABxo6V7rhDchypTx9pxQrbiRDNKIAAAEQAn88c/u3WTcrPl665Rtq82XYqRCsKIAAAEaJqVemll6Q2bcxWcVdeKX3zje1UiEYUQAAAIkj16tLChWb/4C%2B%2BkLp3lw4dsp0K0YYCCADwhOzsbAUCAQWDQdtRjislRVqyRKpVS3r3XalvX3NuIFBeWAYGAOApkbgMzM/5f/9Puvxy6fBh6YEHzIUiQHlgBhAAgAjVqZO5MESSxo2TXn3Vbh5EDwogAAARbMAAafBgM05Plz780G4eRAcKIAAAEW7SJOmyy6TvvzfLw/z737YTwe0ogAAARLiYGGnOHKlpU2n7dqlHD3NeIHCyKIAAALhAnTrSggVSzZrSsmXSiBG2E8HNKIAAALjEuedKf/ubGT/2mFk0GjgZFEAAAFzk2mul0aPNuG9f6eOP7eaBO1EAAQBwmQceOHZRyHXXSaGQ7URwGwogAAAuU7WqNHu2lJoqbdki3XYbO4XgxFAAAQBwodNOk/7xDyk21iwQ/cQTthPBTSiAAAC4VJs20qOPmvHw4dKqVXbzwD0ogAAAT8jOzlYgEFAwGLQdpVxlZEg33GDWBezZU/r2W9uJ4AY%2Bx%2BGsAQCAd4TDYfn9foVCISUlJdmOUy7CYem3v5W%2B%2BMLsFDJvnuTz2U6FSMYMIAAALpeUJP3971JcnFks%2BumnbSdCpKMAAgAQBX77W%2BmRR8z47rul9evt5kFkowACABAlBg2Srr5aKiiQevWSDh60nQiRigIIAECU8Pmk55%2BXGjaUNm2ShgyxnQiRigIIAEAUOe00acYMUwanTTMXhAA/RgEEACDK/P73Zl1AyewXvHu33TyIPBRAAACi0AMPmAtDvv1WuvVWtopDSRRAAEBEKCws1MiRI9WyZUvVqFFDDRs21M0336zdP5q%2B2rdvn9LT0%2BX3%2B%2BX3%2B5Wenq7vvvvOUurIFRcnzZwpJSRIb7zB0jAoiQIIAIgI33//vdatW6dx48Zp3bp1evXVV7V582Z169atxM/17t1b69evV05OjnJycrR%2B/Xqlp6dbSh3Zzjnn2NIwI0ZIn31mNw8iBzuBAAAi1po1a9SmTRtt375djRs31saNGxUIBLRq1Sq1bdtWkrRq1Sq1b99en332mZo3b37c54zGnUB%2BieNIl19uZgGDQWnlSikmxnYq2MYMIAAgYoVCIfl8PtWqVUuS9N5778nv9xeXP0lq166d/H6/Vq5caStmRPP5pL/%2BVapVS1qzRpowwXYiRAIKIAAgIh06dEijRo1S7969i2fq8vLylJyc/JOfTU5OVl5eXqnPk5%2Bfr3A4XOLmNaefLj31lBnffz%2B7hIACCACwZNasWapZs2bx7Z133il%2BrLCwUL169VJRUZEmT55c4vd8Pt9PnstxnFLvl6SsrKziC0b8fr9SU1PL9424RJ8%2BUvfu0uHD0v/9n9ktBN5FAQQAWNGtWzetX7%2B%2B%2BNa6dWtJpvz16NFD27Zt09KlS0ucp1e/fn19/fXXP3mub775RikpKaW%2BzujRoxUKhYpvO3bsqJg3FOF8PumZZ8xC0R99JD30kO1EsImLQAAAEeNo%2BduyZYuWLVumevXqlXj86EUgq1evVps2bSRJq1evVrt27bgIpIxeftnsExwTY84JPO8824lgAwUQABARDh8%2BrOuuu07r1q3T4sWLS8zo1alTR3FxcZKkK664Qrt379bUqVMlSf3791daWpoWLVpUptfxegF0HOmGG6S5c035e/99KTbWdipUNgogACAifPnll2rSpEmpjy1btkwXX3yxJOnbb7/V4MGDtXDhQknmUPLTTz9dfKXw8Xi9AErS119LgYDZJeTBB6UxY2wnQmWjAAIAPIUCaMycKaWnmx1D1q83i0bDO7gIBAAAD%2BrTR7rySnM1cN%2B%2BUlGR7USoTBRAAAA8yOeTpkyRatY0u4NMmWI7ESoTBRAAAI9q3FjKyjLjUaOknTvt5kHloQACAOBhAwdK7dtLBw5Id95pOw0qCwUQAAAPq1JFevZZsy7gggXSvHm2E6EyUAABAPC4Fi2kkSPNeNAgyYPbJXsOBRAAAGjMGKlpU2nXLmncONtpUNEogAAAT8jOzlYgEFAwGLQdJSJVq3bsSuCnn5bWrbObBxWLhaABAJ7CQtC/7MYbpTlzpGBQeu89qWpV24lQEZgBBAAAxR59VEpKktasMReHIDpRAAEAQLEGDcz%2BwJJ0zz3SN9/YzYOKQQEEAAAlDBwonXee9N130ogRttOgIlAAAQBACVWrSpMnm/H06WarOEQXCiAAAPiJ9u2l224z4zvvlI4csZsH5YsCCAAASpWVJdWqJX3wAReERBsKIAAAKFVysvTAA2Y8dqz0n//YzYPyQwEEAAA/6447pJYtpW%2B/ZYeQaEIBBAAAPysmRnrqKTOeOlX68EO7eVA%2BKIAAAOAXXXSR1KOHVFQk3XWXxB5i7kcBBAAAx/Xww1JCgpSbK82dazsNThUFEAAAHFda2rFFoe%2B%2BWzp0yG4enBoKIADAE7KzsxUIBBQMBm1Hca2RI6XTT5e2bzd7BsO9fI7DkXwAgHeEw2H5/X6FQiElJSXZjuM6s2ZJN90k1awpbdki1a9vOxFOBjOAAACgzG68UWrTRjpwwKwNCHeiAFawoiKpsFA6fNh2EgAATl2VKtJjj5nx88%2BzLIxbUQArmONIsbGSz2c7CQAA5aNDB7MsjOOYC0I4mcx9KIAVrEoVM/tHAQQARJMJE6S4OOnNN6V//tN2GpwoCmAF8/nMKupV%2BKQBAFGkSRNp8GAzHj6cU53chloCAABOyj33SHXqSBs2SC%2B8YDsNTgQFEAAAnJTataVx48z43nulgwft5kHZUQABAMBJGzhQ%2BtWvpLw8adIk22lQVhRAAABw0uLipPHjzfiRR6S9e%2B3mQdlQAAEAwCm54QapdWuzOPSf/2w7DcqCAggAAE5JlSrSww%2Bb8dSp0hdf2M2D46MAAgA8ITs7W4FAQMFg0HaUqHTJJdLll5vlYNgiLvL5HIf1uwEA3hEOh%2BX3%2BxUKhZSUlGQ7TlRZv146/3wzXrfu2BiRhxlAAABQLs47T%2Brd24zvucduFvwyCiAAACg3f/6z2QErJ0fKzbWdBj%2BHAggAAMpN06ZSv35mfM89EieaRSYKIAAAKFfjxknVqkkrV0pLlthOg9JQAAEAQLlq0EAaNMiMx46Viors5sFPUQABAEC5GzFCSkqSPvxQeuUV22nwYxRAAABQ7urWlYYNM%2BP77jPrAyJyUAABABFpwIAB8vl8evzxx0vcv2/fPqWnp8vv98vv9ys9PV3fffedpZT4JZmZUp060qZN0qxZttPgf1EAAQARZ/78%2BVq9erUaNmz4k8d69%2B6t9evXKycnRzk5OVq/fr3S09MtpMTxJCVJI0ea8f33S4WFdvPgGAogACCi7Nq1S3feeadmzZql2NjYEo9t3LhROTk5eu6559S%2BfXu1b99e06ZN0%2BLFi7Vp0yZLifFLMjKk5GRp2zZp%2BnTbaXAUBRAAEDGKioqUnp6u4cOH69xzz/3J4%2B%2B99578fr/atm1bfF%2B7du3k9/u1cuXKyoyKMqpRQxo92owffFDKz7ebBwYFEAAQMSZOnKiYmBgNHjy41Mfz8vKUnJz8k/uTk5OVl5dX6u/k5%2BcrHA6XuKFyDRhglob56ivp%2Bedtp4FEAQQAWDJr1izVrFmz%2BJabm6snnnhC06dPl8/n%2B9nfK%2B0xx3F%2B9neysrKKLxjx%2B/1KTU0tt/eAsqlW7djewOPHMwsYCSiAAAArunXrpvXr1xffVq5cqb1796px48aKiYlRTEyMtm/frmHDhumMM86QJNWvX19ff/31T57rm2%2B%2BUUpKSqmvM3r0aIVCoeLbjh07KvJt4Wf07Ss1aiTt3Ck995ztNPA5Drv0AQDs%2B89//qM9e/aUuK9Lly5KT0/XrbfequbNm2vjxo0KBAJavXq12rRpI0lavXq12rVrp88%2B%2B0zNmzc/7uuEw2H5/X6FQiElJSVVyHtB6SZPNheFNGokff65lJBgO5F3UQABABHrjDPOUGZmpjIzM4vvu%2BKKK7R7925NnTpVktS/f3%2BlpaVp0aJFZXpOCqA9%2BflS06bSrl3S00%2BbMgg7OAQMAHCVWbNmqWXLlurcubM6d%2B6sX//615oxY4btWCiD%2BPhj5wJOmMC5gDYxAwgA8BRmAO06dEg680wzCzhlinTHHbYTeRMzgAAAoNIkJBzbHSQrSyoosJvHqyiAAACgUvXtK9Wvb9YFfPFF22m8iQIIAAAqVbVq0ogRZpyVJR0%2BbDePF1EAAQBApevfX6pXT9q6VZo923Ya76EAAgCASlejhjR0qBmPHy8dOWI3j9dQAAEAgBUDB0q1a0ubNkmvvmo7jbdQAAEAgBVJSdLgwWb80EMSC9NVHgogAMATsrOzFQgEFAwGbUfB/xg8WKpZU/rwQ2nJEttpvIOFoAEAnsJC0JFnxAjpkUek9u2ld9%2BVfD7biaIfM4AAAMCqoUPNNnHvvSfl5tpO4w0UQAAAYFX9%2BtJtt5lxVpbdLF5BAQQAANYNHy5VrSq98Ya0dq3tNNGPAggAAKxr0kTq1cuMJ0ywm8ULKIAAACAijBxpvs6dK23ebDdLtKMAAgCAiNCypdS1q1kP8JFHbKeJbhRAAAAQMUaNMl9ffFHas8dulmhGAQQAABHjggukDh2kggLpiSdsp4leFEAAABBRjp4LOGWKFA7bzRKtKIAAACCidO0qnXOOKX9Tp9pOE50ogAAAIKJUqSLdfbcZP/64ORyM8kUBBAAAEadPH6lBA2n3bmn2bNtpog8FEADgCdnZ2QoEAgoGg7ajoAzi46W77jLjSZPM0jAoPz7H4SMFAHhHOByW3%2B9XKBRSUlKS7Tj4Bd99J6WmSgcOSK%2B9Jl1xhe1E0YMZQAAAEJFq1ZL69TPjv/zFbpZoQwEEAAAR6667pKpVpbfekj74wHaa6EEBBAAAESstTerRw4wffdRulmhCAQQAABFt6FDzdc4caedOu1miBQUQAABEtNatpYsukg4flp56ynaa6EABBAAAEe/oLOCzz5qrgnFqKIAAACDide0qnXWWWRpm%2BnTbadyPAggAACJelSrHFoZ%2B/HHpyBG7edyOAggAAFzhlluk2rWlL76QFi%2B2ncbdKIAAAMAVatSQ%2Bvc348cft5vF7SiAAADANTIyzMLQy5dL69fbTuNeFEAAgCdkZ2crEAgoGAzajoJTkJoqXX%2B9GT/xhN0sbuZzHMexHQIAgMoSDofl9/sVCoWUlJRkOw5OwqpVUvv2UlyctGOHlJxsO5H7MAMIAABcpV07qW1bqaBAeuYZ22nciQIIAABc5%2BiSMFOmmCKIE0MBBAAArnPddVLDhlJenvTKK7bTuA8FEAAAuE5cnPTHP5rxk0/azeJGFEAAAOBK/fubIvj%2B%2B9Lq1bbTuAsFEAAAuFJysnTjjWbMLOCJoQACAADXGjTIfH3lFXM%2BIMqGAggAiCgbN25Ut27d5Pf7lZiYqHbt2umrr74qfjw/P1%2BDBg3Saaedpho1aqhbt27auXOnxcSwqVUrsyZgYaE0dartNO5BAQQARIwvvvhCHTt21Nlnn63ly5frww8/1Lhx45SQkFD8M5mZmZo3b57mzJmjFStW6MCBA%2BratauOHDliMTlsOjoLOHUqS8KUFTuBAAAiRq9evRQbG6sZM2aU%2BngoFFK9evU0Y8YM9ezZU5K0e/dupaam6rXXXlOXLl2O%2BxrsBBJ9CgqktDRzCHjOHOm/fxr4BcwAAgAiQlFRkZYsWaJmzZqpS5cuSk5OVtu2bTV//vzin1m7dq0KCwvVuXPn4vsaNmyoFi1aaOXKlTZiIwLExUkDBpjx00/bzeIWFEAAQETYu3evDhw4oAkTJujyyy/XG2%2B8oe7du%2Bvaa69Vbm6uJCkvL09xcXGqXbt2id9NSUlR3s9cAZCfn69wOFzihujTv78UEyOtWCF9%2BKHtNJGPAggAsGLWrFmqWbNm8W3Tpk2SpGuuuUZDhgzReeedp1GjRqlr16565jgbvjqOI5/PV%2BpjWVlZ8vv9xbfU1NRyfy%2Bwr2FDafBgaeJEiX/ExxdjOwAAwJu6deumtm3bFn9fr149xcTEKBAIlPi5c845RytWrJAk1a9fXwUFBdq3b1%2BJWcC9e/eqQ4cOpb7O6NGjNXTo0OLvw%2BEwJTBKTZpkO4F7UAABAFYkJiYqMTGxxH3BYLB4JvCozZs3Ky0tTZLUqlUrxcbGaunSperRo4ckac%2BePfrkk0/08MMPl/o68fHxio%2BPr4B3ALgXBRAAEDGGDx%2Bunj176sILL9Qll1yinJwcLVq0SMuXL5ck%2Bf1%2B3X777Ro2bJjq1q2rOnXq6O6771bLli116aWX2g0PuAgFEAAQMbp3765nnnlGWVlZGjx4sJo3b665c%2BeqY8eOxT/z2GOPKSYmRj169NAPP/ygTp06afr06apatarF5IC7sA4gAMBTWAcQ4CpgAAAAz6EAAgAAeAwFEAAAwGMogAAAAB5DAQQAAPAYCiAAAIDHUAABAAA8hgIIAPCE7OxsBQIBBYNB21EA61gIGgDgKSwEDTADCAAA4DkUQAAAAI%2BhAAIAAHgMBRAAAMBjKIAAAAAeQwEEAADwGAogAACAx1AAAQAAPIYCCAAA4DEUQAAAAI%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%3D'}