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g2c_curves • Show schema
{'Lhash': '1846074924137177523', 'abs_disc': 35721, 'analytic_rank': 2, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[6,2]', 'aut_grp_label': '6.2', 'aut_grp_tex': 'C_6', 'bad_lfactors': '[[3,[1,3,3]],[7,[1,5,7]]]', 'bad_primes': [3, 7], 'class': '3969.b', 'cond': 3969, 'disc_sign': 1, 'end_alg': 'CM', 'eqn': '[[0,0,-3,0,3,-2],[1,1,0,1]]', 'g2_inv': "['1350125107/147','57445733/441','-2527307/3969']", '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': "['268','2961','216951','18816']", 'igusa_inv': "['201','573','-563','-110373','35721']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': False, 'label': '3969.b.35721.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.21994490289180735653763237985205622008123681818', 'prec': 163}, 'locally_solvable': True, 'modell_images': ['2.80.1', '3.480.12'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 18, 'num_rat_wpts': 0, 'real_geom_end_alg': 'M_2(R)', 'real_period': {'__RealLiteral__': 0, 'data': '23.234166964744789092793173025', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.00315548079438', 'prec': 54}, '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': 3, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 2, 'two_torsion_field': ['9.9.62523502209.1', [1, 6, -9, -38, 12, 54, -4, -15, 0, 1], [9, 2], True]} -
g2c_endomorphisms • Show schema
{'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': [-35, 21, 63, -14, -21, 0, 1], 'fod_label': '6.6.330812181.1', 'is_simple_base': True, 'is_simple_geom': False, 'label': '3969.b.35721.1', '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'], [['2.2.21.1', [-5, -1, 1], ['9/25', '483/125', '63/125', '-173/125', '-12/125', '9/125']], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_3'], [['3.3.3969.2', [-35, -21, 0, 1], ['-112/25', '-119/125', '391/125', '214/125', '-9/125', '-12/125']], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_2'], [['6.6.330812181.1', [-35, 21, 63, -14, -21, 0, 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': [[['1196447679/50000', '5106100923/250000', '-3288044907/250000', '-1198505889/125000', '81976293/250000', '29108241/62500'], ['-2533145646969/625000', '-86931059013099/25000000', '55772501101191/25000000', '10205353689741/6250000', '-1355533889409/25000000', '-248712019479/3125000']]], 'spl_facs_condnorms': [1], 'spl_fod_coeffs': [-35, 21, 63, -14, -21, 0, 1], 'spl_fod_gen': [0, 1, 0, 0, 0, 0], 'spl_fod_label': '6.6.330812181.1', 'st_group_base': 'E_6', 'st_group_geom': 'E_1'} -
g2c_ratpts • Show schema
{'label': '3969.b.35721.1', 'mw_gens': [[[[-1, 1], [0, 1], [1, 1]], [[0, 1], [-2, 1], [0, 1], [0, 1]]], [[[0, 1], [-2, 1], [1, 1]], [[-1, 1], [-3, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.064863762809723723033895763608375', 'prec': 117}, {'__RealLiteral__': 0, 'data': '0.064863762809723723033895763608325', 'prec': 117}], 'mw_invs': [0, 0], 'num_rat_pts': 18, 'rat_pts': [[-5, -46, 1], [-5, 175, 1], [-1, -1, 1], [-1, 2, 1], [0, -1, 1], [0, 0, 1], [1, -237, 6], [1, -16, 6], [1, -8, 2], [1, -5, 2], [1, -2, 1], [1, -1, 0], [1, -1, 1], [1, 0, 0], [2, -7, 1], [2, -4, 1], [6, -356, 5], [6, -135, 5]], 'rat_pts_v': False} - g2c_galrep • Show schema
- g2c_tamagawa • Show schema
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g2c_plots • Show schema
{'label': '3969.b.35721.1', 'plot': 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6F12kXbt2qW7duicfsGmTObfZtKk5P3ouPB4zfLduXf%2BPbv3sMzNv4fHDr3v2NCOMY2LM199/L1122clr/knmPP%2BPP5r1/46zf/%2Bxrt2WLcc6dkWXFZxhULjvNG/TpuZ3g%2BN/OTnvPPN2vj%2BKe%2B6RUlNLfK0dqq9YZfu%2BvuACqU8fk2s7dmTAcHk7/t%2BtsLAwq8uB%2BJ44GQHQRo4cMV2OH36QfvrJ/F%2B7Zo0ZiFGS2FipRQvpoovM1ry52Ro2ZCUxfwuK36QLC6X//MdclHfxxeY3ghN98YVZyu74oNixo/Tuu%2BY3irOUk2NeqmjbuLH47eHDp39%2BZOSxzuFd2Y8pafn/lXjsHw0u0HND1mvxYrM6zfGd8ObNzZzdd9xxhksYACAIEAADlMdjzrqtXm1uMzJMVy8//9THN2xoLim7%2BGIpPt7ctmhR6rEB8IOgCIClVVhoRpQULQXXpk25vU12tgmDp9pO7HL/W9fqWs0q%2BfUkTXgsX/UbVFZ4uDmjvnCh%2BYWqSLdu5j5%2BQQIQzAiAAWDPHvMfUXr6sduNG099bGSk1KqV2Vq2NFtCgp%2Buy0OZOCoABohDh4p3C7u9lKyEjbNP%2B5wqOqKjOn2Lb8eOUy%2BBDADBglHAFSw314S8lSvN9v33OmnFiyKNGpnGSps20iWXmK1JE65TAoqEh5uOd3x80T1/lkaWHAA3Nu6mgVdWUXa2uRxz717zdzIvT6pc2VwyMWgQ4Q9A8KMDWAHmzZM%2B%2Bkj69ltzGvdUf%2BLNmknt2klt25rbNm1OupbeJz8/X3/729/0%2Beefa%2BPGjXK73erRo4eeeeYZxcbGlu%2BHQYnoAAaAnBzlNGqkSI/n5McqVTJT8iQlVXxdKGbChAl65JFHNGrUKL300ktWl%2BNYv//%2Bux566CHNnTtXhw4d0oUXXqg33nhD7dq1s7o0VAA6gBVgxQrpzTePfR0XZ9azLdratTNz65XWwYMHlZ6ervHjx%2BuSSy7Rvn37NHr0aCUnJ%2Bv777/3/wcA7CIyUve3bKm/b9mi2llZvrv3Vqqk6lOmKIzwZ7mVK1fqtddeU6tWrawuxdH27dunLl26qFu3bpo7d67q1aunDRs2qObZ/GcEWyMAVoBrrjEjeDt2NFtZTy%2B53W4tWLCg2H0TJ05Uhw4dtHXrVjVq1Khsb4CzkpqaqtTUVBWcZoJkVJzXi9a6%2B89/pJ9/lqdKFcXecYfmX3ih/mRtaY534MAB3XLLLZo6daqefPJJq8txtGeffVZxcXF66623fPc1KXHZIwQjxrlVgA4dzGpd115bftcWeTweuVwufnuzwIgRI7R27doKWXsUZ6FLF2nYMO2%2B/HLlSapd0jUVqDAjRoxQr1691KNHD6tLcbxZs2apffv2uvnmm1WvXj21adNGU6dOtbosVCACYBA4fPiwHn74YfXv359rz4DjeL1e3X///brsssuUkJBgdTmONn36dKWnp2vChAlWlwJJGzdu1OTJk3XBBRfoiy%2B%2B0F133aWRI0fq3XfPsJwiggYB0AbS0tJUo0YN37as6BSXzICQlJQUFRYWatKkSRZWCQSee%2B65Rz/%2B%2BKM%2B%2BOADq0txtKysLI0aNUrvvfeeqlatanU5kFRYWKi2bdvq6aefVps2bXTnnXdq6NChmjx5stWloYJwDaANJCcnq2PHjr6vGzRoIMmEv759%2B2rTpk1avHgx3T/gOPfee69mzZqlpUuXqmHDhlaX42irVq3Srl27io0uLSgo0NKlS/XKK68oLy9PISEhFlboPDExMYo/Nn%2BSJKlFixaaMWOGRRWhohEAbSAiIkIRERHF7isKf5mZmVqyZInq1KljUXVAYPF6vbr33ns1c%2BZMffnll2ratKnVJTnelVdeqZ%2BOX25F0u23366LLrpIDz30EOHPAl26dNG6deuK3bd%2B/Xo1btzYoopQ0QiANnT06FHddNNNSk9P15w5c1RQUKDsbLPAfe3atRUaGmpxhYB1RowYoffff1%2BffvqpIiIifH833G63wsPDLa7OmSIiIk66BrN69eqqU6cO12Za5L777lPnzp319NNPq2/fvvruu%2B/02muv6bXXXrO6NFQQJoK2oc2bN5fY1ViyZImuuOKKii0IkpgIOlC4Slgq56233tKgQYMqthiU6IorrlDr1q2ZCNpCc%2BbM0bhx45SZmammTZvq/vvv19ChQ60uCxWEAAj4CQEQAGAXjAIGAABwGAIgAACAwxAAAQAAHIYACJRRamqq4uPjlZiYaHUpAACUCoNAAD9hEAgAwC7oAAIAADgMARAAAMBhCIAAAAAOQwAEAABwGAIgAACAwxAAAQAAHIYACAAA4DAEQAAAAIchAAIAADgMARAAAMBhCIBAGbEWMADAblgLGPAT1gIGANgFHUAAAACHIQACAAA4DAEQAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBEAAAACHIQACAAA4DAEQKCPWAgYA2A1rAQN%2BwlrAAAC7oAMIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiCC3qBBg%2BRyuYptl156abFj8vLydO%2B99yoqKkrVq1dXcnKytm3bZlHFAACULwIgHOGqq67Sjh07fNvnn39e7PHRo0dr5syZmj59ur7%2B%2BmsdOHBAvXv3VkFBgUUVAwBQfipbXQBQEcLCwhQdHX3Kxzwej9544w1NmzZNPXr0kCS99957iouL08KFC9WzZ8%2BKLBUAgHJHBxCO8OWXX6pevXq68MILNXToUO3atcv32KpVq5Sfn6%2BkpCTffbGxsUpISNA333xT4mvm5eUpJyen2AYAgB0QABH0rr76aqWlpWnx4sV64YUXtHLlSnXv3l15eXmSpOzsbIWGhqpWrVrFnle/fn1lZ2eX%2BLoTJkyQ2%2B32bXFxceX6OQAA8BcCIIJKWlqaatSo4duWLVumfv36qVevXkpISFCfPn00d%2B5crV%2B/Xp999tlpX8vr9crlcpX4%2BLhx4%2BTxeHxbVlaWvz8OAADlgmsAEVSSk5PVsWNH39cNGjQ46ZiYmBg1btxYmZmZkqTo6GgdOXJE%2B/btK9YF3LVrlzp37lzie4WFhSksLMyP1QMAUDHoACKoREREqFmzZr4tPDz8pGP27NmjrKwsxcTESJLatWunKlWqaMGCBb5jduzYoTVr1pw2AAIAYFd0ABHUDhw4oMcee0w33nijYmJitHnzZj3yyCOKiorS9ddfL0lyu90aPHiwxowZozp16qh27doaO3asWrZs6RsVDABAMCEAIqiFhITop59%2B0rvvvqv9%2B/crJiZG3bp104cffqiIiAjfcS%2B%2B%2BKIqV66svn376tChQ7ryyiv19ttvKyQkxMLqAQAoHy6v1%2Bu1ugggGOTk5Mjtdsvj8SgyMtLqcgAAKBHXAAIAADgMARAAAMBhCIAAAAAOQwAEAABwGAIgAACAwxAAAQAAHIYACJRRamqq4uPjlZiYaHUpAACUCvMAAn7CPIAAALugAwgAAOAwBEAAAACHIQACAAA4DAEQAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBEAAAACHIQACZcRawAAAu2EtYMBPWAsYAGAXdAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHCYylYXAAAAjIICac4c6YsvpI0bpcOHpRo1pAYNpIsuktq1k9q3l6pVs7pS2B0BEACAALBtm3TdddKqVac/LjRU6tRJuuYa6dprpebNK6Y%2BBBdWAgH8hJVAAJyrggKpY0cT/mrWlG67TWrd2nT6cnKkLVukNWukb7%2BVduwo/tyEBOkvf5H695eaNLGkfNgQARAoo9TUVKWmpqqgoEDr168nAAI4a7NnS8nJJvytXl1ykPN6pd9%2Bk%2BbPN89ZvFjKzz/2eLdu0pAh0g03SFWrVkjpsCkCIOAndAABnKv77pNeekkaMUJ65ZXSP2/fPmnmTCktTVqyxARESapTR7rjDunuu6WmTcunZtgbo4ABALDY9u3m9sILz%2B55tWqZoLdokbRpk/TYY1LDhtKePdJzz0nNmknXXy8tXXosHAISARAAAMtV%2Bt//xoWF5/4ajRtL/%2B//mSD46afSn/9sXu/f/5a6djXXGH70kbneECAAAgBgsehoc7ttW9lfq3Jlcz3h/PnSzz9Lw4aZ6wFXrpT69jXTyUydKuXllf29YF8EQAAALNaihbn98Uf/vm58vDRlirR1q/Too1Lt2mYQybBh0vnnSy%2B/LB065N/3hD0QAAEAsFiHDuZ2xQrp6FH/v37dutL//Z%2BZTuYf/5BiY6Xff5dGjZLOO88MQCEIOgsBEAAAi7VsaQZ05Oaauf7KS40aZsTxxo3Sq6%2Ba6wazs819550nTZxoVh9B8CMAAgBgsZAQ6aqrzP6sWeX/fmFh0p13SuvXS6%2B9diwIjhwpXXCBuUbw%2BPkFEXwIgAAABIDrrjO3H39ccVO2hIZKQ4eaIDh5sllzeNs2c43gxRdLH35YtpHJCFwEQAAAAkCvXmbpt40bzbWAFSk0VLrrLjNA5MUXzTWDmZlSSoqUmCgtXFix9aD8EQABAAgA1aubJdwk6Z13rKmhalVp9GhpwwYzaCQiQkpPN3MK9uwpZWRYUxf8jwAIlFFqaqri4%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%2B%2B9ioqKUvXq1ZWcnKxtJ0xktXXrVvXp00fVq1dXVFSURo4cqSNHjlTUxwCAYq66SkpIkA4cMB02O3C5pL59pV9%2BMdPGhIaa%2BQNbtpTGjbN2bkMURwCE7f3xxx/q0qWLnnnmmRKPGT16tGbOnKnp06fr66%2B/1oEDB9S7d28V/G/ugoKCAvXq1Ut//PGHvv76a02fPl0zZszQmKJfwQGggrlc0gMPmP2XXjKnVu2ienUzTczPP0vXXCPl50vPPMNp4UDCRNAIGps3b1bTpk21evVqtW7d2ne/x%2BNR3bp1NW3aNPXr10%2BStH37dsXFxenzzz9Xz549NXfuXPXu3VtZWVmKjY2VJE2fPl2DBg3Srl27SjWxMxNBA/C3/Hwz3UpWlukC3nmn1RWdPa9XmjXLDGwpGtByzTXSK69ITZtaW5uT0QFE0Fu1apXy8/OVlJTkuy82NlYJCQn65ptvJEnLly9XQkKCL/xJUs%2BePZWXl6dVq1ad8nXz8vKUk5NTbAMAf6pS5di1gH//u3T0qLX1nAuXS7r2WjNtzCOPmM/0%2BedSfLz09NMSV9pYgwCIoJedna3Q0FDVqlWr2P3169dXdna275j69esXe7xWrVoKDQ31HXOiCRMmyO12%2B7a4uLjy%2BQAAHG3IEDM34MaNZoCFXVWrJj31lPTjj1L37uaU9l//KrVuzSTSViAAwlbS0tJUo0YN37Zs2bJzfi2v1yuXy%2BX7%2Bvj9ko453rhx4%2BTxeHxbVlbWOdcCACWpXv3YvIATJpj1gu3sooukhQuladOkevXMgJGuXc2KInv2WF2dcxAAYSvJycnKyMjwbe3btz/jc6Kjo3XkyBHt27ev2P27du3ydf2io6NP6vTt27dP%2Bfn5J3UGi4SFhSkyMrLYBgDl4Z57pMhIM6ji00%2BtrqbsXC7p1lulX389tvbxm2%2BaQSJpaQwSqQgEQNhKRESEmjVr5tvCw8PP%2BJx27dqpSpUqWrBgge%2B%2BHTt2aM2aNercubMkqVOnTlqzZo127NjhO2b%2B/PkKCwtTu3bt/P9BAOAs1KxpQqBkTqMGS0CqVUuaMkX6%2Bmvp4oul3btNMLzqKmnTJqurC24EQNje3r17lZGRobVr10qS1q1bp4w9Mz61AAAad0lEQVSMDF9Hz%2B12a/DgwRozZowWLVqk1atX69Zbb1XLli3Vo0cPSVJSUpLi4%2BM1YMAArV69WosWLdLYsWM1dOhQOnsAAsLo0eY6ulWrpC%2B%2BsLoa/%2BrSxawk8uSTUliYmTvw4oulF16w58AXOyAAwvZmzZqlNm3aqFevXpKklJQUtWnTRq8eN3Pqiy%2B%2BqOuuu059%2B/ZVly5dVK1aNc2ePVshISGSpJCQEH322WeqWrWqunTpor59%2B%2Bq6667T888/b8lnAoAT1a0r3XWX2X/iieDpAhYJDTWDQn78UbriCrOW8Nix0qWXSj/8YHV1wYd5AAE/YR5AAOVtxw4zd15enrRokRlNG4y8XnNN4NixZhm8ypWlBx%2BUxo%2BXqla1urrgQAcQAACbiImRhg41%2B088YW0t5cnlMqOC166VbrzRnAZ%2B%2BmkzZcz/pm9FGREAAQCwkQcfNJMpf/mlGTwRzGJipI8/lmbMkKKjpXXrpMsuM9PisK5w2RAAAQCwkbg46fbbzf7jj1tbS0W54QbTDRw0yJwefvllqWVLackSqyuzLwIgAAA2M26cuS5uwQJpxQqrq6kYtWpJb70lzZsnNWpkponp3l0aPlzKzbW6OvshAAIAYDNNmkgDBph9p3QBi/TsKa1Zc2xE9OTJphu4aJG1ddkNARAoo9TUVMXHxysxMdHqUgA4yCOPSCEh0ty50vffW11NxYqIMMFv0SIThrdskXr0kO6%2BWzpwwOrq7IFpYAA/YRoYABVt4ECzpm6fPtKsWVZXY40DB8zAmMmTzdeJidK335qRxCgZHUAAAGzqr381QWf2bGn1aqursUaNGtKkSaYb2LixmTuQ8HdmBEAAAGyqeXMpJcXsB/O8gKXRvbv0yy9S375WV2IPBEAAAGzsb38zHa%2BZM6WffrK6GmuFh1tdgX0QAAEAsLH4eOmmm8z%2Bk09aWwvsgwAIAIDN/e1v5vajj8xpUOBMCIAAANhcq1bS9debVTLoAqI0CIAAAASBoi7g9OnS%2BvXW1oLARwAEACAItG0r9e4tFRZKTz9tdTUIdARAAACCxPjx5va996SNG62tBYGNAAgAQJDo0EFKSpIKCqRnnrG6GgQyAiAAAEHk0UfN7dtvS1lZlpaCAEYABMooNTVV8fHxSkxMtLoUAFCXLlK3blJ%2BvvTss1ZXg0Dl8nq9XquLAIJBTk6O3G63PB6PIiMjrS4HgIMtXixdeaUUFiZt2iTFxFhdEQINHUAAAIJMt25S585SXp70/PNWV4NARAAEACDIuFzH5gV89VVp925r60HgIQACABCErrrKzA148KD00ktWV4NAQwAEACAIHd8FfOUVaf9%2Ba%2BtBYCEAAgAQpK69Vrr4Yiknx4RAoAgBEACAIFWpkvTII2b/pZekP/6wth4EDgIgAABBrG9fqVkzac8eacoUq6tBoCAAAgAQxCpXlh56yOy/8IKZGgYgAAIAEOQGDJAaNJC2bzdLxAEEQAAAglxYmPTAA2b/73%2BXjh61th5YjwAIlBFrAQOwg6FDpagoaeNG6cMPra4GVmMtYMBPWAsYQKB76ikzN2BCgvTDD2aUMJyJbz0AAA4xYoQUESGtWSN99pnV1cBKBEAAAByiZk1p%2BHCz//TTEucAnYsACACAg4webQaFrFghLV1qdTWwCgEQAAAHiY6Wbr/d7E%2BYYG0tsA4BEAAAh3ngATMA5IsvpNWrra4GViAAAgDgMOedJ/XrZ/affdbaWmANAiAAAA5UtDzcRx9JGzZYWwsqHgEQAAAHuuQS6aqrpMJC6fnnra4GFY0ACACAQxV1Ad9%2BW9q509JSUMEIgAAAOFTXrlKHDtLhw9LEiVZXg4pEAATKiLWAAdiVy3WsCzhpkpSba209qDisBQz4CWsBA7CjggKpRQspM1N68UUzUTSCHx1AAAAcLCREGjvW7P/jH1J%2BvrX1oGIQAAEAcLiBA6X69aWsLOnDD62uBhWBAAgAgMNVrSrde6/Zf/55iYvDgh8BEAAA6O67perVpR9%2BkBYssLoalDcCIAAAUO3a0uDBZp%2BJoYMfARAAAEiS7rvPDApZsMB0AhG8CICwvU8%2B%2BUQ9e/ZUVFSUXC6XMjIyTjrmiiuukMvlKralpKQUO2bfvn0aMGCA3G633G63BgwYoP3791fUxwAAyzVpIt10k9l/4QVLS0E5IwDC9v744w916dJFzzzzzGmPGzp0qHbs2OHbpkyZUuzx/v37KyMjQ/PmzdO8efOUkZGhAQMGlGfpABBwiqaE%2BeADads2a2tB%2BalsdQFAWRWFtM2bN5/2uGrVqik6OvqUj/3yyy%2BaN2%2BeVqxYoY4dO0qSpk6dqk6dOmndunVq3ry5X2sGgEDVvr30pz9JS5ea5eGefdbqilAe6ADCMdLS0hQVFaWLL75YY8eOVe5xax4tX75cbrfbF/4k6dJLL5Xb7dY333xzytfLy8tTTk5OsQ0AgkFRF3DKFJaHC1YEQDjCLbfcog8%2B%2BEBffvmlxo8frxkzZuiGG27wPZ6dna169eqd9Lx69eopOzv7lK85YcIE3/WCbrdbcXFx5VY/AFSkXr2k5s0lj0d6802rq0F5IADCVtLS0lSjRg3ftmzZslI9b%2BjQoerRo4cSEhKUkpKijz/%2BWAsXLlR6errvGJfLddLzvF7vKe%2BXpHHjxsnj8fi2rKysc/tQABBgKlUyI4Il6aWXzHrBCC4EQNhKcnKyMjIyfFv79u3P6XXatm2rKlWqKDMzU5IUHR2tnTt3nnTc7t27Vb9%2B/VO%2BRlhYmCIjI4ttABAsBgyQ6tSRNm%2BWZs60uhr4GwEQthIREaFmzZr5tvDw8HN6nZ9//ln5%2BfmKiYmRJHXq1Ekej0ffffed75hvv/1WHo9HnTt39kvtAGAn1aqZ1UEk6R//sLYW%2BJ/L62XFP9jb3r17tXXrVm3fvl29evXS9OnT1bx5c0VHRys6OlobNmxQWlqarrnmGkVFRWnt2rUaM2aMwsPDtXLlSoWEhEiSrr76am3fvt03PcywYcPUuHFjzZ49u1R15OTkyO12y%2BPx0A0EEBSys6XGjaUjR6QVK6TjxsnB5ugAwvZmzZqlNm3aqFevXpKklJQUtWnTRq%2B%2B%2BqokKTQ0VIsWLVLPnj3VvHlzjRw5UklJSVq4cKEv/Enm%2BsKWLVsqKSlJSUlJatWqlaZNm2bJZwKAQBAdLf3lL2b/xRetrQX%2BRQcQ8BM6gACC0Q8/SK1bmyXiNm6UGjWyuiL4Ax1AAABQoksukbp1MyOBX3nF6mrgLwRAAABwWkVTwkydKh04YG0t8A8CIAAAOK1evaRmzaT9%2B6V337W6GvgDARAAAJxWpUrSyJFm/5//lAoLra0HZUcABAAAZzRokBQZKa1fL82bZ3U1KCsCIFBGqampio%2BPV2JiotWlAEC5iYiQBg82%2B//8p7W1oOyYBgbwE6aBARDsNm2Szj9f8nqltWulFi2srgjnig4gAAAolaZNpeRksz9xorW1oGwIgAAAoNRGjTK377xjRgXDngiAAACg1K64QkpIkA4elN580%2BpqcK4IgAAAoNRcrmNTwrzyilkhBPZDAAQAAGflllukWrXMoJDPP7e6GpwLAiAAADgr1apJQ4aY/ZdftrYWnBsCIAAAOGvDh5sVQhYulH75xepqcLYIgAAA4Kw1aSL16WP2U1MtLQXngAAIAADOyT33mNt33pFycqytBWeHAAgAAM7JlVea1UAOHJDefdfqanA2CIBAGbEWMACncrnMtYCSOQ3M4rL2wVrAgJ%2BwFjAAJ8rJkRo0MF3AhQtNVxCBjw4gAAA4Z5GR0sCBZv%2BVV6ytBaVHAAQAAGUyYoS5nTVLysqythaUDgEQAACUSXy8WSO4sFCaMsXqalAaBEAAAFBmRV3A11%2BXjhyxthacGQEQAACU2bXXSjEx0s6d0iefWF0NzoQACAAAyqxKFWnYMLM/aZK1teDMCIAAAMAvhg6VQkKkZcukNWusrganQwAEAAB%2B0aCBORUsSZMnW1sLTo8ACAAA/Obuu83ttGlmcmgEJgIgAADwm%2B7dpQsukHJzpbQ0q6tBSQiAAADAbypVku66y%2By/%2BirrAwcqAiBQRqmpqYqPj1diYqLVpQBAQLjtNiksTMrIkL77zupqcCour5dsDvhDTk6O3G63PB6PIiMjrS4HACw1cKC5DnDQIOmtt6yuBieiAwgAAPyu6DTwhx9K%2B/ZZWwtORgAEAAB%2B16mT1LKldOiQ6QQisBAAAQCA37lc0p13mv0pUxgMEmgIgAAAoFzceqtUrZq0dq30n/9YXQ2ORwAEAADlwu2WUlLM/pQp1taC4giAAACg3BSdBv74Y2nvXmtrwTEEQAAAUG4SE6VLLpEOH2YwSCAhAAIAgHLjcknDhpn9115jMEigIAACAIBydcstUni4GQyyfLnV1UAiAAIAgHLmdkv9%2Bpn9116zthYYBECgjFgLGADObOhQc/uvf0kej7W1gLWAAb9hLWAAKJnXa1YG%2BflnadIk6e67ra7I2egAAgCAcudySUOGmP2pU62tBQRAAABQQQYMkEJDpdWrpfR0q6txNgIgAACoEHXqSDfcYPbpAlqLAAgAACpM0Wng99%2BXDh60thYnIwACAIAK062b1LSplJNjloeDNQiAAACgwlSqJN1xh9l/4w1ra3EyAiBsLT8/Xw899JBatmyp6tWrKzY2VgMHDtT27duLHbdv3z4NGDBAbrdbbrdbAwYM0P79%2B4sd89NPP6lr164KDw9XgwYN9Pjjj4tZkgDA/wYNMkFw6VIpM9PqapyJAAhbO3jwoNLT0zV%2B/Hilp6frk08%2B0fr165WcnFzsuP79%2BysjI0Pz5s3TvHnzlJGRoQEDBvgez8nJ0Z///GfFxsZq5cqVmjhxop5//nn94x//qOiPBABBr2FDqWdPs//mm9bW4lRMBI2gs3LlSnXo0EFbtmxRo0aN9Msvvyg%2BPl4rVqxQx44dJUkrVqxQp06d9Ouvv6p58%2BaaPHmyxo0bp507dyosLEyS9Mwzz2jixInatm2bXC7XGd%2BXiaABoPRmzJBuukmKiZG2bpUqV7a6ImehA4ig4/F45HK5VLNmTUnS8uXL5Xa7feFPki699FK53W598803vmO6du3qC3%2BS1LNnT23fvl2bN28%2B5fvk5eUpJyen2AYAKJ0%2BfaSoKGnHDmnePKurcR4CIILK4cOH9fDDD6t///6%2BLlx2drbq1at30rH16tVTdna275j69esXe7zo66JjTjRhwgTfNYVut1txcXH%2B/CgAENRCQ83E0BKnga1AAIStpKWlqUaNGr5t2bJlvsfy8/OVkpKiwsJCTZo0qdjzTnUK1%2Bv1Frv/xGOKro4o6fTvuHHj5PF4fFtWVtY5fy4AcKKi0cCzZ0u7dllbi9Nwxh22kpycXOxUboMGDSSZ8Ne3b19t2rRJixcvLnYNXnR0tHbu3HnSa%2B3evdvX5YuOjj6p07frf/8andgZLBIWFlbslDEA4OwkJEiJidLKldJ770n33291Rc5BBxC2EhERoWbNmvm28PBwX/jLzMzUwoULVadOnWLP6dSpkzwej7777jvffd9%2B%2B608Ho86d%2B7sO2bp0qU6cuSI75j58%2BcrNjZWTZo0qZDPBgBOVNQFfOstiWGpFYdRwLC1o0eP6sYbb1R6errmzJlTrFtXu3ZthYaGSpKuvvpqbd%2B%2BXVOmTJEkDRs2TI0bN9bs2bMlmYEjzZs3V/fu3fXII48oMzNTgwYN0qOPPqoxY8aUqhZGAQPA2du/34wEPnxY%2Bu470xFE%2BaMDCFvbtm2bZs2apW3btql169aKiYnxbUUjfCVz7WDLli2VlJSkpKQktWrVStOmTfM97na7tWDBAm3btk3t27fX8OHDdf/99%2Bt%2BzkcAQLmqWVO6/nqz/9Zb1tbiJHQAAT%2BhAwgA52bBAikpyYTBHTukqlWtrij40QEEAACW6t5dioszp4M//dTqapyBAAgAACwVEiLddpvZf/ttS0txDAIgAACwXFEAnD9f%2Bv13a2txAgIgAACwXLNm0mWXSYWFZk5AlC8CIAAACAiDBpnbt99mTsDyRgAEyig1NVXx8fFKZPIqACiTm2%2BWwsOlX381q4Og/DANDOAnTAMDAGV3yy3S%2B%2B9Lw4dLqalWVxO86AACAICAUXQa%2BIMPpLw8S0sJagRAAAAQMLp3lxo0kPbtk%2BbMsbqa4EUABAAAASMkRLr1VrP/zjvW1hLMCIAAACCgDBxobufOlXbvtraWYEUABAAAASU%2BXmrfXjp61FwLCP8jAAIAgIBT1AV8911r6whWBEAAABBwUlKkypWlVauktWutrib4EAABAEDAqVtXuvpqsz9tmrW1BCMCIAAACEhFp4Hfe8%2BsEQz/IQACAICA1Lu3VLOmtG2b9NVXVlcTXAiAQBmxFjAAlI%2BqVc36wBKngf2NtYABP2EtYADwv2XLpD/9SYqIkHbulMLDra4oONABBAAAAatLF6lJEyk3V/r0U6urCR4EQAAAELAqVZJuucXsv/eetbUEEwIgAAAIaEVrA3/xBUvD%2BQsBEAAABLSLLpLatTNLw/3rX1ZXExwIgAAAIOAVdQE5DewfBEAAABDwUlLM9YArVkgbNlhdjf0RAAEAQMCLjpZ69DD7aWnW1hIMCIAAAMAW%2Bvc3t2lpErMYlw0BEAAA2MINN5iJoNevl1atsroaeyMAAgAAW4iIkJKTzf7771tbi90RAAEAgG0UnQaePl0qKLC2FjsjAAJllJqaqvj4eCUmJlpdCgAEvauukmrVknbskL780upq7IsACJTRiBEjtHbtWq1cudLqUgAg6IWGSjffbPY5DXzuCIAAAMBWik4Dz5gh5eVZW4tdEQABAICtXH651KCB5PFIc%2BdaXY09EQABAICtVKpkVgaROA18rgiAAADAdv7yF3M7e7aUm2ttLXZEAAQAALbTtq104YXS4cPSp59aXY39EAABAIDtuFzHuoAffGBtLXZEAAQAALZUdB3g/PnSnj3W1mI3BEAAAGBLF10ktW4tHT1qpoRB6REAAQCAbRWdBp4%2B3do67IYACAAAbKtfP3P75ZfS9u2WlmIrBECgjFgLGACs07ixdOmlktcrffSR1dXYh8vr9XqtLgIIBjk5OXK73fJ4PIqMjLS6HABwjJdflkaNMkFw%2BXKrq7EHOoAAAMDWbr7ZTAuzYoW0ZYvV1dgDARAAANhaTIzUtavZ//BDa2uxCwIgAACwvaLBIATA0iEAAgAA27vxRikkREpPl377zepqAl9lqwsAAAAoq7p1paQk6cgRKTfX6moCHx1A2Fp%2Bfr4eeughtWzZUtWrV1dsbKwGDhyo7SdMBtWkSRO5XK5i28MPP1zsmK1bt6pPnz6qXr26oqKiNHLkSB05cqQiPw4AoAxmz5YWLpTatLG6ksBHBxC2dvDgQaWnp2v8%2BPG65JJLtG/fPo0ePVrJycn6/vvvix37%2BOOPa%2BjQob6va9So4dsvKChQr169VLduXX399dfas2ePbrvtNnm9Xk2cOLHCPg8A4NyFhFhdgX0QAGFrbrdbCxYsKHbfxIkT1aFDB23dulWNGjXy3R8REaHo6OhTvs78%2BfO1du1aZWVlKTY2VpL0wgsvaNCgQXrqqaeY1w8AEFQ4BYyg4/F45HK5VLNmzWL3P/vss6pTp45at26tp556qtjp3eXLlyshIcEX/iSpZ8%2BeysvL06pVqyqsdgAAKgIdQASVw4cP6%2BGHH1b//v2Lde1GjRqltm3bqlatWvruu%2B80btw4bdq0Sa%2B//rokKTs7W/Xr1y/2WrVq1VJoaKiys7NP%2BV55eXnKy8vzfZ2Tk1MOnwgAAP%2BjAwhbSUtLU40aNXzbsmXLfI/l5%2BcrJSVFhYWFmjRpUrHn3XffferatatatWqlIUOG6NVXX9Ubb7yhPXv2%2BI5xuVwnvZ/X6z3l/ZI0YcIEud1u3xYXF%2BenTwkAQPkiAMJWkpOTlZGR4dvat28vyYS/vn37atOmTVqwYMEZr9m79NJLJUm//W%2ByqOjo6JM6ffv27VN%2Bfv5JncEi48aNk8fj8W1ZWVll/XgAAFQITgHDViIiIhQREVHsvqLwl5mZqSVLlqhOnTpnfJ3Vq1dLkmJiYiRJnTp10lNPPaUdO3b47ps/f77CwsLUrl27U75GWFiYwsLCyvJxAACwBAEQtnb06FHddNNNSk9P15w5c1RQUODr5NWuXVuhoaFavny5VqxYoW7dusntdmvlypW67777lJyc7BslnJSUpPj4eA0YMEDPPfec9u7dq7Fjx2ro0KGMAAYABB2X1%2Bv1Wl0EcK42b96spk2bnvKxJUuW6IorrlB6erqGDx%2BuX3/9VXl5eWrcuLFSUlL04IMPqlq1ar7jt27dquHDh2vx4sUKDw9X//799fzzz5e6y5eTkyO32y2Px0NoBAAENAIg4CcEQACAXTAIBAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAoIxSU1MVHx%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%3D%3D'}
Genus 2 curve data - 3969.b.35721.1