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g2c_curves • Show schema
{'Lhash': '615497630361922712', 'abs_disc': 371250, '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,[1,0,1,2]],[3,[1,-1,3,-3]],[5,[1,0,-1]],[11,[1,1,11,11]]]', 'bad_primes': [2, 3, 5, 11], 'class': '1650.a', 'cond': 1650, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[0,1,-11,30,-11,1],[0,1,1]]', 'g2_inv': "['1448946796623435/22','150474103581314/55','3777545308302/25']", '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': "['30180','172689','1721884569','47520000']", 'igusa_inv': "['7545','2364764','985411548','460705338491','371250']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '1650.a.371250.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.5746746889584469024522230045034064635510970089883061999', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.180.3', '3.720.4'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 2, 'num_rat_wpts': 2, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '13.792192535002725658853352108', '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': 12, 'torsion_subgroup': '[2,6]', 'two_selmer_rank': 2, 'two_torsion_field': ['4.4.69696.2', [34, 20, -19, -2, 1], [4, 2], True]} -
g2c_endomorphisms • Show schema
{'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': '1650.a.371250.1', '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': [[[201], [-1701]], [[889], [-24013]]], 'spl_facs_condnorms': [55, 30], 'spl_facs_labels': ['55.a3', '30.a6'], '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 • Show schema
{'label': '1650.a.371250.1', 'mw_gens': [[[[1, 1], [-19, 4], [1, 1]], [[1, 2], [-23, 8], [0, 1], [0, 1]]], [[[1, 1], [-7, 1], [1, 1]], [[1, 1], [-8, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 6], 'num_rat_pts': 2, 'rat_pts': [[0, 0, 1], [1, 0, 0]], 'rat_pts_v': True} - g2c_galrep • Show schema
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g2c_tamagawa • Show schema
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id: 25445
{'label': '1650.a.371250.1', 'p': 2, 'tamagawa_number': 1} -
id: 25446
{'cluster_label': 'c3c2_3~2_0', 'label': '1650.a.371250.1', 'p': 3, 'tamagawa_number': 3} -
id: 25447
{'cluster_label': 'c1c2_1c2_1_0', 'label': '1650.a.371250.1', 'p': 5, 'tamagawa_number': 2} -
id: 25448
{'cluster_label': 'c3c2_1~2_0', 'label': '1650.a.371250.1', 'p': 11, 'tamagawa_number': 1}
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id: 25445
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g2c_plots • Show schema
{'label': '1650.a.371250.1', 'plot': 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NKxxz6t33%2B3Hq2775YeeODgLX%2BR9B7wcwsgy/QehZiYmP2/eZOSpKeftiS31471Sf87NOR%2BTXmg4Z/%2B/Ph4T4MHF/38zZul2bNtPbMZM6SZM22M0qef2iFJsbGz1LVrotq2ldq1s/F%2Bf/bejouLO%2BxPQEfyPZKUlJR0WN8XrtpK02vg9%2Bcfyu%2B5%2BGI7vv5aeugh6ZNPitYWvPlm2%2BrqmGP%2B/LGi%2BTUoie%2BReA2kw3sNIvW5PPqo9PvvD0iy9/9DDx16t2%2Bkvgf8ggUOQuWSS2xaYatWhVflNWwovfyyfTw6BLfffnuxr6tWtU3uBw%2B2/3i2bLGt2IYPt/%2BUqlSR8vMTNWmSzVxMS5Pq1ZNuuUX673%2BlYPDQHudIaguVcNVWml4Dvz//cHxP69a2huDdd7%2Bj886z%2BV4vvCA1amQ7Huy90HRJiMTX4Gi%2B50hE8vMJx2sQac/F82yMX8HCzkOGhH6pl0j9t4lWdAGHwa%2BZmap78slavWqV6qakhOxx8vJsYefPPrOlBr/80ha9LRAfb93EHTtKf/ubTRYJl2jYDSXU/P4alObn/8UX9h/h11/b15UrW2tIz57FZ9yX5tfgUPEaRP9r4HnSgAHS44/b1489VrTV26GI9udfWsQNHsza3KGWHRenJ598UgMGDFDFihVD9jixsVKdOtY6ce21Up8%2BNjawenVrkdi4UVqxwloPR4yw/VFXrbLxh8ccE/oFb%2BPi4nT22Wcr3scbBPv9NSitzz811fYVPvVUm8G/apX9no0da79bTZsWtYyU1tfgcPAaRO9r4HnWw/TUU/b18OFHtrdvtD7/0oQWwDCIlE87v/wiffCBNGmSNH26lJNTdFtysi05c8klUtu2UpkyzsoEolpenvTaa9YCuG6dXffXv0rPPGMLrgPRKi/PtkocNcq%2BHjnSvkZ0IgCGQaQEwL1lZdmOBxMnWijce3u4pCSpc2fpsstszCE7IACHb8cO6YknpCeflHbtshb2Hj1snFTVqq6rAw5PTo6tgfnvf9t7%2BZVXpOuuc10VjgYBMAwiMQDuLSdHmjZNGj/ejvXri26rVMnGC15xhXTBBbYQLoBDt2qV1K%2Bf9NZb9nWNGhYKr7028hfJBSRp927pyiul996zseRjx9r/CYhuBMAwiPQAuLf8fFtm5p13bObw6tVFtyUmWjfxlVfadnV0EwOHbupUmxSycKF9ffbZNnO4cWOnZQEHtX27DQ369FOb0PTOO1KnTq6rQklgGRgUExtrk0iGD7e9h7/5RrrrLiklxdYcfOMN%2B%2BWvVcuWl/n8cxsXciBffPGF/va3v6lOnTqKiYnRhAkTwvdkIsCjjz6q9PR0JSYmKjk5WRdffLEWL17suqywGjlypFq0aFG45lerVq300UcfuS4r7M45R5o3T2rffqqknZo2zRZ8Hzq0%2BHjc0mjw4MGKiYkpdtSqVct1WWH366%2B/qmvXrqpWrZoqVKigk046SXPmzHFd1gFt2WIf9j/91CYLfvTR0YW/%2BvXr7/M%2BiImJYakWRwiAOKCYGOn0022214oVtsRFr14W/rZssYHAbdtaOLz7bluT8I/tyTt27NCJJ56oESNGOHkOrk2fPl233367Zs6cqSlTpig3N1ft2rXTjh07XJcWNnXr1tVjjz2m7777Tt99953OPfdcXXTRRfrxxx9dlxZ28%2BfP1uLFN6pJk8uUmrpI2dk2WeS002wJp9LshBNO0Lp16wqPBQsWuC4prLZs2aLWrVsrISFBH330kRYuXKhhw4apcuXKrkvbr/XrpbPOsg0HqlSx5cXOOefofubs2bOLvQemTJkiSfq///u/EqgYh81DyAWDQU%2BSFwwGXZdSInJzPe%2Bzzzzv5ps9r3Jlz7PYZ0fjxp738MOet2zZvt8nyRs/fnz4C44gGzZs8CR506dPd12KU1WqVPFGjRrluoyw2rZtm3fcccd5U6ZM8c466yzvjjvu9MaM8byqVe13Jz7e84YM8bw9e1xXWvIefPBB78QTT3RdhlP33nuv16ZNG9dlHJJlyzzv2GPtfVmrlud9/31oHufOO%2B/0jj32WC8/Pz80D4CDogUQhy0uTjr3XNvUZP16acIEGxBcrpztWTxokNSwoa1B%2BOKLtoUdTPB/27FU9ek00Ly8PI0bN047duxQq712yfGD22%2B/XR07dtR5550nyVrYu3a1MYGXXmq7iTz4oA3BKI2jBJYuXao6deqoQYMGuvLKK7Vsf5ubl2ITJ07Uqaeeqv/7v/9TcnKyTj75ZL388suuy9rHDz/Ye/CXX6QGDWzf6%2BbNS/5x9uzZozfeeEM33nijb/fidY0AiKNStqxNDPnPf6TffpNGj5bOO8/%2Bc/v6a1v2onZtW1JG6qycHP/%2Bonuep7vvvltt2rRRs2bNXJcTVgsWLFClSpVUtmxZ9ejRQ%2BPHj1daWprrssJm3Lhxmjt3rh599NF9bqtZ0wbWjx1rO4jMni2dfLL00kv7DqmIVi1bttTrr7%2Bujz/%2BWC%2B//LLWr1%2BvM844Q5s2bXJdWtgsW7ZMI0eO1HHHHaePP/5YPXr00B133KHXX3/ddWmFvvnG1qxct05q1szC37HHhuaxJkyYoK1bt%2Br6668PzQPgz7lugvSD0tYFfCh%2B/dXznnzS81q0KN5FnJi42%2BvZ0/Nmz/Y8v7X633bbbV5qaqq3evVq16WEXXZ2trd06VJv9uzZXv/%2B/b3q1at7P/74o%2BuywmLVqlVecnKyl5mZWXjdWWed5d1555373Hf1as9r27bo9%2BXSSz1v8%2BZwVhse27dv92rWrOkNGzbMdSlhk5CQ4LVq1arYdb169fJOP/10RxUV98EHnle%2BvL3vWrXyvE2bQvt47dq18zp16hTaB8FBEQDDwI8BcG/z53tenz6eJ60tFgbT0jzv8cctLJZ2PXv29OrWrest29/gSB9q27at1717d9dlhMX48eM9SV5cXFzhIcmLiYnx4uLivNzc3GL3z8uzD08JCfZ7kprqebNmuak9lM477zyvR48erssIm3r16nk33XRTseuef/55r06dOo4qKjJmjI1BlTyvQwfP2749tI%2B3YsUKLzY21pswYUJoHwgHRRcwQq5FC%2Bmf/5SkFA0aNENXXmnjBRcutA3EU1KkDh2sG3n3btfVlizP89SzZ0%2B9%2B%2B67%2Bvzzz9WgQQPXJUUEz/OUnZ3tuoywaNu2rRYsWKDMzMzC49RTT9U111yjzMxMxcXFFbt/bKztrTpjho2lXbnSxtNmZJSeLuHs7GwtWrRItWvXdl1K2LRu3XqfJaCWLFmi1NRURxWZYcOkbt1sDGrXrrbYcwi3rJckvfrqq0pOTlbHjh1D%2B0A4ONcJtDQbMWKE17RpU%2B/444/3bQvgtm3bvHnz5nnz5s3zJHlPPfWUN2/ePG/BglXeSy95XuvWxbuIK1f2vB49rMWjNHQR33rrrV4gEPCmTZvmrVu3rvDYuXOn69LCZsCAAd4XX3zhLV%2B%2B3Pv%2B%2B%2B%2B9gQMHerGxsd4nn3ziujRnDtQF/Edbt1o3cMHvR9eunrdjRxgKLGF9%2BvTxpk2b5i1btsybOXOm16lTJy8xMdFbsWKF69LC5ttvv/Xi4%2BO9oUOHekuXLvXGjh3rVahQwXvjjTec1JOX53l9%2Bxa9t%2B66y64L/ePmefXq1fPuvffe0D8YDooAGAZ%2B7gKeOnWqJ2mf47rrriu8z5IlnnfffZ6XkuLt00X8xBOet26du/qP1v6euyTv1VdfdV1a2Nx4441eamqqV6ZMGa9GjRpe27ZtfR3%2BPO/QA6Dn2QehYcM8Ly7Ofi/%2B8hfPW7UqxAWWsC5duni1a9f2EhISvDp16niXXnqpb8aA7m3SpEles2bNvLJly3pNmjTxXnrpJSd1ZGfbh4mCv7WPPx6%2BD9wff/yxJ8lbvHhxeB4QB8RWcGEQTVvBuZSfbzuLjB4tvfuutGuXXR8XZ13EN9xgq9CzBR38aOpUW27p999t5vB770ktW7quCtFm2zZblWHKFNvXd9Qo6brrXFcFFwiAYUAAPHzBoPTWW9Krr9rSBAWqV7dxKjfeGJq1qYBItmKFLbv0/fc2jnbMGOnyy11XhWixfr3UsaM0d66N83vnHemCC1xXBVcIgGFAADw6P/1kQfD11%2B0PWIFTT7VWwauusq2KAD/Ytk26%2Bmrp/fft66eesv26gYNZvNjC3ooVUo0a0gcfSOnprquCSwTAMCAAlozcXOnjj6VXXpEmTZJycuz6smVtJ4Ubb7QdSmKZ245SLi9P6t1bKthi%2B557pMceswXYgT/65hsbPrN5sy3sPHmy1KiR66rgGgEwDAiAJW/jRts54ZVXpL33lK9XT7r%2BejtYcQWlmedJTzwh9e9vX99yizRypI2ZBQqMH28txrt3W4vf%2B%2B9Lycmuq0IkIACGAQEwdDxPmjPHguCbb0pbtxbddu651ip4ySVShQruagRC6V//krp3t0lUXbvaJCpCICRrIb7jDvs72amTNG5c6Nf4Q/QgAIYBATA8du2SJkywMPjZZ0WL5iYl2TjBG2%2B0T8B0k6G0eest6ZprbJjENddIr71GCPSz/HxbZN8W4Jf%2B/ncLg/HxbutCZCEAhgEBMPxWrrSWkNGjbdBzgbQ0C4Jdu9pSGkBp8e67UpcuFgJvukl6%2BWU%2B7PjR7t22rMtbb9nXQ4dKAwbwXsC%2BCIBhQAB0Jz9fmjbNWgX/%2B9%2Birebi4205hBtukC68UEpIcFomUCLeflu68kp73/ftKz35pOuKEE6bNkkXXyx99ZX9TXvlFfuwC%2BwPATAMCICRIRi0MTCvvirNmlV0fXKy/ZG84QapWTN39QElYfRoey9L0tNPS3fe6bQchMmyZfZhdvFiKRCwyR/nnOO6KkQyAmAYEAAjz8KFFgTHjJF%2B%2B63o%2BlNPtRnEV10lVa3qrDzgqDz%2BuM0OjomxHUP%2B9jfXFSGUZs2yf%2BONG6WUFOmjj6QTTnBdFSIdATAMCICRKyfH/liOHm1rC%2Bbm2vVlytiOC9dfL7Vrx%2BBpRBfPk3r0kF56SUpMtIDQtKnrqhAKEybYMi%2B7dkknn2zLvNSp47oqRAMCYBgQAKNDwdqCo0dL8%2BcXXV%2B7tnURX3%2B9TSIBokFOjnT%2B%2BdL06Rb%2BZs9mCZDS5tlnbUFwz7Pu3//8R6pUyXVViBYEwDAgAEafefMsCI4dawOrC5x6qs2wu%2BoqqVo1Z%2BUBh%2BS336xVaN06mxk8apTrilAS8vJsks/TT9vXLPOCI0EADAMCYPTas8f2zHztNbss6CJOSLCFVa%2B7TurQwbqMgUg0bZotiu55NsyhUyfXFeFo7Nwpdetmy/5ItgXgPfewzAsOHwEwDAiApcOGDbbbyGuvWQthgWrVrEXw2muthZA/xIg0/frZosDHHCMtWmTjAhF9Nm6UOneWZs60D52vvWbL/gBHggAYQhkZGcrIyFBeXp6WLFlCACxFFiywP75jx0rr1xdd37ixfTrv2lVKTXVXH7C3XbtsiaNly6y16PHHXVeEw/Xzz9bb8PPPUpUqNvnjr391XRWiGQEwDGgBLL1yc6VPP5Vef93%2BIO/aVXTbmWdaGLz8cvuDDbj0/vu2VEjZstLSpbZcCKLDrFnWdf/771L9%2BrZyQZMmrqtCtCMAhgEB0B%2B2bbPdRsaMkaZOLdqLuEwZ23Xkmmvsslw5t3XCnzzPFgaePl3q2VN67jnXFeFQTJxo3by7dkmnnGJBvlYt11WhNCAAhgEB0H/WrJH%2B/W/pjTesu7hAICBddpmt23X22VJcnLMS4UOffSadd55UoYK0dq29HxG5XnxRuu0229qvQwfb35dlXlBSCIBhQAD0t%2B%2B/tyD45psWDAvUri116WITSNLTmTyC0PM8Gwu4cKGFi%2B7dXVeE/fE8afBg6aGH7Osbb7R/L5Z5QUmKdV0AUNq1aCE98YS0cqUtydG9u40JXLfO1vFq2VJq1Ei67z5rLeQjGUIlJsZmq0s2XAGRJzfX1vUrCH8PPGDrNxL%2BUNJoAQwDWgDxR3v2SB9/bK2C771na3sVaNrUWga7dGGgN0rejz9aK2D58lIwaGtaIjLs3m3DQ8aPl2JjpeeftzAIhAIBMAwIgDiYHTtsgd5x42x23549Rbc1by5dcYUdxx/vrkaUHvn5UtWqFv6%2B/97eY3AvK8v2H582zWZqv/mmdMklrqtCaUYADAMCIA5VMGjLyfznP9KUKUU7j0jWlXz55XY0bequRkS/U0%2BV5sxhZ5BI8fvvNsnju%2B9ske6JE22SGBBKjAEEIkggYNvLffih7eP6r39JF1xg43%2B%2B/97GA6WlSSecYOfz5zNmEIdIfba1AAAgAElEQVSv4HPo9u1u64CNBT77bAt/1avbElKEP4QDARCIUFWr2uy/jz6yMPjKK9KFF9qYrYULpYcflk46ySaQ9O0rff21de8Bf6ZgzGnZsm7r8LvVq6WzzrJxmXXqSF98YWv9AeFAF3AY0AWMkrR1q3XdvfuuNHmyDRwvULOm7RV60UVS27YsOo19eZ5Uo4a0aZN1A//lL64r8qdVq6ylb/ly2zby88%2Blhg1dVwU/IQCGAQEQobJjh4XA8eNth4BgsOi2ihWl9u1t%2B6%2BOHe0/feD776UTT7QPB1u30groQkHL3/Ll0rHHWvirV891VfAbAmAYEAARDnv22AzC996z49dfi26LiZFatbIB/x072sxPFp72p969pWeekS6%2B2D44ILzWrZP%2B%2Blfp558t/E2bJtWt67oq%2BBEBMAwIgAg3z5PmzrXZhBMnSpmZxW9PSbHxhB06WFcx20v5w8qVtrbk7t22DmW7dq4r8pfNmy38/fijVL%2B%2BjflLSXFdFfyKABgGBEC4tmaNdRF/8IH06afFxw0mJEhnnmlhsF07WgdLq9xc%2B/edOtW6H6dO5d85nHbutA9bM2fahI8vv2TMH9wiAIYBARCRZNcu%2B8//ww9thvGyZcVvr11bOv98O847T6pVy02dKDmeJ916q%2B0nW7GitQ6zsHj45OVJl15qrfFVqlj4O%2BEE11XB7wiAYUAARKTyPGnpUptIMnmyjUfatav4fZo3t5aLtm2t%2B4q3cHTJybHw969/WYvfO%2B9YGEH43H23NHy4Tbj57DOpdWvXFQEEwLAgACJa7N5t6wl%2B8ontRDJvXvHbY2NtF4lzzrFuxNatCYSRbM0a6ZprbKxZbKyFwOuvd12Vv7z2WtFr/p//2LaOQCQgAIZQRkaGMjIylJeXpyVLlhAAEXV%2B/92WqPjsM7v8%2Befit8fFSSefbC2DZ55pgZDlZtzLz7ewd889ttRLYqL0xhu2RiTCJzNTOv10KTvbdu4ZMsR1RUARAmAY0AKI0mL1ahs/OG2aNH36vuMHJRtb1rq1dMYZdjRpYq1PCD3Ps9bbgQNtnJ8kpadLY8dKxx3ntja/2bHDFtlessSWXpo4kd8DRBYCYBgQAFFarV5tA9q/%2BMIuFy7c9z5JSdJpp0ktW9plerpNNEHJ2bPHdoYZNsz2lJXsdR88WOrVy/aSRnjdeqv0wgu2xl9mplStmuuKgOIIgGFAAIRfbN4szZhhxzffSN9%2BW7Tv7N6OOcb2PD3lFOtCPvlku45lSQ6d50kLFljX7uuv237RklS%2BvNSjhzRgAN3xrkydKp17rp1/9lnRORBJCIBhQACEX%2BXmSj/8YGufffutHYsW2Ri1P6pe3bYoa9HCjubNpaZNpQoVwl93pMrLk2bNsjUdx4%2BXfvqp6LZataS//1267TYpOdldjX63Z4%2B9j3/6yYL4yJGuKwL2jwAYBgRAoMj27Ta7eM4cG6c2b56Fwry8fe8bEyM1aGBBsOA4/ng7atQo/S2GOTnS/PnSV1/ZmMtp02xSR4EyZWxHl%2Buus3FmCQnOSsX/PPecdMcdFsIXL5YqV3ZdEbB/BMAwIAACB7drl22PNX%2B%2B9P331rW5YIHNQj6QQEBq1Mj2U23Y0IJi/fpSaqpUr551hUaTzZstCP/wg70Gc%2Bfa2LG9d22RLFC0b28zejt2tNcBkWHnTnsfbthg4//%2B/nfXFQEHRgAMAwIgcGQ2bLBguGiRdan99JO1qqxebWPgDqZaNRuAX6eOHbVq2eSTGjWsdaZ6dbtPlSpSuXKhfR67dkkbN9o4vXXrpF9/teewcqXNpP755wOH3SpVpFatbJmdc86xdRjj4kJbL47MiBE26aZBA3uf0iKLSEYADAMCIFCydu0qCk6//GLnK1bYsXKldTMfjnLlrCUtKcnWzKtY0cYeVqhguzeULWszaePjbSmPmBgLoHl5Ns4xJ8fWetu1y45t2%2BzYulXasmXf3VUOpG5d2yKsRQubGHPKKbZ8S2nv6i4NPM%2BGKCxebEHw9ttdVwQcHAEwDAiAQPh4ngWv1autpe3XX63Vbf16OzZssNa4TZus23V/E1JCISHBWh5r17YWyZQU665u0MC6sY87TqpUKTy1oOTNmGHrX1asaO%2B3xETXFQEHx%2BpQAEqVmBjrNq1SxVrSDiY/X8rKssC4dWtRy92OHTaea9cuG4O3Z4%2B19OXmFg%2BMsbHWKpiQYK2E5cpZq2Fioh2BgNVRrZq1LtKSV3q9/bZdXnIJ4Q/RgQAIwLdiY21SBTM1cbQ%2B%2BsguL7nEbR3AoWJjGgAAjsKGDTb2LybGJuoA0YAACADAUZg3zy6PP966/IFo4OsAmJOTo3vvvVfNmzdXxYoVVadOHV177bVau3Ztsftt2bJF3bp1UyAQUCAQULdu3bR179VYAQC%2BtWSJXaalua0DOBy%2BDoA7d%2B7U3LlzNWjQIM2dO1fvvvuulixZos6dOxe739VXX63MzExNnjxZkydPVmZmprp16%2BaoagBAJFmzxi7r1XNbB3A4fD0JJBAIaMqUKcWue%2B6553Taaadp1apVqlevnhYtWqTJkydr5syZatmypSTp5ZdfVqtWrbR48WI1btzYRekAgAixZYtdVqvmtg7gcPi6BXB/gsGgYmJiVPl/0wK/%2BeYbBQKBwvAnSaeffroCgYBmzJix35%2BRnZ2trKysYgcAoHTas8cuy5Z1WwdwOAiAe9m9e7f69%2B%2Bvq6%2B%2BunDB5vXr1ys5OXmf%2ByYnJ2v9%2BvX7/TmPPvpo4XjBQCCglJSUkNYNAHCnYGu%2B3Fy3dQCHw1cBcOzYsapUqVLh8eWXXxbelpOToyuvvFL5%2Bfl6/vnni31fzH5Wb/U8b7/XS9KAAQMUDAYLj9WrV5fsEwEARIyChZ/p7EE08dUYwM6dOxfryj3mmGMkWfi74oortHz5cn3%2B%2BefFtmurVauWfvvtt31%2B1saNG1WzZs39Pk7ZsmVVlr4AAPCFgk6iA3QKARHJVwEwMTFRiX/Yo6cg/C1dulRTp05VtT%2BM4m3VqpWCwaC%2B/fZbnXbaaZKkWbNmKRgM6owzzghb7QCAyJSaapfLl7utAzgcMZ7nea6LcCU3N1eXXXaZ5s6dq/fff79Yi17VqlVVpkwZSVKHDh20du1avfjii5Kk7t27KzU1VZMmTTqkx8nKylIgEFAwGCzWuggAiH7ffSelp0s1aki//caez4gOvg6AK1asUIMGDfZ729SpU3X22WdLkjZv3qw77rhDEydOlGRdySNGjCicKfxnCIAAUHrt2mXjAPPypNWrpbp1XVcE/DlfB8BwIQACQOn2l7/YlnDjxklduriuBvhzvpoFDABAKJx5pl1Om%2Ba0DOCQEQABADhK551nl5MnS/SrIRoQAAEAOErnnms7gaxYIf3wg%2BtqgD9HAAQA4ChVrCi1a2fn77zjthbgUBAAAQAoAVdcYZdvvkk3MCIfARAAgBJw0UVShQrS0qXSzJmuqwEOjgAIAEAJSEyULr/czkeNclsL8GcIgAAAlJCbb7bLceOkrVvd1gIcDAEQAIAS0qaNdMIJ0s6d0ujRrqsBDowACABACYmJkXr1svPnnrPt4YBIRAAEAKAEdesmVa0qLVsmTZjguhpg/wiAIZSRkaG0tDSlp6e7LgUAECYVKki33Wbnjz/OkjCITDGex1sz1LKyshQIBBQMBpWUlOS6HABAiG3cKKWmSrt2SVOmFG0VB0QKWgABAChhNWpIt9xi5w8/7LYWYH8IgAAAhEC/flKZMtIXX0hTp7quBiiOAAgAQAjUrVu0LuCDDzIWEJGFAAgAQIgMHCiVLSt9%2BaX0ySeuqwGKEAABAAiRY44pmhF83320AiJyEAABAAihAQOkSpWkOXOkt992XQ1gCIAAAIRQjRpS3752ft99Uk6O23oAiQAIAEDI3X23lJws/fyz9OKLrqsBCIAAAIRcYqI0eLCdDxkiBYNOywEIgAAAhMPNN0uNG0u//y49%2BqjrauB3BEAAAMIgIUF68kk7f/ppacUKp%2BXA5wiAAACESadO0rnnStnZ0r33uq4GfkYABAAgTGJipOHD7fKtt2yBaMAFAiAAAGHUooV0yy123ru3lJfnth74EwEQAIAwe/hhKRCQ5s6VXn3VdTXwIwIgAABhlpwsPfignQ8cKG3d6rYe%2BA8BEAAAB3r2lJo0kTZuLFojEAgXAmAIZWRkKC0tTenp6a5LAQBEmIQE6Zln7HzECOmHH9zWA3%2BJ8TzPc11EaZeVlaVAIKBgMKikpCTX5QAAIsgll0gTJkjnnCN99pnNEAZCjRZAAAAcGj5cKldOmjrVloYBwoEACACAQ/XrS/3723mfPtL27U7LgU8QAAEAcOzee6WGDaVff7UlYoBQIwACAOBYuXJFE0KGD5cWLXJbD0o/AiAAABGgUyc7cnKkXr0kpmgilAiAAABEiGeesdbAzz5jQghCiwAIAECEaNhQGjDAzu%2B%2BW9q2zW09KL0IgAAARJB77pGOPVZau5YdQhA6BEAAACJIuXK2M4hkXcILFritB6UTARAAgAhzwQXSpZdKeXnSrbdK%2BfmuK0JpQwAEACACPf20VLGi9PXX0uuvu64GpQ0BEACACJSSIj34oJ3fc4%2B0ebPbelC6EAABAIhQvXtLaWnSxo3SwIGuq0FpQgAEACBCJSRII0fa%2BUsvSd9%2B67YelB4EQAAAIthf/ypde63tDNKjh00MAY4WARAAgAj35JNS5crSvHnS88%2B7rgalAQEwhDIyMpSWlqb09HTXpQAAolhysvSPf9j5/fdL69a5rQfRL8bz2G461LKyshQIBBQMBpWUlOS6HABAFMrLk1q1kmbPlq6%2BWho71nVFiGa0AAIAEAXi4mxCSEyM9O9/S59/7roiRDMCIAAAUeKUU6TbbrPz226TsrPd1oPoRQAEACCKPPKIVLOmtHixNGyY62oQrQiAAABEkcqVpX/%2B084feURascJpOYhSBEAAAKLMNddIZ58t7dol3XGH62oQjQiAAABEmZgYWw8wIUGaNEmaONF1RYg2BEAAAKJQ06ZSnz52fscd0o4dbutBdCEAAgAQpe6/X6pXT1q5Uho61HU1iCYEQAAAolTFitIzz9j5P/8p/fST23oQPQiAAABEsYsukjp2lHJypNtvl9jfC4eCAAgAQBSLiZGefVYqV852B/nPf1xXhGhAAAQAIMo1bCgNHGjnd98tZWW5rQeRjwC4l7///e%2BKiYnR008/Xez6LVu2qFu3bgoEAgoEAurWrZu2bt3qqEoAAPbVr5/UqJG0bp00eLDrahDpCID/M2HCBM2aNUt16tTZ57arr75amZmZmjx5siZPnqzMzEx169bNQZUAAOxfuXLSiBF2/uyz0vffu60HkY0AKOnXX39Vz549NXbsWCUkJBS7bdGiRZo8ebJGjRqlVq1aqVWrVnr55Zf1/vvva/HixY4qBgBgX%2B3bS5ddJuXlMSEEB%2Bf7AJifn69u3bqpX79%2BOuGEE/a5/ZtvvlEgEFDLli0Lrzv99NMVCAQ0Y8aM/f7M7OxsZWVlFTsAAAiHp56SKlSQvvpKGjPGdTWIVL4PgI8//rji4%2BN1xwE2U1y/fr2Sk5P3uT45OVnr16/f7/c8%2BuijheMFA4GAUlJSSrRmAAAOpF496YEH7LxfP4kh69gfXwXAsWPHqlKlSoXH9OnT9cwzz2j06NGKiYk54Pft7zbP8w74PQMGDFAwGCw8Vq9eXWLPAQCAP3PXXVLjxtKGDUVhENibrwJg586dlZmZWXjMmDFDGzZsUL169RQfH6/4%2BHitXLlSffr0Uf369SVJtWrV0m%2B//bbPz9q4caNq1qy538cpW7askpKSih0AAIRLmTJFE0IyMqTMTLf1IPLEeJ5/h4hu2rRJ69atK3Zd%2B/bt1a1bN91www1q3LixFi1apLS0NM2aNUunnXaaJGnWrFk6/fTT9dNPP6lx48Z/%2BjhZWVkKBAIKBoOEQQBA2HTpIr31ltS6tfTll7ZoNCD5PADuT/369dW7d2/17t278LoOHTpo7dq1evHFFyVJ3bt3V2pqqiZNmnRIP5MACABwYc0aqUkTaccOafRo6brrXFeESOGrLuAjNXbsWDVv3lzt2rVTu3bt1KJFC41hahUAIMLVrSsNGmTn99zDhBAUoQUwDGgBBAC4smeP1KKFtHixdOed0h82u4JP0QIIAEApVqaM9Nxzdj5ihLRggdt6EBkIgAAAlHLnn1%2B0Q0jPnuwQAgIgAAC%2B8NRTUvny0hdfSOPGua4GrhEAAQDwgXr1pIED7bxvX2n7drf1wC0CIAAAPtG3r9SwobR2rfTww66rgUsEQAAAfKJcOemZZ%2Bx8%2BHCbGQx/IgACAOAjnTpJF14o5eRIvXszIcSvCIAAAPjM009LCQnS5MnSIW5qhVKGAAgAgM8cd5zUp4%2Bd33WXtHu323oQfgRAAAB86L77pDp1pGXLpGHDXFeDcCMAAgDgQ5UqSU88Yef/%2BIe0Zo3behBeBEAAAHzq6qul1q2lnTule%2B5xXQ3CiQAYQhkZGUpLS1N6errrUgAA2EdMjO0THBMjvfmm9OWXritCuMR4HhPAQy0rK0uBQEDBYFBJSUmuywEAoJju3aWXX5ZOPlmaPVuKi3NdEUKNFkAAAHxu6FApEJDmzZNeecV1NQgHAiAAAD5Xo4Y0ZIidDxwobd3qth6EHgEQAADottukJk2k33%2BXHnrIdTUINQIgAABQQoLtECLZxJCffnJbD0KLAAgAACRJ7dvbXsG5udLdd7uuBqFEAAQAAIWGDbPWwI8%2BsgOlEwEQAAAUOv546Y477Pyuu6ScHLf1IDQIgAAAoJhBg2xm8OLF0vPPu64GoUAABAAAxQQC0iOP2PngwdKmTU7LQQgQAAEAwD5uuklq0cLWBBw82HU1KGkEQAAAsI%2B4uKJlYUaOlBYudFsPShYBEAAA7Nc550gXXyzl5Ul9%2BriuBiWJAAgAAA7oySdtWZjJk1kWpjQhAAIAgANq1KhoWZg%2BfWyRaEQ/AiAAADio%2B%2B%2BXqlWTFi2SXnrJdTUoCQRAAABwUJUrS0OG2PmDD0rBoNt6cPQIgCGUkZGhtLQ0paenuy4FAICj0r271KSJ9Pvv0tChrqvB0YrxPM9zXURpl5WVpUAgoGAwqKSkJNflAABwRD78UOrYUSpTxrqDGzZ0XRGOFC2AAADgkHToIJ1/vrRnj9S/v%2BtqcDQIgAAA4JDExEj//KcUGyu9/bY0Y4brinCkCIAAAOCQtWgh3XCDnffpIzGQLDoRAAEAwGF5%2BGGpYkVp5kzprbdcV4MjQQAEAACHpXZt6Z577HzAACk72209OHwEQAAAcNj69JHq1JGWL5dGjHBdDQ4XARAAABy2ihWtK1iSHnlE2rTJbT04PARAAABwRK67ziaFbN3K4tDRhgAIAACOSFyc9OSTdj5ihLRsmdt6cOgIgAAA4Ii1a2dHTo5NCEF0IAACAICj8uSTtkj0W29J337ruhocCgIgAAA4Ki1aSNdea%2Bf9%2BrE4dDQgAAIAgKP28MNSuXLSF19Ikya5rgZ/hgAIAACOWkqK1Lu3nffvL%2BXmuq0HB0cABAAAJeLee6Vq1aRFi6RXX3VdDQ6GAAgAAEpE5crS/ffb%2BYMPSjt2uK0HB0YADKGMjAylpaUpPT3ddSkAAITFrbdKDRpI69ZJTz/tuhocSIznMVcn1LKyshQIBBQMBpWUlOS6HAAAQurf/5auuUZKTLTFoatXd10R/ogWQAAAUKKuvFI6%2BWRp2zbbJxiRhwAIAABKVGys9Pjjdv7889Ly5W7rwb4IgAAAoMSdf7503nm2RdwDD7iuBn9EAAQAACHx2GN2OXasNH%2B%2B21pQHAEQAACExCmnSF262NZwAwa4rgZ7IwACAICQeeQRKT5e%2Bugjafp019WgAAEQAACETKNG0i232Hn//tYaCPcIgAAAIKQGDZIqVJBmzpTee891NZAIgAAAIMRq15Z697bz%2B%2B6T8vLc1gMCIAAACIN%2B/aQqVaSFC6UxY1xXAwIgAAAIucqVi2YCDx4sZWc7Lcf3CIAAACAsevaU6tSRVq6UXnjBdTX%2B5vsAuGjRInXu3FmBQECJiYk6/fTTtWrVqsLbs7Oz1atXL1WvXl0VK1ZU586dtWbNGocVAwAQncqXlx580M6HDpW2b3dbj5/5OgD%2B8ssvatOmjZo0aaJp06Zp/vz5GjRokMqVK1d4n969e2v8%2BPEaN26cvvrqK23fvl2dOnVSHiNYAQA4bDfcYEvDbNwoPf2062r8K8bz/Lsiz5VXXqmEhASNOcBo1GAwqBo1amjMmDHq0qWLJGnt2rVKSUnRhx9%2BqPbt2x/S42RlZSkQCCgYDCopKanE6gcAIBq9%2BaZ09dVSUpK0fLlUtarrivzHty2A%2Bfn5%2BuCDD3T88cerffv2Sk5OVsuWLTVhwoTC%2B8yZM0c5OTlq165d4XV16tRRs2bNNGPGDBdlAwAQ9bp0kVq0kLKypCeecF2NP/k2AG7YsEHbt2/XY489pgsuuECffPKJLrnkEl166aWa/r%2B9atavX68yZcqoSpUqxb63Zs2aWr9%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%2BAADAgbVsKf3tb1J%2BftEi0Qg9X7cAhgstgAAAHNj8%2BdJJJxWdt2jhth4/8HULIAAAcO/EE6UrrrDzBx5wW4tfEAABAIBzQ4bYOMD33pNmz3ZdTelHAAQAAM41aSJ17WrntAKGHgEQAABEhAcekOLipMmTJXZcDS0CIAAAiAjHHivdcIOdDxrktpbSjgAIAAAixv33SwkJ0uefS9Omua6m9CIAAgCAiJGaKt1yi50/8IDEYnWhQQAEAAARZeBAqWxZ6csvpU8/dV1N6UQABAAAEeWYY6QePez8wQdpBQwFAiAAAIg4/ftL5cpJ33wjffyx62pKHwIgAACIOLVqSbfdZue0ApY8AiAAAIhI99wjlS8vffut9OGHrqspXQiAIZSRkaG0tDSlp6e7LgUAgKhTs6Z0%2B%2B12PngwrYAlKcbzeDlDLSsrS4FAQMFgUElJSa7LAQAgamzcKNWvL%2B3cKU2aJHXq5Lqi0oEWQAAAELFq1JB69rRzWgFLDgEQAABEtL59pYoVpTlzpPffd11N6UAABAAAEW3vVsAhQ2gFLAkEQAAAEPH69ClqBfzgA9fVRD8CIAAAiHg1ahTNCKYV8OgRAAEAQFTo21eqUEH67jvWBTxaBEAAABAVatQo2h2EVsCjQwAEAABRo18/2x1k9mz2CD4aBEAAABA1kpOlW2%2B1c1oBjxwBEAAARJV%2B/aRy5aSZM6VPP3VdTXQiAAIAgKhSq5bUvbudP/QQrYBHggAIAACizj33SGXLSl99JU2b5rqa6EMABAAAUeeYY6SbbrLzhx92W0s0IgACAICodO%2B9UkKCNHWqtQTi0BEAAQBAVKpXT7r%2BejunFfDwEAABAEDU6t9fiouTPvlE%2BvZb19VEDwJgCGVkZCgtLU3p6emuSwEAoFRq2FDq2tXOH3nEbS3RJMbzmDwdallZWQoEAgoGg0pKSnJdDgAApcrixVLTprYcTGamdOKJriuKfLQAAgCAqNa4sdSli50PHeq2lmhBAAQAAFFv4EC7fOcdadEit7VEg3jXBQAAAByt5s1td5BGjaS6dV1XE/kYAxgGjAEEAACRhC5gAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAyhjIwMpaWlKT093XUpAAAAhWI8z/NcF1HaZWVlKRAIKBgMKikpyXU5AADA52gBBAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAAAACfIQACAAD4DAEQAADAZwiAAAAAPkMABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzvg6A27dvV8%2BePVW3bl2VL19eTZs21ciRI4vdJzs7W7169VL16tVVsWJFde7cWWvWrHFUMQAAwNHzdQC86667NHnyZL3xxhtatGiR7rrrLvXq1Uvvvfde4X169%2B6t8ePHa9y4cfrqq6%2B0fft2derUSXl5eQ4rBwAAOHIxnud5rotwpVmzZurSpYsGDRpUeN0pp5yiCy%2B8UA8//LCCwaBq1KihMWPGqEuXLpKktWvXKiUlRR9%2B%2BKHat29/SI%2BTlZWlQCCgYDCopKSkkDwXAACAQ%2BXrFsA2bdpo4sSJ%2BvXXX%2BV5nqZOnaolS5YUBrs5c%2BYoJydH7dq1K/yeOnXqqFmzZpoxY4arsgEAAI5KvOsCXHr22Wd1yy23qG7duoqPj1dsbKxGjRqlNm3aSJLWr1%2BvMmXKqEqVKsW%2Br2bNmlq/fv0Bf252drays7MLv87KygrNEwAAADgCvmkBHDt2rCpVqlR4fPnll3r22Wc1c1RUgfgAAAK5SURBVOZMTZw4UXPmzNGwYcN022236dNPPz3oz/I8TzExMQe8/dFHH1UgECg8UlJSSvrpAAAAHDHfjAHctm2bfvvtt8KvjznmGAUCAY0fP14dO3YsvP7mm2/WmjVrNHnyZH3%2B%2Bedq27atNm/eXKwV8MQTT9TFF1%2BsIUOG7Pex9tcCmJKSwhhAAAAQEXzTApiYmKhGjRoVHjk5OcrJyVFsbPGXIC4uTvn5%2BZJsQkhCQoKmTJlSePu6dev0ww8/6IwzzjjgY5UtW1ZJSUnFDgAAgEjh2zGASUlJOuuss9SvXz%2BVL19eqampmj59ul5//XU99dRTkqRAIKCbbrpJffr0UbVq1VS1alX17dtXzZs313nnnef4GQAAABwZ3wZASRo3bpwGDBiga665Rps3b1ZqaqqGDh2qHj16FN5n%2BPDhio%2BP1xVXXKFdu3apbdu2Gj16tOLi4hxWDgAAcOR8MwbQJc/ztG3bNiUmJh508ggAAEA4EAABAAB8xjeTQAAAAGAIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD7z/9AayXBglxtcAAAAAElFTkSuQmCC'}
Genus 2 curve data - 1650.a.371250.1