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g2c_curves • Show schema
{'Lhash': '189559735513512141', 'abs_disc': 147456, '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]],[3,[1,0,-1]]]', 'bad_primes': [2, 3], 'class': '1152.a', 'cond': 1152, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[-1,0,2,0,-2,0,1],[]]', 'g2_inv': "['5071050752/9','195344320/9','1016576']", 'geom_aut_grp_id': '[8,3]', 'geom_aut_grp_label': '8.3', 'geom_aut_grp_tex': 'D_4', 'geom_end_alg': 'M_2(Q)', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['152','109','5469','18']", 'igusa_inv': "['608','14240','405504','10942208','147456']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '1152.a.147456.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.4544183772414298600703116667813459996932549202703899141', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.180.5', '3.1080.10'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 2, 'real_geom_end_alg': 'M_2(R)', 'real_period': {'__RealLiteral__': 0, 'data': '7.2706940358628777611249866685', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'J(E_1)', 'st_label': '1.4.E.2.1b', 'st_label_components': [1, 4, 4, 2, 1, 1], 'tamagawa_product': 4, 'torsion_order': 8, 'torsion_subgroup': '[8]', 'two_selmer_rank': 1, 'two_torsion_field': ['4.0.144.1', [1, 0, -1, 0, 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]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['M_2(RR)'], 'fod_coeffs': [1, 0, 1], 'fod_label': '2.0.4.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '1152.a.147456.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'J(E_1)'], [['2.0.4.1', [1, 0, 1], [0, 1]], [['1.1.1.1', [0, 1], 1]], ['M_2(RR)'], [4, 0], 'E_1']], 'ring_base': [2, -1], 'ring_geom': [4, 0], 'spl_facs_coeffs': [[[-32], [-224]], [[-32], [224]]], 'spl_facs_condnorms': [24, 48], 'spl_facs_labels': ['24.a5', '48.a5'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0, 0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'J(E_1)', 'st_group_geom': 'E_1'} -
g2c_ratpts • Show schema
{'label': '1152.a.147456.1', 'mw_gens': [[[[1, 1], [1, 1], [0, 1]], [[1, 1], [0, 1], [0, 1], [1, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [8], 'num_rat_pts': 4, 'rat_pts': [[-1, 0, 1], [1, -1, 0], [1, 0, 1], [1, 1, 0]], 'rat_pts_v': True} - g2c_galrep • Show schema
- g2c_tamagawa • Show schema
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g2c_plots • Show schema
{'label': '1152.a.147456.1', 'plot': 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6EQAgVfzznzZb0tJs1oRCrhOhIiiAAXHCCdKoUXY8ciRvCAEAVN%2BuXdK119rxqFE2a%2BAPFMAAGTvWTs79n/%2BRJk50nQYA4HcTJ9pMyc62GQP/oAAGSL165XcImTDB7hMMYH%2BRSEThcFi5ubmuowBJbf16myWSNG2azRj4R8jzuEdEkHie1Lu39NJLdoX2xYs5XwM4kGg0qqysLBUWFiozM9N1HCCpeJ50zjnSK69IvXrZeYDMEn9hBzBgQiG7R2Pt2vY/7hNPuE4EAPCbJ56wGVK7ts0Uyp//UAAD6NhjpTvusOMbbpC2bXObBwDgH9u2lV9S7I47uOOHX1EAA%2BqWW6RwWPrqKzsGAKAibrnFZkc4zPzwMwpgQKWnS7Nm2fHjj0uvv%2B40DgDAB15/3WaGZDMkPd1pHFQDBTDAuneXrrrKjq%2B80q7iDgDAgXz/vc0KyWZH9%2B5u86B6KIABN3GiXb9p/Xrp7rtdpwEAJKu777ZZkZ3NtWRTAQUw4LKypBkz7HjKFGnVKrd5gFh544031LdvX2VnZysUCunZZ591HQnwrVWrbEZINjOystzmQfVRAKH%2B/aULL5RKSqTLLpOKi10nAqpv586dat%2B%2BvaZPn%2B46CuBrxcU2G0pKbFb07%2B86EWKhpusASA4PPyz9619SXp40ebI0ZozrRED19O7dW71793YdA/C9yZNtNjRoYLMCqYEdQEiSGjeWHnrIjseNk9audZsHAODe2rU2EySbEY0bu82D2KEAYq9Bg6Q%2BfWy7/9JLpT17XCcCEqeoqEjRaHS/DyDI9uyxWVBcbLNh0CDXiRBLFEDsFQpJjz4qHXGE9O675Sf8AkEwYcIEZWVl7f1o3ry560iAU1Om2Cw44gibDdzuLbVQALGf7GzpwQft%2BK67pA8%2BcJsHSJTRo0ersLBw78fmzZtdRwKcWbPGZoBkMyE7220exB4FED8yeLDUt69t%2Bw8ZwruCEQwZGRnKzMzc7wMIouJiaehQ%2B9y3r80EpB4KIH4kFLJb/DRoYNd%2BGj/edSKg8nbs2KG8vDzl5eVJkjZs2KC8vDxt2rTJcTIguY0fb2t/gwY2C3jpNzWFPM/zXIdAcnrqKWnAACktTfr3v6XcXNeJgIp7/fXXdcYZZ/zo94cOHao5c%2BYc8vuj0aiysrJUWFjIbiACY8UK6eST7Zp/f/mLdPHFrhMhXiiAOKiBA20RaNNGWrlSOuww14mAxKAAImi%2B%2B0466STp44/th/8nn3SdCPHES8A4qEjETv79%2BGPptttcpwEAxMttt9lan51taz9SGwUQB9WggfT443Y8fbr00ktu8wAAYu%2Bll2yNl2zNb9DAbR7EHwUQh9SrlzRypB3/7nfS11%2B7zQMAiJ2vv7a1XbK1vlcvt3mQGBRAVMikSdLxx0tbtkhXXCFx5igA%2BJ/n2Zq%2BZYut8ZMmuU6ERKEAokIOO0xasECqVUt65hnpz392nQgAUF1//rOt6bVq2RrPG/2CgwKICjvppPJrAo4aJeXnu80DAKi6/HxbyyVb2086yW0eJBYFEJVy883SL35hlwsYNEjavdt1IiC2IpGIwuGwcrnwJVLY7t22hn/3na3pN9/sOhESjesAotK%2B%2BEI68URp2zZbNKZMcZ0IiD2uA4hUdsst0tSp9m7f99%2BXcnJcJ0KisQOISsvJKT8HcOpUafFit3kAABW3eLGt3ZJd8oXyF0wUQFTJeedJV19tx0OGSFu3us0DADi0rVttzZZsDe/f320euEMBRJXdf790wgm2oAwdKpWWuk4EAPgppaW2Vm/damv3/fe7TgSXKICosjp17D7BderYVeRZTAAgeU2damv1vms3gosCiGpp21Z68EE7vv126Z133OYBAPzYO%2B9IY8bY8YMP2tqNYKMAotqGDZMuukjas0caOFD69lvXiQAAZb791tbmPXtsrR42zHUiJAMKIKotFJJmzZKOOUbauFG6/HJuFQcAycDzbE3euNHW6FmzbM0GKICIiaws6amn7HZCCxdKM2a4TgQAmDHD1uRatWyNzspynQjJggKImOncWZo82Y5vvFFaudJtHgAIspUrbS2WbG3u3NltHiQXCiBiatQoqV8/u83QxRdL0ajrRAAQPNGorcG7d9uaXHbPX6AMBRAxFQpJs2dLLVpIn3wiDR/O%2BYAAkEieJ115pa3BLVrYmsx5f/ghCiBirkEDu8ZUzZr2edYs14mAiotEIgqHw8rNzXUdBaiSWbPsfL%2ByNbhBA9eJkIxCnsf%2BDOJjyhTp1luljAxp%2BXLpxBNdJwIqLhqNKisrS4WFhcrMzHQdB6iQ99%2BXunSRiorsvL9bbnGdCMmKHUDEzU03SeeeawvRhRdKO3a4TgQAqWvHDltri4ps7b3pJteJkMwogIibGjWkOXOknBwpP99uPM5%2BMwDEnufZGpufLzVrZmtvDSY8DoKnB%2BKqYUM7ByUtTZo/3xYlAEBszZlja2xamvTkk7b2AgdDAUTcnXqqdPfddjxypPThh27zAEAq%2BfBDW1slW2tPPdVtHvgDBRAJcdtt0llnSd99Z9em%2Bv5714kAwP%2B%2B/97W1O%2B%2BszX29793nQh%2BQQFEQqSlSf/1X1KjRtKaNdLNN7tOBAD%2Bd/PNtqY2amRrLOf9oaJ4qiBhmjSR5s2z4xkzpOeec5sHAPzsuefK77s%2Bb56tsUBFUQCRUL16le/%2BXXaZ9MUXbvMAgB998YWtoZKtqb16uc0D/6EAIuHuvVc66SRp2zbp0kul0lLXiQDAP0pLbe3cts3W0nvvdZ0IfkQBRMKlp0tPPCHVqSO98or04IOuEwGAfzz4oK2dderYWpqe7joR/IgCCCfatJEeeMCOR4%2BW1q51mwcA/GDtWlszJVtD27Rxmwf%2BRQGEM8OHS7/6ld22aMgQqbjYdSJAikQiCofDys3NdR0F2E9xsa2VRUW2dg4f7joR/CzkedycC%2B5s2SK1bSt9841dwPQPf3CdCDDRaFRZWVkqLCxUZmam6ziA7r5buusuqX592wls2tR1IvgZO4BwqmlTafp0O77nHumDD9zmAYBktGaNNH68HU%2BfTvlD9VEA4dzAgVK/fvbyxrBhUkmJ60QAkDxKSmxtLC62tXLgQNeJkAoogHAuFLKLmWZmSsuWSTNnuk4EAMlj5kxp%2BXJbI2fMsDUTqC4KIJJCTo40YYIdjxkjFRS4zQMAyaCgwNZEydbInBy3eZA6KIBIGsOHS507S9EoNzQHAMnWwmjU1kbe9YtYogAiaaSlSZGIHc%2BdK737rts8AODSu%2B/aWijZ2piW5jYPUgsFEEmlSxdp8GA7vvlmiYsUAQgizyu/b/rgwbY2ArHEdQCRdDZtkn72M7vY6YsvcpNzuMF1AOHSiy9KvXtLGRlSfr7UooXrREg17AAi6bRoIV1zjR3feSe7gACCxfPsgs%2BSNGIE5Q/xwQ4gktLWrdLRR0u7dkkvvyyddZbrRAgadgDhyiuvSGefLdWpI23YIDVu7DoRUhE7gEhKjRvbhU8l6f773WYBgESaOtU%2BX3455Q/xww4gktb//I/UurW9HJKfb8dAvEUiEUUiEZWUlCg/P58dQCTU%2BvV2DnQoZMfHHus6EVIVO4BIWsceaydBS9Jjj7nNguAYMWKE1q1bpxUrVriOggAqW%2Bt696b8Ib4ogEhqZS8DL1gglZa6zQIA8VRaamudVL72AfFCAURS%2B9WvpKws6YsvpLffdp0GAOLn7bdtrcvKsrUPiCcKIJJaRobUp48dP/%2B82ywAEE9//7t97tPH1j4gniiASHpl5wG%2B8orbHAAQT2VrXNmaB8QTBRBJ7/TT7fOqVdKOHU6jAEBc7Ngh5eXZcdmaB8QTBRBJLydHys62E6RXr3adBgBib/VqW%2BNycuwDiDcKIJJbaan06KN6Y3sH7VKGOvRpJo0ZI33zjetkAFB933wjjRmjDn2aaZcytCTaQXr0US57gLjjQtBIXp4nDRkizZ//46%2BFw9LSpVKDBonPhUDgVnCIu23bpNNOk9at%2B/HXLrlEmjfPrggNxAE7gEheixcfuPxJtmBOmpTYPAAQSxMnHrj8Sbb2vfxyYvMgUGq6DuBnnudp%2B/btrmOkrj/96eBff/xxezkYiIGioiIVFRXt/XXZ/9vRaNRVJKS6xx8/%2BNf/9CepW7fEZAmoevXqKRTQXVZeAq6GspeIAACA/wT5FA8KYDVUZQcwGo2qefPm2rx5c5WfdLm5udW6T2l1vj%2Bh//b110uzZ//01485xq4NE49/O8m%2BvzrfW93nXFAetx/uAG7ZskVdunTRunXrlFPFt2X65v%2B1GH6/6zXOV49bx47Sp5/%2B9Nd/9ztp2rT4/Nsx/n6X/3Z1nnNB3gHkJeBqCIVCVV7gMjMzq/y9aWlp1fqJpTrfn9B/%2B9prpTlz7M0gBzJihFSJLC4ft%2Bp%2Bf3X/banqz7mgP2716tVz8u/7%2BXGX3K1xrv%2B7K/X911wj3Xzzgb8WCknXXeebNc714y5V7zkXRLwJxIdGjBjh7PsT%2Bm937ChNnXrgr/Xvb4tjvP7tJPv%2B6v7b1cHjVnW%2B%2BX8tDt/v6t92/d9dqe8fNcrWsh8KhWzt69Ahfv92jL/f9eOOyuMl4ATj0hJV8O67eqHvTNUv%2BFD/0WadM3%2BiDhs4UKrBzy8VwXOuaj7//PO9Lys1a9bMdRzf4PlWSaWl0nPPaeO4x/Xl6q%2B1rXEb9Xl%2BhNS5s%2BtkvsFzrmp4CTjBMjIydNdddymDO31XmNepswYX/VnfSLryykfU%2Bze/ofxVAs%2B5qil7vHjcKofnWyXVqCH9%2Btfamn2uundLV/3dnr7uFFIwz0qrGp5zVcMOIJLexx9Lxx8vZWRI0aiUnu46EYKAXQUk0u7ddrpfUZH00UdSmzauEyHVsY2CpPfqq/b55JMpfwBSU3p6%2BSX/ytY8IJ4ogEh6L7xgn88%2B220OAIinc86xz2VrHhBPFEAktW%2B/Lb8b0oHeLAcAqaJsjXv5ZVv7gHiiACKp/fd/27kxbdvaBwCkqnDY1rndu6W//c11GqQ6CmCcLFy4UL169VLDhg0VCoWUl5d3yO%2BZM2eOQqHQjz527dqVgMTJ6bHH7PNJJ62p9OMZJJ7naezYscrOzladOnV0%2Bumna%2B3atQf9nrFjx/7oudakSZMEJYafzZgxQ61atVLt2rXVqVMnLV269Cf/LOvawb3xxhvq27evsrOzVaNGSJ06fSDp0LdCD4p9H59QKKRnn332oH/%2B9ddfP%2BDz7aOPPkpQYv%2BgAMbJzp071b17d02cOLFS35eZmaktW7bs91G7du04pUxuy5ZJy5fbydFdu35UpcczKCZPnqwHHnhA06dP14oVK9SkSROdffbZh7xVYdu2bfd7rq1ZsyZBieFXTz31lK6//nqNGTNGq1at0mmnnabevXtr06ZNP/k9rGs/befOnWrfvr2mT58uSTrjjE1KT7e1b/lyx%2BGSwA8fn4r6%2BOOP93u%2BtW7dOk4J/YvrAMbJ4MGDJUkbN26s1PexC1Ou7CYgAwZII0ZcKKnyj2cQeJ6nadOmacyYMTr//PMlSXPnzlXjxo31xBNPaPjw4T/5vTVr1uT59gORSESRSEQlJSWuoySlBx54QJdffrmGDRsmSZo2bZpeeuklzZw5UxMmTDjg97Cu/bTevXurd%2B/ee399xBG7NWCANG%2BeNGWKnQYTZD98fCqqUaNGOuKII%2BKQKHWwA5hkduzYoZYtW6pZs2bq06ePVq1a5TqSEx9%2BKD39tB3/1K0yYTZs2KCCggKdU/YWQtmFUXv27Km33377oN%2B7fv16ZWdnq1WrVhowYIA%2BPdiN6QNixIgRWrduXbVubJ%2Bqdu/erffee2%2B/55oknXPOOQd9rrGuVU7Zmvf007YWovI6duyopk2b6swzz9Rrr73mOk5SogAmkeOPP15z5szRokWL9OSTT6p27drq3r271q9f7zpawo0bJ3medN55Urt2rtMkt4KCAklS48aN9/v9xo0b7/3agXTt2lXz5s3TSy%2B9pD/96U8qKCjQKaecoq%2B//jqueeFf//u//6uSkpJKPddY1yqvXTtb%2BzzP1kJUXNOmTTVr1iw9/fTTWrhwodq0aaMzzzxTb7zxhutoSYcCGAMLFizQ4YcfvvfjYCdEH0y3bt10ySWXqH379jrttNP017/%2BVT/72c/08MMPxzhxchs//p966ik7fvHFblV%2BPFPVD59vxcXFkuxltn15nvej39tX7969dcEFF6hdu3Y666yz9ML/X3xs7ty58QuPlFCZ5xrrWtWMHWvgfIUiAAAZ4ElEQVSfn3pKWrnSaRRfadOmja644gqddNJJOvnkkzVjxgyde%2B65mlp2ThH24hzAGOjXr5%2B6du2699c5OTkx%2BXtr1Kih3NzcQP2k7HnS4sV2xed%2B/bbr/vvnx%2BzxTBU/fL4VFRVJsp3Apk2b7v39//znPz/aqTmYunXrql27doF6vqFyGjZsqLS0tB/t9lXmuRbEda0q2reXfvtbacEC6aab7O4gB/l5DgfRrVs3zZ8/33WMpMMOYAzUq1dPxx133N6POnXqxOTv9TxPeXl5%2Bw31VLdwobR0aU3Vri099FC9mD6eqeKHz7dwOKwmTZro5bIrZsvO1VqyZIlOOeWUCv%2B9RUVF%2BvDDDwP1fEPlpKenq1OnTvs91yTp5ZdfrvBzLYjrWlXde6/dA/31121tRNWsWrWK59sBsAMYJ9u2bdOmTZv05ZdfSrK3pEtSkyZN9r4bbsiQIcrJydn7zrlx48apW7duat26taLRqB566CHl5eUpEom4%2BY9IsJ07pRtusONbbpFatiz/WkUez6AKhUK6/vrrdd9996l169Zq3bq17rvvPh122GEaNGjQ3j935pln6te//rVGjhwpSbr55pvVt29ftWjRQv/5z380fvx4RaNRDR061NV/Cnzgxhtv1ODBg9W5c2edfPLJmjVrljZt2qSrrrpKEutaZe3YsUOffPLJ3l9v2LBBeXl5atCggVq2bKFbb5XuucfWxl/%2BUqpb12FYBw72%2BLRo0UKjR4/WF198oXnz5kmyd6UfffTRatu2rXbv3q358%2Bfr6aef1tNl7ypEOQ9xMXv2bE/Sjz7uuuuuvX%2BmZ8%2Be3tChQ/f%2B%2Bvrrr/datGjhpaene0cddZR3zjnneG%2B//Xbiwztyyy2eJ3ley5aet3Pn/l%2BryOMZZKWlpd5dd93lNWnSxMvIyPB69OjhrVmzZr8/07Jly/0er4svvthr2rSpV6tWLS87O9s7//zzvbVr1yY4efIqLCz0JHmFhYWuoySdSCTitWzZ0ktPT/dOOukkb8mSJXu/xrpWOa%2B99toB17ayx3DnTlsTJVsjg%2BZQj8/QoUO9nj177v3zkyZN8o499livdu3aXv369b1TTz3Ve%2BGFF9yET3Ihz/O8hDZO4ABWrZJyc6WSEunvf5f69HGdCEEXjUaVlZWlwsJCZWZmuo6DAHv%2BealvXyktTVqxQurY0XUipALOAYRzxcXS5Zdb%2BbvwQsofAOyrTx9bG0tKbK38/zf%2BA9VCAYRzkybZDmCDBhJXhgCAH3voIal%2BfVsrJ01ynQapgAIIp1avlu6%2B244fekiqxFVLACAwmjQp/wH57rtt7QSqgwIIZ4qKpMGD7eWM886T9nnDKgDgBwYNsrWyuNjWzv%2B/BChQJRRAODNmjLRmjXTUUdKjj3KRUySHSCSicDis3Nxc11GA/YRCtlYedZStnWPGuE4EP%2BNdwHDiX/%2BSzjrLjhctsne4AcmEdwEjWS1aJPXvb8evvCKdeabbPPAndgCRcF9/LQ0ZYsfDh1P%2BAKAy%2BvWTrrzSjocMsTUVqCwKIBLK8%2BwyBl9%2BKbVpI91/v%2BtEAOA/Dzxga%2BiXX9qaymt5qCwKIBJqxgzpueek9HTpySeDd1sjAIiFunVtDU1PtzV1xgzXieA3FEAkzOrV0k032fHkyVzNHgCqo2PH8msC3nQTl4ZB5VAAkRA7dkgXX2yXLTj3XOm661wnAgD/GzXK1tSiIltjd%2BxwnQh%2BQQFEQowcKX38sZSdLc2ZwyVfACAWQiFbU7OzbY0dOdJ1IvgFBRBxN2%2BeNHeuVKOGnbPSsKHrRACQOho2tLW1Rg1ba%2BfNc50IfkABRFx9%2BKF09dV2PG6c1KOH2zwAkIp69JDGjrXjq6%2B2tRc4GC4Ejbj57jupa1fpgw/sQqUvvSSlpblOBVQMF4KG35SUSL162YX2TzhBWrZMOuww16mQrNgBRNyMGmXlr3Fjaf58yh8AxFNamq21jRvb2jtqlOtESGYUQMTFE09Ijz1mJygvWCA1aeI6EQCkviZNbM0NhWwNfuIJ14mQrCiAiLn8fLvFmyT94Q/cpxL%2BEolEFA6HlZub6zoKUCVnnmlrr2RrcX6%2B2zxITpwDiJjatUvq1s0uSNqzp52Lwku/8CPOAYSflZRYEVyyRGrfXnrnHal2bdepkEzYAURM3Xijlb%2BjjrKXHih/AJB4aWm2Bh91lK3JN97oOhGSDQUQMfO3v0kzZ9rxf/2XXZgUAOBGdratxZKtzX/7m9s8SC4UQMTEhg3S5Zfb8e9/b5ciAAC41auXrcmSrdEbNrjNg%2BRBAUS17d4tDRggRaPSKadId9/tOhEAoMzdd9vaHI3aWr17t%2BtESAYUQFTbmDHS8uVS/fp2O6JatVwnAgCUqVXLzgc84ghbq8eMcZ0IyYACiGp58UVp6lQ7fvxxqUULt3kAAD/WsqU0e7YdT51qazeCjQKIKisokIYMseMRI6TzznObBwDw0847z9ZqydbuggK3eeAWBRBVUlpqC8hXX0knnli%2BCwgASF5Tp9qa/dVXtoaXlrpOBFcogKiSBx6QXn5ZqlNH%2BstfuMAoAPhB7dq2ZtepY2v4Aw%2B4TgRXKICotPfek26/3Y6nTZN%2B/nO3eQAAFffzn9vaLdla/t57bvPADQogKmXnTmnQIKm4WDr/fOmKK1wnAmKLewEjCK64wtbw4mJb03fudJ0Iica9gFEpV14p/elPUk6O9P77UoMGrhMB8cG9gJHqtm2z8wG/%2BMIK4axZrhMhkdgBRIU9%2B6yVv1DIbi9E%2BQMA/2rQwNbyUMjW9mefdZ0IiUQBRIVs2SING2bHN98snXGG2zwAgOo74wxb0yXbBdyyxW0eJA4FEIfkeXYPya%2B/ljp0kO65x3UiAECs3HOPre3/%2B7%2B21nNiWDBQAHFIjzwi/fOfUkaGtGCBfQYApIZ91/Z//tPWfKQ%2BCiAOKj9fuukmO540SQqH3eYBAMReOGxrvGRrfn6%2B2zyIPwogftKePXal%2BO%2B/l37xC%2Bnaa10nAgDEy7XX2lr//ffS0KE2A5C6KID4SZMnS8uWSZmZ0pw5Ug2eLQCQsmrUsLU%2BM1N65x2bAUhdjHQc0PvvS2PH2vHDD0vNmzuNA1TKwoUL1atXLzVs2FChUEh5eXmuIwG%2B0Ly5rfmSzYD333caB3FEAcSP7N5tL/0WF0v9%2B0uDB7tOBFTOzp071b17d02cONF1FMB3Bg%2B2tb%2B42F4K3r3bdSLEQ03XAZB87rtPWr1aOvJI6dFH7SKhgJ8M/v%2BfWjZu3Og2COBDoZCt/W%2B%2BKeXl2Uwoe0UIqYMdQOwnL0%2B69147jkSkxo3d5gESpaioSNFodL8PIKgaN7YZINlM4CyK1EMBxF7FxdKll9o7vy64QLroIteJgMSZMGGCsrKy9n4058RXBNxFF9ks2LPHZkNxsetEiCUKIPaaOLH8pd8ZM3jpF/6wYMECHX744Xs/li5dWqW/Z/To0SosLNz7sXnz5hgnBfwlFLJdwCOPtNnAKbWpJeR53PQF0tq1UseO9hPeggXSoEGuEwEVs337dm3dunXvr3NyclSnTh1Jdg5gq1attGrVKnXo0KFSf280GlVWVpYKCwuVmZkZ08yAnzzxhPTb30q1akmrVklt27pOhFjgTSBQSYnd/7G4WOrbVxo40HUioOLq1aunevXquY4BpKyBA6W//EX6%2B99tVrz1lpSW5joVqouXgKGHHy6/4PPMmbz0C//btm2b8vLytG7dOknSxx9/rLy8PBUUFDhOBvhPKGSzITPTZkXZdQLhbxTAgNu4URozxo6nTJFycpzGAWJi0aJF6tixo84991xJ0oABA9SxY0c9wl3ugSrJybEZIdnM4ApL/sc5gAHmedKvfiW9%2BKLUo4f02mvc7g0owzmAwP5KS6UzzpDeeEP65S%2Blf/yDV4z8jHEfYE8%2BaeUvI0OaNYvyBwD4aTVq2KzIyLDZ8eSTrhOhOhj5AbVtm3T99XZ8xx1SmzZu8wAAkl%2BbNjYzJJsh27a5zYOqowAG1G23SV99JYXD0q23uk4DAPCLW2%2B12fHVVzZL4E8UwAB66y3pscfs%2BJFHpPR0t3kAAP6Rnm6zQ7JZ8tZbbvOgaiiAAVNcLF11lR1ffrl02mlu8wAA/Oe002yGSDZTuE2c/1AAA2baNOmDD6SGDaVJk1ynAQD41aRJNks%2B%2BMBmC/yFAhggmzdLY8fa8eTJdn9HAPuLRCIKh8PKzc11HQVIakceabNEstnC7bP9hesABshvfiM9/bR06qnSkiVc9gU4GK4DCBxaaanUs6f05pvSBRdIf/ub60SoKCpAQCxebOUvLU2KRCh/AIDqq1HDZkpams2YxYtdJ0JFUQMCoKhIuvZaOx45UjrxRLd5AACp48QTbbZINmt273abBxVDAQyAadOk/HypcWNp3DjXaQAAqWbcOJsx%2BfnSH//oOg0qggKY4r74QrrnHjuePFnKynKbBwCQerKyyt8Qcs89NnuQ3CiAKe6226SdO6WTT5YuucR1GgBAqrrkEps1O3dyhxA/oACmsLfekhYskEIh6eGHeeMHACB%2BatSwWRMK2ezhDiHJjUqQokpLpVGj7Pjyy6VOndzmAQCkvk6dpMsus%2BNRo2wWITlRAFPUnDnSe%2B9JmZnSvfe6TgMACIr77rPZ89570ty5rtPgp1AAU9D27dKYMXZ8551So0Zu8wAAgqNRI%2BkPf7Dj22%2B3mYTkQwFMQRMnSgUF0nHHlV//DwCARLnuOptBBQU2k5B8KIAp5rPPpPvvt%2BOpU6X0dLd5AADBk55uM0iymfTZZ27z4McogClm9Gi788fpp0v9%2BrlOA/hPJBJROBxWbm6u6yiAr/XrZ7OoqMhmE5JLyPM8z3UIxMby5VLXrvYW/Pfekzp2dJ0I8K9oNKqsrCwVFhYqMzPTdRzAl1atsncGe560bJnUpYvrRCjDDmCK8DzpppvseMgQyh8AwL2OHW0mSTaj2HJKHhTAFPHcc9Kbb0p16kjjx7tOAwCAGT/eZtObb9qsQnKgAKaA4uLy2%2B7ceKPUrJnbPAAAlGnWTLrhBju%2B7TabWXCPApgCHntMys%2BXjjpKuvVW12kAANjfbbdJDRvarPrzn12ngUQB9L0dO6Rx4%2Bz4zjvt6usAACSTzEybUZI0dqzNLrhFAfS5P/5R2rpVOvZY6corXacBAODAhg%2B3WbV1q80uuEUB9LGvvpKmTLHj8eO56DMAIHmlp5e/SXHKFJthcIcC6GP33Wf3WDzpJOmii1ynAQDg4C66yGbW9u02w%2BAOF4L2qU2bpNatpd27pcWLpbPPdp0ISC1cCBqIj8WLpV69bEdw/XqpRQvXiYKJHUCfGjvWyt8ZZ0hnneU6DQAAFXP22Ta7du%2B2WQY3KIA%2B9NFH0ty5djxhgt36DUBscC9gIL5CofKXf%2BfOtZmGxOMlYB%2B66CLpv//bbrTNVdWB%2BOAlYCC%2B%2BveXFi2SLrxQ%2ButfXacJHgqgz%2BTl2b0VQyFp9WqpXTvXiYDURAEE4mvNGql9e7s/8KpVUocOrhMFCy8B%2B0zZhTQvvpjyBwDwr3btbJZJ5bMNicMOoI8sXy517SrVqCGtWye1aeM6EZC62AEE4u/jj6VwWCotlZYtk7p0cZ0oONgB9JGyn5AGD6b8AQD8r00bm2kSu4CJxg6gT7z9ttS9u1Szpv3EdMwxrhMBqY0dQCAxPv3UiuCePdJbb0mnnOI6UTCwA%2BgTZT8ZXXop5Q8AkDqOOcZmmyTddZfTKIHCDqAPvPmmdNpptvu3fr109NGuEwGpjx1AIHE2brS7W%2B3ZIy1dKp16qutEqY8dQB8ou1L6ZZdR/gAAqefoo23GSdK4cU6jBAY7gEnurbfsJ6GaNaVPPpFatnSdCAgGdgCBxPrsM%2Bm442wX8M037bx3xA87gEnu7rvt8%2B9%2BR/kDAKSuli1t1knlsw/xww5gElu2TOrWzXb/8vOlVq1cJwKCgx1AIPE2bLBzAUtKpHfesWvfIj7YAUxi99xjnwcPpvwBiRKJRBQOh5Wbm%2Bs6ChA4rVqVXxewbAYiPtgBTFIrV0qdOtldPz76yH4iApA47AACbqxfLx1/vN0dZOVKqWNH14lSEzuASeree%2B3zgAGUPwBAcLRubbNPksaPd5sllbEDmITWrZPatrXjDz4oPwaQOOwAAu6sXSudcIIUCtkcDIddJ0o97AAmoQkT7PP551P%2BAADB07at9OtfS55XPhMRW%2BwAJpl93wH17rt2HiCAxGMHEHDrvfekzp2ltDQ7L5A3Q8YWO4BJZvJkK3/nnEP5AwAEV6dONgtLSqQpU1ynST3sACaRggK7HU5RkfT661LPnq4TAcHFDiDg3pIl0umnSxkZdr/gJk1cJ0od7AAmkWnTrPx16yb16OE6DeBPxcXFuu2229SuXTvVrVtX2dnZGjJkiL788kvX0QBUUo8eNhOLimxGInYogEmisFCaOdOOf/97e%2BcTgMr77rvvtHLlSv3hD3/QypUrtXDhQuXn56tfv36uowGopFDIZqJkM7Kw0G2eVMJLwEli8mTpttukn//c3vJeg2oOxMyKFSvUpUsXffbZZ2rRokWFvoeXgIHkUFpql4T58ENp0iTp1ltdJ0oN1IwksO/W9q23Uv6AWCssLFQoFNIRRxzhOgqASqpRo7z0lZ0qheqjaiSB%2BfOlLVuknBxp0CDXaYDUsmvXLv3%2B97/XoEGDDrqTV1RUpGg0ut8HgOQwaJDNyC1bbGai%2BiiAjpWWSlOn2vENN0jp6W7zAH6zYMECHX744Xs/li5duvdrxcXFGjBggEpLSzVjxoyD/j0TJkxQVlbW3o/mzZvHOzqACkpPtxkp2cwsLXWbJxVwDqBjf/%2B71K%2BflJUlbdokcaoRUDnbt2/X1q1b9/46JydHderUUXFxsS666CJ9%2BumnevXVV3XkkUce9O8pKipS0T6vLUWjUTVv3pxzAIEkEY1KLVrYG0EWLZL69nWdyN9qug4QdGUXtxw%2BnPIHVEW9evVUr169/X6vrPytX79er7322iHLnyRlZGQoIyMjXjEBVFNmps3KyZNtdlIAq4cdQIeWL5e6dpVq1bJbwOXkuE4E%2BN%2BePXt0wQUXaOXKlXr%2B%2BefVuHHjvV9r0KCB0it4ngXvAgaSzxdf2C3hioulZcukLl1cJ/IvzgF06P777fPAgZQ/IFY%2B//xzLVq0SJ9//rk6dOigpk2b7v14%2B%2B23XccDUA05OTYzpfIZiqphB9CRjRulY4%2B1E1nz8qT27V0nArAvdgCB5LR6tdShg10e5tNPpZYtXSfyJ3YAHXnoISt/Z51F%2BQMAoKLat7fZWVoqPfig6zT%2BRQF0IBqVHnvMjm%2B80W0WAAD8pmx2PvaYzVRUHgXQgT//Wdq%2B3W779stfuk4DAIC//PKXNkO3b7eZisqjACZYSYm9/CtJ119vN7oGAAAVFwrZDJVsppaUuM3jRxTABHvuOXsDyJFHSoMHu04DAIA/DR5ss3TjRputqBwKYIJNm2afhw%2BX6tRxmwUAAL%2BqU8dmqVQ%2BW1FxFMAEWrlSWrpUqllTuuYa12kAHEgkElE4HFZubq7rKAAO4ZprbKYuXWozFhVHAUygsnP/LryQCz8DyWrEiBFat26dVqxY4ToKgEPIyZF%2B8xs7LpuxqBguBJ0g//mP1Ly5tHu39O9/S926uU4E4GC4EDTgD%2B%2B8I518spSeLm3eLDVq5DqRP7ADmCCzZln569KF8gcAQKx062azdfdum7WoGApgAhQXSzNn2vF117nNAgBAqimbrTNn2szFoVEAE2DhQunLL6XGje38PwAAEDsXXmgz9ssvpWeecZ3GHyiACTB9un0ePtzOUQAAALGTnl5%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'}
Genus 2 curve data - 1152.a.147456.1