Genus 2 curve data - 10005.a.50025.1

Formats: - HTML - YAML - JSON - 2025-01-28T15:36:02.638875

• 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': '2012502121525681589', 'abs_disc': 50025, 'analytic_rank': 1, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[3,[1,0,2,-3]],[5,[1,1,3,-5]],[23,[1,-3,25,-23]],[29,[1,0,28,29]]]', 'bad_primes': [3, 5, 23, 29], 'class': '10005.a', 'cond': 10005, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-1,1],[1,1,0,1]]', 'g2_inv': "['5584059449/50025','1409938/50025','-35612816/50025']", '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': "['356','7873','1025121','6403200']", 'igusa_inv': "['89','2','-4496','-100037','50025']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '10005.a.50025.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.74209097598435500428239336951333354159049712010337', 'prec': 173}, 'locally_solvable': True, 'modell_images': ['2.20.2'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '10.892123060765341297012458155', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0340654880524', 'prec': 50}, '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': 2, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.2.8012006001.1', [-80, -82, 45, -45, 5, -2, 1], [6, 9], 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': '10005.a.50025.1', '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': '10005.a.50025.1', 'mw_gens': [[[[1, 1], [0, 1], [1, 1]], [[0, 1], [1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.034065488052437205693100385940796', 'prec': 117}], 'mw_invs': [0], 'num_rat_pts': 4, 'rat_pts': [[1, -3, 1], [1, -1, 0], [1, 0, 0], [1, 0, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 10005, 'lmfdb_label': '10005.a.50025.1', 'modell_image': '2.20.2', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 26
    {'cluster_label': 'c4c2_1~2_0', 'label': '10005.a.50025.1', 'p': 3, 'tamagawa_number': 1}
  • id: 27
    {'cluster_label': 'c4c2_1_0', 'label': '10005.a.50025.1', 'p': 5, 'tamagawa_number': 2}
  • id: 28
    {'cluster_label': 'c4c2_1~2_0', 'label': '10005.a.50025.1', 'p': 23, 'tamagawa_number': 1}
  • id: 29
    {'cluster_label': 'c4c2_1~2_0', 'label': '10005.a.50025.1', 'p': 29, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '10005.a.50025.1', 'plot': 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oAwRwFEkVSvLj36qDkeM0b6%2BWe7eYBws2rVKj333HNq2bKl7SgAHIACiCK7/XapRQvp4EFTAgGUjcOHD%2Bumm27SrFmzVK1aNdtxADgABRBFFhUlPfWUOX72WWndOrt5gHCRmpqqa6%2B9VlddddVZH%2Bvz%2BeT1ek/aAKC4KIAolo4dpeuvN%2BsDDx0qsZAgUDqvv/661qxZo7S0tCI9Pi0tTQkJCflbcnJyOScEEI4ogCi2KVOkChWkpUult96ynQYIXXv27NHdd9%2BtOXPmqEKFCkV6zsiRI%2BXxePK3PXv2lHNKAOHI5fczhoPiGz9eGjdOSk6WNm%2BWKlWynQgIPQsWLFDv3r0VGRmZf19OTo5cLpciIiLk8/lO%2BllhvF6vEhIS5PF4FB8fX96RAYQJCiBK5OhRKSVF2rVLGj1aevhh24mA0JOZmaldu3addN/gwYPVtGlTPfDAA2revPlZfwcFEEBJRNkOgNBUqZJZJ7hvX3NKePBgqVEj26mA0FKlSpVTSl5cXJxq1KhRpPIHACXFdwBRYn36SJ06ScePS/fdZzsNAAAoKk4Bo1S%2B%2BUa68EIpJ0datEgqwiwWAMoQp4ABlAQjgCiV5s2l1FRzPHSolJVlNw8AADg7CiBKbfx46ZxzpE2bpBkzbKcBAABnQwFEqVWtKk2YYI7HjZP277caBwAAnAUFEGVi8GDp4oslr1caOdJ2GgAAcCYUQJSJyMiC078vvih98YXdPAAA4PQogCgz7dpJgwaZ4yFDzHrBAMpHenq6UlJS5Ha7bUcBEIKYBgZlat8%2BqUkTKTNTmj3bnBoGUH6YBgZASTACiDKVmCiNGWOOR4yQPB67eQAAwKkogChzQ4eaUcADB8wUMQAAILhQAFHmYmKkJ580x08/beYHBAAAwYMCiHLRpYvUvbuUnS0NGybxTVMAAIIHBRDlZto0Mxr44YfSwoW20wAAgDwUQJSb88%2BX7rvPHN9zj%2BTz2c0DAAAMCiDK1YMPmiuDt283I4IAAMA%2BCiDKVeXK0qRJ5vixx6QffrCbBwAAUAARADfdZFYJOXLEjAgCAAC7KIAodxERBdPCvPwy6wQDAGAbBRAB0bZtwTrBTAsDAIBdFEAEzIQJUlyc9Omn0htv2E4DhLb09HSlpKTI7XbbjgIgBLn8fsZiEDiPPGLWCq5fX9q8WapY0XYiILR5vV4lJCTI4/EoPj7edhwAIYIRQATU/fdLycnS7t3SE0/YTgMAgDNRABFQFStKaWnmOC1N2r/fbh4AAJyIAoiA699fcrulw4elsWNtpwEAwHkogAi4iAhp6lRzPGuWtHGj3TwAADgNBRBWXHaZ1KuXlJsrjRhhOw0AAM5CAYQ1EydKkZHSwoXSsmW20wAA4BwUQFjTpIl0223m%2BIEHmBwaAIBAoQDCqrFjpUqVpM8%2Bk95%2B23YaAACcgQIIqxITzdJwkvTQQ1JOjt08AAA4AQUQ1t1/v1S1qrRhg/T667bTAAAQ/iiAsK5aNWn4cHM8bpyUnW01DhASWAsYQGmwFjCCQmamdO650k8/SS%2B9JN1yi%2B1EQGhgLWAAJcEIIIJClSrmVLAkPfooo4AAAJQnCiCCRmqqVKOG9O230ltv2U4DAED4ogAiaFSuXHBFcFoa8wICAFBeKIAIKqmppgiuXy/95z%2B20wAAEJ4ogAgq1apJd9xhjh9/3G4WAADCFQUQQefuu80awR9/LK1bZzsNAADhhwKIoFO/vtS7tzmeMcNuFqC8paWlye12q0qVKqpVq5Z69eqlLVu22I4FIMxRABGUhgwx%2B7lzJa/XbhagPC1dulSpqan67LPPtGjRImVnZ6tz5846cuSI7WgAwhgTQSMo%2Bf1Ss2bSpk3S3/9e8L1AINz9%2BOOPqlWrlpYuXarLL7/8rI9nImgAJcEIIIKSyyX96U/m%2BKWXrEYBAsrj8UiSqlevXujPfT6fvF7vSRsAFBcjgAha%2B/ZJ9epJubnSd9%2BZpeKAcOb3%2B9WzZ08dPHhQy5cvL/Qx48aN0/jx40%2B5nxFAAMXBCCCCVmKi1KmTOWZlEDjBkCFDtG7dOr322munfczIkSPl8Xjytz179gQwIYBwQQFEUPvjH81%2BwQK7OYDydtddd%2Bmdd97RkiVLVK9evdM%2BLjY2VvHx8SdtAFBcFEAEte7dzf7zz6WffrKbBSgPfr9fQ4YM0bx58/Txxx%2BrUaNGtiMBcAAKIIJa3bpS8%2BbmquD//td2GqDspaamas6cOXr11VdVpUoVZWRkKCMjQ8eOHbMdDUAYowAi6HXoYPYrVtjNAZSHZ555Rh6PRx07dlRiYmL%2B9sYbb9iOBiCMRdkOAJxNu3ZSerq0erXtJEDZYyIGADYwAoig16qV2W/YYE4FAwCA0qEAIuidf77ZHzokHTxoNwsAAOGAAoigV7GilLcowr59drMAABAOKIAICQkJZs%2BqVwAAlB4FECEh6n%2BXK%2BXk2M0BAEA4oAAiJBw5YvaVKtnNAQSL9PR0paSkyO12244CIAS5/MxBgCCXnS1VqGBG/77/3kwODcDwer1KSEiQx%2BNhWTgARcYIIILeli2m/MXFSYmJttMAABD6KIAIesuXm32bNlIE71gAAEqN/5wi6P3rX2bfpYvdHAAAhAsKIILali3SRx%2BZ43797GYBACBcUAAR1MaONfsePaRGjexmAQAgXFAAEbT%2B/W/pjTckl0saP952GgAAwgcFEEFp927pllvM8dCh0oUX2s0DAEA4oQAi6Pz4o9S1q9lfeKGUlmY7EQAA4YUCiKCye7fUsaO0caOZ8Pntt6WKFW2nAgAgvFAAETSWLpXati0of4sXS/Xr204FAED4oQDCuuPHpZEjpU6dpP37pZYtpZUrpSZNbCcDACA8UQBhjd8vvfuu1KKFNHGilJsr3XqrKX%2BM/AFnlp6erpSUFLndbttRAIQgl9/v99sOAWfx%2B6Vly6QxY8xekpKSpPR0qVcvu9mAUOP1epWQkCCPx6P4%2BHjbcQCEiCjbAeAc2dnmoo5p08wonyTFxkp33y2NGiXx3y4AAAKDAohyt3279PLL0uzZ0vffm/tiYqQ//Ul68EEpOdluPgAAnIYCiHLxww/SvHnS669Ln3xScH/NmtKdd0qpqVJior18AAA4GQUQZcLvl77%2BWnr/fWnhQumzzwp%2B5nJJV11lRvx69zanfQEAgD0UQJSI3y/t3CktWSJ9/LH00UdmCpdfa9dOuuEGs9WtayUmAAAoBAUQRXLihLR2rRnZW7lSWrFC2rv35MfExZm5/K69Vure3VzZCwAAgg8FEKc4flz65htT%2BNaskVavNqd3T5w4%2BXFRUWbljiuukK68UvrDHzi9CwBAKKAAOlhWlvTtt2bptY0bTelbv17aulXKyTn18dWrS5dcYope%2B/bmuFKlwOcGAAClQwEMczk55lTtt9%2BabetWads2afNmMz1Ldnbhz6tRQ7rwQql1a%2Bnii6U2baRzzzUXdAAAgNBGAQxxOTlSRoa0e7e0a5fZduwwF2hs3272WVmnf35cnPS730kpKVLz5mZr2dJ8f4%2ByBwBAeKIABrGjR6V9%2B8ycennb3r1mMuW8be/e04/i5YmOlho2lBo3LtiaNDFbvXoUPSAUpaenKz09XTmFfV8DAM6CtYADyO%2BXvF7pxx/NduCA2fbvL9hnZBRsXm/Rfm9kpJlmpUEDszVsaLZzz5UaNTIrbURGludfBsAW1gIGUBKMAAbAU09JEydKP/105tOxhalY0ZyOzdvq1jVbvXqm2CUnmxU1KHgAAKCoKIABkJNjTuXmiYuTzjlHqlWrYKtdu2CrU8dsiYlSfDynaAEAQNniFHAA7Ntntpo1TfGrWNF2IgDhglPAAEqCEcAASEw0GwAAQDCIsB0AAAAAgcUIIAAATnP0qFncXZLatWNZJwdiBBAAAKfw%2B6Xx4820Eldeaba6daVHHjE/g2MwAggAgFM8%2BKCZl%2BzXDh2SxoyRfD7p0UfL7J/KyZGOHTPb8eOFbz7fqduJEwX7X29ZWafu87bs7IL9nXdK/fqV2Z8RtiiAAAA4wS%2B/SNOnn/bHudOe0Pqr79chVVVmppSZKR0%2BbPZHjpjjvP3Ro%2Bb41/ujR03Zy9sXd97bsnLNNXb%2B3VBDASwFv9%2BvzMxM2zEAOIjP55PP58u/nfcZ5C3q0kEIO7m5ksdj%2Bt3Bgydvhw6ZzeORUjbO15Djx0//i44d1biOC7RAfco8Y1SUmQItNlaqUEGKiTH72NiC4%2Bjogp9FR5t93pZ3Ozr65C0q6uTjqCipRYuir6RVpUoVuRw62S7zAJZC3vxbAAAg9Dh5/kwKYCn8dgTQ6/UqOTlZe/bsKdEbyu12a9WqVUH/vJI%2BtzSvT6j8jSV9nlPeOyV9Hu%2BdAr8dAdy3b5/atm2rjRs3qm7dugHJWZrnOv29k7cy1N69ZvvhB7Pt2ye9//4aJSa21v795jRqcVWqJFWrJlWvLlWtao6rVTPHCQlSveydun7ChYrQaf6zHxEhrVtn1hgtxd9YXs8tj%2Bc5eQSQU8Cl4HK5Cv1AiY%2BPL9F/xCMjI0PieaV9bklen1D6GwP92pTm3wyV5%2BXhvXN6VapUCdhrU5rnhvt7JzdXys1N1FdfxWvHDmn7dmnnTmnXLrN9/70pgYXrqB07Cm5VrlywPGjt2gVLh55zjtnyVpeqWVPq1KmVNm/%2B%2BizpWkrf3SC98UbhP%2B7fX2rWrEh/p5PeO%2BGKAhhEUlNTQ%2BJ5pX1uIP89G39joF%2Bb0vybofK80uC9Uz7/ntPfOwcPSps2SZs3S1u2SFu3Stu2Sd99Jx0//l917Hj63xsVZWZeSU42%2B3r1zKws33zzgW69tYuSkszqUXFxRc961113FO2BL7xgLrFdsODk%2B/v0kZ57rsj/Hu%2Bd0Mcp4DLEmpxnxutzerw2Z8brc3rff/99/inOevXq2Y4TdEr73jl%2BXNqwQfr6a2n9erNt2CBlZJz%2BOZGRUsOGUqNG0rnnmuOGDaUGDcxWp455jFUbNuj4ggUa9dBDGv/556rctq3lQAg0RgDLUGxsrMaOHavY2FjbUYISr8/p8dqcGa/P6eW9Jrw2hSvOeycryxS9L76QVq%2BWvvxS2rjRzC1XmHr1pKZNpSZNzNa4sdkaNDCjfEGtWTO5zj9fVbKyFN2qle00sIARQAAIYYyOltzhw9Inn0jLlkkrVkirVpn5636rRg2pVSupZUszxUizZlJKilSlSuAzA2Ul2P8fBQCAMuH3S199Jb3/vvThh9Knn546uletmuR2S23bSm3aSK1bm5E%2Bh14oijBGAQQAhK3cXGn5cunNN6W33zZTr/xagwZShw7SZZdJl14qXXCBmQ0FCHcUQABA2Nm%2BXXr%2Beekf/zBTr%2BSJi5Ouvlrq0sXszzvPXkbAJgogACBsrFwpTZwovfuuOeUrmYmQe/eW%2BvaVOnUyy40BTsdAdxnJysrSAw88oBYtWiguLk5JSUkaNGiQfvjhB9vRAmrevHnq0qWLatasKZfLpbVr1571OS%2B99JJcLtcp2/EzrVkZZkryuoUbv9%2BvcePGKSkpSRUrVlTHjh21YcOGMz5n3Lhxp7xv6tSpE6DECAYzZ85Uo0aNFBNzkRISVqp9e2nhQlP%2BOneW3nrLTNkye7Z04MBLqljR2Z81krRs2TJ1795dSUlJcrlcWvDbOQHhCBTAMnL06FGtWbNGo0eP1po1azRv3jxt3bpVPXr0sB0toI4cOaL27dtr4sSJxXpefHy89u3bd9JWwUH/m17S1y2cTJ48WdOmTdOMGTO0atUq1alTR1dfffVJyy0WplmzZie9b9avXx%2BgxHalp6crJSVFbrfbdhRr3njjDd19931q3vxf8vvXyOv9g6Qs9et3WJs3Sx98YEb9fj0DjNM/ayTzedOqVSvNmDHDdhRYxCngMpKQkKBFixaddN/TTz%2Bttm3bavfu3apfv76lZIE1cOBASdLOnTuL9Tynj9yU9HULF36/X9OnT9eoUaPUp08fSdLLL7%2Bs2rVr69VXX9Wdd9552udGRUU58r2Tmpqq1NTU/GlgnGjKlL%2Brdu2v9O67TSVJvXpJ69Zdq4YNL1aTJmmFPsfpnzWS1LVrV3Xt2tV2DFjGCGA58ng8crlcqlq1qu0oQe/w4cNq0KCB6tWrp%2Buuu05fffWV7UgIoB07digjI0OdO3fOvy82NlYdOnTQypUrz/jcbdu2KSkpSY0aNVK/fv20ffv28o6LIJCZeUJffvmI9u5tqsqVpddek%2BbPl6677ndnfM/wWQMYFMDk5eCIAAATzElEQVRycvz4cY0YMUIDBgxgctazaNq0qV566SW98847eu2111ShQgW1b99e27Ztsx0NAZLxv3W1ateufdL9tWvXzv9ZYS655BK98sor%2BuCDDzRr1ixlZGToD3/4g37%2B%2BedyzQv7Ro70SWqvKlWytXSp1K%2Bfuf9M7xk%2Ba4ACFMASmjt3ripXrpy/LV%2B%2BPP9nWVlZ6tevn3JzczVz5kyLKcvXmV6D4mjXrp1uvvlmtWrVSpdddpnefPNNXXDBBXr66afLOHFwKKvXLZT99jXIysqSZE7P/Zrf7z/lvl/r2rWr/vjHP6pFixa66qqr9N5770kyp48Rvn7%2BWXr%2B%2BcqSpFGjvlXr1gU/O9N7xmmfNcCZ8B3AEurRo4cuueSS/Nt169aVZMrfDTfcoB07dujjjz8O69G/070GpRURESG32x22/1deXq9bKPnta%2BDz%2BSSZkcDExMT8%2Bw8cOHDKqOCZxMXFqUWLFmH73oGxeLHk87kkrVPjxt9Japr/s%2BK8Z8L9swY4EwpgCVWpUkVVfrMQZF7527Ztm5YsWaIaNWpYShcYhb0GZcHv92vt2rVq0aJFmf/uYFBer1so%2Be1r4Pf7VadOHS1atEgXXXSRJOnEiRNaunSpJk2aVOTf6/P5tGnTJl122WVlnhnBI281j%2BrV9%2BmjjxapT5/e%2BT9btGiRevbsWaTfE%2B6fNcCZUADLSHZ2tvr27as1a9bo3XffVU5OTv73UKpXr66YmBjLCQPjl19%2B0e7du/PnP9yyZYskqU6dOvlX3g0aNEh169ZVWpq5Sm/8%2BPFq166dGjduLK/Xq6eeekpr165Venq6nT/CgqK8buHM5XJp2LBhmjBhgho3bqzGjRtrwoQJqlSpkgYMGJD/uCuvvFK9e/fWkCFDJEn333%2B/unfvrvr16%2BvAgQN69NFH5fV6dcstt9j6UxAA1aub/TnntNbzz3dXmzZt9Pvf/17PPfecdu/erb/85S%2BS%2BKw5ncOHD%2Bvbb7/Nv71jxw6tXbtW1atXd8yMFZDkR5nYsWOHX1Kh25IlS2zHC5gXX3yx0Ndg7Nix%2BY/p0KGD/5Zbbsm/PWzYMH/9%2BvX9MTEx/nPOOcffuXNn/8qVKwMf3qKivG7hLjc31z927Fh/nTp1/LGxsf7LL7/cv379%2BpMe06BBg5NekxtvvNGfmJjoj46O9iclJfn79Onj37BhQ4CT2%2BXxePyS/B6Px3aUgNm%2B3e%2BX/P6ICL9//Ph/%2BBs0aOCPiYnxt27d2r906dL8x/FZU7glS5YU%2Bnnz69cK4c/l9%2BctlgMACDV58wB6PJ6w/s7xb11zjZnouVcvad486QzXCgEoBFcBAwBCzpQpUlSUtGCBxIIWQPFRAAEAIadFCynv%2BqBhw6Q337SbBwg1FEAAQEi65x7pjjuk3FxpwACJ6R%2BBoqMAAkAISk9PV0pKitxut%2B0o1rhc0syZ0uDBUk6OdOut0tixphACODMuAgGAEObUi0B%2BLTdXGjlSmjzZ3O7e3YwGVqtmNxcQzBgBBACEtIgI833AF1%2BUYmOlhQuliy6SVqywnQwIXhRAAEBYuPVWaeVK6bzzpF27pA4dzMjg/1YaBPArFEAAQNho3Vpas0YaNMicGp44Ubr4Yunzz20nA4ILBRAAEFbi4813AOfNk845R9qwQfrDH8x0MZmZttMBwYECCAAIS717Sxs3SjffbEYDn3xS%2Bt3vpH/%2BU%2BLyRzgdBRAAELZq1pT%2B8Q%2BzbNy550p790rXX2%2BWktu82XY6wB4KIAAg7HXuLH3zjTR6tBQTI334oVlNZPhwyeu1nQ4IPAogAMARKlaUHn7YfCfw2mul7Gzp8celxo2lF14wk0kDTkEBBAA4yvnnS%2B%2B%2BK733nnTBBdKBA9Jtt0lut/Tf/9pOBwQGBRAA4Ejduknr10tTp0oJCdJXX0lXXGEuHtm2zXY6oHxRAAEgBLEWcNmIiZHuvdcUvr/9TYqMlBYskFJSzLQxP/9sOyFQPlgLGABCGGsBl62NG82FIe%2B/b25XrSqNGiXddZdZZg4IF4wAAgDwPykp5ruBixZJLVtKhw6ZQti0qfT668wfiPBBAQQA4DeuusosKTd7tpSUJO3cKfXvL7VrJy1fbjsdUHoUQAAAChEZKQ0eLG3daqaPiYuTvvhCuvxyc6HIli22EwIlRwEEAOAM4uLMBNLffivdeacUEWEuFGne3Hw38McfbScEio8CCABAEdSpIz37rJk6Jm8i6RkzzLyCkyZJx4/bTggUHQUQAIBiSEkxE0kvXixddJFZSm7ECHOhyGuvcaEIQgMFEAAsyMrK0gMPPKAWLVooLi5OSUlJGjRokH744Qfb0VBEnTpJq1dLL78s1asn7dolDRhgLhT55BPb6YAzowACgAVHjx7VmjVrNHr0aK1Zs0bz5s3T1q1b1aNHD9vRUAwREdKgQeaCkEceKbhQ5NJLpRtvlHbssJ0QKBwTQQNAkFi1apXatm2rXbt2qX79%2BkV6DhNBB5eMDHPByOzZUm6umTz6nnukBx%2BUqlSxnQ4owAggAAQJj8cjl8ulqlWrnvYxPp9PXq/3pA3Bo04dadYsM4dgp06SzydNnCg1biy9%2BKIphUAwoAACQBA4fvy4RowYoQEDBpxxJC8tLU0JCQn5W3JycgBToqhatZI%2B%2BshMF3P%2B%2BdL%2B/dKf/iS1bcv3AxEcKIAAEABz585V5cqV87flv1pOIisrS/369VNubq5mzpx5xt8zcuRIeTye/G3Pnj3lHR0l5HJJPXtKGzZIU6ZI8fHSl1%2Ba7wfedJO0d6/thHAyvgMIAAGQmZmp/fv359%2BuW7euKlasqKysLN1www3avn27Pv74Y9WoUaNYv5fvAIaOAwekUaOkF14wU8XExUkPPWS%2BIxgbazsdnIYCCACW5JW/bdu2acmSJTrnnHOK/TsogKHnyy%2BloUOllSvN7caNpSeflLp2tZsLzsIpYACwIDs7W3379tXq1as1d%2B5c5eTkKCMjQxkZGTpx4oTteChHF18srVghvfKKuWhk2zapWzepVy9p507b6eAUjAACgAU7d%2B5Uo0aNCv3ZkiVL1LFjxyL9HkYAQ5vXK40fLz31lFlarmJFc1r4vvs4LYzyRQEEgBBGAQwPGzZIqanS0qXmdpMm0syZZioZoDxwChgAAMuaNZOWLJHmzJFq1zYri1x5pTRwoLl4BChrFEAAAIKAy2Wmh9m82YwGulymEDZtKj3/PJNIo2xRAAEACCJVq0ozZkiffy5ddJF08KB0%2B%2B3SFVeYcgiUBQogAABByO2WvvhCmjpVqlRJWrbMrDDyyCMSF4qjtCiAAAAEqago6d57pY0bzTyBJ05IY8aYqWS%2B%2BMJ2OoQyCiAAhKD09HSlpKTI7XbbjoIAaNBAeu89ae5cqWZN6ZtvpN//Xho%2BXDp2zHY6hCKmgQGAEMY0MM7z00/S3XdLr75qbjduLM2ebdYYBoqKEUAAAEJIzZpmJHDhQikpyawkcvnlZk1hRgNRVBRAAABC0HXXmQmkBw%2BW/H5p%2BnTpwgulzz6znQyhgAIIAECIqlrVnP597z0zGrh1q9S%2BvfTgg1wpjDOjAAIAEOK6dTMXhtx8s5kwOi1NatvW3AcUhgIIAEAYqFZN%2Bsc/pH/9y3xP8OuvzXQxTzzBKiI4FQUQAIAw0qePtH69dO215jTwvfdKnTtLe/faToZgQgEEACDM1KljrhJ%2B9lmpYkVp8WKpZUtp/nxJu3ZJ998vtWhhtuHDpd27bUdGgDEPIACEMOYBxNls2SLddJP05ZeSW1/ovzGdVemE5%2BQHVa0qLVoktWljJyQCjhFAAADCWJMm0sqV0vD7/Zqjm08tf5J06JA0cGDgw8EaCiAAAGEuJkaafO1SXaBtp3/Q5s3S8uWBCwWrKIAAADjBjh1l8xiEBQogAISg9PR0paSkyO12246CUFGv3tkfk5xc/jkQFLgIBABCGBeBoMhyc6XzzpN27iz0x9td52rp899q8J9cgc0FKxgBBADACSIipBdflCpVOuVHxyLjdKv/Rf3pzy7ddpt07JiFfAgoCiAAAE7RsaO0apV0223mlHBysnT77Ypdt1qdH7lcERHSCy9Il1562oFChAlOAQNACOMUMMrSokVS//7Szz9L1atLr71mVhFB%2BGEEEAAASJKuvlpas0Zyu6VffpG6dpUmTZIYKgo/FEAAAJCvfn1p2TLpz382142MGCH16ycdOWI7GcoSBRAAAJykQgVp1izpmWek6GjpzTel9u35XmA4oQACAIBTuFzSX/4iffyxVKuW9PXX5tTwsmW2k6EsUAABAMBpXXqptHq11Lq19NNP0lVXmSuFEdoogAAA4IySk80ywTfcIGVlmVlk7rtPysmxnQwlRQEEAABnVamS9Prr0rhx5va0aVKvXtLhw1ZjoYQogAAQglgLGDa4XNLYsaYIVqggvfuuOUX8/fe2k6G4mAgaAEIYE0HDls8/l3r0kA4ckJKSpPfeky680HYqFBUjgAAAoNguucSUwJQU6YcfpMsuk/7zH9upUFQUQAAAUCING0qffCJ16mS%2BC3jdddLs2bZToSgogAAAoMSqVpX%2B/W9p4EBzVfCf/yyNH8/yccGOAggAAEolJkZ6%2BWXpwQfN7XHjpDvvlLKzrcbCGVAAAQBAqblc0mOPSTNnShERZim5vn2lY8dsJ0NhKIAAAKDM/PWv0j//KcXGSm%2B/LXXpIh06ZDsVfosCCABB4M4775TL5dL06dNtRwFKrXdv6cMPpfh4s4JIx45SRobtVPg1CiAAWLZgwQJ9/vnnSkpKsh0FKDOXXy4tWybVri19/bWZMHrnTtupkIcCCAAW7d27V0OGDNHcuXMVHR1tOw5Qplq1klasMNPFfPedKYGbNtlOBYkCCADW5ObmauDAgRo%2BfLiaNWtWpOf4fD55vd6TNiCYnX%2B%2BKYEpKdLevWZk8KuvbKcCBRAALJk0aZKioqI0dOjQIj8nLS1NCQkJ%2BVtycnI5JgTKRt260tKl0sUXSz/9JF1xhfTZZ7ZTORsFEAACYO7cuapcuXL%2BtnTpUj355JN66aWX5HK5ivx7Ro4cKY/Hk7/t2bOnHFMDZadmTWnxYnMa2OORrr7alELY4fL7masbAMpbZmam9u/fn3/7rbfe0qhRoxQRUfD/4Tk5OYqIiFBycrJ2FvHb8l6vVwkJCfJ4PIqPjy/r2ECZO3JE6tVL%2BugjqWJF6Z13pKuusp3KeSiAAGDBzz//rH379p10X5cuXTRw4EANHjxYTZo0KdLvoQAiFB0/Lv3xj9L775v5AhcskK65xnYqZ4myHQAAnKhGjRqqUaPGSfdFR0erTp06RS5/QKiqUEGaN0%2B68UYzWXTPnub2tdfaTuYcfAcQAAAEXGys9NZbUp8%2B0okTZv/ee7ZTOQengAEghHEKGKEuK0saMMAsHxcTI82fL3XrZjtV%2BGMEEAAAWBMdLb36qtS3rxkJ7N1b%2Bs9/bKcKfxRAAABgVV4JzDsdnHeVMMoPBRAAAFgXHS299pq5IMTnk3r0YJ7A8kQBBAAAQSEmRnrjDalrV%2BnYMem661gxpLxQAAEAQNCIjTVTwlx5pXT4sJkfkLWDyx4FEABCUHp6ulJSUuR2u21HAcpchQpmfsD27c2ycZ07S5s22U4VXpgGBgBCGNPAIJx5PGYk8Msvpbp1pRUrpIYNbacKD4wAAgCAoJSQYKaESUmR9u41awZnZNhOFR4ogAAAIGjVrCl9%2BKHUqJH03XdSly7SoUO2U4U%2BCiAAAAhqdetKixZJdepI69ZJ3bubq4RRchRAAAAQ9M47T/rgA3NaeMUK6cYbpexs26lCFwUQAACEhJYtpYULzVXCCxdKd94pcSlryVAAAQBAyLjsMjNZdESENHu2NHq07UShiQIIAABCSo8e0t//bo4fe0x65hm7eUIRBRAAAISc226Txo83x0OGmImjUXQUQAAAEJJGj5Zuv13KzZX692fd4OKgAAIAgJDkckkzZ0rduplpYbp3N3MF4uwogAAQglgLGDCiosxFIa1bSz/9ZMqgx2M7VfBjLWAACGGsBQwY%2B/ZJ7dpJAwaYC0MiGOI6oyjbAQAAAEorMVH6%2BmupalXbSUID/RgAAIQFyl/RUQABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIACEoPT1dKSkpcrvdtqMACEEuv9/vtx0CAFAyXq9XCQkJ8ng8io%2BPtx0HQIhgBBAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIdhHkAACGF%2Bv1%2BZmZmqUqWKXC6X7TgAQgQFEAAAwGE4BQwAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACH%2BX8Ph%2Bi2vzlOBwAAAABJRU5ErkJggg%3D%3D'}