Transaction
ID: 475de021fe...d6a3
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
5.01 ERG
Tokens:
Loading assets...
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.002 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
5.01 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 591,083
Total coins transferred: 5.01 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000253 ERG
Raw Transaction Data
{
"id": "475de021fe1de359b5bdf135e0b9cebf8ad33bc159abd55fafcf37718b10d6a3",
"blockId": "30832398d1cf8fdc4e19f03416ac1d382d53d8d181a7822f237b880821bf34db",
"inclusionHeight": 1172814,
"timestamp": 1704624343450,
"index": 4,
"globalIndex": 6424905,
"numConfirmations": 591083,
"inputs": [
{
"boxId": "12090db014abb49ff4456c80b6246b3e5a0ee3e3a6df240de26c9cb2036042d9",
"value": 1000000,
"index": 0,
"spendingProof": "329dfdd6686b47b75ee61b32b5668e52472f367736d7ed432a88af897c342a2523936193cd11343b2d58e86fd7f99becfb960adcf73cc948",
"outputBlockId": "0a82e70eb283f9396b5b5cb54e21766b4ef46226d922f15f2be62500f3ed0dba",
"outputTransactionId": "15e4574b63baab48f04bd21cc93cd5eff277d2b37726f42161384718e9081f55",
"outputIndex": 3,
"outputGlobalIndex": 35824092,
"outputCreatedAt": 1171751,
"outputSettledAt": 1171753,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "c3596bc7136b6b3eab9c2f7302316211f02d8e97c294f6f134156ff72572b69e",
"index": 0,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {}
},
{
"boxId": "ebcdea2c4a9605a78a9f8ab5c98f33600679b6987be15f99360d4e86281d2381",
"value": 5011283922,
"index": 1,
"spendingProof": "13b71624b7cf6750ce3950c32e48162db6e49c474daf3a90570b2482795d7939d6bc27a43782f7f7d1d42f9c128fa985eb8861f903cc035c",
"outputBlockId": "9dbb96e2d7686e9bfd16785f8fc3cdf95c41abf9b78841da3a04007f1d5ccf74",
"outputTransactionId": "3009214c92d8d1370ecd7fa565ad894630a876ed3f8d202b9631470b4bf8dc58",
"outputIndex": 2,
"outputGlobalIndex": 35846964,
"outputCreatedAt": 1172356,
"outputSettledAt": 1172358,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "00de06c61282578684b377ffd6314c38ce596f2c054107fe2314810677859269",
"index": 0,
"amount": 99699999999997,
"name": "MyFirstDAOToken",
"decimals": 6,
"type": "EIP-004"
},
{
"tokenId": "0fdb7ff8b37479b6eb7aab38d45af2cfeefabbefdc7eebc0348d25dd65bc2c91",
"index": 1,
"amount": 69,
"name": "$Lambo",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "0040ae650c4ed77bcd20391493abe84c1a9bb58ee88e87f15670c801e2fc5983",
"index": 2,
"amount": 487801968998,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [],
"outputs": [
{
"boxId": "991e20d6c8b029e1a6c94c29628d5dc5ba7083d9fef10e9970c5f0d6e8acb970",
"transactionId": "475de021fe1de359b5bdf135e0b9cebf8ad33bc159abd55fafcf37718b10d6a3",
"blockId": "30832398d1cf8fdc4e19f03416ac1d382d53d8d181a7822f237b880821bf34db",
"value": 2000000,
"index": 0,
"globalIndex": 35865213,
"creationHeight": 1172812,
"settlementHeight": 1172814,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: Coll(3,-99,42,10,33,-115,124,72,-56,-3,70,-88,-46,-108,49,-67,5,108,-124,-120,42,33,106,86,115,38,-61,-57,-104,78,-56,53)\n2: Coll(-121,90,-57,120,55,-56,-44,115,10,-122,-18,34,-128,67,79,98,-112,-83,-101,-32,-76,73,-76,58,-71,44,-77,-34,-101,7,99,55)\n3: Coll(-65,23,-80,-4,-80,121,-100,-67,-107,-64,90,96,11,10,108,-1,-30,83,-64,29,120,-33,-124,107,56,-2,-99,-67,66,109,-90,31)\n4: Coll(30,33,26,-16,124,56,-30,-114,7,75,117,127,39,113,51,117,125,83,-120,111,-78,3,1,-61,-102,52,-45,11,-17,-69,28,-62)\n5: Coll(-67,87,21,19,-28,-29,-27,-80,-50,-3,122,27,97,-24,6,66,71,-91,-97,-56,120,79,108,-16,-69,-45,-95,-91,-124,55,100,79)\n6: Coll(-113,-112,-104,30,7,17,115,-5,27,-72,-40,-105,65,114,10,72,56,-18,102,20,99,90,41,49,87,95,70,-52,27,-94,59,97)\n7: Coll(4,-49,53,5,11,-94,79,13,121,120,-24,-15,104,110,65,-71,-111,-29,88,-20,-69,-38,-80,-48,-1,9,-79,-112,122,68,-120,91)\n8: Coll(-57,71,-35,-2,-114,-81,-32,27,-46,123,39,78,-122,-93,87,-16,-90,-50,102,61,20,25,-30,-14,91,-116,-85,-47,-31,29,-59,102)\n9: Coll(-86,14,126,94,59,100,68,84,-53,73,-18,18,83,-113,-64,-77,-121,44,28,-101,46,49,117,-59,123,-27,8,-30,-51,96,-119,-104)\n10: Coll(14,-44,-42,101,21,102,-43,117,-22,-84,-79,72,0,102,4,113,-77,21,-15,-69,124,-109,-21,13,-21,99,-3,-50,-41,-70,-104,79)\n11: Coll(99,-6,-127,-116,-67,-17,-10,111,109,-124,60,-81,100,101,-5,89,-78,78,3,104,41,-84,59,-71,-78,103,79,-17,42,-11,75,85)\n12: Coll(-4,98,-59,-31,-2,4,-93,-38,-100,28,89,-85,38,85,62,-8,-117,-65,-78,-44,13,-65,-5,-6,22,69,-73,-1,-49,58,-37,30)\n13: Coll(54,81,-127,41,-82,100,93,107,77,-3,-48,44,40,-69,28,90,111,-87,-96,7,-46,62,-62,19,-31,-77,-19,45,13,32,71,82)\n14: Coll(-85,111,-85,11,63,-33,-97,-118,-123,28,-34,80,23,113,62,41,-59,-9,-91,-44,-113,-61,-91,45,26,41,-28,58,-128,7,77,-22)\n15: Coll(103,-36,20,-108,67,49,53,36,52,-105,-117,4,14,3,-47,-56,-104,85,111,85,23,-86,-54,69,17,-54,-75,66,31,-109,-84,-4)\n16: Coll(-92,49,28,114,107,53,-79,-114,-14,43,-125,78,-32,-1,87,-1,55,13,-125,-52,-108,-56,-97,-64,109,-39,125,0,-110,21,-104,113)\n17: Coll(12,-47,45,108,-50,127,-17,93,-86,-112,-76,94,122,11,63,-21,123,-120,-100,-116,-76,-37,-28,88,-8,27,-111,-53,45,-64,-83,-9)\n18: Coll(102,3,28,-67,115,53,-112,117,59,42,-8,-85,-126,-116,-20,-71,93,123,108,-12,-38,101,2,-77,31,-66,-45,13,-97,105,-25,69)\n19: Coll(-61,3,-35,-14,-100,4,3,72,-66,92,49,-127,-66,23,106,-73,94,119,33,-31,83,-50,-1,-125,106,-107,15,-39,5,-99,-125,-51)\n20: Coll(82,-115,-55,-29,10,-98,122,-105,-122,-18,-93,9,63,-35,-50,60,62,-126,-56,127,-28,19,110,5,-102,-76,-93,-70,-95,-92,-11,73)\n21: Coll(-104,-16,-56,-32,-28,6,-38,-87,123,112,89,-128,-108,-71,-26,98,106,-54,-38,-60,25,-38,102,123,77,113,-13,48,100,-8,-43,-35)\n22: Coll(73,118,0,-124,-9,-43,90,-71,4,112,-21,-12,-44,-70,-81,5,125,-117,66,-40,-52,45,-85,97,-98,-38,-94,-103,-118,16,71,-99)\n23: Coll(-77,-96,-29,91,74,2,-34,-95,126,-55,58,42,-124,-57,-35,-128,23,53,53,-64,17,-38,66,-34,91,27,3,-62,106,88,-35,69)\n24: Coll(-18,-85,59,86,-82,63,-53,10,-46,-1,-57,84,-121,72,-50,-29,-50,100,29,-94,-103,-117,74,120,-31,85,-98,-57,50,14,64,69)\n25: Coll(1,54,-18,76,64,-60,-36,95,-70,58,4,-122,-27,7,75,43,-104,17,-114,72,-102,-20,29,10,60,67,37,-10,25,115,-19,-56)\n26: Coll(19,37,111,124,19,57,-63,75,-110,-125,1,-30,90,-27,64,113,-83,73,122,25,47,124,-8,25,62,38,97,-69,14,-83,125,-83)\n27: Coll(110,-98,-11,-34,103,65,69,-105,-39,51,99,-101,20,-36,42,-66,6,36,22,58,-59,110,-74,-93,-80,-79,79,83,-20,-64,80,-88)\n28: 1\n29: 0\n30: Coll(61,-86,102,-128,-114,105,-62,90,-23,50,-35,102,97,28,95,11,60,-70,-54,-47,18,82,3,-24,125,-115,-60,-111,-122,28,113,-26)\n31: Coll(-70,-86,-101,22,76,-76,-72,13,115,94,16,66,78,38,-92,113,-71,98,28,2,-17,-54,23,70,19,90,-61,-10,-37,16,20,16)\n32: Coll(-54,11,-75,-24,-54,-41,24,34,92,-113,45,2,97,-96,-48,-107,-14,93,94,-121,70,10,-122,51,-12,0,-36,60,-55,7,-111,47)\n33: Coll(-17,-60,-10,3,-34,-90,4,18,-122,-88,-97,91,-43,22,-84,-106,-22,91,37,-38,79,8,-41,108,105,39,-32,29,97,-78,42,-33)\n34: Coll(65,-13,-104,-128,101,82,-124,94,82,0,-112,50,16,95,89,-27,-53,44,-79,65,-34,99,-52,115,123,109,-103,87,83,-61,110,-127)\n35: 1\n36: 6\n37: 38\n38: 1\n39: 3\n40: 0\n41: 5\n42: 2\n43: 6\n44: 38\n45: 0\n46: 6\n47: 38\n48: 2\n49: 7\n50: 3\n51: 9\n52: 4\n53: 11\n54: 5\n55: 15\n56: 7\n57: 17\n58: 8\n59: 19\n60: 9\n61: 13\n62: 6\n63: 21\n64: 10\n65: 25\n66: 12\n67: 23\n68: 11\n69: 0\n70: 1\n71: 2\n72: 1\n73: 0\n74: Coll(105,109,46,112,97,105,100,101,105,97,46,99,111,110,116,114,97,99,116,115,46,97,99,116,105,111,110,46)\n75: 2\n76: 78\n77: -58\n78: 31\n79: 72\n80: 91\n81: -104\n82: -21\n83: -121\n84: 21\n85: 63\n86: 124\n87: 87\n88: -37\n89: 79\n90: 94\n91: -51\n92: 117\n93: 85\n94: 111\n95: -35\n96: -68\n97: 64\n98: 59\n99: 65\n100: -84\n101: -8\n102: 68\n103: 31\n104: -34\n105: -114\n106: 22\n107: 9\n108: 0\n109: 0\n110: 3\n111: 4\n112: 5\n113: 6\n114: 7\n115: 8\n116: 9\n117: 10\n118: 0\n119: Coll(0,-1,-106,50,18,-70,0,58,-21,-89,21,-64,103,-23,46,-14,-103,-59,95,53,-82,-41,-74,-82,-12,-55,117,-82,-127,-91,-45,-7)\n120: 2\n121: 1\n122: 6\n123: 7\n124: 32\n125: 4\n126: 1\n127: 6\n128: 2\n129: 32\n130: 6\n131: 1\n132: 6\n133: 33\n134: 35\n135: 32\n136: 8\n137: 1\n138: 6\n139: 7\n140: 9\n141: 32\n142: 10\n143: 1\n144: 6\n145: 11\n146: 32\n147: 12\n148: 1\n149: 6\n150: 31\n151: 32\n152: 16\n153: 1\n154: 6\n155: 70\n156: 32\n157: 18\n158: 1\n159: 6\n160: 24\n161: 32\n162: 20\n163: 1\n164: 6\n165: 135\n166: 32\n167: 14\n168: 1\n169: 6\n170: 28\n171: 32\n172: 22\n173: 1\n174: 6\n175: 12\n176: 32\n177: 26\n178: 1\n179: 6\n180: 27\n181: 32\n182: 24\n183: 1\n184: 6\n185: 63\n186: 32\n187: 0\n188: 1\n189: 33\n190: 1000000\n191: 0\n192: 0\n193: 9223372036854775807\n194: 9223372036854775807\n195: 3\n196: 1\n197: 33\n198: 1000000\n199: 1\n200: 1\n201: Coll(-57,-59,55,-26,-58,53,-109,14,-53,74,-50,-107,-91,73,38,-77,-85,119,105,-115,-97,73,34,-16,-79,-59,-114,-88,113,86,72,59)\n202: Coll(-87,85,-114,65,-122,-53,-43,-86,87,35,-88,82,-44,-63,-36,101,125,-98,-127,67,-126,-1,-120,-115,90,-118,-20,82,21,49,48,29)\n203: 0\n204: 1\n205: Coll(105,109,46,112,97,105,100,101,105,97,46,99,111,110,116,114,97,99,116,115,46,112,114,111,112,111,115,97,108,46)\n206: 2\n207: Coll(-120,48,97,44,82,53,95,111,40,13,18,-105,-15,-97,103,-80,120,-55,-38,-89,-41,-80,75,69,-100,-111,-52,100,73,87,-62,-128)\n208: Coll(3,-110,8,-68,78,-17,-102,3,-24,-41,-117,-122,99,-93,1,-69,95,-83,-36,-89,-117,-31,-99,127,-27,53,-77,-58,76,-66,-2,66)\n209: Coll(-119,46,111,71,-95,13,92,-112,-72,122,-44,-122,51,85,-50,-83,0,-61,-30,-104,50,23,-18,21,83,50,83,-51,-102,96,37,-62)\n210: Coll(58,17,-107,92,71,25,-27,-120,-68,-26,-89,97,29,39,-67,31,-33,-37,87,56,92,-82,-30,102,-40,4,12,-119,79,28,46,29)\n211: Coll(79,-40,-80,-42,-39,-126,66,114,111,87,-77,-33,-90,-122,18,103,-110,-72,-27,5,110,29,81,-74,-23,13,104,-128,-49,45,-51,-59)\n212: Coll(-34,-82,-49,91,100,-70,-42,-11,87,11,-83,10,97,12,78,72,73,87,-49,71,-126,48,-124,0,-68,-112,64,76,29,20,16,-38)\n213: Coll(9,-126,15,-53,-120,113,-5,69,12,62,6,-73,-53,94,39,-80,69,80,-121,-93,102,98,26,-99,-34,117,-126,-96,25,17,30,62)\n214: Coll(-117,-57,-113,28,106,-82,-55,30,98,-114,21,-49,102,-116,22,-52,30,-101,-40,-28,-71,-73,-31,109,99,24,-75,-11,35,-91,-23,-67)\n215: 1\n216: 33\n217: 1000000\n218: 0\n219: 3\n220: 6\n221: 38\n222: 1\n223: 1\n224: 2\n225: 4\n226: 1\n227: 9\n228: 1\n229: 0\n230: 0\n231: 0\n232: 1000000\n233: 1\n234: 33\n235: 1000000\n236: 1\n237: 33\n238: 1000000\n239: 1\n240: 33\n241: 1000000\n242: 1\n243: 33\n244: 1000000\n245: 1\n246: 33\n247: 1000000\n248: 1\n249: 33\n250: 1000000\n251: 1\n252: 33",
"ergoTreeScript": "{\n val box1 = CONTEXT.dataInputs(placeholder[Int](0))\n val coll2 = box1.R4[AvlTree].get.getMany(\n Coll[Coll[Byte]](\n placeholder[Coll[Byte]](1), placeholder[Coll[Byte]](2), placeholder[Coll[Byte]](3), placeholder[Coll[Byte]](4), placeholder[Coll[Byte]](5), placeholder[\n Coll[Byte]\n ](6), placeholder[Coll[Byte]](7), placeholder[Coll[Byte]](8), placeholder[Coll[Byte]](9), placeholder[Coll[Byte]](10), placeholder[Coll[Byte]](\n 11\n ), placeholder[Coll[Byte]](12), placeholder[Coll[Byte]](13), placeholder[Coll[Byte]](14), placeholder[Coll[Byte]](15), placeholder[Coll[Byte]](\n 16\n ), placeholder[Coll[Byte]](17), placeholder[Coll[Byte]](18), placeholder[Coll[Byte]](19), placeholder[Coll[Byte]](20), placeholder[Coll[Byte]](\n 21\n ), placeholder[Coll[Byte]](22), placeholder[Coll[Byte]](23), placeholder[Coll[Byte]](24), placeholder[Coll[Byte]](25), placeholder[Coll[Byte]](\n 26\n ), placeholder[Coll[Byte]](27)\n ), getVar[Coll[Byte]](0.toByte).get\n )\n val coll3 = coll2(placeholder[Int](28)).get\n val box4 = INPUTS(placeholder[Int](29))\n val avlTree5 = box4.R4[AvlTree].get\n val coll6 = avlTree5.getMany(\n Coll[Coll[Byte]](\n placeholder[Coll[Byte]](30), placeholder[Coll[Byte]](31), placeholder[Coll[Byte]](32), placeholder[Coll[Byte]](33), placeholder[Coll[Byte]](34)\n ), getVar[Coll[Byte]](1.toByte).get\n )\n val coll7 = coll6(placeholder[Int](35)).get.slice(placeholder[Int](36), placeholder[Int](37))\n val coll8 = Coll[Coll[Byte]](coll7)\n val coll9 = getVar[Coll[Coll[Byte]]](3.toByte).get\n val coll10 = coll9(placeholder[Int](38))\n val coll11 = coll2(placeholder[Int](39)).get\n val coll12 = coll9(placeholder[Int](40))\n val coll13 = coll2(placeholder[Int](41)).get\n val coll14 = coll6(placeholder[Int](42)).get.slice(placeholder[Int](43), placeholder[Int](44))\n val coll15 = coll6(placeholder[Int](45)).get.slice(placeholder[Int](46), placeholder[Int](47))\n val coll16 = Coll[Coll[Byte]](coll14, coll15)\n val coll17 = coll9(placeholder[Int](48))\n val coll18 = coll2(placeholder[Int](49)).get\n val coll19 = coll9(placeholder[Int](50))\n val coll20 = coll2(placeholder[Int](51)).get\n val coll21 = Coll[Coll[Byte]](coll14)\n val coll22 = coll9(placeholder[Int](52))\n val coll23 = coll2(placeholder[Int](53)).get\n val coll24 = coll9(placeholder[Int](54))\n val coll25 = coll2(placeholder[Int](55)).get\n val coll26 = coll9(placeholder[Int](56))\n val coll27 = coll2(placeholder[Int](57)).get\n val coll28 = coll9(placeholder[Int](58))\n val coll29 = coll2(placeholder[Int](59)).get\n val coll30 = coll9(placeholder[Int](60))\n val coll31 = coll2(placeholder[Int](61)).get\n val coll32 = coll9(placeholder[Int](62))\n val coll33 = coll2(placeholder[Int](63)).get\n val coll34 = coll9(placeholder[Int](64))\n val coll35 = coll2(placeholder[Int](65)).get\n val coll36 = coll9(placeholder[Int](66))\n val coll37 = coll2(placeholder[Int](67)).get\n val coll38 = coll9(placeholder[Int](68))\n val box39 = OUTPUTS(placeholder[Int](69))\n val coll40 = box39.tokens\n val coll41 = box4.tokens\n val tuple42 = coll40(placeholder[Int](70))\n val tuple43 = coll40(placeholder[Int](71))\n val box44 = OUTPUTS(placeholder[Int](72))\n val coll45 = box44.tokens\n val tuple46 = coll45(placeholder[Int](73))\n val coll47 = placeholder[Coll[Byte]](74)\n val coll48 = getVar[Coll[Coll[Byte]]](4.toByte).get\n val box49 = OUTPUTS(placeholder[Int](75))\n val coll50 = box49.tokens\n val coll51 = Coll[Byte](\n placeholder[Byte](76), placeholder[Byte](77), placeholder[Byte](78), placeholder[Byte](79), placeholder[Byte](80), placeholder[Byte](81), placeholder[Byte](\n 82\n ), placeholder[Byte](83), placeholder[Byte](84), placeholder[Byte](85), placeholder[Byte](86), placeholder[Byte](87), placeholder[Byte](88), placeholder[\n Byte\n ](89), placeholder[Byte](90), placeholder[Byte](91), placeholder[Byte](92), placeholder[Byte](93), placeholder[Byte](94), placeholder[Byte](\n 95\n ), placeholder[Byte](96), placeholder[Byte](97), placeholder[Byte](98), placeholder[Byte](99), placeholder[Byte](100), placeholder[Byte](101), placeholder[\n Byte\n ](102), placeholder[Byte](103), placeholder[Byte](104), placeholder[Byte](105), placeholder[Byte](106), placeholder[Byte](107), placeholder[Byte](108)\n )\n val coll52 = box49.R5[Coll[Long]].get\n val l53 = coll52(placeholder[Int](109))\n val l54 = CONTEXT.preHeader.timestamp\n val box55 = OUTPUTS(placeholder[Int](110))\n val box56 = OUTPUTS(placeholder[Int](111))\n val box57 = OUTPUTS(placeholder[Int](112))\n val box58 = OUTPUTS(placeholder[Int](113))\n val box59 = OUTPUTS(placeholder[Int](114))\n val box60 = OUTPUTS(placeholder[Int](115))\n val box61 = OUTPUTS(placeholder[Int](116))\n val box62 = OUTPUTS(placeholder[Int](117))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box1.tokens(placeholder[Int](118))._1 == placeholder[Coll[Byte]](119), allOf(\n Coll[Boolean](\n coll2(placeholder[Int](120)).get.patch(\n placeholder[Int](121), blake2b256(\n substConstants(coll3.slice(placeholder[Int](122), coll3.size), Coll[Int](placeholder[Int](123)), coll8)\n ), placeholder[Int](124)\n ) == coll10, coll2(placeholder[Int](125)).get.patch(\n placeholder[Int](126), blake2b256(\n substConstants(coll11.slice(placeholder[Int](127), coll11.size), Coll[Int](placeholder[Int](128)), coll8)\n ), placeholder[Int](129)\n ) == coll12, coll2(placeholder[Int](130)).get.patch(\n placeholder[Int](131), blake2b256(\n substConstants(coll13.slice(placeholder[Int](132), coll13.size), Coll[Int](placeholder[Int](133), placeholder[Int](134)), coll16)\n ), placeholder[Int](135)\n ) == coll17, coll2(placeholder[Int](136)).get.patch(\n placeholder[Int](137), blake2b256(\n substConstants(coll18.slice(placeholder[Int](138), coll18.size), Coll[Int](placeholder[Int](139), placeholder[Int](140)), coll16)\n ), placeholder[Int](141)\n ) == coll19, coll2(placeholder[Int](142)).get.patch(\n placeholder[Int](143), blake2b256(\n substConstants(coll20.slice(placeholder[Int](144), coll20.size), Coll[Int](placeholder[Int](145)), coll21)\n ), placeholder[Int](146)\n ) == coll22, coll2(placeholder[Int](147)).get.patch(\n placeholder[Int](148), blake2b256(\n substConstants(coll23.slice(placeholder[Int](149), coll23.size), Coll[Int](placeholder[Int](150)), coll21)\n ), placeholder[Int](151)\n ) == coll24, coll2(placeholder[Int](152)).get.patch(\n placeholder[Int](153), blake2b256(\n substConstants(coll25.slice(placeholder[Int](154), coll25.size), Coll[Int](placeholder[Int](155)), coll21)\n ), placeholder[Int](156)\n ) == coll26, coll2(placeholder[Int](157)).get.patch(\n placeholder[Int](158), blake2b256(\n substConstants(coll27.slice(placeholder[Int](159), coll27.size), Coll[Int](placeholder[Int](160)), coll21)\n ), placeholder[Int](161)\n ) == coll28, coll2(placeholder[Int](162)).get.patch(\n placeholder[Int](163), blake2b256(\n substConstants(coll29.slice(placeholder[Int](164), coll29.size), Coll[Int](placeholder[Int](165)), coll21)\n ), placeholder[Int](166)\n ) == coll30, coll2(placeholder[Int](167)).get.patch(\n placeholder[Int](168), blake2b256(\n substConstants(coll31.slice(placeholder[Int](169), coll31.size), Coll[Int](placeholder[Int](170)), coll21)\n ), placeholder[Int](171)\n ) == coll32, coll2(placeholder[Int](172)).get.patch(\n placeholder[Int](173), blake2b256(\n substConstants(coll33.slice(placeholder[Int](174), coll33.size), Coll[Int](placeholder[Int](175)), coll21)\n ), placeholder[Int](176)\n ) == coll34, coll2(placeholder[Int](177)).get.patch(\n placeholder[Int](178), blake2b256(\n substConstants(coll35.slice(placeholder[Int](179), coll35.size), Coll[Int](placeholder[Int](180)), coll21)\n ), placeholder[Int](181)\n ) == coll36, coll2(placeholder[Int](182)).get.patch(\n placeholder[Int](183), blake2b256(\n substConstants(coll37.slice(placeholder[Int](184), coll37.size), Coll[Int](placeholder[Int](185)), coll21)\n ), placeholder[Int](186)\n ) == coll38\n )\n ), allOf(\n Coll[Boolean](\n blake2b256(box39.propositionBytes) == coll2(placeholder[Int](187)).get.slice(\n placeholder[Int](188), placeholder[Int](189)\n ), box39.value >= placeholder[Long](190), coll40(placeholder[Int](191)) == coll41(\n placeholder[Int](192)\n ), tuple42._1 == coll15, tuple42._2 == placeholder[Long](193), tuple43._1 == coll7, tuple43._2 == placeholder[Long](\n 194\n ), coll40.size == placeholder[Int](195), box39.R4[Coll[Byte]].get == coll14\n )\n ), allOf(\n Coll[Boolean](\n blake2b256(box44.propositionBytes) == coll10.slice(placeholder[Int](196), placeholder[Int](197)), box44.value >= placeholder[Long](\n 198\n ), tuple46._1 == coll14, tuple46._2 == placeholder[Long](199), coll45.size == placeholder[Int](200), box44.R4[\n AvlTree\n ].get.digest == avlTree5.insert(\n Coll[(Coll[Byte], Coll[Byte])](\n (placeholder[Coll[Byte]](201), coll12), (placeholder[Coll[Byte]](202), coll10), (\n blake2b256(coll47.append(coll48(placeholder[Int](203)))), coll17\n ), (blake2b256(coll47.append(coll48(placeholder[Int](204)))), coll19), (\n blake2b256(placeholder[Coll[Byte]](205).append(coll48(placeholder[Int](206)))), coll22\n ), (placeholder[Coll[Byte]](207), coll24), (placeholder[Coll[Byte]](208), coll32), (placeholder[Coll[Byte]](209), coll26), (\n placeholder[Coll[Byte]](210), coll28\n ), (placeholder[Coll[Byte]](211), coll30), (placeholder[Coll[Byte]](212), coll34), (placeholder[Coll[Byte]](213), coll38), (\n placeholder[Coll[Byte]](214), coll36\n )\n ), getVar[Coll[Byte]](2.toByte).get\n ).get.digest\n )\n ), allOf(\n Coll[Boolean](\n blake2b256(box49.propositionBytes) == coll34.slice(placeholder[Int](215), placeholder[Int](216)), box49.value >= placeholder[Long](217), coll50(\n placeholder[Int](218)\n )._1 == coll6(placeholder[Int](219)).get.slice(placeholder[Int](220), placeholder[Int](221)), coll50(placeholder[Int](222)) == coll41(\n placeholder[Int](223)\n ), coll50.size == placeholder[Int](224), box49.R4[Coll[AvlTree]].get.forall(\n {(avlTree63: AvlTree) => avlTree63.digest == coll51 }\n ), l53 > l54, l53 < l54 + byteArrayToLong(coll6(placeholder[Int](225)).get.slice(placeholder[Int](226), placeholder[Int](227))), coll52.slice(\n placeholder[Int](228), coll52.size\n ).forall({(l63: Long) => l63 == placeholder[Long](229) }), box49.R6[Coll[Coll[Long]]].get.flatMap({(coll63: Coll[Long]) => coll63 }).forall(\n {(l63: Long) => l63 == placeholder[Long](230) }\n ), box49.R7[Coll[(AvlTree, AvlTree)]].get.forall(\n {(tuple63: (AvlTree, AvlTree)) => (tuple63._1.digest == coll51) && (tuple63._2.digest == coll51) }\n ), box49.R8[Coll[Long]].get.forall({(l63: Long) => l63 == placeholder[Long](231) })\n )\n ), allOf(\n Coll[Boolean](\n box55.value >= placeholder[Long](232), blake2b256(box55.propositionBytes) == coll24.slice(\n placeholder[Int](233), placeholder[Int](234)\n ), box56.value >= placeholder[Long](235), blake2b256(box56.propositionBytes) == coll32.slice(\n placeholder[Int](236), placeholder[Int](237)\n ), box57.value >= placeholder[Long](238), blake2b256(box57.propositionBytes) == coll36.slice(\n placeholder[Int](239), placeholder[Int](240)\n ), box58.value >= placeholder[Long](241), blake2b256(box58.propositionBytes) == coll26.slice(\n placeholder[Int](242), placeholder[Int](243)\n ), box59.value >= placeholder[Long](244), blake2b256(box59.propositionBytes) == coll30.slice(\n placeholder[Int](245), placeholder[Int](246)\n ), box60.value >= placeholder[Long](247), blake2b256(box60.propositionBytes) == coll38.slice(\n placeholder[Int](248), placeholder[Int](249)\n ), box61.value >= placeholder[Long](250), blake2b256(box61.propositionBytes) == coll28.slice(placeholder[Int](251), placeholder[Int](252))\n )\n ), allOf(Coll[Boolean](box62.value >= SELF.value, box62.propositionBytes == SELF.propositionBytes))\n )\n )\n )\n}",
"address": "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",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "3f83fe02023560e531dec404e610f53878f62355b66c7bb5b7c5dbb4447aadeb",
"mainChain": true
},
{
"boxId": "1aad24e821ed1d8b5dd204afaeaf0b426f46c23817e9b3a903e0f26d54778639",
"transactionId": "475de021fe1de359b5bdf135e0b9cebf8ad33bc159abd55fafcf37718b10d6a3",
"blockId": "30832398d1cf8fdc4e19f03416ac1d382d53d8d181a7822f237b880821bf34db",
"value": 1100000,
"index": 1,
"globalIndex": 35865214,
"creationHeight": 1172812,
"settlementHeight": 1172814,
"ergoTree": "1005040004000e36100204a00b08cd0279be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798ea02d192a39a8cc7a701730073011001020402d19683030193a38cc7b2a57300000193c2b2a57301007473027303830108cdeeac93b1a57304",
"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(16,2,4,-96,11,8,-51,2,121,-66,102,126,-7,-36,-69,-84,85,-96,98,-107,-50,-121,11,7,2,-101,-4,-37,45,-50,40,-39,89,-14,-127,91,22,-8,23,-104,-22,2,-47,-110,-93,-102,-116,-57,-89,1,115,0,115,1)\n3: Coll(1)\n4: 1",
"ergoTreeScript": "{sigmaProp(\n allOf(\n Coll[Boolean](\n HEIGHT == OUTPUTS(placeholder[Int](0)).creationInfo._1, OUTPUTS(placeholder[Int](1)).propositionBytes == substConstants(\n placeholder[Coll[Byte]](2), placeholder[Coll[Int]](3), Coll[SigmaProp](proveDlog(decodePoint(minerPubKey)))\n ), OUTPUTS.size == placeholder[Int](4)\n )\n )\n)}",
"address": "2iHkR7CWvD1R4j1yZg5bkeDRQavjAaVPeTDFGGLZduHyfWMuYpmhHocX8GJoaieTx78FntzJbCBVL6rf96ocJoZdmWBL2fci7NqWgAirppPQmZ7fN9V6z13Ay6brPriBKYqLp1bT2Fk4FkFLCfdPpe",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "d77f81b3cd3995231415a92852a977113a15e261c67a7b98e7ad88b93dce227c",
"mainChain": true
},
{
"boxId": "6fec5d470b1ea44e13109d7b62eb4b6cc082809f3c645264e5202d41829d5ae3",
"transactionId": "475de021fe1de359b5bdf135e0b9cebf8ad33bc159abd55fafcf37718b10d6a3",
"blockId": "30832398d1cf8fdc4e19f03416ac1d382d53d8d181a7822f237b880821bf34db",
"value": 5009183922,
"index": 2,
"globalIndex": 35865215,
"creationHeight": 1172812,
"settlementHeight": 1172814,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "c3596bc7136b6b3eab9c2f7302316211f02d8e97c294f6f134156ff72572b69e",
"index": 0,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "00de06c61282578684b377ffd6314c38ce596f2c054107fe2314810677859269",
"index": 1,
"amount": 99699999999997,
"name": "MyFirstDAOToken",
"decimals": 6,
"type": "EIP-004"
},
{
"tokenId": "0fdb7ff8b37479b6eb7aab38d45af2cfeefabbefdc7eebc0348d25dd65bc2c91",
"index": 2,
"amount": 69,
"name": "$Lambo",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "0040ae650c4ed77bcd20391493abe84c1a9bb58ee88e87f15670c801e2fc5983",
"index": 3,
"amount": 487801968998,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "09bc4bebe9943d397b99ec0adc7540242373e58b29d98ea8a23e09652a63641a",
"mainChain": true
}
],
"size": 4345,
"isUnconfirmed": false
}