Transaction
ID: 620fa8a79b...f1e7
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0010746 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.68050828 ERG
Tokens:
Loading assets...
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0010746 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.67940828 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 59,285
Total coins transferred: 0.68158288 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000214 ERG
Raw Transaction Data
{
"id": "620fa8a79be12ab89196f29841699b6e28f16382b1e7773b0c72986c3d8af1e7",
"blockId": "20ba47568f3b9e83af9a268fe925e47997678c3c928cd971ced135373be1dbe0",
"inclusionHeight": 1711212,
"timestamp": 1769793811854,
"index": 2,
"globalIndex": 10226023,
"numConfirmations": 59285,
"inputs": [
{
"boxId": "7a4e9f2a74560f389e36192b26ef66465392bc4464d7162796659e14877afa7b",
"value": 1074600,
"index": 0,
"spendingProof": null,
"outputBlockId": "863926d2369412f888069a105d80decd2f86da1a20c91ee53fcbe0676ac1c659",
"outputTransactionId": "c259f84ae715c542829d02b59d5b97211f38fd42d22bf028f4c288fe657eb536",
"outputIndex": 0,
"outputGlobalIndex": 53283553,
"outputCreatedAt": 1711180,
"outputSettledAt": 1711184,
"ergoTree": "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",
"ergoTreeConstants": "0: 2\n1: Coll(-89,-81,-66,-72,-29,-33,-41,49,121,2,2,-101,4,-84,-100,16,70,102,-46,-50,71,-50,-127,-116,-70,-70,91,42,9,107,65,66)\n2: 0\n3: 1\n4: 4\n5: 0\n6: 5\n7: 0\n8: 3\n9: 0\n10: 1\n11: 0\n12: true\n13: Coll(-109,-14,117,-63,82,-13,-93,50,4,-81,-25,-120,-56,69,30,85,71,-64,61,96,-113,-54,93,-118,-54,92,-15,-117,1,59,-66,-92)\n14: 1\n15: 0\n16: false\n17: true\n18: Coll(122,-94,-75,-37,-122,16,-18,-68,-23,35,110,21,-55,109,-47,84,-91,113,115,65,96,4,98,-91,-79,-37,114,2,-73,30,-45,38)\n19: 0\n20: 0\n21: 1\n22: 10\n23: false\n24: Coll(58,-108,98,24,98,-10,116,84,-53,-35,97,62,-45,-90,68,-126,-71,-33,-29,9,60,97,-126,53,107,-92,-12,-95,68,17,-9,-107)\n25: 0\n26: Coll(-126,-103,-39,-114,21,-21,-18,127,-93,-102,-41,22,-34,124,-117,-79,-111,121,10,27,-12,-73,-61,-7,26,-13,90,14,54,24,119,6)\n27: 0\n28: 0\n29: 1\n30: 1\n31: 0\n32: 1\n33: 1\n34: 2\n35: 3\n36: 4\n37: 5\n38: 5\n39: 6\n40: 6\n41: 7\n42: 5\n43: 0\n44: 0\n45: 1\n46: 3\n47: 1\n48: false\n49: false\n50: false\n51: 2\n52: 0\n53: 5\n54: Coll(-101,3,101,37,50,25,-79,-73,50,0,107,-13,-81,-26,110,-50,-104,-49,-42,19,44,42,119,-41,74,81,-23,-88,8,-34,63,122)\n55: 1\n56: 1\n57: 2\n58: 30\n59: false\n60: 0\n61: 0\n62: 0\n63: false",
"ergoTreeScript": "{\n val i1 = SELF.R4[Int].get\n val l2 = HEIGHT.toLong\n val coll3 = SELF.R8[Coll[Long]].get\n val l4 = coll3(placeholder[Int](0))\n val coll5 = placeholder[Coll[Byte]](1)\n val coll6 = SELF.R6[Coll[Byte]].get\n val coll7 = SELF.tokens(placeholder[Int](2))._1\n val coll8 = CONTEXT.dataInputs\n val coll9 = SELF.R5[Coll[Byte]].get\n val coll10 = SELF.R9[Coll[Coll[Byte]]].get\n val coll11 = coll10(placeholder[Int](3))\n val l12 = coll3(placeholder[Int](4))\n val l13 = coll3(placeholder[Int](5))\n val l14 = l4 - placeholder[Long](6)\n val func15 = {(box15: Box) =>\n box15.tokens.filter({(tuple17: (Coll[Byte], Long)) => tuple17._1 == coll11 }).fold(\n placeholder[Long](7), {(tuple17: (Long, (Coll[Byte], Long))) => tuple17._1 + tuple17._2._2 }\n )\n }\n val coll16 = SELF.R7[Coll[Coll[Byte]]].get\n val l17 = coll3(placeholder[Int](8))\n sigmaProp((i1 == placeholder[Int](9)) && ((if (l2 >= l4) {(\n val coll18 = OUTPUTS.filter({(box18: Box) => blake2b256(box18.propositionBytes) == coll5 })\n if (coll18.size == placeholder[Int](10)) {(\n val box19 = coll18(placeholder[Int](11))\n val tuple20 = box19.R6[(Coll[Byte], Coll[Byte])].get\n val coll21 = tuple20._1\n val coll22 = tuple20._2\n val coll23 = Coll[Byte]()\n if (((blake2b256(coll21) == coll6) && (blake2b256(box19.propositionBytes) == coll5)) && if (coll22 == coll23) { placeholder[Boolean](12) } else {(\n val coll24 = coll8.filter({(box24: Box) => ((blake2b256(box24.propositionBytes) == placeholder[Coll[Byte]](13)) && (box24.R6[Coll[Byte]].get == coll7)) && (box24.R5[Coll[Byte]].get == coll22) })\n if (coll24.size == placeholder[Int](14)) {(\n val box25 = coll24(placeholder[Int](15))\n val coll26 = box25.R9[Coll[Long]].get\n (((coll26.fold(placeholder[Boolean](16), {(tuple27: (Boolean, Long)) =>\n val bool29 = tuple27._1\n if (bool29) { bool29 } else { if (blake2b256(box25.R7[Coll[Byte]].get.append(coll9).append(longToByteArray(tuple27._2)).append(box25.R8[Coll[Byte]].get).append(box25.R4[Coll[Byte]].get).append(coll21)) == box25.R5[Coll[Byte]].get) { placeholder[Boolean](17) } else { bool29 } }\n }) && if (coll11 == coll23) { box25.value >= l12 } else { box25.tokens.exists({(tuple27: (Coll[Byte], Long)) => (tuple27._1 == coll11) && (tuple27._2 >= l12) }) }) && ({(box27: Box) =>\n val coll29 = coll8.filter({(box29: Box) =>\n val opt31 = box29.R4[Coll[Byte]]\n (opt31.isDefined && (opt31.get == box27.R7[Coll[Byte]].get)) && (blake2b256(box29.propositionBytes) == placeholder[Coll[Byte]](18))\n })\n if (coll29.size > placeholder[Int](19)) {(\n val l30 = coll29.fold(coll29(placeholder[Int](20)), {(tuple30: (Box, Box)) =>\n val box32 = tuple30._2\n val box33 = tuple30._1\n if (box32.creationInfo._1 < box33.creationInfo._1) { box32 } else { box33 }\n }).creationInfo._1.toLong\n if (l30 < l13) { l13 } else { l30 }\n )} else { l4 }\n }(box25) < l14 - placeholder[Long](21))) && (box25.creationInfo._1.toLong < l4)) && (coll26.size.toLong <= placeholder[Long](22))\n )} else { placeholder[Boolean](23) }\n )}) {(\n val coll24 = coll8.filter({(box24: Box) => ((((((blake2b256(box24.propositionBytes) == placeholder[Coll[Byte]](24)) && (box24.tokens.size > placeholder[Int](25))) && (box24.R4[Coll[Byte]].get == placeholder[Coll[Byte]](26))) && (box24.R5[Coll[Byte]].get == coll7)) && box24.R6[Boolean].get) && box24.R8[Boolean].get) && coll16.exists({(coll26: Coll[Byte]) => coll26 == box24.tokens(placeholder[Int](27))._1 }) })\n val coll25 = coll24.map({(box25: Box) => box25.tokens(placeholder[Int](28))._1 })\n ((((((((((((((((func15(box19) >= func15(SELF)) && (box19.tokens.filter({(tuple26: (Coll[Byte], Long)) => tuple26._1 == coll7 }).size == placeholder[Int](29))) && (box19.R4[Int].get == placeholder[Int](30))) && (box19.R5[Coll[Byte]].get == coll9)) && (box19.R7[Coll[Coll[Byte]]].get == coll16)) && (box19.R8[Coll[Long]].get(placeholder[Int](31)) == l13)) && (box19.R8[Coll[Long]].get(placeholder[Int](32)) == coll3(placeholder[Int](33)))) && (box19.R8[Coll[Long]].get(placeholder[Int](34)) == l4)) && (box19.R8[Coll[Long]].get(placeholder[Int](35)) == l17)) && (box19.R8[Coll[Long]].get(placeholder[Int](36)) == l12)) && (box19.R8[Coll[Long]].get(placeholder[Int](37)) == coll3(placeholder[Int](38)))) && (box19.R8[Coll[Long]].get(placeholder[Int](39)) >= coll3(placeholder[Int](40)))) && (box19.R8[Coll[Long]].get(placeholder[Int](41)) >= l2 + placeholder[Long](42))) && (box19.R9[Coll[Coll[Byte]]].get(placeholder[Int](43)) == coll10(placeholder[Int](44)))) && (box19.R9[Coll[Coll[Byte]]].get(placeholder[Int](45)) == coll11)) && (box19.R9[Coll[Coll[Byte]]].get.size == placeholder[Int](46))) && coll25.indices.forall({(i26: Int) => !coll25.slice(i26 + placeholder[Int](47), coll24.size).exists({(coll28: Coll[Byte]) => coll28 == coll25(i26) }) })\n )} else { placeholder[Boolean](48) }\n )} else { placeholder[Boolean](49) }\n )} else { placeholder[Boolean](50) } || if ((l2 < l4) && (OUTPUTS.size >= placeholder[Int](51))) {(\n val box18 = OUTPUTS(placeholder[Int](52))\n val l19 = func15(SELF) - l17 / placeholder[Long](53)\n ((((((((blake2b256(box18.propositionBytes) == placeholder[Coll[Byte]](54)) && (func15(box18) >= l19)) && (box18.tokens.filter({(tuple20: (Coll[Byte], Long)) => (tuple20._1 == coll7) && (tuple20._2 == placeholder[Long](55)) }).size == placeholder[Int](56))) && (box18.R4[Int].get == placeholder[Int](57))) && (box18.R5[Long].get >= l2 + placeholder[Long](58))) && (blake2b256(box18.R6[Coll[Byte]].get) == coll6)) && (box18.R7[Long].get == l19)) && (box18.R8[Long].get == l4)) && (box18.R9[Coll[Coll[Byte]]].get == coll10)\n )} else { placeholder[Boolean](59) }) || if ((l2 < l14) && (OUTPUTS.size > placeholder[Int](60))) {(\n val box18 = OUTPUTS.filter({(box18: Box) => blake2b256(box18.propositionBytes) == blake2b256(SELF.propositionBytes) })(placeholder[Int](61))\n (((((((box18.tokens(placeholder[Int](62))._1 == coll7) && (func15(box18) >= func15(SELF))) && (box18.R5[Coll[Byte]].get == blake2b256(coll9.append(SELF.id)))) && (box18.R4[Int].get == i1)) && (box18.R6[Coll[Byte]].get == coll6)) && (box18.R7[Coll[Coll[Byte]]].get == coll16)) && (box18.R8[Coll[Long]].get == coll3)) && (box18.R9[Coll[Coll[Byte]]].get == coll10)\n )} else { placeholder[Boolean](63) }))\n}",
"address": "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",
"assets": [
{
"tokenId": "10804c3078d2198bbab3bee7c7cf7cec181c780d51d9eba99794948fc5d6dba2",
"index": 0,
"amount": 1,
"name": "Test V1",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "aa59253a0a9d75d658eec7aeda90f350675b2e1eccfeeed17e5380f619603c71",
"index": 1,
"amount": 2000,
"name": "CAT",
"decimals": 2,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e20424adf67f4146dc4a9f5704824c15ed40e50a49f7353c1811773c4ad9de07d6f",
"sigmaType": "Coll[SByte]",
"renderedValue": "424adf67f4146dc4a9f5704824c15ed40e50a49f7353c1811773c4ad9de07d6f"
},
"R6": {
"serializedValue": "0e20b4b3b5bb7d059e48b3acb460389c2ce69e90b426fe53d719da72873d846eaf2a",
"sigmaType": "Coll[SByte]",
"renderedValue": "b4b3b5bb7d059e48b3acb460389c2ce69e90b426fe53d719da72873d846eaf2a"
},
"R8": {
"serializedValue": "110788f1d00100bcf1d001d00fc801a09c0180b518",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1711172,0,1711198,1000,100,10000,200000]"
},
"R7": {
"serializedValue": "1a0120758eb79665224492c8fb849a08751d41cd5974a7af1099e5558f5424a3821fc9",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[758eb79665224492c8fb849a08751d41cd5974a7af1099e5558f5424a3821fc9]"
},
"R9": {
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},
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},
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"additionalRegisters": {
"R5": {
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},
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"sigmaType": "Coll[SLong]",
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},
"R4": {
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}
}
},
{
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"value": 1000000,
"index": 2,
"outputBlockId": "863926d2369412f888069a105d80decd2f86da1a20c91ee53fcbe0676ac1c659",
"outputTransactionId": "dca396ec31f556743140da487ae0094d5049ef7065f80a98537542a28a95f241",
"outputIndex": 0,
"ergoTree": "1906010100d17300",
"address": "FxyP1tYDuHYhZ28RZ",
"assets": [],
"additionalRegisters": {
"R4": {
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"renderedValue": "b160aea9543707bc6e3fb5a6299b7119e321fa09bf8a36fba0306e3cf6f55325"
}
}
}
],
"outputs": [
{
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"transactionId": "620fa8a79be12ab89196f29841699b6e28f16382b1e7773b0c72986c3d8af1e7",
"blockId": "20ba47568f3b9e83af9a268fe925e47997678c3c928cd971ced135373be1dbe0",
"value": 1074600,
"index": 0,
"globalIndex": 53284655,
"creationHeight": 1711211,
"settlementHeight": 1711212,
"ergoTree": "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",
"ergoTreeConstants": "0: 7\n1: Coll(-109,-14,117,-63,82,-13,-93,50,4,-81,-25,-120,-56,69,30,85,71,-64,61,96,-113,-54,93,-118,-54,92,-15,-117,1,59,-66,-92)\n2: 0\n3: 2\n4: 0\n5: Coll(122,-94,-75,-37,-122,16,-18,-68,-23,35,110,21,-55,109,-47,84,-91,113,115,65,96,4,98,-91,-79,-37,114,2,-73,30,-45,38)\n6: 0\n7: 0\n8: 1\n9: 1\n10: 0\n11: 4\n12: -1\n13: false\n14: true\n15: 1\n16: 3\n17: 5\n18: 6\n19: 0\n20: Coll(58,-108,98,24,98,-10,116,84,-53,-35,97,62,-45,-90,68,-126,-71,-33,-29,9,60,97,-126,53,107,-92,-12,-95,68,17,-9,-107)\n21: 0\n22: Coll(-10,-127,-98,11,124,-7,-100,-116,120,114,-74,47,73,-123,-72,-39,0,-58,21,9,37,-48,30,-78,121,120,117,23,-88,72,-74,-40)\n23: Coll(-90,-127,-98,11,124,-7,-100,-116,120,114,-74,47,73,-123,-72,-39,0,-58,21,9,37,-48,30,-78,121,120,117,23,-88,72,-74,-39)\n24: true\n25: 0\n26: 0\n27: 1\n28: 0\n29: 0\n30: 2\n31: 1\n32: 1\n33: 0\n34: 1\n35: 0\n36: 7\n37: 5\n38: 0\n39: 1\n40: 0\n41: 1\n42: 2\n43: 3\n44: 4\n45: 5\n46: 0\n47: 6\n48: 0\n49: false\n50: false\n51: false\n52: 1\n53: 1\n54: 1\n55: 1\n56: 0\n57: 5\n58: 1\n59: 10\n60: -1\n61: 1\n62: 0\n63: -1\n64: 1\n65: 1\n66: 0\n67: 0\n68: 1\n69: 0\n70: 1\n71: 2\n72: 3\n73: 4\n74: 5\n75: 6\n76: 7\n77: 0\n78: 0\n79: 1\n80: 2\n81: 2\n82: 5\n83: 5\n84: 3\n85: false\n86: false\n87: false\n88: false\n89: false\n90: true\n91: false\n92: 0\n93: Coll(93,65,122,-25,-52,15,-15,8,73,69,11,-16,33,-111,-26,-21,89,45,-106,-60,-123,-82,-124,71,-61,60,0,-11,-84,-108,-126,-98)\n94: 1\n95: 0\n96: 0\n97: 1\n98: 0\n99: 1\n100: 2\n101: 3\n102: 4\n103: 5\n104: 6\n105: 7\n106: false\n107: false",
"ergoTreeScript": "{\n val i1 = SELF.R4[Int].get\n val l2 = HEIGHT.toLong\n val coll3 = SELF.R8[Coll[Long]].get\n val l4 = coll3(placeholder[Int](0))\n val bool5 = l2 < l4\n val coll6 = placeholder[Coll[Byte]](1)\n val coll7 = SELF.tokens(placeholder[Int](2))._1\n val tuple8 = SELF.R6[(Coll[Byte], Coll[Byte])].get\n val coll9 = tuple8._2\n val coll10 = CONTEXT.dataInputs\n val l11 = coll3(placeholder[Int](3))\n val l12 = coll3(placeholder[Int](4))\n val func13 = {(box13: Box) =>\n val coll15 = coll10.filter({(box15: Box) =>\n val opt17 = box15.R4[Coll[Byte]]\n (opt17.isDefined && (opt17.get == box13.R7[Coll[Byte]].get)) && (blake2b256(box15.propositionBytes) == placeholder[Coll[Byte]](5))\n })\n if (coll15.size > placeholder[Int](6)) {(\n val l16 = coll15.fold(coll15(placeholder[Int](7)), {(tuple16: (Box, Box)) =>\n val box18 = tuple16._2\n val box19 = tuple16._1\n if (box18.creationInfo._1 < box19.creationInfo._1) { box18 } else { box19 }\n }).creationInfo._1.toLong\n val l17 = l12 + placeholder[Long](8)\n if (l16 < l17) { l17 } else { l16 }\n )} else { l11 }\n }\n val coll14 = SELF.R9[Coll[Coll[Byte]]].get\n val coll15 = coll14(placeholder[Int](9))\n val func16 = {(box16: Box) =>\n box16.tokens.filter({(tuple18: (Coll[Byte], Long)) => tuple18._1 == coll15 }).fold(\n placeholder[Long](10), {(tuple18: (Long, (Coll[Byte], Long))) => tuple18._1 + tuple18._2._2 }\n )\n }\n val l17 = coll3(placeholder[Int](11))\n val coll18 = SELF.R5[Coll[Byte]].get\n val coll19 = tuple8._1\n val func20 = {(box20: Box) => box20.R9[Coll[Long]].get.fold((placeholder[Long](12), placeholder[Boolean](13)), {(tuple22: ((Long, Boolean), Long)) =>\n val tuple24 = tuple22._1\n val l25 = tuple22._2\n if (tuple24._2) { tuple24 } else { if (blake2b256(box20.R7[Coll[Byte]].get.append(coll18).append(longToByteArray(l25)).append(box20.R8[Coll[Byte]].get).append(box20.R4[Coll[Byte]].get).append(coll19)) == box20.R5[Coll[Byte]].get) { (l25, placeholder[Boolean](14)) } else { tuple24 } }\n }) }\n val l21 = coll3(placeholder[Int](15))\n val coll22 = SELF.R7[Coll[Coll[Byte]]].get\n val l23 = coll3(placeholder[Int](16))\n val l24 = coll3(placeholder[Int](17))\n val l25 = coll3(placeholder[Int](18))\n val func26 = {(bool26: Boolean) => if (bool5 && (coll10.size > placeholder[Int](19))) {(\n val coll28 = coll10.filter({(box28: Box) => (((((blake2b256(box28.propositionBytes) == placeholder[Coll[Byte]](20)) && (box28.tokens.size > placeholder[Int](21))) && (box28.R4[Coll[Byte]].get == if (bool26) { placeholder[Coll[Byte]](22) } else { placeholder[Coll[Byte]](23) })) && (box28.R5[Coll[Byte]].get == coll9)) && if (bool26) { box28.R6[Boolean].get && (!box28.R8[Boolean].get) } else { placeholder[Boolean](24) }) && coll22.exists({(coll30: Coll[Byte]) => coll30 == box28.tokens(placeholder[Int](25))._1 }) })\n val coll29 = coll28.map({(box29: Box) => box29.tokens(placeholder[Int](26))._1 })\n val i30 = coll28.size\n val i31 = coll22.size\n val coll32 = OUTPUTS.filter({(box32: Box) => box32.propositionBytes == SELF.propositionBytes })\n if ((coll29.indices.forall({(i33: Int) => !coll29.slice(i33 + placeholder[Int](27), i30).exists({(coll35: Coll[Byte]) => coll35 == coll29(i33) }) }) && (i30 >= if (i31 == placeholder[Int](28)) { placeholder[Int](29) } else { i31 / placeholder[Int](30) + placeholder[Int](31) })) && (coll32.size == placeholder[Int](32))) {(\n val box33 = coll32(placeholder[Int](33))\n val coll34 = INPUTS.filter({(box34: Box) => (blake2b256(box34.propositionBytes) == coll6) && (box34.R5[Coll[Byte]].get == coll9) })\n if ((box33.R6[(Coll[Byte], Coll[Byte])].get._2 == Coll[Byte]()) && (coll34.size == placeholder[Int](34))) { ((func16(box33) >= func16(SELF) + func16(coll34(placeholder[Int](35)))) && (box33.R8[Coll[Long]].get(placeholder[Int](36)) >= l2 + placeholder[Long](37))) && (((((((((((((box33.tokens(placeholder[Int](38))._1 == coll7) && (box33.R4[Int].get == i1)) && (i1 == placeholder[Int](39))) && (box33.R5[Coll[Byte]].get == coll18)) && (box33.R7[Coll[Coll[Byte]]].get == coll22)) && (box33.R8[Coll[Long]].get(placeholder[Int](40)) == l12)) && (box33.R8[Coll[Long]].get(placeholder[Int](41)) == l21)) && (box33.R8[Coll[Long]].get(placeholder[Int](42)) == l11)) && (box33.R8[Coll[Long]].get(placeholder[Int](43)) == l23)) && (box33.R8[Coll[Long]].get(placeholder[Int](44)) == l17)) && (box33.R8[Coll[Long]].get(placeholder[Int](45)) == l24 + if (bool26) { l25 } else { placeholder[Long](46) })) && (box33.R8[Coll[Long]].get(placeholder[Int](47)) == if (bool26) { placeholder[Long](48) } else { l25 })) && (box33.R9[Coll[Coll[Byte]]].get == coll14)) } else { placeholder[Boolean](49) }\n )} else { placeholder[Boolean](50) }\n )} else { placeholder[Boolean](51) } }\n sigmaProp(i1 == placeholder[Int](52)) && sigmaProp(if ((bool5 && (INPUTS.size > placeholder[Int](53))) && (OUTPUTS.size > placeholder[Int](54))) {(\n val coll27 = coll10.filter({(box27: Box) => ((blake2b256(box27.propositionBytes) == coll6) && (box27.R6[Coll[Byte]].get == coll7)) && (box27.R5[Coll[Byte]].get != coll9) })\n if (coll27.size == placeholder[Int](55)) {(\n val box28 = coll27(placeholder[Int](56))\n val l29 = func13(box28)\n if ((((((blake2b256(box28.propositionBytes) == coll6) && (box28.R6[Coll[Byte]].get == coll7)) && (box28.creationInfo._1.toLong < l11)) && (l29 < l11 - placeholder[Long](57) - placeholder[Long](58))) && (func16(box28) >= l17)) && (box28.R9[Coll[Long]].get.size.toLong <= placeholder[Long](59))) {(\n val l30 = func20(box28)._1\n val bool31 = l30 != placeholder[Long](60)\n if (bool31) {(\n val coll32 = Coll[Byte]()\n val coll33 = if (coll9 == coll32) { box28.R5[Coll[Byte]].get } else {(\n val coll33 = coll10.filter({(box33: Box) => ((blake2b256(box33.propositionBytes) == coll6) && (box33.R5[Coll[Byte]].get == coll9)) && (box33.R6[Coll[Byte]].get == coll7) })\n if (coll33.size == placeholder[Int](61)) {(\n val box34 = coll33(placeholder[Int](62))\n val tuple35 = func20(box34)\n val l36 = tuple35._1\n if (tuple35._2 && (l36 != placeholder[Long](63))) {(\n val l37 = l30 * placeholder[Long](64) + l21 * l11 - l29\n val l38 = func13(box34)\n val l39 = l36 * placeholder[Long](65) + l21 * l11 - l38\n if ((l37 > l39) || ((l37 == l39) && (l29 < l38))) { box28.R5[Coll[Byte]].get } else { coll32 }\n )} else { coll32 }\n )} else { coll32 }\n )}\n if ((coll33 != coll32) && (coll33 != coll9)) {(\n val box34 = OUTPUTS(placeholder[Int](66))\n ((((((((((((((((((((func16(box34) >= func16(SELF)) && (box34.tokens(placeholder[Int](67))._1 == coll7)) && (box34.R4[Int].get == i1)) && (i1 == placeholder[Int](68))) && (box34.R5[Coll[Byte]].get == coll18)) && (box34.R6[(Coll[Byte], Coll[Byte])].get._1 == coll19)) && (box34.R6[(Coll[Byte], Coll[Byte])].get._2 == coll33)) && (box34.R7[Coll[Coll[Byte]]].get == coll22)) && (box34.R8[Coll[Long]].get(placeholder[Int](69)) == l12)) && (box34.R8[Coll[Long]].get(placeholder[Int](70)) == l21)) && (box34.R8[Coll[Long]].get(placeholder[Int](71)) == l11)) && (box34.R8[Coll[Long]].get(placeholder[Int](72)) == l23)) && (box34.R8[Coll[Long]].get(placeholder[Int](73)) == l17)) && (box34.R8[Coll[Long]].get(placeholder[Int](74)) == l24)) && (box34.R8[Coll[Long]].get(placeholder[Int](75)) == l25)) && (box34.R8[Coll[Long]].get(placeholder[Int](76)) == l4)) && (box34.R9[Coll[Coll[Byte]]].get(placeholder[Int](77)) == coll14(placeholder[Int](78)))) && (box34.R9[Coll[Coll[Byte]]].get(placeholder[Int](79)) == coll15)) && ((box34.R9[Coll[Coll[Byte]]].get(placeholder[Int](80)) == coll14(placeholder[Int](81))) || (l4 - placeholder[Long](82) + placeholder[Long](83) < l2))) && (box34.R9[Coll[Coll[Byte]]].get.size == placeholder[Int](84))) && bool31\n )} else { placeholder[Boolean](85) }\n )} else { placeholder[Boolean](86) }\n )} else { placeholder[Boolean](87) }\n )} else { placeholder[Boolean](88) }\n )} else { placeholder[Boolean](89) }) || sigmaProp(func26(placeholder[Boolean](90))) || sigmaProp(func26(placeholder[Boolean](91))) || if ((l2 >= l4) && (\n OUTPUTS.size > placeholder[Int](92)\n )) {(\n val coll27 = OUTPUTS.filter({(box27: Box) => blake2b256(box27.propositionBytes) == placeholder[Coll[Byte]](93) })\n if (coll27.size == placeholder[Int](94)) {(\n val box28 = coll27(placeholder[Int](95))\n sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (((box28.tokens(placeholder[Int](96))._1 == coll7) && (box28.R4[Int].get == i1)) && (i1 == placeholder[Int](97))) && (\n box28.R5[Coll[Byte]].get == coll18\n )\n ) && (box28.R6[(Coll[Byte], Coll[Byte])].get._1 == coll19)\n ) && (box28.R6[(Coll[Byte], Coll[Byte])].get._2 == coll9)\n ) && (box28.R7[Coll[Coll[Byte]]].get == coll22)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](98)) == l12)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](99)) == l21)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](100)) == l11)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](101)) == l23)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](102)) == l17)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](103)) == l24)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](104)) == l25)\n ) && (box28.R8[Coll[Long]].get(placeholder[Int](105)) == l4)\n ) && (box28.R9[Coll[Coll[Byte]]].get == coll14)\n ) && (func16(box28) >= func16(SELF))\n )\n )} else { sigmaProp(placeholder[Boolean](106)) }\n )} else { sigmaProp(placeholder[Boolean](107)) }\n}",
"address": "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",
"assets": [
{
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{
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}
],
"additionalRegisters": {
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"sigmaType": "Coll[SByte]",
"renderedValue": "424adf67f4146dc4a9f5704824c15ed40e50a49f7353c1811773c4ad9de07d6f"
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"renderedValue": "[35aa11186c18d3e04f81656248213a1a3c43e89a67045763287e644db60c3f21,42a14b79ce52d511d447cb7f12d574deb099136a6099ae10ceda1297445ad1cf]"
},
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"sigmaType": "Coll[SLong]",
"renderedValue": "[1711172,0,1711198,1000,100,10000,200000,1711226]"
},
"R7": {
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"sigmaType": "Coll[Coll[SByte]]",
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},
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},
"R4": {
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"renderedValue": "1"
}
},
"spentTransactionId": "982f01159d53dacb5a7a9693d77f8a9c3d9212bb7b5a40047c4781d36f17c502",
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},
{
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"ergoTree": "1005040004000e36100204a00b08cd0279be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798ea02d192a39a8cc7a701730073011001020402d19683030193a38cc7b2a57300000193c2b2a57301007473027303830108cdeeac93b1a57304",
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"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",
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{
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"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(910cc5,459442,...)))}",
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{
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