Transaction
ID: 5943420e33...aad0
Inputs (12)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.011 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Tokens:
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Transaction Details
Confirmations: 701,983
Total coins transferred: 0.0132 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000588 ERG
Raw Transaction Data
{
"id": "5943420e3375e620ea84f6ad1e41f770f73aaf004c2a55facffff0e6b8e5aad0",
"blockId": "af1b9c1021948e4cba09d2539be99a68014471cf83badd0f2a52cc43f398ef4a",
"inclusionHeight": 1063849,
"timestamp": 1691405638226,
"index": 6,
"globalIndex": 5631493,
"numConfirmations": 701983,
"inputs": [
{
"boxId": "e5a0177bb294281446c328954b7f79fb505c9b173b42a28d4de4feaf8885d584",
"value": 1100000,
"index": 0,
"spendingProof": null,
"outputBlockId": "12f371477fd1ff04ab252330f411785758399eacc2c7c929cac88f3e56ee6390",
"outputTransactionId": "0888329b94a6fda701bac36259ff6c69dcb556328afb39ad2b70944e892b9ffe",
"outputIndex": 1,
"outputGlobalIndex": 31586474,
"outputCreatedAt": 1063836,
"outputSettledAt": 1063838,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: Coll(66,11,46,-97,-78,-68,-35,-19,-1,101,11,-26,59,26,-53,-62,107,-64,-67,-35,37,91,0,50,80,-121,-86,85,-107,-88,-102,58)\n2: 0\n3: 0\n4: 1\n5: 0\n6: 0\n7: 0\n8: 0\n9: 0\n10: 0\n11: 3\n12: 2\n13: 1\n14: 1\n15: 100\n16: 0\n17: 0\n18: 0\n19: 0\n20: 1\n21: 0\n22: 0\n23: 0\n24: 0\n25: 1\n26: 0\n27: 0",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
"address": "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",
"assets": [
{
"tokenId": "3825b2b4acaaaba626440113153246c65ddb2e9df406c4a56418b5842c9f839a",
"index": 0,
"amount": 10000,
"name": "rptconfErgoRWTV1",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "1a01202228e54299db6c13479c6ced3705df36f2ed0e4e815ed7536ba99be14a13179d",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[2228e54299db6c13479c6ced3705df36f2ed0e4e815ed7536ba99be14a13179d]"
},
"R5": {
"serializedValue": "1a0120b9f6fb8aaa0e8cc459d822f17db2fbeaa4e22ae4de4f79bfbd106badd36bf078",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[b9f6fb8aaa0e8cc459d822f17db2fbeaa4e22ae4de4f79bfbd106badd36bf078]"
},
"R6": {
"serializedValue": "0e2008b8ff77688d59bd95c5ed3668092357a66f301ade4575c8ddc3603712f1092f",
"sigmaType": "Coll[SByte]",
"renderedValue": "08b8ff77688d59bd95c5ed3668092357a66f301ade4575c8ddc3603712f1092f"
},
"R7": {
"serializedValue": "0e20aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07",
"sigmaType": "Coll[SByte]",
"renderedValue": "aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07"
}
}
},
{
"boxId": "e69bb7920532c9f72dd0ed5d19b0c482405d0f348a175dc24bde7bc541403c84",
"value": 1100000,
"index": 1,
"spendingProof": null,
"outputBlockId": "12f371477fd1ff04ab252330f411785758399eacc2c7c929cac88f3e56ee6390",
"outputTransactionId": "5cd94a3a169029ace1a3de3d223bb557f7934c9cfaef76d017c9645c37a93b86",
"outputIndex": 1,
"outputGlobalIndex": 31586478,
"outputCreatedAt": 1063836,
"outputSettledAt": 1063838,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: Coll(66,11,46,-97,-78,-68,-35,-19,-1,101,11,-26,59,26,-53,-62,107,-64,-67,-35,37,91,0,50,80,-121,-86,85,-107,-88,-102,58)\n2: 0\n3: 0\n4: 1\n5: 0\n6: 0\n7: 0\n8: 0\n9: 0\n10: 0\n11: 3\n12: 2\n13: 1\n14: 1\n15: 100\n16: 0\n17: 0\n18: 0\n19: 0\n20: 1\n21: 0\n22: 0\n23: 0\n24: 0\n25: 1\n26: 0\n27: 0",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
"address": "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",
"assets": [
{
"tokenId": "3825b2b4acaaaba626440113153246c65ddb2e9df406c4a56418b5842c9f839a",
"index": 0,
"amount": 10000,
"name": "rptconfErgoRWTV1",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "1a01205589db926e6760d840c919948169a559abcab58ef0d2100126e48f9d2ef12320",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[5589db926e6760d840c919948169a559abcab58ef0d2100126e48f9d2ef12320]"
},
"R5": {
"serializedValue": "1a0120b9f6fb8aaa0e8cc459d822f17db2fbeaa4e22ae4de4f79bfbd106badd36bf078",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[b9f6fb8aaa0e8cc459d822f17db2fbeaa4e22ae4de4f79bfbd106badd36bf078]"
},
"R6": {
"serializedValue": "0e2022f003e21c3640838871db985ff4edfcddc10d4946af6e0c3bb0acca0be3b588",
"sigmaType": "Coll[SByte]",
"renderedValue": "22f003e21c3640838871db985ff4edfcddc10d4946af6e0c3bb0acca0be3b588"
},
"R7": {
"serializedValue": "0e20aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07",
"sigmaType": "Coll[SByte]",
"renderedValue": "aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07"
}
}
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{
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"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
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"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
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"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
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"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val coll2 = placeholder[Coll[Byte]](1)\n val bool3 = blake2b256(box1.propositionBytes) == coll2\n val coll4 = SELF.R4[Coll[Coll[Byte]]].get\n val box5 = if (bool3) { box1 } else { OUTPUTS(placeholder[Int](2)) }\n val coll6 = box5.R4[Coll[Coll[Byte]]].get\n val coll7 = box5.R5[Coll[Coll[Byte]]].get\n val coll8 = coll7.fold(Coll[Byte](placeholder[Byte](3)), {(tuple8: (Coll[Byte], Coll[Byte])) => tuple8._1.append(tuple8._2) })\n val coll9 = coll8.slice(placeholder[Int](4), coll8.size)\n val box10 = OUTPUTS(placeholder[Int](5))\n if (bool3) {(\n val box11 = OUTPUTS.filter({(box11: Box) =>\n val opt13 = box11.R4[Coll[Coll[Byte]]]\n opt13.isDefined && (opt13.get == coll4)\n })(placeholder[Int](6))\n val tuple12 = box11.tokens(placeholder[Int](7))\n val tuple13 = SELF.tokens(placeholder[Int](8))\n sigmaProp(\n allOf(\n Coll[Boolean](\n blake2b256(box11.propositionBytes) == SELF.R7[Coll[Byte]].get, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, !coll6.exists(\n {(coll14: Coll[Byte]) => coll4 == Coll[Coll[Byte]](coll14) }\n ), blake2b256(coll9.append(coll4(placeholder[Int](9)))) == SELF.R6[Coll[Byte]].get\n )\n )\n )\n )} else {(\n val coll11 = blake2b256(box10.propositionBytes)\n if (coll11 == coll2) {(\n val coll12 = INPUTS.filter({(box12: Box) => SELF.propositionBytes == box12.propositionBytes })\n val i13 = coll12.size\n val l14 = i13.toLong\n val box15 = CONTEXT.dataInputs(placeholder[Int](10))\n val coll16 = box15.R6[Coll[Long]].get\n val l17 = coll16(placeholder[Int](11))\n val l18 = coll16(placeholder[Int](12)) + coll16(placeholder[Int](13)) * box15.R4[Coll[Coll[Byte]]].get.size.toLong - placeholder[Long](14) / placeholder[\n Long\n ](15)\n val tuple19 = SELF.tokens(placeholder[Int](16))\n val l20 = coll16(placeholder[Int](17))\n val tuple21 = box5.tokens(placeholder[Int](18))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box10.value >= coll12.map({(box22: Box) => box22.value }).fold(\n placeholder[Long](19), {(tuple22: (Long, Long)) => tuple22._1 + tuple22._2 }\n ), coll12.filter({(box22: Box) => box22.R4[Coll[Coll[Byte]]].get == coll4 }).size == placeholder[Int](20), coll6.exists(\n {(coll22: Coll[Byte]) => Coll[Coll[Byte]](coll22) == coll4 }\n ), box5.R6[Coll[Byte]].get == SELF.R7[Coll[Byte]].get, coll6.size == i13, blake2b256(coll9.append(coll4(placeholder[Int](21)))) == SELF.R6[\n Coll[Byte]\n ].get, SELF.R5[Coll[Coll[Byte]]].get == Coll[Coll[Byte]](blake2b256(coll7(placeholder[Int](22)))), l14 > if (l17 < l18) { l17 } else {\n l18\n }, tuple19._2 == l20, tuple21._2 == l20 * l14, tuple21._1 == tuple19._1\n )\n )\n )\n )} else {(\n val tuple12 = box10.tokens(placeholder[Int](23))\n val tuple13 = SELF.tokens(placeholder[Int](24))\n sigmaProp(\n allOf(\n Coll[Boolean](\n SELF.id == box1.id, tuple12._1 == tuple13._1, tuple12._2 == tuple13._2, box10.R4[Coll[Coll[Byte]]].get == coll4, INPUTS(\n placeholder[Int](25)\n ).tokens(placeholder[Int](26))._1 == coll4(placeholder[Int](27)), coll11 == SELF.R7[Coll[Byte]].get\n )\n )\n )\n )}\n )}\n}",
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"name": "rptconfRSNV1",
"decimals": 3,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [
{
"boxId": "8531bdff9172c4897a9806b82186b7785130f666528f6da4e3f72858ee22081f",
"value": 1100000,
"index": 0,
"outputBlockId": "e29988c2e0cd821fc0c44cc2e501372bc36576836c8c5979e0d05b2e7418d83d",
"outputTransactionId": "94a4553c69c53af44b5a1d87050b37326b5bde6420a45f01cda3955779ab2d36",
"outputIndex": 0,
"ergoTree": "102c040004000e20c691b75b137752e0fcbd590b9942ca2455f528db8e553b8632988c555eef2bc40e20793d6c566f238cd234176f7007b1e50c2e5ae4367b668351451ec6c9e9e97ca901010402040204040404040004010402040404000e20aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f070401040204020406040a040004000400040805000400010104020402040004000400040004020402040004000402040204000400040004000406d805d601b2a5730000d602db63087201d603b27202730100d6048c720301d6057302959372047303d17304d811d606db6308a7d607b27206730500d6088c720702d609b27202730600d60a8c720902d60bb27202730700d60c8c720b02d60db27206730800d60e8c720d02d60fe4c6a70611d610b27206730900d6118c720d01d6129683060193c27201c2a793e4c672010611720f9372048c721001938c7203028c721002938c7209018c720701938c720b017211d613e4c6a70511d614b17213d615e4c672010511d616b1721595917208720ad805d617e5c672010704730ad618997208720ad619b2a5730b00d61ab2a5730c00d61b96830401721293721899720c720e938cb2db63087219730d0002721893cbc27219730e95937217730fd806d61ce4c67201041ad61d9a72147310d61e9972167311d61fc5a7d620b2a5731200d621b2720f731300d196830d01721b93b1721c721d93b4721c7314721ee4c6a7041a93b2721c721e00721f937216721d93b472157315721e721393b27215721e00721893e4c67219041a83010e721f938cb2db6308721a73160001721f93cbc27220720593e4c67220040e721f92c17220b2720f731700959172217318d801d622b2db6308722073190096830201938c7222017211928c7222027221731ad803d61ce4c6a7041ad61db2721c721700d61e9a7217731bd196830801721b93721d8cb2db6308b2a4731c00731d000193e4c67201041a721c93b272157217009ab27213721700721893b47215731e7217b47213731f721793b47215721e7216b47213721e721693e4c67219041a83010e721d938cb2db6308721a73200001721dd803d617e4c672010704d61899720a7208d619b27213721700d19683040172129383010eb2e4c6a7041a721700e4c6b2a4732100041a93721899720e720c959172197218d801d61a9a72177322968304019372199ab27215721700721893e4c6a7041ae4c67201041a93b4721373237217b472157324721793b47213721a7214b47215721a7214d804d61ae4c6a7041ad61be4c67201041ad61c9a72177325d61d997214732696830601937219721893b4721a73277217b4721b7328721793b4721a721c7214b4721b7217721d93b4721373297217b47215732a721793b47213721c7214b472157217721d93cbc2b2a4732b007205",
"address": "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",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "1a0b046572676f20e61a83c5bca59e98c21d925faff5dfc079aa438f21df08702e4c4bf2bada02dc20d050d5e741b9e18bedb81be435f893c8c6ae32ecbbf88554480df8cb9cc0d58920995ca29a94c2290c7648b56e4a84dafda75765c6e793c5cdcd4fba55ff261e9f2090829fb915dba5c834d14b0e1e1387f6c2e4760965b8cd338892e4ad13baab3d20fe83cb8a2843fa6f5751d65e4f957fc622c67ab9211f30b44991a1fb03145c01208abd3c4a0e14dd3632919a758ccfee875cfcf8ea7e0ec85844d4188834fd602d203d36709eb83dc46cc60298ab183a1e4ab1d85363337b951b9494dc1ab1418eb1205589db926e6760d840c919948169a559abcab58ef0d2100126e48f9d2ef1232020aba9cf9df2a03cc07c1adc7515d4d70346c93bd7a250525536a90008f73d289d202228e54299db6c13479c6ced3705df36f2ed0e4e815ed7536ba99be14a13179d",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[6572676f,e61a83c5bca59e98c21d925faff5dfc079aa438f21df08702e4c4bf2bada02dc,d050d5e741b9e18bedb81be435f893c8c6ae32ecbbf88554480df8cb9cc0d589,995ca29a94c2290c7648b56e4a84dafda75765c6e793c5cdcd4fba55ff261e9f,90829fb915dba5c834d14b0e1e1387f6c2e4760965b8cd338892e4ad13baab3d,fe83cb8a2843fa6f5751d65e4f957fc622c67ab9211f30b44991a1fb03145c01,8abd3c4a0e14dd3632919a758ccfee875cfcf8ea7e0ec85844d4188834fd602d,3d36709eb83dc46cc60298ab183a1e4ab1d85363337b951b9494dc1ab1418eb1,5589db926e6760d840c919948169a559abcab58ef0d2100126e48f9d2ef12320,aba9cf9df2a03cc07c1adc7515d4d70346c93bd7a250525536a90008f73d289d,2228e54299db6c13479c6ced3705df36f2ed0e4e815ed7536ba99be14a13179d]"
},
"R5": {
"serializedValue": "110b0080dac40980dac40980dac40980dac40980dac40980dac40980dac40980dac40980dac40980dac409",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,10000000,10000000,10000000,10000000,10000000,10000000,10000000,10000000,10000000,10000000]"
},
"R6": {
"serializedValue": "1106a09c018c01022880a8d6b90700",
"sigmaType": "Coll[SLong]",
"renderedValue": "[10000,70,1,20,1000000000,0]"
}
}
}
],
"outputs": [
{
"boxId": "28142567fb1bdc1d7880a72dc47955d4c0564620ea23e04e7582dd8f90e731d7",
"transactionId": "5943420e3375e620ea84f6ad1e41f770f73aaf004c2a55facffff0e6b8e5aad0",
"blockId": "af1b9c1021948e4cba09d2539be99a68014471cf83badd0f2a52cc43f398ef4a",
"value": 11000000,
"index": 0,
"globalIndex": 31586717,
"creationHeight": 1063837,
"settlementHeight": 1063849,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 0\n4: 0\n5: 0\n6: Coll(-114,30,-46,70,96,13,44,-117,-112,95,56,-10,96,-39,72,0,108,-53,-91,-43,22,45,-64,37,-98,-83,-5,120,68,69,-3,-55)\n7: 1\n8: 0\n9: Coll(-28,-36,-91,-57,-77,94,-83,20,-26,86,-103,80,91,-35,101,-81,92,0,-78,36,-109,39,-32,-19,-101,-96,-30,-75,9,16,26,-126)\n10: 7200\n11: Coll(70,36,70,31,-62,-119,64,54,-23,-68,80,103,-65,-15,-28,103,-108,42,-46,-17,42,-11,33,-114,-92,83,108,-75,23,-64,14,43)",
"ergoTreeScript": "{\n val coll1 = SELF.R4[Coll[Coll[Byte]]].get\n val i2 = coll1.size\n val coll3 = OUTPUTS.slice(placeholder[Int](0), i2)\n val coll4 = OUTPUTS(placeholder[Int](1)).propositionBytes\n val coll5 = blake2b256(coll4)\n sigmaProp(allOf(Coll[Boolean](i2 == coll3.size, coll1.zip(coll3).forall({(tuple6: (Coll[Byte], Box)) =>\n val box8 = tuple6._2\n (((Coll[Coll[Byte]](tuple6._1) == box8.R4[Coll[Coll[Byte]]].get) && (box8.propositionBytes == coll4)) && (box8.tokens(placeholder[Int](2))._1 == SELF.tokens(placeholder[Int](3))._1)) && (box8.tokens(placeholder[Int](4))._2 == SELF.tokens(placeholder[Int](5))._2 / i2.toLong)\n }), if (coll5 == placeholder[Coll[Byte]](6)) { allOf(Coll[Boolean](INPUTS(placeholder[Int](7)).tokens(placeholder[Int](8))._1 == placeholder[Coll[Byte]](9), HEIGHT - placeholder[Int](10) >= SELF.creationInfo._1)) } else { allOf(Coll[Boolean](INPUTS.exists({(box6: Box) => blake2b256(box6.propositionBytes) == placeholder[Coll[Byte]](11) }), coll5 == SELF.R6[Coll[Byte]].get)) })))\n}",
"address": "BRNzuPCshtnBeoxCJ8iQiVbfd6yZNTf3KfxSfpsfDeC18pKRVoNNgDUo91X8TQRdLcnRwFHCU7doEUu4vciu5sfQbKyyd5FQD2x4X24eJFyR52vTtE4sVP46utpskdbwB9C3By3ueXBuDF6gmT6o83tN5b9wPjXtdpJowqgu2ZLDruYsqEdrPgGLsE1rfvBJM2NCYH9Gcp2UvJSGMiCu1p4XiLg1HpFhvcUz4wFwYFPBdUwoZVjERf6VVHv2hWzybdfxLBSEBPGys9xLPX4ck8Ms2LgBdX9GsAiXDnmrJ4A1NHqbJUZzUBfHEBUUUR94nJCoGfyAaFvDBafbouRuxe1MkArntvv6j6CQYe436e4hfZmrnC8aHGUaMvXQjtCwp4sF3yXe6c7Gf8GKw9LT1Tsk6AbMy4nP1X8jmaztJJhc8f7yopXSPEGM7JyNBNHyvp",
"assets": [
{
"tokenId": "3825b2b4acaaaba626440113153246c65ddb2e9df406c4a56418b5842c9f839a",
"index": 0,
"amount": 100000,
"name": "rptconfErgoRWTV1",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "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",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[2228e54299db6c13479c6ced3705df36f2ed0e4e815ed7536ba99be14a13179d,5589db926e6760d840c919948169a559abcab58ef0d2100126e48f9d2ef12320,e61a83c5bca59e98c21d925faff5dfc079aa438f21df08702e4c4bf2bada02dc,fe83cb8a2843fa6f5751d65e4f957fc622c67ab9211f30b44991a1fb03145c01,90829fb915dba5c834d14b0e1e1387f6c2e4760965b8cd338892e4ad13baab3d,995ca29a94c2290c7648b56e4a84dafda75765c6e793c5cdcd4fba55ff261e9f,3d36709eb83dc46cc60298ab183a1e4ab1d85363337b951b9494dc1ab1418eb1,d050d5e741b9e18bedb81be435f893c8c6ae32ecbbf88554480df8cb9cc0d589,8abd3c4a0e14dd3632919a758ccfee875cfcf8ea7e0ec85844d4188834fd602d,aba9cf9df2a03cc07c1adc7515d4d70346c93bd7a250525536a90008f73d289d]"
},
"R5": {
"serializedValue": "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",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[37323337663638376464353639613238646265663261326566653130633139613534306231386338346465373862656336653230623030646131623339366465,6572676f,63617264616e6f,396763724b6e374c354c6e337252736a466f53456b45736f76556462354a523564366f327648515635464358584e6b6b454144,616464723171396e77676c6c6c74376a3030796634307776656163327467746d787879377930366e74757273366c3833776e39673337646a6c6e7a35686d6d6d6b78656e786c386d77716c366839366775377a74706867786a66306e67776866717761646d3764,000000012a05f200,0000000077359400,000000002faf0800,657267,61737365743132647a63796c6d6579386d7a7166346c7178376c7533746c6e657677727471347675726a3832,39313931343537656634626138383866313638653738336138636633393536616261393363333766616338333165663761356632346236363462343363333733,0000000000103b96]"
},
"R6": {
"serializedValue": "0e20aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07",
"sigmaType": "Coll[SByte]",
"renderedValue": "aedb01e6a2594d7dacf6f63b2f3247e3b1ecf01409960758934d2fb4c3004f07"
}
},
"spentTransactionId": "ef8f88187b84a6e73bf95743774a499b599616b8c6e1a34bd8042b9667c153bc",
"mainChain": true
},
{
"boxId": "5ae399679e51a3e6b5f6d7d434033c637a4dbf8e80560d32707338d8063b8073",
"transactionId": "5943420e3375e620ea84f6ad1e41f770f73aaf004c2a55facffff0e6b8e5aad0",
"blockId": "af1b9c1021948e4cba09d2539be99a68014471cf83badd0f2a52cc43f398ef4a",
"value": 1100000,
"index": 1,
"globalIndex": 31586718,
"creationHeight": 1063837,
"settlementHeight": 1063849,
"ergoTree": "0008cd02638d1bd6fa628630b5fc668b06a6a1b398be1c8de8234b232e2913c21df675cd",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(638d1b,f156dd,...)))}",
"address": "9fGuR8wsLfdWVjWkn9extivJ5vY56vdRi4VSWF66ymKexNUjXMw",
"assets": [
{
"tokenId": "f8fe64d3d94d4eb193ea9d6304646db67bd914ed42cebd3a4f614d9d9de75cf0",
"index": 0,
"amount": 240,
"name": "rptconfRSNV1",
"decimals": 3,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "2a883c106fa0ad84936bbdd59bf5cd54022530912f1ba7424fc1de414f98b61d",
"mainChain": true
},
{
"boxId": "c42e974799e31560813ac592b0d75e777f6b0a71f73a4bbecbd411e8987f4418",
"transactionId": "5943420e3375e620ea84f6ad1e41f770f73aaf004c2a55facffff0e6b8e5aad0",
"blockId": "af1b9c1021948e4cba09d2539be99a68014471cf83badd0f2a52cc43f398ef4a",
"value": 1100000,
"index": 2,
"globalIndex": 31586719,
"creationHeight": 1063837,
"settlementHeight": 1063849,
"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": "6256ebaaff303c4b285a446d82becce8a8bf983a9ac40a231062e216d7b2d23f",
"mainChain": true
}
],
"size": 1870,
"isUnconfirmed": false
}