Transaction
ID: e731bb815e...106c
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0021 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
1.29 ERG
Outputs (5)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0021 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.007396322 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
1.27 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0063645 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Transaction Details
Confirmations: 839,009
Total coins transferred: 1.29 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000249 ERG
Raw Transaction Data
{
"id": "e731bb815e31720a4abe3dff3cb14dce9bcbde20111e2a3023fa1bbb79b0106c",
"blockId": "3c34f76cbe80a2b721cb2252f459e38e05b6e7e6c7d51191df15be23a8f0e9a9",
"inclusionHeight": 919132,
"timestamp": 1673826573934,
"index": 7,
"globalIndex": 4623281,
"numConfirmations": 839009,
"inputs": [
{
"boxId": "ee09a32f0c5fb7814dd0201c22fd1b5e2346226ceb7aa3df08544c37cc80794b",
"value": 2100000,
"index": 0,
"spendingProof": null,
"outputBlockId": "53c7d59434dc25e3ddb2747f4905d9bb96df5b8b8374c33096d5fce8b9979367",
"outputTransactionId": "2d884684b80ed0a86d435236dcd67310f53f1bb8240cf36d7e5778286919c73f",
"outputIndex": 0,
"outputGlobalIndex": 25659903,
"outputCreatedAt": 919124,
"outputSettledAt": 919126,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 3\n4: 1\n5: 0\n6: 0\n7: 2\n8: 100\n9: 0\n10: 7\n11: Coll(119,119,119,-27,5,31,-118,107,-61,8,39,-18,65,-35,57,-32,75,-54,29,-70,53,35,107,-102,38,-76,-108,-32,-70,-56,79,51)\n12: Coll(48)\n13: 1\n14: 2100000\n15: 1\n16: false\n17: 1\n18: 1\n19: 0\n20: 1\n21: 0\n22: CBigInt(1000000)\n23: 86400000\n24: 2\n25: 1\n26: 86400000\n27: 2100000\n28: 1\n29: 1\n30: 2\n31: 1\n32: 2100000\n33: 1\n34: 9\n35: false\n36: 14400000\n37: 2\n38: 0\n39: 0\n40: 0\n41: 0\n42: 0\n43: 100\n44: 500\n45: 1000\n46: 2000\n47: 4000\n48: 9000\n49: 13000\n50: 20000\n51: 30000\n52: 40000\n53: 50000\n54: 70000\n55: 110000\n56: 140000\n57: 170000\n58: 210000\n59: 250000\n60: 300000\n61: 500000\n62: 1\n63: 0\n64: 1\n65: 0\n66: 1\n67: 4\n68: 4\n69: 1775840000\n70: CBigInt(0)\n71: 5\n72: 1000\n73: 6\n74: 177584000\n75: 10000\n76: 0\n77: Coll(102,102,102,-116,-29,83,-2,-3,20,109,20,-76,20,-126,-43,38,-21,-14,106,90,125,32,79,54,87,12,89,-59,88,19,107,-125)\n78: 30\n79: 2\n80: 2\n81: 3\n82: 3\n83: 8\n84: CBigInt(1000)\n85: false\n86: 2\n87: 4\n88: 0\n89: 1\n90: 0\n91: 2\n92: 0\n93: 0\n94: 1\n95: 0\n96: 0\n97: 1\n98: false\n99: 2100000\n100: 1\n101: 1\n102: 1100000\n103: false",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val tuple2 = (Coll[Byte](), placeholder[Long](0))\n val tuple3 = coll1.getOrElse(placeholder[Int](1), tuple2)\n val box4 = SELF.R7[Box].get\n val bool5 = (tuple3._1 == box4.id) && (SELF.propositionBytes == box4.propositionBytes)\n val box6 = if (bool5) { box4 } else { SELF }\n val prop7 = box6.R9[SigmaProp].get\n val bool8 = !bool5\n val box9 = OUTPUTS(placeholder[Int](2))\n val coll10 = box9.propositionBytes\n val coll11 = prop7.propBytes\n val bool12 = coll10 == coll11\n val coll13 = box6.R8[Coll[Long]].get\n val l14 = coll13(placeholder[Int](3))\n val l15 = CONTEXT.preHeader.timestamp\n val l16 = l14 - l15\n val coll17 = box9.tokens\n val tuple18 = coll17.getOrElse(placeholder[Int](4), tuple2)\n val l19 = tuple18._2\n val l20 = tuple3._2\n val bool21 = coll13(placeholder[Int](5)) == placeholder[Long](6)\n val l22 = coll13(placeholder[Int](7))\n val l23 = l22 * placeholder[Long](8)\n val tuple24 = coll17.getOrElse(placeholder[Int](9), tuple2)\n val l25 = tuple24._2\n val l26 = SELF.value\n val l27 = coll13(placeholder[Int](10))\n val coll28 = tuple24._1\n val coll29 = box6.id\n val coll30 = placeholder[Coll[Byte]](11)\n val coll31 = placeholder[Coll[Byte]](12)\n val bool32 = if (coll10 == SELF.propositionBytes) {\n (\n (\n (\n (\n (((coll28 == coll29) && (l25 >= placeholder[Long](13))) && (box9.value >= placeholder[Long](14))) && (\n ((bool21 && (tuple18._1 == coll30)) && (l19 >= placeholder[Long](15))) || (!bool21)\n )\n ) && (box9.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)\n ) && (box9.R5[Coll[Byte]].get == coll31)\n ) && (box9.R6[Coll[Byte]].get == coll31)\n ) && (box9.R7[Box].get == box6)\n } else { placeholder[Boolean](16) }\n val box33 = OUTPUTS(placeholder[Int](17))\n val coll34 = box6.R5[Coll[Byte]].get\n val bool35 = l15 <= l14\n val l36 = box9.value\n val tuple37 = coll1.getOrElse(placeholder[Int](18), tuple2)\n val l38 = tuple37._2\n val coll39 = box33.tokens\n val tuple40 = coll39.getOrElse(placeholder[Int](19), tuple2)\n val coll41 = tuple40._1\n val l42 = tuple40._2\n val bool43 = coll13(placeholder[Int](20)) == placeholder[Long](21)\n val bi44 = placeholder[BigInt](22)\n val bool45 = l15 > l14\n val bool46 = if (bool43) { (bool5 && bool45) && (l15 < l14 + placeholder[Long](23)) } else { bool5 && bool35 }\n prop7 && sigmaProp((bool8 && (OUTPUTS.size == placeholder[Int](24))) && bool12) || sigmaProp(\n (\n (\n if ((bool8 && (INPUTS.size == placeholder[Int](25))) && (l16 >= placeholder[Long](26))) {\n (\n (\n bool32 && (\n ((box9.value >= placeholder[Long](27)) && (box9.R7[Box].get == SELF)) && (\n (\n ((bool21 && (l19 == l20)) && (l25 == l20 - placeholder[Long](28) / l23 + placeholder[Long](29))) && (coll17.size == placeholder[Int](30))\n ) || (((!bool21) && (coll17.size == placeholder[Int](31))) && (l25 == l26 - placeholder[Long](32) / l27 + placeholder[Long](33)))\n )\n )\n ) && (box33.propositionBytes == coll34)\n ) && (box33.value >= coll13(placeholder[Int](34)))\n } else { placeholder[Boolean](35) } || if (((!(bool35 && (l15 > l14 - placeholder[Long](36)))) && (INPUTS.size == placeholder[Int](37))) && (\n CONTEXT.dataInputs.size > placeholder[Int](38)\n )) {(\n val box47 = CONTEXT.dataInputs(placeholder[Int](39))\n val l48 = l20 - l25\n val l49 = box47.R4[Long].get\n val l50 = if (bool21) { max(placeholder[Long](40), l49 - l27 * l22) } else { max(placeholder[Long](41), l27 - l49 * l22) }\n val coll51 = Coll[Long](\n placeholder[Long](42), placeholder[Long](43), placeholder[Long](44), placeholder[Long](45), placeholder[Long](46), placeholder[Long](\n 47\n ), placeholder[Long](48), placeholder[Long](49), placeholder[Long](50), placeholder[Long](51), placeholder[Long](52), placeholder[Long](\n 53\n ), placeholder[Long](54), placeholder[Long](55), placeholder[Long](56), placeholder[Long](57), placeholder[Long](58), placeholder[Long](\n 59\n ), placeholder[Long](60), placeholder[Long](61)\n )\n val coll52 = coll51.map({(l52: Long) => l52 * l52 }).zip(coll51)\n val i53 = coll52.map({(tuple53: (Long, Long)) => if (tuple53._1 >= l16) { placeholder[Long](62) } else { placeholder[Long](63) } }).indexOf(\n placeholder[Long](64), placeholder[Int](65)\n )\n val tuple54 = coll52(i53 - placeholder[Int](66))\n val l55 = tuple54._2\n val tuple56 = coll52(i53)\n val l57 = tuple54._1\n val bi58 = l55.toBigInt + tuple56._2 - l55.toBigInt * l16 - l57.toBigInt / tuple56._1 - l57.toBigInt\n val bi59 = placeholder[Long](67) * coll13(placeholder[Int](68)).toBigInt * l22.toBigInt * l27.toBigInt * bi58 / placeholder[Int](69).toBigInt\n val bi60 = max(\n placeholder[BigInt](70), bi59 - bi59 * coll13(placeholder[Int](71)).toBigInt * max(l49 - l27, l27 - l49).toBigInt / placeholder[Long](\n 72\n ) * l27.toBigInt\n )\n val bi61 = if (bool43) { l50.toBigInt + bi60 } else {\n l50.toBigInt + bi60 + bi60 * coll13(placeholder[Int](73)).toBigInt * bi58 / placeholder[Int](74).toBigInt\n }\n val bi62 = l48.toBigInt * bi61 - bi61 % placeholder[Long](75).toBigInt\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (box47.tokens(placeholder[Int](76))._1 == placeholder[Coll[Byte]](77)) && (HEIGHT <= box47.R5[Int].get + placeholder[Int](78))\n ) && bool32\n ) && (l36 == l26)\n ) && (l19 == l38)\n ) && (coll41 == coll29)\n ) && (l42 == l48)\n ) && (OUTPUTS(placeholder[Int](79)).propositionBytes == coll11)\n ) && (OUTPUTS(placeholder[Int](80)).value.toBigInt >= max(bi44, bi62))\n ) && (OUTPUTS(placeholder[Int](81)).propositionBytes == coll34)\n ) && (OUTPUTS(placeholder[Int](82)).value.toBigInt >= max(bi44, bi62 * coll13(placeholder[Int](83)).toBigInt / placeholder[BigInt](84)))\n )} else { placeholder[Boolean](85) }\n ) || if (((bool46 && (INPUTS.size == placeholder[Int](86))) && (OUTPUTS.size == placeholder[Int](87))) && (\n CONTEXT.dataInputs.size == placeholder[Int](88)\n )) {(\n val l47 = if (bool21) { l38 - l19 } else { l26 - l36 }\n val l48 = if (bool21) { l47 / l23 } else { l47 / l27 * l22 }\n val tuple49 = INPUTS(placeholder[Int](89)).tokens.getOrElse(placeholder[Int](90), tuple2)\n val box50 = OUTPUTS(placeholder[Int](91))\n val coll51 = box50.tokens\n val tuple52 = coll51.getOrElse(placeholder[Int](92), tuple2)\n (\n (((l48 == if (tuple49._1 == coll29) { tuple49._2 } else { placeholder[Long](93) }) && bool32) && (l25 == l20)) && (\n (\n ((((bool21 && (coll41 == coll30)) && (l42 == l47)) && (coll39.size == placeholder[Int](94))) && (box50.value >= l48 * l27 * l22)) && (\n coll51.size == placeholder[Int](95)\n )\n ) || (\n (((((!bool21) && (box33.value >= l47)) && (coll39.size == placeholder[Int](96))) && (tuple52._1 == coll30)) && (tuple52._2 >= l48 * l23)) && (\n coll51.size == placeholder[Int](97)\n )\n )\n )\n ) && (box50.propositionBytes == coll11)\n )} else { placeholder[Boolean](98) }\n ) || if ((bool45 && (!bool46)) || (((l26 == placeholder[Long](99)) && (l20 == placeholder[Long](100))) && (l38 <= placeholder[Long](101)))) {\n ((bool12 && (l36 >= l26 - placeholder[Long](102))) && (coll28 == tuple37._1)) && (l25 == l38)\n } else { placeholder[Boolean](103) }\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "306a097fafaa0ed7452b79aabffa60149579e2f9bf2f042e9ccc66f31b98e9ba",
"index": 0,
"amount": 3,
"name": "Call_A_fakeUSD_ERG_606499999_2023-04-17_per_1",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "777777e5051f8a6bc30827ee41dd39e04bca1dba35236b9a26b494e0bac84f33",
"index": 1,
"amount": 201,
"name": "fakeUSD",
"decimals": 2,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "0e2d43616c6c5f415f66616b655553445f4552475f3630363439393939395f323032332d30342d31375f7065725f31",
"sigmaType": "Coll[SByte]",
"renderedValue": "43616c6c5f415f66616b655553445f4552475f3630363439393939395f323032332d30342d31375f7065725f31"
},
"R5": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
},
"R6": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
},
"R7": {
"serializedValue": "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",
"sigmaType": null,
"renderedValue": null
}
}
},
{
"boxId": "03d02791c6f962fdddb30869ef8a7d806c4e78fe57b41b5be6a3d34cdcb6d955",
"value": 1287760822,
"index": 1,
"spendingProof": null,
"outputBlockId": "17c49e0d0935c750bc99031b9152812d98e3e90dd0a03436dd64d1d1437a87c2",
"outputTransactionId": "c3e97ff79be39b80dfb3f9e0fe47f6e1e840df06269b35247db045f32b59fee4",
"outputIndex": 0,
"outputGlobalIndex": 25659970,
"outputCreatedAt": 919128,
"outputSettledAt": 919130,
"ergoTree": "100504020400050004020100d804d601e4c6a70408d602b2a5730000d603db63087202d604b2720373010186028300027302eb027201d19593b172037303eded93c27202d07201938c720401e4c6a7050e928c720402e4c6a706057304",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 1\n4: false",
"ergoTreeScript": "{\n val prop1 = SELF.R4[SigmaProp].get\n val box2 = OUTPUTS(placeholder[Int](0))\n val coll3 = box2.tokens\n val tuple4 = coll3.getOrElse(placeholder[Int](1), (Coll[Byte](), placeholder[Long](2)))\n prop1 || sigmaProp(\n if (coll3.size == placeholder[Int](3)) {\n ((box2.propositionBytes == prop1.propBytes) && (tuple4._1 == SELF.R5[Coll[Byte]].get)) && (tuple4._2 >= SELF.R6[Long].get)\n } else { placeholder[Boolean](4) }\n )\n}",
"address": "MZ4DGZnZL4rrxJ45amiyx8cvHrifo5PL82vwbrNEd19cMG2ALKk94md25VU8exnSexUfexvhDX8HHpUpLh3nSHno1VFt5pSoTcoHBDNzUHAGgf8N5mCD1KpG1GM1o6AKCLBKv",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "08cd02042a3559387831c22aaec64a86969b6f2e4b7626b3f94cbec08123c5dc88d23e",
"sigmaType": "SSigmaProp",
"renderedValue": "02042a3559387831c22aaec64a86969b6f2e4b7626b3f94cbec08123c5dc88d23e"
},
"R5": {
"serializedValue": "0e20306a097fafaa0ed7452b79aabffa60149579e2f9bf2f042e9ccc66f31b98e9ba",
"sigmaType": "Coll[SByte]",
"renderedValue": "306a097fafaa0ed7452b79aabffa60149579e2f9bf2f042e9ccc66f31b98e9ba"
},
"R6": {
"serializedValue": "0504",
"sigmaType": "SLong",
"renderedValue": "2"
}
}
}
],
"dataInputs": [
{
"boxId": "ee1acabd9b4ca0ea168e1807b92928534237dd234b88c416d1762aaf67f90ec1",
"value": 1000000,
"index": 0,
"outputBlockId": "992a708cd5ec3f8733d9b78f56471716b71b84b89760eedc5f2d461d4dfdb09d",
"outputTransactionId": "9318109f3b667c32abbce3080bafef5afee6c481b8d96aa19476209d9dc1ccab",
"outputIndex": 0,
"ergoTree": "100308cd039ed9a6df20fca487da2d3b58e822cdcc5bcfad4cca794eadf132afa3113f31a605000502ea027300d19473017302",
"address": "5p1BsJCGPFvhshBqXAaGkMUoBayaSLmMS8uVi4nThb1i4h7WbnULp8r1XgMLURrjg2RE9JEdo6uE",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "05beeacdfe08",
"sigmaType": "SLong",
"renderedValue": "1206499999"
},
"R5": {
"serializedValue": "0480897a",
"sigmaType": "SInt",
"renderedValue": "1000000"
}
}
}
],
"outputs": [
{
"boxId": "cb2a383435c78fe82ba0779137f9f776a65def872d3e7ff0d55f214b89e92da9",
"transactionId": "e731bb815e31720a4abe3dff3cb14dce9bcbde20111e2a3023fa1bbb79b0106c",
"blockId": "3c34f76cbe80a2b721cb2252f459e38e05b6e7e6c7d51191df15be23a8f0e9a9",
"value": 2100000,
"index": 0,
"globalIndex": 25659996,
"creationHeight": 919128,
"settlementHeight": 919132,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 3\n4: 1\n5: 0\n6: 0\n7: 2\n8: 100\n9: 0\n10: 7\n11: Coll(119,119,119,-27,5,31,-118,107,-61,8,39,-18,65,-35,57,-32,75,-54,29,-70,53,35,107,-102,38,-76,-108,-32,-70,-56,79,51)\n12: Coll(48)\n13: 1\n14: 2100000\n15: 1\n16: false\n17: 1\n18: 1\n19: 0\n20: 1\n21: 0\n22: CBigInt(1000000)\n23: 86400000\n24: 2\n25: 1\n26: 86400000\n27: 2100000\n28: 1\n29: 1\n30: 2\n31: 1\n32: 2100000\n33: 1\n34: 9\n35: false\n36: 14400000\n37: 2\n38: 0\n39: 0\n40: 0\n41: 0\n42: 0\n43: 100\n44: 500\n45: 1000\n46: 2000\n47: 4000\n48: 9000\n49: 13000\n50: 20000\n51: 30000\n52: 40000\n53: 50000\n54: 70000\n55: 110000\n56: 140000\n57: 170000\n58: 210000\n59: 250000\n60: 300000\n61: 500000\n62: 1\n63: 0\n64: 1\n65: 0\n66: 1\n67: 4\n68: 4\n69: 1775840000\n70: CBigInt(0)\n71: 5\n72: 1000\n73: 6\n74: 177584000\n75: 10000\n76: 0\n77: Coll(102,102,102,-116,-29,83,-2,-3,20,109,20,-76,20,-126,-43,38,-21,-14,106,90,125,32,79,54,87,12,89,-59,88,19,107,-125)\n78: 30\n79: 2\n80: 2\n81: 3\n82: 3\n83: 8\n84: CBigInt(1000)\n85: false\n86: 2\n87: 4\n88: 0\n89: 1\n90: 0\n91: 2\n92: 0\n93: 0\n94: 1\n95: 0\n96: 0\n97: 1\n98: false\n99: 2100000\n100: 1\n101: 1\n102: 1100000\n103: false",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val tuple2 = (Coll[Byte](), placeholder[Long](0))\n val tuple3 = coll1.getOrElse(placeholder[Int](1), tuple2)\n val box4 = SELF.R7[Box].get\n val bool5 = (tuple3._1 == box4.id) && (SELF.propositionBytes == box4.propositionBytes)\n val box6 = if (bool5) { box4 } else { SELF }\n val prop7 = box6.R9[SigmaProp].get\n val bool8 = !bool5\n val box9 = OUTPUTS(placeholder[Int](2))\n val coll10 = box9.propositionBytes\n val coll11 = prop7.propBytes\n val bool12 = coll10 == coll11\n val coll13 = box6.R8[Coll[Long]].get\n val l14 = coll13(placeholder[Int](3))\n val l15 = CONTEXT.preHeader.timestamp\n val l16 = l14 - l15\n val coll17 = box9.tokens\n val tuple18 = coll17.getOrElse(placeholder[Int](4), tuple2)\n val l19 = tuple18._2\n val l20 = tuple3._2\n val bool21 = coll13(placeholder[Int](5)) == placeholder[Long](6)\n val l22 = coll13(placeholder[Int](7))\n val l23 = l22 * placeholder[Long](8)\n val tuple24 = coll17.getOrElse(placeholder[Int](9), tuple2)\n val l25 = tuple24._2\n val l26 = SELF.value\n val l27 = coll13(placeholder[Int](10))\n val coll28 = tuple24._1\n val coll29 = box6.id\n val coll30 = placeholder[Coll[Byte]](11)\n val coll31 = placeholder[Coll[Byte]](12)\n val bool32 = if (coll10 == SELF.propositionBytes) {\n (\n (\n (\n (\n (((coll28 == coll29) && (l25 >= placeholder[Long](13))) && (box9.value >= placeholder[Long](14))) && (\n ((bool21 && (tuple18._1 == coll30)) && (l19 >= placeholder[Long](15))) || (!bool21)\n )\n ) && (box9.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)\n ) && (box9.R5[Coll[Byte]].get == coll31)\n ) && (box9.R6[Coll[Byte]].get == coll31)\n ) && (box9.R7[Box].get == box6)\n } else { placeholder[Boolean](16) }\n val box33 = OUTPUTS(placeholder[Int](17))\n val coll34 = box6.R5[Coll[Byte]].get\n val bool35 = l15 <= l14\n val l36 = box9.value\n val tuple37 = coll1.getOrElse(placeholder[Int](18), tuple2)\n val l38 = tuple37._2\n val coll39 = box33.tokens\n val tuple40 = coll39.getOrElse(placeholder[Int](19), tuple2)\n val coll41 = tuple40._1\n val l42 = tuple40._2\n val bool43 = coll13(placeholder[Int](20)) == placeholder[Long](21)\n val bi44 = placeholder[BigInt](22)\n val bool45 = l15 > l14\n val bool46 = if (bool43) { (bool5 && bool45) && (l15 < l14 + placeholder[Long](23)) } else { bool5 && bool35 }\n prop7 && sigmaProp((bool8 && (OUTPUTS.size == placeholder[Int](24))) && bool12) || sigmaProp(\n (\n (\n if ((bool8 && (INPUTS.size == placeholder[Int](25))) && (l16 >= placeholder[Long](26))) {\n (\n (\n bool32 && (\n ((box9.value >= placeholder[Long](27)) && (box9.R7[Box].get == SELF)) && (\n (\n ((bool21 && (l19 == l20)) && (l25 == l20 - placeholder[Long](28) / l23 + placeholder[Long](29))) && (coll17.size == placeholder[Int](30))\n ) || (((!bool21) && (coll17.size == placeholder[Int](31))) && (l25 == l26 - placeholder[Long](32) / l27 + placeholder[Long](33)))\n )\n )\n ) && (box33.propositionBytes == coll34)\n ) && (box33.value >= coll13(placeholder[Int](34)))\n } else { placeholder[Boolean](35) } || if (((!(bool35 && (l15 > l14 - placeholder[Long](36)))) && (INPUTS.size == placeholder[Int](37))) && (\n CONTEXT.dataInputs.size > placeholder[Int](38)\n )) {(\n val box47 = CONTEXT.dataInputs(placeholder[Int](39))\n val l48 = l20 - l25\n val l49 = box47.R4[Long].get\n val l50 = if (bool21) { max(placeholder[Long](40), l49 - l27 * l22) } else { max(placeholder[Long](41), l27 - l49 * l22) }\n val coll51 = Coll[Long](\n placeholder[Long](42), placeholder[Long](43), placeholder[Long](44), placeholder[Long](45), placeholder[Long](46), placeholder[Long](\n 47\n ), placeholder[Long](48), placeholder[Long](49), placeholder[Long](50), placeholder[Long](51), placeholder[Long](52), placeholder[Long](\n 53\n ), placeholder[Long](54), placeholder[Long](55), placeholder[Long](56), placeholder[Long](57), placeholder[Long](58), placeholder[Long](\n 59\n ), placeholder[Long](60), placeholder[Long](61)\n )\n val coll52 = coll51.map({(l52: Long) => l52 * l52 }).zip(coll51)\n val i53 = coll52.map({(tuple53: (Long, Long)) => if (tuple53._1 >= l16) { placeholder[Long](62) } else { placeholder[Long](63) } }).indexOf(\n placeholder[Long](64), placeholder[Int](65)\n )\n val tuple54 = coll52(i53 - placeholder[Int](66))\n val l55 = tuple54._2\n val tuple56 = coll52(i53)\n val l57 = tuple54._1\n val bi58 = l55.toBigInt + tuple56._2 - l55.toBigInt * l16 - l57.toBigInt / tuple56._1 - l57.toBigInt\n val bi59 = placeholder[Long](67) * coll13(placeholder[Int](68)).toBigInt * l22.toBigInt * l27.toBigInt * bi58 / placeholder[Int](69).toBigInt\n val bi60 = max(\n placeholder[BigInt](70), bi59 - bi59 * coll13(placeholder[Int](71)).toBigInt * max(l49 - l27, l27 - l49).toBigInt / placeholder[Long](\n 72\n ) * l27.toBigInt\n )\n val bi61 = if (bool43) { l50.toBigInt + bi60 } else {\n l50.toBigInt + bi60 + bi60 * coll13(placeholder[Int](73)).toBigInt * bi58 / placeholder[Int](74).toBigInt\n }\n val bi62 = l48.toBigInt * bi61 - bi61 % placeholder[Long](75).toBigInt\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (box47.tokens(placeholder[Int](76))._1 == placeholder[Coll[Byte]](77)) && (HEIGHT <= box47.R5[Int].get + placeholder[Int](78))\n ) && bool32\n ) && (l36 == l26)\n ) && (l19 == l38)\n ) && (coll41 == coll29)\n ) && (l42 == l48)\n ) && (OUTPUTS(placeholder[Int](79)).propositionBytes == coll11)\n ) && (OUTPUTS(placeholder[Int](80)).value.toBigInt >= max(bi44, bi62))\n ) && (OUTPUTS(placeholder[Int](81)).propositionBytes == coll34)\n ) && (OUTPUTS(placeholder[Int](82)).value.toBigInt >= max(bi44, bi62 * coll13(placeholder[Int](83)).toBigInt / placeholder[BigInt](84)))\n )} else { placeholder[Boolean](85) }\n ) || if (((bool46 && (INPUTS.size == placeholder[Int](86))) && (OUTPUTS.size == placeholder[Int](87))) && (\n CONTEXT.dataInputs.size == placeholder[Int](88)\n )) {(\n val l47 = if (bool21) { l38 - l19 } else { l26 - l36 }\n val l48 = if (bool21) { l47 / l23 } else { l47 / l27 * l22 }\n val tuple49 = INPUTS(placeholder[Int](89)).tokens.getOrElse(placeholder[Int](90), tuple2)\n val box50 = OUTPUTS(placeholder[Int](91))\n val coll51 = box50.tokens\n val tuple52 = coll51.getOrElse(placeholder[Int](92), tuple2)\n (\n (((l48 == if (tuple49._1 == coll29) { tuple49._2 } else { placeholder[Long](93) }) && bool32) && (l25 == l20)) && (\n (\n ((((bool21 && (coll41 == coll30)) && (l42 == l47)) && (coll39.size == placeholder[Int](94))) && (box50.value >= l48 * l27 * l22)) && (\n coll51.size == placeholder[Int](95)\n )\n ) || (\n (((((!bool21) && (box33.value >= l47)) && (coll39.size == placeholder[Int](96))) && (tuple52._1 == coll30)) && (tuple52._2 >= l48 * l23)) && (\n coll51.size == placeholder[Int](97)\n )\n )\n )\n ) && (box50.propositionBytes == coll11)\n )} else { placeholder[Boolean](98) }\n ) || if ((bool45 && (!bool46)) || (((l26 == placeholder[Long](99)) && (l20 == placeholder[Long](100))) && (l38 <= placeholder[Long](101)))) {\n ((bool12 && (l36 >= l26 - placeholder[Long](102))) && (coll28 == tuple37._1)) && (l25 == l38)\n } else { placeholder[Boolean](103) }\n )\n}",
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"ergoTree": "0008cd039ed9a6df20fca487da2d3b58e822cdcc5bcfad4cca794eadf132afa3113f31a6",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(9ed9a6,83e77d,...)))}",
"address": "9hfmJjrmeyhNgQZks1NzvrJyNgtHuMinnZbrupk6gGxTZWQQf87",
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},
{
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"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",
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"spentTransactionId": "1e5ebfad396c41c51ffdd887a6b888d7ac8349f2081855a162a8c6e089781a1b",
"mainChain": true
}
],
"size": 4413,
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}