Transaction
ID: 06630023ba...5d2d
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.951264537 ERG
Tokens:
Spent
Address:
Output transaction:
Settlement height:
Value:
95.5 ERG
Tokens:
Loading assets...
Outputs (4)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.93430901 ERG
Tokens:
Spent
Address:
Spent in transaction:
Settlement height:
Value:
95.5 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.017055527 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.002 ERG
Transaction Details
Confirmations: 639,722
Total coins transferred: 96.45 ERG
Fees: 0.002 ERG
Fees per byte: 0.000000506 ERG
Raw Transaction Data
{
"id": "06630023baa77b9d60324d036ffed91e3fa362a4fd6dd9ec5a7df8ad96b35d2d",
"blockId": "76985ebb7317c7cc15636a7c696b905e000510abfff0e66294ea4f5eb113d6a0",
"inclusionHeight": 1126395,
"timestamp": 1698993605447,
"index": 4,
"globalIndex": 6063328,
"numConfirmations": 639722,
"inputs": [
{
"boxId": "80fdd0f658cf255dcb56647ef439f79218471d9111c39981a65ab1757f762ae9",
"value": 951264537,
"index": 0,
"spendingProof": "ec20f0b3c5888fe2157b50fd939bd724ccdc8fe5dd7cb7bf9ac4a29883b7c98905d6f567070a1da57a8403c92d76cd76dfdf3ac148c1efbc",
"outputBlockId": "9de5e06df0a144dfd76cca25b489faa1d6d42c549b286f0c824faf387094a1a9",
"outputTransactionId": "ebecbc717040a299a3ef63569477d7d1a3b6434036d16692be8f87a2de55ff32",
"outputIndex": 1,
"outputGlobalIndex": 33972230,
"outputCreatedAt": 1126307,
"outputSettledAt": 1126309,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: CBigInt(1)\n2: 0\n3: 1\n4: 0\n5: 100000000\n6: 100000000\n7: 2\n8: 1\n9: 0\n10: 100000000\n11: 100000000\n12: 100000000\n13: Coll(-71,-9,10,-114,77,16,110,124,-5,-85,36,-38,-59,48,38,126,-14,66,-3,54,92,62,40,10,-72,5,-67,-52,-126,123,-75,-74)\n14: 0\n15: 0\n16: Coll(6,-31,-40,-5,72,-71,-10,-83,51,86,51,5,70,1,-45,-71,-44,-102,-70,-32,119,1,26,-1,-26,-105,58,-94,38,126,-85,-108)\n17: 0\n18: Coll(-117,8,-51,-43,68,-102,-107,-110,-87,-25,-105,17,-41,-41,-110,73,-41,-96,60,83,93,23,-17,-82,-24,62,33,110,-128,-92,76,75)\n19: 0\n20: 3\n21: 5000000\n22: 1000\n23: 2\n24: 2\n25: CBigInt(100)\n26: 3000000\n27: 1000000\n28: 0\n29: 0\n30: 1\n31: 0\n32: 1000000\n33: 1\n34: Coll(70,98,-118,89,125,70,75,-39,91,-88,-107,32,109,-93,126,26,-42,-95,20,49,-80,29,31,-4,108,-1,-107,91,-8,-8,-34,-70)\n35: 1000000\n36: 1000000\n37: 1\n38: 0\n39: 3\n40: 5000000\n41: 1000\n42: 2\n43: 2\n44: CBigInt(100)\n45: 2\n46: 0\n47: 0\n48: false\n49: 1\n50: 3\n51: 0\n52: 3\n53: 5000000\n54: 1000\n55: 2\n56: 2\n57: CBigInt(100)\n58: 3000000\n59: 1000000\n60: 0\n61: 100000000\n62: 5\n63: 100000000\n64: false\n65: false",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val ge2 = SELF.R8[GroupElement].get\n val coll3 = SELF.propositionBytes\n val coll4 = CONTEXT.dataInputs\n val tuple5 = SELF.R6[(Long, Long)].get\n val l6 = tuple5._1\n val bi7 = placeholder[BigInt](1)\n val coll8 = SELF.tokens\n val tuple9 = coll8(placeholder[Int](2))\n val l10 = tuple9._2\n val bi11 = l10.toBigInt\n val box12 = coll4(placeholder[Int](3))\n val coll13 = box12.R4[Coll[Long]].get\n val i14 = coll13.size\n val tuple15 = SELF.R5[(Int, Int)].get\n val i16 = tuple15._1\n val box17 = coll4(placeholder[Int](4))\n val coll18 = box17.R4[Coll[Long]].get\n val l19 = coll18.slice(tuple15._2, coll18.size).fold(placeholder[Long](5), {(tuple19: (Long, Long)) => tuple19._1 * tuple19._2 / placeholder[Long](6) })\n val box20 = coll4(placeholder[Int](7))\n val coll21 = box20.R4[Coll[Long]].get\n val bi22 = if (i14 == i16) { l19 } else {(\n val i22 = i16 + placeholder[Int](8)\n val l23 = coll21.slice(placeholder[Int](9), coll21.size).fold(l19, {(tuple23: (Long, Long)) => tuple23._1 * tuple23._2 / placeholder[Long](10) })\n if (i14 == i22) { l23 } else { coll13.slice(i22, i14).fold(l23, {(tuple24: (Long, Long)) => tuple24._1 * tuple24._2 / placeholder[Long](11) }) }\n )}.toBigInt\n val l23 = placeholder[Long](12)\n val bi24 = bi7 + bi11 * bi22 / l23.toBigInt\n val coll25 = SELF.R4[Coll[Byte]].get\n val coll26 = SELF.R7[Coll[Byte]].get\n val tuple27 = SELF.R9[(Long, Long)].get\n val l28 = tuple27._1\n val l29 = tuple27._2\n val coll30 = placeholder[Coll[Byte]](13)\n val bool31 = box17.tokens(placeholder[Int](14))._1 == coll30\n val bool32 = box17.R6[Int].get == i16\n val bool33 = box12.tokens(placeholder[Int](15))._1 == placeholder[Coll[Byte]](16)\n val bool34 = (box20.tokens(placeholder[Int](17))._1 == coll30) && (box20.R6[Int].get == i14)\n val l35 = SELF.value\n val coll36 = placeholder[Coll[Byte]](18)\n val coll37 = tuple9._1\n if (box1 == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](19))\n val box39 = coll4(placeholder[Int](20))\n val coll40 = box39.tokens\n val l41 = box38.value\n val bi42 = l41.toBigInt - placeholder[Int](21).toBigInt\n val bi43 = box39.R4[Int].get.toBigInt\n val bi44 = box39.value.toBigInt\n val i45 = placeholder[Int](22)\n val tuple46 = box38.R9[(Long, Long)].get\n proveDlog(ge2) && sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (box38.propositionBytes == coll3) && (\n (\n coll40(placeholder[Int](23))._2.toBigInt * bi42 * bi43 / bi44 + bi44 * placeholder[Int](24).toBigInt / placeholder[BigInt](\n 25\n ) * i45.toBigInt + bi42 * bi43 > bi24 * l6.toBigInt / i45.toBigInt\n ) && (l41 >= placeholder[Int](26).toLong + placeholder[Long](27))\n )\n ) && (box38.tokens(placeholder[Int](28)) == tuple9)\n ) && (\n (\n (\n (\n (((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (\n box38.R7[Coll[Byte]].get == coll26\n )\n ) && (box38.R8[GroupElement].get == ge2)\n ) && (tuple46._1 == l28)\n ) && (tuple46._2 == l29)\n )\n ) && bool31\n ) && bool32\n ) && bool33\n ) && (coll40(placeholder[Int](29))._1 == coll26)\n ) && bool34\n )\n )} else { if (INPUTS(placeholder[Int](30)) == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](31))\n val coll39 = box38.propositionBytes\n val l40 = box38.value\n val bool41 = l40 >= l35 - placeholder[Long](32)\n val box42 = OUTPUTS(placeholder[Int](33))\n val bool43 = blake2b256(box42.propositionBytes) == placeholder[Coll[Byte]](34)\n val bool44 = box42.value >= placeholder[Int](35).toLong + placeholder[Long](36)\n val coll45 = box42.tokens\n val tuple46 = coll45(placeholder[Int](37))\n val bool47 = tuple46._1 == coll36\n val l48 = tuple46._2\n val tuple49 = coll45(placeholder[Int](38))\n val bool50 = tuple49 == tuple9\n val coll51 = box1.tokens\n val coll52 = box38.tokens\n sigmaProp((((((((((((coll39 == coll25) && bool41) && bool43) && bool44) && (bool47 && (l48.toBigInt > bi24))) && bool50) && bool31) && bool32) && bool33) && bool34) || if (coll51.size >= placeholder[Int](39)) {(\n val bi53 = l35.toBigInt - placeholder[Int](40).toBigInt\n val bi54 = box1.R4[Int].get.toBigInt\n val bi55 = box1.value.toBigInt\n val i56 = placeholder[Int](41)\n val bi57 = coll51(placeholder[Int](42))._2.toBigInt * bi53 * bi54 / bi55 + bi55 * placeholder[Int](43).toBigInt / placeholder[BigInt](44) * i56.toBigInt + bi53 * bi54\n val bi58 = bi57 - bi24\n val bi59 = tuple5._2.toBigInt\n val bi60 = bi58 * i56.toBigInt - bi59 / i56.toBigInt\n ((((((((((((bi57 <= bi24 * l6.toBigInt / i56.toBigInt) && (HEIGHT.toLong >= l29)) || (HEIGHT.toLong > l28)) && bool43) && bool47) && bool44) && if (bi60 < bi7) { l48.toBigInt >= bi57 } else {(\n val box61 = OUTPUTS(placeholder[Int](45))\n val tuple62 = box61.tokens(placeholder[Int](46))\n (((l48.toBigInt >= bi24 + bi58 * bi59 / i56.toBigInt) && (tuple62._2.toBigInt >= bi60)) && (box61.propositionBytes == coll25)) && (tuple62._1 == coll36)\n )}) && bool50) && (coll51(placeholder[Int](47))._1 == coll26)) && bool31) && bool32) && bool33) && bool34\n )} else { placeholder[Boolean](48) }) || if ((coll52.size >= placeholder[Int](49)) && (box1.tokens.size < placeholder[Int](50))) {(\n val tuple53 = box38.R9[(Long, Long)].get\n val l54 = tuple53._2\n val bool55 = coll39 == coll3\n val tuple56 = coll52(placeholder[Int](51))\n val l57 = tuple56._2\n val bi58 = l57.toBigInt\n val bool59 = (((((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (box38.R7[Coll[Byte]].get == coll26)) && (box38.R8[GroupElement].get == ge2)) && (tuple53._1 == l28)\n val box60 = coll4(placeholder[Int](52))\n val coll61 = box60.tokens\n val bi62 = l40.toBigInt - placeholder[Int](53).toBigInt\n val bi63 = box60.R4[Int].get.toBigInt\n val bi64 = box60.value.toBigInt\n val i65 = placeholder[Int](54)\n val bool66 = (coll61(placeholder[Int](55))._2.toBigInt * bi62 * bi63 / bi64 + bi64 * placeholder[Int](56).toBigInt / placeholder[BigInt](57) * i65.toBigInt + bi62 * bi63 >= bi7 + bi58 * bi22 / l23.toBigInt * l6.toBigInt / i65.toBigInt) && (l40 >= placeholder[Int](58).toLong + placeholder[Long](59))\n val bool67 = coll61(placeholder[Int](60))._1 == coll26\n if (l54 == placeholder[Long](61)) { ((((((((((((((bool55 && (l40 >= l35)) && (tuple56._1 == coll37)) && (bi58 >= bi11 - l48.toBigInt * l23.toBigInt / bi22)) && bool59) && (l29 == l54)) && bool43) && bool44) && bool47) && ((tuple49._2 == l10 - l57) && (tuple49._1 == coll37))) && bool31) && bool32) && bool33) && bool34) && bool66) && bool67 } else { (((((((((((!bool66) && bool55) && bool41) && (coll52 == coll8)) && bool59) && ((l54 > HEIGHT.toLong) && (l54 < HEIGHT + placeholder[Int](62).toLong))) && (l29 == placeholder[Long](63))) && bool31) && bool32) && bool33) && bool67) && bool34 }\n )} else { placeholder[Boolean](64) })\n )} else { sigmaProp(placeholder[Boolean](65)) } }\n}",
"address": "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",
"assets": [
{
"tokenId": "4849995b1295d8205c581c25c8f146ad01d3cae741013487a6eb80ee36ba7dd4",
"index": 0,
"amount": 30000,
"name": "Borrow Token RSN-0e",
"decimals": 3,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "580202",
"sigmaType": "(SInt, SInt)",
"renderedValue": "[1,1]"
},
"R6": {
"serializedValue": "59c413d804",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1250,300]"
},
"R8": {
"serializedValue": "07036b1001ad3e368f9ca5272a21affee9e8d0f21df023ee9e8ba0ab0047dc6b9608",
"sigmaType": "SGroupElement",
"renderedValue": "036b1001ad3e368f9ca5272a21affee9e8d0f21df023ee9e8ba0ab0047dc6b9608"
},
"R7": {
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"ergoTree": "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",
"address": "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",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "04ca0f",
"sigmaType": "SInt",
"renderedValue": "997"
}
}
}
],
"outputs": [
{
"boxId": "d62edfd2b55c41363ced9d37467da6498119e1a08e490128a730e800bab019e1",
"transactionId": "06630023baa77b9d60324d036ffed91e3fa362a4fd6dd9ec5a7df8ad96b35d2d",
"blockId": "76985ebb7317c7cc15636a7c696b905e000510abfff0e66294ea4f5eb113d6a0",
"value": 934309010,
"index": 0,
"globalIndex": 33974577,
"creationHeight": 1126393,
"settlementHeight": 1126395,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: CBigInt(1)\n2: 0\n3: 1\n4: 0\n5: 100000000\n6: 100000000\n7: 2\n8: 1\n9: 0\n10: 100000000\n11: 100000000\n12: 100000000\n13: Coll(-71,-9,10,-114,77,16,110,124,-5,-85,36,-38,-59,48,38,126,-14,66,-3,54,92,62,40,10,-72,5,-67,-52,-126,123,-75,-74)\n14: 0\n15: 0\n16: Coll(6,-31,-40,-5,72,-71,-10,-83,51,86,51,5,70,1,-45,-71,-44,-102,-70,-32,119,1,26,-1,-26,-105,58,-94,38,126,-85,-108)\n17: 0\n18: Coll(-117,8,-51,-43,68,-102,-107,-110,-87,-25,-105,17,-41,-41,-110,73,-41,-96,60,83,93,23,-17,-82,-24,62,33,110,-128,-92,76,75)\n19: 0\n20: 3\n21: 5000000\n22: 1000\n23: 2\n24: 2\n25: CBigInt(100)\n26: 3000000\n27: 1000000\n28: 0\n29: 0\n30: 1\n31: 0\n32: 1000000\n33: 1\n34: Coll(70,98,-118,89,125,70,75,-39,91,-88,-107,32,109,-93,126,26,-42,-95,20,49,-80,29,31,-4,108,-1,-107,91,-8,-8,-34,-70)\n35: 1000000\n36: 1000000\n37: 1\n38: 0\n39: 3\n40: 5000000\n41: 1000\n42: 2\n43: 2\n44: CBigInt(100)\n45: 2\n46: 0\n47: 0\n48: false\n49: 1\n50: 3\n51: 0\n52: 3\n53: 5000000\n54: 1000\n55: 2\n56: 2\n57: CBigInt(100)\n58: 3000000\n59: 1000000\n60: 0\n61: 100000000\n62: 5\n63: 100000000\n64: false\n65: false",
"ergoTreeScript": "{\n val box1 = INPUTS(placeholder[Int](0))\n val ge2 = SELF.R8[GroupElement].get\n val coll3 = SELF.propositionBytes\n val coll4 = CONTEXT.dataInputs\n val tuple5 = SELF.R6[(Long, Long)].get\n val l6 = tuple5._1\n val bi7 = placeholder[BigInt](1)\n val coll8 = SELF.tokens\n val tuple9 = coll8(placeholder[Int](2))\n val l10 = tuple9._2\n val bi11 = l10.toBigInt\n val box12 = coll4(placeholder[Int](3))\n val coll13 = box12.R4[Coll[Long]].get\n val i14 = coll13.size\n val tuple15 = SELF.R5[(Int, Int)].get\n val i16 = tuple15._1\n val box17 = coll4(placeholder[Int](4))\n val coll18 = box17.R4[Coll[Long]].get\n val l19 = coll18.slice(tuple15._2, coll18.size).fold(placeholder[Long](5), {(tuple19: (Long, Long)) => tuple19._1 * tuple19._2 / placeholder[Long](6) })\n val box20 = coll4(placeholder[Int](7))\n val coll21 = box20.R4[Coll[Long]].get\n val bi22 = if (i14 == i16) { l19 } else {(\n val i22 = i16 + placeholder[Int](8)\n val l23 = coll21.slice(placeholder[Int](9), coll21.size).fold(l19, {(tuple23: (Long, Long)) => tuple23._1 * tuple23._2 / placeholder[Long](10) })\n if (i14 == i22) { l23 } else { coll13.slice(i22, i14).fold(l23, {(tuple24: (Long, Long)) => tuple24._1 * tuple24._2 / placeholder[Long](11) }) }\n )}.toBigInt\n val l23 = placeholder[Long](12)\n val bi24 = bi7 + bi11 * bi22 / l23.toBigInt\n val coll25 = SELF.R4[Coll[Byte]].get\n val coll26 = SELF.R7[Coll[Byte]].get\n val tuple27 = SELF.R9[(Long, Long)].get\n val l28 = tuple27._1\n val l29 = tuple27._2\n val coll30 = placeholder[Coll[Byte]](13)\n val bool31 = box17.tokens(placeholder[Int](14))._1 == coll30\n val bool32 = box17.R6[Int].get == i16\n val bool33 = box12.tokens(placeholder[Int](15))._1 == placeholder[Coll[Byte]](16)\n val bool34 = (box20.tokens(placeholder[Int](17))._1 == coll30) && (box20.R6[Int].get == i14)\n val l35 = SELF.value\n val coll36 = placeholder[Coll[Byte]](18)\n val coll37 = tuple9._1\n if (box1 == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](19))\n val box39 = coll4(placeholder[Int](20))\n val coll40 = box39.tokens\n val l41 = box38.value\n val bi42 = l41.toBigInt - placeholder[Int](21).toBigInt\n val bi43 = box39.R4[Int].get.toBigInt\n val bi44 = box39.value.toBigInt\n val i45 = placeholder[Int](22)\n val tuple46 = box38.R9[(Long, Long)].get\n proveDlog(ge2) && sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (box38.propositionBytes == coll3) && (\n (\n coll40(placeholder[Int](23))._2.toBigInt * bi42 * bi43 / bi44 + bi44 * placeholder[Int](24).toBigInt / placeholder[BigInt](\n 25\n ) * i45.toBigInt + bi42 * bi43 > bi24 * l6.toBigInt / i45.toBigInt\n ) && (l41 >= placeholder[Int](26).toLong + placeholder[Long](27))\n )\n ) && (box38.tokens(placeholder[Int](28)) == tuple9)\n ) && (\n (\n (\n (\n (((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (\n box38.R7[Coll[Byte]].get == coll26\n )\n ) && (box38.R8[GroupElement].get == ge2)\n ) && (tuple46._1 == l28)\n ) && (tuple46._2 == l29)\n )\n ) && bool31\n ) && bool32\n ) && bool33\n ) && (coll40(placeholder[Int](29))._1 == coll26)\n ) && bool34\n )\n )} else { if (INPUTS(placeholder[Int](30)) == SELF) {(\n val box38 = OUTPUTS(placeholder[Int](31))\n val coll39 = box38.propositionBytes\n val l40 = box38.value\n val bool41 = l40 >= l35 - placeholder[Long](32)\n val box42 = OUTPUTS(placeholder[Int](33))\n val bool43 = blake2b256(box42.propositionBytes) == placeholder[Coll[Byte]](34)\n val bool44 = box42.value >= placeholder[Int](35).toLong + placeholder[Long](36)\n val coll45 = box42.tokens\n val tuple46 = coll45(placeholder[Int](37))\n val bool47 = tuple46._1 == coll36\n val l48 = tuple46._2\n val tuple49 = coll45(placeholder[Int](38))\n val bool50 = tuple49 == tuple9\n val coll51 = box1.tokens\n val coll52 = box38.tokens\n sigmaProp((((((((((((coll39 == coll25) && bool41) && bool43) && bool44) && (bool47 && (l48.toBigInt > bi24))) && bool50) && bool31) && bool32) && bool33) && bool34) || if (coll51.size >= placeholder[Int](39)) {(\n val bi53 = l35.toBigInt - placeholder[Int](40).toBigInt\n val bi54 = box1.R4[Int].get.toBigInt\n val bi55 = box1.value.toBigInt\n val i56 = placeholder[Int](41)\n val bi57 = coll51(placeholder[Int](42))._2.toBigInt * bi53 * bi54 / bi55 + bi55 * placeholder[Int](43).toBigInt / placeholder[BigInt](44) * i56.toBigInt + bi53 * bi54\n val bi58 = bi57 - bi24\n val bi59 = tuple5._2.toBigInt\n val bi60 = bi58 * i56.toBigInt - bi59 / i56.toBigInt\n ((((((((((((bi57 <= bi24 * l6.toBigInt / i56.toBigInt) && (HEIGHT.toLong >= l29)) || (HEIGHT.toLong > l28)) && bool43) && bool47) && bool44) && if (bi60 < bi7) { l48.toBigInt >= bi57 } else {(\n val box61 = OUTPUTS(placeholder[Int](45))\n val tuple62 = box61.tokens(placeholder[Int](46))\n (((l48.toBigInt >= bi24 + bi58 * bi59 / i56.toBigInt) && (tuple62._2.toBigInt >= bi60)) && (box61.propositionBytes == coll25)) && (tuple62._1 == coll36)\n )}) && bool50) && (coll51(placeholder[Int](47))._1 == coll26)) && bool31) && bool32) && bool33) && bool34\n )} else { placeholder[Boolean](48) }) || if ((coll52.size >= placeholder[Int](49)) && (box1.tokens.size < placeholder[Int](50))) {(\n val tuple53 = box38.R9[(Long, Long)].get\n val l54 = tuple53._2\n val bool55 = coll39 == coll3\n val tuple56 = coll52(placeholder[Int](51))\n val l57 = tuple56._2\n val bi58 = l57.toBigInt\n val bool59 = (((((box38.R4[Coll[Byte]].get == coll25) && (box38.R5[(Int, Int)].get == tuple15)) && (box38.R6[(Long, Long)].get == tuple5)) && (box38.R7[Coll[Byte]].get == coll26)) && (box38.R8[GroupElement].get == ge2)) && (tuple53._1 == l28)\n val box60 = coll4(placeholder[Int](52))\n val coll61 = box60.tokens\n val bi62 = l40.toBigInt - placeholder[Int](53).toBigInt\n val bi63 = box60.R4[Int].get.toBigInt\n val bi64 = box60.value.toBigInt\n val i65 = placeholder[Int](54)\n val bool66 = (coll61(placeholder[Int](55))._2.toBigInt * bi62 * bi63 / bi64 + bi64 * placeholder[Int](56).toBigInt / placeholder[BigInt](57) * i65.toBigInt + bi62 * bi63 >= bi7 + bi58 * bi22 / l23.toBigInt * l6.toBigInt / i65.toBigInt) && (l40 >= placeholder[Int](58).toLong + placeholder[Long](59))\n val bool67 = coll61(placeholder[Int](60))._1 == coll26\n if (l54 == placeholder[Long](61)) { ((((((((((((((bool55 && (l40 >= l35)) && (tuple56._1 == coll37)) && (bi58 >= bi11 - l48.toBigInt * l23.toBigInt / bi22)) && bool59) && (l29 == l54)) && bool43) && bool44) && bool47) && ((tuple49._2 == l10 - l57) && (tuple49._1 == coll37))) && bool31) && bool32) && bool33) && bool34) && bool66) && bool67 } else { (((((((((((!bool66) && bool55) && bool41) && (coll52 == coll8)) && bool59) && ((l54 > HEIGHT.toLong) && (l54 < HEIGHT + placeholder[Int](62).toLong))) && (l29 == placeholder[Long](63))) && bool31) && bool32) && bool33) && bool67) && bool34 }\n )} else { placeholder[Boolean](64) })\n )} else { sigmaProp(placeholder[Boolean](65)) } }\n}",
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"assets": [
{
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}
],
"additionalRegisters": {
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"sigmaType": "(SInt, SInt)",
"renderedValue": "[1,1]"
},
"R6": {
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"sigmaType": "(SLong, SLong)",
"renderedValue": "[1250,300]"
},
"R8": {
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"sigmaType": "SGroupElement",
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"R7": {
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"sigmaType": "Coll[SByte]",
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"R9": {
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"sigmaType": "(SLong, SLong)",
"renderedValue": "[1526331,100000000]"
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"R4": {
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"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd036b1001ad3e368f9ca5272a21affee9e8d0f21df023ee9e8ba0ab0047dc6b9608"
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},
{
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"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(6b1001,e0825c,...)))}",
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],
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"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(6b1001,e0825c,...)))}",
"address": "9hGxTdZKhZ12eZNzg7mCGRpuHTvxayNm7gxPz6rmbMtVpjdjYQh",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "e061c570032364fe5410ecc3fd057fda6bc4b5d180ff0aeb6834b6b7fcff99ab",
"mainChain": true
},
{
"boxId": "a387849721e6618f21f721621476270e6e24bb2c7edbb531c86fa971bcc90dbd",
"transactionId": "06630023baa77b9d60324d036ffed91e3fa362a4fd6dd9ec5a7df8ad96b35d2d",
"blockId": "76985ebb7317c7cc15636a7c696b905e000510abfff0e66294ea4f5eb113d6a0",
"value": 2000000,
"index": 3,
"globalIndex": 33974580,
"creationHeight": 1126393,
"settlementHeight": 1126395,
"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": "69e71539e06f5262e110283f3ad462f72899a838e3592243127c5e83659d9021",
"mainChain": true
}
],
"size": 3954,
"isUnconfirmed": false
}