Transaction
ID: 0b946edf49...be00
Inputs (4)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001080016 ERG
Tokens:
795
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001815729 ERG
Spent
Address:
Output transaction:
Settlement height:
Value:
0.25828377 ERG
Tokens:
Loading assets...
Outputs (8)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.181179515 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
790.42
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.005 ERG
Transaction Details
Confirmations: 584,221
Total coins transferred: 0.266179515 ERG
Fees: 0.005 ERG
Fees per byte: 0.000000559 ERG
Raw Transaction Data
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"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 2\n4: 0\n5: 0\n6: 2\n7: 0\n8: 1\n9: 0\n10: 0\n11: 3\n12: 0\n13: 0\n14: 2\n15: 1\n16: 1000\n17: 0\n18: 2\n19: 10\n20: 2\n21: 0\n22: 0\n23: 0\n24: 0\n25: 0\n26: 3\n27: 0\n28: 1000\n29: 1000\n30: 1000\n31: 0\n32: 0\n33: 2\n34: 10\n35: 3\n36: 0\n37: 1000\n38: 2\n39: 1000\n40: 0\n41: 3\n42: 3\n43: 0\n44: 0\n45: false\n46: true\n47: 0\n48: 1\n49: 0\n50: 1\n51: 0\n52: 1\n53: 0\n54: 0\n55: 0\n56: 1\n57: 0\n58: 1\n59: 2\n60: 0\n61: false\n62: 0\n63: Coll(-98,-68,-42,-108,-65,52,-37,78,-29,-30,-52,-22,0,-121,-54,66,-105,7,67,-71,-32,25,-95,-24,-47,69,-24,86,4,103,-58,14)",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val coll2 = Coll[Byte]()\n val l3 = SELF.value\n val tuple4 = coll1.getOrElse(placeholder[Int](0), (coll2, l3))\n val l5 = tuple4._2\n val opt6 = SELF.R6[Coll[Long]]\n val coll7 = opt6.get\n val l8 = coll7(placeholder[Int](1))\n val coll9 = SELF.R7[Coll[Byte]].get\n val l10 = if ((l5 < l8) || (tuple4._1 != coll9)) { placeholder[Long](2) } else { l5 }\n val bool11 = l10 < l8\n val coll12 = SELF.R4[Coll[Byte]].get\n val l13 = CONTEXT.preHeader.timestamp\n val tuple14 = SELF.R5[(Long, Long)].get\n val l15 = tuple14._1\n val l16 = tuple14._2\n val l17 = coll7.getOrElse(placeholder[Int](3), placeholder[Long](4))\n val box18 = CONTEXT.dataInputs(placeholder[Int](5))\n val i19 = box18.R4[Int].get\n val box20 = OUTPUTS.getOrElse(placeholder[Int](6), SELF)\n val coll21 = box20.tokens\n val l22 = box20.value\n val tuple23 = (coll2, l22)\n val tuple24 = coll21.getOrElse(placeholder[Int](7), tuple23)\n val box25 = OUTPUTS.getOrElse(placeholder[Int](8), SELF)\n val coll26 = box25.tokens\n val l27 = box25.value\n val tuple28 = (coll2, l27)\n val tuple29 = coll26.getOrElse(placeholder[Int](9), tuple28)\n val coll30 = SELF.R8[Coll[Byte]].get\n val bool31 = INPUTS(placeholder[Int](10)).id == SELF.id\n sigmaProp(bool11) && proveDlog(decodePoint(coll12.slice(placeholder[Int](11), coll12.size))) || sigmaProp(if (l13 > l15) { if (l13 > l16) {(\n val bool32 = (l10 >= l8) || ((l17 > placeholder[Long](12)) && (l10 >= l17))\n val i33 = OUTPUTS.size\n val box34 = OUTPUTS(placeholder[Int](13))\n val l35 = placeholder[Long](14) * box18.R6[Long].get\n val box36 = box20\n val coll37 = coll21\n val l38 = l22\n val tuple39 = tuple23\n val tuple40 = tuple24\n val box41 = box25\n val box42 = box41.R4[Box].getOrElse(OUTPUTS(i33 - placeholder[Int](15)))\n val opt43 = box42.R4[Int]\n val bool44 = opt43.isDefined\n val i45 = placeholder[Int](16) - i19 - if (bool44) {(\n val i45 = opt43.getOrElse(placeholder[Int](17))\n if ((i45 < placeholder[Int](18)) || (i45 >= placeholder[Int](19))) { i45 } else { if ((i45 == placeholder[Int](20)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) { box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]()).fold(placeholder[Int](21), {(tuple46: (Int, (Coll[Byte], Int))) => tuple46._1 + tuple46._2._2 }) } else { placeholder[Int](22) } }\n )} else { placeholder[Int](23) }\n val coll46 = coll26\n val l47 = l27\n val tuple48 = tuple28\n val tuple49 = tuple29\n val box50 = OUTPUTS.getOrElse(placeholder[Int](24), SELF)\n val tuple51 = coll1(placeholder[Int](25))\n ((!bool32) && allOf(Coll[Boolean](i33 == placeholder[Int](26), box34.tokens == coll1, box34.value >= l3 - l35, box34.propositionBytes == coll12))) || (bool32 && allOf(Coll[Boolean](bool31, ((box36.propositionBytes == coll12) && (tuple40._2 >= if (coll9.size > placeholder[Int](27)) { l10 * i45.toLong / placeholder[Long](28) } else { l10 * i45.toLong / placeholder[Long](29) - l35 })) && (tuple40._1 == coll9), ((box41.propositionBytes == box18.R5[Coll[Byte]].get) && (tuple49._2 >= l10 * i19.toLong / placeholder[Long](30))) && (tuple49._1 == coll9), box50.propositionBytes == coll30, tuple51 == box50.tokens(placeholder[Int](31)), if (bool44) {(\n val i52 = opt43.getOrElse(placeholder[Int](32))\n if ((i52 < placeholder[Int](33)) || (i52 >= placeholder[Int](34))) {(\n val box53 = OUTPUTS.getOrElse(placeholder[Int](35), SELF)\n val tuple54 = box53.tokens.getOrElse(placeholder[Int](36), (coll2, box53.value))\n ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](37)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n )} else { if ((i52 == placeholder[Int](38)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) {(\n val coll53 = box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]())\n val i54 = coll53.size\n coll53.map({(tuple55: (Coll[Byte], Int)) => (tuple55._1, tuple55._2.toLong * l10 / placeholder[Long](39)) }).slice(placeholder[Int](40), i54) == OUTPUTS.slice(placeholder[Int](41), placeholder[Int](42) + i54).map({(box55: Box) =>\n val tuple57 = box55.tokens.getOrElse(placeholder[Int](43), (coll2, box55.value))\n if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](44)) }\n })\n )} else { placeholder[Boolean](45) } }\n )} else { placeholder[Boolean](46) } && (blake2b256(box42.bytes) == tuple51._1))))\n )} else {(\n val box32 = OUTPUTS(placeholder[Int](47))\n val coll33 = box32.tokens\n val tuple34 = coll33.getOrElse(placeholder[Int](48), (coll2, box32.value))\n val l35 = tuple34._2\n val bool36 = l17 > placeholder[Long](49)\n val coll37 = box18.R7[Coll[Long]].get\n val box38 = OUTPUTS(placeholder[Int](50))\n val tuple39 = box38.tokens.getOrElse(placeholder[Int](51), (coll2, box38.value))\n allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](52)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](53)) == coll33(placeholder[Int](54)), box32.propositionBytes == SELF.propositionBytes, box32.R4[Coll[Byte]].get == coll12, if (bool36 && (l35 >= l17)) {(\n val tuple40 = box32.R5[(Long, Long)].get\n (tuple40._1 == l15) && (tuple40._2 <= l13)\n )} else { box32.R5[(Long, Long)].get == (l15, if (l16 - l13 <= coll37(placeholder[Int](55))) { l16 + coll37(placeholder[Int](56)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](57)) { placeholder[Int](58) } else { placeholder[Int](59) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](60))))\n )} } else { placeholder[Boolean](61) }) && sigmaProp(box18.tokens(placeholder[Int](62))._1 == placeholder[Coll[Byte]](63))\n}",
"address": "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",
"assets": [
{
"tokenId": "a500898895f20a86122fb86ef82f6eb2467fed1c0b7243373af32eac0ad8bdda",
"index": 0,
"amount": 20,
"name": "Donald Duck 100x ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "59aaecbba2a763c0e0f2deaa63",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1706077682453,1706543700000]"
},
"R6": {
"serializedValue": "110380c284920580c2d72f8094adb433",
"sigmaType": "Coll[SLong]",
"renderedValue": "[690000000,50000000,6900000000]"
},
"R8": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
},
"R7": {
"serializedValue": "0e00",
"sigmaType": "Coll[SByte]",
"renderedValue": ""
},
"R4": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
}
},
"spentTransactionId": "4e0331292b9109d607d589d14c3b6486e65e24538d80b09d90854132dfab1685",
"mainChain": true
},
{
"boxId": "5f40b4b9e08d4c78534bac6b619c693cff45c1b83d208483bae1a8ca9287a2ab",
"transactionId": "0b946edf491a79d00163fab34a12477379e718174c25d3d7d0aa2ca63fdabe00",
"blockId": "127429ee851d605d4ac67e749b61a8143e8740acddf4b3e37208c9db900dbfd8",
"value": 15000000,
"index": 1,
"globalIndex": 36360471,
"creationHeight": 1184790,
"settlementHeight": 1184793,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 2\n4: 0\n5: 0\n6: 2\n7: 0\n8: 1\n9: 0\n10: 0\n11: 3\n12: 0\n13: 0\n14: 2\n15: 1\n16: 1000\n17: 0\n18: 2\n19: 10\n20: 2\n21: 0\n22: 0\n23: 0\n24: 0\n25: 0\n26: 3\n27: 0\n28: 1000\n29: 1000\n30: 1000\n31: 0\n32: 0\n33: 2\n34: 10\n35: 3\n36: 0\n37: 1000\n38: 2\n39: 1000\n40: 0\n41: 3\n42: 3\n43: 0\n44: 0\n45: false\n46: true\n47: 0\n48: 1\n49: 0\n50: 1\n51: 0\n52: 1\n53: 0\n54: 0\n55: 0\n56: 1\n57: 0\n58: 1\n59: 2\n60: 0\n61: false\n62: 0\n63: Coll(-98,-68,-42,-108,-65,52,-37,78,-29,-30,-52,-22,0,-121,-54,66,-105,7,67,-71,-32,25,-95,-24,-47,69,-24,86,4,103,-58,14)",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val coll2 = Coll[Byte]()\n val l3 = SELF.value\n val tuple4 = coll1.getOrElse(placeholder[Int](0), (coll2, l3))\n val l5 = tuple4._2\n val opt6 = SELF.R6[Coll[Long]]\n val coll7 = opt6.get\n val l8 = coll7(placeholder[Int](1))\n val coll9 = SELF.R7[Coll[Byte]].get\n val l10 = if ((l5 < l8) || (tuple4._1 != coll9)) { placeholder[Long](2) } else { l5 }\n val bool11 = l10 < l8\n val coll12 = SELF.R4[Coll[Byte]].get\n val l13 = CONTEXT.preHeader.timestamp\n val tuple14 = SELF.R5[(Long, Long)].get\n val l15 = tuple14._1\n val l16 = tuple14._2\n val l17 = coll7.getOrElse(placeholder[Int](3), placeholder[Long](4))\n val box18 = CONTEXT.dataInputs(placeholder[Int](5))\n val i19 = box18.R4[Int].get\n val box20 = OUTPUTS.getOrElse(placeholder[Int](6), SELF)\n val coll21 = box20.tokens\n val l22 = box20.value\n val tuple23 = (coll2, l22)\n val tuple24 = coll21.getOrElse(placeholder[Int](7), tuple23)\n val box25 = OUTPUTS.getOrElse(placeholder[Int](8), SELF)\n val coll26 = box25.tokens\n val l27 = box25.value\n val tuple28 = (coll2, l27)\n val tuple29 = coll26.getOrElse(placeholder[Int](9), tuple28)\n val coll30 = SELF.R8[Coll[Byte]].get\n val bool31 = INPUTS(placeholder[Int](10)).id == SELF.id\n sigmaProp(bool11) && proveDlog(decodePoint(coll12.slice(placeholder[Int](11), coll12.size))) || sigmaProp(if (l13 > l15) { if (l13 > l16) {(\n val bool32 = (l10 >= l8) || ((l17 > placeholder[Long](12)) && (l10 >= l17))\n val i33 = OUTPUTS.size\n val box34 = OUTPUTS(placeholder[Int](13))\n val l35 = placeholder[Long](14) * box18.R6[Long].get\n val box36 = box20\n val coll37 = coll21\n val l38 = l22\n val tuple39 = tuple23\n val tuple40 = tuple24\n val box41 = box25\n val box42 = box41.R4[Box].getOrElse(OUTPUTS(i33 - placeholder[Int](15)))\n val opt43 = box42.R4[Int]\n val bool44 = opt43.isDefined\n val i45 = placeholder[Int](16) - i19 - if (bool44) {(\n val i45 = opt43.getOrElse(placeholder[Int](17))\n if ((i45 < placeholder[Int](18)) || (i45 >= placeholder[Int](19))) { i45 } else { if ((i45 == placeholder[Int](20)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) { box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]()).fold(placeholder[Int](21), {(tuple46: (Int, (Coll[Byte], Int))) => tuple46._1 + tuple46._2._2 }) } else { placeholder[Int](22) } }\n )} else { placeholder[Int](23) }\n val coll46 = coll26\n val l47 = l27\n val tuple48 = tuple28\n val tuple49 = tuple29\n val box50 = OUTPUTS.getOrElse(placeholder[Int](24), SELF)\n val tuple51 = coll1(placeholder[Int](25))\n ((!bool32) && allOf(Coll[Boolean](i33 == placeholder[Int](26), box34.tokens == coll1, box34.value >= l3 - l35, box34.propositionBytes == coll12))) || (bool32 && allOf(Coll[Boolean](bool31, ((box36.propositionBytes == coll12) && (tuple40._2 >= if (coll9.size > placeholder[Int](27)) { l10 * i45.toLong / placeholder[Long](28) } else { l10 * i45.toLong / placeholder[Long](29) - l35 })) && (tuple40._1 == coll9), ((box41.propositionBytes == box18.R5[Coll[Byte]].get) && (tuple49._2 >= l10 * i19.toLong / placeholder[Long](30))) && (tuple49._1 == coll9), box50.propositionBytes == coll30, tuple51 == box50.tokens(placeholder[Int](31)), if (bool44) {(\n val i52 = opt43.getOrElse(placeholder[Int](32))\n if ((i52 < placeholder[Int](33)) || (i52 >= placeholder[Int](34))) {(\n val box53 = OUTPUTS.getOrElse(placeholder[Int](35), SELF)\n val tuple54 = box53.tokens.getOrElse(placeholder[Int](36), (coll2, box53.value))\n ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](37)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n )} else { if ((i52 == placeholder[Int](38)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) {(\n val coll53 = box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]())\n val i54 = coll53.size\n coll53.map({(tuple55: (Coll[Byte], Int)) => (tuple55._1, tuple55._2.toLong * l10 / placeholder[Long](39)) }).slice(placeholder[Int](40), i54) == OUTPUTS.slice(placeholder[Int](41), placeholder[Int](42) + i54).map({(box55: Box) =>\n val tuple57 = box55.tokens.getOrElse(placeholder[Int](43), (coll2, box55.value))\n if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](44)) }\n })\n )} else { placeholder[Boolean](45) } }\n )} else { placeholder[Boolean](46) } && (blake2b256(box42.bytes) == tuple51._1))))\n )} else {(\n val box32 = OUTPUTS(placeholder[Int](47))\n val coll33 = box32.tokens\n val tuple34 = coll33.getOrElse(placeholder[Int](48), (coll2, box32.value))\n val l35 = tuple34._2\n val bool36 = l17 > placeholder[Long](49)\n val coll37 = box18.R7[Coll[Long]].get\n val box38 = OUTPUTS(placeholder[Int](50))\n val tuple39 = box38.tokens.getOrElse(placeholder[Int](51), (coll2, box38.value))\n allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](52)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](53)) == coll33(placeholder[Int](54)), box32.propositionBytes == SELF.propositionBytes, box32.R4[Coll[Byte]].get == coll12, if (bool36 && (l35 >= l17)) {(\n val tuple40 = box32.R5[(Long, Long)].get\n (tuple40._1 == l15) && (tuple40._2 <= l13)\n )} else { box32.R5[(Long, Long)].get == (l15, if (l16 - l13 <= coll37(placeholder[Int](55))) { l16 + coll37(placeholder[Int](56)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](57)) { placeholder[Int](58) } else { placeholder[Int](59) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](60))))\n )} } else { placeholder[Boolean](61) }) && sigmaProp(box18.tokens(placeholder[Int](62))._1 == placeholder[Coll[Byte]](63))\n}",
"address": "GZJ5yvPXrP52Pc7JYv5gt8E5hQbQTgwVm6xiRkWLqh3gdEgsWtHomjmDVCPdFGoscG3YZ5z9yWENPJG3Fnn12KrJ89Zc2gsAa3uBYugxaut69LWsi2UTjjXs9xD7Sf26SvpUmoBBqj1i6duhL76iDSJVihZh7yZiZMLRuSBmiUxsf4S16CVtthYwvZ9Z9LyFGEPXTcWiuTHzG3HhPyji429vfRqyZXXTcEWckAim4k9vU3nZ7Q4dAzqjZDJpgD5QJKP8co7uUJEu4Ef1drk1ha2hPH6ZbC9h2awzbUbCqBVUgPkYZRY5cxHSJGQJw7jAQsd6As2eabaH5iwByu7FKvXbNMiWnqHUPekr6bAdja5gYBnHak4sopo5ihXs1TYufRwp6cxGD1Dt8VWLWJHQKgmSzVmd7CGJfZpVg354Q4YSdK93yxLMUEWdnsatu42UNWbwUGpqsgg3inKDdBYgG6mD2BpmJknMbahrMpaCQhcySZcweySkD7E9Ab5oPPbjV8J3XzoFM344C7iBmXPhYEjenocn21WXFkM1dzQQqRju26zVJFtBdgUxc9DUW9WUTxuE6yu8YLjWNRW5zxXy1zVqsmaVmRa7dSDRg63YrsfrBgFXHSqKongEnXzDnzcHV8qirE7NVEqXT6NpfLDAwB7oxyPx3xwz1b4zy2PbXjG7w5N4MaVYvSG13JRxM5pxEgHF1fwh2SuvGKcF3dZnVEPJDChGFDgc2CmZbn1k4UAcheS7ZvVXBRmiZUcYehE9igQPum22yTvAgtLzkAocvM7yzdPGbm32DVMkiJRKkYEMizQvS3RjtXpUrLSDacRpwc1qxF1DKFbGF8JcdC5peR69mhyQT9r6yiVgLYBWD6t3M5otQ4jDCWUkeLy6zfNwead8VPU1DeTtwZ5neeGR4MwMYZbvvEJSFsZFJDwxEnRxjSzwzkG7SCnLmDUAhjbBXT3fXmoEMB4S7b34qGYk8V9GVhcBEyTYYQStEZxdDsKQUvd14twknkna6xkjFJWU6t18vvdxYpK7PkyJBtrXKyj7R3gesqDmgRaDTnPB47MKaJ3K953uVVefrfFovRQ4fibP6GcH2m6thn2pDvPwTvufXSCAxMpheER65DaSKa3UZnXwKr4hTN7sCPPDYDL3zCoFJBSFK9qoy28jA67Kiet4kbnBjj3c3Z66fC6EXKcS5EDCwNXBSBofR1zoFXdN29yS4dUbjMPygS9v6dnySyP7GiCrxhpi9stkbT7BG317SCP3LEcVBC6kLZwLRtbgsbf61bpcunVvTjEKxvQc66se5k66qfk3fwGMAiP7YypepNFtgfcy6YXQwCUGC8CNreCLh6ae6Q2PapspTAxmMu7m8QMmPMnSiUwr2soea8ui5fn8yMFy4ZnFpnJtvny7Bm2KmYfjC4gwKtg89HJD3KuAQKN83oXiS8h9YSEiYsTzAoXgG8h7ecmb3Z5Y9vxy9T9NGx5CBxsBa8QiwBfdy5C3C6f4pX8eeuWoqAW5HFuVy96vKyQWTVEgJ6jtDgEcRHJkB1uCZdawAXttNbZnj5xF4Ev5X4xe5Lptgr2Vcr2n9WuoDChohbsJSFMP2ucrDAzq1DuDCC1dUH5Vk8pFV9CYeyrgndWwb4XTZAJ1APMtZhLTcRtGJ9piaMEZZEvyK3ZidRWVguAWdGR5Xg3o1SeM95UGwhfJHmNDfnXekcFfy9zBZ8pprWucVUXQsjeCbde1ftEukPmjCVVUc4kCTpe7sq8FVjpixso4ytHcR2fpwMM1nDc8SH1DDtVYC82dfi2n2QX1h3qatt",
"assets": [
{
"tokenId": "a500898895f20a86122fb86ef82f6eb2467fed1c0b7243373af32eac0ad8bdda",
"index": 0,
"amount": 10,
"name": "Donald Duck 100x ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "59aaecbba2a763c0c0d883ac63",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1706077682453,1706716500000]"
},
"R6": {
"serializedValue": "11038094ebdc0380c2d72f8094adb433",
"sigmaType": "Coll[SLong]",
"renderedValue": "[500000000,50000000,6900000000]"
},
"R8": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
},
"R7": {
"serializedValue": "0e00",
"sigmaType": "Coll[SByte]",
"renderedValue": ""
},
"R4": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
}
},
"spentTransactionId": "24524e26daeb0a7add2830d81242e3d08755ead3f9a043f4383f359377534b9b",
"mainChain": true
},
{
"boxId": "5a7149bc8f1e24701b8786c49dc079e7b6d942c3ddefba63726501e36b4574f5",
"transactionId": "0b946edf491a79d00163fab34a12477379e718174c25d3d7d0aa2ca63fdabe00",
"blockId": "127429ee851d605d4ac67e749b61a8143e8740acddf4b3e37208c9db900dbfd8",
"value": 15000000,
"index": 2,
"globalIndex": 36360472,
"creationHeight": 1184790,
"settlementHeight": 1184793,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 2\n4: 0\n5: 0\n6: 2\n7: 0\n8: 1\n9: 0\n10: 0\n11: 3\n12: 0\n13: 0\n14: 2\n15: 1\n16: 1000\n17: 0\n18: 2\n19: 10\n20: 2\n21: 0\n22: 0\n23: 0\n24: 0\n25: 0\n26: 3\n27: 0\n28: 1000\n29: 1000\n30: 1000\n31: 0\n32: 0\n33: 2\n34: 10\n35: 3\n36: 0\n37: 1000\n38: 2\n39: 1000\n40: 0\n41: 3\n42: 3\n43: 0\n44: 0\n45: false\n46: true\n47: 0\n48: 1\n49: 0\n50: 1\n51: 0\n52: 1\n53: 0\n54: 0\n55: 0\n56: 1\n57: 0\n58: 1\n59: 2\n60: 0\n61: false\n62: 0\n63: Coll(-98,-68,-42,-108,-65,52,-37,78,-29,-30,-52,-22,0,-121,-54,66,-105,7,67,-71,-32,25,-95,-24,-47,69,-24,86,4,103,-58,14)",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val coll2 = Coll[Byte]()\n val l3 = SELF.value\n val tuple4 = coll1.getOrElse(placeholder[Int](0), (coll2, l3))\n val l5 = tuple4._2\n val opt6 = SELF.R6[Coll[Long]]\n val coll7 = opt6.get\n val l8 = coll7(placeholder[Int](1))\n val coll9 = SELF.R7[Coll[Byte]].get\n val l10 = if ((l5 < l8) || (tuple4._1 != coll9)) { placeholder[Long](2) } else { l5 }\n val bool11 = l10 < l8\n val coll12 = SELF.R4[Coll[Byte]].get\n val l13 = CONTEXT.preHeader.timestamp\n val tuple14 = SELF.R5[(Long, Long)].get\n val l15 = tuple14._1\n val l16 = tuple14._2\n val l17 = coll7.getOrElse(placeholder[Int](3), placeholder[Long](4))\n val box18 = CONTEXT.dataInputs(placeholder[Int](5))\n val i19 = box18.R4[Int].get\n val box20 = OUTPUTS.getOrElse(placeholder[Int](6), SELF)\n val coll21 = box20.tokens\n val l22 = box20.value\n val tuple23 = (coll2, l22)\n val tuple24 = coll21.getOrElse(placeholder[Int](7), tuple23)\n val box25 = OUTPUTS.getOrElse(placeholder[Int](8), SELF)\n val coll26 = box25.tokens\n val l27 = box25.value\n val tuple28 = (coll2, l27)\n val tuple29 = coll26.getOrElse(placeholder[Int](9), tuple28)\n val coll30 = SELF.R8[Coll[Byte]].get\n val bool31 = INPUTS(placeholder[Int](10)).id == SELF.id\n sigmaProp(bool11) && proveDlog(decodePoint(coll12.slice(placeholder[Int](11), coll12.size))) || sigmaProp(if (l13 > l15) { if (l13 > l16) {(\n val bool32 = (l10 >= l8) || ((l17 > placeholder[Long](12)) && (l10 >= l17))\n val i33 = OUTPUTS.size\n val box34 = OUTPUTS(placeholder[Int](13))\n val l35 = placeholder[Long](14) * box18.R6[Long].get\n val box36 = box20\n val coll37 = coll21\n val l38 = l22\n val tuple39 = tuple23\n val tuple40 = tuple24\n val box41 = box25\n val box42 = box41.R4[Box].getOrElse(OUTPUTS(i33 - placeholder[Int](15)))\n val opt43 = box42.R4[Int]\n val bool44 = opt43.isDefined\n val i45 = placeholder[Int](16) - i19 - if (bool44) {(\n val i45 = opt43.getOrElse(placeholder[Int](17))\n if ((i45 < placeholder[Int](18)) || (i45 >= placeholder[Int](19))) { i45 } else { if ((i45 == placeholder[Int](20)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) { box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]()).fold(placeholder[Int](21), {(tuple46: (Int, (Coll[Byte], Int))) => tuple46._1 + tuple46._2._2 }) } else { placeholder[Int](22) } }\n )} else { placeholder[Int](23) }\n val coll46 = coll26\n val l47 = l27\n val tuple48 = tuple28\n val tuple49 = tuple29\n val box50 = OUTPUTS.getOrElse(placeholder[Int](24), SELF)\n val tuple51 = coll1(placeholder[Int](25))\n ((!bool32) && allOf(Coll[Boolean](i33 == placeholder[Int](26), box34.tokens == coll1, box34.value >= l3 - l35, box34.propositionBytes == coll12))) || (bool32 && allOf(Coll[Boolean](bool31, ((box36.propositionBytes == coll12) && (tuple40._2 >= if (coll9.size > placeholder[Int](27)) { l10 * i45.toLong / placeholder[Long](28) } else { l10 * i45.toLong / placeholder[Long](29) - l35 })) && (tuple40._1 == coll9), ((box41.propositionBytes == box18.R5[Coll[Byte]].get) && (tuple49._2 >= l10 * i19.toLong / placeholder[Long](30))) && (tuple49._1 == coll9), box50.propositionBytes == coll30, tuple51 == box50.tokens(placeholder[Int](31)), if (bool44) {(\n val i52 = opt43.getOrElse(placeholder[Int](32))\n if ((i52 < placeholder[Int](33)) || (i52 >= placeholder[Int](34))) {(\n val box53 = OUTPUTS.getOrElse(placeholder[Int](35), SELF)\n val tuple54 = box53.tokens.getOrElse(placeholder[Int](36), (coll2, box53.value))\n ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](37)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n )} else { if ((i52 == placeholder[Int](38)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) {(\n val coll53 = box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]())\n val i54 = coll53.size\n coll53.map({(tuple55: (Coll[Byte], Int)) => (tuple55._1, tuple55._2.toLong * l10 / placeholder[Long](39)) }).slice(placeholder[Int](40), i54) == OUTPUTS.slice(placeholder[Int](41), placeholder[Int](42) + i54).map({(box55: Box) =>\n val tuple57 = box55.tokens.getOrElse(placeholder[Int](43), (coll2, box55.value))\n if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](44)) }\n })\n )} else { placeholder[Boolean](45) } }\n )} else { placeholder[Boolean](46) } && (blake2b256(box42.bytes) == tuple51._1))))\n )} else {(\n val box32 = OUTPUTS(placeholder[Int](47))\n val coll33 = box32.tokens\n val tuple34 = coll33.getOrElse(placeholder[Int](48), (coll2, box32.value))\n val l35 = tuple34._2\n val bool36 = l17 > placeholder[Long](49)\n val coll37 = box18.R7[Coll[Long]].get\n val box38 = OUTPUTS(placeholder[Int](50))\n val tuple39 = box38.tokens.getOrElse(placeholder[Int](51), (coll2, box38.value))\n allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](52)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](53)) == coll33(placeholder[Int](54)), box32.propositionBytes == SELF.propositionBytes, box32.R4[Coll[Byte]].get == coll12, if (bool36 && (l35 >= l17)) {(\n val tuple40 = box32.R5[(Long, Long)].get\n (tuple40._1 == l15) && (tuple40._2 <= l13)\n )} else { box32.R5[(Long, Long)].get == (l15, if (l16 - l13 <= coll37(placeholder[Int](55))) { l16 + coll37(placeholder[Int](56)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](57)) { placeholder[Int](58) } else { placeholder[Int](59) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](60))))\n )} } else { placeholder[Boolean](61) }) && sigmaProp(box18.tokens(placeholder[Int](62))._1 == placeholder[Coll[Byte]](63))\n}",
"address": "GZJ5yvPXrP52Pc7JYv5gt8E5hQbQTgwVm6xiRkWLqh3gdEgsWtHomjmDVCPdFGoscG3YZ5z9yWENPJG3Fnn12KrJ89Zc2gsAa3uBYugxaut69LWsi2UTjjXs9xD7Sf26SvpUmoBBqj1i6duhL76iDSJVihZh7yZiZMLRuSBmiUxsf4S16CVtthYwvZ9Z9LyFGEPXTcWiuTHzG3HhPyji429vfRqyZXXTcEWckAim4k9vU3nZ7Q4dAzqjZDJpgD5QJKP8co7uUJEu4Ef1drk1ha2hPH6ZbC9h2awzbUbCqBVUgPkYZRY5cxHSJGQJw7jAQsd6As2eabaH5iwByu7FKvXbNMiWnqHUPekr6bAdja5gYBnHak4sopo5ihXs1TYufRwp6cxGD1Dt8VWLWJHQKgmSzVmd7CGJfZpVg354Q4YSdK93yxLMUEWdnsatu42UNWbwUGpqsgg3inKDdBYgG6mD2BpmJknMbahrMpaCQhcySZcweySkD7E9Ab5oPPbjV8J3XzoFM344C7iBmXPhYEjenocn21WXFkM1dzQQqRju26zVJFtBdgUxc9DUW9WUTxuE6yu8YLjWNRW5zxXy1zVqsmaVmRa7dSDRg63YrsfrBgFXHSqKongEnXzDnzcHV8qirE7NVEqXT6NpfLDAwB7oxyPx3xwz1b4zy2PbXjG7w5N4MaVYvSG13JRxM5pxEgHF1fwh2SuvGKcF3dZnVEPJDChGFDgc2CmZbn1k4UAcheS7ZvVXBRmiZUcYehE9igQPum22yTvAgtLzkAocvM7yzdPGbm32DVMkiJRKkYEMizQvS3RjtXpUrLSDacRpwc1qxF1DKFbGF8JcdC5peR69mhyQT9r6yiVgLYBWD6t3M5otQ4jDCWUkeLy6zfNwead8VPU1DeTtwZ5neeGR4MwMYZbvvEJSFsZFJDwxEnRxjSzwzkG7SCnLmDUAhjbBXT3fXmoEMB4S7b34qGYk8V9GVhcBEyTYYQStEZxdDsKQUvd14twknkna6xkjFJWU6t18vvdxYpK7PkyJBtrXKyj7R3gesqDmgRaDTnPB47MKaJ3K953uVVefrfFovRQ4fibP6GcH2m6thn2pDvPwTvufXSCAxMpheER65DaSKa3UZnXwKr4hTN7sCPPDYDL3zCoFJBSFK9qoy28jA67Kiet4kbnBjj3c3Z66fC6EXKcS5EDCwNXBSBofR1zoFXdN29yS4dUbjMPygS9v6dnySyP7GiCrxhpi9stkbT7BG317SCP3LEcVBC6kLZwLRtbgsbf61bpcunVvTjEKxvQc66se5k66qfk3fwGMAiP7YypepNFtgfcy6YXQwCUGC8CNreCLh6ae6Q2PapspTAxmMu7m8QMmPMnSiUwr2soea8ui5fn8yMFy4ZnFpnJtvny7Bm2KmYfjC4gwKtg89HJD3KuAQKN83oXiS8h9YSEiYsTzAoXgG8h7ecmb3Z5Y9vxy9T9NGx5CBxsBa8QiwBfdy5C3C6f4pX8eeuWoqAW5HFuVy96vKyQWTVEgJ6jtDgEcRHJkB1uCZdawAXttNbZnj5xF4Ev5X4xe5Lptgr2Vcr2n9WuoDChohbsJSFMP2ucrDAzq1DuDCC1dUH5Vk8pFV9CYeyrgndWwb4XTZAJ1APMtZhLTcRtGJ9piaMEZZEvyK3ZidRWVguAWdGR5Xg3o1SeM95UGwhfJHmNDfnXekcFfy9zBZ8pprWucVUXQsjeCbde1ftEukPmjCVVUc4kCTpe7sq8FVjpixso4ytHcR2fpwMM1nDc8SH1DDtVYC82dfi2n2QX1h3qatt",
"assets": [
{
"tokenId": "a500898895f20a86122fb86ef82f6eb2467fed1c0b7243373af32eac0ad8bdda",
"index": 0,
"amount": 10,
"name": "Donald Duck 100x ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "59aaecbba2a763a0b9a3d9be63",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1706077682453,1709222162000]"
},
"R6": {
"serializedValue": "11038094ebdc0380c2d72f80c8c38b8204",
"sigmaType": "Coll[SLong]",
"renderedValue": "[500000000,50000000,69000000000]"
},
"R8": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
},
"R7": {
"serializedValue": "0e00",
"sigmaType": "Coll[SByte]",
"renderedValue": ""
},
"R4": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
}
},
"spentTransactionId": "0ce452e52e0e8824f9e04b06efbdb31fed7e37df1d46eaaf0947c5b0230c34d8",
"mainChain": true
},
{
"boxId": "d17a27b80bdcac228e60638bcd4cbdcff7f46d051867975dfc052f5a17d36eec",
"transactionId": "0b946edf491a79d00163fab34a12477379e718174c25d3d7d0aa2ca63fdabe00",
"blockId": "127429ee851d605d4ac67e749b61a8143e8740acddf4b3e37208c9db900dbfd8",
"value": 15000000,
"index": 3,
"globalIndex": 36360473,
"creationHeight": 1184790,
"settlementHeight": 1184793,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 2\n4: 0\n5: 0\n6: 2\n7: 0\n8: 1\n9: 0\n10: 0\n11: 3\n12: 0\n13: 0\n14: 2\n15: 1\n16: 1000\n17: 0\n18: 2\n19: 10\n20: 2\n21: 0\n22: 0\n23: 0\n24: 0\n25: 0\n26: 3\n27: 0\n28: 1000\n29: 1000\n30: 1000\n31: 0\n32: 0\n33: 2\n34: 10\n35: 3\n36: 0\n37: 1000\n38: 2\n39: 1000\n40: 0\n41: 3\n42: 3\n43: 0\n44: 0\n45: false\n46: true\n47: 0\n48: 1\n49: 0\n50: 1\n51: 0\n52: 1\n53: 0\n54: 0\n55: 0\n56: 1\n57: 0\n58: 1\n59: 2\n60: 0\n61: false\n62: 0\n63: Coll(-98,-68,-42,-108,-65,52,-37,78,-29,-30,-52,-22,0,-121,-54,66,-105,7,67,-71,-32,25,-95,-24,-47,69,-24,86,4,103,-58,14)",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val coll2 = Coll[Byte]()\n val l3 = SELF.value\n val tuple4 = coll1.getOrElse(placeholder[Int](0), (coll2, l3))\n val l5 = tuple4._2\n val opt6 = SELF.R6[Coll[Long]]\n val coll7 = opt6.get\n val l8 = coll7(placeholder[Int](1))\n val coll9 = SELF.R7[Coll[Byte]].get\n val l10 = if ((l5 < l8) || (tuple4._1 != coll9)) { placeholder[Long](2) } else { l5 }\n val bool11 = l10 < l8\n val coll12 = SELF.R4[Coll[Byte]].get\n val l13 = CONTEXT.preHeader.timestamp\n val tuple14 = SELF.R5[(Long, Long)].get\n val l15 = tuple14._1\n val l16 = tuple14._2\n val l17 = coll7.getOrElse(placeholder[Int](3), placeholder[Long](4))\n val box18 = CONTEXT.dataInputs(placeholder[Int](5))\n val i19 = box18.R4[Int].get\n val box20 = OUTPUTS.getOrElse(placeholder[Int](6), SELF)\n val coll21 = box20.tokens\n val l22 = box20.value\n val tuple23 = (coll2, l22)\n val tuple24 = coll21.getOrElse(placeholder[Int](7), tuple23)\n val box25 = OUTPUTS.getOrElse(placeholder[Int](8), SELF)\n val coll26 = box25.tokens\n val l27 = box25.value\n val tuple28 = (coll2, l27)\n val tuple29 = coll26.getOrElse(placeholder[Int](9), tuple28)\n val coll30 = SELF.R8[Coll[Byte]].get\n val bool31 = INPUTS(placeholder[Int](10)).id == SELF.id\n sigmaProp(bool11) && proveDlog(decodePoint(coll12.slice(placeholder[Int](11), coll12.size))) || sigmaProp(if (l13 > l15) { if (l13 > l16) {(\n val bool32 = (l10 >= l8) || ((l17 > placeholder[Long](12)) && (l10 >= l17))\n val i33 = OUTPUTS.size\n val box34 = OUTPUTS(placeholder[Int](13))\n val l35 = placeholder[Long](14) * box18.R6[Long].get\n val box36 = box20\n val coll37 = coll21\n val l38 = l22\n val tuple39 = tuple23\n val tuple40 = tuple24\n val box41 = box25\n val box42 = box41.R4[Box].getOrElse(OUTPUTS(i33 - placeholder[Int](15)))\n val opt43 = box42.R4[Int]\n val bool44 = opt43.isDefined\n val i45 = placeholder[Int](16) - i19 - if (bool44) {(\n val i45 = opt43.getOrElse(placeholder[Int](17))\n if ((i45 < placeholder[Int](18)) || (i45 >= placeholder[Int](19))) { i45 } else { if ((i45 == placeholder[Int](20)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) { box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]()).fold(placeholder[Int](21), {(tuple46: (Int, (Coll[Byte], Int))) => tuple46._1 + tuple46._2._2 }) } else { placeholder[Int](22) } }\n )} else { placeholder[Int](23) }\n val coll46 = coll26\n val l47 = l27\n val tuple48 = tuple28\n val tuple49 = tuple29\n val box50 = OUTPUTS.getOrElse(placeholder[Int](24), SELF)\n val tuple51 = coll1(placeholder[Int](25))\n ((!bool32) && allOf(Coll[Boolean](i33 == placeholder[Int](26), box34.tokens == coll1, box34.value >= l3 - l35, box34.propositionBytes == coll12))) || (bool32 && allOf(Coll[Boolean](bool31, ((box36.propositionBytes == coll12) && (tuple40._2 >= if (coll9.size > placeholder[Int](27)) { l10 * i45.toLong / placeholder[Long](28) } else { l10 * i45.toLong / placeholder[Long](29) - l35 })) && (tuple40._1 == coll9), ((box41.propositionBytes == box18.R5[Coll[Byte]].get) && (tuple49._2 >= l10 * i19.toLong / placeholder[Long](30))) && (tuple49._1 == coll9), box50.propositionBytes == coll30, tuple51 == box50.tokens(placeholder[Int](31)), if (bool44) {(\n val i52 = opt43.getOrElse(placeholder[Int](32))\n if ((i52 < placeholder[Int](33)) || (i52 >= placeholder[Int](34))) {(\n val box53 = OUTPUTS.getOrElse(placeholder[Int](35), SELF)\n val tuple54 = box53.tokens.getOrElse(placeholder[Int](36), (coll2, box53.value))\n ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](37)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n )} else { if ((i52 == placeholder[Int](38)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) {(\n val coll53 = box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]())\n val i54 = coll53.size\n coll53.map({(tuple55: (Coll[Byte], Int)) => (tuple55._1, tuple55._2.toLong * l10 / placeholder[Long](39)) }).slice(placeholder[Int](40), i54) == OUTPUTS.slice(placeholder[Int](41), placeholder[Int](42) + i54).map({(box55: Box) =>\n val tuple57 = box55.tokens.getOrElse(placeholder[Int](43), (coll2, box55.value))\n if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](44)) }\n })\n )} else { placeholder[Boolean](45) } }\n )} else { placeholder[Boolean](46) } && (blake2b256(box42.bytes) == tuple51._1))))\n )} else {(\n val box32 = OUTPUTS(placeholder[Int](47))\n val coll33 = box32.tokens\n val tuple34 = coll33.getOrElse(placeholder[Int](48), (coll2, box32.value))\n val l35 = tuple34._2\n val bool36 = l17 > placeholder[Long](49)\n val coll37 = box18.R7[Coll[Long]].get\n val box38 = OUTPUTS(placeholder[Int](50))\n val tuple39 = box38.tokens.getOrElse(placeholder[Int](51), (coll2, box38.value))\n allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](52)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](53)) == coll33(placeholder[Int](54)), box32.propositionBytes == SELF.propositionBytes, box32.R4[Coll[Byte]].get == coll12, if (bool36 && (l35 >= l17)) {(\n val tuple40 = box32.R5[(Long, Long)].get\n (tuple40._1 == l15) && (tuple40._2 <= l13)\n )} else { box32.R5[(Long, Long)].get == (l15, if (l16 - l13 <= coll37(placeholder[Int](55))) { l16 + coll37(placeholder[Int](56)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](57)) { placeholder[Int](58) } else { placeholder[Int](59) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](60))))\n )} } else { placeholder[Boolean](61) }) && sigmaProp(box18.tokens(placeholder[Int](62))._1 == placeholder[Coll[Byte]](63))\n}",
"address": "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",
"assets": [
{
"tokenId": "a500898895f20a86122fb86ef82f6eb2467fed1c0b7243373af32eac0ad8bdda",
"index": 0,
"amount": 10,
"name": "Donald Duck 100x ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "59aaecbba2a763a0e9c2fbe563",
"sigmaType": "(SLong, SLong)",
"renderedValue": "[1706077682453,1714492562000]"
},
"R6": {
"serializedValue": "110380c284920580c2d72f80c8c38b8204",
"sigmaType": "Coll[SLong]",
"renderedValue": "[690000000,50000000,69000000000]"
},
"R8": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
},
"R7": {
"serializedValue": "0e00",
"sigmaType": "Coll[SByte]",
"renderedValue": ""
},
"R4": {
"serializedValue": "0e240008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
}
},
"spentTransactionId": "26bb7fbce458a5106e256f6ee0edd300b50a87a0be7d5e480f2ef22207db0898",
"mainChain": true
},
{
"boxId": "ca0a0cf236890c22749ed9ce72191a38dc0d81533097a06316f6ed03ed664a4e",
"transactionId": "0b946edf491a79d00163fab34a12477379e718174c25d3d7d0aa2ca63fdabe00",
"blockId": "127429ee851d605d4ac67e749b61a8143e8740acddf4b3e37208c9db900dbfd8",
"value": 15000000,
"index": 4,
"globalIndex": 36360474,
"creationHeight": 1184790,
"settlementHeight": 1184793,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 2\n4: 0\n5: 0\n6: 2\n7: 0\n8: 1\n9: 0\n10: 0\n11: 3\n12: 0\n13: 0\n14: 2\n15: 1\n16: 1000\n17: 0\n18: 2\n19: 10\n20: 2\n21: 0\n22: 0\n23: 0\n24: 0\n25: 0\n26: 3\n27: 0\n28: 1000\n29: 1000\n30: 1000\n31: 0\n32: 0\n33: 2\n34: 10\n35: 3\n36: 0\n37: 1000\n38: 2\n39: 1000\n40: 0\n41: 3\n42: 3\n43: 0\n44: 0\n45: false\n46: true\n47: 0\n48: 1\n49: 0\n50: 1\n51: 0\n52: 1\n53: 0\n54: 0\n55: 0\n56: 1\n57: 0\n58: 1\n59: 2\n60: 0\n61: false\n62: 0\n63: Coll(-98,-68,-42,-108,-65,52,-37,78,-29,-30,-52,-22,0,-121,-54,66,-105,7,67,-71,-32,25,-95,-24,-47,69,-24,86,4,103,-58,14)",
"ergoTreeScript": "{\n val coll1 = SELF.tokens\n val coll2 = Coll[Byte]()\n val l3 = SELF.value\n val tuple4 = coll1.getOrElse(placeholder[Int](0), (coll2, l3))\n val l5 = tuple4._2\n val opt6 = SELF.R6[Coll[Long]]\n val coll7 = opt6.get\n val l8 = coll7(placeholder[Int](1))\n val coll9 = SELF.R7[Coll[Byte]].get\n val l10 = if ((l5 < l8) || (tuple4._1 != coll9)) { placeholder[Long](2) } else { l5 }\n val bool11 = l10 < l8\n val coll12 = SELF.R4[Coll[Byte]].get\n val l13 = CONTEXT.preHeader.timestamp\n val tuple14 = SELF.R5[(Long, Long)].get\n val l15 = tuple14._1\n val l16 = tuple14._2\n val l17 = coll7.getOrElse(placeholder[Int](3), placeholder[Long](4))\n val box18 = CONTEXT.dataInputs(placeholder[Int](5))\n val i19 = box18.R4[Int].get\n val box20 = OUTPUTS.getOrElse(placeholder[Int](6), SELF)\n val coll21 = box20.tokens\n val l22 = box20.value\n val tuple23 = (coll2, l22)\n val tuple24 = coll21.getOrElse(placeholder[Int](7), tuple23)\n val box25 = OUTPUTS.getOrElse(placeholder[Int](8), SELF)\n val coll26 = box25.tokens\n val l27 = box25.value\n val tuple28 = (coll2, l27)\n val tuple29 = coll26.getOrElse(placeholder[Int](9), tuple28)\n val coll30 = SELF.R8[Coll[Byte]].get\n val bool31 = INPUTS(placeholder[Int](10)).id == SELF.id\n sigmaProp(bool11) && proveDlog(decodePoint(coll12.slice(placeholder[Int](11), coll12.size))) || sigmaProp(if (l13 > l15) { if (l13 > l16) {(\n val bool32 = (l10 >= l8) || ((l17 > placeholder[Long](12)) && (l10 >= l17))\n val i33 = OUTPUTS.size\n val box34 = OUTPUTS(placeholder[Int](13))\n val l35 = placeholder[Long](14) * box18.R6[Long].get\n val box36 = box20\n val coll37 = coll21\n val l38 = l22\n val tuple39 = tuple23\n val tuple40 = tuple24\n val box41 = box25\n val box42 = box41.R4[Box].getOrElse(OUTPUTS(i33 - placeholder[Int](15)))\n val opt43 = box42.R4[Int]\n val bool44 = opt43.isDefined\n val i45 = placeholder[Int](16) - i19 - if (bool44) {(\n val i45 = opt43.getOrElse(placeholder[Int](17))\n if ((i45 < placeholder[Int](18)) || (i45 >= placeholder[Int](19))) { i45 } else { if ((i45 == placeholder[Int](20)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) { box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]()).fold(placeholder[Int](21), {(tuple46: (Int, (Coll[Byte], Int))) => tuple46._1 + tuple46._2._2 }) } else { placeholder[Int](22) } }\n )} else { placeholder[Int](23) }\n val coll46 = coll26\n val l47 = l27\n val tuple48 = tuple28\n val tuple49 = tuple29\n val box50 = OUTPUTS.getOrElse(placeholder[Int](24), SELF)\n val tuple51 = coll1(placeholder[Int](25))\n ((!bool32) && allOf(Coll[Boolean](i33 == placeholder[Int](26), box34.tokens == coll1, box34.value >= l3 - l35, box34.propositionBytes == coll12))) || (bool32 && allOf(Coll[Boolean](bool31, ((box36.propositionBytes == coll12) && (tuple40._2 >= if (coll9.size > placeholder[Int](27)) { l10 * i45.toLong / placeholder[Long](28) } else { l10 * i45.toLong / placeholder[Long](29) - l35 })) && (tuple40._1 == coll9), ((box41.propositionBytes == box18.R5[Coll[Byte]].get) && (tuple49._2 >= l10 * i19.toLong / placeholder[Long](30))) && (tuple49._1 == coll9), box50.propositionBytes == coll30, tuple51 == box50.tokens(placeholder[Int](31)), if (bool44) {(\n val i52 = opt43.getOrElse(placeholder[Int](32))\n if ((i52 < placeholder[Int](33)) || (i52 >= placeholder[Int](34))) {(\n val box53 = OUTPUTS.getOrElse(placeholder[Int](35), SELF)\n val tuple54 = box53.tokens.getOrElse(placeholder[Int](36), (coll2, box53.value))\n ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](37)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n )} else { if ((i52 == placeholder[Int](38)) && box42.R5[Coll[(Coll[Byte], Int)]].isDefined) {(\n val coll53 = box42.R5[Coll[(Coll[Byte], Int)]].getOrElse(Coll[(Coll[Byte], Int)]())\n val i54 = coll53.size\n coll53.map({(tuple55: (Coll[Byte], Int)) => (tuple55._1, tuple55._2.toLong * l10 / placeholder[Long](39)) }).slice(placeholder[Int](40), i54) == OUTPUTS.slice(placeholder[Int](41), placeholder[Int](42) + i54).map({(box55: Box) =>\n val tuple57 = box55.tokens.getOrElse(placeholder[Int](43), (coll2, box55.value))\n if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](44)) }\n })\n )} else { placeholder[Boolean](45) } }\n )} else { placeholder[Boolean](46) } && (blake2b256(box42.bytes) == tuple51._1))))\n )} else {(\n val box32 = OUTPUTS(placeholder[Int](47))\n val coll33 = box32.tokens\n val tuple34 = coll33.getOrElse(placeholder[Int](48), (coll2, box32.value))\n val l35 = tuple34._2\n val bool36 = l17 > placeholder[Long](49)\n val coll37 = box18.R7[Coll[Long]].get\n val box38 = OUTPUTS(placeholder[Int](50))\n val tuple39 = box38.tokens.getOrElse(placeholder[Int](51), (coll2, box38.value))\n allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](52)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](53)) == coll33(placeholder[Int](54)), box32.propositionBytes == SELF.propositionBytes, box32.R4[Coll[Byte]].get == coll12, if (bool36 && (l35 >= l17)) {(\n val tuple40 = box32.R5[(Long, Long)].get\n (tuple40._1 == l15) && (tuple40._2 <= l13)\n )} else { box32.R5[(Long, Long)].get == (l15, if (l16 - l13 <= coll37(placeholder[Int](55))) { l16 + coll37(placeholder[Int](56)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](57)) { placeholder[Int](58) } else { placeholder[Int](59) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](60))))\n )} } else { placeholder[Boolean](61) }) && sigmaProp(box18.tokens(placeholder[Int](62))._1 == placeholder[Coll[Byte]](63))\n}",
"address": "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",
"assets": [
{
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"index": 0,
"amount": 10,
"name": "Donald Duck 100x ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
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"sigmaType": "(SLong, SLong)",
"renderedValue": "[1706077682453,1711900562000]"
},
"R6": {
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"sigmaType": "Coll[SLong]",
"renderedValue": "[690000000,50000000,69000000000]"
},
"R8": {
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"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
},
"R7": {
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"sigmaType": "Coll[SByte]",
"renderedValue": ""
},
"R4": {
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"sigmaType": "Coll[SByte]",
"renderedValue": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f"
}
},
"spentTransactionId": "9875f260698641950fd9ab11bb844f7aea70b3456800ae12ff10e7e7bcae49ed",
"mainChain": true
},
{
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"value": 181179515,
"index": 5,
"globalIndex": 36360475,
"creationHeight": 1184790,
"settlementHeight": 1184793,
"ergoTree": "0008cd02e43a9fa53e78eef987d2cabbcfa76894929dce7c494421412c69c3b4ac83b02f",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e43a9f,15fd7b,...)))}",
"address": "9gFaKRc9c6xDMJ4AZzJQxWenDGsrRCgggcvzvL5o33d7o78XMok",
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},
{
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},
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{
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{
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{
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{
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},
{
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