Transaction
ID: 59776d3400...7c82
Inputs (1)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Outputs (3)
Unspent
Address:
Settlement height:
Value:
0.002 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
500
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.002 ERG
Transaction Details
Confirmations: 216,034
Total coins transferred: 0.005 ERG
Fees: 0.002 ERG
Fees per byte: 0.000000472 ERG
Raw Transaction Data
{
"id": "59776d3400a85bc0a1a66c8cf6042a2be6a1ee9fc27e7b7fe51524d1bf677c82",
"blockId": "cf9f613270b1a1a2f048b9b1c20a53dd5fa0b73d65c61f7e8195e2576e13d4d0",
"inclusionHeight": 1541619,
"timestamp": 1749263501805,
"index": 1,
"globalIndex": 9056048,
"numConfirmations": 216034,
"inputs": [
{
"boxId": "59d45025a43322cdd77a1b07704e4bbc009299b988e8d8fa7313538f6761cac9",
"value": 5000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "bbcfcb608b3afbdc80be0f43555ed26eca446fcbb10709d9752cb45b76cb7bc9",
"outputTransactionId": "e9981d0bf5bc83047a4e3973846d73f244243e2e2ccbb6e533574e40ee55b87d",
"outputIndex": 2,
"outputGlobalIndex": 48202066,
"outputCreatedAt": 1539499,
"outputSettledAt": 1539502,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(-104,-70,111,12,-4,119,88,99,61,82,-22,48,-126,-82,59,-117,56,-76,93,-65,-24,-89,36,125,-23,102,-36,-74,8,-71,0,-114)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(-89,-10,-44,-52,-15,40,-118,43,121,-29,-40,116,30,30,32,56,40,19,90,18,-71,47,-115,-60,-14,-46,30,64,115,-114,14,1)",
"ergoTreeScript": "{\n val func1 = {(box1: Box) => box1.tokens(0) }\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.exists({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val preHeader3 = CONTEXT.preHeader\n val b4 = getVar[Byte](0.toByte).get\n val func5 = {(box5: Box) =>\n val coll7 = box5.R5[Coll[Long]].get\n coll7.slice(2, coll7.size)\n }\n val opt6 = getVar[(Int, Long)](10.toByte)\n val tuple7 = (-2, 0L)\n val tuple8 = tuple7\n val tuple9 = opt6.getOrElse(tuple8)\n val i10 = tuple9._1\n val l11 = tuple9._2\n val func12 = {(tuple12: (Coll[Box], Coll[Byte])) => tuple12._1.filter({(box14: Box) => blake2b256(box14.propositionBytes) == tuple12._2 }) }\n val func13 = {(box13: Box) => box13.R4[Coll[Int]].get(0) }\n val func14 = {(box14: Box) => box14.R5[Coll[Long]].get(1) }\n val func15 = {(box15: Box) => box15.R6[AvlTree].get }\n val func16 = {(box16: Box) => box16.R4[Coll[Int]].get(1) }\n val coll17 = placeholder[Coll[Byte]](3)\n val func18 = {(tuple18: (Coll[Box], Coll[Byte])) =>\n tuple18._1.filter({(box20: Box) => box20.tokens.exists({(tuple22: (Coll[Byte], Long)) => tuple22._1 == tuple18._2 }) })\n }\n val func19 = {(box19: Box) => box19.R4[AvlTree].get }\n val func20 = {(tuple20: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple22 = tuple20._2\n val l23 = tuple22._1\n val tuple24 = tuple22._2\n val l25 = tuple24._1\n val l26 = tuple24._2\n tuple20._1.map({(coll27: Coll[Byte]) => if (coll27.size == 9) {(\n val l29 = byteArrayToLong(coll27.slice(1, 9))\n if (l29 < l23) { l23 } else { if (l29 > l25) { l25 } else { l29 } }\n )} else { l26 } }).getOrElse(l26)\n }\n val func21 = {(box21: Box) => box21.R5[Coll[Long]].get(0) }\n sigmaProp(\n anyOf(\n Coll[Boolean](\n {(b22: Byte) =>\n if (b22 == 12.toByte) {\n {(coll24: Coll[Coll[Byte]]) =>\n allOf(\n Coll[Boolean](\n preHeader3.height - SELF.creationInfo._1 > 788400, coll24.forall({(coll26: Coll[Byte]) => !func2((OUTPUTS, coll26)) }), INPUTS.size == 1\n )\n )\n }(Coll[Coll[Byte]](func1(SELF)._1))\n } else { false }\n }(b4), {(b22: Byte) => if (b22 == 13.toByte) {(\n val coll24 = func5(SELF)\n val coll25 = SELF.propositionBytes\n val box26 = func12((OUTPUTS, blake2b256(coll25)))(0)\n val l27 = box26.value\n val l28 = func14(SELF)\n val coll29 = CONTEXT.dataInputs\n val coll30 = Coll[Byte]()\n val coll31 = func19(func18((coll29, placeholder[Coll[Byte]](0)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-10.toByte, -1.toByte, -117.toByte, 114.toByte, 16.toByte, 1.toByte, 85.toByte, 69.toByte, -44.toByte, -77.toByte, -84.toByte, 95.toByte, -58.toByte, 12.toByte, -112.toByte, -128.toByte, -110.toByte, -48.toByte, 53.toByte, -95.toByte, -95.toByte, 97.toByte, 85.toByte, -64.toByte, 41.toByte, -24.toByte, -43.toByte, 17.toByte, 98.toByte, 124.toByte, 122.toByte, 44.toByte), Coll[Byte](-81.toByte, 120.toByte, 91.toByte, 10.toByte, -35.toByte, -128.toByte, 92.toByte, 92.toByte, 49.toByte, -15.toByte, -52.toByte, 58.toByte, 62.toByte, -106.toByte, -56.toByte, -112.toByte, 8.toByte, -3.toByte, 113.toByte, 39.toByte, 50.toByte, 1.toByte, 7.toByte, -93.toByte, 120.toByte, 75.toByte, 127.toByte, 73.toByte, -23.toByte, 86.toByte, 72.toByte, 66.toByte)), getVar[Coll[Byte]](1.toByte).getOrElse(coll30))\n val tuple32 = (1L, (999L, 500L))\n val opt33 = opt6\n val tuple34 = tuple7\n val tuple35 = tuple8\n val tuple36 = tuple9\n val coll37 = func19(func18((coll29, placeholder[Coll[Byte]](1)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-11.toByte, -111.toByte, -114.toByte, -76.toByte, -80.toByte, 40.toByte, 60.toByte, 102.toByte, -101.toByte, -35.toByte, -118.toByte, 25.toByte, 86.toByte, 64.toByte, 118.toByte, 108.toByte, 25.toByte, -28.toByte, 10.toByte, 105.toByte, 58.toByte, 102.toByte, -105.toByte, -73.toByte, 117.toByte, -80.toByte, -114.toByte, 9.toByte, 5.toByte, 37.toByte, 35.toByte, -44.toByte), Coll[Byte](118.toByte, 124.toByte, -86.toByte, -128.toByte, -71.toByte, -114.toByte, 73.toByte, 106.toByte, -40.toByte, -87.toByte, -10.toByte, -119.toByte, -60.toByte, 65.toByte, 10.toByte, -28.toByte, 83.toByte, 50.toByte, 127.toByte, 15.toByte, -107.toByte, -23.toByte, 80.toByte, -124.toByte, -64.toByte, -82.toByte, 32.toByte, 99.toByte, 80.toByte, 121.toByte, 59.toByte, 119.toByte)), getVar[Coll[Byte]](2.toByte).getOrElse(coll30))\n val box38 = func12((OUTPUTS, {(opt38: Option[Coll[Byte]]) => opt38.get.slice(1, 33) }(coll37(1))))(0)\n allOf(Coll[Boolean]((coll24.indices.forall({(i39: Int) => if (i39 == i10) { coll24(i39) == l11 } else { coll24(i39) <= l11 } }) && (coll24.size > i10)) && (i10 >= 0), allOf(Coll[Boolean](box26.propositionBytes == coll25, l27 >= SELF.value - 3000000L, l27 >= 2000000L, func1(box26) == func1(SELF), func13(box26) == func13(SELF), func14(box26) == l28, func5(box26) == coll24, func15(box26) == func15(SELF), func16(box26) == if ((l28 >= {(box39: Box) => box39.R5[Coll[Long]].get(1) }(func18((coll29, coll17))(0)) * func20((coll31(0), tuple32)) / 1000L) && \n val l39 = l11\n l39 >= l28 * func20((coll31(1), tuple32)) / 1000L\n ) { i10 } else { -2 })), preHeader3.timestamp > func21(SELF), allOf(Coll[Boolean](box38.value >= 1000000L, {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.flatMap({(box41: Box) => box41.tokens }).fold(0L, {(tuple41: (Long, (Coll[Byte], Long))) =>\n val tuple43 = tuple41._2\n tuple41._1 + if (tuple43._1 == tuple39._2) { tuple43._2 } else { 0L }\n }) }((Coll[Box](box38), placeholder[Coll[Byte]](2))) >= min(SELF.tokens(1)._2, byteArrayToLong(coll37(0).get.slice(1, 9))))), func16(SELF) == -1))\n )} else { false } }(b4), {(b22: Byte) => if (b22 == 6.toByte) {(\n val coll24 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll25 = coll24(4)\n val coll26 = coll24(3)\n val coll27 = coll26.indices.slice(0, coll26.size / 8).map({(i27: Int) => byteArrayToLong(coll26.slice(i27 * 8, i27 + 1 * 8)) })\n val l28 = coll27.fold(0L, {(tuple28: (Long, Long)) => tuple28._1 + tuple28._2 })\n val coll29 = SELF.propositionBytes\n val box30 = func12((OUTPUTS, blake2b256(coll29)))(0)\n val l31 = func21(SELF)\n val avlTree32 = func15(SELF)\n val opt33 = avlTree32.get(coll25, coll24(0))\n val bool34 = opt33.isDefined\n val coll35 = coll24(1)\n val coll36 = func5(SELF)\n val coll37 = func5(box30)\n allOf(Coll[Boolean](byteArrayToLong({(box38: Box) => box38.R4[Coll[AvlTree]].get(0) }(func18((INPUTS, coll17))(0)).get(coll25, coll24(2)).get.slice(8, 16)) >= l28, box30.propositionBytes == coll29, box30.value >= SELF.value, box30.tokens == SELF.tokens, func13(box30) == func13(SELF), func16(box30) == func16(SELF), func21(box30) == l31, func15(box30).digest == if (bool34) { avlTree32.update(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get } else { avlTree32.insert(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get }.digest, preHeader3.timestamp < l31, if (bool34) {(\n val coll38 = opt33.get\n val coll39 = coll38.indices.slice(0, coll38.size / 8).map({(i39: Int) => byteArrayToLong(coll38.slice(i39 * 8, i39 + 1 * 8)) })\n allOf(Coll[Boolean](func14(box30) == func14(SELF) - coll39.fold(0L, {(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 }) + l28, coll37 == coll36.zip(coll39.zip(coll27).map({(tuple40: (Long, Long)) => tuple40._2 - tuple40._1 })).map({(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 })))\n )} else { allOf(Coll[Boolean](func14(box30) == func14(SELF) + l28, coll37 == coll36.zip(coll27).map({(tuple38: (Long, Long)) => tuple38._1 + tuple38._2 }))) }, func2((INPUTS, coll25)), coll37 != coll36))\n )} else { false } }(b4)\n )\n )\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "4afba4014c298e92a214caea22a3d9bd57240449c03f5a136267998cf8e5bd89",
"index": 0,
"amount": 1,
"name": "Walrus DAO Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "1fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad489",
"index": 1,
"amount": 5000000,
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],
"outputs": [
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"transactionId": "59776d3400a85bc0a1a66c8cf6042a2be6a1ee9fc27e7b7fe51524d1bf677c82",
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"creationHeight": 1541617,
"settlementHeight": 1541619,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(-104,-70,111,12,-4,119,88,99,61,82,-22,48,-126,-82,59,-117,56,-76,93,-65,-24,-89,36,125,-23,102,-36,-74,8,-71,0,-114)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(-89,-10,-44,-52,-15,40,-118,43,121,-29,-40,116,30,30,32,56,40,19,90,18,-71,47,-115,-60,-14,-46,30,64,115,-114,14,1)",
"ergoTreeScript": "{\n val func1 = {(box1: Box) => box1.tokens(0) }\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.exists({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val preHeader3 = CONTEXT.preHeader\n val b4 = getVar[Byte](0.toByte).get\n val func5 = {(box5: Box) =>\n val coll7 = box5.R5[Coll[Long]].get\n coll7.slice(2, coll7.size)\n }\n val opt6 = getVar[(Int, Long)](10.toByte)\n val tuple7 = (-2, 0L)\n val tuple8 = tuple7\n val tuple9 = opt6.getOrElse(tuple8)\n val i10 = tuple9._1\n val l11 = tuple9._2\n val func12 = {(tuple12: (Coll[Box], Coll[Byte])) => tuple12._1.filter({(box14: Box) => blake2b256(box14.propositionBytes) == tuple12._2 }) }\n val func13 = {(box13: Box) => box13.R4[Coll[Int]].get(0) }\n val func14 = {(box14: Box) => box14.R5[Coll[Long]].get(1) }\n val func15 = {(box15: Box) => box15.R6[AvlTree].get }\n val func16 = {(box16: Box) => box16.R4[Coll[Int]].get(1) }\n val coll17 = placeholder[Coll[Byte]](3)\n val func18 = {(tuple18: (Coll[Box], Coll[Byte])) =>\n tuple18._1.filter({(box20: Box) => box20.tokens.exists({(tuple22: (Coll[Byte], Long)) => tuple22._1 == tuple18._2 }) })\n }\n val func19 = {(box19: Box) => box19.R4[AvlTree].get }\n val func20 = {(tuple20: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple22 = tuple20._2\n val l23 = tuple22._1\n val tuple24 = tuple22._2\n val l25 = tuple24._1\n val l26 = tuple24._2\n tuple20._1.map({(coll27: Coll[Byte]) => if (coll27.size == 9) {(\n val l29 = byteArrayToLong(coll27.slice(1, 9))\n if (l29 < l23) { l23 } else { if (l29 > l25) { l25 } else { l29 } }\n )} else { l26 } }).getOrElse(l26)\n }\n val func21 = {(box21: Box) => box21.R5[Coll[Long]].get(0) }\n sigmaProp(\n anyOf(\n Coll[Boolean](\n {(b22: Byte) =>\n if (b22 == 12.toByte) {\n {(coll24: Coll[Coll[Byte]]) =>\n allOf(\n Coll[Boolean](\n preHeader3.height - SELF.creationInfo._1 > 788400, coll24.forall({(coll26: Coll[Byte]) => !func2((OUTPUTS, coll26)) }), INPUTS.size == 1\n )\n )\n }(Coll[Coll[Byte]](func1(SELF)._1))\n } else { false }\n }(b4), {(b22: Byte) => if (b22 == 13.toByte) {(\n val coll24 = func5(SELF)\n val coll25 = SELF.propositionBytes\n val box26 = func12((OUTPUTS, blake2b256(coll25)))(0)\n val l27 = box26.value\n val l28 = func14(SELF)\n val coll29 = CONTEXT.dataInputs\n val coll30 = Coll[Byte]()\n val coll31 = func19(func18((coll29, placeholder[Coll[Byte]](0)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-10.toByte, -1.toByte, -117.toByte, 114.toByte, 16.toByte, 1.toByte, 85.toByte, 69.toByte, -44.toByte, -77.toByte, -84.toByte, 95.toByte, -58.toByte, 12.toByte, -112.toByte, -128.toByte, -110.toByte, -48.toByte, 53.toByte, -95.toByte, -95.toByte, 97.toByte, 85.toByte, -64.toByte, 41.toByte, -24.toByte, -43.toByte, 17.toByte, 98.toByte, 124.toByte, 122.toByte, 44.toByte), Coll[Byte](-81.toByte, 120.toByte, 91.toByte, 10.toByte, -35.toByte, -128.toByte, 92.toByte, 92.toByte, 49.toByte, -15.toByte, -52.toByte, 58.toByte, 62.toByte, -106.toByte, -56.toByte, -112.toByte, 8.toByte, -3.toByte, 113.toByte, 39.toByte, 50.toByte, 1.toByte, 7.toByte, -93.toByte, 120.toByte, 75.toByte, 127.toByte, 73.toByte, -23.toByte, 86.toByte, 72.toByte, 66.toByte)), getVar[Coll[Byte]](1.toByte).getOrElse(coll30))\n val tuple32 = (1L, (999L, 500L))\n val opt33 = opt6\n val tuple34 = tuple7\n val tuple35 = tuple8\n val tuple36 = tuple9\n val coll37 = func19(func18((coll29, placeholder[Coll[Byte]](1)))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-11.toByte, -111.toByte, -114.toByte, -76.toByte, -80.toByte, 40.toByte, 60.toByte, 102.toByte, -101.toByte, -35.toByte, -118.toByte, 25.toByte, 86.toByte, 64.toByte, 118.toByte, 108.toByte, 25.toByte, -28.toByte, 10.toByte, 105.toByte, 58.toByte, 102.toByte, -105.toByte, -73.toByte, 117.toByte, -80.toByte, -114.toByte, 9.toByte, 5.toByte, 37.toByte, 35.toByte, -44.toByte), Coll[Byte](118.toByte, 124.toByte, -86.toByte, -128.toByte, -71.toByte, -114.toByte, 73.toByte, 106.toByte, -40.toByte, -87.toByte, -10.toByte, -119.toByte, -60.toByte, 65.toByte, 10.toByte, -28.toByte, 83.toByte, 50.toByte, 127.toByte, 15.toByte, -107.toByte, -23.toByte, 80.toByte, -124.toByte, -64.toByte, -82.toByte, 32.toByte, 99.toByte, 80.toByte, 121.toByte, 59.toByte, 119.toByte)), getVar[Coll[Byte]](2.toByte).getOrElse(coll30))\n val box38 = func12((OUTPUTS, {(opt38: Option[Coll[Byte]]) => opt38.get.slice(1, 33) }(coll37(1))))(0)\n allOf(Coll[Boolean]((coll24.indices.forall({(i39: Int) => if (i39 == i10) { coll24(i39) == l11 } else { coll24(i39) <= l11 } }) && (coll24.size > i10)) && (i10 >= 0), allOf(Coll[Boolean](box26.propositionBytes == coll25, l27 >= SELF.value - 3000000L, l27 >= 2000000L, func1(box26) == func1(SELF), func13(box26) == func13(SELF), func14(box26) == l28, func5(box26) == coll24, func15(box26) == func15(SELF), func16(box26) == if ((l28 >= {(box39: Box) => box39.R5[Coll[Long]].get(1) }(func18((coll29, coll17))(0)) * func20((coll31(0), tuple32)) / 1000L) && \n val l39 = l11\n l39 >= l28 * func20((coll31(1), tuple32)) / 1000L\n ) { i10 } else { -2 })), preHeader3.timestamp > func21(SELF), allOf(Coll[Boolean](box38.value >= 1000000L, {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.flatMap({(box41: Box) => box41.tokens }).fold(0L, {(tuple41: (Long, (Coll[Byte], Long))) =>\n val tuple43 = tuple41._2\n tuple41._1 + if (tuple43._1 == tuple39._2) { tuple43._2 } else { 0L }\n }) }((Coll[Box](box38), placeholder[Coll[Byte]](2))) >= min(SELF.tokens(1)._2, byteArrayToLong(coll37(0).get.slice(1, 9))))), func16(SELF) == -1))\n )} else { false } }(b4), {(b22: Byte) => if (b22 == 6.toByte) {(\n val coll24 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll25 = coll24(4)\n val coll26 = coll24(3)\n val coll27 = coll26.indices.slice(0, coll26.size / 8).map({(i27: Int) => byteArrayToLong(coll26.slice(i27 * 8, i27 + 1 * 8)) })\n val l28 = coll27.fold(0L, {(tuple28: (Long, Long)) => tuple28._1 + tuple28._2 })\n val coll29 = SELF.propositionBytes\n val box30 = func12((OUTPUTS, blake2b256(coll29)))(0)\n val l31 = func21(SELF)\n val avlTree32 = func15(SELF)\n val opt33 = avlTree32.get(coll25, coll24(0))\n val bool34 = opt33.isDefined\n val coll35 = coll24(1)\n val coll36 = func5(SELF)\n val coll37 = func5(box30)\n allOf(Coll[Boolean](byteArrayToLong({(box38: Box) => box38.R4[Coll[AvlTree]].get(0) }(func18((INPUTS, coll17))(0)).get(coll25, coll24(2)).get.slice(8, 16)) >= l28, box30.propositionBytes == coll29, box30.value >= SELF.value, box30.tokens == SELF.tokens, func13(box30) == func13(SELF), func16(box30) == func16(SELF), func21(box30) == l31, func15(box30).digest == if (bool34) { avlTree32.update(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get } else { avlTree32.insert(Coll[(Coll[Byte], Coll[Byte])]((coll25, coll26)), coll35).get }.digest, preHeader3.timestamp < l31, if (bool34) {(\n val coll38 = opt33.get\n val coll39 = coll38.indices.slice(0, coll38.size / 8).map({(i39: Int) => byteArrayToLong(coll38.slice(i39 * 8, i39 + 1 * 8)) })\n allOf(Coll[Boolean](func14(box30) == func14(SELF) - coll39.fold(0L, {(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 }) + l28, coll37 == coll36.zip(coll39.zip(coll27).map({(tuple40: (Long, Long)) => tuple40._2 - tuple40._1 })).map({(tuple40: (Long, Long)) => tuple40._1 + tuple40._2 })))\n )} else { allOf(Coll[Boolean](func14(box30) == func14(SELF) + l28, coll37 == coll36.zip(coll27).map({(tuple38: (Long, Long)) => tuple38._1 + tuple38._2 }))) }, func2((INPUTS, coll25)), coll37 != coll36))\n )} else { false } }(b4)\n )\n )\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "4afba4014c298e92a214caea22a3d9bd57240449c03f5a136267998cf8e5bd89",
"index": 0,
"amount": 1,
"name": "Walrus DAO Proposal",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "10020602",
"sigmaType": "Coll[SInt]",
"renderedValue": "[3,1]"
},
"R5": {
"serializedValue": "1104cef0be83e965a0fe0a00a0fe0a",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1749263309863,90000,0,90000]"
},
"R6": {
"serializedValue": "646075be9f4977816bbf9b342a16cfa2c80aed1c5dee5e4c82454dd06a1dfa747002072000",
"sigmaType": null,
"renderedValue": null
},
"R7": {
"serializedValue": "0e0953656c6c2050414c4d",
"sigmaType": "Coll[SByte]",
"renderedValue": "53656c6c2050414c4d"
}
},
"spentTransactionId": null,
"mainChain": true
},
{
"boxId": "6788a151ebf39ce178975718acc89c2c4f976651d9adaf8c5a111d0092c3dbe5",
"transactionId": "59776d3400a85bc0a1a66c8cf6042a2be6a1ee9fc27e7b7fe51524d1bf677c82",
"blockId": "cf9f613270b1a1a2f048b9b1c20a53dd5fa0b73d65c61f7e8195e2576e13d4d0",
"value": 1000000,
"index": 1,
"globalIndex": 48246176,
"creationHeight": 1541617,
"settlementHeight": 1541619,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n1: Coll(35,53,54,38,26,-40,-110,11,-123,100,77,48,-1,-8,-26,-116,71,4,112,19,-119,80,49,122,-43,32,-77,0,-24,-63,-27,115)",
"ergoTreeScript": "{\n val func1 = {(opt1: Option[Coll[Byte]]) => opt1.get.slice(1, 33) }\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.filter({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val coll3 = {(box3: Box) => box3.R4[AvlTree].get }(func2((CONTEXT.dataInputs, placeholder[Coll[Byte]](0)))(0)).getMany(\n Coll[Coll[Byte]](\n Coll[Byte](\n -57.toByte, -59.toByte, 55.toByte, -26.toByte, -58.toByte, 53.toByte, -109.toByte, 14.toByte, -53.toByte, 74.toByte, -50.toByte, -107.toByte, -91.toByte, 73.toByte, 38.toByte, -77.toByte, -85.toByte, 119.toByte, 105.toByte, -115.toByte, -97.toByte, 73.toByte, 34.toByte, -16.toByte, -79.toByte, -59.toByte, -114.toByte, -88.toByte, 113.toByte, 86.toByte, 72.toByte, 59.toByte\n ), Coll[Byte](\n -34.toByte, -82.toByte, -49.toByte, 91.toByte, 100.toByte, -70.toByte, -42.toByte, -11.toByte, 87.toByte, 11.toByte, -83.toByte, 10.toByte, 97.toByte, 12.toByte, 78.toByte, 72.toByte, 73.toByte, 87.toByte, -49.toByte, 71.toByte, -126.toByte, 48.toByte, -124.toByte, 0.toByte, -68.toByte, -112.toByte, 64.toByte, 76.toByte, 29.toByte, 20.toByte, 16.toByte, -38.toByte\n ), Coll[Byte](\n -2.toByte, 33.toByte, -71.toByte, 115.toByte, -52.toByte, -76.toByte, -39.toByte, 31.toByte, 40.toByte, -117.toByte, 28.toByte, 91.toByte, 58.toByte, 79.toByte, 109.toByte, -98.toByte, 12.toByte, 82.toByte, -10.toByte, -79.toByte, 99.toByte, -61.toByte, -121.toByte, -94.toByte, 55.toByte, 14.toByte, -76.toByte, -7.toByte, -76.toByte, 0.toByte, 26.toByte, 62.toByte\n ), Coll[Byte](\n -72.toByte, -61.toByte, 44.toByte, 11.toByte, -98.toByte, 66.toByte, -52.toByte, -122.toByte, -48.toByte, 48.toByte, -78.toByte, 97.toByte, -114.toByte, 90.toByte, 6.toByte, -64.toByte, -46.toByte, -21.toByte, 43.toByte, -96.toByte, 100.toByte, 31.toByte, 9.toByte, 6.toByte, -123.toByte, -71.toByte, -123.toByte, -19.toByte, -85.toByte, 16.toByte, -107.toByte, 111.toByte\n )\n ), getVar[Coll[Byte]](0.toByte).get\n )\n val coll4 = func1(coll3(0))\n val coll5 = func1(coll3(1))\n val box6 = OUTPUTS.filter({(box6: Box) =>\n val coll8 = blake2b256(box6.propositionBytes)\n (coll8 != coll4) && (coll8 != coll5)\n })(0)\n val func7 = {(coll7: Coll[Box]) =>\n coll7.flatMap({(box9: Box) => box9.tokens }).fold(0L, {(tuple9: (Long, (Coll[Byte], Long))) => tuple9._1 + tuple9._2._2 })\n }\n val box8 = {(tuple8: (Coll[Box], Coll[Byte])) => tuple8._1.filter({(box10: Box) => blake2b256(box10.propositionBytes) == tuple8._2 }) }((OUTPUTS, coll4))(0)\n val l9 = box8.value\n val b10 = coll3(2).get(1)\n val coll11 = placeholder[Coll[Byte]](1)\n val func12 = {(tuple12: (Coll[Box], Coll[Byte])) => tuple12._1.flatMap({(box14: Box) => box14.tokens }).fold(0L, {(tuple14: (Long, (Coll[Byte], Long))) =>\n val tuple16 = tuple14._2\n tuple14._1 + if (tuple16._1 == tuple12._2) { tuple16._2 } else { 0L }\n }) }\n sigmaProp(\n allOf(Coll[Boolean](box6.value <= 5000000L, box6.tokens.size == 0, func7(INPUTS) == func7(OUTPUTS), l9 >= 1000000L)) && if (b10.toInt <= 0) {\n OUTPUTS.size == 2\n } else {(\n val box13 = func2((OUTPUTS, coll11))(0)\n val box14 = func2((INPUTS, coll11))(0)\n val l15 = box13.value - box14.value\n val l16 = b10.toLong\n allOf(\n Coll[Boolean](\n OUTPUTS.size == 4, blake2b256(box13.propositionBytes) == coll5, l15 + l9 - 1000000L * l16 / 100L == l15, Coll[Coll[Byte]](\n {(opt17: Option[Coll[Byte]]) => opt17.get.slice(6, 38) }(coll3(3))\n ).forall({(coll17: Coll[Byte]) =>\n val l19 = func12((Coll[Box](box13), coll17)) - func12((Coll[Box](box14), coll17))\n l19 + func12((Coll[Box](box8), coll17)) * l16 / 100L == l19\n })\n )\n )\n )}\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "1fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad489",
"index": 0,
"amount": 5000000,
"name": "Paideia",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "31d78d1125cd224ab0e730a5ad34c514d8b97434ed4311c0f23e6410bf967bf9",
"mainChain": true
},
{
"boxId": "d888f64181e5635b91365d82f413cb490d25d9edfd1e1ff1e8d608b7c9241196",
"transactionId": "59776d3400a85bc0a1a66c8cf6042a2be6a1ee9fc27e7b7fe51524d1bf677c82",
"blockId": "cf9f613270b1a1a2f048b9b1c20a53dd5fa0b73d65c61f7e8195e2576e13d4d0",
"value": 2000000,
"index": 2,
"globalIndex": 48246177,
"creationHeight": 1541617,
"settlementHeight": 1541619,
"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": "2c3abdf0a36a89344f0081c3ea208a77b9938611561968fd4b060dcb9e674163",
"mainChain": true
}
],
"size": 4237,
"isUnconfirmed": false
}