Transaction
ID: 9a44668272...2124
Inputs (3)
Spent
Address:
Output transaction:
Settlement height:
Value:
1 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
9.86 ERG
Tokens:
Loading assets...
Outputs (6)
Unspent
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Unspent
Address:
Settlement height:
Value:
0.002 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.00185 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
9.85 ERG
Tokens:
Loading assets...
Transaction Details
Status: Confirmed
Size: 13.18 KB
Received time: 10/11/2024 10:42:10 AM
Included in blocks: 1,371,193
Confirmations: 396,264
Total coins transferred: 10.86 ERG
Fees: 0.00185 ERG
Fees per byte: 0.000000137 ERG
Raw Transaction Data
{
"id": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"inclusionHeight": 1371193,
"timestamp": 1728643330725,
"index": 6,
"globalIndex": 7894581,
"numConfirmations": 396264,
"inputs": [
{
"boxId": "97c0e78cc83908cef561d900786f0b7d09864532ea671013864c0775e832a914",
"value": 1000000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "ad6d695020a4016837ca6edc556a7e514789803c100727fa99527bab90629d17",
"outputTransactionId": "9da3e650f6a12ea1774f1426ff319278a3fa248f7d03c095422c3cfb9fa99a0f",
"outputIndex": 1,
"outputGlobalIndex": 43176499,
"outputCreatedAt": 1371169,
"outputSettledAt": 1371174,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n1: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n2: Coll(0,64,-82,101,12,78,-41,123,-51,32,57,20,-109,-85,-24,76,26,-101,-75,-114,-24,-114,-121,-15,86,112,-56,1,-30,-4,89,-125)\n3: Coll(0,-84,-15,-2,-1,-9,24,30,-116,79,-108,118,17,45,-44,64,2,18,-94,-59,-123,109,16,56,79,-103,-116,-24,106,-118,76,120)",
"ergoTreeScript": "{\n val coll1 = placeholder[Coll[Byte]](0)\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 func3 = {(box3: Box) => box3.R4[AvlTree].get }\n val coll4 = Coll[Byte](\n 3.toByte, -99.toByte, 42.toByte, 10.toByte, 33.toByte, -115.toByte, 124.toByte, 72.toByte, -56.toByte, -3.toByte, 70.toByte, -88.toByte, -46.toByte, -108.toByte, 49.toByte, -67.toByte, 5.toByte, 108.toByte, -124.toByte, -120.toByte, 42.toByte, 33.toByte, 106.toByte, 86.toByte, 115.toByte, 38.toByte, -61.toByte, -57.toByte, -104.toByte, 78.toByte, -56.toByte, 53.toByte\n )\n val func5 = {(opt5: Option[Coll[Byte]]) => opt5.get.slice(1, 33) }\n val func6 = {(box6: Box) => box6.tokens(0) }\n val func7 = {(box7: Box) => box7.tokens(1) }\n val func8 = {(box8: Box) => box8.tokens(2) }\n val func9 = {(tuple9: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple11 = tuple9._2\n val l12 = tuple11._1\n val tuple13 = tuple11._2\n val l14 = tuple13._1\n val l15 = tuple13._2\n tuple9._1.map({(coll16: Coll[Byte]) => if (coll16.size == 9) {(\n val l18 = byteArrayToLong(coll16.slice(1, 9))\n if (l18 < l12) { l12 } else { if (l18 > l14) { l14 } else { l18 } }\n )} else { l15 } }).getOrElse(l15)\n }\n val b10 = getVar[Byte](0.toByte).get\n val i11 = INPUTS.indexOf(SELF, 0)\n val func12 = {(l12: Long) =>\n if (l12 < 128L) { 1 } else {\n if (l12 < 16384L) { 2 } else {\n if (l12 < 2097152L) { 3 } else {\n if (l12 < 268435456L) { 4 } else {\n if (l12 < 34359738368L) { 5 } else {\n if (l12 < 4398046511104L) { 6 } else { if (l12 < 562949953421312L) { 7 } else { if (l12 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n sigmaProp(anyOf(Coll[Boolean]({(b13: Byte) => if (b13 == 11.toByte) {(\n val box15 = OUTPUTS(0)\n val coll16 = CONTEXT.dataInputs\n val box17 = getVar[Box](2.toByte).get\n val coll18 = box17.propositionBytes\n val coll19 = getVar[Coll[Box]](3.toByte).get\n val coll20 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll21 = func3(func2((coll16, coll1))(0)).getMany(Coll[Coll[Byte]](coll4, Coll[Byte](-16.toByte, 54.toByte, 125.toByte, -33.toByte, 52.toByte, 53.toByte, -104.toByte, 31.toByte, 74.toByte, -91.toByte, 87.toByte, 27.toByte, -111.toByte, -35.toByte, 70.toByte, -2.toByte, -127.toByte, 54.toByte, 103.toByte, -14.toByte, -120.toByte, -115.toByte, 79.toByte, -58.toByte, -111.toByte, -106.toByte, -113.toByte, -34.toByte, 21.toByte, -121.toByte, -101.toByte, -105.toByte), Coll[Byte](88.toByte, 30.toByte, -80.toByte, 92.toByte, -24.toByte, 20.toByte, -36.toByte, 63.toByte, 36.toByte, 121.toByte, -28.toByte, -80.toByte, 2.toByte, 125.toByte, -79.toByte, -59.toByte, 73.toByte, 79.toByte, -28.toByte, 32.toByte, 61.toByte, 117.toByte, -124.toByte, 18.toByte, 101.toByte, -10.toByte, -57.toByte, 82.toByte, -105.toByte, -43.toByte, -20.toByte, -63.toByte), blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 112.toByte, 114.toByte, 111.toByte, 112.toByte, 111.toByte, 115.toByte, 97.toByte, 108.toByte, 46.toByte).append(coll18))).append(coll19.map({(box21: Box) => blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 97.toByte, 99.toByte, 116.toByte, 105.toByte, 111.toByte, 110.toByte, 46.toByte).append(box21.propositionBytes)) })), coll20(1))\n val tuple22 = func7(box15)\n val tuple23 = func7(SELF)\n val coll24 = tuple23._1\n val l25 = tuple23._2\n val tuple26 = func8(box15)\n val tuple27 = func8(SELF)\n val coll28 = tuple27._1\n val i29 = coll19.size\n val box30 = OUTPUTS(1)\n val l31 = box30.value\n val l32 = 9223372036854775807L - l25\n val tuple33 = {(box33: Box) => box33.tokens(0) }(box30)\n val tuple34 = {(box34: Box) => box34.tokens(1) }(box30)\n val coll35 = box30.propositionBytes\n val coll36 = OUTPUTS.slice(2, i29 + 2)\n val box37 = func2((coll16, placeholder[Coll[Byte]](3)))(0)\n val coll38 = coll20(2)\n allOf(Coll[Boolean](allOf(Coll[Boolean](blake2b256(box15.propositionBytes) == func5(coll21(0)), box15.value >= SELF.value, func6(box15) == func6(SELF), tuple22._1 == coll24, tuple22._2 == l25 - 1L, tuple26._1 == coll28, tuple26._2 == tuple27._2 - i29.toLong, box15.tokens.size == 3)), allOf(Coll[Boolean](l31 >= box17.value, l31 >= 5000000L, {(box39: Box) => box39.R4[Coll[Int]].get(0) }(box30).toLong == l32, tuple33._1 == coll24, tuple33._2 == 1L, tuple34._1 == placeholder[Coll[Byte]](2), tuple34._2 == func9((func3(func2((coll16, 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)), coll20(0))(0), (0L, (100000000000L, 1000L)))), coll35 == coll18, {(box39: Box) => box39.R5[Coll[Long]].get(0) }(box30) >= CONTEXT.preHeader.timestamp + func9((coll21(1), (43200000L, (2626560000L, 86400000L)))), blake2b256(coll35) == func5(coll21(3)), {(box39: Box) => box39.R5[Coll[Long]].get(1) }(box30) == 0L, {(box39: Box) =>\n val coll41 = box39.R5[Coll[Long]].get\n coll41.slice(2, coll41.size)\n }(box30).forall({(l39: Long) => l39 == 0L }))), coll36.indices.forall({(i39: Int) =>\n val box41 = coll36(i39)\n val l42 = box41.value\n val tuple43 = {(box43: Box) => box43.tokens(0) }(box41)\n allOf(Coll[Boolean](l42 >= coll19(i39).value, l42 >= 2000000L, tuple43._1 == coll28, tuple43._2 == 1L, {(box44: Box) => box44.R4[Coll[Long]].get(0) }(box41) == l32, {(box44: Box) => box44.R4[Coll[Long]].get(1) }(box41) > 0L, blake2b256(box41.propositionBytes) == coll21.slice(4, coll21.size).map({(opt44: Option[Coll[Byte]]) => func5(opt44) })(i39)))\n }), {(coll39: Coll[Byte]) => byteArrayToLong(coll39.slice(8, 16)) }({(box39: Box) => box39.R4[Coll[AvlTree]].get(0) }(box37).get(coll38, coll20(3)).get) >= func9((coll21(2), (0L, ({(box39: Box) => box39.R5[Coll[Long]].get(1) }(box37) / 2L, 0L)))), {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.exists({(box41: Box) => box41.tokens.exists({(tuple43: (Coll[Byte], Long)) => tuple43._1 == tuple39._2 }) }) }((OUTPUTS, coll38))))\n )} else { false } }(b10), {(b13: Byte) => if (b13 == 7.toByte) { {(tuple15: (Coll[Byte], Box)) => if (i11 >= OUTPUTS.size) { false } else {(\n val box17 = OUTPUTS(i11)\n val l18 = box17.value\n val l19 = SELF.value\n val coll20 = box17.propositionBytes\n val coll21 = SELF.bytesWithoutRef\n val coll22 = SELF.propositionBytes\n val i23 = SELF.creationInfo._1\n val coll24 = box17.bytesWithoutRef\n val i25 = box17.creationInfo._1\n allOf(Coll[Boolean](l18 >= l19 - 2000000L, blake2b256(coll20) == func5(func3(tuple15._2).getMany(Coll[Coll[Byte]](tuple15._1), getVar[Coll[Byte]](1.toByte).getOrElse(Coll[Byte]()))(0)), coll21.slice(func12(l19) + coll22.size + func12(i23.toLong), coll21.size) == coll24.slice(func12(l18) + coll20.size + func12(i25.toLong), coll24.size), anyOf(Coll[Boolean](i25 - i23 >= 504000, coll22 != coll20))))\n )} }((coll4, func2((CONTEXT.dataInputs, coll1))(0))) } else { false } }(b10))))\n}",
"address": "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",
"assets": [
{
"tokenId": "00eecc3039d053630a03d8922ba8107bcfa1f503230ad20edbfc2212f5f7fce8",
"index": 0,
"amount": 1,
"name": "Paideia DAO",
"decimals": 0,
"type": "EIP-004"
},
{
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"index": 1,
"amount": 9223372036854776000,
"name": "Paideia Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "005a993f953394bb898f7a2de29757acee1f31b77db262e807d01553f3c1a062",
"index": 2,
"amount": 9223372036854776000,
"name": "Paideia Action",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {}
},
{
"boxId": "518331fbd2f86deb9de89f31286059a71af3e7a6ce5b3e9b39f248d64ca37acd",
"value": 1000000,
"index": 1,
"spendingProof": "b18b77703f4cccb3b5370b1e3c20930318139857f985da927205739aad7b1641a5b13dd3633738e183463d6109b4d11d3be009236f122ee4",
"outputBlockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"outputTransactionId": "cf9c83d00b4a6a3e496b6a44263d1540d267bfa7b3f54e1d6396ebe0e88ae4ef",
"outputIndex": 2,
"outputGlobalIndex": 43176755,
"outputCreatedAt": 1371190,
"outputSettledAt": 1371193,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "bfdfe251205e8c9ca309df336290a43f5f43aeb2c6639e81eee5dfec28db6c47",
"index": 0,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "0e1150616964656961205374616b65204b6579",
"sigmaType": "Coll[SByte]",
"renderedValue": "50616964656961205374616b65204b6579"
},
"R5": {
"serializedValue": "0e12506f77657265642062792050616964656961",
"sigmaType": "Coll[SByte]",
"renderedValue": "506f77657265642062792050616964656961"
},
"R6": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
}
}
},
{
"boxId": "b0efb2c2f44bc5ce070be4173cac5037b21ce4887b03c6358df784033c204926",
"value": 9859193686,
"index": 2,
"spendingProof": "ca254765f5546a4db29c3f503d491623534feeb3435e876a97829b8479504817cfa08899df947374ce2b30ca6afe00b068886798f47998da",
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"outputTransactionId": "cf9c83d00b4a6a3e496b6a44263d1540d267bfa7b3f54e1d6396ebe0e88ae4ef",
"outputIndex": 4,
"outputGlobalIndex": 43176757,
"outputCreatedAt": 1371190,
"outputSettledAt": 1371193,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
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},
{
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},
{
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},
{
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"index": 3,
"amount": 69,
"name": "$Lambo",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "045d2f3ae2f7c17c83bd6a56d9ea64209802a62598ee138ed5289ea947393eee",
"index": 4,
"amount": 9997,
"name": "RosenGuard",
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},
{
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"index": 5,
"amount": 98698999999997,
"name": "MyFirstDAOToken",
"decimals": 6,
"type": "EIP-004"
},
{
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"type": "EIP-004"
},
{
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"index": 7,
"amount": 1,
"name": "Sigmanauts Stake Key",
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"type": "EIP-004"
},
{
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"amount": 1,
"name": "RosenGuards Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
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"index": 9,
"amount": 1,
"name": "MyFirstDAO Stake Key",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [
{
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"value": 1000000000,
"index": 0,
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"outputTransactionId": "9da3e650f6a12ea1774f1426ff319278a3fa248f7d03c095422c3cfb9fa99a0f",
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"assets": [],
"additionalRegisters": {
"R4": {
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"sigmaType": null,
"renderedValue": null
}
}
},
{
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"value": 1000000000,
"index": 2,
"outputBlockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"outputTransactionId": "cf9c83d00b4a6a3e496b6a44263d1540d267bfa7b3f54e1d6396ebe0e88ae4ef",
"outputIndex": 0,
"ergoTree": "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",
"address": "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",
"assets": [],
"additionalRegisters": {
"R5": {
"serializedValue": "110780a4e6cbcf6480a8d6b9070200000000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1728669600000,1000000000,1,0,0,0,0]"
},
"R6": {
"serializedValue": "1d0501000100010001000100",
"sigmaType": "Coll[Coll[SLong]]",
"renderedValue": "[[0],[0],[0],[0],[0]]"
},
"R8": {
"serializedValue": "11020000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,0]"
},
"R7": {
"serializedValue": "0c3c6464014ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
},
"R4": {
"serializedValue": "0c6402a9dfc28b92074adf65a29310bfc3429c86a1ffb50775d1553ca2d63741ccbd38010720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
}
}
}
],
"outputs": [
{
"boxId": "e51d5b519796f736d7c6c34cd801fedd231d081d0e2e8d8e4ef0c9ea379f7843",
"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"value": 1000000000,
"index": 0,
"globalIndex": 43176777,
"creationHeight": 1371191,
"settlementHeight": 1371193,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n1: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n2: Coll(0,64,-82,101,12,78,-41,123,-51,32,57,20,-109,-85,-24,76,26,-101,-75,-114,-24,-114,-121,-15,86,112,-56,1,-30,-4,89,-125)\n3: Coll(0,-84,-15,-2,-1,-9,24,30,-116,79,-108,118,17,45,-44,64,2,18,-94,-59,-123,109,16,56,79,-103,-116,-24,106,-118,76,120)",
"ergoTreeScript": "{\n val coll1 = placeholder[Coll[Byte]](0)\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 func3 = {(box3: Box) => box3.R4[AvlTree].get }\n val coll4 = Coll[Byte](\n 3.toByte, -99.toByte, 42.toByte, 10.toByte, 33.toByte, -115.toByte, 124.toByte, 72.toByte, -56.toByte, -3.toByte, 70.toByte, -88.toByte, -46.toByte, -108.toByte, 49.toByte, -67.toByte, 5.toByte, 108.toByte, -124.toByte, -120.toByte, 42.toByte, 33.toByte, 106.toByte, 86.toByte, 115.toByte, 38.toByte, -61.toByte, -57.toByte, -104.toByte, 78.toByte, -56.toByte, 53.toByte\n )\n val func5 = {(opt5: Option[Coll[Byte]]) => opt5.get.slice(1, 33) }\n val func6 = {(box6: Box) => box6.tokens(0) }\n val func7 = {(box7: Box) => box7.tokens(1) }\n val func8 = {(box8: Box) => box8.tokens(2) }\n val func9 = {(tuple9: (Option[Coll[Byte]], (Long, (Long, Long)))) =>\n val tuple11 = tuple9._2\n val l12 = tuple11._1\n val tuple13 = tuple11._2\n val l14 = tuple13._1\n val l15 = tuple13._2\n tuple9._1.map({(coll16: Coll[Byte]) => if (coll16.size == 9) {(\n val l18 = byteArrayToLong(coll16.slice(1, 9))\n if (l18 < l12) { l12 } else { if (l18 > l14) { l14 } else { l18 } }\n )} else { l15 } }).getOrElse(l15)\n }\n val b10 = getVar[Byte](0.toByte).get\n val i11 = INPUTS.indexOf(SELF, 0)\n val func12 = {(l12: Long) =>\n if (l12 < 128L) { 1 } else {\n if (l12 < 16384L) { 2 } else {\n if (l12 < 2097152L) { 3 } else {\n if (l12 < 268435456L) { 4 } else {\n if (l12 < 34359738368L) { 5 } else {\n if (l12 < 4398046511104L) { 6 } else { if (l12 < 562949953421312L) { 7 } else { if (l12 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n sigmaProp(anyOf(Coll[Boolean]({(b13: Byte) => if (b13 == 11.toByte) {(\n val box15 = OUTPUTS(0)\n val coll16 = CONTEXT.dataInputs\n val box17 = getVar[Box](2.toByte).get\n val coll18 = box17.propositionBytes\n val coll19 = getVar[Coll[Box]](3.toByte).get\n val coll20 = getVar[Coll[Coll[Byte]]](1.toByte).get\n val coll21 = func3(func2((coll16, coll1))(0)).getMany(Coll[Coll[Byte]](coll4, Coll[Byte](-16.toByte, 54.toByte, 125.toByte, -33.toByte, 52.toByte, 53.toByte, -104.toByte, 31.toByte, 74.toByte, -91.toByte, 87.toByte, 27.toByte, -111.toByte, -35.toByte, 70.toByte, -2.toByte, -127.toByte, 54.toByte, 103.toByte, -14.toByte, -120.toByte, -115.toByte, 79.toByte, -58.toByte, -111.toByte, -106.toByte, -113.toByte, -34.toByte, 21.toByte, -121.toByte, -101.toByte, -105.toByte), Coll[Byte](88.toByte, 30.toByte, -80.toByte, 92.toByte, -24.toByte, 20.toByte, -36.toByte, 63.toByte, 36.toByte, 121.toByte, -28.toByte, -80.toByte, 2.toByte, 125.toByte, -79.toByte, -59.toByte, 73.toByte, 79.toByte, -28.toByte, 32.toByte, 61.toByte, 117.toByte, -124.toByte, 18.toByte, 101.toByte, -10.toByte, -57.toByte, 82.toByte, -105.toByte, -43.toByte, -20.toByte, -63.toByte), blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 112.toByte, 114.toByte, 111.toByte, 112.toByte, 111.toByte, 115.toByte, 97.toByte, 108.toByte, 46.toByte).append(coll18))).append(coll19.map({(box21: Box) => blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 97.toByte, 99.toByte, 116.toByte, 105.toByte, 111.toByte, 110.toByte, 46.toByte).append(box21.propositionBytes)) })), coll20(1))\n val tuple22 = func7(box15)\n val tuple23 = func7(SELF)\n val coll24 = tuple23._1\n val l25 = tuple23._2\n val tuple26 = func8(box15)\n val tuple27 = func8(SELF)\n val coll28 = tuple27._1\n val i29 = coll19.size\n val box30 = OUTPUTS(1)\n val l31 = box30.value\n val l32 = 9223372036854775807L - l25\n val tuple33 = {(box33: Box) => box33.tokens(0) }(box30)\n val tuple34 = {(box34: Box) => box34.tokens(1) }(box30)\n val coll35 = box30.propositionBytes\n val coll36 = OUTPUTS.slice(2, i29 + 2)\n val box37 = func2((coll16, placeholder[Coll[Byte]](3)))(0)\n val coll38 = coll20(2)\n allOf(Coll[Boolean](allOf(Coll[Boolean](blake2b256(box15.propositionBytes) == func5(coll21(0)), box15.value >= SELF.value, func6(box15) == func6(SELF), tuple22._1 == coll24, tuple22._2 == l25 - 1L, tuple26._1 == coll28, tuple26._2 == tuple27._2 - i29.toLong, box15.tokens.size == 3)), allOf(Coll[Boolean](l31 >= box17.value, l31 >= 5000000L, {(box39: Box) => box39.R4[Coll[Int]].get(0) }(box30).toLong == l32, tuple33._1 == coll24, tuple33._2 == 1L, tuple34._1 == placeholder[Coll[Byte]](2), tuple34._2 == func9((func3(func2((coll16, 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)), coll20(0))(0), (0L, (100000000000L, 1000L)))), coll35 == coll18, {(box39: Box) => box39.R5[Coll[Long]].get(0) }(box30) >= CONTEXT.preHeader.timestamp + func9((coll21(1), (43200000L, (2626560000L, 86400000L)))), blake2b256(coll35) == func5(coll21(3)), {(box39: Box) => box39.R5[Coll[Long]].get(1) }(box30) == 0L, {(box39: Box) =>\n val coll41 = box39.R5[Coll[Long]].get\n coll41.slice(2, coll41.size)\n }(box30).forall({(l39: Long) => l39 == 0L }))), coll36.indices.forall({(i39: Int) =>\n val box41 = coll36(i39)\n val l42 = box41.value\n val tuple43 = {(box43: Box) => box43.tokens(0) }(box41)\n allOf(Coll[Boolean](l42 >= coll19(i39).value, l42 >= 2000000L, tuple43._1 == coll28, tuple43._2 == 1L, {(box44: Box) => box44.R4[Coll[Long]].get(0) }(box41) == l32, {(box44: Box) => box44.R4[Coll[Long]].get(1) }(box41) > 0L, blake2b256(box41.propositionBytes) == coll21.slice(4, coll21.size).map({(opt44: Option[Coll[Byte]]) => func5(opt44) })(i39)))\n }), {(coll39: Coll[Byte]) => byteArrayToLong(coll39.slice(8, 16)) }({(box39: Box) => box39.R4[Coll[AvlTree]].get(0) }(box37).get(coll38, coll20(3)).get) >= func9((coll21(2), (0L, ({(box39: Box) => box39.R5[Coll[Long]].get(1) }(box37) / 2L, 0L)))), {(tuple39: (Coll[Box], Coll[Byte])) => tuple39._1.exists({(box41: Box) => box41.tokens.exists({(tuple43: (Coll[Byte], Long)) => tuple43._1 == tuple39._2 }) }) }((OUTPUTS, coll38))))\n )} else { false } }(b10), {(b13: Byte) => if (b13 == 7.toByte) { {(tuple15: (Coll[Byte], Box)) => if (i11 >= OUTPUTS.size) { false } else {(\n val box17 = OUTPUTS(i11)\n val l18 = box17.value\n val l19 = SELF.value\n val coll20 = box17.propositionBytes\n val coll21 = SELF.bytesWithoutRef\n val coll22 = SELF.propositionBytes\n val i23 = SELF.creationInfo._1\n val coll24 = box17.bytesWithoutRef\n val i25 = box17.creationInfo._1\n allOf(Coll[Boolean](l18 >= l19 - 2000000L, blake2b256(coll20) == func5(func3(tuple15._2).getMany(Coll[Coll[Byte]](tuple15._1), getVar[Coll[Byte]](1.toByte).getOrElse(Coll[Byte]()))(0)), coll21.slice(func12(l19) + coll22.size + func12(i23.toLong), coll21.size) == coll24.slice(func12(l18) + coll20.size + func12(i25.toLong), coll24.size), anyOf(Coll[Boolean](i25 - i23 >= 504000, coll22 != coll20))))\n )} }((coll4, func2((CONTEXT.dataInputs, coll1))(0))) } else { false } }(b10))))\n}",
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{
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"index": 0,
"amount": 1,
"name": "Paideia DAO",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "00aa649808f01c850b4e022368fdfa836ddeabcf3c09d56d0d0fd19cf3f62302",
"index": 1,
"amount": 9223372036854776000,
"name": "Paideia Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
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"index": 2,
"amount": 9223372036854776000,
"name": "Paideia Action",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": null,
"mainChain": true
},
{
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"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
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"creationHeight": 1371191,
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"ergoTreeConstants": "0: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n1: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n2: Coll(0,64,-82,101,12,78,-41,123,-51,32,57,20,-109,-85,-24,76,26,-101,-75,-114,-24,-114,-121,-15,86,112,-56,1,-30,-4,89,-125)\n3: Coll(0,-84,-15,-2,-1,-9,24,30,-116,79,-108,118,17,45,-44,64,2,18,-94,-59,-123,109,16,56,79,-103,-116,-24,106,-118,76,120)",
"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": "00aa649808f01c850b4e022368fdfa836ddeabcf3c09d56d0d0fd19cf3f62302",
"index": 0,
"amount": 1,
"name": "Paideia Proposal",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "0040ae650c4ed77bcd20391493abe84c1a9bb58ee88e87f15670c801e2fc5983",
"index": 1,
"amount": 10000000,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "10020001",
"sigmaType": "Coll[SInt]",
"renderedValue": "[0,-1]"
},
"R5": {
"serializedValue": "1104809cf98bd064000000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1728736864000,0,0,0]"
},
"R6": {
"serializedValue": "644ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
},
"R7": {
"serializedValue": "0e0f53656e642066756e64732074657374",
"sigmaType": "Coll[SByte]",
"renderedValue": "53656e642066756e64732074657374"
}
},
"spentTransactionId": "b615ca8e1ad0fe7c5cf05f399cd9711a5367695a5b784be0af42fd112d8f36c8",
"mainChain": true
},
{
"boxId": "27c181fb55696a51a0585ef8d7d5629cf03f97d84b40d431fc718e31ffa6c06d",
"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"value": 2000000,
"index": 2,
"globalIndex": 43176779,
"creationHeight": 1371191,
"settlementHeight": 1371193,
"ergoTree": "10020e2000f8f5bbe544e16c5d51098f361ac5fd975beee0abca8ac76f0d7a388c32ef260e2000aa649808f01c850b4e022368fdfa836ddeabcf3c09d56d0d0fd19cf3f62302d80ed601d9010163b2db63087201040000d602d901023c0c630eae8c720201d9010463aedb63087204d901064d0e938c7206018c720202d603db6503fed6047301d605d901053c0c630eb58c720501d9010763aedb63087207d901094d0e938c7209018c720502d606d9010663b2e4c672060411040000d607d9010763b2e4c672070411040200d608d9010863b2e4c672080411040600d609d9010963b2e4c672090411040400d60ad9010a63e4c6720a050c63d60bd9010b63b2e4c6720b0411040800d60cd9010c05958f720c0580020402958f720c058080020404958f720c05808080020406958f720c0580808080020408958f720c05808080808002040a958f720c0580808080808002040c958f720c058080808080808002040e958f720c0580808080808080800204100412d60dd9010d63d802d60fc4720dd6109ada720c01c1720db1c2720db3b4720f04007210b4720f9a7210da720c017e8cc7720d0105b1720fd60ed9010e0c63b0dc0c0f720e01d9011063db630872100500d90110414d0e9a8c7210018c8c721002029593e4e30002020cd1dad9010f1a968303019199db690572038cc7a70104e09e60af720fd901110eefda7202018602a5721193b1a404020183010e8cda720101a701d80ad60fdb6501fed610b2da7205018602720f7204040000d611da720901a7d6129172110500d613da720a01a7d614b17213d615b1a5d616b2a5997215040200d61793cbc27216dad9011732b4e472170402044201b2dc640bdad9011763e4c67217046401b2da7205018602720f73000400000283010e83200202c702c5023702e602c602350293020e02cb024a02ce029502a50249022602b302ab02770269028d029f0249022202f002b102c5028e02a8027102560248023be4e3010e040000d618957217b2a59972150404007216d19683070196830301938cdad9011963b2db63087219040000017210017204937edad9011963b2e4c67219041004000001721005da720601a7937edad9011963b2e4c67219041004020001721005da720701a792db69037203da720801a7957212d803d619da720a01a7d61ab2a5b1721900d61bda720b01a79683090193c1721ac1a793db6308721adb6308a793da720601721ada720601a793da720701721ada720701a793da720901721a997211050293da720801721a9ada720801a7721b93da720b01721a721b93da720a01721a721993c2721ac2a7efda7202018602a58cda720101a701afdc0c1d721301b4a504007214d901193c636393da720d018c721901da720d018c7219029372159a9a7214957212040204009572170404040293da720e01a49ada720e01a5957212050005029683020190c172180580ade20493b1db630872180400",
"ergoTreeConstants": "0: Coll(0,-8,-11,-69,-27,68,-31,108,93,81,9,-113,54,26,-59,-3,-105,91,-18,-32,-85,-54,-118,-57,111,13,122,56,-116,50,-17,38)\n1: Coll(0,-86,100,-104,8,-16,28,-123,11,78,2,35,104,-3,-6,-125,109,-34,-85,-49,60,9,-43,109,13,15,-47,-100,-13,-10,35,2)",
"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 coll4 = placeholder[Coll[Byte]](1)\n val func5 = {(tuple5: (Coll[Box], Coll[Byte])) =>\n tuple5._1.filter({(box7: Box) => box7.tokens.exists({(tuple9: (Coll[Byte], Long)) => tuple9._1 == tuple5._2 }) })\n }\n val func6 = {(box6: Box) => box6.R4[Coll[Long]].get(0) }\n val func7 = {(box7: Box) => box7.R4[Coll[Long]].get(1) }\n val func8 = {(box8: Box) => box8.R4[Coll[Long]].get(3) }\n val func9 = {(box9: Box) => box9.R4[Coll[Long]].get(2) }\n val func10 = {(box10: Box) => box10.R5[Coll[Box]].get }\n val func11 = {(box11: Box) => box11.R4[Coll[Long]].get(4) }\n val func12 = {(l12: Long) =>\n if (l12 < 128L) { 1 } else {\n if (l12 < 16384L) { 2 } else {\n if (l12 < 2097152L) { 3 } else {\n if (l12 < 268435456L) { 4 } else {\n if (l12 < 34359738368L) { 5 } else {\n if (l12 < 4398046511104L) { 6 } else { if (l12 < 562949953421312L) { 7 } else { if (l12 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n val func13 = {(box13: Box) =>\n val coll15 = box13.bytesWithoutRef\n val i16 = func12(box13.value) + box13.propositionBytes.size\n coll15.slice(0, i16).append(coll15.slice(i16 + func12(box13.creationInfo._1.toLong), coll15.size))\n }\n val func14 = {(coll14: Coll[Box]) =>\n coll14.flatMap({(box16: Box) => box16.tokens }).fold(0L, {(tuple16: (Long, (Coll[Byte], Long))) => tuple16._1 + tuple16._2._2 })\n }\n if (getVar[Byte](0.toByte).get == 12.toByte) {\n sigmaProp(\n {(coll15: Coll[Coll[Byte]]) =>\n allOf(\n Coll[Boolean](\n preHeader3.height - SELF.creationInfo._1 > 788400, coll15.forall({(coll17: Coll[Byte]) => !func2((OUTPUTS, coll17)) }), INPUTS.size == 1\n )\n )\n }(Coll[Coll[Byte]](func1(SELF)._1))\n )\n } else {(\n val coll15 = CONTEXT.dataInputs\n val box16 = func5((coll15, coll4))(0)\n val l17 = func9(SELF)\n val bool18 = l17 > 0L\n val coll19 = func10(SELF)\n val i20 = coll19.size\n val i21 = OUTPUTS.size\n val box22 = OUTPUTS(i21 - 1)\n val bool23 = blake2b256(box22.propositionBytes) == {(opt23: Option[Coll[Byte]]) => opt23.get.slice(1, 33) }(\n {(box23: Box) => box23.R4[AvlTree].get }(func5((coll15, 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 )\n ), getVar[Coll[Byte]](1.toByte).get\n )(0)\n )\n val box24 = if (bool23) { OUTPUTS(i21 - 2) } else { box22 }\n sigmaProp(\n allOf(\n Coll[Boolean](\n allOf(\n Coll[Boolean](\n {(box25: Box) => box25.tokens(0) }(box16)._1 == coll4, {(box25: Box) => box25.R4[Coll[Int]].get(0) }(box16).toLong == func6(SELF), {(\n box25: Box\n ) => box25.R4[Coll[Int]].get(1) }(box16).toLong == func7(SELF)\n )\n ), preHeader3.timestamp >= func8(SELF), if (bool18) {(\n val coll25 = func10(SELF)\n val box26 = OUTPUTS(coll25.size)\n val l27 = func11(SELF)\n allOf(\n Coll[Boolean](\n box26.value == SELF.value, box26.tokens == SELF.tokens, func6(box26) == func6(SELF), func7(box26) == func7(SELF), func9(\n box26\n ) == l17 - 1L, func8(box26) == func8(SELF) + l27, func11(box26) == l27, func10(box26) == coll25, box26.propositionBytes == SELF.propositionBytes\n )\n )\n )} else { !func2((OUTPUTS, func1(SELF)._1)) }, coll19.zip(OUTPUTS.slice(0, i20)).forall(\n {(tuple25: (Box, Box)) => func13(tuple25._1) == func13(tuple25._2) }\n ), i21 == i20 + if (bool18) { 1 } else { 0 } + if (bool23) { 2 } else { 1 }, func14(INPUTS) == func14(OUTPUTS) + if (bool18) { 0L } else {\n 1L\n }, allOf(Coll[Boolean](box24.value <= 5000000L, box24.tokens.size == 0))\n )\n )\n )\n )}\n}",
"address": "7MHZRMHDsuBgntP138LPYq6BdnR17sZoGYQERzz8xnCws6Ckgkgr3dSFarMYarTMAP6h9dgDFJJr46auTN1B2R5CvKsGCsQYfRH3yfarvbpUVWGuQmHpLbtQSUJPadW5bNq7ckDTPgZCv5qF3y66TSFPVG8LBHQxPnPjau39W93y2ASCCo11DfqtzUfWKqLWwN7F8qRqM5LmnhC3NNFA5YsHG5FQse4ACSmuC3bn8oZp3Qc8frKkfPm3zVm4hnp8zkXwCk4N9BQeBqCTG2hxbFFKnTCx8bG677s8tt7X7nTcz7P64oatSSWCv84TFGChBurZq3QxAz7e57Uo9Ye9trPS8kzkVD9iqFfAbe2KtjX18SkBksr2tr378YrM81ZaN5TXtaV2hKURLLRx1EAurqVXHSHLvHHsVLL3ifhNTVYHQGzf3NFHHzHWAwmGC3afwvxdDmCtPBuDbq5wR4BT4G1mRWxQBa8MRcu5BceUjYauQuF3siHTGQGPw39WWnPqbM8emD6DZrMvtwcV3Eac2Q7drVsiKhh4MRN9fE6zqiG6pTKm7PerXMxPXEiKSahiKVgzmjMzTvbHxq2rBLxLpKJZdiH6fcg4RAtob2CivNmyZwd8hjAvRtyPnrZariSGtD2mBUQdr3ezrnPfbdddYh7hUSrVcVc3hd6boRZHjTPHtNU3QqooGUTFvcYznTazgsfgf663k3s4XFhMgCsf8XJAPvPWH9wQK2eZT51WkmxApJCGsC2A5Nvbw8LGQxEKxUAtXPd8yJY7ajPjdzAy1vC5RPZgue1UuMNQZrWTTruPjhES29AcULWJnHF2xSa3qG1g24RF9CH1iey8k1s7uSypwzJybYepXxv6bxbrf3bgQySzGMKrjJHnZGfVsbbnkdE8ChvJ5kWyzdFcGRWHBjTBGGXSNBwruWvayWg9bAeFvo8uXMfts1ExN45hvq2gfX1W5FsNT7AsB6oS9oy8tF3LPZzhCBVRqVgLoyV5NjLK6YTe1SSYmQkk4rE3D6LMPmNUA17U94X4nEg3RUNCdGVSjbE73hPUSs3fdq5pGYZEpSB63eDteMb1FRTx2yUUP9NSWW3nYoTRERjSatumvb528BRPA5Vy7jzZBmkSupkvnStoTsGoF7D1pXAdaS89GaXfLNFxEUXGiaL583LcBEXHa5nUYhu8mVfxwWXXCo9PPP7MrZxkSBUP86YztxsJio7wwLq6AVuWchJ29ZpniEWNaZHRjs4GcX9Q33jS229NsuJCVgxi6NZcYn5PNULaMwuEGpZkgaRjtvHzgRG4ic91B3QAq4Tm5KmkqvNF1jCDJniUeBFobTS8XHSj4WLRY3APbe6YmXHFWuQspVRQdxogJmxMfAWW3BA9NZZugPmhhSR48os9MEzWEbyFXAy9mFSknggZ9KUFRRLqvosme36zPd4z7qnDYa9WwHSkQVfur7vkKFkSJc7d7MDpHA9eV",
"assets": [
{
"tokenId": "005a993f953394bb898f7a2de29757acee1f31b77db262e807d01553f3c1a062",
"index": 0,
"amount": 1,
"name": "Paideia Action",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R4": {
"serializedValue": "110500020090ea9db2f26100",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,1,0,1681800805000,0]"
},
"R5": {
"serializedValue": "0c6301c0843d0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221eb7d8530140d1e9028a53c165ffdffd0a080b05ff0c8df6d95aa59456a17ee9d699332e91a08d060098e1c09eab6125869091753c6c4626b1835cb3710f8453335e4e3897b7d6e41b00",
"sigmaType": null,
"renderedValue": null
}
},
"spentTransactionId": null,
"mainChain": true
},
{
"boxId": "46b90c94d414e9af5936be598c5353a3dd4dc3c0b69eedbff67393daf4558653",
"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"value": 1000000,
"index": 3,
"globalIndex": 43176780,
"creationHeight": 1371191,
"settlementHeight": 1371193,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "bfdfe251205e8c9ca309df336290a43f5f43aeb2c6639e81eee5dfec28db6c47",
"index": 0,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "b615ca8e1ad0fe7c5cf05f399cd9711a5367695a5b784be0af42fd112d8f36c8",
"mainChain": true
},
{
"boxId": "b246dc546a7fd50e4f95ce4a9dd5c1cdfd73ead2d75d3baba8f8ab27518fcd5d",
"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"value": 1850000,
"index": 4,
"globalIndex": 43176781,
"creationHeight": 1371191,
"settlementHeight": 1371193,
"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": "ed86b152d5e7de9d2fdf1eacde12fd51c096eb8c039b7a11cc331e2b7b320f83",
"mainChain": true
},
{
"boxId": "234f916de19bad24f9cc6d4b0537463d2b9e65cf3e92653087fb67055c90ee17",
"transactionId": "9a44668272af2abf233ef9ad1bbdc99f941cd0e53c9b2bf941c6f04088202124",
"blockId": "90c7bd0e8296fa22e330e973c6ec90c3e75517f27bea6f54d76a50a7e7b83c5b",
"value": 9850343686,
"index": 5,
"globalIndex": 43176782,
"creationHeight": 1371191,
"settlementHeight": 1371193,
"ergoTree": "0008cd02e4cb952261186ec0fd2dc4c2baa8dbfd9c8f6012c5efa9f702f9450a58fe221e",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(e4cb95,7c781d,...)))}",
"address": "9gFpnLUGwRyohhoh5CXQS8eNdranNyNVGreCmc4xFYHPg5JUGWL",
"assets": [
{
"tokenId": "0040ae650c4ed77bcd20391493abe84c1a9bb58ee88e87f15670c801e2fc5983",
"index": 0,
"amount": 476717928647,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "7b976757ded023ccdffc1c6958376727fc392e60ed5627fb4e405177a8a090c1",
"index": 1,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "59ee24951ce668f0ed32bdb2e2e5731b6c36128748a3b23c28407c5f8ccbf0f6",
"index": 2,
"amount": 4,
"name": "WALRUS",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "0fdb7ff8b37479b6eb7aab38d45af2cfeefabbefdc7eebc0348d25dd65bc2c91",
"index": 3,
"amount": 69,
"name": "$Lambo",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "045d2f3ae2f7c17c83bd6a56d9ea64209802a62598ee138ed5289ea947393eee",
"index": 4,
"amount": 9997,
"name": "RosenGuard",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "00de06c61282578684b377ffd6314c38ce596f2c054107fe2314810677859269",
"index": 5,
"amount": 98698999999997,
"name": "MyFirstDAOToken",
"decimals": 6,
"type": "EIP-004"
},
{
"tokenId": "24b33d72bc82d430fd68b20dc22af598be6994189a96d1ee1eef78fd93b5f763",
"index": 6,
"amount": 1,
"name": "Paideia Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "4679238680dadf89f6670c1d83563e87e16616407845ce7bdf5b9e3aee1ae8b8",
"index": 7,
"amount": 1,
"name": "Sigmanauts Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "94073554b6fed07682cc819527a0188ab913698233fedd1bb908368e275a24b1",
"index": 8,
"amount": 1,
"name": "RosenGuards Stake Key",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "669438571de1006db69e406bab15881b8c85f5b8a231b54c12db1923654b8220",
"index": 9,
"amount": 1,
"name": "MyFirstDAO Stake Key",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "b615ca8e1ad0fe7c5cf05f399cd9711a5367695a5b784be0af42fd112d8f36c8",
"mainChain": true
}
],
"size": 13496,
"isUnconfirmed": false
}