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Transaction

11/10/2023 02:53:32 PM
ID: 9f58d75024...4c6c
Inputs (3)
#0
efd0131ab80a...9445
Spent
Address:
9f3Rnk46ajhP...xRKv
Output transaction:
5c4e3a3bcfc7...6e23
Settlement height:
1,120,971
Value:
199.57 ERG
Tokens:
Loading assets...
#1
9348d7bc093f...b41a
Spent
Address:
9f3Rnk46ajhP...xRKv
Output transaction:
152cd9c3e7dc...8586
Settlement height:
1,120,482
Value:
0.001 ERG
Tokens:
Loading assets...
#2
e25c5b262ac8...5086
Spent
Address:
9f3Rnk46ajhP...xRKv
Output transaction:
595c7333a2cf...93ca
Settlement height:
1,120,425
Value:
0.001 ERG
Tokens:
Loading assets...
Outputs (12)
#0
4f73fc4b229a...85cf
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
c00b1d4a9913...4bd1
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#1
e1226ebf76a1...74dc
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
12408c3f5bac...7182
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#2
e1ec744f70a1...a455
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
b1f893714acc...098a
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#3
6a5a2963c963...5bf6
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
d4d70a0adbc2...9697
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#4
9274fede3290...7bfa
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
10f764da3c64...01df
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#5
f777e87ae106...2b24
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
9a21eef73991...24d7
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#6
985a92b3ae7d...84b9
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
d5ad159740e1...1693
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#7
99829e6ba1cf...ad52
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
90bf9f1cb3a3...5cca
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#8
cb357e03050e...99fb
Spent
Address:
3NtW3QbiLwaF...bV75
Spent in transaction:
f8781b201dbf...0ff3
Settlement height:
1,131,597
Value:
0.015 ERG
Tokens:
Loading assets...
#9
79b88ab06231...8acd
Spent
Address:
9f3Rnk46ajhP...xRKv
Spent in transaction:
91b7aa3de517...2e3f
Settlement height:
1,131,597
Value:
198.08 ERG
Tokens:
Loading assets...
#10
8d9c8c5fd8be...56e0
Spent
Address:
9gjghouPwq9T...Azvo
Spent in transaction:
55e94006bc4f...72f1
Settlement height:
1,131,597
Value:
1.35 ERG
#11
ae14e0d88cf7...58d4
Spent
Address:
Ergo Platform (Miner Fee)
Spent in transaction:
02562af02832...f1b6
Settlement height:
1,131,597
Value:
0.005 ERG
Transaction Details
Status: Confirmed
Size: 13.55 KB
Received time: 11/10/2023 02:53:32 PM
Included in blocks: 1,131,597
Confirmations: 628,528
Total coins transferred: 199.57 ERG
Fees: 0.005 ERG
Fees per byte: 0.00000036 ERG
Raw Transaction Data
{
  "id": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
  "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
  "inclusionHeight": 1131597,
  "timestamp": 1699628012450,
  "index": 2,
  "globalIndex": 6098025,
  "numConfirmations": 628528,
  "inputs": [
    {
      "boxId": "efd0131ab80aa68e3b63645d4a41676b49f04fca37b18499d4f4dbe0ac409445",
      "value": 199572083827,
      "index": 0,
      "spendingProof": "ad3cb2cc09166c2e43aa850f3773a413e98470259b7c3ce5070f3386bebd8282bc2c3b952b1a4f5031385dd4f389ee9169e2d81d73f153dd",
      "outputBlockId": "2e327c8f3a7227c3e1a2e2906d3701d12d0ede89615061a89e5abd75a4c16dc9",
      "outputTransactionId": "5c4e3a3bcfc77b031cabc04e03c2b8d08c7a1b9cc18b3cc3185a8cceeecd6e23",
      "outputIndex": 2,
      "outputGlobalIndex": 33764709,
      "outputCreatedAt": 1120969,
      "outputSettledAt": 1120971,
      "ergoTree": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(44f397,ec80b9,...)))}",
      "address": "9f3Rnk46ajhPUmxuZ3Vo8XDNPnF6zBcwKkL1aGNMjrsiF8sxRKv",
      "assets": [
        {
          "tokenId": "99c302393d6d9459627a26a62bfd63a6c64a0b73222a767ea4d9f408d93afed2",
          "index": 0,
          "amount": 1,
          "name": "Lucky Rabbits Foot",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "c56f0a0c272cc9d6c5269ef84876795f1874198e3e2988b49c91e6170d306cee",
          "index": 1,
          "amount": 1,
          "name": "Ice Chest",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "ec9c7a71b5a3e6bbee6809e6405265216d807e38f4027cc324732f6f093c3e04",
          "index": 2,
          "amount": 1,
          "name": "Razor Knife",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "51fdcaacd13fe7ad11ca51b8878f6889e0b90e5f98cdd608f93b567bcfed93e4",
          "index": 3,
          "amount": 1,
          "name": "Diner Coffee",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "47f016dc89ab5af8528b3823d50a6c173372b1ab68597ed17a52f8335e60bd61",
          "index": 4,
          "amount": 1,
          "name": "\"Pinner\" Joint",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "7f2d9f2417dfe9f44816546d22dfd2acafeceee01f178b107621045366b6f7b1",
          "index": 5,
          "amount": 1,
          "name": "Fresh Socks",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "e4389fb68981f9fa0a87c16dab5c2879448d0c05230c46db1895516bb34204ed",
          "index": 6,
          "amount": 1,
          "name": "An Orange",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "d2df433282a109ab136e286838e31d8737a0848088acbb23f38a65b6ef47cad9",
          "index": 7,
          "amount": 1,
          "name": "\"Healing\" Crystals",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "2fb54776e80f8c43fde61fc3b7132aee09f16e161b65d91f451dfa4db6ef3d95",
          "index": 8,
          "amount": 1,
          "name": "Used Covid Mask",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "9a06d9e545a41fd51eeffc5e20d818073bf820c635e2a9d922269913e0de369d",
          "index": 9,
          "amount": 3201352,
          "name": "SPF",
          "decimals": 6,
          "type": "EIP-004"
        },
        {
          "tokenId": "fcfca7654fb0da57ecf9a3f489bcbeb1d43b56dce7e73b352f7bc6f2561d2a1b",
          "index": 10,
          "amount": 497510000,
          "name": "ErgOne",
          "decimals": 8,
          "type": "EIP-004"
        },
        {
          "tokenId": "94683e22a61a79c89c941136d5589c72598c897d2775572e1e5a3e3b821431e9",
          "index": 11,
          "amount": 1,
          "name": "duckpools Public Vesting Key",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "089990451bb430f05a85f4ef3bcb6ebf852b3d6ee68d86d78658b9ccef20074f",
          "index": 12,
          "amount": 230082623,
          "name": "QUACKS",
          "decimals": 6,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {}
    },
    {
      "boxId": "9348d7bc093f9a869a7d62b840e1ccfc3d0f75e8c8bc77b7873f8a7770e8b41a",
      "value": 1000000,
      "index": 1,
      "spendingProof": "9aac0b9291feb795c1be5b293abe1e892bddcc7119731247d8f296dcc6e3e35f3da5d510a1ae36bb4a78d9faba03af5886326c6a0dea8f6d",
      "outputBlockId": "9913b062ea85d95ccc76603f87793dfeb97ea5f1437311b2cfd960ff4996f7a8",
      "outputTransactionId": "152cd9c3e7dc18f1a497b48c7fa60c9841977afa4d0cef8344bef9d2049d8586",
      "outputIndex": 0,
      "outputGlobalIndex": 33737256,
      "outputCreatedAt": 1120480,
      "outputSettledAt": 1120482,
      "ergoTree": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(44f397,ec80b9,...)))}",
      "address": "9f3Rnk46ajhPUmxuZ3Vo8XDNPnF6zBcwKkL1aGNMjrsiF8sxRKv",
      "assets": [
        {
          "tokenId": "51fdcaacd13fe7ad11ca51b8878f6889e0b90e5f98cdd608f93b567bcfed93e4",
          "index": 0,
          "amount": 1,
          "name": "Diner Coffee",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R4": {
          "serializedValue": "0e4037333232353839633137633734396631393634653463636561633438393734303332633765353863316137656339653235383334643039366631303438393263",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "37333232353839633137633734396631393634653463636561633438393734303332633765353863316137656339653235383334643039366631303438393263"
        }
      }
    },
    {
      "boxId": "e25c5b262ac82be71d5c3688659dc420ebf3ff5a34176fe0e9fa1e6f82c35086",
      "value": 1000000,
      "index": 2,
      "spendingProof": "8d47c9082726453c845a3280d33b0059aad2db2e2f7c5b2476853ad6eac32703d1c75b7e41412ed539d64e250e354cfefed34af60b76506f",
      "outputBlockId": "75934f681a404442d716c9b1b8a6a0c4d7c67d00a8578fbee55eac79755d06fc",
      "outputTransactionId": "595c7333a2cf192ddec0d29e51ed8a051ced697614880889b18ada10e1f493ca",
      "outputIndex": 0,
      "outputGlobalIndex": 33735839,
      "outputCreatedAt": 1120423,
      "outputSettledAt": 1120425,
      "ergoTree": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(44f397,ec80b9,...)))}",
      "address": "9f3Rnk46ajhPUmxuZ3Vo8XDNPnF6zBcwKkL1aGNMjrsiF8sxRKv",
      "assets": [
        {
          "tokenId": "3443d56b0ed9581f5ae91e38f83cb246b74feaec9a60cb9c4832c48909270449",
          "index": 0,
          "amount": 1,
          "name": "OG Blacksmith Tools",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R4": {
          "serializedValue": "0e4034356234306333633532323562373031613837313739663639616635623463386637383061323966393463653263316165663364326432393064646134653433",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "34356234306333633532323562373031613837313739663639616635623463386637383061323966393463653263316165663364326432393064646134653433"
        }
      }
    }
  ],
  "dataInputs": [
    {
      "boxId": "573d9010b852894b9f99bc072933e6cfa115b490f857451e385dbc2e8448cf7c",
      "value": 4219364,
      "index": 0,
      "outputBlockId": "b3cfb684232fe21e89a8132c966b08c6772b72369822c397d25a7bc8e98c9bda",
      "outputTransactionId": "aa58e9d389d3044fda4201159d5d62752e0820f8d0b8506cec28ac4fd0ea826e",
      "outputIndex": 0,
      "ergoTree": "0008cd03b04048a9708f0a2b109d75513a2483e3b9ede622efb3cc03bdddf93bccd93ac5",
      "address": "9hoRnjysKfkwZSCgSFNzSXMohwYn8DqruuYxD6vT7Ubw55qnwiZ",
      "assets": [],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "0e240008cd03d4e3f9c9956945986a2bc2621a2fc02b9f7492fe57c87be07a33a0d7ccef5bc8",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd03d4e3f9c9956945986a2bc2621a2fc02b9f7492fe57c87be07a33a0d7ccef5bc8"
        },
        "R6": {
          "serializedValue": "0580dac409",
          "sigmaType": "SLong",
          "renderedValue": "10000000"
        },
        "R8": {
          "serializedValue": "058084af5f",
          "sigmaType": "SLong",
          "renderedValue": "100000000"
        },
        "R7": {
          "serializedValue": "110280dddb0180fca402",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1800000,2400000]"
        },
        "R4": {
          "serializedValue": "0428",
          "sigmaType": "SInt",
          "renderedValue": "20"
        }
      }
    }
  ],
  "outputs": [
    {
      "boxId": "4f73fc4b229a08c6323c31e99364cadce07161ba66e00954f347468b97e585cf",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 0,
      "globalIndex": 34158662,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "3NtW3QbiLwaF7f2h7NyiFZwSV3LqNwFNBH5HQgzeGtttvsLCS4e19bjkpDbuNbzBXtjvK3eyi94G9YNjeR94F4ix5iAXHSwmSsZ2ftbMCBfxf9JfgouBAvbrcKoDnioJmQdTfQLi8ubzsnzfi3aCE9sV1mXLxob6Dto5XCCGhuBXEB631qRpxaWkASSpgwHhAJdJQcM1ikTtfTXcVmx7n3rtQ83i1xvb4zaJYHA4SGBwT4je2dFd27k8gRpvTgk5RbxxzsiBTpdbyB1a2eEfHfyQztYrC7NPD6M94aiHKtfzCvXjGZPpFohMLsdV4H8yggtqeiay3Nxj2MctjkyTWfKkr1dbWit9bHGyT58fXMpPbBr96NQfZsPNgWFMpusN2VJo9LyMFaGZYZgDyzykTuD6UvwfC2GBLuW1bk12YvuTbqhszbxVaWjf47Kzm13xkotdseL6a7NTULKFbFv8uLUVindCiqu4Gbv26V6kh9xCSM9vPvbqw67uH4QXUcj2N2THFS8CjZkatjHBQhMzDngiAyd8jKfhqa9w6N87S2jJNnZoKT87tKqLXA5oyvVfz4BNFqhHANfGfrQvAXLfoiZ2nCRyYYcy1BCvQ8TWiwf48MPuhKcipa5UdrndgAe7EZrVB3SbFnP7a3KfHkX36LfZBxbH7V2g9C3a9JnkkFhAFM1wfBYrvbJbSN8siGqEusfPh3D6UFAwNNN9aAKiYXZE3H1u4zVb8trNawRp7Ye3Rr8bTBEqX65xs1jpdZCzHUzQMikjBsUaRrCtkWut3PEtmVeQXUVRKwz2eYFaE4sX2zUuMuswKY911Lw38jNHcmUdwXYXCEEZNGY4b2LbMjyMpFHcnM5PMdoAPtx21LTB2oSMqGGcDPr5kigNeYUacjxEjimhMSnxYiVhnoQPKjftvzJwtvdT55fewwLGcRUDP8Koa31pkjubLtpPbiXCyiZ4iXZ2c1M82Wpxsb6EpGvkuhm5iiUi6Kdj6Ya4fzJhmgZLJZu57TxxTr1AB3EfH4AYqnKhrJeBwb5jcv5YD8vtWVeZ53wDaBGyEgkCZxZvgff4rkX2yBu3pCpQZheTxUepQzykqNJck3Y2gMJcC8nxYyjbtpgckReo2LT4bZi5DKsyv6QNWruenSEd3KavrMuyaiBu3LFxxvKwGJFXfZiEku5pALnbi15npqHuxhmwXwhbQFXUVfwviHsJH71qY889oQjERkweQkEB9AFk8hkkLG5aGyWruGLJa3546CPc6Zvbqwt33CZGbpo7jcuuG4rP4G2dpefASG8H45A8NeiGcQq3LXCvAw9MyzLmkVpvFG71nqurqdJCexWcawiLC1QiQwis4AFn9qgf4TLNprpAHPWr1nYAXa78f8DiXy5ahCNjArVq1SdseuFG3S1aoCEP8hbTWmHzBeRmJVsGhJXpNi2h9UiePxWsBBzVm5usMpsaBuckmxxXJyQAantCqFCVXcB71ZLi3pUjTYv2E4Ldf8QEjuSHMqmUv8HWvUudHRiPpCnqoyxyzjQtjbbcqAaSo7zKF1i3d6swUQbcjyULQTkL2oPwYy6vB7yenYXrCEWmyV85iH3wdYzdJj6CYoD5vRKAoNh1kMxZ82nkyB7KoCqr3AePVdSoWned8LMURXdGDdGCpVr7QtcHBL2E3RY4A3drPYpc1DWct8gwPGbowLJkHLNhPwsxTuLReLuotmT7Gheyd6NYUFtz1aWM516uNdDDg5bEPGXzJAGEx1UjZArrStByZcMKRDYVMG6NtNjLvDJCZEvLgBRG1Bi79mgBBbV75",
      "assets": [
        {
          "tokenId": "7f2d9f2417dfe9f44816546d22dfd2acafeceee01f178b107621045366b6f7b1",
          "index": 0,
          "amount": 1,
          "name": "Fresh Socks",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "c00b1d4a991311a206d6c553d8044eda78961b5c6ed16bee7d333d520de84bd1",
      "mainChain": true
    },
    {
      "boxId": "e1226ebf76a18409e7fe691ba4f22b258884abfedbfa4a8d1f53fbbc300e74dc",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 1,
      "globalIndex": 34158663,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "99c302393d6d9459627a26a62bfd63a6c64a0b73222a767ea4d9f408d93afed2",
          "index": 0,
          "amount": 1,
          "name": "Lucky Rabbits Foot",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "12408c3f5bace10d9ea3716819c5bee5ff4418cd3503bfdeca7624210e0f7182",
      "mainChain": true
    },
    {
      "boxId": "e1ec744f70a1be4ab241a35aa249f3b9c74963eb247d420201c876fba165a455",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 2,
      "globalIndex": 34158664,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "51fdcaacd13fe7ad11ca51b8878f6889e0b90e5f98cdd608f93b567bcfed93e4",
          "index": 0,
          "amount": 1,
          "name": "Diner Coffee",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "b1f893714acc06a7ab1585b05dd4f4b50696d42d1991918b298b01493a28098a",
      "mainChain": true
    },
    {
      "boxId": "6a5a2963c9632507deb433214135f0e7045abfdb6b40194c13a06dfcb8055bf6",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 3,
      "globalIndex": 34158665,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "c56f0a0c272cc9d6c5269ef84876795f1874198e3e2988b49c91e6170d306cee",
          "index": 0,
          "amount": 1,
          "name": "Ice Chest",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "d4d70a0adbc2701f5da3d292c0f4225db895facdde70c7c8c313af10ae7f9697",
      "mainChain": true
    },
    {
      "boxId": "9274fede3290fb9d45b799953ea40a20b3b9d021b7ceeb85eda0d634738c7bfa",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 4,
      "globalIndex": 34158666,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "ec9c7a71b5a3e6bbee6809e6405265216d807e38f4027cc324732f6f093c3e04",
          "index": 0,
          "amount": 1,
          "name": "Razor Knife",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "10f764da3c645db26675818d52f190f7cb4fac1d0f79d0917f559002221a01df",
      "mainChain": true
    },
    {
      "boxId": "f777e87ae1062ce24b25b7e3977d6995965c28107201636af8452cc979092b24",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 5,
      "globalIndex": 34158667,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "3443d56b0ed9581f5ae91e38f83cb246b74feaec9a60cb9c4832c48909270449",
          "index": 0,
          "amount": 1,
          "name": "OG Blacksmith Tools",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "9a21eef739919791c1329ca553c3ed76498076e5d1b245f7c7f378f3362324d7",
      "mainChain": true
    },
    {
      "boxId": "985a92b3ae7d924b06bf2af556f7d08ff7a67fdf4a3f13cb8673460061f684b9",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 6,
      "globalIndex": 34158668,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "e4389fb68981f9fa0a87c16dab5c2879448d0c05230c46db1895516bb34204ed",
          "index": 0,
          "amount": 1,
          "name": "An Orange",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "d5ad159740e1aa102e385fb2deeced735939475bf9df0ed48efc92f274111693",
      "mainChain": true
    },
    {
      "boxId": "99829e6ba1cf543b76bd613a920bcbad19f857541fc51224f03b26d5a728ad52",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 7,
      "globalIndex": 34158669,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "2fb54776e80f8c43fde61fc3b7132aee09f16e161b65d91f451dfa4db6ef3d95",
          "index": 0,
          "amount": 1,
          "name": "Used Covid Mask",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "90bf9f1cb3a366dbc51d7c7c4193a4d52ee10c25aa88ef721362667343745cca",
      "mainChain": true
    },
    {
      "boxId": "cb357e03050e017d4c181aef05f6ea3329284a6a2beeddb3caa0b8232a1d99fb",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 15000000,
      "index": 8,
      "globalIndex": 34158670,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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: 2\n33: 10\n34: 3\n35: 0\n36: 1000\n37: 2\n38: 1000\n39: 0\n40: 3\n41: 3\n42: 0\n43: 0\n44: false\n45: true\n46: 0\n47: 1\n48: 0\n49: 1\n50: 0\n51: 1\n52: 0\n53: 0\n54: 0\n55: 1\n56: 0\n57: 1\n58: 2\n59: 0\n60: false\n61: 0\n62: 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.get\n                if ((i52 < placeholder[Int](32)) || (i52 >= placeholder[Int](33))) {(\n                  val box53 = OUTPUTS.getOrElse(placeholder[Int](34), SELF)\n                  val tuple54 = box53.tokens.getOrElse(placeholder[Int](35), (coll2, box53.value))\n                  ((tuple54._2 >= l10 * i52.toLong / placeholder[Long](36)) && (tuple54._1 == coll9)) && (box53.propositionBytes == box42.propositionBytes)\n                )} else { if ((i52 == placeholder[Int](37)) && 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](38)) }).slice(placeholder[Int](39), i54) == OUTPUTS.slice(placeholder[Int](40), placeholder[Int](41) + i54).map({(box55: Box) =>\n                        val tuple57 = box55.tokens.getOrElse(placeholder[Int](42), (coll2, box55.value))\n                        if (tuple57._1 == coll9) { (box55.propositionBytes, tuple57._2) } else { (coll2, placeholder[Long](43)) }\n                      })\n                  )} else { placeholder[Boolean](44) } }\n              )} else { placeholder[Boolean](45) } && (blake2b256(box42.bytes) == tuple51._1))))\n      )} else {(\n        val box32 = OUTPUTS(placeholder[Int](46))\n        val coll33 = box32.tokens\n        val tuple34 = coll33.getOrElse(placeholder[Int](47), (coll2, box32.value))\n        val l35 = tuple34._2\n        val bool36 = l17 > placeholder[Long](48)\n        val coll37 = box18.R7[Coll[Long]].get\n        val box38 = OUTPUTS(placeholder[Int](49))\n        val tuple39 = box38.tokens.getOrElse(placeholder[Int](50), (coll2, box38.value))\n        allOf(Coll[Boolean](bool31, tuple34._1 == coll9, (l35 >= if (bool11) { l8 } else { l10 + coll7(placeholder[Int](51)) }) || (bool36 && (l35 >= l17)), coll1(placeholder[Int](52)) == coll33(placeholder[Int](53)), 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](54))) { l16 + coll37(placeholder[Int](55)) } else { l16 }) }, box32.R6[Coll[Long]] == opt6, box32.R7[Coll[Byte]].get == coll9, coll33.size == if (coll9.size == placeholder[Int](56)) { placeholder[Int](57) } else { placeholder[Int](58) }, box38.propositionBytes == coll30, ((tuple39._1 == coll9) && (tuple39._2 >= l10)) || (l10 == placeholder[Long](59))))\n      )} } else { placeholder[Boolean](60) }) && sigmaProp(box18.tokens(placeholder[Int](61))._1 == placeholder[Coll[Byte]](62))\n}",
      "address": "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",
      "assets": [
        {
          "tokenId": "d2df433282a109ab136e286838e31d8737a0848088acbb23f38a65b6ef47cad9",
          "index": 0,
          "amount": 1,
          "name": "\"Healing\" Crystals",
          "decimals": 0,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "59deb4e99bf762dea584dcfb62",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1699628264751,1700232464751]"
        },
        "R6": {
          "serializedValue": "110380a8d6b90780a8d6b9078098dc9334",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[1000000000,1000000000,7000000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f"
        }
      },
      "spentTransactionId": "f8781b201dbf2893730aa946c11eb14d81e26d9ef027952ba2639de4625a0ff3",
      "mainChain": true
    },
    {
      "boxId": "79b88ab06231698ceb081fbc4cbbb369c873c4a742edf2a3a7d4882963038acd",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 198084083827,
      "index": 9,
      "globalIndex": 34158671,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "ergoTree": "0008cd0244f397e1f15a952354034a671f9f2b420d61642c448b058e8e7bd0450248f21f",
      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(44f397,ec80b9,...)))}",
      "address": "9f3Rnk46ajhPUmxuZ3Vo8XDNPnF6zBcwKkL1aGNMjrsiF8sxRKv",
      "assets": [
        {
          "tokenId": "51fdcaacd13fe7ad11ca51b8878f6889e0b90e5f98cdd608f93b567bcfed93e4",
          "index": 0,
          "amount": 1,
          "name": "Diner Coffee",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "47f016dc89ab5af8528b3823d50a6c173372b1ab68597ed17a52f8335e60bd61",
          "index": 1,
          "amount": 1,
          "name": "\"Pinner\" Joint",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "9a06d9e545a41fd51eeffc5e20d818073bf820c635e2a9d922269913e0de369d",
          "index": 2,
          "amount": 3201352,
          "name": "SPF",
          "decimals": 6,
          "type": "EIP-004"
        },
        {
          "tokenId": "fcfca7654fb0da57ecf9a3f489bcbeb1d43b56dce7e73b352f7bc6f2561d2a1b",
          "index": 3,
          "amount": 497510000,
          "name": "ErgOne",
          "decimals": 8,
          "type": "EIP-004"
        },
        {
          "tokenId": "94683e22a61a79c89c941136d5589c72598c897d2775572e1e5a3e3b821431e9",
          "index": 4,
          "amount": 1,
          "name": "duckpools Public Vesting Key",
          "decimals": 0,
          "type": "EIP-004"
        },
        {
          "tokenId": "089990451bb430f05a85f4ef3bcb6ebf852b3d6ee68d86d78658b9ccef20074f",
          "index": 5,
          "amount": 230082623,
          "name": "QUACKS",
          "decimals": 6,
          "type": "EIP-004"
        }
      ],
      "additionalRegisters": {},
      "spentTransactionId": "91b7aa3de517da9bc42ce9f5c666c13d7e9077b632673f70195573b03ae82e3f",
      "mainChain": true
    },
    {
      "boxId": "8d9c8c5fd8beae1a58df942cb9daa91041d9c3d446263b1b6c447e0105c356e0",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 1350000000,
      "index": 10,
      "globalIndex": 34158672,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "ergoTree": "0008cd03240e71da7ba42ff1e2f68c425ee64ca7d9504a1c84b0c95de4a6effe68804d1d",
      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(240e71,dfb102,...)))}",
      "address": "9gjghouPwq9TCKyFAAvHPsoE1n9moZWec9oKeRzAjnV9p4hAzvo",
      "assets": [],
      "additionalRegisters": {},
      "spentTransactionId": "55e94006bc4f5ee0cf7d2faa33b422f1da507b5e38418b1468b05446755a72f1",
      "mainChain": true
    },
    {
      "boxId": "ae14e0d88cf7bbd5043dd54a9c7d747ec3c4d2ea02379ac1b2fb29bdf88958d4",
      "transactionId": "9f58d75024875dc96e444568e9628addc5398e052d5e6b3da4c5fab1ad9a4c6c",
      "blockId": "01a26f42cb292f2a4c34092be1f943f199b2fa44e81755a87b338dc3dbdbf5bf",
      "value": 5000000,
      "index": 11,
      "globalIndex": 34158673,
      "creationHeight": 1131594,
      "settlementHeight": 1131597,
      "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": [],
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}