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Inputs (4)
Output transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Output transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Output transaction:
Settlement height:
Value:
3.09 ERG
Tokens:
Loading assets...
Output transaction:
Settlement height:
Value:
0.005 ERG
Tokens:
Loading assets...
Outputs (5)
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent in transaction:
Settlement height:
Value:
0.015 ERG
Tokens:
Loading assets...
Spent in transaction:
Settlement height:
Value:
2.87 ERG
Tokens:
Loading assets...
Spent in transaction:
Settlement height:
Value:
0.2 ERG
Spent in transaction:
Settlement height:
Value:
0.005 ERG
Transaction Details
Status: Confirmed
Size: 2.99 KB
Received time: 5/30/2023 06:26:28 PM
Included in blocks: 1,015,058
Confirmations: 761,753
Total coins transferred: 3.1 ERG
Fees: 0.005 ERG
Fees per byte: 0.000001631 ERG
Raw Transaction Data
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  "timestamp": 1685471188578,
  "index": 1,
  "globalIndex": 5305226,
  "numConfirmations": 761753,
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      "ergoTree": "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",
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      "ergoTreeScript": "{\n  val l1 = CONTEXT.preHeader.timestamp\n  val tuple2 = SELF.R5[(Long, Long)].get\n  val l3 = tuple2._1\n  val bool4 = l1 > l3\n  val coll5 = SELF.R4[Coll[Byte]].get\n  val l6 = tuple2._2\n  val coll7 = SELF.tokens\n  val coll8 = Coll[Byte]()\n  val l9 = SELF.value\n  val tuple10 = coll7.getOrElse(placeholder[Int](0), (coll8, l9))\n  val l11 = tuple10._2\n  val opt12 = SELF.R6[Coll[Long]]\n  val coll13 = opt12.get\n  val l14 = coll13(placeholder[Int](1))\n  val coll15 = SELF.R7[Coll[Byte]].get\n  val l16 = if ((l11 < l14) || (tuple10._1 != coll15)) { placeholder[Long](2) } else { l11 }\n  val box17 = CONTEXT.dataInputs(placeholder[Int](3))\n  sigmaProp(bool4) && proveDlog(decodePoint(coll5.slice(placeholder[Int](4), coll5.size))) || sigmaProp(if (bool4) { if (l1 > l6) {(\n        val box18 = OUTPUTS(placeholder[Int](5))\n        val bool19 = l16 >= l14\n        ((l16 < l14) && allOf(Coll[Boolean](OUTPUTS.size == placeholder[Int](6), box18.tokens == coll7, box18.value >= l9 - placeholder[Long](7) * box17.R6[Long].get, box18.propositionBytes == coll5))) || (bool19 && bool19)\n      )} else {(\n        val box18 = OUTPUTS(placeholder[Int](8))\n        val coll19 = box18.tokens\n        val tuple20 = coll19.getOrElse(placeholder[Int](9), (coll8, box18.value))\n        val coll21 = box17.R7[Coll[Long]].get\n        val box22 = OUTPUTS(placeholder[Int](10))\n        val tuple23 = box22.tokens.getOrElse(placeholder[Int](11), (coll8, box22.value))\n        allOf(Coll[Boolean](INPUTS(placeholder[Int](12)).id == SELF.id, tuple20._1 == coll15, tuple20._2 >= max(l16 + coll13(placeholder[Int](13)), l14), coll7(placeholder[Int](14)) == coll19(placeholder[Int](15)), box18.propositionBytes == SELF.propositionBytes, box18.R4[Coll[Byte]].get == coll5, box18.R5[(Long, Long)].get == (l3, if (l6 - l1 <= coll21(placeholder[Int](16))) { l6 + coll21(placeholder[Int](17)) } else { l6 }), box18.R6[Coll[Long]] == opt12, box18.R7[Coll[Byte]].get == coll15, coll19.size == if (coll15.size == placeholder[Int](18)) { placeholder[Int](19) } else { placeholder[Int](20) }, box22.propositionBytes == SELF.R8[Coll[Byte]].get, ((tuple23._1 == coll15) && (tuple23._2 >= l16)) || (l16 == placeholder[Long](21))))\n      )} } else { placeholder[Boolean](22) }) && sigmaProp(box17.tokens(placeholder[Int](23))._1 == placeholder[Coll[Byte]](24))\n}",
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      "assets": [
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          "index": 0,
          "amount": 1,
          "name": "Character #27. Anime Girl",
          "decimals": 0,
          "type": "EIP-004"
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      ],
      "additionalRegisters": {
        "R5": {
          "serializedValue": "599ad387df8d629ac4a29f9262",
          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1685471687885,1686075887885]"
        },
        "R6": {
          "serializedValue": "110280b8c9e5ae048084af5f",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[75000000000,100000000]"
        },
        "R8": {
          "serializedValue": "0e240008cd0336477547b008d32d8c9e263fa9925235207488de98a0f314e0ec5e0f1ebb5285",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0336477547b008d32d8c9e263fa9925235207488de98a0f314e0ec5e0f1ebb5285"
        },
        "R7": {
          "serializedValue": "0e00",
          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
        },
        "R4": {
          "serializedValue": "0e240008cd0336477547b008d32d8c9e263fa9925235207488de98a0f314e0ec5e0f1ebb5285",
          "sigmaType": "Coll[SByte]",
          "renderedValue": "0008cd0336477547b008d32d8c9e263fa9925235207488de98a0f314e0ec5e0f1ebb5285"
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      },
      "spentTransactionId": "eeefe32f13e6b6a0df4b6c1e3393847d55297baa63c14bca6918bed99586d397",
      "mainChain": true
    },
    {
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      "blockId": "35683700ba93deea3ae0ce062bdfbfedb11f57c19c808381997548d61366d721",
      "value": 15000000,
      "index": 1,
      "globalIndex": 29644156,
      "creationHeight": 1015056,
      "settlementHeight": 1015058,
      "ergoTree": "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",
      "ergoTreeConstants": "0: 1\n1: 0\n2: 0\n3: 0\n4: 3\n5: 0\n6: 3\n7: 2\n8: 0\n9: 1\n10: 1\n11: 0\n12: 0\n13: 1\n14: 0\n15: 0\n16: 0\n17: 1\n18: 0\n19: 1\n20: 2\n21: 0\n22: false\n23: 0\n24: 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 l1 = CONTEXT.preHeader.timestamp\n  val tuple2 = SELF.R5[(Long, Long)].get\n  val l3 = tuple2._1\n  val bool4 = l1 > l3\n  val coll5 = SELF.R4[Coll[Byte]].get\n  val l6 = tuple2._2\n  val coll7 = SELF.tokens\n  val coll8 = Coll[Byte]()\n  val l9 = SELF.value\n  val tuple10 = coll7.getOrElse(placeholder[Int](0), (coll8, l9))\n  val l11 = tuple10._2\n  val opt12 = SELF.R6[Coll[Long]]\n  val coll13 = opt12.get\n  val l14 = coll13(placeholder[Int](1))\n  val coll15 = SELF.R7[Coll[Byte]].get\n  val l16 = if ((l11 < l14) || (tuple10._1 != coll15)) { placeholder[Long](2) } else { l11 }\n  val box17 = CONTEXT.dataInputs(placeholder[Int](3))\n  sigmaProp(bool4) && proveDlog(decodePoint(coll5.slice(placeholder[Int](4), coll5.size))) || sigmaProp(if (bool4) { if (l1 > l6) {(\n        val box18 = OUTPUTS(placeholder[Int](5))\n        val bool19 = l16 >= l14\n        ((l16 < l14) && allOf(Coll[Boolean](OUTPUTS.size == placeholder[Int](6), box18.tokens == coll7, box18.value >= l9 - placeholder[Long](7) * box17.R6[Long].get, box18.propositionBytes == coll5))) || (bool19 && bool19)\n      )} else {(\n        val box18 = OUTPUTS(placeholder[Int](8))\n        val coll19 = box18.tokens\n        val tuple20 = coll19.getOrElse(placeholder[Int](9), (coll8, box18.value))\n        val coll21 = box17.R7[Coll[Long]].get\n        val box22 = OUTPUTS(placeholder[Int](10))\n        val tuple23 = box22.tokens.getOrElse(placeholder[Int](11), (coll8, box22.value))\n        allOf(Coll[Boolean](INPUTS(placeholder[Int](12)).id == SELF.id, tuple20._1 == coll15, tuple20._2 >= max(l16 + coll13(placeholder[Int](13)), l14), coll7(placeholder[Int](14)) == coll19(placeholder[Int](15)), box18.propositionBytes == SELF.propositionBytes, box18.R4[Coll[Byte]].get == coll5, box18.R5[(Long, Long)].get == (l3, if (l6 - l1 <= coll21(placeholder[Int](16))) { l6 + coll21(placeholder[Int](17)) } else { l6 }), box18.R6[Coll[Long]] == opt12, box18.R7[Coll[Byte]].get == coll15, coll19.size == if (coll15.size == placeholder[Int](18)) { placeholder[Int](19) } else { placeholder[Int](20) }, box22.propositionBytes == SELF.R8[Coll[Byte]].get, ((tuple23._1 == coll15) && (tuple23._2 >= l16)) || (l16 == placeholder[Long](21))))\n      )} } else { placeholder[Boolean](22) }) && sigmaProp(box17.tokens(placeholder[Int](23))._1 == placeholder[Coll[Byte]](24))\n}",
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      "assets": [
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          "decimals": 0,
          "type": "EIP-004"
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      ],
      "additionalRegisters": {
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          "sigmaType": "(SLong, SLong)",
          "renderedValue": "[1685471691621,1686421491000]"
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        "R6": {
          "serializedValue": "110280b8c9e5ae048084af5f",
          "sigmaType": "Coll[SLong]",
          "renderedValue": "[75000000000,100000000]"
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          "sigmaType": "Coll[SByte]",
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        "R7": {
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          "sigmaType": "Coll[SByte]",
          "renderedValue": ""
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        "R4": {
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      "ergoTreeConstants": "",
      "ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(364775,7ff808,...)))}",
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