Transaction
ID: fe00e6f5e6...3a79
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0064 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
2,029.84 ERG
Tokens:
Loading assets...
Outputs (4)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0042 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
0
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0022 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
2,029.84 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 17,063
Total coins transferred: 2,029.85 ERG
Fees: 0.0022 ERG
Fees per byte: 0.000000316 ERG
Raw Transaction Data
{
"id": "fe00e6f5e6e42f249d2bfafa0688f2030a85d4c017298bfde156cde2193e3a79",
"blockId": "ce2aef8eea0153ae03f1721dd161f6dea3cef52ed9a77c70e3cf3a3373cb8cf0",
"inclusionHeight": 1757814,
"timestamp": 1775437599663,
"index": 6,
"globalIndex": 10553880,
"numConfirmations": 17063,
"inputs": [
{
"boxId": "76136ea48a3d7fd85499f9c64b00e80bd68fd19938d9ad060f1d3c65f369c9ae",
"value": 6400000,
"index": 0,
"spendingProof": null,
"outputBlockId": "03ca6b244cdbc747f22a201df52b6bac5bf065a8b1da812bfb91d008b62c0433",
"outputTransactionId": "d20c0d975fc525f8b057b2ab392f2ca6e5b8de7260c1f1483b711196d8ee425b",
"outputIndex": 0,
"outputGlobalIndex": 54557963,
"outputCreatedAt": 1757806,
"outputSettledAt": 1757808,
"ergoTree": 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"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 0\n4: 8\n5: 7\n6: Coll(-22,123,54,-30,-108,-79,-87,84,-88,7,82,-22,-62,-120,113,23,40,-27,-71,27,11,60,5,-106,84,-116,117,86,101,5,11,-120)\n7: 0\n8: Coll(-91,91,-121,53,-19,26,-103,-28,108,44,-119,-8,-103,74,-84,-33,75,17,9,-67,-49,104,47,30,91,52,71,-100,110,57,38,105)\n9: Coll(3,-6,-14,-53,50,-97,46,-112,-42,-46,59,88,-39,27,-69,108,4,106,-95,67,38,28,-62,31,82,-5,-30,-126,75,-4,-65,4)\n10: 10\n11: 4\n12: 2\n13: 6\n14: 1000000\n15: false\n16: 5\n17: 0\n18: 1\n19: 9\n20: 1\n21: 1\n22: 0\n23: 1\n24: 3\n25: 720\n26: 1\n27: 0\n28: 0\n29: 1\n30: 0\n31: 1\n32: 2\n33: 1\n34: 3\n35: 0\n36: 1000000\n37: 1000000\n38: 0\n39: 0\n40: 0\n41: 17\n42: 0\n43: 0\n44: 0\n45: 0\n46: 0\n47: 0\n48: 0\n49: 0\n50: 1000\n51: 100\n52: 1000\n53: 0\n54: 100\n55: 0\n56: 2\n57: 0\n58: 17\n59: 0\n60: 1000000\n61: 0\n62: 1\n63: 2\n64: 17\n65: 0\n66: 17\n67: 1000000\n68: 0\n69: 3\n70: 2000000\n71: 1\n72: 1\n73: 1\n74: 1\n75: 2\n76: 1\n77: 1000000\n78: 1000000\n79: 0\n80: 1\n81: 2\n82: 1\n83: 0\n84: false\n85: 1\n86: 3\n87: 11\n88: 11\n89: 0\n90: 0\n91: 1\n92: 1000000\n93: 1000000\n94: 1\n95: 1000000\n96: 1\n97: 12\n98: 12\n99: 0\n100: Coll(16,17,4,0,4,0,4,0,4,0,4,0,5,0,4,2,14,32,-91,91,-121,53,-19,26,-103,-28,108,44,-119,-8,-103,74,-84,-33,75,17,9,-67,-49,104,47,30,91,52,71,-100,110,57,38,105,4,0,4,2,5,-48,15,5,0,5,0,1,1,5,0,4,4,1,1,-40,12,-42,1,-78,-37,99,8,-89,115,0,0,-42,2,-78,-91,115,1,0,-42,3,-37,99,8,114,2,-42,4,-107,-19,-111)\n101: Coll(16,17,4,0,4,0,4,0,4,0,4,0,5,0,4,2,14,32,3,-6,-14,-53,50,-97,46,-112,-42,-46,59,88,-39,27,-69,108,4,106,-95,67,38,28,-62,31,82,-5,-30,-126,75,-4,-65,4,4,0,4,2,5,-48,15,5,0,5,0,1,1,5,0,4,4,1,1,-40,12,-42,1,-78,-37,99,8,-89,115,0,0,-42,2,-78,-91,115,1,0,-42,3,-37,99,8,114,2,-42,4,-107,-19,-111)\n102: 0\n103: 0\n104: 1000000\n105: false\n106: 0\n107: 0\n108: 0\n109: 0\n110: 0\n111: 1000000\n112: 1000000\n113: 1000000\n114: 0\n115: 0\n116: 1000\n117: 0\n118: 17\n119: 0\n120: 17\n121: 2\n122: 1\n123: 2\n124: 0\n125: true\n126: 1\n127: 2\n128: 0\n129: true\n130: false\n131: 1\n132: 0\n133: 1\n134: 0\n135: 0\n136: 0\n137: 0\n138: 1000000\n139: 1000000\n140: 0\n141: Coll(49,-126,103,79,7,-37,-71,-115,105,109,56,-19,-91,62,99,-21,59,-11,-2,87,15,113,-34,-24,94,-71,84,-42,-49,-112,59,-70)\n142: 0\n143: 0\n144: 0\n145: true\n146: false\n147: 2\n148: false",
"ergoTreeScript": "{\n val coll1 = SELF.R9[Coll[Coll[Byte]]].get\n val prop2 = proveDlog(decodePoint(coll1(placeholder[Int](0))))\n val coll3 = SELF.tokens\n val tuple4 = (Coll[Byte](), placeholder[Long](1))\n val tuple5 = coll3.getOrElse(placeholder[Int](2), tuple4)\n val coll6 = tuple5._1\n val coll7 = SELF.R7[Coll[Byte]].get\n val bool8 = coll6 == coll7\n val bool9 = !bool8\n val box10 = OUTPUTS(placeholder[Int](3))\n val coll11 = box10.propositionBytes\n val coll12 = prop2.propBytes\n val coll13 = SELF.R8[Coll[Long]].get\n val l14 = coll13(placeholder[Int](4))\n val l15 = coll13(placeholder[Int](5))\n val coll16 = placeholder[Coll[Byte]](6)\n val l17 = coll13(placeholder[Int](7))\n val coll18 = placeholder[Coll[Byte]](8)\n val coll19 = placeholder[Coll[Byte]](9)\n val l20 = coll13(placeholder[Int](10))\n val l21 = coll13(placeholder[Int](11))\n val l22 = coll13(placeholder[Int](12))\n val l23 = coll13(placeholder[Int](13))\n val l24 = l23 + placeholder[Long](14)\n val coll25 = SELF.R5[Coll[Byte]].get\n val bool26 = if (coll11 == SELF.propositionBytes) {\n (\n (\n (((box10.value >= l24) && (box10.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)) && (box10.R5[Coll[Byte]].get == coll25)) && (\n box10.R6[Coll[Byte]].get == SELF.R6[Coll[Byte]].get\n )\n ) && (box10.R8[Coll[Long]].get == coll13)\n ) && (box10.R9[Coll[Coll[Byte]]].get == coll1)\n } else { placeholder[Boolean](15) }\n val coll27 = SELF.id\n val l28 = coll13(placeholder[Int](16))\n val coll29 = box10.tokens\n val tuple30 = coll29.getOrElse(placeholder[Int](17), tuple4)\n val coll31 = tuple30._1\n val tuple32 = coll29.getOrElse(placeholder[Int](18), tuple4)\n val l33 = tuple5._2\n val l34 = tuple30._2\n val coll35 = tuple32._1\n val l36 = coll13(placeholder[Int](19))\n val box37 = OUTPUTS(placeholder[Int](20))\n val coll38 = box37.tokens\n val bool39 = bool8 && (l33 == placeholder[Long](21))\n val i40 = coll13.size\n val tuple41 = coll38.getOrElse(placeholder[Int](22), tuple4)\n val tuple42 = coll3.getOrElse(placeholder[Int](23), tuple4)\n val l43 = HEIGHT.toLong\n val l44 = coll13(placeholder[Int](24))\n val l45 = l44 + placeholder[Long](25)\n val bool46 = if (coll13(placeholder[Int](26)) == placeholder[Long](27)) { (bool39 && (l43 >= l44)) && (l43 <= l45) } else { bool39 && (l43 <= l45) }\n val l47 = INPUTS.fold(placeholder[Long](28), {(tuple47: (Long, Box)) =>\n val box49 = tuple47._2\n val l50 = tuple47._1\n if (box49.id != coll27) { box49.tokens.fold(l50, {(tuple51: (Long, (Coll[Byte], Long))) =>\n val tuple53 = tuple51._2\n val l54 = tuple51._1\n if (tuple53._1 == coll7) { l54 + tuple53._2 } else { l54 }\n }) } else { l50 }\n })\n val bool48 = ((bool26 && (box10.R7[Coll[Byte]].get == coll7)) && \n val coll48 = coll31\n coll48 == coll6\n ) && (l34 == placeholder[Long](29))\n val bool49 = OUTPUTS.fold(placeholder[Long](30), {(tuple49: (Long, Box)) => tuple49._2.tokens.fold(tuple49._1, {(tuple51: (Long, (Coll[Byte], Long))) =>\n val tuple53 = tuple51._2\n val l54 = tuple51._1\n if (tuple53._1 == coll7) { l54 + tuple53._2 } else { l54 }\n }) }) == placeholder[Long](31)\n val l50 = tuple32._2\n prop2 && sigmaProp((bool9 && (OUTPUTS.size == placeholder[Int](32))) && (coll11 == coll12)) || sigmaProp(\n (((if ((bool9 && (INPUTS.size == placeholder[Int](33))) && (OUTPUTS.size == placeholder[Int](34))) {(\n val bool51 = l14 == placeholder[Long](35)\n val l52 = l21 * l22 / placeholder[Long](36) * l20 / placeholder[Long](37)\n val bool53 = l52 > placeholder[Long](38)\n ((((((((((if (bool51) {(\n val box54 = CONTEXT.dataInputs(placeholder[Int](39))\n val i55 = l15.toInt\n val l56 = box54.R5[Coll[Long]].get(i55)\n val bool57 = box54.tokens(placeholder[Int](40))._1 == coll16\n val coll58 = box54.R4[Coll[Coll[Byte]]].get(i55)\n if (l15 == placeholder[Long](41)) { bool57 && (l56 > placeholder[Long](42)) } else { if (l17 == placeholder[Long](43)) { ((bool57 && (coll58.size > placeholder[Int](44))) && (l56 > placeholder[Long](45))) && (coll3(placeholder[Int](46))._1 == coll58) } else { (bool57 && (coll58.size > placeholder[Int](47))) && (l56 > placeholder[Long](48)) } }\n )} else {(\n val coll54 = coll3(placeholder[Int](49))._1\n (coll54 == coll18) || (coll54 == coll19)\n )} && if (bool51) { (l20 == placeholder[Long](50)) || (l20 == placeholder[Long](51)) } else { ((l20 == placeholder[Long](52)) && (coll3(placeholder[Int](53))._1 == coll18)) || ((l20 == placeholder[Long](54)) && (coll3(placeholder[Int](55))._1 == coll19)) }) && bool53) && bool26) && (box10.R7[Coll[Byte]].get == coll27)) && (box10.value == SELF.value - l23 - l28)) && (box10.value >= placeholder[Long](56) * l24)) && (coll31 == coll27)) && \n val bool54 = l17 == placeholder[Long](57)\n (((((bool54 && bool51) && (l15 != placeholder[Long](58))) && \n val l55 = l22 * CONTEXT.dataInputs(placeholder[Int](59)).R5[Coll[Long]].get(l15.toInt) / placeholder[Long](60)\n ((((l55 > placeholder[Long](61)) && \n val coll56 = coll35\n coll56 == coll25\n ) && (tuple32 == tuple5)) && (l34 == l33 / l55 + placeholder[Long](62))) && (coll29.size == placeholder[Int](63))\n ) || (((bool54 && bool51) && (l15 == placeholder[Long](64))) && \n val l55 = l22 * CONTEXT.dataInputs(placeholder[Int](65)).R5[Coll[Long]].get(placeholder[Int](66)) / placeholder[Long](67)\n val l56 = SELF.value\n (((l55 > placeholder[Long](68)) && (l34 == l56 - placeholder[Long](69) * l23 - l28 - placeholder[Long](70) / l55 + placeholder[Long](71))) && (coll29.size == placeholder[Int](72))) && (box10.value >= l56 - l23 - l28)\n )) || (((((((l17 == placeholder[Long](73)) && bool51) && (coll35 == coll25)) && (tuple32 == tuple5)) && bool53) && (l34 == l33 / l52 + placeholder[Long](74))) && (coll29.size == placeholder[Int](75)))) || ((l14 == placeholder[Long](76)) && \n val l55 = l36 * l22 / placeholder[Long](77) * l20 / placeholder[Long](78)\n ((((l55 > placeholder[Long](79)) && (coll35 == coll25)) && (tuple32 == tuple5)) && (l34 == l33 / l55 + placeholder[Long](80))) && (coll29.size == placeholder[Int](81))\n )\n ) && (box37.propositionBytes == coll1(placeholder[Int](82)))) && (coll38.size == placeholder[Int](83))) && (box37.value >= l28)\n )} else { placeholder[Boolean](84) } || if (((bool8 && (!bool39)) && (INPUTS.size == placeholder[Int](85))) && (OUTPUTS.size == placeholder[Int](86))) {(\n val bool51 = if (i40 > placeholder[Int](87)) { coll13(placeholder[Int](88)) } else { placeholder[Long](89) } == placeholder[Long](90)\n val bool52 = (tuple41._1 == coll6) && (tuple41._2 == l33 - placeholder[Long](91))\n val bool53 = (((((box10.value == SELF.value - l23 - if (bool51) { placeholder[Long](92) } else { placeholder[Long](93) + l23 }) && bool26) && (box10.R7[Coll[Byte]].get == coll7)) && (coll31 == coll6)) && (l34 == placeholder[Long](94))) && (tuple32 == tuple42)\n if (bool51) { (((bool53 && bool52) && (box37.propositionBytes == coll12)) && (box37.value == placeholder[Long](95))) && (coll38.size == placeholder[Int](96)) } else {(\n val coll54 = box37.propositionBytes\n val l55 = if (i40 > placeholder[Int](97)) { coll13(placeholder[Int](98)) } else { placeholder[Long](99) }\n ((((bool53 && bool52) && ((coll54 == placeholder[Coll[Byte]](100)) || (coll54 == placeholder[Coll[Byte]](101)))) && (box37.R4[SigmaProp].get == prop2)) && ((box37.R5[Coll[Long]].get(placeholder[Int](102)) == l55) && (l55 > placeholder[Long](103)))) && (box37.value >= placeholder[Long](104) + l23)\n )}\n )} else { placeholder[Boolean](105) }) || if ((bool46 && (l14 == placeholder[Long](106))) && (l47 > placeholder[Long](107))) {(\n val box51 = CONTEXT.dataInputs(placeholder[Int](108))\n val i52 = l15.toInt\n val l53 = box51.R5[Coll[Long]].get(i52)\n val bool54 = l53 > placeholder[Long](109)\n val bool55 = box51.tokens(placeholder[Int](110))._1 == coll16\n val l56 = l22 * l53 / placeholder[Long](111)\n val l57 = l21 * l22 / placeholder[Long](112) * l20 / placeholder[Long](113)\n val bool58 = (l56 > placeholder[Long](114)) && (l57 > placeholder[Long](115))\n val l59 = l56 * l47\n val coll60 = if (l20 == placeholder[Long](116)) { coll18 } else { coll19 }\n val l61 = l57 * l47\n val coll62 = box51.R4[Coll[Coll[Byte]]].get(i52)\n val bool63 = (coll62.size > placeholder[Int](117)) || (l15 == placeholder[Long](118))\n if (l17 == placeholder[Long](119)) { if (l15 == placeholder[Long](120)) {(\n val box64 = OUTPUTS(placeholder[Int](121))\n ((((((bool55 && bool54) && bool58) && (box37.value >= l59)) && ((box64.propositionBytes == coll12) && box64.tokens.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll60) && (tuple65._2 >= l61) }))) && bool48) && bool49) && (box10.value >= SELF.value - l59 - l23)\n )} else {(\n val tuple64 = coll3(placeholder[Int](122))\n val box65 = OUTPUTS(placeholder[Int](123))\n ((((((((((bool55 && bool63) && bool54) && bool58) && (tuple64._1 == coll62)) && coll38.exists({(tuple66: (Coll[Byte], Long)) => (tuple66._1 == coll62) && (tuple66._2 >= l59) })) && ((box65.propositionBytes == coll12) && box65.tokens.exists({(tuple66: (Coll[Byte], Long)) => (tuple66._1 == coll60) && (tuple66._2 >= l61) }))) && if (l50 > placeholder[Long](124)) { coll35 == coll62 } else { placeholder[Boolean](125) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple64._2 - l59)\n )} } else {(\n val tuple64 = coll3(placeholder[Int](126))\n val coll65 = tuple64._1\n val box66 = OUTPUTS(placeholder[Int](127))\n ((((((((((bool55 && bool63) && bool54) && bool58) && ((coll65 == coll18) || (coll65 == coll19))) && coll38.exists({(tuple67: (Coll[Byte], Long)) => (tuple67._1 == coll65) && (tuple67._2 >= l61) })) && ((box66.propositionBytes == coll12) && box66.tokens.exists({(tuple67: (Coll[Byte], Long)) => (tuple67._1 == coll62) && (tuple67._2 >= l59) }))) && if (l50 > placeholder[Long](128)) { coll35 == coll65 } else { placeholder[Boolean](129) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple64._2 - l61)\n )}\n )} else { placeholder[Boolean](130) }) || if ((bool46 && (l14 == placeholder[Long](131))) && (l47 > placeholder[Long](132))) {(\n val tuple51 = coll3(placeholder[Int](133))\n val coll52 = tuple51._1\n val box53 = CONTEXT.dataInputs(placeholder[Int](134))\n val l54 = box53.R8[Coll[Long]].get(l15.toInt)\n val l55 = if (l17 == placeholder[Long](135)) { if (l54 > l21) {(\n val l55 = l54 - l21\n if (l55 > l36) { l36 } else { l55 }\n )} else { placeholder[Long](136) } } else { if (l54 < l21) {(\n val l55 = l21 - l54\n if (l55 > l36) { l36 } else { l55 }\n )} else { placeholder[Long](137) } }\n val l56 = l55 * l22 / placeholder[Long](138) * l20 / placeholder[Long](139)\n val l57 = l56 * l47\n ((((((((((coll52 == coll18) || (coll52 == coll19)) && (box53.tokens(placeholder[Int](140))._1 == placeholder[Coll[Byte]](141))) && (l55 > placeholder[Long](142))) && (l56 > placeholder[Long](143))) && coll38.exists({(tuple58: (Coll[Byte], Long)) => (tuple58._1 == coll52) && (tuple58._2 >= l57) })) && if (l50 > placeholder[Long](144)) { coll35 == coll52 } else { placeholder[Boolean](145) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple51._2 - l57)\n )} else { placeholder[Boolean](146) }) || if (l43 > l45) {\n ((((OUTPUTS.size == placeholder[Int](147)) && (coll11 == coll12)) && (box10.value >= SELF.value - l23)) && (coll31 == tuple42._1)) && (l34 == tuple42._2)\n } else { placeholder[Boolean](148) }\n )\n}",
"address": "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",
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"tokenId": "f834b2d4e7cd66fd1d7e3c5089739ce19600f0a5a68321dcbdfc11341fb49159",
"index": 74,
"amount": 1,
"name": "Ergobotz #2",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "edd9f92be0b130e2a89bc8cc6b0b8185f2cc3478ede7f868e51cbf183b23d397",
"index": 75,
"amount": 1,
"name": "rosen watcher tcg #4 fateme",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "29bf87aa876d6abc6d46bec46ebddcf819aa213303915ff174019dba5220c60c",
"index": 76,
"amount": 1,
"name": "Sigma BYTES",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "0cd8c9f416e5b1ca9f986a7f10a84191dfb85941619e49e53c0dc30ebf83324b",
"index": 77,
"amount": 22989,
"name": "COMET",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "b780a9682ba1ea68eedfe3dd00169e414ea8968d96142ee95e2391d64df05c3d",
"index": 78,
"amount": 155795376433,
"name": "rsDIS",
"decimals": 8,
"type": "EIP-004"
},
{
"tokenId": "abe0a3c2f646dcd430aac9c29d80feee865bd8b5231edb545a41105d4c8e4985",
"index": 79,
"amount": 7569,
"name": "BUNS",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "8b08cdd5449a9592a9e79711d7d79249d7a03c535d17efaee83e216e80a44c4b",
"index": 80,
"amount": 2132991,
"name": "RSN",
"decimals": 3,
"type": "EIP-004"
},
{
"tokenId": "e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297",
"index": 81,
"amount": 3000000,
"name": "rsADA",
"decimals": 6,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [
{
"boxId": "05a779fa3c8be3f315273fc88cd339023cf6e9ac3db3adf7f862d99b86f648f9",
"value": 10000000,
"index": 0,
"outputBlockId": "823b7611f644a16aadf6f16b572b55e989ed3bf58626c9a175b7da8ce46eabed",
"outputTransactionId": "ac0da50fc7d80672541421fb3d85b5123d2b5ddd05bb4ea727ce48cfa46dd3ab",
"outputIndex": 2,
"ergoTree": "1006040004000400040008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b0400d802d601b2db6308a7730000d602b2b5a5d9010263d801d604db63087202ed91b172047301938cb27204730200018c720101730300ea02ea02ea02ea02ea027304d193b2db630872027305007201d193c27202c2a7d193e4c67202070ee4c6a7070ed192c17202c1a7d1edede6c672020411e6c672020511e6c672020604",
"address": "zLQUkB1HjNMi11pafYwCfPugsQm8YQRTYhr5tfgCwK5jRZjTe6CqkG1SShzuEY1unaCQB2566C52j79g5GmpRyybHfiAYgFAM1zj4NVn5x9WbjeGQowzGjBMysU4hTnDVnZnqf5beZCyXLd2ZQRtordB96TQcxz9FnWQbPoAh62iJA5n5URMtVFCzC31zQMW91CAHLG54P5BDcGmGtdJXfPQQQUUBPEn4iWTYYggNJ8tTDCmgc",
"assets": [],
"additionalRegisters": {
"R5": {
"serializedValue": "1115c23c842ddc2ccc38be508076c85ae83dcc6800008ec201d0c001a4c411f6cd16bae31be0b60bce4cbe52ea920100",
"sigmaType": "Coll[SLong]",
"renderedValue": "[3873,2882,2862,3622,5151,7552,5796,3956,6694,0,0,12423,12328,143634,185211,227549,93616,4903,5279,9397,0]"
},
"R6": {
"serializedValue": "04f81a",
"sigmaType": "SInt",
"renderedValue": "1724"
},
"R8": {
"serializedValue": "1115c0d1f9eb0f80ddcaaa840480d687c10488b30bce921f8c4eaa16a4cecf01fa9d0aa083df8531e0cca7beda02d0d2eb44e09db005c0f9d269c0e5a96be0b4db02e09d8912988a24ae96dfdb2382914a00",
"sigmaType": "Coll[SLong]",
"renderedValue": "[2126460000,69301000000,605091200,93380,255143,4998,1429,1700754,83837,6582690000,46504670000,72185000,5638000,110780000,112540000,2846000,18950000,295564,4793820567,607297,0]"
},
"R7": {
"serializedValue": "0e20f7f008ad8fcaad4490d8e78ab6d3f11efe7213a13f7b243795818b155e1acc92",
"sigmaType": "Coll[SByte]",
"renderedValue": "f7f008ad8fcaad4490d8e78ab6d3f11efe7213a13f7b243795818b155e1acc92"
},
"R4": {
"serializedValue": "1115d2b3f2b20feadd81ecf703e2fdf0b604a68e0b88f91db64ea016a4decd01c0930adee3de8531a69ba7beda02b0c9bf45cc8eb405b8bcc56884f9a66bc8f8d702b2af8b12d6ee23a2dbcdfa2382d34a00",
"sigmaType": "Coll[SLong]",
"renderedValue": "[2066631913,67624777589,594419569,91027,245316,5019,1424,1685394,83168,6582687983,46504666835,72872536,5669798,109621020,112516674,2817572,18967513,293803,4826183377,611521,0]"
}
}
}
],
"outputs": [
{
"boxId": "d91d2145b3559a9d0a1ed4e6bb6d6851703235e85d8954636d9847f8d528eaaa",
"transactionId": "fe00e6f5e6e42f249d2bfafa0688f2030a85d4c017298bfde156cde2193e3a79",
"blockId": "ce2aef8eea0153ae03f1721dd161f6dea3cef52ed9a77c70e3cf3a3373cb8cf0",
"value": 4200000,
"index": 0,
"globalIndex": 54558244,
"creationHeight": 1757813,
"settlementHeight": 1757814,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 0\n4: 8\n5: 7\n6: Coll(-22,123,54,-30,-108,-79,-87,84,-88,7,82,-22,-62,-120,113,23,40,-27,-71,27,11,60,5,-106,84,-116,117,86,101,5,11,-120)\n7: 0\n8: Coll(-91,91,-121,53,-19,26,-103,-28,108,44,-119,-8,-103,74,-84,-33,75,17,9,-67,-49,104,47,30,91,52,71,-100,110,57,38,105)\n9: Coll(3,-6,-14,-53,50,-97,46,-112,-42,-46,59,88,-39,27,-69,108,4,106,-95,67,38,28,-62,31,82,-5,-30,-126,75,-4,-65,4)\n10: 10\n11: 4\n12: 2\n13: 6\n14: 1000000\n15: false\n16: 5\n17: 0\n18: 1\n19: 9\n20: 1\n21: 1\n22: 0\n23: 1\n24: 3\n25: 720\n26: 1\n27: 0\n28: 0\n29: 1\n30: 0\n31: 1\n32: 2\n33: 1\n34: 3\n35: 0\n36: 1000000\n37: 1000000\n38: 0\n39: 0\n40: 0\n41: 17\n42: 0\n43: 0\n44: 0\n45: 0\n46: 0\n47: 0\n48: 0\n49: 0\n50: 1000\n51: 100\n52: 1000\n53: 0\n54: 100\n55: 0\n56: 2\n57: 0\n58: 17\n59: 0\n60: 1000000\n61: 0\n62: 1\n63: 2\n64: 17\n65: 0\n66: 17\n67: 1000000\n68: 0\n69: 3\n70: 2000000\n71: 1\n72: 1\n73: 1\n74: 1\n75: 2\n76: 1\n77: 1000000\n78: 1000000\n79: 0\n80: 1\n81: 2\n82: 1\n83: 0\n84: false\n85: 1\n86: 3\n87: 11\n88: 11\n89: 0\n90: 0\n91: 1\n92: 1000000\n93: 1000000\n94: 1\n95: 1000000\n96: 1\n97: 12\n98: 12\n99: 0\n100: Coll(16,17,4,0,4,0,4,0,4,0,4,0,5,0,4,2,14,32,-91,91,-121,53,-19,26,-103,-28,108,44,-119,-8,-103,74,-84,-33,75,17,9,-67,-49,104,47,30,91,52,71,-100,110,57,38,105,4,0,4,2,5,-48,15,5,0,5,0,1,1,5,0,4,4,1,1,-40,12,-42,1,-78,-37,99,8,-89,115,0,0,-42,2,-78,-91,115,1,0,-42,3,-37,99,8,114,2,-42,4,-107,-19,-111)\n101: Coll(16,17,4,0,4,0,4,0,4,0,4,0,5,0,4,2,14,32,3,-6,-14,-53,50,-97,46,-112,-42,-46,59,88,-39,27,-69,108,4,106,-95,67,38,28,-62,31,82,-5,-30,-126,75,-4,-65,4,4,0,4,2,5,-48,15,5,0,5,0,1,1,5,0,4,4,1,1,-40,12,-42,1,-78,-37,99,8,-89,115,0,0,-42,2,-78,-91,115,1,0,-42,3,-37,99,8,114,2,-42,4,-107,-19,-111)\n102: 0\n103: 0\n104: 1000000\n105: false\n106: 0\n107: 0\n108: 0\n109: 0\n110: 0\n111: 1000000\n112: 1000000\n113: 1000000\n114: 0\n115: 0\n116: 1000\n117: 0\n118: 17\n119: 0\n120: 17\n121: 2\n122: 1\n123: 2\n124: 0\n125: true\n126: 1\n127: 2\n128: 0\n129: true\n130: false\n131: 1\n132: 0\n133: 1\n134: 0\n135: 0\n136: 0\n137: 0\n138: 1000000\n139: 1000000\n140: 0\n141: Coll(49,-126,103,79,7,-37,-71,-115,105,109,56,-19,-91,62,99,-21,59,-11,-2,87,15,113,-34,-24,94,-71,84,-42,-49,-112,59,-70)\n142: 0\n143: 0\n144: 0\n145: true\n146: false\n147: 2\n148: false",
"ergoTreeScript": "{\n val coll1 = SELF.R9[Coll[Coll[Byte]]].get\n val prop2 = proveDlog(decodePoint(coll1(placeholder[Int](0))))\n val coll3 = SELF.tokens\n val tuple4 = (Coll[Byte](), placeholder[Long](1))\n val tuple5 = coll3.getOrElse(placeholder[Int](2), tuple4)\n val coll6 = tuple5._1\n val coll7 = SELF.R7[Coll[Byte]].get\n val bool8 = coll6 == coll7\n val bool9 = !bool8\n val box10 = OUTPUTS(placeholder[Int](3))\n val coll11 = box10.propositionBytes\n val coll12 = prop2.propBytes\n val coll13 = SELF.R8[Coll[Long]].get\n val l14 = coll13(placeholder[Int](4))\n val l15 = coll13(placeholder[Int](5))\n val coll16 = placeholder[Coll[Byte]](6)\n val l17 = coll13(placeholder[Int](7))\n val coll18 = placeholder[Coll[Byte]](8)\n val coll19 = placeholder[Coll[Byte]](9)\n val l20 = coll13(placeholder[Int](10))\n val l21 = coll13(placeholder[Int](11))\n val l22 = coll13(placeholder[Int](12))\n val l23 = coll13(placeholder[Int](13))\n val l24 = l23 + placeholder[Long](14)\n val coll25 = SELF.R5[Coll[Byte]].get\n val bool26 = if (coll11 == SELF.propositionBytes) {\n (\n (\n (((box10.value >= l24) && (box10.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)) && (box10.R5[Coll[Byte]].get == coll25)) && (\n box10.R6[Coll[Byte]].get == SELF.R6[Coll[Byte]].get\n )\n ) && (box10.R8[Coll[Long]].get == coll13)\n ) && (box10.R9[Coll[Coll[Byte]]].get == coll1)\n } else { placeholder[Boolean](15) }\n val coll27 = SELF.id\n val l28 = coll13(placeholder[Int](16))\n val coll29 = box10.tokens\n val tuple30 = coll29.getOrElse(placeholder[Int](17), tuple4)\n val coll31 = tuple30._1\n val tuple32 = coll29.getOrElse(placeholder[Int](18), tuple4)\n val l33 = tuple5._2\n val l34 = tuple30._2\n val coll35 = tuple32._1\n val l36 = coll13(placeholder[Int](19))\n val box37 = OUTPUTS(placeholder[Int](20))\n val coll38 = box37.tokens\n val bool39 = bool8 && (l33 == placeholder[Long](21))\n val i40 = coll13.size\n val tuple41 = coll38.getOrElse(placeholder[Int](22), tuple4)\n val tuple42 = coll3.getOrElse(placeholder[Int](23), tuple4)\n val l43 = HEIGHT.toLong\n val l44 = coll13(placeholder[Int](24))\n val l45 = l44 + placeholder[Long](25)\n val bool46 = if (coll13(placeholder[Int](26)) == placeholder[Long](27)) { (bool39 && (l43 >= l44)) && (l43 <= l45) } else { bool39 && (l43 <= l45) }\n val l47 = INPUTS.fold(placeholder[Long](28), {(tuple47: (Long, Box)) =>\n val box49 = tuple47._2\n val l50 = tuple47._1\n if (box49.id != coll27) { box49.tokens.fold(l50, {(tuple51: (Long, (Coll[Byte], Long))) =>\n val tuple53 = tuple51._2\n val l54 = tuple51._1\n if (tuple53._1 == coll7) { l54 + tuple53._2 } else { l54 }\n }) } else { l50 }\n })\n val bool48 = ((bool26 && (box10.R7[Coll[Byte]].get == coll7)) && \n val coll48 = coll31\n coll48 == coll6\n ) && (l34 == placeholder[Long](29))\n val bool49 = OUTPUTS.fold(placeholder[Long](30), {(tuple49: (Long, Box)) => tuple49._2.tokens.fold(tuple49._1, {(tuple51: (Long, (Coll[Byte], Long))) =>\n val tuple53 = tuple51._2\n val l54 = tuple51._1\n if (tuple53._1 == coll7) { l54 + tuple53._2 } else { l54 }\n }) }) == placeholder[Long](31)\n val l50 = tuple32._2\n prop2 && sigmaProp((bool9 && (OUTPUTS.size == placeholder[Int](32))) && (coll11 == coll12)) || sigmaProp(\n (((if ((bool9 && (INPUTS.size == placeholder[Int](33))) && (OUTPUTS.size == placeholder[Int](34))) {(\n val bool51 = l14 == placeholder[Long](35)\n val l52 = l21 * l22 / placeholder[Long](36) * l20 / placeholder[Long](37)\n val bool53 = l52 > placeholder[Long](38)\n ((((((((((if (bool51) {(\n val box54 = CONTEXT.dataInputs(placeholder[Int](39))\n val i55 = l15.toInt\n val l56 = box54.R5[Coll[Long]].get(i55)\n val bool57 = box54.tokens(placeholder[Int](40))._1 == coll16\n val coll58 = box54.R4[Coll[Coll[Byte]]].get(i55)\n if (l15 == placeholder[Long](41)) { bool57 && (l56 > placeholder[Long](42)) } else { if (l17 == placeholder[Long](43)) { ((bool57 && (coll58.size > placeholder[Int](44))) && (l56 > placeholder[Long](45))) && (coll3(placeholder[Int](46))._1 == coll58) } else { (bool57 && (coll58.size > placeholder[Int](47))) && (l56 > placeholder[Long](48)) } }\n )} else {(\n val coll54 = coll3(placeholder[Int](49))._1\n (coll54 == coll18) || (coll54 == coll19)\n )} && if (bool51) { (l20 == placeholder[Long](50)) || (l20 == placeholder[Long](51)) } else { ((l20 == placeholder[Long](52)) && (coll3(placeholder[Int](53))._1 == coll18)) || ((l20 == placeholder[Long](54)) && (coll3(placeholder[Int](55))._1 == coll19)) }) && bool53) && bool26) && (box10.R7[Coll[Byte]].get == coll27)) && (box10.value == SELF.value - l23 - l28)) && (box10.value >= placeholder[Long](56) * l24)) && (coll31 == coll27)) && \n val bool54 = l17 == placeholder[Long](57)\n (((((bool54 && bool51) && (l15 != placeholder[Long](58))) && \n val l55 = l22 * CONTEXT.dataInputs(placeholder[Int](59)).R5[Coll[Long]].get(l15.toInt) / placeholder[Long](60)\n ((((l55 > placeholder[Long](61)) && \n val coll56 = coll35\n coll56 == coll25\n ) && (tuple32 == tuple5)) && (l34 == l33 / l55 + placeholder[Long](62))) && (coll29.size == placeholder[Int](63))\n ) || (((bool54 && bool51) && (l15 == placeholder[Long](64))) && \n val l55 = l22 * CONTEXT.dataInputs(placeholder[Int](65)).R5[Coll[Long]].get(placeholder[Int](66)) / placeholder[Long](67)\n val l56 = SELF.value\n (((l55 > placeholder[Long](68)) && (l34 == l56 - placeholder[Long](69) * l23 - l28 - placeholder[Long](70) / l55 + placeholder[Long](71))) && (coll29.size == placeholder[Int](72))) && (box10.value >= l56 - l23 - l28)\n )) || (((((((l17 == placeholder[Long](73)) && bool51) && (coll35 == coll25)) && (tuple32 == tuple5)) && bool53) && (l34 == l33 / l52 + placeholder[Long](74))) && (coll29.size == placeholder[Int](75)))) || ((l14 == placeholder[Long](76)) && \n val l55 = l36 * l22 / placeholder[Long](77) * l20 / placeholder[Long](78)\n ((((l55 > placeholder[Long](79)) && (coll35 == coll25)) && (tuple32 == tuple5)) && (l34 == l33 / l55 + placeholder[Long](80))) && (coll29.size == placeholder[Int](81))\n )\n ) && (box37.propositionBytes == coll1(placeholder[Int](82)))) && (coll38.size == placeholder[Int](83))) && (box37.value >= l28)\n )} else { placeholder[Boolean](84) } || if (((bool8 && (!bool39)) && (INPUTS.size == placeholder[Int](85))) && (OUTPUTS.size == placeholder[Int](86))) {(\n val bool51 = if (i40 > placeholder[Int](87)) { coll13(placeholder[Int](88)) } else { placeholder[Long](89) } == placeholder[Long](90)\n val bool52 = (tuple41._1 == coll6) && (tuple41._2 == l33 - placeholder[Long](91))\n val bool53 = (((((box10.value == SELF.value - l23 - if (bool51) { placeholder[Long](92) } else { placeholder[Long](93) + l23 }) && bool26) && (box10.R7[Coll[Byte]].get == coll7)) && (coll31 == coll6)) && (l34 == placeholder[Long](94))) && (tuple32 == tuple42)\n if (bool51) { (((bool53 && bool52) && (box37.propositionBytes == coll12)) && (box37.value == placeholder[Long](95))) && (coll38.size == placeholder[Int](96)) } else {(\n val coll54 = box37.propositionBytes\n val l55 = if (i40 > placeholder[Int](97)) { coll13(placeholder[Int](98)) } else { placeholder[Long](99) }\n ((((bool53 && bool52) && ((coll54 == placeholder[Coll[Byte]](100)) || (coll54 == placeholder[Coll[Byte]](101)))) && (box37.R4[SigmaProp].get == prop2)) && ((box37.R5[Coll[Long]].get(placeholder[Int](102)) == l55) && (l55 > placeholder[Long](103)))) && (box37.value >= placeholder[Long](104) + l23)\n )}\n )} else { placeholder[Boolean](105) }) || if ((bool46 && (l14 == placeholder[Long](106))) && (l47 > placeholder[Long](107))) {(\n val box51 = CONTEXT.dataInputs(placeholder[Int](108))\n val i52 = l15.toInt\n val l53 = box51.R5[Coll[Long]].get(i52)\n val bool54 = l53 > placeholder[Long](109)\n val bool55 = box51.tokens(placeholder[Int](110))._1 == coll16\n val l56 = l22 * l53 / placeholder[Long](111)\n val l57 = l21 * l22 / placeholder[Long](112) * l20 / placeholder[Long](113)\n val bool58 = (l56 > placeholder[Long](114)) && (l57 > placeholder[Long](115))\n val l59 = l56 * l47\n val coll60 = if (l20 == placeholder[Long](116)) { coll18 } else { coll19 }\n val l61 = l57 * l47\n val coll62 = box51.R4[Coll[Coll[Byte]]].get(i52)\n val bool63 = (coll62.size > placeholder[Int](117)) || (l15 == placeholder[Long](118))\n if (l17 == placeholder[Long](119)) { if (l15 == placeholder[Long](120)) {(\n val box64 = OUTPUTS(placeholder[Int](121))\n ((((((bool55 && bool54) && bool58) && (box37.value >= l59)) && ((box64.propositionBytes == coll12) && box64.tokens.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll60) && (tuple65._2 >= l61) }))) && bool48) && bool49) && (box10.value >= SELF.value - l59 - l23)\n )} else {(\n val tuple64 = coll3(placeholder[Int](122))\n val box65 = OUTPUTS(placeholder[Int](123))\n ((((((((((bool55 && bool63) && bool54) && bool58) && (tuple64._1 == coll62)) && coll38.exists({(tuple66: (Coll[Byte], Long)) => (tuple66._1 == coll62) && (tuple66._2 >= l59) })) && ((box65.propositionBytes == coll12) && box65.tokens.exists({(tuple66: (Coll[Byte], Long)) => (tuple66._1 == coll60) && (tuple66._2 >= l61) }))) && if (l50 > placeholder[Long](124)) { coll35 == coll62 } else { placeholder[Boolean](125) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple64._2 - l59)\n )} } else {(\n val tuple64 = coll3(placeholder[Int](126))\n val coll65 = tuple64._1\n val box66 = OUTPUTS(placeholder[Int](127))\n ((((((((((bool55 && bool63) && bool54) && bool58) && ((coll65 == coll18) || (coll65 == coll19))) && coll38.exists({(tuple67: (Coll[Byte], Long)) => (tuple67._1 == coll65) && (tuple67._2 >= l61) })) && ((box66.propositionBytes == coll12) && box66.tokens.exists({(tuple67: (Coll[Byte], Long)) => (tuple67._1 == coll62) && (tuple67._2 >= l59) }))) && if (l50 > placeholder[Long](128)) { coll35 == coll65 } else { placeholder[Boolean](129) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple64._2 - l61)\n )}\n )} else { placeholder[Boolean](130) }) || if ((bool46 && (l14 == placeholder[Long](131))) && (l47 > placeholder[Long](132))) {(\n val tuple51 = coll3(placeholder[Int](133))\n val coll52 = tuple51._1\n val box53 = CONTEXT.dataInputs(placeholder[Int](134))\n val l54 = box53.R8[Coll[Long]].get(l15.toInt)\n val l55 = if (l17 == placeholder[Long](135)) { if (l54 > l21) {(\n val l55 = l54 - l21\n if (l55 > l36) { l36 } else { l55 }\n )} else { placeholder[Long](136) } } else { if (l54 < l21) {(\n val l55 = l21 - l54\n if (l55 > l36) { l36 } else { l55 }\n )} else { placeholder[Long](137) } }\n val l56 = l55 * l22 / placeholder[Long](138) * l20 / placeholder[Long](139)\n val l57 = l56 * l47\n ((((((((((coll52 == coll18) || (coll52 == coll19)) && (box53.tokens(placeholder[Int](140))._1 == placeholder[Coll[Byte]](141))) && (l55 > placeholder[Long](142))) && (l56 > placeholder[Long](143))) && coll38.exists({(tuple58: (Coll[Byte], Long)) => (tuple58._1 == coll52) && (tuple58._2 >= l57) })) && if (l50 > placeholder[Long](144)) { coll35 == coll52 } else { placeholder[Boolean](145) }) && bool48) && bool49) && (box10.value >= SELF.value - l23)) && (l50 >= tuple51._2 - l57)\n )} else { placeholder[Boolean](146) }) || if (l43 > l45) {\n ((((OUTPUTS.size == placeholder[Int](147)) && (coll11 == coll12)) && (box10.value >= SELF.value - l23)) && (coll31 == tuple42._1)) && (l34 == tuple42._2)\n } else { placeholder[Boolean](148) }\n )\n}",
"address": "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",
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