Transaction
ID: 15a5faefd3...05c9
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.002 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.218 ERG
Tokens:
9,997.96
Outputs (4)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.002 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0005 ERG
Tokens:
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0015 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.216 ERG
Tokens:
9,997.95
Transaction Details
Confirmations: 782,813
Total coins transferred: 0.22 ERG
Fees: 0.0015 ERG
Fees per byte: 0.000000217 ERG
Raw Transaction Data
{
"id": "15a5faefd3dedcfa64cfec0f1d0cd0d003b9d04a084df89c31c38ba7b68005c9",
"blockId": "298b39b94802a6aa098784209508c102203c87ffc0dc6eea3008b26f12a09bed",
"inclusionHeight": 974406,
"timestamp": 1680526512026,
"index": 10,
"globalIndex": 4996614,
"numConfirmations": 782813,
"inputs": [
{
"boxId": "b2898078097baed5c6e1a40d25f1028d73704bd7d23287e7083e851ea58f21d1",
"value": 2000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "298b39b94802a6aa098784209508c102203c87ffc0dc6eea3008b26f12a09bed",
"outputTransactionId": "0315c18ce485dd1543c34fb1a2a719d3c9d5a6359956a4c0cbb87a8608f3a002",
"outputIndex": 0,
"outputGlobalIndex": 27981998,
"outputCreatedAt": 974404,
"outputSettledAt": 974406,
"ergoTree": "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"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(-80,-71,7,-85,-81,-83,-115,-1,-50,47,-97,29,-6,21,53,-64,34,-35,-96,83,102,-12,-5,-46,127,88,29,19,47,75,35,-10)\n3: Coll(-101,22,-79,-128,-127,39,77,-62,47,24,-24,89,25,-106,-94,102,-45,-71,33,-73,113,-127,12,-54,12,-72,-25,18,74,-40,8,-66)\n4: Coll(65,-13,-104,-128,101,82,-124,94,82,0,-112,50,16,95,89,-27,-53,44,-79,65,-34,99,-52,115,123,109,-103,87,83,-61,110,-127)\n5: Coll(73,50,-62,-121,84,-14,-28,-6,-72,-24,90,-8,-18,61,-21,91,-66,73,36,-73,88,84,102,-46,13,-18,-44,-55,-98,65,-111,-94)\n6: Coll(-25,-6,33,-9,44,66,-82,28,61,83,-76,42,-109,-105,5,-47,29,55,-22,-30,-19,-55,50,-11,-18,-108,90,14,100,-21,86,-31)\n7: Coll(8,71,-125,65,-34,-120,-6,-107,-30,-125,50,36,-84,-119,121,-10,88,-62,114,-121,18,103,-26,32,-43,-63,-42,-98,56,-4,-12,89)\n8: 0\n9: 1\n10: 1\n11: 3\n12: 0\n13: 6\n14: 37\n15: 6\n16: 37\n17: 5\n18: 6\n19: 37\n20: 1\n21: 3\n22: 2\n23: 0\n24: 2\n25: 0\n26: 1\n27: 2\n28: 1\n29: 1\n30: 9\n31: 3\n32: 0\n33: 0\n34: 0\n35: Coll(1,55,-55,24,-126,-73,89,-83,70,-94,14,57,-86,77,3,92,-29,37,37,-36,118,-48,33,-18,100,62,113,-48,-108,70,64,15)\n36: 0\n37: 5\n38: 5\n39: 1\n40: 33\n41: 0\n42: 0\n43: 1\n44: 0\n45: 0\n46: 8\n47: 8\n48: 0\n49: 1\n50: 2\n51: 1\n52: 1\n53: 0\n54: true\n55: 1\n56: 8\n57: 8\n58: 8\n59: 8\n60: 0\n61: 0\n62: 1\n63: 0\n64: 2\n65: 1\n66: 0\n67: 1\n68: 1\n69: 2\n70: -3\n71: 2\n72: 0\n73: true\n74: 2\n75: 0\n76: 8\n77: 8\n78: 0\n79: 1\n80: 0\n81: 2\n82: 1\n83: 0\n84: -3\n85: 2\n86: true\n87: 3\n88: 1\n89: 0\n90: 1\n91: 1\n92: 1\n93: 1\n94: 1\n95: 0\n96: 0\n97: 1\n98: 9\n99: 0\n100: 78\n101: -58\n102: 31\n103: 72\n104: 91\n105: -104\n106: -21\n107: -121\n108: 21\n109: 63\n110: 124\n111: 87\n112: -37\n113: 79\n114: 94\n115: -51\n116: 117\n117: 85\n118: 111\n119: -35\n120: -68\n121: 64\n122: 59\n123: 65\n124: -84\n125: -8\n126: 68\n127: 31\n128: -34\n129: -114\n130: 22\n131: 9\n132: 0\n133: 0\n134: 1\n135: 0\n136: 1\n137: 0\n138: 0\n139: 2\n140: 1\n141: 9\n142: true\n143: 4\n144: 8\n145: 8\n146: -1\n147: 8\n148: 8\n149: 0\n150: 0\n151: 8\n152: 8\n153: 0\n154: true\n155: 0\n156: 0\n157: 0\n158: 0\n159: 0\n160: 0\n161: 0\n162: 0\n163: 2\n164: 0\n165: 0\n166: 8\n167: 0\n168: true\n169: 5\n170: 0\n171: 1\n172: 1\n173: 0\n174: 0\n175: 0\n176: 2\n177: -1\n178: 2\n179: 0\n180: 0\n181: -3\n182: 0\n183: 2\n184: 0\n185: true",
"ergoTreeScript": "{\n val box1 = CONTEXT.dataInputs(placeholder[Int](0))\n val b2 = getVar[Byte](1.toByte).get\n val i3 = b2.toInt\n val box4 = OUTPUTS(placeholder[Int](1))\n val coll5 = box1.R4[AvlTree].get.getMany(\n Coll[Coll[Byte]](\n placeholder[Coll[Byte]](2), placeholder[Coll[Byte]](3), placeholder[Coll[Byte]](4), placeholder[Coll[Byte]](5), placeholder[Coll[Byte]](6), placeholder[\n Coll[Byte]\n ](7)\n ), getVar[Coll[Byte]](0.toByte).get\n )\n val coll6 = box4.tokens\n val tuple7 = coll6(placeholder[Int](8))\n val coll8 = SELF.tokens\n val tuple9 = coll6(placeholder[Int](9))\n val tuple10 = coll8(placeholder[Int](10))\n val coll11 = SELF.R5[Coll[Long]].get\n val i12 = coll11.size\n val coll13 = coll5(placeholder[Int](11)).get\n val coll14 = coll13.slice(placeholder[Int](12), coll13.size - placeholder[Int](13) / placeholder[Int](14)).indices\n val coll15 = coll14.map(\n {(i15: Int) =>\n coll13.slice(\n placeholder[Int](15) + placeholder[Int](16) * i15 + placeholder[Int](17), placeholder[Int](18) + placeholder[Int](19) * i15 + placeholder[Int](20)\n )\n }\n )\n val coll16 = coll11.slice(placeholder[Int](21), i12).append(\n coll15.slice(i12 - placeholder[Int](22), coll14.size).map({(coll16: Coll[Byte]) => placeholder[Long](23) })\n )\n val coll17 = coll16.indices\n val l18 = tuple9._2\n val l19 = tuple10._2\n val coll20 = box4.R5[Coll[Long]].get\n val l21 = coll11(placeholder[Int](24))\n val avlTree22 = SELF.R4[AvlTree].get\n val coll23 = getVar[Coll[(Coll[Byte], Coll[Byte])]](2.toByte).get\n val tuple24 = coll23(placeholder[Int](25))\n val coll25 = tuple24._1\n val coll26 = getVar[Coll[Byte]](3.toByte).get\n val l27 = l18 - l19\n val coll28 = box4.R4[AvlTree].get.digest\n val bool29 = coll23.size == placeholder[Int](26)\n val l30 = SELF.value\n val l31 = box4.value\n val i32 = coll8.size\n val coll33 = coll8.slice(placeholder[Int](27), i32)\n val coll34 = SELF.R7[Coll[AvlTree]].get\n val i35 = byteArrayToLong(coll5(placeholder[Int](28)).get.slice(placeholder[Int](29), placeholder[Int](30))).toInt\n val coll36 = SELF.R6[Coll[Long]].get\n val coll37 = coll20.slice(placeholder[Int](31), coll20.size)\n val l38 = coll11(placeholder[Int](32))\n val avlTree39 = coll34(placeholder[Int](33))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box1.tokens(placeholder[Int](34))._1 == placeholder[Coll[Byte]](35), (i3 >= placeholder[Int](36)) && (i3 <= placeholder[Int](37)), allOf(\n Coll[Boolean](\n blake2b256(box4.propositionBytes) == coll5(placeholder[Int](38)).get.slice(placeholder[Int](39), placeholder[Int](40)), tuple7 == coll8(\n placeholder[Int](41)\n ), tuple9._1 == tuple10._1\n )\n ), if (b2 == placeholder[Byte](42)) {(\n val coll40 = SELF.id\n val tuple41 = OUTPUTS(placeholder[Int](43)).tokens(placeholder[Int](44))\n val coll42 = getVar[Coll[(Coll[Byte], Coll[Byte])]](2.toByte).get\n val tuple43 = coll42(placeholder[Int](45))\n val coll44 = coll17.map({(i44: Int) =>\n val i46 = i44 * placeholder[Int](46)\n byteArrayToLong(tuple43._2.slice(i46, i46 + placeholder[Int](47)))\n })\n val l45 = coll44(placeholder[Int](48))\n allOf(\n Coll[Boolean](\n (coll40 == tuple41._1) && (coll40 == tuple43._1), tuple41._2 == placeholder[Long](49), (l45 == l18 - l19) && (\n l45 == coll20(placeholder[Int](50)) - l21\n ), coll42.size == placeholder[Int](51), avlTree22.insert(coll42, getVar[Coll[Byte]](3.toByte).get).get.digest == box4.R4[\n AvlTree\n ].get.digest, coll44.slice(placeholder[Int](52), coll17.size).forall({(l46: Long) => l46 == placeholder[Long](53) })\n )\n )\n )} else { placeholder[Boolean](54) }, if (b2 == placeholder[Byte](55)) {(\n val coll40 = coll17.map({(i40: Int) =>\n val i42 = i40 * placeholder[Int](56)\n byteArrayToLong(tuple24._2.slice(i42, i42 + placeholder[Int](57)))\n })\n val coll41 = coll17.map({(i41: Int) =>\n val i43 = i41 * placeholder[Int](58)\n byteArrayToLong(avlTree22.get(coll25, coll26).get.slice(i43, i43 + placeholder[Int](59)))\n })\n val l42 = coll40(placeholder[Int](60)) - coll41(placeholder[Int](61))\n val coll43 = coll41.zip(coll40)\n allOf(\n Coll[Boolean](\n OUTPUTS(placeholder[Int](62)).tokens.getOrElse(placeholder[Int](63), tuple7)._1 == coll25, (l42 == l27) && (\n l42 == coll20(placeholder[Int](64)) - l21\n ), bool29, avlTree22.update(coll23, coll26).get.digest == coll28, coll43.slice(placeholder[Int](65), coll43.size).forall(\n {(tuple44: (Long, Long)) =>\n val l46 = tuple44._2\n (tuple44._1 >= l46) && (l46 >= placeholder[Long](66))\n }\n ), coll41(placeholder[Int](67)) - coll40(placeholder[Int](68)) == SELF.value - box4.value, coll8.slice(placeholder[Int](69), coll8.size).forall(\n {(tuple44: (Coll[Byte], Long)) =>\n val coll46 = tuple44._1\n val i47 = coll15.indexOf(coll46, placeholder[Int](70)) + placeholder[Int](71)\n tuple44._2 - coll6.fold(placeholder[Long](72), {(tuple48: (Long, (Coll[Byte], Long))) =>\n val tuple50 = tuple48._2\n val l51 = tuple48._1\n if (tuple50._1 == coll46) { l51 + tuple50._2 } else { l51 }\n }) == coll41(i47) - coll40(i47)\n }\n )\n )\n )\n )} else { placeholder[Boolean](73) }, if (b2 == placeholder[Byte](74)) {(\n val coll40 = coll23.map({(tuple40: (Coll[Byte], Coll[Byte])) => tuple40._1 })\n val coll41 = coll40(placeholder[Int](75))\n val coll42 = coll17.map({(i42: Int) =>\n val i44 = i42 * placeholder[Int](76)\n byteArrayToLong(avlTree22.get(coll41, coll26).get.slice(i44, i44 + placeholder[Int](77)))\n })\n val l43 = coll42(placeholder[Int](78))\n allOf(\n Coll[Boolean](\n INPUTS(placeholder[Int](79)).tokens(placeholder[Int](80))._1 == coll41, (l43 == l19 - l18) && (l43 == l21 - coll20(placeholder[Int](81))), coll42(\n placeholder[Int](82)\n ) == l30 - l31, coll33.forall({(tuple44: (Coll[Byte], Long)) =>\n val coll46 = tuple44._1\n tuple44._2 - coll6.fold(placeholder[Long](83), {(tuple47: (Long, (Coll[Byte], Long))) =>\n val tuple49 = tuple47._2\n val l50 = tuple47._1\n if (tuple49._1 == coll46) { l50 + tuple49._2 } else { l50 }\n }) == coll42(coll15.indexOf(coll46, placeholder[Int](84)) + placeholder[Int](85))\n }), bool29, avlTree22.remove(coll40, getVar[Coll[Byte]](4.toByte).get).get.digest == coll28\n )\n )\n )} else { placeholder[Boolean](86) }, if (b2 == placeholder[Byte](87)) {(\n val coll40 = box4.R6[Coll[Long]].get\n val i41 = coll40.size\n val coll42 = box4.R7[Coll[AvlTree]].get\n val i43 = coll42.size\n val coll44 = box4.R8[Coll[Coll[Long]]].get\n val i45 = coll44.size\n val coll46 = coll44(i45 - placeholder[Int](88))\n val l47 = coll16(placeholder[Int](89))\n val i48 = i35 - placeholder[Int](90)\n allOf(\n Coll[Boolean](\n allOf(\n Coll[Boolean](\n coll40(i41 - placeholder[Int](91)) == l21, coll42(i43 - placeholder[Int](92)).digest == avlTree22.digest, coll46.slice(\n placeholder[Int](93), coll16.size\n ).indices.forall({(i49: Int) =>\n val i51 = i49 + placeholder[Int](94)\n coll46(i51) == coll16(i51)\n }), coll46(placeholder[Int](95)) == l47 + min(\n byteArrayToLong(coll5(placeholder[Int](96)).get.slice(placeholder[Int](97), placeholder[Int](98))), l19 - l21 - l47\n )\n )\n ), allOf(\n Coll[Boolean](\n coll34(placeholder[Int](99)).digest == Coll[Int](\n placeholder[Int](100), placeholder[Int](101), placeholder[Int](102), placeholder[Int](103), placeholder[Int](104), placeholder[Int](\n 105\n ), placeholder[Int](106), placeholder[Int](107), placeholder[Int](108), placeholder[Int](109), placeholder[Int](110), placeholder[Int](\n 111\n ), placeholder[Int](112), placeholder[Int](113), placeholder[Int](114), placeholder[Int](115), placeholder[Int](116), placeholder[Int](\n 117\n ), placeholder[Int](118), placeholder[Int](119), placeholder[Int](120), placeholder[Int](121), placeholder[Int](122), placeholder[Int](\n 123\n ), placeholder[Int](124), placeholder[Int](125), placeholder[Int](126), placeholder[Int](127), placeholder[Int](128), placeholder[Int](\n 129\n ), placeholder[Int](130), placeholder[Int](131), placeholder[Int](132)\n ).map({(i49: Int) => i49.toByte }), coll42.slice(placeholder[Int](133), i48) == coll34.slice(placeholder[Int](134), i35), coll40.slice(\n placeholder[Int](135), i48\n ).indices.forall({(i49: Int) => coll40(i49) == coll36(i49 + placeholder[Int](136)) })\n )\n ), ((i43 == i35) && (i41 == i35)) && (i45 == i35), coll37.forall({(l49: Long) => l49 == placeholder[Long](137) }), coll20(\n placeholder[Int](138)\n ) == l38 + byteArrayToLong(\n coll5(placeholder[Int](139)).get.slice(placeholder[Int](140), placeholder[Int](141))\n ), l38 <= CONTEXT.preHeader.timestamp\n )\n )\n )} else { placeholder[Boolean](142) }, if (b2 == placeholder[Byte](143)) {(\n val coll40 = coll23.map({(tuple40: (Coll[Byte], Coll[Byte])) => tuple40._1 })\n val coll41 = coll40.indices\n val coll42 = avlTree22.getMany(coll40, coll26).map({(opt42: Option[Coll[Byte]]) => if (opt42.isDefined) { coll17.map({(i44: Int) =>\n val i46 = i44 * placeholder[Int](144)\n byteArrayToLong(opt42.get.slice(i46, i46 + placeholder[Int](145)))\n }) } else { coll16.map({(l44: Long) => placeholder[Long](146) }) } })\n val coll43 = avlTree39.getMany(coll40, getVar[Coll[Byte]](4.toByte).get).map({(opt43: Option[Coll[Byte]]) => coll17.map({(i45: Int) =>\n val i47 = i45 * placeholder[Int](147)\n byteArrayToLong(opt43.get.slice(i47, i47 + placeholder[Int](148)))\n }) })\n val l44 = coll36(placeholder[Int](149))\n val coll45 = SELF.R8[Coll[Coll[Long]]].get(placeholder[Int](150))\n val coll46 = coll23.map({(tuple46: (Coll[Byte], Coll[Byte])) => coll17.map({(i48: Int) =>\n val i50 = i48 * placeholder[Int](151)\n byteArrayToLong(tuple46._2.slice(i50, i50 + placeholder[Int](152)))\n }) })\n val tuple47 = (coll45.map({(l47: Long) => placeholder[Long](153) }), placeholder[Boolean](154))\n allOf(Coll[Boolean](allOf(coll41.map({(i48: Int) =>\n val coll50 = coll42(i48)\n if (coll50(placeholder[Int](155)) >= placeholder[Long](156)) {(\n val coll51 = coll45.map({(l51: Long) => coll43(i48)(placeholder[Int](157)) * l51 / l44 })\n (coll51, coll50.zip(coll51).map({(tuple52: (Long, Long)) => tuple52._1 + tuple52._2 }) == coll46(i48))\n )} else { tuple47 }._2\n })), l21 + coll41.map({(i48: Int) =>\n val coll50 = coll42(i48)\n if (coll50(placeholder[Int](158)) >= placeholder[Long](159)) {(\n val coll51 = coll45.map({(l51: Long) => coll43(i48)(placeholder[Int](160)) * l51 / l44 })\n (coll51, coll50.zip(coll51).map({(tuple52: (Long, Long)) => tuple52._1 + tuple52._2 }) == coll46(i48))\n )} else { tuple47 }\n }).fold(placeholder[Long](161), {(tuple48: (Long, (Coll[Long], Boolean))) => tuple48._1 + tuple48._2._1(placeholder[Int](162)) }) == coll20(placeholder[Int](163)), avlTree39.remove(coll40, getVar[Coll[Byte]](5.toByte).get).get.digest == box4.R7[Coll[AvlTree]].get(placeholder[Int](164)).digest, avlTree22.update(coll23.filter({(tuple48: (Coll[Byte], Coll[Byte])) => byteArrayToLong(tuple48._2.slice(placeholder[Int](165), placeholder[Int](166))) > placeholder[Long](167) }), coll26).get.digest == coll28))\n )} else { placeholder[Boolean](168) }, if (b2 == placeholder[Byte](169)) {(\n val l40 = l31 - l30\n val i41 = coll6.size\n allOf(\n Coll[Boolean](\n (l40 >= placeholder[Long](170)) && (coll37(placeholder[Int](171)) - coll16(placeholder[Int](172)) == l40), (l27 >= placeholder[Long](173)) && (\n coll37(placeholder[Int](174)) - coll16(placeholder[Int](175)) == l27\n ), coll33.zip(coll6.slice(placeholder[Int](176), i32)).forall({(tuple42: ((Coll[Byte], Long), (Coll[Byte], Long))) =>\n val tuple44 = tuple42._1\n val coll45 = tuple44._1\n val tuple46 = tuple42._2\n val i47 = coll15.indexOf(coll45, placeholder[Int](177))\n val l48 = tuple46._2 - tuple44._2\n val i49 = i47 + placeholder[Int](178)\n allOf(Coll[Boolean](coll45 == tuple46._1, i47 >= placeholder[Int](179), l48 == coll37(i49) - coll16(i49), l48 >= placeholder[Long](180)))\n }), coll6.slice(i32, i41).forall({(tuple42: (Coll[Byte], Long)) =>\n val i44 = coll15.indexOf(tuple42._1, placeholder[Int](181))\n val l45 = tuple42._2\n allOf(Coll[Boolean](i44 >= placeholder[Int](182), l45 == coll37(i44 + placeholder[Int](183)), l45 >= placeholder[Long](184)))\n }), i41 >= i32\n )\n )\n )} else { placeholder[Boolean](185) }\n )\n )\n )\n}",
"address": "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",
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{
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"amount": 1,
"name": "PaideiaAlpha Stake State",
"decimals": 0,
"type": "EIP-004"
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"amount": 397738071199,
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"additionalRegisters": {
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"outputSettledAt": 974406,
"ergoTree": "1033040004000e2000b44a84993674c57c4fc23c6c1bb221470463e4e711b2260ffd8ed01f1aab4201000e20012aec95af24812a01775de090411ba70a648fe859013f896ca2a1a95882ce5f0e205bcff102254366780cd5191257050a6ed33ac5d12eef0e304139ed5d981f4bfa0400040004000402050005000500040004000e20225e3fc5d189f547d9c626bebdc671398b6c007c783dc47f903f24bf7f3484790e20bc4a5ab9e45ab74b79faecbf67496cc2bf8c2b0e55dbe8accfc39d14891174900e20767caa80b98e496ad8a9f689c4410ae453327f0f95e95084c0ae206350793b7704000402041204020502040404020442040004000406040204120500050004020402041204040404040204020e20ecf2d04bae48a00a8a6e49c0567263c9f5d23f26c82358a176abd1f021d8b1300402040204120500050004000402041201000100d803d601aea4d9010163d801d603db630872019591b172037300938cb272037301000173027303d6027304d6037305d197830201720195ef7201d80fd604b2a5730600d605e4c672040511d606b2a4730700d607e4c672060511d608c2a7d609b2b5a5d901096393c272097208730800d60ab2db6501fe730900d60be4e3000ed60cb5a4d9010c6393c2720c7208d60db0720c730ad9010d41639a8c720d01c18c720d02d60edb63087209d60fd9010f0eb0dc0c0f720c01d9011163db63087211730bd90111414d0ed801d6138c7211029a8c72110195938c721301720f8c721302730cd610da720f017202d611afb5720ed901114d0e948c7211017202d901114d0e938c721102da720f018c721101d612afdc0c0f720c01d9011263db63087212d901124d0ed801d6148c721201ec9372147202ae720ed901154d0e938c72150172149591b27205730d00b27207730e00d803d613dc640be4c6720a04640283040e730f731073117203720bd6149a9c7cb4e4b2721373120073137314b272057315007316d615b2db6308b2b5a5d901156393cbc27215b4e4b2721373170073187319731a00731b009683060192c1720999720d7cb4e4b27213731c00731d731e92b0720e731fd90116414d0ed801d6188c7216029a8c72160195938c72180172028c72180273209999721072147cb4e4b272137321007322732372117212938c7215017202928c721502721495ed91b27205732400b27207732500938cb2db63087204732600028cb2db6308720673270002d801d613dc640be4c6720a04640283020e73287203720b9683040192c1720999720d7cb4e4b27213732900732a732b92b0720e732cd90114414d0ed801d6168c7214029a8c72140195938c72160172028c721602732d9972107cb4e4b27213732e00732f73307211721273317332",
"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(0,-76,74,-124,-103,54,116,-59,124,79,-62,60,108,27,-78,33,71,4,99,-28,-25,17,-78,38,15,-3,-114,-48,31,26,-85,66)\n3: false\n4: Coll(1,42,-20,-107,-81,36,-127,42,1,119,93,-32,-112,65,27,-89,10,100,-113,-24,89,1,63,-119,108,-94,-95,-87,88,-126,-50,95)\n5: Coll(91,-49,-15,2,37,67,102,120,12,-43,25,18,87,5,10,110,-45,58,-59,-47,46,-17,14,48,65,57,-19,93,-104,31,75,-6)\n6: 0\n7: 0\n8: 0\n9: 1\n10: 0\n11: 0\n12: 0\n13: 0\n14: 0\n15: Coll(34,94,63,-59,-47,-119,-11,71,-39,-58,38,-66,-67,-58,113,57,-117,108,0,124,120,61,-60,127,-112,63,36,-65,127,52,-124,121)\n16: Coll(-68,74,90,-71,-28,90,-73,75,121,-6,-20,-65,103,73,108,-62,-65,-116,43,14,85,-37,-24,-84,-49,-61,-99,20,-119,17,116,-112)\n17: Coll(118,124,-86,-128,-71,-114,73,106,-40,-87,-10,-119,-60,65,10,-28,83,50,127,15,-107,-23,80,-124,-64,-82,32,99,80,121,59,119)\n18: 0\n19: 1\n20: 9\n21: 1\n22: 1\n23: 2\n24: 1\n25: 33\n26: 0\n27: 0\n28: 3\n29: 1\n30: 9\n31: 0\n32: 0\n33: 1\n34: 1\n35: 9\n36: 2\n37: 2\n38: 1\n39: 1\n40: Coll(-20,-14,-48,75,-82,72,-96,10,-118,110,73,-64,86,114,99,-55,-11,-46,63,38,-56,35,88,-95,118,-85,-47,-16,33,-40,-79,48)\n41: 1\n42: 1\n43: 9\n44: 0\n45: 0\n46: 0\n47: 1\n48: 9\n49: false\n50: false",
"ergoTreeScript": "{\n val bool1 = INPUTS.exists({(box1: Box) =>\n val coll3 = box1.tokens\n if (coll3.size > placeholder[Int](0)) { coll3(placeholder[Int](1))._1 == placeholder[Coll[Byte]](2) } else { placeholder[Boolean](3) }\n })\n val coll2 = placeholder[Coll[Byte]](4)\n val coll3 = placeholder[Coll[Byte]](5)\n sigmaProp(anyOf(Coll[Boolean](bool1, if (!bool1) {(\n val box4 = OUTPUTS(placeholder[Int](6))\n val coll5 = box4.R5[Coll[Long]].get\n val box6 = INPUTS(placeholder[Int](7))\n val coll7 = box6.R5[Coll[Long]].get\n val coll8 = SELF.propositionBytes\n val box9 = OUTPUTS.filter({(box9: Box) => box9.propositionBytes == coll8 })(placeholder[Int](8))\n val box10 = CONTEXT.dataInputs(placeholder[Int](9))\n val coll11 = getVar[Coll[Byte]](0.toByte).get\n val coll12 = INPUTS.filter({(box12: Box) => box12.propositionBytes == coll8 })\n val l13 = coll12.fold(placeholder[Long](10), {(tuple13: (Long, Box)) => tuple13._1 + tuple13._2.value })\n val coll14 = box9.tokens\n val func15 = {(coll15: Coll[Byte]) => coll12.flatMap({(box17: Box) => box17.tokens }).fold(placeholder[Long](11), {(tuple17: (Long, (Coll[Byte], Long))) =>\n val tuple19 = tuple17._2\n tuple17._1 + if (tuple19._1 == coll15) { tuple19._2 } else { placeholder[Long](12) }\n }) }\n val l16 = func15(coll2)\n val bool17 = coll14.filter({(tuple17: (Coll[Byte], Long)) => tuple17._1 != coll2 }).forall({(tuple17: (Coll[Byte], Long)) => tuple17._2 == func15(tuple17._1) })\n val bool18 = coll12.flatMap({(box18: Box) => box18.tokens }).forall({(tuple18: (Coll[Byte], Long)) =>\n val coll20 = tuple18._1\n (coll20 == coll2) || coll14.exists({(tuple21: (Coll[Byte], Long)) => tuple21._1 == coll20 })\n })\n if (coll5(placeholder[Int](13)) > coll7(placeholder[Int](14))) {(\n val coll19 = box10.R4[AvlTree].get.getMany(Coll[Coll[Byte]](placeholder[Coll[Byte]](15), placeholder[Coll[Byte]](16), placeholder[Coll[Byte]](17), coll3), coll11)\n val l20 = byteArrayToLong(coll19(placeholder[Int](18)).get.slice(placeholder[Int](19), placeholder[Int](20))) * coll5(placeholder[Int](21)) + placeholder[Long](22)\n val tuple21 = OUTPUTS.filter({(box21: Box) => blake2b256(box21.propositionBytes) == coll19(placeholder[Int](23)).get.slice(placeholder[Int](24), placeholder[Int](25)) })(placeholder[Int](26)).tokens(placeholder[Int](27))\n allOf(Coll[Boolean](box9.value >= l13 - byteArrayToLong(coll19(placeholder[Int](28)).get.slice(placeholder[Int](29), placeholder[Int](30))), coll14.fold(placeholder[Long](31), {(tuple22: (Long, (Coll[Byte], Long))) =>\n val tuple24 = tuple22._2\n tuple22._1 + if (tuple24._1 == coll2) { tuple24._2 } else { placeholder[Long](32) }\n }) >= l16 - l20 - byteArrayToLong(coll19(placeholder[Int](33)).get.slice(placeholder[Int](34), placeholder[Int](35))), bool17, bool18, tuple21._1 == coll2, tuple21._2 >= l20))\n )} else { if ((coll5(placeholder[Int](36)) > coll7(placeholder[Int](37))) && (box4.tokens(placeholder[Int](38))._2 == box6.tokens(placeholder[Int](39))._2)) {(\n val coll19 = box10.R4[AvlTree].get.getMany(Coll[Coll[Byte]](placeholder[Coll[Byte]](40), coll3), coll11)\n allOf(Coll[Boolean](box9.value >= l13 - byteArrayToLong(coll19(placeholder[Int](41)).get.slice(placeholder[Int](42), placeholder[Int](43))), coll14.fold(placeholder[Long](44), {(tuple20: (Long, (Coll[Byte], Long))) =>\n val tuple22 = tuple20._2\n tuple20._1 + if (tuple22._1 == coll2) { tuple22._2 } else { placeholder[Long](45) }\n }) >= l16 - byteArrayToLong(coll19(placeholder[Int](46)).get.slice(placeholder[Int](47), placeholder[Int](48))), bool17, bool18))\n )} else { placeholder[Boolean](49) } }\n )} else { placeholder[Boolean](50) })))\n}",
"address": "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",
"assets": [
{
"tokenId": "012aec95af24812a01775de090411ba70a648fe859013f896ca2a1a95882ce5f",
"index": 0,
"amount": 99979642,
"name": "PaideiaAlpha",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [
{
"boxId": "41c9d527ba797e1c2f203ab8cf4697354db68365b1fb7076c009ef7cc2318b23",
"value": 1000000,
"index": 0,
"outputBlockId": "785f16991e8b801c0bd00d2d33f5fad2ef5633779b515fa830d81647db48dfbd",
"outputTransactionId": "5269247d007c9db083d7c368f89325915fa6ade30402205908d33a3bc04c6a94",
"outputIndex": 2,
"ergoTree": "100904000e20a9558e4186cbd5aa5723a852d4c1dc657d9e814382ff888d5a8aec521531301d040004020442040004000e2000b44a84993674c57c4fc23c6c1bb221470463e4e711b2260ffd8ed01f1aab420100d801d601b2a5730000d19683040193db6308a7db6308720190c1a7c1720193cbc27201b4e4b2dc640be4c6720104640283010e7301e4e3000e73020073037304aea4d9010263d801d604db630872029591b172047305938cb272047306000173077308",
"address": "FDdVv3XcPnh67Hm9GfPJpFCLuVeaYKY9MGf67RZfgNcGhsxDZPTz5JVn86hKGoSf3aCbfCxw59UhLdaDK4PZaQesVrvc6gsvXWqr4iDQTFpsVM9Jrw7gCRZwk5Y2BJHoYuPWdGqHJfFsQrpa5dedXz1kXDR7ojGmN5JGmNF1Tj6cXdWdhnkEyzytShfP6kNeLssQ5va1N5Qo5U4qKvU9Jw7RTU8jCYZ6RRby6z3TwxKGN8VA29m5fHZNaVuwwxVK",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "64ea50861a6af284a10217c88b26e5f20c476bea1f03c399498871eb29d3ba2b5207072000",
"sigmaType": null,
"renderedValue": null
}
}
}
],
"outputs": [
{
"boxId": "be96fa3a95013d6630017f8bdbce21058a1190ffaf77deb5ccbe70d25889c43a",
"transactionId": "15a5faefd3dedcfa64cfec0f1d0cd0d003b9d04a084df89c31c38ba7b68005c9",
"blockId": "298b39b94802a6aa098784209508c102203c87ffc0dc6eea3008b26f12a09bed",
"value": 2000000,
"index": 0,
"globalIndex": 27982003,
"creationHeight": 974404,
"settlementHeight": 974406,
"ergoTree": 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"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(-80,-71,7,-85,-81,-83,-115,-1,-50,47,-97,29,-6,21,53,-64,34,-35,-96,83,102,-12,-5,-46,127,88,29,19,47,75,35,-10)\n3: Coll(-101,22,-79,-128,-127,39,77,-62,47,24,-24,89,25,-106,-94,102,-45,-71,33,-73,113,-127,12,-54,12,-72,-25,18,74,-40,8,-66)\n4: Coll(65,-13,-104,-128,101,82,-124,94,82,0,-112,50,16,95,89,-27,-53,44,-79,65,-34,99,-52,115,123,109,-103,87,83,-61,110,-127)\n5: Coll(73,50,-62,-121,84,-14,-28,-6,-72,-24,90,-8,-18,61,-21,91,-66,73,36,-73,88,84,102,-46,13,-18,-44,-55,-98,65,-111,-94)\n6: Coll(-25,-6,33,-9,44,66,-82,28,61,83,-76,42,-109,-105,5,-47,29,55,-22,-30,-19,-55,50,-11,-18,-108,90,14,100,-21,86,-31)\n7: Coll(8,71,-125,65,-34,-120,-6,-107,-30,-125,50,36,-84,-119,121,-10,88,-62,114,-121,18,103,-26,32,-43,-63,-42,-98,56,-4,-12,89)\n8: 0\n9: 1\n10: 1\n11: 3\n12: 0\n13: 6\n14: 37\n15: 6\n16: 37\n17: 5\n18: 6\n19: 37\n20: 1\n21: 3\n22: 2\n23: 0\n24: 2\n25: 0\n26: 1\n27: 2\n28: 1\n29: 1\n30: 9\n31: 3\n32: 0\n33: 0\n34: 0\n35: Coll(1,55,-55,24,-126,-73,89,-83,70,-94,14,57,-86,77,3,92,-29,37,37,-36,118,-48,33,-18,100,62,113,-48,-108,70,64,15)\n36: 0\n37: 5\n38: 5\n39: 1\n40: 33\n41: 0\n42: 0\n43: 1\n44: 0\n45: 0\n46: 8\n47: 8\n48: 0\n49: 1\n50: 2\n51: 1\n52: 1\n53: 0\n54: true\n55: 1\n56: 8\n57: 8\n58: 8\n59: 8\n60: 0\n61: 0\n62: 1\n63: 0\n64: 2\n65: 1\n66: 0\n67: 1\n68: 1\n69: 2\n70: -3\n71: 2\n72: 0\n73: true\n74: 2\n75: 0\n76: 8\n77: 8\n78: 0\n79: 1\n80: 0\n81: 2\n82: 1\n83: 0\n84: -3\n85: 2\n86: true\n87: 3\n88: 1\n89: 0\n90: 1\n91: 1\n92: 1\n93: 1\n94: 1\n95: 0\n96: 0\n97: 1\n98: 9\n99: 0\n100: 78\n101: -58\n102: 31\n103: 72\n104: 91\n105: -104\n106: -21\n107: -121\n108: 21\n109: 63\n110: 124\n111: 87\n112: -37\n113: 79\n114: 94\n115: -51\n116: 117\n117: 85\n118: 111\n119: -35\n120: -68\n121: 64\n122: 59\n123: 65\n124: -84\n125: -8\n126: 68\n127: 31\n128: -34\n129: -114\n130: 22\n131: 9\n132: 0\n133: 0\n134: 1\n135: 0\n136: 1\n137: 0\n138: 0\n139: 2\n140: 1\n141: 9\n142: true\n143: 4\n144: 8\n145: 8\n146: -1\n147: 8\n148: 8\n149: 0\n150: 0\n151: 8\n152: 8\n153: 0\n154: true\n155: 0\n156: 0\n157: 0\n158: 0\n159: 0\n160: 0\n161: 0\n162: 0\n163: 2\n164: 0\n165: 0\n166: 8\n167: 0\n168: true\n169: 5\n170: 0\n171: 1\n172: 1\n173: 0\n174: 0\n175: 0\n176: 2\n177: -1\n178: 2\n179: 0\n180: 0\n181: -3\n182: 0\n183: 2\n184: 0\n185: true",
"ergoTreeScript": "{\n val box1 = CONTEXT.dataInputs(placeholder[Int](0))\n val b2 = getVar[Byte](1.toByte).get\n val i3 = b2.toInt\n val box4 = OUTPUTS(placeholder[Int](1))\n val coll5 = box1.R4[AvlTree].get.getMany(\n Coll[Coll[Byte]](\n placeholder[Coll[Byte]](2), placeholder[Coll[Byte]](3), placeholder[Coll[Byte]](4), placeholder[Coll[Byte]](5), placeholder[Coll[Byte]](6), placeholder[\n Coll[Byte]\n ](7)\n ), getVar[Coll[Byte]](0.toByte).get\n )\n val coll6 = box4.tokens\n val tuple7 = coll6(placeholder[Int](8))\n val coll8 = SELF.tokens\n val tuple9 = coll6(placeholder[Int](9))\n val tuple10 = coll8(placeholder[Int](10))\n val coll11 = SELF.R5[Coll[Long]].get\n val i12 = coll11.size\n val coll13 = coll5(placeholder[Int](11)).get\n val coll14 = coll13.slice(placeholder[Int](12), coll13.size - placeholder[Int](13) / placeholder[Int](14)).indices\n val coll15 = coll14.map(\n {(i15: Int) =>\n coll13.slice(\n placeholder[Int](15) + placeholder[Int](16) * i15 + placeholder[Int](17), placeholder[Int](18) + placeholder[Int](19) * i15 + placeholder[Int](20)\n )\n }\n )\n val coll16 = coll11.slice(placeholder[Int](21), i12).append(\n coll15.slice(i12 - placeholder[Int](22), coll14.size).map({(coll16: Coll[Byte]) => placeholder[Long](23) })\n )\n val coll17 = coll16.indices\n val l18 = tuple9._2\n val l19 = tuple10._2\n val coll20 = box4.R5[Coll[Long]].get\n val l21 = coll11(placeholder[Int](24))\n val avlTree22 = SELF.R4[AvlTree].get\n val coll23 = getVar[Coll[(Coll[Byte], Coll[Byte])]](2.toByte).get\n val tuple24 = coll23(placeholder[Int](25))\n val coll25 = tuple24._1\n val coll26 = getVar[Coll[Byte]](3.toByte).get\n val l27 = l18 - l19\n val coll28 = box4.R4[AvlTree].get.digest\n val bool29 = coll23.size == placeholder[Int](26)\n val l30 = SELF.value\n val l31 = box4.value\n val i32 = coll8.size\n val coll33 = coll8.slice(placeholder[Int](27), i32)\n val coll34 = SELF.R7[Coll[AvlTree]].get\n val i35 = byteArrayToLong(coll5(placeholder[Int](28)).get.slice(placeholder[Int](29), placeholder[Int](30))).toInt\n val coll36 = SELF.R6[Coll[Long]].get\n val coll37 = coll20.slice(placeholder[Int](31), coll20.size)\n val l38 = coll11(placeholder[Int](32))\n val avlTree39 = coll34(placeholder[Int](33))\n sigmaProp(\n allOf(\n Coll[Boolean](\n box1.tokens(placeholder[Int](34))._1 == placeholder[Coll[Byte]](35), (i3 >= placeholder[Int](36)) && (i3 <= placeholder[Int](37)), allOf(\n Coll[Boolean](\n blake2b256(box4.propositionBytes) == coll5(placeholder[Int](38)).get.slice(placeholder[Int](39), placeholder[Int](40)), tuple7 == coll8(\n placeholder[Int](41)\n ), tuple9._1 == tuple10._1\n )\n ), if (b2 == placeholder[Byte](42)) {(\n val coll40 = SELF.id\n val tuple41 = OUTPUTS(placeholder[Int](43)).tokens(placeholder[Int](44))\n val coll42 = getVar[Coll[(Coll[Byte], Coll[Byte])]](2.toByte).get\n val tuple43 = coll42(placeholder[Int](45))\n val coll44 = coll17.map({(i44: Int) =>\n val i46 = i44 * placeholder[Int](46)\n byteArrayToLong(tuple43._2.slice(i46, i46 + placeholder[Int](47)))\n })\n val l45 = coll44(placeholder[Int](48))\n allOf(\n Coll[Boolean](\n (coll40 == tuple41._1) && (coll40 == tuple43._1), tuple41._2 == placeholder[Long](49), (l45 == l18 - l19) && (\n l45 == coll20(placeholder[Int](50)) - l21\n ), coll42.size == placeholder[Int](51), avlTree22.insert(coll42, getVar[Coll[Byte]](3.toByte).get).get.digest == box4.R4[\n AvlTree\n ].get.digest, coll44.slice(placeholder[Int](52), coll17.size).forall({(l46: Long) => l46 == placeholder[Long](53) })\n )\n )\n )} else { placeholder[Boolean](54) }, if (b2 == placeholder[Byte](55)) {(\n val coll40 = coll17.map({(i40: Int) =>\n val i42 = i40 * placeholder[Int](56)\n byteArrayToLong(tuple24._2.slice(i42, i42 + placeholder[Int](57)))\n })\n val coll41 = coll17.map({(i41: Int) =>\n val i43 = i41 * placeholder[Int](58)\n byteArrayToLong(avlTree22.get(coll25, coll26).get.slice(i43, i43 + placeholder[Int](59)))\n })\n val l42 = coll40(placeholder[Int](60)) - coll41(placeholder[Int](61))\n val coll43 = coll41.zip(coll40)\n allOf(\n Coll[Boolean](\n OUTPUTS(placeholder[Int](62)).tokens.getOrElse(placeholder[Int](63), tuple7)._1 == coll25, (l42 == l27) && (\n l42 == coll20(placeholder[Int](64)) - l21\n ), bool29, avlTree22.update(coll23, coll26).get.digest == coll28, coll43.slice(placeholder[Int](65), coll43.size).forall(\n {(tuple44: (Long, Long)) =>\n val l46 = tuple44._2\n (tuple44._1 >= l46) && (l46 >= placeholder[Long](66))\n }\n ), coll41(placeholder[Int](67)) - coll40(placeholder[Int](68)) == SELF.value - box4.value, coll8.slice(placeholder[Int](69), coll8.size).forall(\n {(tuple44: (Coll[Byte], Long)) =>\n val coll46 = tuple44._1\n val i47 = coll15.indexOf(coll46, placeholder[Int](70)) + placeholder[Int](71)\n tuple44._2 - coll6.fold(placeholder[Long](72), {(tuple48: (Long, (Coll[Byte], Long))) =>\n val tuple50 = tuple48._2\n val l51 = tuple48._1\n if (tuple50._1 == coll46) { l51 + tuple50._2 } else { l51 }\n }) == coll41(i47) - coll40(i47)\n }\n )\n )\n )\n )} else { placeholder[Boolean](73) }, if (b2 == placeholder[Byte](74)) {(\n val coll40 = coll23.map({(tuple40: (Coll[Byte], Coll[Byte])) => tuple40._1 })\n val coll41 = coll40(placeholder[Int](75))\n val coll42 = coll17.map({(i42: Int) =>\n val i44 = i42 * placeholder[Int](76)\n byteArrayToLong(avlTree22.get(coll41, coll26).get.slice(i44, i44 + placeholder[Int](77)))\n })\n val l43 = coll42(placeholder[Int](78))\n allOf(\n Coll[Boolean](\n INPUTS(placeholder[Int](79)).tokens(placeholder[Int](80))._1 == coll41, (l43 == l19 - l18) && (l43 == l21 - coll20(placeholder[Int](81))), coll42(\n placeholder[Int](82)\n ) == l30 - l31, coll33.forall({(tuple44: (Coll[Byte], Long)) =>\n val coll46 = tuple44._1\n tuple44._2 - coll6.fold(placeholder[Long](83), {(tuple47: (Long, (Coll[Byte], Long))) =>\n val tuple49 = tuple47._2\n val l50 = tuple47._1\n if (tuple49._1 == coll46) { l50 + tuple49._2 } else { l50 }\n }) == coll42(coll15.indexOf(coll46, placeholder[Int](84)) + placeholder[Int](85))\n }), bool29, avlTree22.remove(coll40, getVar[Coll[Byte]](4.toByte).get).get.digest == coll28\n )\n )\n )} else { placeholder[Boolean](86) }, if (b2 == placeholder[Byte](87)) {(\n val coll40 = box4.R6[Coll[Long]].get\n val i41 = coll40.size\n val coll42 = box4.R7[Coll[AvlTree]].get\n val i43 = coll42.size\n val coll44 = box4.R8[Coll[Coll[Long]]].get\n val i45 = coll44.size\n val coll46 = coll44(i45 - placeholder[Int](88))\n val l47 = coll16(placeholder[Int](89))\n val i48 = i35 - placeholder[Int](90)\n allOf(\n Coll[Boolean](\n allOf(\n Coll[Boolean](\n coll40(i41 - placeholder[Int](91)) == l21, coll42(i43 - placeholder[Int](92)).digest == avlTree22.digest, coll46.slice(\n placeholder[Int](93), coll16.size\n ).indices.forall({(i49: Int) =>\n val i51 = i49 + placeholder[Int](94)\n coll46(i51) == coll16(i51)\n }), coll46(placeholder[Int](95)) == l47 + min(\n byteArrayToLong(coll5(placeholder[Int](96)).get.slice(placeholder[Int](97), placeholder[Int](98))), l19 - l21 - l47\n )\n )\n ), allOf(\n Coll[Boolean](\n coll34(placeholder[Int](99)).digest == Coll[Int](\n placeholder[Int](100), placeholder[Int](101), placeholder[Int](102), placeholder[Int](103), placeholder[Int](104), placeholder[Int](\n 105\n ), placeholder[Int](106), placeholder[Int](107), placeholder[Int](108), placeholder[Int](109), placeholder[Int](110), placeholder[Int](\n 111\n ), placeholder[Int](112), placeholder[Int](113), placeholder[Int](114), placeholder[Int](115), placeholder[Int](116), placeholder[Int](\n 117\n ), placeholder[Int](118), placeholder[Int](119), placeholder[Int](120), placeholder[Int](121), placeholder[Int](122), placeholder[Int](\n 123\n ), placeholder[Int](124), placeholder[Int](125), placeholder[Int](126), placeholder[Int](127), placeholder[Int](128), placeholder[Int](\n 129\n ), placeholder[Int](130), placeholder[Int](131), placeholder[Int](132)\n ).map({(i49: Int) => i49.toByte }), coll42.slice(placeholder[Int](133), i48) == coll34.slice(placeholder[Int](134), i35), coll40.slice(\n placeholder[Int](135), i48\n ).indices.forall({(i49: Int) => coll40(i49) == coll36(i49 + placeholder[Int](136)) })\n )\n ), ((i43 == i35) && (i41 == i35)) && (i45 == i35), coll37.forall({(l49: Long) => l49 == placeholder[Long](137) }), coll20(\n placeholder[Int](138)\n ) == l38 + byteArrayToLong(\n coll5(placeholder[Int](139)).get.slice(placeholder[Int](140), placeholder[Int](141))\n ), l38 <= CONTEXT.preHeader.timestamp\n )\n )\n )} else { placeholder[Boolean](142) }, if (b2 == placeholder[Byte](143)) {(\n val coll40 = coll23.map({(tuple40: (Coll[Byte], Coll[Byte])) => tuple40._1 })\n val coll41 = coll40.indices\n val coll42 = avlTree22.getMany(coll40, coll26).map({(opt42: Option[Coll[Byte]]) => if (opt42.isDefined) { coll17.map({(i44: Int) =>\n val i46 = i44 * placeholder[Int](144)\n byteArrayToLong(opt42.get.slice(i46, i46 + placeholder[Int](145)))\n }) } else { coll16.map({(l44: Long) => placeholder[Long](146) }) } })\n val coll43 = avlTree39.getMany(coll40, getVar[Coll[Byte]](4.toByte).get).map({(opt43: Option[Coll[Byte]]) => coll17.map({(i45: Int) =>\n val i47 = i45 * placeholder[Int](147)\n byteArrayToLong(opt43.get.slice(i47, i47 + placeholder[Int](148)))\n }) })\n val l44 = coll36(placeholder[Int](149))\n val coll45 = SELF.R8[Coll[Coll[Long]]].get(placeholder[Int](150))\n val coll46 = coll23.map({(tuple46: (Coll[Byte], Coll[Byte])) => coll17.map({(i48: Int) =>\n val i50 = i48 * placeholder[Int](151)\n byteArrayToLong(tuple46._2.slice(i50, i50 + placeholder[Int](152)))\n }) })\n val tuple47 = (coll45.map({(l47: Long) => placeholder[Long](153) }), placeholder[Boolean](154))\n allOf(Coll[Boolean](allOf(coll41.map({(i48: Int) =>\n val coll50 = coll42(i48)\n if (coll50(placeholder[Int](155)) >= placeholder[Long](156)) {(\n val coll51 = coll45.map({(l51: Long) => coll43(i48)(placeholder[Int](157)) * l51 / l44 })\n (coll51, coll50.zip(coll51).map({(tuple52: (Long, Long)) => tuple52._1 + tuple52._2 }) == coll46(i48))\n )} else { tuple47 }._2\n })), l21 + coll41.map({(i48: Int) =>\n val coll50 = coll42(i48)\n if (coll50(placeholder[Int](158)) >= placeholder[Long](159)) {(\n val coll51 = coll45.map({(l51: Long) => coll43(i48)(placeholder[Int](160)) * l51 / l44 })\n (coll51, coll50.zip(coll51).map({(tuple52: (Long, Long)) => tuple52._1 + tuple52._2 }) == coll46(i48))\n )} else { tuple47 }\n }).fold(placeholder[Long](161), {(tuple48: (Long, (Coll[Long], Boolean))) => tuple48._1 + tuple48._2._1(placeholder[Int](162)) }) == coll20(placeholder[Int](163)), avlTree39.remove(coll40, getVar[Coll[Byte]](5.toByte).get).get.digest == box4.R7[Coll[AvlTree]].get(placeholder[Int](164)).digest, avlTree22.update(coll23.filter({(tuple48: (Coll[Byte], Coll[Byte])) => byteArrayToLong(tuple48._2.slice(placeholder[Int](165), placeholder[Int](166))) > placeholder[Long](167) }), coll26).get.digest == coll28))\n )} else { placeholder[Boolean](168) }, if (b2 == placeholder[Byte](169)) {(\n val l40 = l31 - l30\n val i41 = coll6.size\n allOf(\n Coll[Boolean](\n (l40 >= placeholder[Long](170)) && (coll37(placeholder[Int](171)) - coll16(placeholder[Int](172)) == l40), (l27 >= placeholder[Long](173)) && (\n coll37(placeholder[Int](174)) - coll16(placeholder[Int](175)) == l27\n ), coll33.zip(coll6.slice(placeholder[Int](176), i32)).forall({(tuple42: ((Coll[Byte], Long), (Coll[Byte], Long))) =>\n val tuple44 = tuple42._1\n val coll45 = tuple44._1\n val tuple46 = tuple42._2\n val i47 = coll15.indexOf(coll45, placeholder[Int](177))\n val l48 = tuple46._2 - tuple44._2\n val i49 = i47 + placeholder[Int](178)\n allOf(Coll[Boolean](coll45 == tuple46._1, i47 >= placeholder[Int](179), l48 == coll37(i49) - coll16(i49), l48 >= placeholder[Long](180)))\n }), coll6.slice(i32, i41).forall({(tuple42: (Coll[Byte], Long)) =>\n val i44 = coll15.indexOf(tuple42._1, placeholder[Int](181))\n val l45 = tuple42._2\n allOf(Coll[Boolean](i44 >= placeholder[Int](182), l45 == coll37(i44 + placeholder[Int](183)), l45 >= placeholder[Long](184)))\n }), i41 >= i32\n )\n )\n )} else { placeholder[Boolean](185) }\n )\n )\n )\n}",
"address": "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",
"assets": [
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"index": 0,
"amount": 1,
"name": "PaideiaAlpha Stake State",
"decimals": 0,
"type": "EIP-004"
},
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"index": 1,
"amount": 397738071199,
"name": "PaideiaAlpha",
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"additionalRegisters": {
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"sigmaType": "Coll[SLong]",
"renderedValue": "[1680516000000,2,12806421168,0,0]"
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"R6": {
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"sigmaType": "Coll[SLong]",
"renderedValue": "[11710541172,11984511171,12258481170,12532451169]"
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"R8": {
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"sigmaType": "Coll[Coll[SLong]]",
"renderedValue": "[[273970000,0],[273970000,0],[273970000,0],[273970000,0]]"
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"sigmaType": null,
"renderedValue": null
},
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"sigmaType": null,
"renderedValue": null
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"spentTransactionId": "f3a8cf82aa6ed23f0bccc1e2a16087815406b3b5987ae90b0481ce92a15cf8e7",
"mainChain": true
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{
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"transactionId": "15a5faefd3dedcfa64cfec0f1d0cd0d003b9d04a084df89c31c38ba7b68005c9",
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"globalIndex": 27982004,
"creationHeight": 974404,
"settlementHeight": 974406,
"ergoTree": "0008cd03553448c194fdd843c87d080f5e8ed983f5bb2807b13b45a9683bba8c7bfb5ae8",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(553448,8bebb3,...)))}",
"address": "9h7L7sUHZk43VQC3PHtSp5ujAWcZtYmWATBH746wi75C5XHi68b",
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"additionalRegisters": {},
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"index": 2,
"globalIndex": 27982005,
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"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)}",
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{
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"value": 216000000,
"index": 3,
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"creationHeight": 974404,
"settlementHeight": 974406,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(0,-76,74,-124,-103,54,116,-59,124,79,-62,60,108,27,-78,33,71,4,99,-28,-25,17,-78,38,15,-3,-114,-48,31,26,-85,66)\n3: false\n4: Coll(1,42,-20,-107,-81,36,-127,42,1,119,93,-32,-112,65,27,-89,10,100,-113,-24,89,1,63,-119,108,-94,-95,-87,88,-126,-50,95)\n5: Coll(91,-49,-15,2,37,67,102,120,12,-43,25,18,87,5,10,110,-45,58,-59,-47,46,-17,14,48,65,57,-19,93,-104,31,75,-6)\n6: 0\n7: 0\n8: 0\n9: 1\n10: 0\n11: 0\n12: 0\n13: 0\n14: 0\n15: Coll(34,94,63,-59,-47,-119,-11,71,-39,-58,38,-66,-67,-58,113,57,-117,108,0,124,120,61,-60,127,-112,63,36,-65,127,52,-124,121)\n16: Coll(-68,74,90,-71,-28,90,-73,75,121,-6,-20,-65,103,73,108,-62,-65,-116,43,14,85,-37,-24,-84,-49,-61,-99,20,-119,17,116,-112)\n17: Coll(118,124,-86,-128,-71,-114,73,106,-40,-87,-10,-119,-60,65,10,-28,83,50,127,15,-107,-23,80,-124,-64,-82,32,99,80,121,59,119)\n18: 0\n19: 1\n20: 9\n21: 1\n22: 1\n23: 2\n24: 1\n25: 33\n26: 0\n27: 0\n28: 3\n29: 1\n30: 9\n31: 0\n32: 0\n33: 1\n34: 1\n35: 9\n36: 2\n37: 2\n38: 1\n39: 1\n40: Coll(-20,-14,-48,75,-82,72,-96,10,-118,110,73,-64,86,114,99,-55,-11,-46,63,38,-56,35,88,-95,118,-85,-47,-16,33,-40,-79,48)\n41: 1\n42: 1\n43: 9\n44: 0\n45: 0\n46: 0\n47: 1\n48: 9\n49: false\n50: false",
"ergoTreeScript": "{\n val bool1 = INPUTS.exists({(box1: Box) =>\n val coll3 = box1.tokens\n if (coll3.size > placeholder[Int](0)) { coll3(placeholder[Int](1))._1 == placeholder[Coll[Byte]](2) } else { placeholder[Boolean](3) }\n })\n val coll2 = placeholder[Coll[Byte]](4)\n val coll3 = placeholder[Coll[Byte]](5)\n sigmaProp(anyOf(Coll[Boolean](bool1, if (!bool1) {(\n val box4 = OUTPUTS(placeholder[Int](6))\n val coll5 = box4.R5[Coll[Long]].get\n val box6 = INPUTS(placeholder[Int](7))\n val coll7 = box6.R5[Coll[Long]].get\n val coll8 = SELF.propositionBytes\n val box9 = OUTPUTS.filter({(box9: Box) => box9.propositionBytes == coll8 })(placeholder[Int](8))\n val box10 = CONTEXT.dataInputs(placeholder[Int](9))\n val coll11 = getVar[Coll[Byte]](0.toByte).get\n val coll12 = INPUTS.filter({(box12: Box) => box12.propositionBytes == coll8 })\n val l13 = coll12.fold(placeholder[Long](10), {(tuple13: (Long, Box)) => tuple13._1 + tuple13._2.value })\n val coll14 = box9.tokens\n val func15 = {(coll15: Coll[Byte]) => coll12.flatMap({(box17: Box) => box17.tokens }).fold(placeholder[Long](11), {(tuple17: (Long, (Coll[Byte], Long))) =>\n val tuple19 = tuple17._2\n tuple17._1 + if (tuple19._1 == coll15) { tuple19._2 } else { placeholder[Long](12) }\n }) }\n val l16 = func15(coll2)\n val bool17 = coll14.filter({(tuple17: (Coll[Byte], Long)) => tuple17._1 != coll2 }).forall({(tuple17: (Coll[Byte], Long)) => tuple17._2 == func15(tuple17._1) })\n val bool18 = coll12.flatMap({(box18: Box) => box18.tokens }).forall({(tuple18: (Coll[Byte], Long)) =>\n val coll20 = tuple18._1\n (coll20 == coll2) || coll14.exists({(tuple21: (Coll[Byte], Long)) => tuple21._1 == coll20 })\n })\n if (coll5(placeholder[Int](13)) > coll7(placeholder[Int](14))) {(\n val coll19 = box10.R4[AvlTree].get.getMany(Coll[Coll[Byte]](placeholder[Coll[Byte]](15), placeholder[Coll[Byte]](16), placeholder[Coll[Byte]](17), coll3), coll11)\n val l20 = byteArrayToLong(coll19(placeholder[Int](18)).get.slice(placeholder[Int](19), placeholder[Int](20))) * coll5(placeholder[Int](21)) + placeholder[Long](22)\n val tuple21 = OUTPUTS.filter({(box21: Box) => blake2b256(box21.propositionBytes) == coll19(placeholder[Int](23)).get.slice(placeholder[Int](24), placeholder[Int](25)) })(placeholder[Int](26)).tokens(placeholder[Int](27))\n allOf(Coll[Boolean](box9.value >= l13 - byteArrayToLong(coll19(placeholder[Int](28)).get.slice(placeholder[Int](29), placeholder[Int](30))), coll14.fold(placeholder[Long](31), {(tuple22: (Long, (Coll[Byte], Long))) =>\n val tuple24 = tuple22._2\n tuple22._1 + if (tuple24._1 == coll2) { tuple24._2 } else { placeholder[Long](32) }\n }) >= l16 - l20 - byteArrayToLong(coll19(placeholder[Int](33)).get.slice(placeholder[Int](34), placeholder[Int](35))), bool17, bool18, tuple21._1 == coll2, tuple21._2 >= l20))\n )} else { if ((coll5(placeholder[Int](36)) > coll7(placeholder[Int](37))) && (box4.tokens(placeholder[Int](38))._2 == box6.tokens(placeholder[Int](39))._2)) {(\n val coll19 = box10.R4[AvlTree].get.getMany(Coll[Coll[Byte]](placeholder[Coll[Byte]](40), coll3), coll11)\n allOf(Coll[Boolean](box9.value >= l13 - byteArrayToLong(coll19(placeholder[Int](41)).get.slice(placeholder[Int](42), placeholder[Int](43))), coll14.fold(placeholder[Long](44), {(tuple20: (Long, (Coll[Byte], Long))) =>\n val tuple22 = tuple20._2\n tuple20._1 + if (tuple22._1 == coll2) { tuple22._2 } else { placeholder[Long](45) }\n }) >= l16 - byteArrayToLong(coll19(placeholder[Int](46)).get.slice(placeholder[Int](47), placeholder[Int](48))), bool17, bool18))\n )} else { placeholder[Boolean](49) } }\n )} else { placeholder[Boolean](50) })))\n}",
"address": "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",
"assets": [
{
"tokenId": "012aec95af24812a01775de090411ba70a648fe859013f896ca2a1a95882ce5f",
"index": 0,
"amount": 99979542,
"name": "PaideiaAlpha",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "f3a8cf82aa6ed23f0bccc1e2a16087815406b3b5987ae90b0481ce92a15cf8e7",
"mainChain": true
}
],
"size": 6898,
"isUnconfirmed": false
}