Token
0d9753ffd979a45a561090a88ae2ece69b5044e54579d18bf801d982d9866d95
ID
0d9753ffd979a45a561090a88ae2ece69b5044e54579d18bf801d982d9866d95
Name
Paideia Stake Key
Emission amount
1
Decimals
0
Description
Powered by Paideia
Type
EIP-004
Issuer Box
{
"boxId": "0d9753ffd979a45a561090a88ae2ece69b5044e54579d18bf801d982d9866d95",
"transactionId": "5269247d007c9db083d7c368f89325915fa6ade30402205908d33a3bc04c6a94",
"blockId": "785f16991e8b801c0bd00d2d33f5fad2ef5633779b515fa830d81647db48dfbd",
"value": 2000000,
"index": 3,
"globalIndex": 27906328,
"creationHeight": 972233,
"settlementHeight": 972235,
"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": [
{
"tokenId": "011740cc8daf203f5d60127a0e9ef1328c8c2540d7c9d78d0416fae0571c8d7d",
"index": 0,
"amount": 1,
"name": "PaideiaAlpha Stake State",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "012aec95af24812a01775de090411ba70a648fe859013f896ca2a1a95882ce5f",
"index": 1,
"amount": 400000000001,
"name": "PaideiaAlpha",
"decimals": 4,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "110580dcd8dae56100000000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1680098400000,0,0,0,0]"
},
"R6": {
"serializedValue": "110400000000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,0,0,0]"
},
"R8": {
"serializedValue": "1d04020000020000020000020000",
"sigmaType": "Coll[Coll[SLong]]",
"renderedValue": "[[0,0],[0,0],[0,0],[0,0]]"
},
"R7": {
"serializedValue": "0c64044ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e1609000720004ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
},
"R4": {
"serializedValue": "644ec61f485b98eb87153f7c57db4f5ecd75556fddbc403b41acf8441fde8e160900072000",
"sigmaType": null,
"renderedValue": null
}
},
"spentTransactionId": "1a15edb4e5d99bdc7769559288a046f898d90979d44509debf231640a516af83",
"mainChain": true
}