Transaction
ID: d9bf81d1eb...11eb
Inputs (1)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.024 ERG
Tokens:
Loading assets...
Outputs (2)
Unspent
Address:
Settlement height:
Value:
0.0218 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0022 ERG
Transaction Details
Confirmations: 8,782
Total coins transferred: 0.024 ERG
Fees: 0.0022 ERG
Fees per byte: 0.000007666 ERG
Raw Transaction Data
{
"id": "d9bf81d1eb36d1b9a166b74a4bf8fb1aa4a7d77423c4d39d0fa453597cfc11eb",
"blockId": "9637b8f242e6e4574c104e11a2d57ebe3a199fc4dccf45814993f573fedba0ed",
"inclusionHeight": 1763882,
"timestamp": 1776171111246,
"index": 1,
"globalIndex": 10597980,
"numConfirmations": 8782,
"inputs": [
{
"boxId": "4d49570cffb1fad472e1ebe8bb7e17beebc4bc59c863b8c7ad4005c2473b7795",
"value": 24000000,
"index": 0,
"spendingProof": "3644f64e178b12f881062439b82eaad4958681a3925ee23fc00c8303090373518c54fee29e1e351398a50f1420598cdb1b1e9f6d71a96d65",
"outputBlockId": "630c00afbd3bdf668279f7dd10f74fc0b9a707611b2875c767679de74b2b4b59",
"outputTransactionId": "eff30b705a1db465df4a934970ff1ab4a4382808fa7c7c4fdb4a2159a94e9ed8",
"outputIndex": 0,
"outputGlobalIndex": 54726423,
"outputCreatedAt": 1763874,
"outputSettledAt": 1763875,
"ergoTree": "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",
"ergoTreeConstants": "0: 0\n1: 0\n2: 0\n3: 0\n4: 3\n5: 13\n6: 13\n7: 0\n8: 14\n9: 14\n10: 0\n11: 0\n12: 0\n13: 6\n14: 0\n15: 1\n16: 8\n17: 7\n18: 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)\n19: 0\n20: 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)\n21: 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)\n22: 10\n23: 4\n24: 2\n25: 1000000\n26: 13\n27: 15\n28: 0\n29: 13\n30: 13\n31: 5\n32: 13\n33: 14\n34: false\n35: 5\n36: 1\n37: 9\n38: 1\n39: 1\n40: 0\n41: 720\n42: 1\n43: 0\n44: 1\n45: 1\n46: 2\n47: 0\n48: 90\n49: 0\n50: 0\n51: false\n52: 1\n53: 3\n54: 13\n55: 0\n56: 1000000\n57: 1000000\n58: 0\n59: 0\n60: 0\n61: 17\n62: 0\n63: 0\n64: 0\n65: 0\n66: 0\n67: 0\n68: 0\n69: 0\n70: 1000\n71: 100\n72: 1000\n73: 0\n74: 100\n75: 0\n76: 2\n77: 0\n78: 17\n79: 0\n80: 1000000\n81: 0\n82: 1\n83: 2\n84: 17\n85: 0\n86: 17\n87: 1000000\n88: 0\n89: 3\n90: 2000000\n91: 1\n92: 1\n93: 1\n94: 1\n95: 2\n96: 1\n97: 1000000\n98: 1000000\n99: 0\n100: 1\n101: 2\n102: 1\n103: 0\n104: false\n105: 1\n106: 3\n107: 11\n108: 11\n109: 0\n110: 0\n111: 1\n112: 1000000\n113: 1000000\n114: 1\n115: 1000000\n116: 1\n117: 12\n118: 12\n119: 0\n120: 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)\n121: 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)\n122: 0\n123: 0\n124: 1000000\n125: false\n126: 0\n127: 0\n128: 0\n129: 0\n130: 0\n131: 1000000\n132: 1000000\n133: 1000000\n134: 0\n135: 0\n136: 1000\n137: 0\n138: 17\n139: 0\n140: 17\n141: 2\n142: 1\n143: 2\n144: 0\n145: true\n146: 1\n147: 2\n148: 0\n149: true\n150: false\n151: 1\n152: 0\n153: 1\n154: 0\n155: 0\n156: 0\n157: 0\n158: 1000000\n159: 1000000\n160: 0\n161: 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)\n162: 0\n163: 0\n164: 0\n165: true\n166: false\n167: 2\n168: 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 l13 = HEIGHT.toLong\n val coll14 = SELF.R8[Coll[Long]].get\n val l15 = coll14(placeholder[Int](4))\n val i16 = coll14.size\n val l17 = if (i16 > placeholder[Int](5)) { coll14(placeholder[Int](6)) } else { placeholder[Long](7) }\n val l18 = if (i16 > placeholder[Int](8)) { coll14(placeholder[Int](9)) } else { placeholder[Long](10) }\n val coll19 = SELF.id\n val l20 = INPUTS.fold(placeholder[Long](11), {(tuple20: (Long, Box)) =>\n val box22 = tuple20._2\n val l23 = tuple20._1\n if (box22.id != coll19) { box22.tokens.fold(l23, {(tuple24: (Long, (Coll[Byte], Long))) =>\n val tuple26 = tuple24._2\n val l27 = tuple24._1\n if (tuple26._1 == coll7) { l27 + tuple26._2 } else { l27 }\n }) } else { l23 }\n })\n val l21 = tuple5._2\n val l22 = OUTPUTS.fold(placeholder[Long](12), {(tuple22: (Long, Box)) => tuple22._2.tokens.fold(tuple22._1, {(tuple24: (Long, (Coll[Byte], Long))) =>\n val tuple26 = tuple24._2\n val l27 = tuple24._1\n if (tuple26._1 == coll7) { l27 + tuple26._2 } else { l27 }\n }) })\n val l23 = coll14(placeholder[Int](13))\n val coll24 = box10.tokens\n val tuple25 = coll24.getOrElse(placeholder[Int](14), tuple4)\n val coll26 = tuple25._1\n val tuple27 = coll3.getOrElse(placeholder[Int](15), tuple4)\n val coll28 = tuple27._1\n val l29 = tuple25._2\n val l30 = tuple27._2\n val l31 = coll14(placeholder[Int](16))\n val l32 = coll14(placeholder[Int](17))\n val coll33 = placeholder[Coll[Byte]](18)\n val l34 = coll14(placeholder[Int](19))\n val coll35 = placeholder[Coll[Byte]](20)\n val coll36 = placeholder[Coll[Byte]](21)\n val l37 = coll14(placeholder[Int](22))\n val l38 = coll14(placeholder[Int](23))\n val l39 = coll14(placeholder[Int](24))\n val l40 = l23 + placeholder[Long](25)\n val coll41 = SELF.R5[Coll[Byte]].get\n val bool42 = if (coll11 == SELF.propositionBytes) {(\n val coll42 = box10.R8[Coll[Long]].get\n (\n (\n (((box10.value >= l40) && (box10.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)) && (box10.R5[Coll[Byte]].get == coll41)) && (\n box10.R6[Coll[Byte]].get == SELF.R6[Coll[Byte]].get\n )\n ) && if (coll14.size == placeholder[Int](26)) {\n (\n (\n ((coll42.size == placeholder[Int](27)) && (coll42.slice(placeholder[Int](28), placeholder[Int](29)) == coll14)) && (\n coll42(placeholder[Int](30)) >= HEIGHT - placeholder[Int](31).toLong\n )\n ) && (coll42(placeholder[Int](32)) <= l13)\n ) && \n val l43 = l29\n coll42(placeholder[Int](33)) == l43\n \n } else { coll42 == coll14 }\n ) && (box10.R9[Coll[Coll[Byte]]].get == coll1)\n )} else { placeholder[Boolean](34) }\n val l43 = coll14(placeholder[Int](35))\n val tuple44 = coll24.getOrElse(placeholder[Int](36), tuple4)\n val coll45 = tuple44._1\n val l46 = coll14(placeholder[Int](37))\n val box47 = OUTPUTS(placeholder[Int](38))\n val coll48 = box47.tokens\n val bool49 = bool8 && (l21 == placeholder[Long](39))\n val tuple50 = coll48.getOrElse(placeholder[Int](40), tuple4)\n val l51 = l15 + placeholder[Long](41)\n val bool52 = if (coll14(placeholder[Int](42)) == placeholder[Long](43)) { (bool49 && (l13 >= l15)) && (l13 <= l51) } else { bool49 && (l13 <= l51) }\n val bool53 = ((bool42 && (box10.R7[Coll[Byte]].get == coll7)) && (coll26 == coll6)) && (l29 == placeholder[Long](44))\n val bool54 = l22 == placeholder[Long](45)\n val l55 = tuple44._2\n prop2 && sigmaProp((bool9 && (OUTPUTS.size == placeholder[Int](46))) && (coll11 == coll12)) || prop2 && sigmaProp(\n if (bool8 && (l13 < l15)) {\n (\n (((l17 > placeholder[Long](47)) && (l13 >= l17 + placeholder[Long](48))) && ((l18 > placeholder[Long](49)) && (l20 + l21 == l18))) && (\n l22 == placeholder[Long](50)\n )\n ) && ((((coll11 == coll12) && (box10.value >= SELF.value - l23)) && (coll26 == coll28)) && (l29 == l30))\n } else { placeholder[Boolean](51) }\n ) || sigmaProp(\n (((if (((bool9 && (INPUTS.size == placeholder[Int](52))) && (OUTPUTS.size == placeholder[Int](53))) && (coll14.size == placeholder[Int](54))) {(\n val bool56 = l31 == placeholder[Long](55)\n val l57 = l38 * l39 / placeholder[Long](56) * l37 / placeholder[Long](57)\n val bool58 = l57 > placeholder[Long](58)\n ((((((((((if (bool56) {(\n val box59 = CONTEXT.dataInputs(placeholder[Int](59))\n val i60 = l32.toInt\n val l61 = box59.R5[Coll[Long]].get(i60)\n val bool62 = box59.tokens(placeholder[Int](60))._1 == coll33\n val coll63 = box59.R4[Coll[Coll[Byte]]].get(i60)\n if (l32 == placeholder[Long](61)) { bool62 && (l61 > placeholder[Long](62)) } else { if (l34 == placeholder[Long](63)) { ((bool62 && (coll63.size > placeholder[Int](64))) && (l61 > placeholder[Long](65))) && (coll3(placeholder[Int](66))._1 == coll63) } else { (bool62 && (coll63.size > placeholder[Int](67))) && (l61 > placeholder[Long](68)) } }\n )} else {(\n val coll59 = coll3(placeholder[Int](69))._1\n (coll59 == coll35) || (coll59 == coll36)\n )} && if (bool56) { (l37 == placeholder[Long](70)) || (l37 == placeholder[Long](71)) } else { ((l37 == placeholder[Long](72)) && (coll3(placeholder[Int](73))._1 == coll35)) || ((l37 == placeholder[Long](74)) && (coll3(placeholder[Int](75))._1 == coll36)) }) && bool58) && bool42) && (box10.R7[Coll[Byte]].get == coll19)) && (box10.value == SELF.value - l23 - l43)) && (box10.value >= placeholder[Long](76) * l40)) && (coll26 == coll19)) && \n val bool59 = l34 == placeholder[Long](77)\n (((((bool59 && bool56) && (l32 != placeholder[Long](78))) && \n val l60 = l39 * CONTEXT.dataInputs(placeholder[Int](79)).R5[Coll[Long]].get(l32.toInt) / placeholder[Long](80)\n ((((l60 > placeholder[Long](81)) && \n val coll61 = coll45\n coll61 == coll41\n ) && (tuple44 == tuple5)) && (l29 == l21 / l60 + placeholder[Long](82))) && (coll24.size == placeholder[Int](83))\n ) || (((bool59 && bool56) && (l32 == placeholder[Long](84))) && \n val l60 = l39 * CONTEXT.dataInputs(placeholder[Int](85)).R5[Coll[Long]].get(placeholder[Int](86)) / placeholder[Long](87)\n val l61 = SELF.value\n (((l60 > placeholder[Long](88)) && (l29 == l61 - placeholder[Long](89) * l23 - l43 - placeholder[Long](90) / l60 + placeholder[Long](91))) && (coll24.size == placeholder[Int](92))) && (box10.value >= l61 - l23 - l43)\n )) || (((((((l34 == placeholder[Long](93)) && bool56) && (coll45 == coll41)) && (tuple44 == tuple5)) && bool58) && (l29 == l21 / l57 + placeholder[Long](94))) && (coll24.size == placeholder[Int](95)))) || ((l31 == placeholder[Long](96)) && \n val l60 = l46 * l39 / placeholder[Long](97) * l37 / placeholder[Long](98)\n ((((l60 > placeholder[Long](99)) && (coll45 == coll41)) && (tuple44 == tuple5)) && (l29 == l21 / l60 + placeholder[Long](100))) && (coll24.size == placeholder[Int](101))\n )\n ) && (box47.propositionBytes == coll1(placeholder[Int](102)))) && (coll48.size == placeholder[Int](103))) && (box47.value >= l43)\n )} else { placeholder[Boolean](104) } || if (((bool8 && (!bool49)) && (INPUTS.size == placeholder[Int](105))) && (OUTPUTS.size == placeholder[Int](106))) {(\n val bool56 = if (i16 > placeholder[Int](107)) { coll14(placeholder[Int](108)) } else { placeholder[Long](109) } == placeholder[Long](110)\n val bool57 = (tuple50._1 == coll6) && (tuple50._2 == l21 - placeholder[Long](111))\n val bool58 = (((((box10.value == SELF.value - l23 - if (bool56) { placeholder[Long](112) } else { placeholder[Long](113) + l23 }) && bool42) && (box10.R7[Coll[Byte]].get == coll7)) && (coll26 == coll6)) && (l29 == placeholder[Long](114))) && (tuple44 == tuple27)\n if (bool56) { (((bool58 && bool57) && (box47.propositionBytes == coll12)) && (box47.value == placeholder[Long](115))) && (coll48.size == placeholder[Int](116)) } else {(\n val coll59 = box47.propositionBytes\n val l60 = if (i16 > placeholder[Int](117)) { coll14(placeholder[Int](118)) } else { placeholder[Long](119) }\n ((((bool58 && bool57) && ((coll59 == placeholder[Coll[Byte]](120)) || (coll59 == placeholder[Coll[Byte]](121)))) && (box47.R4[SigmaProp].get == prop2)) && ((box47.R5[Coll[Long]].get(placeholder[Int](122)) == l60) && (l60 > placeholder[Long](123)))) && (box47.value >= placeholder[Long](124) + l23)\n )}\n )} else { placeholder[Boolean](125) }) || if ((bool52 && (l31 == placeholder[Long](126))) && (l20 > placeholder[Long](127))) {(\n val box56 = CONTEXT.dataInputs(placeholder[Int](128))\n val i57 = l32.toInt\n val l58 = box56.R5[Coll[Long]].get(i57)\n val bool59 = l58 > placeholder[Long](129)\n val bool60 = box56.tokens(placeholder[Int](130))._1 == coll33\n val l61 = l39 * l58 / placeholder[Long](131)\n val l62 = l38 * l39 / placeholder[Long](132) * l37 / placeholder[Long](133)\n val bool63 = (l61 > placeholder[Long](134)) && (l62 > placeholder[Long](135))\n val l64 = l61 * l20\n val coll65 = if (l37 == placeholder[Long](136)) { coll35 } else { coll36 }\n val l66 = l62 * l20\n val coll67 = box56.R4[Coll[Coll[Byte]]].get(i57)\n val bool68 = (coll67.size > placeholder[Int](137)) || (l32 == placeholder[Long](138))\n if (l34 == placeholder[Long](139)) { if (l32 == placeholder[Long](140)) {(\n val box69 = OUTPUTS(placeholder[Int](141))\n ((((((bool60 && bool59) && bool63) && (box47.value >= l64)) && ((box69.propositionBytes == coll12) && box69.tokens.exists({(tuple70: (Coll[Byte], Long)) => (tuple70._1 == coll65) && (tuple70._2 >= l66) }))) && bool53) && bool54) && (box10.value >= SELF.value - l64 - l23)\n )} else {(\n val tuple69 = coll3(placeholder[Int](142))\n val box70 = OUTPUTS(placeholder[Int](143))\n ((((((((((bool60 && bool68) && bool59) && bool63) && (tuple69._1 == coll67)) && coll48.exists({(tuple71: (Coll[Byte], Long)) => (tuple71._1 == coll67) && (tuple71._2 >= l64) })) && ((box70.propositionBytes == coll12) && box70.tokens.exists({(tuple71: (Coll[Byte], Long)) => (tuple71._1 == coll65) && (tuple71._2 >= l66) }))) && if (l55 > placeholder[Long](144)) { coll45 == coll67 } else { placeholder[Boolean](145) }) && bool53) && bool54) && (box10.value >= SELF.value - l23)) && (l55 >= tuple69._2 - l64)\n )} } else {(\n val tuple69 = coll3(placeholder[Int](146))\n val coll70 = tuple69._1\n val box71 = OUTPUTS(placeholder[Int](147))\n ((((((((((bool60 && bool68) && bool59) && bool63) && ((coll70 == coll35) || (coll70 == coll36))) && coll48.exists({(tuple72: (Coll[Byte], Long)) => (tuple72._1 == coll70) && (tuple72._2 >= l66) })) && ((box71.propositionBytes == coll12) && box71.tokens.exists({(tuple72: (Coll[Byte], Long)) => (tuple72._1 == coll67) && (tuple72._2 >= l64) }))) && if (l55 > placeholder[Long](148)) { coll45 == coll70 } else { placeholder[Boolean](149) }) && bool53) && bool54) && (box10.value >= SELF.value - l23)) && (l55 >= tuple69._2 - l66)\n )}\n )} else { placeholder[Boolean](150) }) || if ((bool52 && (l31 == placeholder[Long](151))) && (l20 > placeholder[Long](152))) {(\n val tuple56 = coll3(placeholder[Int](153))\n val coll57 = tuple56._1\n val box58 = CONTEXT.dataInputs(placeholder[Int](154))\n val l59 = box58.R8[Coll[Long]].get(l32.toInt)\n val l60 = if (l34 == placeholder[Long](155)) { if (l59 > l38) {(\n val l60 = l59 - l38\n if (l60 > l46) { l46 } else { l60 }\n )} else { placeholder[Long](156) } } else { if (l59 < l38) {(\n val l60 = l38 - l59\n if (l60 > l46) { l46 } else { l60 }\n )} else { placeholder[Long](157) } }\n val l61 = l60 * l39 / placeholder[Long](158) * l37 / placeholder[Long](159)\n val l62 = l61 * l20\n ((((((((((coll57 == coll35) || (coll57 == coll36)) && (box58.tokens(placeholder[Int](160))._1 == placeholder[Coll[Byte]](161))) && (l60 > placeholder[Long](162))) && (l61 > placeholder[Long](163))) && coll48.exists({(tuple63: (Coll[Byte], Long)) => (tuple63._1 == coll57) && (tuple63._2 >= l62) })) && if (l55 > placeholder[Long](164)) { coll45 == coll57 } else { placeholder[Boolean](165) }) && bool53) && bool54) && (box10.value >= SELF.value - l23)) && (l55 >= tuple56._2 - l62)\n )} else { placeholder[Boolean](166) }) || if (l13 > l51) {\n ((((OUTPUTS.size == placeholder[Int](167)) && (coll11 == coll12)) && (box10.value >= SELF.value - l23)) && \n val coll56 = coll28\n coll26 == coll56\n ) && (l29 == l30)\n } else { placeholder[Boolean](168) }\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "6122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad",
"index": 0,
"amount": 31,
"name": "DexyGold",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e206122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad",
"sigmaType": "Coll[SByte]",
"renderedValue": "6122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad"
},
"R6": {
"serializedValue": "0e0130",
"sigmaType": "Coll[SByte]",
"renderedValue": "30"
},
"R8": {
"serializedValue": "110d0002e80794aed70180fca40280dac40980c78c02240080fca402c801020e",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,1,500,1764234,2400000,10000000,2200000,18,0,2400000,100,1,7]"
},
"R7": {
"serializedValue": "0e200000000000000000000000000000000000000000000000000000000000000000",
"sigmaType": "Coll[SByte]",
"renderedValue": "0000000000000000000000000000000000000000000000000000000000000000"
},
"R9": {
"serializedValue": "1a022102795f3b7a0ebae08365c6a3a4e82cad02fb51c7b0e13d53026d695a5ff9287f73240008cd02383747243fed0a3ae9fcf0f3936d92447b57bb34c53faf5c5c0a105fbf42b4c8",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[02795f3b7a0ebae08365c6a3a4e82cad02fb51c7b0e13d53026d695a5ff9287f73,0008cd02383747243fed0a3ae9fcf0f3936d92447b57bb34c53faf5c5c0a105fbf42b4c8]"
},
"R4": {
"serializedValue": "0e12476f6c642043616c6c2024343830302e3030",
"sigmaType": "Coll[SByte]",
"renderedValue": "476f6c642043616c6c2024343830302e3030"
}
}
}
],
"dataInputs": [],
"outputs": [
{
"boxId": "f4dfb7a882276b0eec024a4e30850f0b4cb46df40754e2efbe441f77abbdbe6b",
"transactionId": "d9bf81d1eb36d1b9a166b74a4bf8fb1aa4a7d77423c4d39d0fa453597cfc11eb",
"blockId": "9637b8f242e6e4574c104e11a2d57ebe3a199fc4dccf45814993f573fedba0ed",
"value": 21800000,
"index": 0,
"globalIndex": 54726640,
"creationHeight": 1763881,
"settlementHeight": 1763882,
"ergoTree": "0008cd02795f3b7a0ebae08365c6a3a4e82cad02fb51c7b0e13d53026d695a5ff9287f73",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(795f3b,350403,...)))}",
"address": "9fSWoM7h4mnQBJXzKAaGrVtB6qaQevmB7cZEjEVEAVgTKnYAF6P",
"assets": [
{
"tokenId": "6122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad",
"index": 0,
"amount": 31,
"name": "DexyGold",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": null,
"mainChain": true
},
{
"boxId": "10af5c0f9cb866c886934f1affca2f7a8f5ef8d99d56bc55d64f9af608d3494f",
"transactionId": "d9bf81d1eb36d1b9a166b74a4bf8fb1aa4a7d77423c4d39d0fa453597cfc11eb",
"blockId": "9637b8f242e6e4574c104e11a2d57ebe3a199fc4dccf45814993f573fedba0ed",
"value": 2200000,
"index": 1,
"globalIndex": 54726641,
"creationHeight": 1763881,
"settlementHeight": 1763882,
"ergoTree": "1005040004000e36100204a00b08cd0279be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798ea02d192a39a8cc7a701730073011001020402d19683030193a38cc7b2a57300000193c2b2a57301007473027303830108cdeeac93b1a57304",
"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(16,2,4,-96,11,8,-51,2,121,-66,102,126,-7,-36,-69,-84,85,-96,98,-107,-50,-121,11,7,2,-101,-4,-37,45,-50,40,-39,89,-14,-127,91,22,-8,23,-104,-22,2,-47,-110,-93,-102,-116,-57,-89,1,115,0,115,1)\n3: Coll(1)\n4: 1",
"ergoTreeScript": "{sigmaProp(\n allOf(\n Coll[Boolean](\n HEIGHT == OUTPUTS(placeholder[Int](0)).creationInfo._1, OUTPUTS(placeholder[Int](1)).propositionBytes == substConstants(\n placeholder[Coll[Byte]](2), placeholder[Coll[Int]](3), Coll[SigmaProp](proveDlog(decodePoint(minerPubKey)))\n ), OUTPUTS.size == placeholder[Int](4)\n )\n )\n)}",
"address": "2iHkR7CWvD1R4j1yZg5bkeDRQavjAaVPeTDFGGLZduHyfWMuYpmhHocX8GJoaieTx78FntzJbCBVL6rf96ocJoZdmWBL2fci7NqWgAirppPQmZ7fN9V6z13Ay6brPriBKYqLp1bT2Fk4FkFLCfdPpe",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "5f52aab554c034ae24509dd7513c198d46da49a8c8e60e73821a1db15abf8c41",
"mainChain": true
}
],
"size": 287,
"isUnconfirmed": false
}