Transaction
ID: 7abb20dc20...cd2f
Inputs (1)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.0128 ERG
Tokens:
0
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0096 ERG
Tokens:
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0022 ERG
Transaction Details
Confirmations: 16,910
Total coins transferred: 0.0128 ERG
Fees: 0.0022 ERG
Fees per byte: 0.000000758 ERG
Raw Transaction Data
{
"id": "7abb20dc20909536b256b5bb31163b2bd6f4d40901d8bbd7607de490e107cd2f",
"blockId": "af8665a29f343cabfe68d7bf79c06e3728c091043f2bae992d26a2ce74306df8",
"inclusionHeight": 1746465,
"timestamp": 1774061991844,
"index": 1,
"globalIndex": 10476406,
"numConfirmations": 16910,
"inputs": [
{
"boxId": "b46236ab1dcc25c2d0e06b1d4e0471dda00d52487cce1b3f68b1f3e950df5d91",
"value": 12800000,
"index": 0,
"spendingProof": null,
"outputBlockId": "6a22d511b5821c291fb97b26ef68bff34ea228b8417b9f2d808e2a92d6b85e2d",
"outputTransactionId": "34d0c231ae733d6a191bf45f613935b36e2303cd18a337d54d813101301fa7bd",
"outputIndex": 0,
"outputGlobalIndex": 54253236,
"outputCreatedAt": 1746463,
"outputSettledAt": 1746464,
"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: 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)\n8: 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)\n9: 10\n10: 4\n11: 6\n12: 1000000\n13: false\n14: 5\n15: 0\n16: 0\n17: 1\n18: 2\n19: 9\n20: 1\n21: 1\n22: 1\n23: 0\n24: 3\n25: 5\n26: 1\n27: 0\n28: 0\n29: 1\n30: 2\n31: 1\n32: 3\n33: 0\n34: 1000000\n35: 0\n36: 0\n37: 0\n38: 17\n39: 0\n40: 0\n41: 0\n42: 0\n43: 0\n44: 1000\n45: 100\n46: 1000\n47: 0\n48: 100\n49: 0\n50: 2\n51: 0\n52: 17\n53: 1\n54: 2\n55: 17\n56: 0\n57: 17\n58: 1000000\n59: 0\n60: 3\n61: 2000000\n62: 1\n63: 1\n64: 1\n65: 1\n66: 2\n67: 1\n68: 1000000\n69: 0\n70: 1\n71: 2\n72: 1\n73: 0\n74: false\n75: 1\n76: 3\n77: 1000000\n78: 1\n79: 1000000\n80: 1\n81: 1\n82: false\n83: 0\n84: 0\n85: 0\n86: 0\n87: 0\n88: 1000000\n89: 1000000\n90: 0\n91: 0\n92: 1000\n93: 0\n94: 17\n95: 0\n96: 17\n97: 2\n98: 1\n99: 2\n100: 0\n101: true\n102: 1\n103: 2\n104: 0\n105: true\n106: false\n107: 1\n108: 0\n109: 1\n110: 0\n111: 0\n112: 0\n113: 0\n114: 1000000\n115: 0\n116: 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)\n117: 0\n118: 0\n119: 0\n120: true\n121: false\n122: 2\n123: 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 coll17 = placeholder[Coll[Byte]](7)\n val coll18 = placeholder[Coll[Byte]](8)\n val l19 = coll13(placeholder[Int](9))\n val l20 = coll13(placeholder[Int](10))\n val l21 = coll13(placeholder[Int](11))\n val l22 = l21 + placeholder[Long](12)\n val coll23 = SELF.R5[Coll[Byte]].get\n val bool24 = if (coll11 == SELF.propositionBytes) {\n (\n (\n (((box10.value >= l22) && (box10.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)) && (box10.R5[Coll[Byte]].get == coll23)) && (\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](13) }\n val coll25 = SELF.id\n val l26 = coll13(placeholder[Int](14))\n val coll27 = box10.tokens\n val tuple28 = coll27.getOrElse(placeholder[Int](15), tuple4)\n val coll29 = tuple28._1\n val l30 = coll13(placeholder[Int](16))\n val tuple31 = coll27.getOrElse(placeholder[Int](17), tuple4)\n val l32 = tuple5._2\n val l33 = coll13(placeholder[Int](18))\n val l34 = tuple28._2\n val coll35 = tuple31._1\n val l36 = coll13(placeholder[Int](19))\n val box37 = OUTPUTS(placeholder[Int](20))\n val coll38 = box37.tokens\n val bool39 = bool8 && (l32 == placeholder[Long](21))\n val tuple40 = coll3.getOrElse(placeholder[Int](22), tuple4)\n val tuple41 = coll38.getOrElse(placeholder[Int](23), tuple4)\n val l42 = HEIGHT.toLong\n val l43 = coll13(placeholder[Int](24))\n val l44 = l43 + placeholder[Long](25)\n val bool45 = if (coll13(placeholder[Int](26)) == placeholder[Long](27)) { (bool39 && (l42 >= l43)) && (l42 <= l44) } else { bool39 && (l42 <= l44) }\n val l46 = INPUTS.fold(placeholder[Long](28), {(tuple46: (Long, Box)) =>\n val box48 = tuple46._2\n val l49 = tuple46._1\n if (box48.id != coll25) { box48.tokens.fold(l49, {(tuple50: (Long, (Coll[Byte], Long))) =>\n val tuple52 = tuple50._2\n val l53 = tuple50._1\n if (tuple52._1 == coll7) { l53 + tuple52._2 } else { l53 }\n }) } else { l49 }\n })\n val bool47 = ((bool24 && (box10.R7[Coll[Byte]].get == coll7)) && \n val coll47 = coll29\n coll47 == coll6\n ) && (l34 == placeholder[Long](29))\n val l48 = tuple31._2\n prop2 && sigmaProp((bool9 && (OUTPUTS.size == placeholder[Int](30))) && (coll11 == coll12)) || sigmaProp(\n (((if ((bool9 && (INPUTS.size == placeholder[Int](31))) && (OUTPUTS.size == placeholder[Int](32))) {(\n val bool49 = l14 == placeholder[Long](33)\n val l50 = l20 * l19 / placeholder[Long](34)\n val bool51 = l50 > placeholder[Long](35)\n ((((((((((if (bool49) {(\n val box52 = CONTEXT.dataInputs(placeholder[Int](36))\n val i53 = l15.toInt\n val l54 = box52.R5[Coll[Long]].get(i53)\n val bool55 = box52.tokens(placeholder[Int](37))._1 == coll16\n val coll56 = box52.R4[Coll[Coll[Byte]]].get(i53)\n if (l15 == placeholder[Long](38)) { bool55 && (l54 > placeholder[Long](39)) } else { ((bool55 && (coll56.size > placeholder[Int](40))) && (l54 > placeholder[Long](41))) && (coll3(placeholder[Int](42))._1 == coll56) }\n )} else {(\n val coll52 = coll3(placeholder[Int](43))._1\n (coll52 == coll17) || (coll52 == coll18)\n )} && if (bool49) { (l19 == placeholder[Long](44)) || (l19 == placeholder[Long](45)) } else { ((l19 == placeholder[Long](46)) && (coll3(placeholder[Int](47))._1 == coll17)) || ((l19 == placeholder[Long](48)) && (coll3(placeholder[Int](49))._1 == coll18)) }) && bool51) && bool24) && (box10.R7[Coll[Byte]].get == coll25)) && (box10.value == SELF.value - l21 - l26)) && (box10.value >= placeholder[Long](50) * l22)) && (coll29 == coll25)) && \n val bool52 = l30 == placeholder[Long](51)\n ((((((((bool52 && bool49) && (l15 != placeholder[Long](52))) && \n val coll53 = coll35\n coll53 == coll23\n ) && (tuple31 == tuple5)) && (l34 == l32 / l33 + placeholder[Long](53))) && (coll27.size == placeholder[Int](54))) || (((bool52 && bool49) && (l15 == placeholder[Long](55))) && \n val l53 = l33 * CONTEXT.dataInputs(placeholder[Int](56)).R5[Coll[Long]].get(placeholder[Int](57)) / placeholder[Long](58)\n val l54 = SELF.value\n (((l53 > placeholder[Long](59)) && (l34 == l54 - placeholder[Long](60) * l21 - l26 - placeholder[Long](61) / l53 + placeholder[Long](62))) && (coll27.size == placeholder[Int](63))) && (box10.value >= l54 - l21 - l26)\n )) || (((((((l30 == placeholder[Long](64)) && bool49) && (coll35 == coll23)) && (tuple31 == tuple5)) && bool51) && (l34 == l32 / l50 + placeholder[Long](65))) && (coll27.size == placeholder[Int](66)))) || ((l14 == placeholder[Long](67)) && \n val l53 = l36 * l19 / placeholder[Long](68)\n ((((l53 > placeholder[Long](69)) && (coll35 == coll23)) && (tuple31 == tuple5)) && (l34 == l32 / l53 + placeholder[Long](70))) && (coll27.size == placeholder[Int](71))\n )\n ) && (box37.propositionBytes == coll1(placeholder[Int](72)))) && (coll38.size == placeholder[Int](73))) && (box37.value >= l26)\n )} else { placeholder[Boolean](74) } || if (((bool8 && (!bool39)) && (INPUTS.size == placeholder[Int](75))) && (OUTPUTS.size == placeholder[Int](76))) { ((((((((((box10.value == SELF.value - l21 - placeholder[Long](77)) && bool24) && (box10.R7[Coll[Byte]].get == coll7)) && (coll29 == coll6)) && (l34 == placeholder[Long](78))) && (tuple31 == tuple40)) && (box37.propositionBytes == coll12)) && (box37.value == placeholder[Long](79))) && (coll38.size == placeholder[Int](80))) && (tuple41._1 == coll6)) && (tuple41._2 == l32 - placeholder[Long](81)) } else { placeholder[Boolean](82) }) || if ((bool45 && (l14 == placeholder[Long](83))) && (l46 > placeholder[Long](84))) {(\n val box49 = CONTEXT.dataInputs(placeholder[Int](85))\n val i50 = l15.toInt\n val l51 = box49.R5[Coll[Long]].get(i50)\n val bool52 = l51 > placeholder[Long](86)\n val bool53 = box49.tokens(placeholder[Int](87))._1 == coll16\n val l54 = l33 * l51 / placeholder[Long](88)\n val l55 = l20 * l19 / placeholder[Long](89)\n val bool56 = (l54 > placeholder[Long](90)) && (l55 > placeholder[Long](91))\n val l57 = l54 * l46\n val coll58 = if (l19 == placeholder[Long](92)) { coll17 } else { coll18 }\n val l59 = l55 * l46\n val coll60 = box49.R4[Coll[Coll[Byte]]].get(i50)\n val bool61 = (coll60.size > placeholder[Int](93)) || (l15 == placeholder[Long](94))\n if (l30 == placeholder[Long](95)) { if (l15 == placeholder[Long](96)) {(\n val box62 = OUTPUTS(placeholder[Int](97))\n (((((bool53 && bool52) && bool56) && (box37.value >= l57)) && ((box62.propositionBytes == coll12) && box62.tokens.exists({(tuple63: (Coll[Byte], Long)) => (tuple63._1 == coll58) && (tuple63._2 >= l59) }))) && bool47) && (box10.value >= SELF.value - l57 - l21)\n )} else {(\n val tuple62 = coll3(placeholder[Int](98))\n val box63 = OUTPUTS(placeholder[Int](99))\n (((((((((bool53 && bool61) && bool52) && bool56) && (tuple62._1 == coll60)) && coll38.exists({(tuple64: (Coll[Byte], Long)) => (tuple64._1 == coll60) && (tuple64._2 >= l57) })) && ((box63.propositionBytes == coll12) && box63.tokens.exists({(tuple64: (Coll[Byte], Long)) => (tuple64._1 == coll58) && (tuple64._2 >= l59) }))) && if (l48 > placeholder[Long](100)) { coll35 == coll60 } else { placeholder[Boolean](101) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple62._2 - l57)\n )} } else {(\n val tuple62 = coll3(placeholder[Int](102))\n val coll63 = tuple62._1\n val box64 = OUTPUTS(placeholder[Int](103))\n (((((((((bool53 && bool61) && bool52) && bool56) && ((coll63 == coll17) || (coll63 == coll18))) && coll38.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll63) && (tuple65._2 >= l59) })) && ((box64.propositionBytes == coll12) && box64.tokens.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll60) && (tuple65._2 >= l57) }))) && if (l48 > placeholder[Long](104)) { coll35 == coll63 } else { placeholder[Boolean](105) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple62._2 - l59)\n )}\n )} else { placeholder[Boolean](106) }) || if ((bool45 && (l14 == placeholder[Long](107))) && (l46 > placeholder[Long](108))) {(\n val tuple49 = coll3(placeholder[Int](109))\n val coll50 = tuple49._1\n val box51 = CONTEXT.dataInputs(placeholder[Int](110))\n val l52 = box51.R8[Coll[Long]].get(l15.toInt)\n val l53 = if (l30 == placeholder[Long](111)) { if (l52 > l20) {(\n val l53 = l52 - l20\n if (l53 > l36) { l36 } else { l53 }\n )} else { placeholder[Long](112) } } else { if (l52 < l20) {(\n val l53 = l20 - l52\n if (l53 > l36) { l36 } else { l53 }\n )} else { placeholder[Long](113) } }\n val l54 = l53 * l19 / placeholder[Long](114)\n val l55 = l54 * l46\n (((((((((coll50 == coll17) || (coll50 == coll18)) && (box51.tokens(placeholder[Int](115))._1 == placeholder[Coll[Byte]](116))) && (l53 > placeholder[Long](117))) && (l54 > placeholder[Long](118))) && coll38.exists({(tuple56: (Coll[Byte], Long)) => (tuple56._1 == coll50) && (tuple56._2 >= l55) })) && if (l48 > placeholder[Long](119)) { coll35 == coll50 } else { placeholder[Boolean](120) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple49._2 - l55)\n )} else { placeholder[Boolean](121) }) || if (l42 > l44) {\n ((((OUTPUTS.size == placeholder[Int](122)) && (coll11 == coll12)) && (box10.value >= SELF.value - l21)) && (coll29 == tuple40._1)) && (l34 == tuple40._2)\n } else { placeholder[Boolean](123) }\n )\n}",
"address": "pqMdDMu6mc5JkfpoGKk1QMAYxUPLTAEfyTqSWoZ4jtsLkR9c9YceyjsYJyZFnGZzPuNu9v7G6vJDt8E9XDzEHrEjjZHvkCHcd1WufQayFUTQkVfw9F8Tg8UoeiYVfUF8dzH98JyWR4GVuUmDah3GBntBLN1j9VbzxPeKyLu9YRTrVuyko5GPZBRxLshKijRnvzwX3eL449x2R6eh58CEvAgWZZC1NmpXHMZd3gx27UFeGFzK2MqRkHocjaYtHdMJYePSkojpmd6CsAiJoBm8rCqreZ9j6K5b6Z7sTGDpFc6dwp9xjsnPatoJ3w8ysxngNU8dT4U7dLxCcDuDUE5uURgQotqVRNmXV2aV5maJCGtdkaXw7E4PJoT6v7sefDxU26GvipdALVHdAU4P7MNfNHvTvgXbyozxSkgMa4YdghP9kFDri7TuCCgPQQELVbwbPkqYpsEwetSnJ96SFy62tPtFkH3R6pLg3oNEaFtkN39UMgzyHqGsGUvBSn9GRaT9sJJAZ5bnEbRSqAY5Kjp9nmAn3rQkwMXJRQ1XrHT7YN4BKS8Mp51kTJViuKWm7x5ak5s3fsayqqs1FjsgrBydyTABjmD44CK9AcRyi3Ufpsh6b4EfsADCrpzGPtDEfoM5UuPW1Gs91DuQBDr5TS8EVigRzXZqjUKsBVLxE6aLLcVAEWWzvS8NPsrUBZdrdevuw93fxv5J4KrabuYZGNQDaQA3MyTXsKC3qH7isqEYtVsDPFncegW5fqse93NRC81mn3u31Qb1fPJWUUtAuruFwHMeahgsqJq172QmfGFQRBbsC9VJ21G39yPcqCj2vZf9Nxco5bzHNBCJTY844gAYySVsKZAn9zrDjbXA8MbR3apvei1qQ4wQRcJon1HdTqzVFhQGpV4wvQSwK88ZfQtfiAUA2KeXCajcUDzrZ8ihkrv4n1QtTZAERskKoLHgcYKeXVfq94KHaPeeesL9LszhmttHJYeLEZpwHDu3q7VVscK1ZomruKPitwFTszwGb34Ea7YJSbRwGR1xfP66RVdzK2AeNS44gevsbWnNZ5iD3LS5vM1Js9gnuzMAJYWKpvNn9tJPTJKjivP2YcKru685GWaLyB9cs4wy25C74e4wryxNz54bqvq4buVn3E3WXqv1T84qdkL5H6H31kzGaPdyZwxfBQbWKHKqtWsKsT9gUycr1cP6Wvk7SEeNMJJbX6HxZSXcpPfDWULZMBdjVk1PVJ4Jytpjs9exMpzUgFH5bxuWYBkxDN6a7PZkeBxtR3vfiDTbqs3gXCmdFKcnx8gFzapxdNNKKbtoe2qt1SCJeS6P3S32qzEGhPf7zLSVZ5BGciq1VSAW4TRTNHmWraWyHNyLoyCaNWeVPjgALpkDHEtrdZ9m2PMHi3KhnbNEtK43LxGGQRkacJU4MwSZTAVeT32PZkmsocCFimo8VDGtAdstcCyZPeHa3PyKN2rFtBz4geZuwQasYEczPBibw1JKNnMgJo4tBNqLUpTM7PDU1pTUSu1g7Md5jcgqV3QP8iBUD5y5z3t8zcnuM5zUTf9dFjta59umrsz324vZgmSmATW4UuvbiPzJPKCpsKdTVsoewoU5eePRDL5DbLLXtHNSN7QkAVzb639ZzNGaU8ZAE7fhyRERcF4kKoPKzhqLA86oRM2HcEEv6GFdJ2RNPrQjZBmXrzEdcsrSqv8j4aUSvpd3s4rYwKQ3m8HjvGXB9YCJ9Bd4qFeUGhxWpFSnfYnJqnRbicuoCBEE7EoxmUKNWKDy1DFLhMiHoG3qtX3uBYjWHKAPViSki8vhYyN12MHw9VFNkaPvcGcZNJBsA4LbVWwyc3zXiCdxP7cUaP7XGWQGr4GuG3r5zV3mnvcvPg72hxCdVfCq2aKzhp2SsvzXbnMXfC7HiUsyJFkSDXxoPQoGNFK4N9FPkry8hh62uLfpACTeRNLg85g7d3qhzovk8oyg1n5a4zKADk6CQvNiCT7ndecucaHznAGgErH3Y3DEKhVoJ5ns4UHhC7xk9dm73NcMZoZqo3hMemPYb1jH8nm8JSpasMPwKVYsgY1Z2CSJAKKMmKx18BzYsxHqGH6pNjCNvZWuxnY2MZRyJ1M4b1srggXCKPuPzCQ9sQ78f8nHfu1n1BdSEGhDqjUHLX8SpTDMbV1TyRgrBMwk91nzLctrRL1ZppkEiSjE95Pucmjq2LsjDAK3xPALkqTW2XYTi564gKUykCYSdE9QoEQDXDJUbr1jGvWgsJEZUWHuFBFJV7CeducNxqHKYAmxrBrCKj9Xv7PdJfRTctnGZ8WvCcUQfrVituBPYg31nPDchWtemGnKaJD3FGKwhmX3JfxZd8qXRfERgdsMeyYhyN7RSsH8Cjga1k5HJbxx2HQepkp465QNXcdNWkSzBAhjgDLBnM9zykiTywofgjNzMoA6UTcNSwZz3EnchFp9Y1SJ4K7oKbnSMA2V62GQYs8iRhwJRAWfs8m6TBfLujLNMSfs4dSDKEaaQWDGZRpTW79tov9sRdX7Ki7Us66TkzXRe6k6Rb2GJqBPoeFbG2kURod1snMr3MgkEnx7hca5G1cQVFhxa3escF3kbN4faYCQxtBHuACvJwcSvRznCQDDGWops5g5ipCwszvHt6GM5qxLEkSGvbB8x8UjycJQqrjfgjVZJya1f4XipHTFUgXdA5SMgAvpptSDTdeBnTMkeeCALw8aP5ZiqjVtKkuaj1EioZ5n1c37Nd1XwAJZFSfbUU6u84eGoHwPvA26xAYkUHY2nkxwTXRqL3oZZyLTrJueS6udZ4gpFxgzmjVJPy8niZmCvujmU95vhrdDF8GdWyZBABNnyYDeSjCnJ3BLbPbGW8RkcQkokZ9P7x3HLf3qAnEFPFG2bXDKcjXMEBAejrX1tpR6Comtd2uAVVV7AdqHrkHiihbaicD4iTaRh4QkQZKt6RoT6oDtY8CtqNnJXJ4URtyrCzVVhBe8UHXrqN21jyuMstwznhowKxCrHduLk7YkNjeNgKDZvXFaFyw3TvRM4MJuyG5KKsxoet3i6PaWqb7QXEWp8xevPevuVYXwrxmpDX83uxNsi6224fDSKsXUPsLBRbNMGwhLyQw6oHPtTv47n5K6qcgU22QpQobxDQYm1fBzm7cgpcH9gsubPRr92m1uYNMrucwHXRYeZby41nEszBmtgk4TdrxSevxC6Pzq3JcmfLsb5yCiNqGUMzUbnHxmgGrKpGF1kzRYAk8C7XnNpWFY3sHYpz661gazjJVVJ2Uy48o3ZCM2qD2jfesA7x",
"assets": [
{
"tokenId": "e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297",
"index": 0,
"amount": 250000,
"name": "rsADA",
"decimals": 6,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e20e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297",
"sigmaType": "Coll[SByte]",
"renderedValue": "e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297"
},
"R6": {
"serializedValue": "0e0136",
"sigmaType": "Coll[SByte]",
"renderedValue": "36"
},
"R8": {
"serializedValue": "110b0002c09a0cfa98d501a0c21e80897a80c78c02080000d00f",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,1,100000,1746493,250000,1000000,2200000,4,0,0,1000]"
},
"R7": {
"serializedValue": "0e200000000000000000000000000000000000000000000000000000000000000000",
"sigmaType": "Coll[SByte]",
"renderedValue": "0000000000000000000000000000000000000000000000000000000000000000"
},
"R9": {
"serializedValue": "1a022103bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b240008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b,0008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b]"
},
"R4": {
"serializedValue": "0e19544553542072734144412043616c6c2049544d2024302e3235",
"sigmaType": "Coll[SByte]",
"renderedValue": "544553542072734144412043616c6c2049544d2024302e3235"
}
}
}
],
"dataInputs": [
{
"boxId": "ac99a1eadde51e62cb48b85731e46ef56136a0a2c2c0d035416475f79e736ef0",
"value": 150000000,
"index": 0,
"outputBlockId": "a0572fca9270bb8f8253e852730a68dadb254a03b7e96cfd573736e51e63a85e",
"outputTransactionId": "16ac127b686954c7163ac83517e271083b90f1e79438d8a2fe08236efef46552",
"outputIndex": 0,
"ergoTree": "10040e20ea7b36e294b1a954a80752eac288711728e5b91b0b3c0596548c755665050b88040004000400d804d6017300d602b2a5730100d603b1e4c67202041ad604b1e4c672020511ea02d1ededededededed938cb2db6308a7730200017201938cb2db6308720273030001720193c27202c2a7927203b1e4c6a7041a927204b1e4c6a70511937203720492c17202c1a7e6c672020608e4c6a70608",
"address": "QqJr1n1xDwmVKHreupY52BAX35qjNng6DCjpvB2dUpqTWaBxXtHJvuY5qFYg25xQ8fQ7kFdodBmXqGDAoRcsJHoaTt4MvjCyAz2hJPpG1Br9o3YBwoFvJyufMSjTwbSVGrPKSxPq9f1TBpJuthgw7rMgd6DDwDndMyiQ4M9wnm9FXuup12EzDTZavsJ1D6bPD81eS1iR6ppRNcVyEbAr95kRkry",
"assets": [],
"additionalRegisters": {
"R4": {
"serializedValue": "1a1520203ef3066a912f35c488487cc2cb94bdb0d30680dab22551c7e6fdbc70dfcc8e207a51950e5f548549ec1aa63ffdc38279505b11e7e803d01bcf8347e0123c88b020050322548722d36f094e341f59ed93eb22118b363eb4efe8c461a52c4d93e2c32048132396ebd00831e603c73cf01e01f248dd1966d2cc976caf52ef76f7ac6e3620e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c29700000000000000000000000000206122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad0000",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[203ef3066a912f35c488487cc2cb94bdb0d30680dab22551c7e6fdbc70dfcc8e,7a51950e5f548549ec1aa63ffdc38279505b11e7e803d01bcf8347e0123c88b0,050322548722d36f094e341f59ed93eb22118b363eb4efe8c461a52c4d93e2c3,48132396ebd00831e603c73cf01e01f248dd1966d2cc976caf52ef76f7ac6e36,e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297,,,,,,,,,,,,,,6122f7289e7bb2df2de273e09d4b2756cda6aeb0f40438dc9d257688f45183ad,,]"
},
"R5": {
"serializedValue": "111580a8d6b9078084af5f80a8d6b90780897a80897a00000000000000000000000080a8d6b907fee5030000",
"sigmaType": "Coll[SLong]",
"renderedValue": "[1000000000,100000000,1000000000,1000000,1000000,0,0,0,0,0,0,0,0,0,0,0,0,1000000000,31103,0,0]"
},
"R6": {
"serializedValue": "08cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b",
"sigmaType": "SSigmaProp",
"renderedValue": "03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b"
}
}
}
],
"outputs": [
{
"boxId": "91012f2827ec89e8adab165d2d2872f284bec6073505e5781cac61c7fdfd1b79",
"transactionId": "7abb20dc20909536b256b5bb31163b2bd6f4d40901d8bbd7607de490e107cd2f",
"blockId": "af8665a29f343cabfe68d7bf79c06e3728c091043f2bae992d26a2ce74306df8",
"value": 9600000,
"index": 0,
"globalIndex": 54253242,
"creationHeight": 1746463,
"settlementHeight": 1746465,
"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: 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)\n8: 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)\n9: 10\n10: 4\n11: 6\n12: 1000000\n13: false\n14: 5\n15: 0\n16: 0\n17: 1\n18: 2\n19: 9\n20: 1\n21: 1\n22: 1\n23: 0\n24: 3\n25: 5\n26: 1\n27: 0\n28: 0\n29: 1\n30: 2\n31: 1\n32: 3\n33: 0\n34: 1000000\n35: 0\n36: 0\n37: 0\n38: 17\n39: 0\n40: 0\n41: 0\n42: 0\n43: 0\n44: 1000\n45: 100\n46: 1000\n47: 0\n48: 100\n49: 0\n50: 2\n51: 0\n52: 17\n53: 1\n54: 2\n55: 17\n56: 0\n57: 17\n58: 1000000\n59: 0\n60: 3\n61: 2000000\n62: 1\n63: 1\n64: 1\n65: 1\n66: 2\n67: 1\n68: 1000000\n69: 0\n70: 1\n71: 2\n72: 1\n73: 0\n74: false\n75: 1\n76: 3\n77: 1000000\n78: 1\n79: 1000000\n80: 1\n81: 1\n82: false\n83: 0\n84: 0\n85: 0\n86: 0\n87: 0\n88: 1000000\n89: 1000000\n90: 0\n91: 0\n92: 1000\n93: 0\n94: 17\n95: 0\n96: 17\n97: 2\n98: 1\n99: 2\n100: 0\n101: true\n102: 1\n103: 2\n104: 0\n105: true\n106: false\n107: 1\n108: 0\n109: 1\n110: 0\n111: 0\n112: 0\n113: 0\n114: 1000000\n115: 0\n116: 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)\n117: 0\n118: 0\n119: 0\n120: true\n121: false\n122: 2\n123: 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 coll17 = placeholder[Coll[Byte]](7)\n val coll18 = placeholder[Coll[Byte]](8)\n val l19 = coll13(placeholder[Int](9))\n val l20 = coll13(placeholder[Int](10))\n val l21 = coll13(placeholder[Int](11))\n val l22 = l21 + placeholder[Long](12)\n val coll23 = SELF.R5[Coll[Byte]].get\n val bool24 = if (coll11 == SELF.propositionBytes) {\n (\n (\n (((box10.value >= l22) && (box10.R4[Coll[Byte]].get == SELF.R4[Coll[Byte]].get)) && (box10.R5[Coll[Byte]].get == coll23)) && (\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](13) }\n val coll25 = SELF.id\n val l26 = coll13(placeholder[Int](14))\n val coll27 = box10.tokens\n val tuple28 = coll27.getOrElse(placeholder[Int](15), tuple4)\n val coll29 = tuple28._1\n val l30 = coll13(placeholder[Int](16))\n val tuple31 = coll27.getOrElse(placeholder[Int](17), tuple4)\n val l32 = tuple5._2\n val l33 = coll13(placeholder[Int](18))\n val l34 = tuple28._2\n val coll35 = tuple31._1\n val l36 = coll13(placeholder[Int](19))\n val box37 = OUTPUTS(placeholder[Int](20))\n val coll38 = box37.tokens\n val bool39 = bool8 && (l32 == placeholder[Long](21))\n val tuple40 = coll3.getOrElse(placeholder[Int](22), tuple4)\n val tuple41 = coll38.getOrElse(placeholder[Int](23), tuple4)\n val l42 = HEIGHT.toLong\n val l43 = coll13(placeholder[Int](24))\n val l44 = l43 + placeholder[Long](25)\n val bool45 = if (coll13(placeholder[Int](26)) == placeholder[Long](27)) { (bool39 && (l42 >= l43)) && (l42 <= l44) } else { bool39 && (l42 <= l44) }\n val l46 = INPUTS.fold(placeholder[Long](28), {(tuple46: (Long, Box)) =>\n val box48 = tuple46._2\n val l49 = tuple46._1\n if (box48.id != coll25) { box48.tokens.fold(l49, {(tuple50: (Long, (Coll[Byte], Long))) =>\n val tuple52 = tuple50._2\n val l53 = tuple50._1\n if (tuple52._1 == coll7) { l53 + tuple52._2 } else { l53 }\n }) } else { l49 }\n })\n val bool47 = ((bool24 && (box10.R7[Coll[Byte]].get == coll7)) && \n val coll47 = coll29\n coll47 == coll6\n ) && (l34 == placeholder[Long](29))\n val l48 = tuple31._2\n prop2 && sigmaProp((bool9 && (OUTPUTS.size == placeholder[Int](30))) && (coll11 == coll12)) || sigmaProp(\n (((if ((bool9 && (INPUTS.size == placeholder[Int](31))) && (OUTPUTS.size == placeholder[Int](32))) {(\n val bool49 = l14 == placeholder[Long](33)\n val l50 = l20 * l19 / placeholder[Long](34)\n val bool51 = l50 > placeholder[Long](35)\n ((((((((((if (bool49) {(\n val box52 = CONTEXT.dataInputs(placeholder[Int](36))\n val i53 = l15.toInt\n val l54 = box52.R5[Coll[Long]].get(i53)\n val bool55 = box52.tokens(placeholder[Int](37))._1 == coll16\n val coll56 = box52.R4[Coll[Coll[Byte]]].get(i53)\n if (l15 == placeholder[Long](38)) { bool55 && (l54 > placeholder[Long](39)) } else { ((bool55 && (coll56.size > placeholder[Int](40))) && (l54 > placeholder[Long](41))) && (coll3(placeholder[Int](42))._1 == coll56) }\n )} else {(\n val coll52 = coll3(placeholder[Int](43))._1\n (coll52 == coll17) || (coll52 == coll18)\n )} && if (bool49) { (l19 == placeholder[Long](44)) || (l19 == placeholder[Long](45)) } else { ((l19 == placeholder[Long](46)) && (coll3(placeholder[Int](47))._1 == coll17)) || ((l19 == placeholder[Long](48)) && (coll3(placeholder[Int](49))._1 == coll18)) }) && bool51) && bool24) && (box10.R7[Coll[Byte]].get == coll25)) && (box10.value == SELF.value - l21 - l26)) && (box10.value >= placeholder[Long](50) * l22)) && (coll29 == coll25)) && \n val bool52 = l30 == placeholder[Long](51)\n ((((((((bool52 && bool49) && (l15 != placeholder[Long](52))) && \n val coll53 = coll35\n coll53 == coll23\n ) && (tuple31 == tuple5)) && (l34 == l32 / l33 + placeholder[Long](53))) && (coll27.size == placeholder[Int](54))) || (((bool52 && bool49) && (l15 == placeholder[Long](55))) && \n val l53 = l33 * CONTEXT.dataInputs(placeholder[Int](56)).R5[Coll[Long]].get(placeholder[Int](57)) / placeholder[Long](58)\n val l54 = SELF.value\n (((l53 > placeholder[Long](59)) && (l34 == l54 - placeholder[Long](60) * l21 - l26 - placeholder[Long](61) / l53 + placeholder[Long](62))) && (coll27.size == placeholder[Int](63))) && (box10.value >= l54 - l21 - l26)\n )) || (((((((l30 == placeholder[Long](64)) && bool49) && (coll35 == coll23)) && (tuple31 == tuple5)) && bool51) && (l34 == l32 / l50 + placeholder[Long](65))) && (coll27.size == placeholder[Int](66)))) || ((l14 == placeholder[Long](67)) && \n val l53 = l36 * l19 / placeholder[Long](68)\n ((((l53 > placeholder[Long](69)) && (coll35 == coll23)) && (tuple31 == tuple5)) && (l34 == l32 / l53 + placeholder[Long](70))) && (coll27.size == placeholder[Int](71))\n )\n ) && (box37.propositionBytes == coll1(placeholder[Int](72)))) && (coll38.size == placeholder[Int](73))) && (box37.value >= l26)\n )} else { placeholder[Boolean](74) } || if (((bool8 && (!bool39)) && (INPUTS.size == placeholder[Int](75))) && (OUTPUTS.size == placeholder[Int](76))) { ((((((((((box10.value == SELF.value - l21 - placeholder[Long](77)) && bool24) && (box10.R7[Coll[Byte]].get == coll7)) && (coll29 == coll6)) && (l34 == placeholder[Long](78))) && (tuple31 == tuple40)) && (box37.propositionBytes == coll12)) && (box37.value == placeholder[Long](79))) && (coll38.size == placeholder[Int](80))) && (tuple41._1 == coll6)) && (tuple41._2 == l32 - placeholder[Long](81)) } else { placeholder[Boolean](82) }) || if ((bool45 && (l14 == placeholder[Long](83))) && (l46 > placeholder[Long](84))) {(\n val box49 = CONTEXT.dataInputs(placeholder[Int](85))\n val i50 = l15.toInt\n val l51 = box49.R5[Coll[Long]].get(i50)\n val bool52 = l51 > placeholder[Long](86)\n val bool53 = box49.tokens(placeholder[Int](87))._1 == coll16\n val l54 = l33 * l51 / placeholder[Long](88)\n val l55 = l20 * l19 / placeholder[Long](89)\n val bool56 = (l54 > placeholder[Long](90)) && (l55 > placeholder[Long](91))\n val l57 = l54 * l46\n val coll58 = if (l19 == placeholder[Long](92)) { coll17 } else { coll18 }\n val l59 = l55 * l46\n val coll60 = box49.R4[Coll[Coll[Byte]]].get(i50)\n val bool61 = (coll60.size > placeholder[Int](93)) || (l15 == placeholder[Long](94))\n if (l30 == placeholder[Long](95)) { if (l15 == placeholder[Long](96)) {(\n val box62 = OUTPUTS(placeholder[Int](97))\n (((((bool53 && bool52) && bool56) && (box37.value >= l57)) && ((box62.propositionBytes == coll12) && box62.tokens.exists({(tuple63: (Coll[Byte], Long)) => (tuple63._1 == coll58) && (tuple63._2 >= l59) }))) && bool47) && (box10.value >= SELF.value - l57 - l21)\n )} else {(\n val tuple62 = coll3(placeholder[Int](98))\n val box63 = OUTPUTS(placeholder[Int](99))\n (((((((((bool53 && bool61) && bool52) && bool56) && (tuple62._1 == coll60)) && coll38.exists({(tuple64: (Coll[Byte], Long)) => (tuple64._1 == coll60) && (tuple64._2 >= l57) })) && ((box63.propositionBytes == coll12) && box63.tokens.exists({(tuple64: (Coll[Byte], Long)) => (tuple64._1 == coll58) && (tuple64._2 >= l59) }))) && if (l48 > placeholder[Long](100)) { coll35 == coll60 } else { placeholder[Boolean](101) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple62._2 - l57)\n )} } else {(\n val tuple62 = coll3(placeholder[Int](102))\n val coll63 = tuple62._1\n val box64 = OUTPUTS(placeholder[Int](103))\n (((((((((bool53 && bool61) && bool52) && bool56) && ((coll63 == coll17) || (coll63 == coll18))) && coll38.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll63) && (tuple65._2 >= l59) })) && ((box64.propositionBytes == coll12) && box64.tokens.exists({(tuple65: (Coll[Byte], Long)) => (tuple65._1 == coll60) && (tuple65._2 >= l57) }))) && if (l48 > placeholder[Long](104)) { coll35 == coll63 } else { placeholder[Boolean](105) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple62._2 - l59)\n )}\n )} else { placeholder[Boolean](106) }) || if ((bool45 && (l14 == placeholder[Long](107))) && (l46 > placeholder[Long](108))) {(\n val tuple49 = coll3(placeholder[Int](109))\n val coll50 = tuple49._1\n val box51 = CONTEXT.dataInputs(placeholder[Int](110))\n val l52 = box51.R8[Coll[Long]].get(l15.toInt)\n val l53 = if (l30 == placeholder[Long](111)) { if (l52 > l20) {(\n val l53 = l52 - l20\n if (l53 > l36) { l36 } else { l53 }\n )} else { placeholder[Long](112) } } else { if (l52 < l20) {(\n val l53 = l20 - l52\n if (l53 > l36) { l36 } else { l53 }\n )} else { placeholder[Long](113) } }\n val l54 = l53 * l19 / placeholder[Long](114)\n val l55 = l54 * l46\n (((((((((coll50 == coll17) || (coll50 == coll18)) && (box51.tokens(placeholder[Int](115))._1 == placeholder[Coll[Byte]](116))) && (l53 > placeholder[Long](117))) && (l54 > placeholder[Long](118))) && coll38.exists({(tuple56: (Coll[Byte], Long)) => (tuple56._1 == coll50) && (tuple56._2 >= l55) })) && if (l48 > placeholder[Long](119)) { coll35 == coll50 } else { placeholder[Boolean](120) }) && bool47) && (box10.value >= SELF.value - l21)) && (l48 >= tuple49._2 - l55)\n )} else { placeholder[Boolean](121) }) || if (l42 > l44) {\n ((((OUTPUTS.size == placeholder[Int](122)) && (coll11 == coll12)) && (box10.value >= SELF.value - l21)) && (coll29 == tuple40._1)) && (l34 == tuple40._2)\n } else { placeholder[Boolean](123) }\n )\n}",
"address": "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",
"assets": [
{
"tokenId": "b46236ab1dcc25c2d0e06b1d4e0471dda00d52487cce1b3f68b1f3e950df5d91",
"index": 0,
"amount": 3,
"name": "TEST rsADA Call ITM $0.25",
"decimals": 6,
"type": "EIP-004"
},
{
"tokenId": "e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297",
"index": 1,
"amount": 250000,
"name": "rsADA",
"decimals": 6,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e20e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297",
"sigmaType": "Coll[SByte]",
"renderedValue": "e023c5f382b6e96fbd878f6811aac73345489032157ad5affb84aefd4956c297"
},
"R6": {
"serializedValue": "0e0136",
"sigmaType": "Coll[SByte]",
"renderedValue": "36"
},
"R8": {
"serializedValue": "110b0002c09a0cfa98d501a0c21e80897a80c78c02080000d00f",
"sigmaType": "Coll[SLong]",
"renderedValue": "[0,1,100000,1746493,250000,1000000,2200000,4,0,0,1000]"
},
"R7": {
"serializedValue": "0e20b46236ab1dcc25c2d0e06b1d4e0471dda00d52487cce1b3f68b1f3e950df5d91",
"sigmaType": "Coll[SByte]",
"renderedValue": "b46236ab1dcc25c2d0e06b1d4e0471dda00d52487cce1b3f68b1f3e950df5d91"
},
"R9": {
"serializedValue": "1a022103bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b240008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b",
"sigmaType": "Coll[Coll[SByte]]",
"renderedValue": "[03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b,0008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b]"
},
"R4": {
"serializedValue": "0e19544553542072734144412043616c6c2049544d2024302e3235",
"sigmaType": "Coll[SByte]",
"renderedValue": "544553542072734144412043616c6c2049544d2024302e3235"
}
},
"spentTransactionId": "e4ce275125759e76a2895381ce54eb291a673803dd73bf748539ce5c1153b196",
"mainChain": true
},
{
"boxId": "b62a08f282c68639c3576cabae155dc77f54296a2c5c8867df2decd8e9713b1e",
"transactionId": "7abb20dc20909536b256b5bb31163b2bd6f4d40901d8bbd7607de490e107cd2f",
"blockId": "af8665a29f343cabfe68d7bf79c06e3728c091043f2bae992d26a2ce74306df8",
"value": 1000000,
"index": 1,
"globalIndex": 54253243,
"creationHeight": 1746463,
"settlementHeight": 1746465,
"ergoTree": "0008cd03bda2691a9f1a2adf122741390847e7dae2c75bd2eb3a0dc896388d4ec3e9577b",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(bda269,1a4560,...)))}",
"address": "9huKeYQbBnuGcndNCSbxUrRc2sRQNCvfyex8nMorKkEwLcQW4vd",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "728a73d92c8f5ca4ae3e16f19ee9b9dfe6ced06c32dcbdfafa573501dc413470",
"mainChain": true
},
{
"boxId": "b59d2f5719969e6cc062702fda8e2467f707f8e7105460d6d1f4bf6eeb3cf237",
"transactionId": "7abb20dc20909536b256b5bb31163b2bd6f4d40901d8bbd7607de490e107cd2f",
"blockId": "af8665a29f343cabfe68d7bf79c06e3728c091043f2bae992d26a2ce74306df8",
"value": 2200000,
"index": 2,
"globalIndex": 54253244,
"creationHeight": 1746463,
"settlementHeight": 1746465,
"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": "01ff6ccb3fa8809adcffe965e550691a28e62b1e46a64235acfc4d4d0346962a",
"mainChain": true
}
],
"size": 2903,
"isUnconfirmed": false
}