Transaction
ID: 7437f5eed8...2219
Inputs (2)
Spent
Address:
Output transaction:
Settlement height:
Value:
0.108 ERG
Tokens:
Loading assets...
Spent
Address:
Output transaction:
Settlement height:
Value:
0.3211 ERG
Outputs (4)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.208 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.01 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.21 ERG
Transaction Details
Confirmations: 97,067
Total coins transferred: 0.4291 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000402 ERG
Raw Transaction Data
{
"id": "7437f5eed8b738faf6a6587f2cdc37ff6f10f607fb8f5090293b7a3307462219",
"blockId": "fe30041c026cf3fd119974ffcc1e01937cb930a5254689eb8cc29831f7136857",
"inclusionHeight": 1670774,
"timestamp": 1764907677850,
"index": 17,
"globalIndex": 9914016,
"numConfirmations": 97067,
"inputs": [
{
"boxId": "130843a2cbe8349dffbd037515382d3ba7bf2e387bcdbfd235dfc4933e45b327",
"value": 108000000,
"index": 0,
"spendingProof": null,
"outputBlockId": "fe30041c026cf3fd119974ffcc1e01937cb930a5254689eb8cc29831f7136857",
"outputTransactionId": "2bdd3b122fd20e6d8742b0a2d5ef827fd8323c1b9e93d06b2b7f11652b0c8471",
"outputIndex": 0,
"outputGlobalIndex": 52042505,
"outputCreatedAt": 1670770,
"outputSettledAt": 1670774,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 1\n3: 1\n4: 0\n5: 1\n6: 3\n7: 0\n8: 69\n9: 82\n10: 71\n11: 1\n12: 2\n13: 0\n14: 0\n15: 1\n16: 1\n17: 1\n18: 0\n19: 0\n20: 2\n21: 2\n22: 1\n23: 1\n24: 1\n25: 1\n26: 3\n27: 2\n28: 3\n29: 0\n30: 1\n31: 2\n32: 10000000\n33: 10000000\n34: 10000000\n35: 1\n36: 2\n37: 3\n38: 2\n39: 3\n40: 2\n41: 0\n42: 2\n43: 10000000\n44: 10000000\n45: 10000000\n46: 1\n47: false\n48: 1\n49: 0\n50: 1\n51: 2\n52: 0\n53: 1\n54: 0\n55: 10000000\n56: 0\n57: 3\n58: 2\n59: 0\n60: 0\n61: 0\n62: 0\n63: 0\n64: 0\n65: 1\n66: 0\n67: 1\n68: 0\n69: 2\n70: 0\n71: 1\n72: 2\n73: 100\n74: 0\n75: 1\n76: 0\n77: 0\n78: 0\n79: 0\n80: 1\n81: true\n82: 2\n83: 1\n84: 1\n85: 0\n86: 0\n87: 0\n88: 5\n89: 1\n90: 2\n91: 3\n92: 10000000\n93: 2\n94: 10000000\n95: 2\n96: 10000000\n97: 2\n98: 10000000\n99: 3\n100: 2\n101: 0\n102: 0\n103: SigmaProp(ProveDlog(ECPoint(e45f52,5253a8,...)))\n104: 1\n105: 0\n106: 0\n107: 1\n108: 1\n109: 0\n110: true\n111: 0\n112: 0\n113: 0\n114: 0\n115: 0\n116: 0\n117: true\n118: false",
"ergoTreeScript": "{\n val coll1 = SELF.R7[Coll[Int]].get\n val i2 = coll1(placeholder[Int](0))\n val i3 = coll1(placeholder[Int](1)) + i2\n val coll4 = SELF.propositionBytes\n val bool5 = OUTPUTS.filter({(box5: Box) => box5.propositionBytes == coll4 }).size == placeholder[Int](2)\n val bool6 = INPUTS.filter({(box6: Box) => box6.propositionBytes == coll4 }).size == placeholder[Int](3)\n val l7 = SELF.R4[Long].get\n val coll8 = SELF.tokens\n val coll9 = coll8(placeholder[Int](4))._1\n val tuple10 = coll8(placeholder[Int](5))\n val coll11 = tuple10._1\n val i12 = coll1(placeholder[Int](6))\n val coll13 = SELF.R8[Coll[Int]].get\n val coll14 = SELF.R9[Coll[Long]].get\n val l15 = coll14(placeholder[Int](7))\n val coll16 = SELF.R6[Coll[Byte]].get\n val bool17 = coll16 == Coll[Byte](placeholder[Byte](8), placeholder[Byte](9), placeholder[Byte](10))\n val coll18 = SELF.R5[Coll[Byte]].get\n val l19 = coll14(placeholder[Int](11))\n val i20 = coll1(placeholder[Int](12))\n if (HEIGHT < i3) {(\n val box21 = OUTPUTS(placeholder[Int](13))\n val coll22 = box21.tokens\n val i23 = coll22.size\n val tuple24 = coll22(placeholder[Int](14))\n val tuple25 = coll22(placeholder[Int](15))\n val l26 = tuple25._2\n val i27 = i12 + placeholder[Int](16) - l26.toInt\n val coll28 = box21.R8[Coll[Int]].get\n val box29 = OUTPUTS(placeholder[Int](17))\n val b30 = box29.R4[Byte].get\n val i31 = b30.toInt\n val tuple32 = box29.tokens(placeholder[Int](18))\n val opt33 = box29.R6[Coll[Byte]]\n sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (((bool6 && bool5) && (l7 > placeholder[Long](19))) && (OUTPUTS.size >= placeholder[Int](20))) && (i23 >= placeholder[Int](21))\n ) && ((tuple24._1 == coll9) && (tuple24._2 == placeholder[Long](22)))\n ) && ((tuple25._1 == coll11) && (l26 == tuple10._2 - placeholder[Long](23)))\n ) && (i27 <= i12)\n ) && (((coll28(i31) == coll13(i31) + placeholder[Int](24)) && \n val i34 = i31 + placeholder[Int](25) % placeholder[Int](26)\n coll28(i34) == coll13(i34)\n ) && \n val i34 = i31 + placeholder[Int](27) % placeholder[Int](28)\n coll28(i34) == coll13(i34)\n )\n ) && (coll28(placeholder[Int](29)) + coll28(placeholder[Int](30)) + coll28(placeholder[Int](31)) == i27)\n ) && if (bool17) {(\n val l34 = box21.value\n val l35 = SELF.value\n val l36 = l34 - placeholder[Long](32) - l35 - placeholder[Long](33)\n ((((l36 >= l15) && (l34 == l35 + l36)) && (box29.value == placeholder[Long](34))) && (box29.tokens.size == placeholder[Int](35))) && (\n box29.R7[Long].get == l36\n )\n )} else {(\n val i34 = coll8.size\n val bool35 = (i34 >= placeholder[Int](36)) && (i23 >= placeholder[Int](37))\n bool35 && if (bool35) {(\n val tuple36 = coll22(placeholder[Int](38))\n val l37 = tuple36._2\n val l38 = if (i34 >= placeholder[Int](39)) { coll8(placeholder[Int](40))._2 } else { placeholder[Long](41) }\n val l39 = l37 - l38\n (\n (\n (\n ((((coll8(placeholder[Int](42))._1 == coll16) && (tuple36._1 == coll16)) && (l39 >= l15)) && (l37 == l38 + l39)) && (\n (box21.value == placeholder[Long](43)) && (SELF.value == placeholder[Long](44))\n )\n ) && (box29.value == placeholder[Long](45))\n ) && (box29.tokens.size == placeholder[Int](46))\n ) && (box29.R7[Long].get == l39)\n )} else { placeholder[Boolean](47) }\n )}\n ) && (\n (\n (((box21.R4[Long].get == l7) && (box21.R5[Coll[Byte]].get == coll18)) && (box21.R6[Coll[Byte]].get == coll16)) && (\n box21.R7[Coll[Int]].get == coll1\n )\n ) && (box21.R9[Coll[Long]].get == coll14)\n )\n ) && (box21.propositionBytes == coll4)\n ) && ((tuple32._1 == coll11) && (tuple32._2 == placeholder[Long](48)))\n ) && (((b30 == placeholder[Byte](49)) || (b30 == placeholder[Byte](50))) || (b30 == placeholder[Byte](51)))\n ) && box29.R5[Coll[Byte]].isDefined\n ) && (opt33.isDefined && (opt33.get == coll9))\n )\n )} else { if (HEIGHT >= i3 + i20) {(\n val box21 = CONTEXT.dataInputs(placeholder[Int](52))\n val coll22 = box21.tokens\n val l23 = box21.R4[Long].get\n val coll24 = INPUTS.filter({(box24: Box) =>\n val coll26 = box24.tokens\n (coll26.size >= placeholder[Int](53)) && (coll26(placeholder[Int](54))._1 == coll11)\n })\n val i25 = coll24.size\n val tuple26 = if (bool17) {(\n val l26 = SELF.value - placeholder[Long](55)\n (l26, l26 == coll24.fold(placeholder[Long](56), {(tuple27: (Long, Box)) => tuple27._1 + tuple27._2.R7[Long].get }) + l19)\n )} else { if (coll8.size >= placeholder[Int](57)) {(\n val l26 = coll8(placeholder[Int](58))._2\n (l26, l26 == coll24.fold(placeholder[Long](59), {(tuple27: (Long, Box)) => tuple27._1 + tuple27._2.R7[Long].get }) + l19)\n )} else { (placeholder[Long](60), (i25 == placeholder[Int](61)) && (l19 == placeholder[Long](62))) } }\n val box27 = OUTPUTS(placeholder[Int](63))\n val coll28 = box27.tokens\n val i29 = coll28.size\n val tuple30 = coll28(placeholder[Int](64))\n val tuple31 = coll28(placeholder[Int](65))\n val coll32 = box27.R7[Coll[Int]].get\n val i33 = coll32(placeholder[Int](66))\n val coll34 = coll24.filter({(box34: Box) => box34.R4[Byte].get == if (l23 > l7) { placeholder[Byte](67) } else { if (l23 < l7) { placeholder[Byte](68) } else { placeholder[Byte](69) } } })\n val bool35 = coll34.size > placeholder[Int](70)\n val coll36 = box27.R9[Coll[Long]].get\n val l37 = coll36(placeholder[Int](71))\n val l38 = tuple26._1\n val l39 = l38 * placeholder[Int](72).toLong / placeholder[Long](73)\n val l40 = l38 - l39\n val bool41 = l38 > placeholder[Long](74)\n sigmaProp(((((((((((((((((bool6 && bool5) && ((coll22.size >= placeholder[Int](75)) && (coll22(placeholder[Int](76))._1 == coll18))) && (l23 > placeholder[Long](77))) && tuple26._2) && if (i25 > placeholder[Int](78)) { coll24.forall({(box42: Box) =>\n val tuple44 = box42.tokens(placeholder[Int](79))\n (((((tuple44._1 == coll11) && (tuple44._2 == placeholder[Long](80))) && box42.R4[Byte].isDefined) && box42.R5[Coll[Byte]].isDefined) && box42.R6[Coll[Byte]].isDefined) && box42.R7[Long].isDefined\n }) } else { placeholder[Boolean](81) }) && (i29 >= placeholder[Int](82))) && ((tuple30._1 == coll9) && (tuple30._2 == placeholder[Long](83)))) && ((tuple31._1 == coll11) && (tuple31._2 == i12 + placeholder[Int](84).toLong))) && (box27.R8[Coll[Int]].get == Coll[Int](placeholder[Int](85), placeholder[Int](86), placeholder[Int](87)))) && ((((((box27.R4[Long].get == l23) && ((i33 >= HEIGHT) && (i33 <= HEIGHT + placeholder[Int](88)))) && (coll32(placeholder[Int](89)) == i2)) && (coll32(placeholder[Int](90)) == i20)) && (coll32(placeholder[Int](91)) == i12)) && (box27.R6[Coll[Byte]].get == coll16))) && (box27.R5[Coll[Byte]].get == coll18)) && (box27.propositionBytes == coll4)) && if (bool35) { if (bool17) { (box27.value == placeholder[Long](92)) && (i29 == placeholder[Int](93)) } else { (box27.value == placeholder[Long](94)) && (i29 == placeholder[Int](95)) } } else { if (bool17) { (box27.value == placeholder[Long](96) + l37) && (i29 == placeholder[Int](97)) } else { ((box27.value == placeholder[Long](98)) && (i29 == placeholder[Int](99))) && (coll28(placeholder[Int](100))._2 == l37) } }) && (coll36(placeholder[Int](101)) == l15)) && (l37 == if (bool35) { placeholder[Long](102) } else { l40 })) && if (bool41) {(\n val coll42 = OUTPUTS.filter({(box42: Box) => box42.propositionBytes == placeholder[SigmaProp](103).propBytes })\n if (bool17) { (coll42.size >= placeholder[Int](104)) && (coll42(placeholder[Int](105)).value >= l39) } else {(\n val coll43 = coll42(placeholder[Int](106)).tokens.filter({(tuple43: (Coll[Byte], Long)) => tuple43._1 == coll16 })\n ((coll42.size >= placeholder[Int](107)) && (coll43.size >= placeholder[Int](108))) && (coll43(placeholder[Int](109))._2 >= l39)\n )}\n )} else { placeholder[Boolean](110) }) && if (bool35 && bool41) { coll34.forall({(box42: Box) =>\n val coll44 = box42.R5[Coll[Byte]].get\n val coll45 = OUTPUTS.filter({(box45: Box) => box45.propositionBytes == coll44 })\n val l46 = coll34.fold(placeholder[Long](111), {(tuple46: (Long, Box)) =>\n val box48 = tuple46._2\n val l49 = tuple46._1\n if (box48.R5[Coll[Byte]].get == coll44) { l49 + box48.R7[Long].get } else { l49 }\n }) * l40 / coll34.fold(placeholder[Long](112), {(tuple46: (Long, Box)) => tuple46._1 + tuple46._2.R7[Long].get })\n if (bool17) { coll45.fold(placeholder[Long](113), {(tuple47: (Long, Box)) => tuple47._1 + tuple47._2.value }) >= l46 } else { coll45.fold(placeholder[Long](114), {(tuple47: (Long, Box)) =>\n val coll49 = tuple47._2.tokens.filter({(tuple49: (Coll[Byte], Long)) => tuple49._1 == coll16 })\n val l50 = tuple47._1\n if (coll49.size > placeholder[Int](115)) { l50 + coll49(placeholder[Int](116))._2 } else { l50 }\n }) >= l46 }\n }) } else { placeholder[Boolean](117) })\n )} else { sigmaProp(placeholder[Boolean](118)) } }\n}",
"address": "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",
"assets": [
{
"tokenId": "1e7f688ad7c8f2e6cbbd1c0f93fcf889ffea4774c8b10e08382f4f1dc09d7f6c",
"index": 0,
"amount": 1,
"name": " ",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "dfcf04f2b9329acbd0fd09263d557b831cce53370187cbc16b5967d1d83fe107",
"index": 1,
"amount": 11,
"name": " ",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {
"R5": {
"serializedValue": "0e206a2b821b5727e85beb5e78b4efb9f0250d59cd48481d2ded2c23e91ba1d07c66",
"sigmaType": "Coll[SByte]",
"renderedValue": "6a2b821b5727e85beb5e78b4efb9f0250d59cd48481d2ded2c23e91ba1d07c66"
},
"R6": {
"serializedValue": "0e03455247",
"sigmaType": "Coll[SByte]",
"renderedValue": "455247"
},
"R8": {
"serializedValue": "1003000000",
"sigmaType": "Coll[SInt]",
"renderedValue": "[0,0,0]"
},
"R7": {
"serializedValue": "1004ecf9cb010a0a14",
"sigmaType": "Coll[SInt]",
"renderedValue": "[1670774,5,5,10]"
},
"R9": {
"serializedValue": "11028084af5f80f2ba5d",
"sigmaType": "Coll[SLong]",
"renderedValue": "[100000000,98000000]"
},
"R4": {
"serializedValue": "05d0ecd0d40e",
"sigmaType": "SLong",
"renderedValue": "1967790888"
}
}
},
{
"boxId": "0296d8dc851b44e9184a9f8d0193103581454711d0e011dd08aea8d2b57074b9",
"value": 321100000,
"index": 1,
"spendingProof": "0b0a9c30862b9c70fa6f0c66b7559d3ac648ea4ec20a504982e6073f3eee24b40cd2ea32e37e691d025ac7863c4faafeabbbb0b8def05c0b",
"outputBlockId": "fb4f225bede5d477f4b3e393ac297cf7974c06fd17b4d3fc1577bca7ba0c20b3",
"outputTransactionId": "9b5873e6cecb67393c39d50e8c989e1d1659aaccae473d2b87ba4e0ec33ec453",
"outputIndex": 3,
"outputGlobalIndex": 52033575,
"outputCreatedAt": 1670464,
"outputSettledAt": 1670465,
"ergoTree": "0008cd03848b1c77c1a24096d22ef8a8f78f4d97a5f411ff2d89138bc8bdca2cbac600b2",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(848b1c,dd161b,...)))}",
"address": "9hUBLNSpZWLngoKBCUMpR6cG28UEJ97ogcsw2AJDNF47BBdycTR",
"assets": [],
"additionalRegisters": {}
}
],
"dataInputs": [],
"outputs": [
{
"boxId": "2385230f179910ab0cfb0bc3a1b1489a2150949d4af55728f1d24ce7412bf163",
"transactionId": "7437f5eed8b738faf6a6587f2cdc37ff6f10f607fb8f5090293b7a3307462219",
"blockId": "fe30041c026cf3fd119974ffcc1e01937cb930a5254689eb8cc29831f7136857",
"value": 208000000,
"index": 0,
"globalIndex": 52042521,
"creationHeight": 1670773,
"settlementHeight": 1670774,
"ergoTree": "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",
"ergoTreeConstants": "0: 1\n1: 0\n2: 1\n3: 1\n4: 0\n5: 1\n6: 3\n7: 0\n8: 69\n9: 82\n10: 71\n11: 1\n12: 2\n13: 0\n14: 0\n15: 1\n16: 1\n17: 1\n18: 0\n19: 0\n20: 2\n21: 2\n22: 1\n23: 1\n24: 1\n25: 1\n26: 3\n27: 2\n28: 3\n29: 0\n30: 1\n31: 2\n32: 10000000\n33: 10000000\n34: 10000000\n35: 1\n36: 2\n37: 3\n38: 2\n39: 3\n40: 2\n41: 0\n42: 2\n43: 10000000\n44: 10000000\n45: 10000000\n46: 1\n47: false\n48: 1\n49: 0\n50: 1\n51: 2\n52: 0\n53: 1\n54: 0\n55: 10000000\n56: 0\n57: 3\n58: 2\n59: 0\n60: 0\n61: 0\n62: 0\n63: 0\n64: 0\n65: 1\n66: 0\n67: 1\n68: 0\n69: 2\n70: 0\n71: 1\n72: 2\n73: 100\n74: 0\n75: 1\n76: 0\n77: 0\n78: 0\n79: 0\n80: 1\n81: true\n82: 2\n83: 1\n84: 1\n85: 0\n86: 0\n87: 0\n88: 5\n89: 1\n90: 2\n91: 3\n92: 10000000\n93: 2\n94: 10000000\n95: 2\n96: 10000000\n97: 2\n98: 10000000\n99: 3\n100: 2\n101: 0\n102: 0\n103: SigmaProp(ProveDlog(ECPoint(e45f52,5253a8,...)))\n104: 1\n105: 0\n106: 0\n107: 1\n108: 1\n109: 0\n110: true\n111: 0\n112: 0\n113: 0\n114: 0\n115: 0\n116: 0\n117: true\n118: false",
"ergoTreeScript": "{\n val coll1 = SELF.R7[Coll[Int]].get\n val i2 = coll1(placeholder[Int](0))\n val i3 = coll1(placeholder[Int](1)) + i2\n val coll4 = SELF.propositionBytes\n val bool5 = OUTPUTS.filter({(box5: Box) => box5.propositionBytes == coll4 }).size == placeholder[Int](2)\n val bool6 = INPUTS.filter({(box6: Box) => box6.propositionBytes == coll4 }).size == placeholder[Int](3)\n val l7 = SELF.R4[Long].get\n val coll8 = SELF.tokens\n val coll9 = coll8(placeholder[Int](4))._1\n val tuple10 = coll8(placeholder[Int](5))\n val coll11 = tuple10._1\n val i12 = coll1(placeholder[Int](6))\n val coll13 = SELF.R8[Coll[Int]].get\n val coll14 = SELF.R9[Coll[Long]].get\n val l15 = coll14(placeholder[Int](7))\n val coll16 = SELF.R6[Coll[Byte]].get\n val bool17 = coll16 == Coll[Byte](placeholder[Byte](8), placeholder[Byte](9), placeholder[Byte](10))\n val coll18 = SELF.R5[Coll[Byte]].get\n val l19 = coll14(placeholder[Int](11))\n val i20 = coll1(placeholder[Int](12))\n if (HEIGHT < i3) {(\n val box21 = OUTPUTS(placeholder[Int](13))\n val coll22 = box21.tokens\n val i23 = coll22.size\n val tuple24 = coll22(placeholder[Int](14))\n val tuple25 = coll22(placeholder[Int](15))\n val l26 = tuple25._2\n val i27 = i12 + placeholder[Int](16) - l26.toInt\n val coll28 = box21.R8[Coll[Int]].get\n val box29 = OUTPUTS(placeholder[Int](17))\n val b30 = box29.R4[Byte].get\n val i31 = b30.toInt\n val tuple32 = box29.tokens(placeholder[Int](18))\n val opt33 = box29.R6[Coll[Byte]]\n sigmaProp(\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (\n (((bool6 && bool5) && (l7 > placeholder[Long](19))) && (OUTPUTS.size >= placeholder[Int](20))) && (i23 >= placeholder[Int](21))\n ) && ((tuple24._1 == coll9) && (tuple24._2 == placeholder[Long](22)))\n ) && ((tuple25._1 == coll11) && (l26 == tuple10._2 - placeholder[Long](23)))\n ) && (i27 <= i12)\n ) && (((coll28(i31) == coll13(i31) + placeholder[Int](24)) && \n val i34 = i31 + placeholder[Int](25) % placeholder[Int](26)\n coll28(i34) == coll13(i34)\n ) && \n val i34 = i31 + placeholder[Int](27) % placeholder[Int](28)\n coll28(i34) == coll13(i34)\n )\n ) && (coll28(placeholder[Int](29)) + coll28(placeholder[Int](30)) + coll28(placeholder[Int](31)) == i27)\n ) && if (bool17) {(\n val l34 = box21.value\n val l35 = SELF.value\n val l36 = l34 - placeholder[Long](32) - l35 - placeholder[Long](33)\n ((((l36 >= l15) && (l34 == l35 + l36)) && (box29.value == placeholder[Long](34))) && (box29.tokens.size == placeholder[Int](35))) && (\n box29.R7[Long].get == l36\n )\n )} else {(\n val i34 = coll8.size\n val bool35 = (i34 >= placeholder[Int](36)) && (i23 >= placeholder[Int](37))\n bool35 && if (bool35) {(\n val tuple36 = coll22(placeholder[Int](38))\n val l37 = tuple36._2\n val l38 = if (i34 >= placeholder[Int](39)) { coll8(placeholder[Int](40))._2 } else { placeholder[Long](41) }\n val l39 = l37 - l38\n (\n (\n (\n ((((coll8(placeholder[Int](42))._1 == coll16) && (tuple36._1 == coll16)) && (l39 >= l15)) && (l37 == l38 + l39)) && (\n (box21.value == placeholder[Long](43)) && (SELF.value == placeholder[Long](44))\n )\n ) && (box29.value == placeholder[Long](45))\n ) && (box29.tokens.size == placeholder[Int](46))\n ) && (box29.R7[Long].get == l39)\n )} else { placeholder[Boolean](47) }\n )}\n ) && (\n (\n (((box21.R4[Long].get == l7) && (box21.R5[Coll[Byte]].get == coll18)) && (box21.R6[Coll[Byte]].get == coll16)) && (\n box21.R7[Coll[Int]].get == coll1\n )\n ) && (box21.R9[Coll[Long]].get == coll14)\n )\n ) && (box21.propositionBytes == coll4)\n ) && ((tuple32._1 == coll11) && (tuple32._2 == placeholder[Long](48)))\n ) && (((b30 == placeholder[Byte](49)) || (b30 == placeholder[Byte](50))) || (b30 == placeholder[Byte](51)))\n ) && box29.R5[Coll[Byte]].isDefined\n ) && (opt33.isDefined && (opt33.get == coll9))\n )\n )} else { if (HEIGHT >= i3 + i20) {(\n val box21 = CONTEXT.dataInputs(placeholder[Int](52))\n val coll22 = box21.tokens\n val l23 = box21.R4[Long].get\n val coll24 = INPUTS.filter({(box24: Box) =>\n val coll26 = box24.tokens\n (coll26.size >= placeholder[Int](53)) && (coll26(placeholder[Int](54))._1 == coll11)\n })\n val i25 = coll24.size\n val tuple26 = if (bool17) {(\n val l26 = SELF.value - placeholder[Long](55)\n (l26, l26 == coll24.fold(placeholder[Long](56), {(tuple27: (Long, Box)) => tuple27._1 + tuple27._2.R7[Long].get }) + l19)\n )} else { if (coll8.size >= placeholder[Int](57)) {(\n val l26 = coll8(placeholder[Int](58))._2\n (l26, l26 == coll24.fold(placeholder[Long](59), {(tuple27: (Long, Box)) => tuple27._1 + tuple27._2.R7[Long].get }) + l19)\n )} else { (placeholder[Long](60), (i25 == placeholder[Int](61)) && (l19 == placeholder[Long](62))) } }\n val box27 = OUTPUTS(placeholder[Int](63))\n val coll28 = box27.tokens\n val i29 = coll28.size\n val tuple30 = coll28(placeholder[Int](64))\n val tuple31 = coll28(placeholder[Int](65))\n val coll32 = box27.R7[Coll[Int]].get\n val i33 = coll32(placeholder[Int](66))\n val coll34 = coll24.filter({(box34: Box) => box34.R4[Byte].get == if (l23 > l7) { placeholder[Byte](67) } else { if (l23 < l7) { placeholder[Byte](68) } else { placeholder[Byte](69) } } })\n val bool35 = coll34.size > placeholder[Int](70)\n val coll36 = box27.R9[Coll[Long]].get\n val l37 = coll36(placeholder[Int](71))\n val l38 = tuple26._1\n val l39 = l38 * placeholder[Int](72).toLong / placeholder[Long](73)\n val l40 = l38 - l39\n val bool41 = l38 > placeholder[Long](74)\n sigmaProp(((((((((((((((((bool6 && bool5) && ((coll22.size >= placeholder[Int](75)) && (coll22(placeholder[Int](76))._1 == coll18))) && (l23 > placeholder[Long](77))) && tuple26._2) && if (i25 > placeholder[Int](78)) { coll24.forall({(box42: Box) =>\n val tuple44 = box42.tokens(placeholder[Int](79))\n (((((tuple44._1 == coll11) && (tuple44._2 == placeholder[Long](80))) && box42.R4[Byte].isDefined) && box42.R5[Coll[Byte]].isDefined) && box42.R6[Coll[Byte]].isDefined) && box42.R7[Long].isDefined\n }) } else { placeholder[Boolean](81) }) && (i29 >= placeholder[Int](82))) && ((tuple30._1 == coll9) && (tuple30._2 == placeholder[Long](83)))) && ((tuple31._1 == coll11) && (tuple31._2 == i12 + placeholder[Int](84).toLong))) && (box27.R8[Coll[Int]].get == Coll[Int](placeholder[Int](85), placeholder[Int](86), placeholder[Int](87)))) && ((((((box27.R4[Long].get == l23) && ((i33 >= HEIGHT) && (i33 <= HEIGHT + placeholder[Int](88)))) && (coll32(placeholder[Int](89)) == i2)) && (coll32(placeholder[Int](90)) == i20)) && (coll32(placeholder[Int](91)) == i12)) && (box27.R6[Coll[Byte]].get == coll16))) && (box27.R5[Coll[Byte]].get == coll18)) && (box27.propositionBytes == coll4)) && if (bool35) { if (bool17) { (box27.value == placeholder[Long](92)) && (i29 == placeholder[Int](93)) } else { (box27.value == placeholder[Long](94)) && (i29 == placeholder[Int](95)) } } else { if (bool17) { (box27.value == placeholder[Long](96) + l37) && (i29 == placeholder[Int](97)) } else { ((box27.value == placeholder[Long](98)) && (i29 == placeholder[Int](99))) && (coll28(placeholder[Int](100))._2 == l37) } }) && (coll36(placeholder[Int](101)) == l15)) && (l37 == if (bool35) { placeholder[Long](102) } else { l40 })) && if (bool41) {(\n val coll42 = OUTPUTS.filter({(box42: Box) => box42.propositionBytes == placeholder[SigmaProp](103).propBytes })\n if (bool17) { (coll42.size >= placeholder[Int](104)) && (coll42(placeholder[Int](105)).value >= l39) } else {(\n val coll43 = coll42(placeholder[Int](106)).tokens.filter({(tuple43: (Coll[Byte], Long)) => tuple43._1 == coll16 })\n ((coll42.size >= placeholder[Int](107)) && (coll43.size >= placeholder[Int](108))) && (coll43(placeholder[Int](109))._2 >= l39)\n )}\n )} else { placeholder[Boolean](110) }) && if (bool35 && bool41) { coll34.forall({(box42: Box) =>\n val coll44 = box42.R5[Coll[Byte]].get\n val coll45 = OUTPUTS.filter({(box45: Box) => box45.propositionBytes == coll44 })\n val l46 = coll34.fold(placeholder[Long](111), {(tuple46: (Long, Box)) =>\n val box48 = tuple46._2\n val l49 = tuple46._1\n if (box48.R5[Coll[Byte]].get == coll44) { l49 + box48.R7[Long].get } else { l49 }\n }) * l40 / coll34.fold(placeholder[Long](112), {(tuple46: (Long, Box)) => tuple46._1 + tuple46._2.R7[Long].get })\n if (bool17) { coll45.fold(placeholder[Long](113), {(tuple47: (Long, Box)) => tuple47._1 + tuple47._2.value }) >= l46 } else { coll45.fold(placeholder[Long](114), {(tuple47: (Long, Box)) =>\n val coll49 = tuple47._2.tokens.filter({(tuple49: (Coll[Byte], Long)) => tuple49._1 == coll16 })\n val l50 = tuple47._1\n if (coll49.size > placeholder[Int](115)) { l50 + coll49(placeholder[Int](116))._2 } else { l50 }\n }) >= l46 }\n }) } else { placeholder[Boolean](117) })\n )} else { sigmaProp(placeholder[Boolean](118)) } }\n}",
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"sigmaType": "Coll[SByte]",
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"sigmaType": "Coll[SInt]",
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"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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"spentTransactionId": "0de3bc89d377968dfad3bb97b5a698c6e557ed6c65bbb2342bb274857cc021f4",
"mainChain": true
},
{
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"index": 3,
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"creationHeight": 1670773,
"settlementHeight": 1670774,
"ergoTree": "0008cd03848b1c77c1a24096d22ef8a8f78f4d97a5f411ff2d89138bc8bdca2cbac600b2",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(848b1c,dd161b,...)))}",
"address": "9hUBLNSpZWLngoKBCUMpR6cG28UEJ97ogcsw2AJDNF47BBdycTR",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "52413e4da8323db011130909291cfee5022457cbf3b5d51e5815977a82d4d34e",
"mainChain": true
}
],
"size": 2738,
"isUnconfirmed": false
}