Skip to content

Commit

Permalink
Fix typos (#6288)
Browse files Browse the repository at this point in the history
* fix typo

* fix typos

* fix typo

* fix typo
  • Loading branch information
omahs authored Jul 8, 2024
1 parent 79fc117 commit 317b63d
Show file tree
Hide file tree
Showing 4 changed files with 6 additions and 6 deletions.
2 changes: 1 addition & 1 deletion doc/docusaurus/docs/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ All of these elements are used in combination to write Plutus Core scripts that

To develop and deploy a smart contract, you also need off-chain code for building transactions, submitting transactions, deploying smart contracts, querying for available UTXOs on the chain, and so on. You may also want a front-end interface for your smart contract for a better user experience.

Plutus allows all programming to be done from a [single Haskell library](https://intersectmbo.github.io/plutus/haddock/latest). This lets developers build secure applications, forge new assets, and create smart contracts in a predictable, deterministic environment with the highest level of assurance. Furthemore, developers don’t have to run a full Cardano node to test their work.
Plutus allows all programming to be done from a [single Haskell library](https://intersectmbo.github.io/plutus/haddock/latest). This lets developers build secure applications, forge new assets, and create smart contracts in a predictable, deterministic environment with the highest level of assurance. Furthermore, developers don’t have to run a full Cardano node to test their work.

With Plutus you can:

Expand Down
6 changes: 3 additions & 3 deletions doc/docusaurus/docs/reference/common-weaknesses.md
Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ Any application that makes payments to specific parties needs to ensure that tho
### Solutions

It's possible that a solution will be developed that makes this weakness easier to avoid.
In the mean time, there are workarounds that developers can use.
In the meantime, there are workarounds that developers can use.

#### **Unique outputs**

Expand Down Expand Up @@ -117,12 +117,12 @@ Script size should not itself be a risk (since scripts and their sizes should ge

In the long run, hard limits may be increased, removed, or turned into soft limits.

In the mean time, there are some approaches that developers can use to reduce the risk.
In the meantime, there are some approaches that developers can use to reduce the risk.

- **Careful testing**

It is important to test as many of the execution paths of your application as possible.
This is important for correctness, but also to ensure that there are not unexpected cases where script resource usage spikes.
This is important for correctness, but also to ensure that there are no unexpected cases where script resource usage spikes.

- **Bounding data usage**

Expand Down
2 changes: 1 addition & 1 deletion doc/docusaurus/docs/reference/plutus-language-changes.md
Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ Starting with the release of [Cardano node v.8.8.0-pre](https://github.com/Inter
- Well-known and optimal cryptographic algorithms
- Support for porting of smart contracts from Ethereum
- Creating sidechain bridges
- Improving performance by adding a sums of products (SOPs) feature to support the direct encoding of differrent data types.
- Improving performance by adding a sums of products (SOPs) feature to support the direct encoding of different data types.

### Sums of products

Expand Down
2 changes: 1 addition & 1 deletion doc/docusaurus/docs/simple-example/eutxo-model.md
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ It is *not* responsible for such things as deciding whether it can spend a diffe
Consider it a pure function that returns `Bool`.
Checking transaction validity is done by the ledger rules, and updating the state of a smart contract is done by constructing the transaction to produce a new script UTXO with an updated datum.

<!-- talking about "predicatable transaction fees" -->
<!-- talking about "predictable transaction fees" -->

The immutability of UTXOs leads to the extremely useful property of completely predictable transaction fees.
The Plutus script in a transaction can be run off-chain to determine the fee before submitting the transaction onto the blockchain.
Expand Down

1 comment on commit 317b63d

@github-actions
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

⚠️ Performance Alert ⚠️

Possible performance regression was detected for benchmark 'Plutus Benchmarks'.
Benchmark result of this commit is worse than the previous benchmark result exceeding threshold 1.05.

Benchmark suite Current: 317b63d Previous: 79fc117 Ratio
marlowe-semantics/ffdd68a33afd86f8844c9f5e45b2bda5b035aa02274161b23d57709c0f8b8de6 1071 μs 985.8 μs 1.09
marlowe-semantics/f2a8fd2014922f0d8e01541205d47e9bb2d4e54333bdd408cbe7c47c55e73ae4 815.2 μs 768.7 μs 1.06
marlowe-semantics/ecb5e8308b57724e0f8533921693f111eba942123cf8660aac2b5bac21ec28f0 734.6 μs 681.3 μs 1.08
marlowe-semantics/eb4a605ed3a64961e9e66ad9631c2813dadf7131740212762ae4483ec749fe1d 332.2 μs 308.3 μs 1.08
marlowe-semantics/e3afd22d01ff12f381cf915fd32358634e6c413f979f2492cf3339319d8cc079 335.3 μs 310.2 μs 1.08
marlowe-semantics/e34b48f80d49360e88c612f4016f7d68cb5678dd8cd5ddb981375a028b3a40a5 437.4 μs 404 μs 1.08
marlowe-semantics/e26c1cddba16e05fd10c34cbdb16ea6acdbac7c8323256c31c90c520ee6a1080 414 μs 386.3 μs 1.07
marlowe-semantics/dd11ae574eaeab0e9925319768989313a93913fdc347c704ddaa27042757d990 846.3 μs 758.2 μs 1.12
marlowe-semantics/dc241ac6ad1e04fb056d555d6a4f2d08a45d054c6f7f34355fcfeefebef479f3 517.9 μs 465.7 μs 1.11
marlowe-semantics/d64607eb8a1448595081547ea8780886fcbd9e06036460eea3705c88ea867e33 332.3 μs 301.5 μs 1.10
marlowe-semantics/d1c03759810747b7cab38c4296593b38567e11195d161b5bb0a2b58f89b2c65a 1144 μs 1022 μs 1.12
marlowe-semantics/cf542b7df466b228ca2197c2aaa89238a8122f3330fe5b77b3222f570395d9f5 555.7 μs 497.3 μs 1.12
marlowe-semantics/ced1ea04649e093a501e43f8568ac3e6b37cd3eccec8cac9c70a4857b88a5eb8 944.4 μs 841.5 μs 1.12
marlowe-semantics/cdb9d5c233b288a5a9dcfbd8d5c1831a0bb46eec7a26fa31b80ae69d44805efc 993.7 μs 882.3 μs 1.13
marlowe-semantics/c9efcb705ee057791f7c18a1de79c49f6e40ba143ce0579f1602fd780cabf153 926.9 μs 817.6 μs 1.13
marlowe-semantics/c4bb185380df6e9b66fc1ee0564f09a8d1253a51a0c0c7890f2214df9ac19274 843 μs 742.7 μs 1.14
marlowe-semantics/bb5345bfbbc460af84e784b900ec270df1948bb1d1e29eacecd022eeb168b315 1103 μs 972.7 μs 1.13
marlowe-semantics/b50170cea48ee84b80558c02b15c6df52faf884e504d2c410ad63ba46d8ca35c 868.5 μs 763.5 μs 1.14
marlowe-semantics/b21a4df3b0266ad3481a26d3e3d848aad2fcde89510b29cccce81971e38e0835 1535 μs 1354 μs 1.13
marlowe-semantics/ad6db94ed69b7161c7604568f44358e1cc11e81fea90e41afebd669e51bb60c8 668 μs 590.9 μs 1.13
marlowe-semantics/acce04815e8fd51be93322888250060da173eccf3df3a605bd6bc6a456cde871 318.9 μs 283.1 μs 1.13
marlowe-semantics/acb9c83c2b78dabef8674319ad69ba54912cd9997bdf2d8b2998c6bfeef3b122 746.1 μs 657.2 μs 1.14
marlowe-semantics/a9a853b6d083551f4ed2995551af287880ef42aee239a2d9bc5314d127cce592 574.7 μs 514 μs 1.12
marlowe-semantics/a85173a832db3ea944fafc406dfe3fa3235254897d6d1d0e21bc380147687bd5 415.1 μs 370.5 μs 1.12
marlowe-semantics/9fabc4fc3440cdb776b28c9bb1dd49c9a5b1605fe1490aa3f4f64a3fa8881b25 1179 μs 1045 μs 1.13
marlowe-semantics/96e1a2fa3ceb9a402f2a5841a0b645f87b4e8e75beb636692478ec39f74ee221 342.1 μs 303.4 μs 1.13
marlowe-semantics/8d9ae67656a2911ab15a8e5301c960c69aa2517055197aff6b60a87ff718d66c 409 μs 363.3 μs 1.13
marlowe-semantics/8c7fdc3da6822b5112074380003524f50fb3a1ce6db4e501df1086773c6c0201 1317 μs 1158 μs 1.14
marlowe-semantics/82213dfdb6a812b40446438767c61a388d2c0cfd0cbf7fd4a372b0dc59fa17e1 1427 μs 1287 μs 1.11
marlowe-semantics/7cbc5644b745f4ea635aca42cce5e4a4b9d2e61afdb3ac18128e1688c07071ba 544.9 μs 481.6 μs 1.13
marlowe-semantics/7a758e17486d1a30462c32a5d5309bd1e98322a9dcbe277c143ed3aede9d265f 572.9 μs 514.7 μs 1.11
marlowe-semantics/75a8bb183688bce447e00f435a144c835435e40a5defc6f3b9be68b70b4a3db6 787.9 μs 694.1 μs 1.14
marlowe-semantics/7529b206a78becb793da74b78c04d9d33a2540a1abd79718e681228f4057403a 914.2 μs 802.6 μs 1.14
marlowe-semantics/74c67f2f182b9a0a66c62b95d6fac5ace3f7e71ea3abfc52ffbe3ecb93436ea2 905.5 μs 799.6 μs 1.13
marlowe-semantics/71965c9ccae31f1ffc1d85aa20a356d4ed97a420954018d8301ec4f9783be0d7 541.4 μs 478.1 μs 1.13
marlowe-semantics/70f65b21b77ddb451f3df9d9fb403ced3d10e1e953867cc4900cc25e5b9dec47 887 μs 788.4 μs 1.13
marlowe-semantics/6d88f7294dd2b5ce02c3dc609bc7715bd508009738401d264bf9b3eb7c6f49c1 558.8 μs 495.9 μs 1.13
marlowe-semantics/675d63836cad11b547d1b4cddd498f04c919d4342612accf40913f9ae9419fac 1180 μs 1047 μs 1.13
marlowe-semantics/66af9e473d75e3f464971f6879cc0f2ef84bafcb38fbfa1dbc31ac2053628a38 1387 μs 1263 μs 1.10
marlowe-semantics/64c3d5b43f005855ffc4d0950a02fd159aa1575294ea39061b81a194ebb9eaae 757.2 μs 683.9 μs 1.11
marlowe-semantics/5f3d46c57a56cef6764f96c9de9677ac6e494dd7a4e368d1c8dd9c1f7a4309a5 557.2 μs 501.8 μs 1.11
marlowe-semantics/5f306b4b24ff2b39dab6cdc9ac6ca9bb442c1dc6f4e7e412eeb5a3ced42fb642 860.9 μs 778.1 μs 1.11
marlowe-semantics/5f130d19918807b60eab4c03119d67878fb6c6712c28c54f5a25792049294acc 340.7 μs 309.9 μs 1.10
marlowe-semantics/5e2c68ac9f62580d626636679679b97109109df7ac1a8ce86d3e43dfb5e4f6bc 587.2 μs 530.7 μs 1.11
marlowe-semantics/5e274e0f593511543d41570a4b03646c1d7539062b5728182e073e5760561a66 1148 μs 1032 μs 1.11
marlowe-semantics/5d0a88250f13c49c20e146819357a808911c878a0e0a7d6f7fe1d4a619e06112 1174 μs 1062 μs 1.11
marlowe-semantics/5abae75af26f45658beccbe48f7c88e74efdfc0b8409ba1e98f95fa5b6caf999 556.6 μs 502.4 μs 1.11
marlowe-semantics/57728d8b19b0e06412786f3dfed9e1894cd0ad1d2bc2bd497ec0ecb68f989d2b 337.8 μs 308.8 μs 1.09
marlowe-semantics/56333d4e413dbf1a665463bf68067f63c118f38f7539b7ba7167d577c0c8b8ce 889.1 μs 797.9 μs 1.11
marlowe-semantics/55dfe42688ad683b638df1fa7700219f00f53b335a85a2825502ab1e0687197e 338.8 μs 308.1 μs 1.10
marlowe-semantics/53ed4db7ab33d6f907eec91a861d1188269be5ae1892d07ee71161bfb55a7cb7 415 μs 377.8 μs 1.10
marlowe-semantics/52df7c8dfaa5f801cd837faa65f2fd333665fff00a555ce8c55e36ddc003007a 410.9 μs 374.8 μs 1.10
marlowe-semantics/4f9e8d361b85e62db2350dd3ae77463540e7af0d28e1eb68faeecc45f4655f57 453.2 μs 416 μs 1.09
marlowe-semantics/4d7adf91bfc93cebe95a7e054ec17cfbb912b32bd8aecb48a228b50e02b055c8 786.1 μs 710.3 μs 1.11
marlowe-semantics/4c3efd13b6c69112a8a888372d56c86e60c232125976f29b1c3e21d9f537845c 1144 μs 1042 μs 1.10
marlowe-semantics/44a9e339fa25948b48637fe7e10dcfc6d1256319a7b5ce4202cb54dfef8e37e7 334.8 μs 307.1 μs 1.09
marlowe-semantics/3db496e6cd39a8b888a89d0de07dace4397878958cab3b9d9353978b08c36d8a 922.2 μs 848.3 μs 1.09
marlowe-semantics/3bb75b2e53eb13f718eacd3263ab4535f9137fabffc9de499a0de7cabb335479 333.4 μs 307.2 μs 1.09
marlowe-semantics/383683bfcecdab0f4df507f59631c702bd11a81ca3841f47f37633e8aacbb5de 850 μs 782.6 μs 1.09
marlowe-semantics/33c3efd79d9234a78262b52bc6bbf8124cb321a467dedb278328215167eca455 700.1 μs 641.3 μs 1.09
marlowe-semantics/331e4a1bb30f28d7073c54f9a13c10ae19e2e396c299a0ce101ee6bf4b2020db 516.3 μs 473.2 μs 1.09
marlowe-semantics/322acde099bc34a929182d5b894214fc87ec88446e2d10625119a9d17fa3ec3d 335.4 μs 309.1 μs 1.09
marlowe-semantics/30aa34dfbe89e0c43f569929a96c0d2b74c321d13fec0375606325eee9a34a6a 1297 μs 1172 μs 1.11
marlowe-semantics/2f58c9d884813042bce9cf7c66048767dff166785e8b5183c8139db2aa7312d1 857 μs 785.2 μs 1.09
marlowe-semantics/2cb21612178a2d9336b59d06cbf80488577463d209a453048a66c6eee624a695 879.7 μs 802 μs 1.10
marlowe-semantics/28fdce478e179db0e38fb5f3f4105e940ece450b9ce8a0f42a6e313b752e6f2c 1014.9999999999999 μs 953.5 μs 1.06
marlowe-semantics/26e24ee631a6d927ea4fb4fac530cfd82ff7636986014de2d2aaa460ddde0bc3 633.1 μs 577.9 μs 1.10
marlowe-semantics/238b21364ab5bdae3ddb514d7001c8feba128b4ddcf426852b441f9a9d02c882 332.8 μs 306.8 μs 1.08
marlowe-semantics/21953bf8798b28df60cb459db24843fb46782b19ba72dc4951941fb4c20d2263 403.6 μs 370.4 μs 1.09
marlowe-semantics/1d6e3c137149a440f35e0efc685b16bfb8052ebcf66ec4ad77e51c11501381c7 335.7 μs 309.6 μs 1.08
marlowe-semantics/1d56060c3b271226064c672a282663643b1b0823471c67737f0b076870331260 863.2 μs 795.7 μs 1.08
marlowe-semantics/1a573aed5c46d637919ccb5548dfc22a55c9fc38298d567d15ee9f2eea69d89e 1016.9999999999999 μs 924.7 μs 1.10
marlowe-semantics/1a2f2540121f09321216090b2b1f211e3f020c2c133a1a3c3f3c232a26153a04 336.6 μs 309.4 μs 1.09
marlowe-semantics/18cefc240debc0fcab14efdd451adfd02793093efe7bc76d6322aed6ddb582ad 839.3 μs 765.4 μs 1.10
marlowe-semantics/12910f24d994d451ff379b12c9d1ecdb9239c9b87e5d7bea570087ec506935d5 553.9 μs 505.1 μs 1.10
marlowe-semantics/119fbea4164e2bf21d2b53aa6c2c4e79414fe55e4096f5ce2e804735a7fbaf91 849.8 μs 779.4 μs 1.09
marlowe-semantics/0f1d0110001b121d051e15140c0c05141d151c1f1d201c040f10091b020a0e1a 529.8 μs 480.8 μs 1.10
marlowe-semantics/0be82588e4e4bf2ef428d2f44b7687bbb703031d8de696d90ec789e70d6bc1d8 1534 μs 1392 μs 1.10
marlowe-semantics/0bcfd9487614104ec48de2ea0b2c0979866a95115748c026f9ec129384c262c4 1267 μs 1151 μs 1.10
marlowe-semantics/07070c070510030509010e050d00040907050e0a0d06030f1006030701020607 1144 μs 1037 μs 1.10
marlowe-semantics/0705030002040601010206030604080208020207000101060706050502040301 1113 μs 1028 μs 1.08
marlowe-semantics/0543a00ba1f63076c1db6bf94c6ff13ae7d266dd7544678743890b0e8e1add63 1157 μs 1057 μs 1.09
marlowe-semantics/04000f0b04051006000e060f09080d0b090d0104050a0b0f0506070f0a070008 828.8 μs 761.4 μs 1.09
marlowe-semantics/0104010200020000040103020102020004040300030304040400010301040303 864.7 μs 788.5 μs 1.10
marlowe-semantics/0101080808040600020306010000000302050807010208060100070207080202 863.9 μs 786.9 μs 1.10
marlowe-semantics/0101020201010201010200010102000201000201010102000102010201010000 333.5 μs 307.3 μs 1.09
marlowe-semantics/004025fd712d6c325ffa12c16d157064192992faf62e0b991d7310a2f91666b8 898.9 μs 828.5 μs 1.08
marlowe-semantics/0003040402030103010203030303000200000104030002040304020400000102 1172 μs 1058 μs 1.11
marlowe-semantics/0001000101000000010101000001000001010101010100000001000001010000 489.1 μs 454.4 μs 1.08
marlowe-semantics/0000020002010200020101020201000100010001020101020201010000020102 357.8 μs 328.5 μs 1.09
marlowe-role-payout/ff38b1ec89952d0247630f107a90cbbeb92ecbfcd19b284f60255718e4ec7548 228.5 μs 204.3 μs 1.12
marlowe-role-payout/fc8c5f45ffcdb024c21e0f34b22c23de8045a94d5e1a5bda1555c45ddb059f82 198.9 μs 178.4 μs 1.11
marlowe-role-payout/f7275afb60e33a550df13a132102e7e925dd28965a4efbe510a89b077ff9417f 190.6 μs 170.3 μs 1.12
marlowe-role-payout/f53e8cafe26647ccce51e4c31db13608aea1f39034c0f52dee2e5634ef66e747 208.3 μs 185.9 μs 1.12
marlowe-role-payout/f2932e4ca4bbb94b0a9ffbe95fcb7bd5639d9751d75d56d5e14efa5bbed981df 187.9 μs 168 μs 1.12
marlowe-role-payout/f1a1e6a487f91feca5606f72bbb1e948c71abf043c6a0ea83bfea9ec6a0f08d8 189.6 μs 169.7 μs 1.12
marlowe-role-payout/ee3962fbd7373360f46decef3c9bda536a0b1daf6cda3b8a4bcfd6deeb5b4c53 220.6 μs 195.9 μs 1.13
marlowe-role-payout/ec4712ee820eb959a43ebedfab6735f2325fa52994747526ffd2a4f4f84dd58e 217.1 μs 193.3 μs 1.12
marlowe-role-payout/eabeeae18131af89fa57936c0e9eb8d2c7adba534f7e1a517d75410028fa0d6c 190.8 μs 170 μs 1.12
marlowe-role-payout/df487b2fd5c1583fa33644423849bc1ab5f02f37edc0c235f34ef01cb12604f6 198.2 μs 177 μs 1.12
marlowe-role-payout/dc45c5f1b700b1334db99f50823321daaef0e6925b9b2fabbc9df7cde65af62e 199.5 μs 178.2 μs 1.12
marlowe-role-payout/da353bf9219801fa1bf703fc161497570954e9af7e10ffe95c911a9ef97e77bd 198.4 μs 176.4 μs 1.12
marlowe-role-payout/d6bc8ac4155e22300085784148bbc9d9bbfea896e1009dd396610a90e3943032 222.3 μs 196.9 μs 1.13
marlowe-role-payout/d5cda74eb0947e025e02fb8ed365df39d0a43e4b42cd3573ac2d8fcb29115997 212.4 μs 188.6 μs 1.13
marlowe-role-payout/cc1e82927f6c65b3e912200ae30588793d2066e1d4a6627c21955944ac9bd528 218.2 μs 194.6 μs 1.12
marlowe-role-payout/cb2ab8e22d1f64e8d204dece092e90e9bf1fa8b2a6e9cba5012dbe4978065832 192.3 μs 171.8 μs 1.12
marlowe-role-payout/caa409c40e39aed9b0f59214b4baa178c375526dea6026b4552b88d2cc729716 181.9 μs 163.9 μs 1.11
marlowe-role-payout/c99ecc2146ce2066ba6dffc734923264f8794815acbc2ec74c2c2c42ba272e4d 237.6 μs 209.4 μs 1.13
marlowe-role-payout/c78eeba7681d2ab51b4758efa4c812cc041928837c6e7563d8283cce67ce2e02 204.6 μs 182.1 μs 1.12
marlowe-role-payout/c4d4c88c5fe378a25a034025994a0d0b1642f10c8e6e513f872327fa895bfc7e 206 μs 183 μs 1.13
marlowe-role-payout/c11490431db3a92efdda70933ba411a0423935e73a75c856e326dbcf6672f3bf 191.5 μs 171.4 μs 1.12
marlowe-role-payout/bd79f4a84db23b7c4cd219d498bd581e085cbc3437957e74a8862281a700700b 220.3 μs 195.2 μs 1.13
marlowe-role-payout/bd460b7549b70c52e37b312a4242041eac18fe4a266f018bcea0c78a9085a271 222.2 μs 195.9 μs 1.13
marlowe-role-payout/bcdbc576d63b0454100ad06893812edafc2e7e4934fec1b44e2d06eb34f36eb8 190.3 μs 170.3 μs 1.12
marlowe-role-payout/b869f3928200061abb1c3060425b9354b0e08cbf4400b340b8707c14b34317cd 287 μs 251.7 μs 1.14
marlowe-role-payout/b6243a5b4c353ce4852aa41705111d57867d2783eeef76f6d59beb2360da6e90 258.1 μs 227.7 μs 1.13
marlowe-role-payout/b43564af5f13cc5208b92b1ad6d45369446f378d3891e5cb3e353b30d4f3fb10 190.6 μs 170.1 μs 1.12
marlowe-role-payout/af2e072b5adfaa7211e0b341e1f7319c4f4e7364a4247c9247132a927e914753 228.6 μs 202.4 μs 1.13
marlowe-role-payout/a92b4072cb8601fa697e1150c08463b14ffced54eb963df08d322216e27373cb 190.2 μs 170 μs 1.12
marlowe-role-payout/a7cb09f417c3f089619fe25b7624392026382b458486129efcff18f8912bf302 189.4 μs 169.1 μs 1.12
marlowe-role-payout/a6f064b83b31032ea7f25921364727224707268e472a569f584cc6b1d8c017e8 190.6 μs 169.9 μs 1.12
marlowe-role-payout/a6664a2d2a82f370a34a36a45234f6b33120a39372331678a3b3690312560ce9 232.7 μs 204.9 μs 1.14
marlowe-role-payout/a27524cfad019df45e4e8316f927346d4cc39da6bdd294fb2c33c3f58e6a8994 189.6 μs 169.9 μs 1.12
marlowe-role-payout/a1b25347409c3993feca1a60b6fcaf93d1d4bbaae19ab06fdf50cedc26cee68d 182.8 μs 163.6 μs 1.12
marlowe-role-payout/a0fba5740174b5cd24036c8b008cb1efde73f1edae097b9325c6117a0ff40d3b 211 μs 188.1 μs 1.12
marlowe-role-payout/a004a989c005d59043f996500e110fa756ad1b85800b889d5815a0106388e1d7 202.6 μs 180.8 μs 1.12
marlowe-role-payout/996804e90f2c75fe68886fc8511304b8ab9b36785f8858f5cb098e91c159dde9 197.3 μs 176.3 μs 1.12
marlowe-role-payout/962c2c658b19904372984a56409707401e64e9b03c1986647134cfd329ec5139 206.6 μs 183.6 μs 1.13
marlowe-role-payout/8c0fa5d9d6724c5c72c67e055d4bfc36a385ded7c3c81c08cdbd8705829af6e6 229.6 μs 203.4 μs 1.13
marlowe-role-payout/87167fc5469adac97c1be749326fa79a6b7862ce68aa4abcb438e3c034bd0899 226.4 μs 200.2 μs 1.13
marlowe-role-payout/803eae94d62e2afc0e835c204af8362170301bc329e2d849d5f5a47dddf479ec 215.5 μs 190.5 μs 1.13
marlowe-role-payout/7b1dd76edc27f00eb382bf996378155baf74d6a7c6f3d5ec837c39d29784aade 190 μs 169.9 μs 1.12
marlowe-role-payout/73f044f34a30f26639c58bafe952047f74c7bf1eafebab5aadf5b73cfb9024ed 189.7 μs 169.9 μs 1.12
marlowe-role-payout/6d66bddb4269bdf77392d3894da5341cf019d39787522af4f83f01285991e93c 190.6 μs 170 μs 1.12
marlowe-role-payout/6c364699767a84059ffd99cf718562a8c09d96e343f23dc481e8ffda13af424f 189.7 μs 170.1 μs 1.12
marlowe-role-payout/6b7bc2b9002a71b33cfd535d43f26334a283d0b9ad189b7cd74baac232c3b9fc 181.6 μs 163.5 μs 1.11
marlowe-role-payout/674b0577409957172ad85223c765d17e94c27714276c49c38dfae0a47a561a1e 184.8 μs 165.6 μs 1.12
marlowe-role-payout/6621a69217f09d91f42876a9c0cecf79de0e29bdd5b16c82c6c52cf959092ec4 213.7 μs 189.9 μs 1.13
marlowe-role-payout/622a7f3bc611b5149253c9189da022a9ff296f60a5b7c172a6dc286faa7284fa 229 μs 202.7 μs 1.13
marlowe-role-payout/5efe992e306e31cc857c64a62436ad2f9325acc5b4a74a8cebccdfd853ce63d2 196.4 μs 175.1 μs 1.12
marlowe-role-payout/5d4c62a0671c65a14f6a15093e3efc4f1816d95a5a58fd92486bedaae8d9526b 224 μs 198.5 μs 1.13
marlowe-role-payout/5ade103e9530dd0d572fe1b053ea65ad925c6ebbe321e873ace8b804363fa82c 269.6 μs 236.3 μs 1.14
marlowe-role-payout/5a2aae344e569a2c644dd9fa8c7b1f129850937eb562b7748c275f9e40bed596 189.6 μs 169.5 μs 1.12
marlowe-role-payout/5a0725d49c733130eda8bc6ed5234f7f6ff8c9dd2d201e8806125e5fbcc081f9 201.5 μs 178.7 μs 1.13
marlowe-role-payout/4fbcfdb577a56b842d6f6938187a783f71d9da7519353e3da3ef0c564e1eb344 237.5 μs 210 μs 1.13
marlowe-role-payout/4dd7755b6ca1f0c9747c1fc0ee4da799f6f1c07108e980bd9f820911ad711ff2 257.3 μs 226.6 μs 1.14
marlowe-role-payout/49b8275d0cb817be40865694ab05e3cfe5fc35fb43b78e7de68c1f3519b536bd 197.6 μs 176.1 μs 1.12
marlowe-role-payout/47364cfaf2c00f7d633283dce6cf84e4fd4e8228c0a0aa50e7c55f35c3ecaa1c 189.8 μs 169.5 μs 1.12
marlowe-role-payout/46f8d00030436e4da490a86b331fa6c3251425fb8c19556080e124d75bad7bd6 189.9 μs 169.3 μs 1.12
marlowe-role-payout/452e17d16222a427707fa83f63ffb79f606cc25c755a18b1e3274c964ed5ec99 232.7 μs 206.3 μs 1.13
marlowe-role-payout/4299c7fcf093a5dbfe114c188e32ca199b571a7c25cb7f766bf49f12dab308be 208.6 μs 185.4 μs 1.13
marlowe-role-payout/4121d88f14387d33ac5e1329618068e3848445cdd66b29e5ba382be2e02a174a 227.2 μs 201.3 μs 1.13
marlowe-role-payout/3897ef714bba3e6821495b706c75f8d64264c3fdaa58a3826c808b5a768c303d 195.4 μs 174.5 μs 1.12
marlowe-role-payout/371c10d2526fc0f09dbe9ed59e44dcd949270b27dc42035addd7ff9f7e0d05e7 226.7 μs 200.9 μs 1.13
marlowe-role-payout/36866914aa07cf62ef36cf2cd64c7f240e3371e27bb9fff5464301678e809c40 187.9 μs 168.3 μs 1.12
marlowe-role-payout/3569299fc986f5354d02e627a9eaa48ab46d5af52722307a0af72bae87e256dc 188.4 μs 168.4 μs 1.12
marlowe-role-payout/3565ee025317e065e8555eef288080276716366769aad89e03389f5ec4ce26d7 204.3 μs 182.3 μs 1.12
marlowe-role-payout/332c2b1c11383d1b373e1315201f1128010e0e1518332f273f141b23243f2a07 183.2 μs 163.5 μs 1.12
marlowe-role-payout/224ce46046fab9a17be4197622825f45cc0c59a6bd1604405148e43768c487ef 191.6 μs 171.8 μs 1.12
marlowe-role-payout/21a1426fb3fb3019d5dc93f210152e90b0a6e740ef509b1cdd423395f010e0ca 212.3 μs 188.6 μs 1.13
marlowe-role-payout/211e1b6c10260c4620074d2e372c260d38643a3d605f63772524034f0a4a7632 200.3 μs 178.9 μs 1.12
marlowe-role-payout/1a20b465d48a585ffd622bd8dc26a498a3c12f930ab4feab3a5064cfb3bc536a 210 μs 188 μs 1.12
marlowe-role-payout/195f522b596360690d04586a2563470f2214163435331a6622311f7323433f1c 185.5 μs 165.6 μs 1.12
marlowe-role-payout/159e5a1bf16fe984b5569be7011b61b5e98f5d2839ca7e1b34c7f2afc7ffb58e 190.4 μs 170.2 μs 1.12
marlowe-role-payout/121a0a1b12030616111f02121a0e070716090a0e031c071419121f141409031d 185.1 μs 165 μs 1.12
marlowe-role-payout/1138a04a83edc0579053f9ffa9394b41df38230121fbecebee8c039776a88c0c 191.8 μs 171.5 μs 1.12
marlowe-role-payout/0f010d040810040b10020e040f0e030b0a0d100f0c080c0c05000d04100c100f 219.5 μs 195 μs 1.13
marlowe-role-payout/0e97c9d9417354d9460f2eb35018d3904b7b035af16ab299258adab93be0911a 208.4 μs 185.1 μs 1.13
marlowe-role-payout/0e72f62b0f922e31a2340baccc768104025400cf7fdd7dae62fbba5fc770936d 214.4 μs 191 μs 1.12
marlowe-role-payout/0e00171d0f1e1f14070d0a00091f07101808021d081e1b120219081312081e15 193.1 μs 172.3 μs 1.12
marlowe-role-payout/0dbb692d2bf22d25eeceac461cfebf616f54003077a8473abc0457f18e025960 230.1 μs 203.2 μs 1.13
marlowe-role-payout/0d0f01050a0a0a0b0b050d0404090e0d0506000d0a041003040e0f100e0a0408 198.5 μs 176.4 μs 1.13
marlowe-role-payout/0c9d3634aeae7038f839a1262d1a8bc724dc77af9426459417a56ec73240f0e0 199 μs 177.4 μs 1.12
marlowe-role-payout/0bdca1cb8fa7e38e09062557b82490714052e84e2054e913092cd84ac071b961 224.6 μs 198.8 μs 1.13
marlowe-role-payout/07658a6c898ad6d624c37df1e49e909c2e9349ba7f4c0a6be5f166fe239bfcae 181.7 μs 163 μs 1.11
marlowe-role-payout/06317060a8e488b1219c9dae427f9ce27918a9e09ee8ac424afa33ca923f7954 202.1 μs 180.3 μs 1.12
marlowe-role-payout/057ebc80922f16a5f4bf13e985bf586b8cff37a2f6fe0f3ce842178c16981027 187.5 μs 167.6 μs 1.12
marlowe-role-payout/04f592afc6e57c633b9c55246e7c82e87258f04e2fb910c37d8e2417e9db46e5 266.8 μs 235 μs 1.14
marlowe-role-payout/041a2c3b111139201a3a2c173c392b170e16370d300f2d28342d0f2f0e182e01 226.1 μs 200.6 μs 1.13
marlowe-role-payout/0405010105020401010304080005050800040301010800080207080704020206 223.1 μs 197.6 μs 1.13
marlowe-role-payout/0403020000030204010000030001000202010101000304030001040404030100 204 μs 181.7 μs 1.12
marlowe-role-payout/03d730a62332c51c7b70c16c64da72dd1c3ea36c26b41cd1a1e00d39fda3d6cc 221.8 μs 197.6 μs 1.12
marlowe-role-payout/031d56d71454e2c4216ffaa275c4a8b3eb631109559d0e56f44ea8489f57ba97 233.5 μs 205.8 μs 1.13
marlowe-role-payout/0303020000020001010201060303040208070100050401080304020801030001 191.2 μs 171.1 μs 1.12
marlowe-role-payout/0202010002010100020102020102020001010101020102010001010101000100 192 μs 172 μs 1.12
marlowe-role-payout/0201020201020000020000010201020001020200000002010200000101010100 207.2 μs 184.4 μs 1.12
marlowe-role-payout/01dcc372ea619cb9f23c45b17b9a0a8a16b7ca0e04093ef8ecce291667a99a4c 184.1 μs 164.4 μs 1.12
marlowe-role-payout/0101000100000101010000010101000100010101000001000001000000010101 226 μs 200.6 μs 1.13
marlowe-role-payout/0100000100010000000001000100010101000101000001000000010000010000 297 μs 259.7 μs 1.14
marlowe-role-payout/0004000402010401030101030100040000010104020201030001000204020401 207.8 μs 185.7 μs 1.12

This comment was automatically generated by workflow using github-action-benchmark.

CC: @IntersectMBO/plutus-core

Please sign in to comment.