Generated Computers
Locals: an Apple Silicon engine that runs local LLMs up to 3x faster. A product of Generated Computers.
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Polyglot (.NET/JS/Python) library for management of Annotated Research Contexts (ARCs) using an in-memory representation and runtime-agnostic contract systems.
Developer toolsF# · TypeScriptGitHub @nfdi4plantsOnlineOwner unconfirmed
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Read the full README ↗ARCtrl the easy way to read, manipulate and write ARCs in .NET, JavaScript and Python! ❤️
| Version | Downloads |
|---|---|
#r "nuget: ARCtrl"
<PackageReference Include="ARCtrl" Version="1.1.0" />
npm i @nfdi4plants/arctrl
pip install arctrl
Documentation can be found here
Feel free to reference ARCtrl on slides or elsewhere using our logos:
| Square | Horizontal | Vertical |
|---|---|---|
Measured on 13th Gen Intel(R) Core(TM) i7-13800H
| Name | Description | FSharp Time (ms) | JavaScript Time (ms) | Python Time (ms) |
|---|---|---|---|---|
| Table_GetHashCode | From a table with 1 column and 10000 rows, retrieve the Hash Code | 0 ± 0 | 0 ± 1 | 91 ± 12 |
| Table_AddDistinctRows | Add 10000 distinct rows to a table with 4 columns. | 13 ± 2 | 14 ± 4 | 119 ± 12 |
| Table_AddIdenticalRows | Add 10000 identical rows to a table with 4 columns. | 6 ± 2 | 6 ± 1 | 104 ± 6 |
| Table_AddColumnsWithDistinctValues | Add 4 columns with 10000 distinct values each. | 8 ± 3 | 10 ± 1 | 53 ± 1 |
| Table_AddColumnsWithIdenticalValues | Add 4 columns with 10000 identical values each. | 5 ± 1 | 4 ± 0 | 47 ± 1 |
| Table_fillMissingCells | For a table 6 columns and 20000 rows, where each row has one missing value, fill those values with default values. | 0 ± 0 | 2 ± 1 | 6 ± 4 |
| Table_ToJson | Serialize a table with 5 columns and 10000 rows to json, with 3 fixed and 2 variable columns. | 227 ± 64 | 68 ± 18 | 7851 ± 1411 |
| Table_ToCompressedJson | Serialize a table with 5 columns and 10000 rows to compressed json, with 3 fixed and 2 variable columns. | 147 ± 15 | 2878 ± 135 | 6303 ± 1798 |
| Assay_toJson | Parse an assay with one table with 10000 rows and 6 columns to json, with 3 fixed and 3 variable columns. | 330 ± 36 | 88 ± 9 | 12644 ± 550 |
| Assay_fromJson | Parse an assay with one table with 10000 rows and 6 columns from json, with 3 fixed and 3 variable columns. | 355 ± 66 | 61 ± 6 | 6499 ± 1068 |
| Assay_toISAJson | Parse an assay with one table with 10000 rows and 6 columns to json, with 3 fixed and 3 variable columns | 487 ± 36 | 959 ± 36 | 15618 ± 482 |
| Assay_fromISAJson | Parse an assay with one table with 10000 rows and 6 columns from json, with 3 fixed and 3 variable columns | 359 ± 29 | 706 ± 46 | 9587 ± 621 |
| Study_FromWorkbook | Parse a workbook with one study with 10000 rows and 6 columns to an ArcStudy | 29 ± 14 | 62 ± 5 | 818 ± 36 |
| Investigation_ToWorkbook_ManyStudies | Parse an investigation with 1500 studies to a workbook |
| 240 ± 31 |
| 284 ± 24 |
| 3657 ± 199 |
| Investigation_FromWorkbook_ManyStudies | Parse a workbook with 1500 studies to an ArcInvestigation | 127 ± 20 | 498 ± 21 | 9469 ± 412 |
| ARC_ToROCrate | Parse an ARC with one assay with 10000 rows and 6 columns to a RO-Crate metadata file. | 1431 ± 99 | 3526 ± 264 | 61224 ± 728 |