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61a2e79717133e8dc8db9152349e9543436c0df8c98aed7f3a6a77b44e83e192 | Text | 9,209 | 214 |
# Data overview
- directories
- *annotation*: gene annotations, sample and metastasis pair annotation, pathway definitions
- *bin*: covTest R package and regNet R package (installation see below)
- *conda*: the required conda environment will be installed there (installation see below)
- *data*: expression an... |
9222a678d61846ebcd26673a860ff5afa104808a3a91b86f44e78b9e53586d61 | Text | 9,235 | 136 | # EGGNet: Equivariant Graph-of-Graphs Neural Network
Source code for "[EGGNet, a generalizable geometric deep learning framework for protein complex pose scoring](https://www.biorxiv.org/content/10.1101/2023.03.22.533800v1)"
<img src="figs/GoGs_of_molecules.png">
## Dependencies
All experiments were performed in Py... |
a34e1fec51bf6e2c8e9640be78241c4d7d6050e03754c2e2c2fff4c389ed561e | Text | 9,394 | 149 | # GraphRBF
GraphRBF is a state-of-the-art protein-protein/nucleic acid interaction site prediction model built by enhanced graph neural networks and prioritized radial basis function neural networks.
This project serves users to use our software to directly predict protein binding sites or train our model on a new d... |
14e9395ec1cedca4c8a3a9e74856306f732999a9c04b8cf9ff79ff48dc399caa | Text | 9,399 | 198 | # DeepErwin
DeepErwin is python package that implements and optimizes wave function models for numerical solutions to the multi-electron Schrödinger equation.
DeepErwin is based on JAX and supports:
- Optimizing a wavefunction for a single nuclear geometry
- Optimizing wavefunctions for multiple nuclear geometries in... |
9806034715dba81c7569e642e6800888b00ba5127e7477d0ec1a648df8fd8b65 | Text | 9,518 | 152 | # Higher handgrip strength is linked to higher salience/ventral attention functional network segregation in older adults
## Background
Converging evidence suggests that handgrip strength is linked to cognitive functioning in older adults, and this may be subserved by shared changes in brain function and structure in ... |
e1e29fbc2807fcb4449e731070b7871c38abef309816a3fbb139e7a3ba3f568b | Text | 9,592 | 197 | # ModelAngelo
ModelAngelo is an automatic atomic model building program for cryo-EM maps.
## Compute requirements
It is highly recommended to have access to GPUs with at least 8GB of memory. ModelAngelo performs well on NVIDIA GPUs such as 2080's and beyond.
Please note that the weight files required by both ModelA... |
2e5d55b1c9650d80093803fe13a4a219a4a8a4f125f5e0e34f4ef774c58485a2 | Text | 9,728 | 227 | # Scythe - A Bayesian adapter trimmer (version 0.994 BETA)
Scythe and all supporting documentation
Copyright (c) Vince Buffalo, 2011-2014
Contact: Vince Buffalo <vsbuffaloAAAAA@gmail.com> (with the poly-A tail removed)
If you wish to report a bug, please open an issue on Github
(http://github.com/vsbuffalo/scythe/is... |
0f4907dea65cf6990396b6e8e9159d9dee133f21557aef30e7066ac129c1ea29 | Text | 9,798 | 188 | <div align="center">
<a href="http://www.dietrich-lab.org/vocalmat"><img src="resources/logo.gif" title="VocalMat - Dietrich Lab" alt="VocalMat - Dietrich Lab"></a>
</div>
<div align="center">
<strong>Analysis of ultrasonic vocalizations from mice using computer vision and machine learning</strong>
</div>
<di... |
fc1ff5b8536d76d7a526abf530b140a9a9865cd02b8eb4f77db882e0bbdcf9ba | Text | 9,879 | 107 | # Welcome to the repository for the Behaviour Change Intervention Ontology (BCIO)
The Human Behaviour Change Project (HBCP) funded by the Wellcome Trust, and the Advancing Prevention Research in Cancer through Ontology Tools (APRICOT) Project funded by the US National Institutes of Health, are developing the Behaviour... |
4cb0e5f3bc9523f1865c8361495e0893a52448050c7e7cc790dd97ca40f88726 | Text | 10,206 | 207 | # EduOptimNeurostim
GP-BO optimization of neurostimulation.
Educational material.
Companion code for the following paper : https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(23)00118-0#secsectitle0255
Please cite the present code as:
Bonizzato M.<sup>&</sup>, Macar U.<sup>&</sup>, Guay-Hottin R., Choin... |
da250db4abd28c3ee2fe13afca043ab39a40c8881d89aff8602cdba006ae9ee4 | Text | 10,242 | 148 | =====================================================
Collaborative Brain Wave Analysis Pipeline (Cobrawap)
=====================================================
.. image:: https://readthedocs.org/projects/cobrawap/badge/?version=latest
:target: https://cobrawap.readthedocs.io/en/latest/?badge=latest
:alt: Docum... |
5c09f4ad995682169e2b0f083f4b1db7bef79b94bcea661965045149bd46883c | Text | 10,479 | 217 | ## Modelling Phenomenological Differences in Aetiologically Distinct Visual Hallucinations Using Deep Neural Networks
This repository contains source code necessary to reproduce some of the main results in the paper:
[Suzuki K](https://sites.google.com/view/keisukesuzuki/), David S, [Seth A](https://www.anilseth.com/... |
40661b8d4cd873fd258109a8e63a657cd3368ef97057ea2e5c466c5a2e41810e | Text | 10,489 | 196 | # STRT2-NextSeq analysis pipeline
A pipeline for the analysis of STRT2 RNA-sequencing outputs from NextSeq.
Please cite [Ezer et al. 2021](https://doi.org/10.1016/j.xpro.2021.100995) when you use this pipeline.
```
Ezer S, Yoshihara M, Katayama S, DoGA consortium, Daub C, Lohi H, Krjutskov K, Kere J.
Generation of ... |
6f2f239bf76fc3946934a3d0740199234e7316189bc98fa48e830989bf750d5f | Text | 10,494 | 112 | Article: Sexual coordination in a whole-brain map of prairie vole pair bonding (2023) eLife (DOI: https://doi.org/10.7554/eLife.87029.1)
Authors: Morgan L. Gustison, Rodrigo Munoz-Castenada, Pavel Osten, Steven Phelps
Description: This project contains the prairie vole reference brain and atlas, source data, output f... |
b6eb8830e314c865ed7d33217ad0a6bd6da7110619ce1b6e88bd8c246c6d16e6 | Text | 10,545 | 217 | REINVENT 4
==========
Description
-----------
REINVENT is a molecular design tool for de novo design, scaffold hopping,
R-group replacement, linker design, molecule optimization, and other small
molecule design tasks. REINVENT uses a Reinforcement Learning
(RL) algorithm to generate optimized molecules compliant wi... |
c15125dd90eaeb2c037a550aa0fb289782276417e33e49e8844282c4d63901e9 | Text | 10,602 | 141 | 
# Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling
A reposit... |
0a662fa91c0d2e7e74cf3bf5fbdd2acb2a5a7ec3ce19fb5d6904e5dcf39e95de | Text | 10,708 | 151 | =====================================================
Collaborative Brain Wave Analysis Pipeline (Cobrawap)
=====================================================
.. image:: https://readthedocs.org/projects/cobrawap/badge/?version=latest
:target: https://cobrawap.readthedocs.io/en/latest/?badge=latest
:alt: Docum... |
4d0dc29035188e0fdd2b7e16645defe7b8ac483604235277463688da73acb4ef | Text | 10,708 | 151 | =====================================================
Collaborative Brain Wave Analysis Pipeline (Cobrawap)
=====================================================
.. image:: https://readthedocs.org/projects/cobrawap/badge/?version=latest
:target: https://cobrawap.readthedocs.io/en/latest/?badge=latest
:alt: Docum... |
d5d2a94d04f7f979bbe1079c9f119542931bf0a8fe931a88724b26f16f63e1f0 | Text | 10,835 | 125 | # LF-LDA and LF-DMM latent feature topic models
The implementations of the LF-LDA and LF-DMM latent feature topic models, as described in my TACL paper:
Dat Quoc Nguyen, Richard Billingsley, Lan Du and Mark Johnson. [Improving Topic Models with Latent Feature Word Representations](https://tacl2013.cs.columbia.edu/ojs... |
92aaf73dcea49a7cc29a0a4dd15f82b995e28f38a2c34b81007db1bd9bd41326 | Text | 11,136 | 134 | # sleepy
`sleepy` is a Python tool for semi-automatic slow wave detection and labelling. It has multiple slow wave detection algorithms built in. Its user interface can help to quickly eliminate false-positives generated by these detection algorithms.
The project consists of two parts. The `sleepy.processing` packag... |
2f3795b113e934b0b5b1a7f79715cc7911591288be77cf1069c40dfb2550398c | Text | 11,428 | 294 | # neuroHarmonize
Harmonization tools for multi-site neuroimaging analysis. Part of the work
reported in our paper with data from the ISTAGING consoritum [^1].
> **⚠️ IMPORTANT - Version 2.5.1 NIFTI Precision Fix**
> If you have previously harmonized NIFTI images using `applyModelNIFTIs` with versions < 2.5.1, **you... |
f44ba554cbf4ab5e18ea3c479221cffd7574cbbb2190ce0b95cdd17a9f86a1b5 | Text | 11,497 | 194 | <p align="center">
<img src="https://github.com/divelab/DIG/blob/main/imgs/DIG-logo.jpg" width="500" class="center" alt="logo"/>
<br/>
</p>
[pypi-image]:https://badge.fury.io/py/dive-into-graphs.svg
[pypi-url]:https://pypi.org/project/dive-into-graphs/
[docs-image]: https://readthedocs.org/projects/diveintogra... |
95fcf1027d0ddf67ba5a8c7c275319191b0f799c0147876fb4edc2f756295966 | Text | 11,829 | 195 | # foldingdiff - Diffusion model for protein backbone generation
[](https://github.com/psf/black) 
We present a diffusion model for gener... |
092d50dde8aa12a73297f7cbe2a44bd3a3727bf8aaff2bccd3e04419b301ecf8 | Text | 11,926 | 195 | # foldingdiff - Diffusion model for protein backbone generation
[](https://zenodo.org/doi/10.5281/zenodo.10365889) [](https://github.com/psf/black) .**
# Nenufaar v2.4
DNA NGS pipeline
- [Goals](#goals)
- [Install & Config](#install--config)
- [Warning](#warning)
- [Architecture](#architecture)
-... |
4ed726f9f5db7968a27a86c91939dfa53dd4ba0890c30b5ad7bb262200f37dee | Text | 12,584 | 202 | # HyperPCM
[](https://www.python.org/downloads/release/python-390/)
[](https://pytorch.org/get-started/previous-versions/)
**[De... |
84284aa516263fca27391f1cd2fbee6786cd5e2c83e560fca740ed53744f521b | Text | 12,767 | 280 |
<div align="center">
<img src="https://github.com/user-attachments/assets/e804dce9-9961-4a11-a046-3a7df4836552" alt="ISNetLogo" width="200"/>
</div>
# ISNet & Faster ISNet
Natural and medical images (e.g., X-rays) coommonly present background features that are correlated to the classes we want to classify. For ex... |
5b66a0b1454400db499a542ade020281d517527985167775d649231927976da2 | Text | 13,333 | 346 | https://pymonnto.readthedocs.io/
# PymoNNto
<img width="200" src="https://raw.githubusercontent.com/trieschlab/PymoNNto/Images/logo.png"><br>
The "Python modular neural network toolbox" allows you to create different Neuron-Groups, define their Behavior and connect them with Synapse-Groups.
With thi... |
ceb34463fb15cde21d4205e5f364533d57dc9590275fc37393d440a9c2420173 | Text | 13,532 | 215 | Our paper is now officially published in [___Nature Genetics___](https://doi.org/10.1038/s41588-022-01256-z) and is selected as the [Cover Article](https://www.nature.com/ng/volumes/55/issues/1).
# Overview of SpiceMix
SpiceMix is an unsupervised tool for latent representation le... |
31dca04b08f77110ec8ef8e741b45c2fdfa2817572ec20d8a4c3134b4f7ea54b | Text | 14,219 | 279 | # ABCD-HCP BIDS fMRI Pipeline
[](https://zenodo.org/badge/latestdoi/171551109)
This software takes a BIDS folder as input and determines parameters
for the DCAN Labs' modified HCP pipeline, calling upon the proper code
to run the subject(s).
### Installation
In order to... |
bc5d2fecd5b1720d3219e4fe8b03980e8fe7db436707d1bfb92c29f74fe5b87c | Text | 14,566 | 168 |
[](https://zenodo.org/badge/latestdoi/251371344)
# An fMRI dataset in response to "The Grand Budapest Hotel", a socially-rich, naturalistic movie
This repository contains quality-assurance scripts for an fMRI dataset collected while 25 participants watched [The Grand B... |
91d87f1dd96781289b91d7b4c25eee35ab96b0d6982598f723c4a353e13c86ae | Text | 14,798 | 293 | 
# introNets
Repository for replicating the work in "Detecting introgression at SNP-resolution via
U-Nets and seriation". In this repo, we provide Python routines to create simulated alignments via MS and SLiM, sort and match p... |
f1384e628f5600dc417dcec0d18b589b34a3fc879c35b34cdf9746e316a301de | Text | 15,507 | 325 | .. image:: logo.png
:width: 730
:alt: Geomstats logo
**Geomstats** is a Python package for computations, statistics, machine learning and deep learning on manifolds.
The package is organized into two main modules: *geometry* and *learning*.
The module *geometry* implements differential geometry: manifolds, Lie ... |
2ed88fa5fae2ec703b3dc4c4f0654dd640385e9ef8cee63169d2a431ee4aeb08 | Text | 16,969 | 175 | ## Progressive Growing of GANs for Improved Quality, Stability, and Variation<br><i>— Official TensorFlow implementation of the ICLR 2018 paper</i>
**Tero Karras** (NVIDIA), **Timo Aila** (NVIDIA), **Samuli Laine** (NVIDIA), **Jaakko Lehtinen** (NVIDIA and Aalto University)
* For business inquiries, please cont... |
48ea1fb067a1269dbc93c5b0b9bd8688d91cef3657ffddb29b2d8b65d023dc87 | Text | 17,868 | 270 | # Introduction
Genomic imprinting is a vital phenomenon during mammalian growth and development, which refers to genes that are preferentially expressed from either paternal or maternal allele. In mammals, such imprinting gene expression was identified to be regulated by allele-specific methylation (ASM) in some cis-ac... |
6c4d10d3755b5456bec71a80884a30b955a07dadb5f4189b2e6bfd680b98970c | Text | 18,758 | 151 | # T2T Diversity Panel
This dataset includes sequencing data, assemblies, and analyses for the offspring of ten parent-offspring trios.
Data will be added and updated as technologies improve or new data uses are encountered. If you have issues/questions open an issue on this github page.
# Data description
Each par... |
8e852394f0539409dfbdc964cbdd4b8258d5670f063931dad353c8e601a8c927 | Text | 20,710 | 231 | ## StyleGAN — Official TensorFlow Implementation
Navec is a library of pretrained word embeddings for Russian language. It shows competitive or better results than <a href="http://rusvectores.org">RusVectores</a>, ... |
a4210c729fb9c6ed0685730b8c83cda08d69972746feb6974f8fea35ccf6a02c | Text | 24,202 | 348 | # Seqsharp: Sequence Simulations Have A Real(ism) Problem
This is a tool that uses Convolutional Neural Networks (CNNs) to discriminate simulated and empirical multiple sequence alignments (MSAs) based on their site-wise compositions. The classifier's accuracy, measured as the Balanced Accuracy (BACC), that is the ave... |
1bec0202c8e046bac3f513a0bd4572f1ede41f13304e1f40aafc137695675e00 | Text | 25,107 | 756 | # CurtainUtils
A utility package for preprocessing and uploading processed and analyzed mass spectrometry-based proteomics data to [Curtain](https://curtain.proteo.info) and [CurtainPTM](https://curtainptm.proteo.info) visualization platforms.
> **What is Curtain?** Curtain is a web-based visualization tool for p... |
fad9ea98049f61913c2bac71ee84ee06b2a496aeef573e10909bfc5a9dbdd515 | Text | 27,686 | 648 | <p align="center"><img src="figs/logo_deeppurpose_horizontal.png" alt="logo" width="400px" /></p>
<h3 align="center">
<p> A Deep Learning Library for Compound and Protein Modeling <br>DTI, Drug Property, PPI, DDI, Protein Function Prediction<br></h3>
<h4 align="center">
<p> Applications in Drug Repurposing, Virtual S... |
ed6cac393fd9ffcf810acb0353a65fca5144e132f5767d408e1e011f14fbbcf5 | Text | 27,720 | 200 | 
[](https://zenodo.org/badge/latestdoi/205919974)
# SUNS
Shallow UNet Neuron Segmentation (SUNS) is an automatic algorithm to segment active neurons from two-photon calcium imaging videos. It used temporal filtering and wh... |
f5b0bc2b206e931b41581e86111489de0a4a0963feaa58e5bac164c09bc32559 | Text | 27,935 | 375 | # Multi-fidelity GNNs for drug discovery and quantum mechanics
🎉
Now published in [Nature Communications](https://www.nature.com/articles/s41467-024-45566-8)!
##
## Abstract
We investigate the potential of graph neural networks (GNNs) for transfer learning and improving molecular property predicti... |
ce86d53b18ca7b6167d4791fac5832c37deb680dd0534354575c837c16cfac89 | Text | 29,477 | 489 | # <span style="font-size:larger;">MACE</span>
[](https://GitHub.com/ACEsuit/mace/releases/)
[](https://openreview.net/forum?id=YPpSngE-ZU)
[ [](https://badge.fury.io/py/ratinabox) [](https://pepy.tech/project/ratinabox) [
# Resources for phase recovery
# Released from [here](https://doi.org/10.1038/s41377-023-01340-x)
Here, we refer to *“calculating the phase of a light field from
its amplitude/intensity measurements”* as phase recovery (PR), which contains many techniques an... |
8e609bb71c20b858c77f0e9f90bb1319db8477b13f9f965f1a1e18524bf50881 | TypeScript | 11 | 1 | export {};
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74a16d3477ff412d7276656cd1c8e40890096cad59a9296aae0c996b1b550d11 | TypeScript | 24 | 1 | export * from './hash';
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3fbcf414912ccc7ac802e3e43c1f680f15dbd4e58711f5c040c46c42ca9c0e3d | TypeScript | 26 | 1 | export * from './system';
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5afe88e3ad26ca5697dfc10be2868ff022bfd963014dfb63a9619a2d05bb4e25 | TypeScript | 26 | 1 | export * from './custom';
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5dac25efb383927ec276635632c07136da2c74da3e9b5668c98c53284232769d | TypeScript | 26 | 1 | export * from './errors';
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caade5db2dbf091765793699a4e578152466b6460c4bc4405e6d696630cd3c49 | TypeScript | 26 | 1 | export * from './format';
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51328952ea7d21bf94b67ca09f135aa29ff23690213cd3b13de4eecfddf0a6f6 | TypeScript | 30 | 1 | export * from "./expression";
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3195efa800761d060ec8fb295afdae32eb51a64ff2b50b46a001cfa94c1b732f | TypeScript | 31 | 1 | export * from "./schemaUtils";
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43e818adf60173644896298637f47b01d5819b17eda46eaa32d0c7d64724d012 | TypeScript | 31 | 2 | #!/usr/bin/env node
export {};
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ab9b9a36e5284fd8d3bf2f7d5fcbc60052f25f27e4d20954782099282c60d23e | TypeScript | 39 | 1 | declare var global: typeof globalThis;
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56654a79c97fa7ce41765179a3524530007b524a9a5145e2f9ebece801eba0c5 | TypeScript | 46 | 2 | // src/shims-css.d.ts
declare module '*.css';
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f3ab7c7878742494b587a94babbc659844473792d948527838b7b22fbe9a643a | TypeScript | 52 | 2 | export * from './guard';
export * from './extends';
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5799f8b215edf831c82e6227c71bb92d63519106e9a3a3df8f963554dd85d0cb | TypeScript | 61 | 2 | export * from './conditional';
export * from './structural';
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ac26d4fa29d2ba00092c3ed61a3cefbd0391b2d9fe95072c40072102da8b34be | TypeScript | 71 | 2 | declare const _default: (position?: number) => any;
export = _default;
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2b617a55cc54db2b7a83ff146538c85dd137862fe64d1cf7830f94323e13d5e1 | TypeScript | 75 | 2 | import * as dotenv from "dotenv";
dotenv.config({ path: "../../../.env" }); |
43b49ac250cc2089680ce2af5772020e409e189a239ce2373265c6fe0882dc1a | TypeScript | 76 | 2 | import * as dotenv from "dotenv";
dotenv.config({ path: "../../../.env" });
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978161e29da1b8b1bd2bc8755267c049be6e38df33851ae097cd4b10e2d85f62 | TypeScript | 76 | 3 | import * as dotenv from "dotenv";
dotenv.config({ path: "../../../.env" }); |
3c0e962d54d954664006db3590f90355d80b83ecdce4cad99e155d346c2f5763 | TypeScript | 77 | 3 | import * as dotenv from "dotenv";
dotenv.config({ path: "../../../.env" });
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369e0ee24fa1c766262255f7ab8bf2c0e3dabab63552bf9ea743dc30ff231691 | TypeScript | 85 | 4 | export interface Options {
cwd: string;
}
export declare const options: Options;
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b6cfb5444d75f5b8c5a2988c9658168bdf61603ce20455249c7659d9be8c2b5b | TypeScript | 90 | 2 | export { ValueError, ValueErrorType } from '../errors/index';
export * from './compiler';
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563624bf34635905b08835198e3167586b685853ac230e3b3c5f4f3abd52efbe | TypeScript | 91 | 2 | import { Accession, Parser } from "./classes/UniprotParser";
export { Accession, Parser };
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e6247278b58f59c36d5d5225e76952793a604852031c0e84b0b973363fe92dee | TypeScript | 92 | 3 | export declare namespace ValueClone {
function Clone<T extends unknown>(value: T): T;
}
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e9f542d1e22186235e0a69a3d24c56cd1e6c9621fef48ccf8e3fc8cb0a9cbdd1 | TypeScript | 97 | 3 | export default async function testTeardown() {
await (globalThis as any).stopChromaServer();
}
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6288f75c89689b58e99bf01b18b8f82ea8597e3e7d04a672bc1e5987e13be6ad | TypeScript | 100 | 3 | export declare namespace ValueEqual {
function Equal<T>(left: T, right: unknown): right is T;
}
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9bf47e3dd9756af1826fe6600e6075ff4beb6f764ec90b1523a056f719eb22c1 | TypeScript | 103 | 4 | /**
* Re-exports all functionality from the core package
*/
export * from "@internal/chromadb-core";
|
8a6702b7568200e9e1da8ddefe8785e305d15fb6f7cd08dfd52bcd8c80974f80 | TypeScript | 109 | 3 | // This file is auto-generated by @hey-api/openapi-ts
export * from './types.gen';
export * from './sdk.gen'; |
ab81d04cfb343a4683c99dcd4e7622dc7b674fa39eb3b207603ea2e851e97732 | TypeScript | 115 | 4 | import { config } from "dotenv";
import { resolve } from "path";
config({ path: resolve(__dirname, "../.env") });
|
3f237177e57179d967e2f2e314e4cd3e3120c1acbb512bab2e62fd3122c5ce76 | TypeScript | 116 | 5 | const testTeardown = async () => {
await (globalThis as any).stopChromaServer();
};
export default testTeardown;
|
a8a72efbc8d22f9e9b38368e726d08f15531ab3444bbdb19a8d98ec2a8ae8721 | TypeScript | 130 | 6 | // src/main.ts
import { createApp } from 'vue';
import App from './App.vue';
import './style.css';
createApp(App).mount('#app');
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485ee28fa01e357061b95c9bf15ae2544828361cacc9687e7e70deb5b8235412 | TypeScript | 136 | 8 |
export const environment = {
production: false,
baseApiUrl: "",
pubmedApiKey: "",
umlsApiKey: "",
captchaSiteKey: ""
};
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af55d90ce27282f41152ac62f5c4156f9f0e8a67ce26e9ba473528fb62a89a9b | TypeScript | 140 | 8 |
export const environment = {
production: true,
baseApiUrl: "",
pubmedApiKey: "",
umlsApiKey: "",
captchaSiteKey: ""
};
|
7fbc5fae2ebdb175dc0d5220dd70223b4f7afef9e47d935e06b0f4bad9000d45 | TypeScript | 142 | 7 | import { Raw } from './raw';
describe('Raw', () => {
it('should create an instance', () => {
expect(new Raw()).toBeTruthy();
});
});
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8275150567119f331056bac64b9f369e51340bc02a93d7bd76e1328ba5a70baa | TypeScript | 149 | 7 | import { Raw } from './raw';
describe('Raw', () => {
it('should create an instance', () => {
expect(new Raw()).toBeTruthy();
});
});
|
57b4e8b157b5fa7bbd488e8f44ce37b0d5fec77b2ff7836f0020a084d5ce97d5 | TypeScript | 151 | 6 | /* tslint:disable */
/* eslint-disable */
/* auto-generated by NAPI-RS */
export declare function cli(args?: Array<string> | undefined | null): void
|
e0886a11dc516abf1f341a46dc9548405bfa9997021cde284585c760cd10df73 | TypeScript | 158 | 7 | import { Project } from './project';
describe('Project', () => {
it('should create an instance', () => {
expect(new Project()).toBeTruthy();
});
});
|
ce5071b14f29540f58e59e3c67f46ec0381e39360a5257fd6a56068b73a532dd | TypeScript | 162 | 7 | import { Settings } from './settings';
describe('Settings', () => {
it('should create an instance', () => {
expect(new Settings()).toBeTruthy();
});
});
|
e8746569b0b711a17baec2ee6ccb5458f1356173690060f0deb89519d270653a | TypeScript | 165 | 7 | import { Project } from './project';
describe('Project', () => {
it('should create an instance', () => {
expect(new Project()).toBeTruthy();
});
});
|
1226f59621d8b3482368119b53973c93f6968a3a09a458a0405b4322dff3d5a7 | TypeScript | 166 | 2 | export type Callback = (directory: string, files: string[]) => string | false | void;
export default function (directory: string, callback: Callback): string | void;
|
083ab3f3febbf7ac8a14c20aa3227dc930713aee80cd245f773a02fe77e5000d | TypeScript | 167 | 7 | import { UniprotDb } from './uniprot-db';
describe('UniprotDb', () => {
it('should create an instance', () => {
expect(new UniprotDb()).toBeTruthy();
});
});
|
be9ce354ba35fd337ea10a9e9b0d558aad2810a5cc581b3859d2758a9cedb7a9 | TypeScript | 167 | 7 | import { InputFile } from './input-file';
describe('InputFile', () => {
it('should create an instance', () => {
expect(new InputFile()).toBeTruthy();
});
});
|
d1f1e0d62cb8d8d1e04c26e14de842d8a151f75812d81b046c65b5d1fe8e4b27 | TypeScript | 169 | 6 | import { clsx, type ClassValue } from "clsx";
import { twMerge } from "tailwind-merge";
export function cn(...inputs: ClassValue[]) {
return twMerge(clsx(inputs));
}
|
e0271c4953f25dc25ed27e5244dee73277281a65e34be402725d1e2c396aeb4e | TypeScript | 169 | 7 | import { Settings } from './settings';
describe('Settings', () => {
it('should create an instance', () => {
expect(new Settings()).toBeTruthy();
});
});
|
dcc403e62f8c9ebd1dfea51f51ac56a3aeecfcf57c9608321a0edf6c8b56deff | TypeScript | 170 | 4 | export { ValueError, ValueErrorType } from '../errors/index';
export { Edit, Insert, Update, Delete } from './delta';
export * from './pointer';
export * from './value';
|
801eeb3602d4e52743fc08be713a022808f82c459979b23280cb447b57a986fb | TypeScript | 174 | 7 | import { InputFile } from './input-file';
describe('InputFile', () => {
it('should create an instance', () => {
expect(new InputFile()).toBeTruthy();
});
});
|
f4f8c83563c20f0aafe590697daf831e461bf402ce8ec625b4d31780bb796c3d | TypeScript | 175 | 7 | import { CurtainLink } from './curtain-link';
describe('CurtainLink', () => {
it('should create an instance', () => {
expect(new CurtainLink()).toBeTruthy();
});
});
|
c7a3ea5540470995eb990fde4ee51810592a448c012ce03fffa97078a766c428 | TypeScript | 178 | 7 | import { Differential } from './differential';
describe('Differential', () => {
it('should create an instance', () => {
expect(new Differential()).toBeTruthy();
});
});
|
e1b243a5864a77e7b1b69ebb7b0359c025e7f555cf678c3de6af0134939cf332 | TypeScript | 178 | 9 | import { Argv } from '.';
export = Yargs;
declare function Yargs(
processArgs?: ReadonlyArray<string> | string,
cwd?: string,
parentRequire?: NodeRequire,
): Argv;
|
6d0c6687cd08f222b328582e23309c8f40e249778ef9680778e059bebe7f37af | TypeScript | 182 | 7 | import { CurtainLink } from './curtain-link';
describe('CurtainLink', () => {
it('should create an instance', () => {
expect(new CurtainLink()).toBeTruthy();
});
});
|
3a682c1d8b37e93c79f81770a3a8149f1be41aa53c2a29b6c5c4e98d15e2b263 | TypeScript | 183 | 7 | import { UniprotParser } from './uniprot-parser';
describe('UniprotParser', () => {
it('should create an instance', () => {
expect(new UniprotParser()).toBeTruthy();
});
});
|
edf545c2f6eb0340c66c830b8b1e7cc1fad1aa8c879ff65ef29f0c3e07bcd59c | TypeScript | 185 | 7 | import { Differential } from './differential';
describe('Differential', () => {
it('should create an instance', () => {
expect(new Differential()).toBeTruthy();
});
});
|
6765ca25c70d7a7c7b05da1693a45427cac6669ad7c7d80838f7e15c674273eb | TypeScript | 190 | 7 | import { UniprotParser } from './uniprot-parser';
describe('UniprotParser', () => {
it('should create an instance', () => {
expect(new UniprotParser()).toBeTruthy();
});
});
|
a17a84310544c2b66e9ce7291531ec6338b692e0b9996754297b30661b0a484a | TypeScript | 190 | 10 | import { defineConfig } from "tsup";
export default defineConfig({
entry: ["src/index.ts"],
clean: true,
dts: true,
format: ["cjs", "esm"],
sourcemap: true,
outDir: "dist",
});
|
a69c09dbea52352f479d3e7ac949fde3d17b195abe90b045d619f747b38d6d1a | TypeScript | 201 | 8 | declare module 'assert/strict' {
import { strict } from 'node:assert';
export = strict;
}
declare module 'node:assert/strict' {
import { strict } from 'node:assert';
export = strict;
}
|
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